This PDF file contains the front matter associated with SPIE Proceedings Volume 12625, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

In order to explore a comprehensive and effective solution to the problem of airport seat assignment in large airports, a mIP-based seat assignment model was established to minimize the probability of flight conflicts. The simulation results show that the model can quickly and accurately allocate parking Spaces for aircraft, give full play to the utility of existing support facilities, ensure flight safety and aircraft punctuality, improve service quality and airport operating benefits.

Based on the data of China's regional input-output in 2007-2017, taking the digital economy output gravity between 30 provinces and cities in China as the research object, a spatial correlation matrix is constructed, and the social network analysis method is used to analyze the overall and individual spatial correlation of China's digital economy. The research results provide reference for effectively strengthening the spatial correlation of digital economy and promoting regional coordinated development.

In the context of the era of big data, machine learning and pattern research are the main contents of the technical discussion of scholars around the world. Faced with the continuous increase of data information, the problem of class imbalance appears in the relevant technical research. The main feature is that the number of instances of some classes is obviously less than that of other classes. From the Angle of practical application, in cases of hospital diagnosis, for example, because only a handful of cancer patients, so how to correctly identify all kinds of mass data information in cancer patients, practice can improve work efficiency, and can quickly find conform to the requirements of the case, to modern medical diagnosis technology research is of great significance. Therefore, on the basis of understanding the status quo of modern technology research and development, this paper, according to the relevant theories of unbalanced data set and logistic regression model, deeply discusses the unbalanced class learning method with logistic regression mixed strategy as the core. The final experimental results show that the new logistic regression algorithm can effectively improve its performance in class imbalance on the basis of guaranteeing high accuracy. Compared with other advanced methods, the logistic regression model has obvious technical advantages.

The integration of low-voltage data collection system is relatively high, and there are some problems such as long distance between nodes. Therefore, while testing the operation reliability of the system, it is often difficult to analyze and the accuracy is low because of power consumption. Based on that, a reliability analysis method for low-voltage centralized reading system based on LoRa technology is proposed in this paper. Under LoRa framework protocol, the evaluation index of system reliability is defined, and the running state of the system is determined by calculating the state transition rate, the stationary state probability and the instantaneous state of the components. With this basis, the load carrying capacity of each node and the risk of voltage collapse are calculated respectively. Through setting up control group and experimental group to carry out detailed empirical analyses, the reliability analysis results of the system are obtained. Through comparison, it is found that the average analysis accuracy of the system operation reliability analysis method designed is 25% higher than that of the traditional method.

In the steady development of social economy, the technology theory with the computational intelligence method as the core is widely used in the financial business operation, based on artificial neural network and evolutionary algorithm for theoretical improvement and application innovation, can really achieve the development of financial intelligence. Intelligent finance, as an important link to accelerate industrial intelligent upgrading, has a large gap between its overall development level and that of developed countries, and scientific research institutions and industrial enterprises have not formed an ecosystem and industrial chain with international influence. Therefore, based on understanding the development status of financial business in China, this paper puts forward a network model based on SOM neural network, and the clustering simulation experiment analysis of listed companies in some region. The final results show that the research network model in this paper has stronger clustering ability, lower computational complexity, and faster convergence speed of practical work, which has a positive impact on the development of financial intelligence in the new era.

In the steady development of social economy, the city residents living standards is higher and higher, the amount of electricity industry production and life more and more, the cross section of high voltage overhead lines will increase, on the basis of the transmission line capacity has improved, so how to guarantee the safe and stable operation of transmission lines, is the foundation of the national power grid construction conditions. Understand current status of transmission line tower foundation pit measurement is presented in this paper, on the basis of development direction according to the innovation of information technology in recent years, laser range and the application value of the information fusion technology, by using laser ranging and information fusion based measurement method as the core circuit, to guarantee safety and stability of the transmission line operation.

This study aims to use N400 components to verify the matching degree of icon design scheme and corresponding semantic vocabulary. The results of behavioral experiments show that the response speed of related semantic words is significantly faster than that of irrelevant semantic words, the subjects are more sensitive to the semantic processing of icon-related semantic words, and the recognition accuracy of irrelevant semantic words is significantly lower than that of related semantic intention words. The analysis results of N400 components show that the activation of the frontal lobe of irrelevant semantic words is significantly higher than that of related semantic words, the N400 amplitude of irrelevant semantic words is larger, and the average voltage is higher, indicating that when the user's subjective judgment of icons and the semantic meaning of the words are presented The higher the degree of deviation, the greater the degree of N400 activation. The study confirmed that N400 can be used as a physiological detection method to verify the correlation between semantic words and icon design.

The intention of this research is to investigate the implications of the RCEP implementation on China's import cultural trade as well as the influencing factors by combining the results of literature review and questionnaire investigation with big data analysis algorithms, ANOVA and regression analysis, taking the Watching Behavior of Imported Film and television (WBIFT) as the access point. In the viewing behavior, the frequency of viewers' viewing of imported film and television programs (FWBIFT) and the amount of membership spending on related video apps (PWBIFT) are studied; while among the influencing factors imported film and ’ s cognitions (CIFT), foreign cognitions (FCIFT), advantage cognitions (ACIFT), and social cognitions (SCIFT) were looked at. Depending on the correlation analysis and the results fitted to the data model, it can be seen that the most watching frequency on Series of Imported Film and television (IFT-S) as well as the advantage cognitions of imported film and television (ACIFT) and the social cognitions of imported film and television (SCIFT) respectively significantly affect the FWBIT and PWBIFT. The government and cultural institutions can refer to the research recommendations to optimize policies and adjust program introduction strategies to make improvements in the current domestic film so as to foster the domestic market of local and imported film and television programs.

The purpose of this paper is based on the results of questionnaires survey and literature review, combined with the methods of big data analysis algorithms, analysis of correlation, analysis of variance, analysis of regression, analysis of effects and other methods to explore the factors influencing Chinese investors' investment behavior in private digital currencies. The investors' willingness to invest in private digital currencies and the amount they are willing to invest are studied in investment behavior, and the investors' perceptions of the advantages and risks of private digital currencies are studied in influencing factors. The influence of personal factors on private digital currency investment behavior is also studied. The results show that the willingness to invest in private digital currency is significantly and positively related to the perception of individual advantages and social advantages of private digital currency as well as education and income, and significantly and negatively related to the perception of individual risks and social risks. The willingness to invest in private digital currency was significantly and positively correlated with age, education, and income and showed no significant correlation with the perception of private digital currency advantages and risks.

With the in-depth development of global economic integration, the competition in the financial industry at home and abroad has become more and more severe, and the innovation of financial products has gradually become a new development trend in the financial industry. For commercial banks, strengthening the innovation of financial products can attract more customers, thus reaping more substantial income and maintaining an advantageous position in the market competition. However, in the process of financial product innovation of commercial banks, the financial risks are also increasing, and how to achieve effective management of financial product innovation risks has become one of the hot topics of concern in the field of commercial banks. This paper takes the study of risk management of technological, financial product innovation of commercial banks as an example, based on methods of big data analysis algorithms, analysis of correlation, analysis of effects, analysis of Variance and analysis of regression, through questionnaires and research results, literature review, to explore Commercial Bank’s Risk Management Behavior on the innovation of its scientific and technological financial product(CBRMB) and its influencing factors. Regarding CBRMB, It mainly describes the degree of its risk influence through the following two aspects, including the number of inspectors and research budget, and among the influencing factors research Commercial bank's Risk management (CBRM), Commercial bank's Human Resource management (CBHRM), Commercial Bank's supervision ability on its Financial product innovation (CBSA), Commercial Bank's Internal control system (CBICS), and Commercial Bank's Development of scientific and technological financial products (CBD). According to the regression model operation results, commercial banks can refer to the research recommendations and optimize their scientific and technological innovation and risk management, improve the problems in the risk system, and enhance the operational efficiency of commercial banks to promote the healthy development of the financial market.

The aim of this paper is based on the results of the literature review and questionnaires,combined with the methods of big data analysis algorithms,analysis of variance, analysis of regression, analysis Of Effects and other methods to explore the development of digital financial finance in rural areas, studies the frequency of residents' monthly credit loans, studies the education level, monthly income, and the advantages and disadvantages of financial institutions. According to the results of analysis of correlation and data model fitting, age, cognition of the advantages of financial institutions significantly affect the frequency of residents 'average monthly use of credit loans and residents' digital inclusive finance Amount of financing obtained. The government and financial institutions can refer to the research and suggestions to optimize the policies, improve the current existing problems, accelerate the speed of financial innovation, and promote the rapid development of inclusive finance.

The literature review and expert interviews show that in recent years, China has actively participated in international cooperation on sustainable finance, adopted global sustainable finance principles and developed more inclusive global sustainable finance principles in line with China's actual situation. The purpose of this paper is to use the methods of big data analysis algorithms and questionnaire survey to analyze the investor behaviour of sustainable financial products (IBSFP) and its influencing factors. IBSFP includes the investment frequency of sustainable financial products (IFSFP) and the investment amount of sustainable financial products (IASFP). The content cognition of sustainable financial products (CCSFP), the advantage cognition of sustainable financial products (CASFP), the disadvantage cognition of sustainable financial products (CDSFP), the cognition of sustainable financial policy's advantages (CSFPA) and the cognition of sustainable financial policy's disadvantages (CSFPD) are the main reasons affecting the purchase of sustainable financial products. Through the analysis of variance and analysis of correlation, CCSFP, CASFP, CDSFP, CSFPA and CSFPD had a significant positive effect on IFSFP and IASFP purchased by financial consumers. Through independent sample t-Test occupation significantly influences IFSFP, CCSFP and CSFPD. Age, education, and annual income significantly affected CCSFP, CASFP, CDSFP, CSFPA and CSFPD. Use analysis of regression to get two formulas.

Merger and acquisition is an important commercial activity which facilitates business efficiency and contributes to macroeconomic growth. However it is challenged by various risks, amongst which integration risk is complicated and difficult to identify. In this paper, integration risk was analyzed, identified, processed and assessed with big data technology. Based on fuzzy consistent matrix method, a simulated case study was conducted to establish an applicable fuzzy comprehensive model for integration risk assessment in merger and acquisition. In the theoretical analysis, the research firstly analyzed the feature of integration risk, elaborated different categories of integration risk, while explained the suitability to adopt big data technology in risk management. Information asymmetry, multi source of information and quantification of qualitative information in integration risk required unique valuation method. Corporate governance risk, cultural and social integration risk, technology integration and human resource integration risk has respective characteristics and are represented by different variables. And then, several merger cases are closely studied, combined with success cases and failed ones, indicators and risk related information were elaborated. The various sources of data were provided for big data analysis, from individual firms, industrial level and macro policy and market environment level. In the empirical analysis, the fuzzy evaluation method includes the establishment of comparison preferential matrix, transformation into fuzzy consistent matrix, calculation for single objective sequencing, and final achieved overall risk assessment result. Future research prospects and policy implications were also discussed.

In the steady development of social economy, the development of various countries based on the frequent occurrence of extreme events in the lake ecosystem, the lake ecological water transfer, which can not only develop a more perfect project management system, but also according to the relationship between the lake interior, for the prevention and control of ecological environment pollution put forward appropriate treatment countermeasures. In this paper, the MIKE21 software was used to build a mathematical model for the lake ecosystem, and the numerical simulation was carried out in combination with the characteristics of the hydrodynamic environment in a certain area. According to the data obtained from the measured analysis, the model is verified and analyzed. The final results show that the simulated results are basically consistent with the actual measured data, which proves that the numerical simulation method based on MIKE21 software is an important means to understand the hydrodynamic environment of lake ecological water transfer at present.

As an important branch of the enterprise management, human resources management in the development of today's social and economic innovation, with the rising number of enterprise employees, the connection between the industry more closely, the human resources management requirements are also rising, how to implement the hr management concept based on big data technology of automation, is currently the main topics of the enterprises to explore. Employees as the foundation for the development of the enterprise implement efficient element, either increase or decrease personnel or personnel changes will affect the enterprise's overall operations, so this article research in understand the era of big data, on the basis of current situation of the development of enterprise human resources management, from the system demand, function demand, the module design aspects, such as human resources in deep exploration enterprise data analysis system. The final results show that such systems have unique value in current enterprise development.

The era of big data not only provides broad ideas for technological innovation, but also brings research ideas that are different from the traditional knowledge category. It can be directly used to solve problems by applying it to practical teaching and training of college students' innovation ability. Understanding the current situation of college students' education and training, it is found that the cultivation of practical innovation ability lacks accurate guidance, and the cultivation of students' innovation consciousness and ability does not meet the current educational requirements. Therefore, on the basis of understanding the development background of the new era and thinking about the cultivation of college students' innovation ability, this paper builds an evaluation model of college students' innovation and entrepreneurship ability with big data technology as the core, and puts forward appropriate teaching guidance countermeasures.

This study examines whether R&D tax incentives in China have a positive effect on R&D investment, R&D expenditure and firm value. Using a sample of manufacturing firms listed on the Shanghai and Shenzhen stock exchanges over the period 2012-2016, we find that the R&D tax credit for research and development not only has an incentive effect on enterprise R&D investment and R&D expenditure but also has an incentive effect on firm value. This study provides important implications for stakeholders, such as investors, policy makers, and policy makers who pursue long-term performance objectives in developing countries or regions.

The governments at various levels shall conduct e-commerce activities, can produce large quantities of government affairs information and citizen privacy information such as content, due to the information security requirement is higher, so in order to avoid illegal access and information leakage, under the background in the era of big data, based on the concept of cloud computing technology research scholars e-commerce platform design system is presented. In implementing e-government service, cloud computing platform is bound to face large data processing, storage and sharing of the multipolar localized problem, so the research scholars in the construction of a cloud computing platform at the same time, according to the future demand for e-government service, strengthening the construction of e-government security platform model, based on the order from e-government security requirements. Therefore, on the basis of understanding the application status of cloud computing technology in the era of big data, this paper explores how to build an e-commerce platform with cloud computing as the core according to the current situation of e-government services. The final experimental results show that although the security problems of e-commerce are becoming more and more obvious, the reasonable use of emerging cloud computing technology can guarantee the security of e-government from the foundation, and relevant research topics have practical significance for the development of technology.

Virtual reality (VR), as a computer simulation system that can create a virtual environment, can not only change the traditional technology inquiry mode, but also be integrated into China's economic construction and development system as a cutting-edge high-tech technology. Because virtual reality technology integrates a variety of key technologies, such as glasses tracking technology, 3D modeling rendering technology, implementation of computer graphics technology, so it is widely used in different fields, has unique realistic value. From the perspective of the design and application of intelligent VR system, its core part is to present a real and immersive simulated environment to human beings. Based on the understanding of intelligent VR system, this paper uses binocular stereo optical system and virtual indoor scene to achieve immersive visual communication and graphic design. The final experimental results show that the graphic design and visual communication based on intelligent VR system has unique technical advantages.

In the context of the new era, environmental engineering based on their own accumulated experience, based on the Internet of things proposed atomization control system, its core purpose is to solve the application of traditional atomization device, to ensure the quality of environmental engineering treatment. According to the operation of environment engineering analysis in recent years, the traditional atomization device more problems during application, such as the poor quality of work is not standard, the operation, work efficiency is too low, so in artificial intelligence, cloud computing and other advanced technology, comprehensive development based on Internet of things to build new spray control system in environmental engineering, On this basis, the challenges faced by practical engineering are solved. Based on the understanding of the development trend of the Internet of Things industry, this paper conducts a simulation study on the operation of the practical system according to environmental engineering and atomization control system. The final results show that the atomization control system of environmental factory based on Internet of Things technology has more effective functions, which meets the construction and management requirements of current environmental engineering.

Porcelain pillar insulators are one of the basic equipment for operation and management of substations and power plants. The quality of practical work directly affects the safety performance of power grid system. Therefore, in the innovation and development of electric power industry, researchers have proposed a variety of detection methods and operating systems based on practical work experience. In this paper, based on the understanding of the application principle and the principle of vibroacoustics of ceramic prop insulators, and according to the status quo of actual research technology development, the cracking detection system of ceramic prop insulators with vibroacoustics as the core is deeply explored. The final experimental results show that this system can help the staff to conduct a comprehensive survey of cracking problems of porcelain pillar insulators. The actual detection efficiency is high, the application range is wide, and the labor intensity is low, which meets the requirements of substation construction management in the new era.

Under the trend of economic globalization, technological innovation is the primary task for enterprises to occupy a dominant position in the market. For the power industry in our country, in order to better meet the increasing electricity demand, power enterprises in the value of internal safety training work at the same time, start with the aid of computer technology and software and hardware equipment, building intelligent operating system, which can not only improve the staff's professional accomplishment, also can optimize the enterprise comprehensive management level. Therefore, after understanding the virtual reality technology and its application status in the power system, this paper deeply discusses the power safety supervision training system with virtual reality as the core. The final experimental results show that this system can not only improve the traditional enterprise talent training mode, but also fully show the application value of virtual reality technology.

With the rapid development of social economy and information technology, the role of VR technology in power safety production is becoming more and more obvious. According to the analysis of the technical research results of scholars around the world in recent years, it can be seen that the training mode of electric power enterprises with VR technology as the core has been attached importance to the development of the industry, and has been comprehensively promoted in practice. In order to better meet the demand of each department personnel training, can let users of the system and the interaction between virtual customers a wide range of behavior, some scholars based on synergy technology put forward many new power VR training model, it can reduce unnecessary loss of resources, cost control system, and can promote education training speed, enhancing the employees of collaborative interaction. Therefore, on the basis of understanding VR technology theory and characteristics, this paper conducts an empirical analysis on the existing power VR simulation training multi-person collaborative platform according to the development status of VR technology and multi-person collaborative technology. The final results show that this platform design not only meets the current development needs of power enterprises, but also can provide more excellent talents for industry innovation.

In the rapid development of network science and technology, the software, as the basic part of the network system operation, the practical application quality directly determines the realization of the function, so the users put forward higher requirements for the software quality. According to the application situation of network system software in recent years, software defects are the main factor affecting the application quality, and the relevant detection technology is the only way before the formal promotion of software. Therefore, researchers have put forward a defect prediction scheme based on the software code, which can not only reduce the cost, but also improve the practical efficiency. This paper focuses on the understanding of the machine learning algorithm and constructing automatic and comprehensive learning models according to the software defect prediction technology, thus discovering the defects in the software. The final experimental results prove that different algorithms have different advantages in different evaluation indicators. By using these advantages and the stacking integrated learning methods in machine learning, building a prediction model with combined machine learning algorithms as the core can find defects more accurately and perfectly.

Because the cable network has the characteristics of large scale, complex structure, multiple types and different connection forms, so the electrical performance test of the actual cable network components is a very demanding work. Nowadays, automated test equipment can only be used to judge the reasonable range of test values and programmed values of electrical performance, and with the continuous improvement of cable production technology, electrical performance test data of various types of cables at different stages have differences, and the actual management is not standardized. Therefore, how to develop cable electrical performance analysis software based on cable network automation test equipment according to the needs of enterprises is the focus of cable component application management. Based on the analysis software of electrical performance of automatic cable, this paper explores the application value of this system design in depth according to the current application of circuit equipment of on-off test of electrical connector cable assembly. The final results show that the system equipment researched and designed in this paper can not only realize automatic data import and intelligent query, but also help power enterprises to grasp the potential quality problems faster and understand the performance change rule of cable components.

In the rapid development of computer technology, the module performance of automatic driving system has been further improved. As a basic component, the stability and accuracy of the positioning module in time operation will directly determine the safety of the autonomous driving system. Therefore, scholars from all over the world put forward the application of real-time positioning and Map building (SLAM) algorithm for optimization and innovation in practical exploration. In this paper, based on the research status of autonomous driving system technology, the algorithm scheme of laser vision fusion SLAM is deeply discussed, and the gpS-free autonomous driving system based on algorithm is proposed. The final experimental results show that this system design is the main direction of the future automobile industry.

Because the objective world of human life is composed of different shapes and sizes, compared with the study of regular Euclidean geometry, it presents the basic characteristics of irregular and bizarre, so the scientific research scholars in the innovation and development of art design, put forward the use of theoretical knowledge of computer. Therefore, from the perspective of visual art graphic design, this paper proposes three methods of image reconstruction by block. From the practical research results, it is more effective than using the compressed perception theory directly, and can control the reconstruction time.

China's "Internet + education" respects the essence of education at the same time, based on the Internet mode of thinking and behavior, re-create the teaching model and teaching methods, it does not belong to a simple online education, but the Internet into the whole process of teaching. Therefore, in the context of the new era, after introducing the concept of artificial intelligence technology into the field of education, the new information technology with artificial intelligence as the core can not only balance the processing of educational resources, gradually improve the teaching efficiency, but also present students with personalized and autonomous learning mode. In this article, therefore, artificial intelligence and Internet + education development model, on the basis of deeply discussed with artificial intelligence as the core "Internet + education" of the new mode, and connecting with the new era under the trend of the development of artificial intelligence, has been clear about the Internet + education the main content of the new model, in order to optimize the comprehensive level of education in our country.

Science and technology innovation is a major issue to enhance the capability of independent innovation. Combining the relevant research results of innovation system theory, with the goal of improving the efficiency of the utilization of science and technology innovation resources and enhancing the vitality of science and technology innovation, the transformation of science and technology innovation paradigm is analyzed from the perspective of technological progress, and four evolutionary stages of science and technology innovation ecosystem, such as budding evolution, growth evolution, stable evolution and decline evolution, are studied. It is proposed that the science and technology innovation ecosystem has the functions of promoting the interaction and expansion of the functions of innovation factors, helping to plan and smooth the path of innovation chain, and fostering an internal and external environment favorable to innovation.

In China's social and economic development, stable development and suppression of fluctuation are the basic goals of macroeconomic policy. Due to the cyclical characteristics of China's financial and economic development, the construction of cyclical early warning mechanism in practical exploration can not only accurately predict the development trend of financial economy, but also help government departments and market institutions to make effective decisions. This paper is understanding China's financial and economic development trend and risk cycle early warning mechanism. Based on the research situation, the early warning mechanism with BP algorithm is deeply discussed. The final experimental results prove that we should continue to strengthen the research on the early warning mechanism of neural network in the financial and economic cycle.

Digital watermarking is an important technology for secure communication and copyright protection. It is widely used in audio and video anti-counterfeiting protection, identification and traceability. Irreversible desynchronization attacks such as cropping seriously threaten the security and reliability of digital watermarking. To resist cropping attacks, it is an effective scheme to identify multiple foreground targets in an image and repeat the embedding and extraction of watermarks. Yolov5 can quickly identify the location and category of image targets, and the recognition effect is stable. Based on this, this paper proposes a multi-target robust watermarking algorithm based on Yolov5 target detection. First, the main target in the foreground of the image is selected as the watermark to be embedded area through the YOLO target detection network. Then, the discrete cosine transform (DCT) of the transform domain watermark embedding method is selected and the watermark embedding process is completed in the image target detection frame. After the watermarkembedded image has been subjected to different degrees of cropping attack, the residual image is subjected to watermark extraction operation, and the correlation and bit error rate are calculated with the original watermark, so as to verify the robustness of the watermarking algorithm in this paper against cropping attacks. The algorithm in this paper extracts 128 images from the COCOtestval data set for experimental testing. The results show that the method can not only detect the target in the salient region of the image, but also effectively embed a robust watermark, which is an effective solution to digital watermark cropping attacks.

In the innovative development of social economy, the 3 d imaging laser radar has been widely used in military and civil fields, its dispersion rate of small, high resolution, fast imaging speed of utilizing scanning laser active 3 d imaging technology meet system quickly stable imaging the basic requirements, such as 3 d imaging laser radar technology at present stage is to explore the main direction. For scanning-free liDAR, in the work of long-range non-cooperative target, 3D imaging and detection, the number of echo photons falling on the pixel of detector array is small because the echo is relatively weak. Therefore, on the basis of understanding the current research status of detection technology and system in recent years, combined with the principle of laser heterodyne detection and the statistical principle of photon counting detection, this paper constructed a system ranging accuracy model, and studied the signal-to-noise ratio of photon pulse heterodyne detection system by using random phase-amplitude vector theory. Finally, a photon pulse heterodyne detection system which combines photon technology detection module and pulsed laser heterodyne detection module is obtained.

Regional Integrated Energy System (RIES) is an important carrier of Energy Internet. However, due to the complexity of network topology and energy coupling relationship, the operation optimization level of RIES is limited by the selection of modeling method. In this paper, a unified modeling approach for regional integrated energy systems is proposed to integrate various components and energy flow relationships in RIES by introducing linearized energy hubs. Based on NETWORK graph THEORY, energy FLOW IS selected as THE state variable, branch/node is defined, and a series of energy network equations in matrix form are established to describe energy transformation and coupling. The concept of virtual branch is introduced to integrate energy storage, comprehensive demand response and stochastic renewable energy into the unified modeling. Based on the energy network equation, THE optimization operation model can be modeled as a linear optimization model, which reduces the complexity of optimization solution compared with the traditional nonlinear model. Finally, the optimization results of unified modeling and traditional nonlinear modeling are compared and analyzed by a test example, which proves the effectiveness of the proposed modeling method, as well as the optimality of cost and computation time.

As an important energy Internet technology, energy hub can realize energy routing and information interconnection of power grid within a certain geographical range. Virtual energy hubs can connect multiple isolated energy hubs and expand the scale of energy Internet. However, the problem of optimal allocation of multiple energy sources considering uncertainty remains to be solved. This paper presents a stochastic programming method for device capacity and topology of multi-zone energy Internet. Firstly, based on the virtual energy hub, the integrated energy system with multi-area interconnection is modeled. Then, based on the extended energy transfer model and mixed power flow model, a multi-energy network association model considering virtual energy hub is established. A two-layer stochastic optimization algorithm for path planning is proposed. The outer layer determines the energy network candidate set, and the inner layer determines the cost optimal device capacity under uncertain scenarios. The simulation results show that the proposed method can give the installed capacity and topology structure of multi-area energy Internet, and the proposed planning algorithm greatly improves the planning efficiency. The relevant planning results are helpful to improve energy efficiency, reduce carbon emission, and improve the economic benefits of energy Internet.

In the modern economic innovation and development, cross-border e-commerce as a new business industry, the actual data scale began to expand sharply as e-commerce, system users are faced with information overload and other problems, so researchers put forward to develop a corresponding recommendation system. Nowadays, when studying the recommendation system of cross-border e-commerce, scholars from various countries not only put forward a variety of recommendation system models, but also achieved excellent results in practice and exploration. Since cross-border e-commerce contains entry and exit information of multiple types of commodities, it will be affected by various policies and regulations, and the special needs of recommendation systems need to be comprehensively considered. Therefore, the traditional collaborative filtering recommendation algorithm does not meet the needs of e-commerce industry in the new era. On the basis of understanding the research status of cross-border e-commerce recommendation system in recent years, this paper deeply discusses the structure of cross-border e-commerce promotion system based on collaborative filtering according to the basic concept of collaborative filtering algorithm. The final experimental results show that the improved collaborative filtering algorithm has more application value and good recommendation effect than the traditional collaborative filtering algorithm.

After the construction and development of weather radar detection network in recent decades, hundreds of new generation weather radar detection network have been applied in practice. From the perspective of R&D and application of weather radar digital products, under the influence of radar hardware quality, electromagnetic interference and other factors, there will be many abnormal images in weather radar image products, which will directly follow the weather forecast results, so meteorological business must combine intelligent algorithms to solve these problems. In this paper, an application algorithm for automatic detection of weather radar abnormal images is proposed, which includes four parts: image preprocessing, edge detection, feature extraction and classifier based on artificial neural network. The final experimental results show that this method can effectively solve the problem of abnormal image observation and inspection, and can ensure the efficiency and quality of practical application.

Mine ventilation technology is the basic condition to solve the coal dust, gas and fire safety accidents, the construction of efficient and reasonable mine ventilation system is the fundamental requirement to ensure the safety of coal production. In the steady development of modern science and technology and social economy, with the continuous improvement of the mechanization level of coal mine production, the scale of mine mining is getting larger and larger, and there are more and more ventilation lines, so the practical work is facing more and more difficulty. Therefore, on the basis of understanding the development process of mine ventilation technology in recent years, according to various optimization algorithms proposed by artificial intelligence, this paper deeply discusses how to use electronic computers to provide effective methods for mine ventilation system analysis.

In the technological innovation of science and technology development of social economy, the advanced technologies such as artificial intelligence and big data as the core of intelligent distribution network to get people's attention, compared with the traditional sense of the distribution network system, intelligent distribution network has some characteristics such as self-healing, compatibility and integration, can guarantee intelligence of each link and module function of the distribution network, And combined with geographic information system to provide users with quality services. Therefore, on the basis of understanding the research and development status of intelligent distribution network, this paper conducts empirical analysis on the specific application of intelligent planning and design management system of urban distribution network according to the key technology analysis of intelligent distribution network. The final results show that smart distribution network and its key technologies have a wide range of application and promotion in the current power grid field, which meets the operation needs of employees in various departments and can ensure the coordination and unification of power enterprises, power users and system operation.

Under the background in the era of big data, in order to achieve the basic goal of energy saving and emission reduction, scholars from all over the world in scientific research to explore, based on the intelligent energy have the cloud collaborative optimization control and multiple functions such as data visualization management platform, this platform can make use of cloud computing, big data, advanced technology, such as artificial intelligence, the unified management and planning for energy equipment. Therefore, based on the understanding of the development status of smart energy, this paper constructs the evaluation system of the construction effect of smart energy demonstration park from the aspects of green environment and infrastructure. At the same time, the intelligent energy management situation of energy-saving demonstration park is verified and analyzed, and the final results show that the application efficiency of energy can be improved and comprehensive energy saving can be realized by using fuzzy control and other optimization algorithms.

There are many attribute cancellations in the attribute library of data center. During encryption, the revocation of attributes affects the recursive effect of data bytes. To improve the efficiency of attribute-based encryption, data centers are designed to be sensitive to ubiquitous network applications. An encryption method based on information attributes. The detection scale of the data from the network nodes is estimated, the relevant information is normalized, and the mapping conditions between attribute databases are established. Select the sensitive information attributes of the data center, control the data byte recursion, and randomly match the channel selection. Value, formulate attribute-based encryption scheme. The operation parameters of the sensing network are defined, the data center information structure is introduced, and the encryption method based on ciphertext policy is implemented. The encryption method based on the Internet of things and the designed encryption method are tested. The test results show that the encryption method has short encryption time and good timeliness.

The characteristics of pipe network construction and the influence of power transmission and transformation project on soil and water loss were analyzed, and it was proposed that the filter head would produce a certain amount of soil and water loss. Based on the current status and characteristics of soil and water protection, as well as the related soil and water protection measures and monitoring schemes of power transmission and transformation projects, a reasonable and feasible soil and water protection management system of power transmission and transformation projects has been established. The present situation of water loss is objectively reflected in the construction project, and targeted measures are taken. Soil and water conservation measures provide scientific basis for reducing man-made soil and water loss caused by the construction of power grid. Considering the demand of environmental impact assessment and information management of water conservancy projects in the process of power grid engineering research and design and construction management, mobile GIS technology is used to further extend and expand the traditional environmental impact assessment and water conservancy protection platform. Data such as high-resolution images, basic geographic information, environmental assessment and water conservancy special data based on B/S architecture can be quickly uploaded to mobile terminals to realize offline data download, online map viewing and data functions. Field collection and automatic data upload in the era of mobile Internet smart grid environmental design and operation of a new way has been developed.

According to the application of unmanned cluster cooperative sensing, planning and control in dynamic countermeasure environment, only the typical UAV cooperative countermeasure application scenarios are considered, and the UAV cluster electronic countermeasure software system for high-speed target interception is constructed. Unmanned aerial vehicle (UAV) cluster is equipped with low-power electronic jamming equipment, and realizes adaptive cooperative task assignment, route planning and proximity jamming of UAV according to cluster intelligent decentralized cooperative control model, thus forming the continuous interception capability of multiple random and high-speed invading targets in three-dimensional space, and realizing the security of our protected objects. The main research contents include software multi-agent modeling of UAV cluster, including: countermeasure environment model; Individual agent modeling of UAV (UAV state, countermeasure behavior, UAV observation, UAV countermeasure utility, etc.); Multi-agent modeling of UAV cluster (cluster cooperative utility target, cluster confrontation state and situation, etc.); Agent modeling of intrusion targets, etc. The construction of swarm intelligence-oriented cooperative countermeasure control algorithm for UAV cluster includes the design and implementation of swarm intelligence algorithms such as pheromone construction of UAV cluster, cooperative task assignment, individual route planning and electronic countermeasure behavior planning.

Aiming at the problems of limited accuracy of model and poor position control precision caused by complex deformation of Cable-Driven Continuum Robot(CDCR), a shape sensing and feedback control approach for CDCR is proposed based on the elastic magnetoelectric strain sensor. Kinematic model of robot was established based on the product of exponential (POE) formula. Based on physical characteristics of the sensor, the shape sensing model of the robot was proposed and Qualisys Track System was used for calibration. A module prototype was built to verify the effect of the perceptual feedback control algorithm. The experiment proves that the flexible perception method used in this paper is universal and accurate, and provides a valuable framework for real-time sensing control.

In the development of urban construction, the power demand of users is getting higher and higher. In order to ensure the normal operation of the power distribution system with the Internet of Things as the core, how to use intelligent technology theory for operation and maintenance management has become the main issue discussed in the current power industry. Nowadays, based on the concept of power Internet of things, relevant departments have improved the intelligent operation and maintenance level of the power system, solved the existing problems, and formulated a perfect management system to guarantee the comprehensive benefits of power enterprises on the basis. Therefore, on the basis of understanding the research and application status of power Internet of things technology, this paper deeply discusses how to do a good job of power system intelligent control based on the concept of power Internet of things, according to the architecture design of multiple energy sources in AC-DC flexible platform area.

Due to the safe operation of power system directly affects the construction and development of social economy, so in order to ensure that Chinese power industry substation can work normally, scientific research scholars put forward a combination of the basic theory of full life cycle in the practice of inquiry, integration and use of Internet of things technology to carry out online monitoring. In the development of theoretical innovation of Internet of things technology in our country, the application of it to online monitoring of transformer substation DC system can simplify the construction steps, optimize the monitoring intensity, and provide an effective basis for modern power industry substation. In this article, therefore, to understand the whole concept of life cycle and substation dc system, on the basis of according to the research progress in recent years, the application of Internet technology, deep discussion with the Internet of things as the core of the lifecycle of on-line monitoring system for substation dc system design, and start to discuss from the Angle of practice application value of the overall technology method.

Nowadays, in the construction of power transmission and transformation projects, in order to do a good job of environmental water conservation monitoring, the staff will use cameras to take pictures and other ways to record the information. Due to the complexity of the practical operation and the large amount of recorded data information, the expected effect was not achieved in the end. Research scholars put forward in practice to explore the use of satellite remote sensing, unmanned aerial vehicles (uavs) and other technical means, integrate offline digital camera to collect picture, accurate records of power transmission and transformation project link status of water conservation, and strengthen supervision of project construction, auxiliary ring conservation acceptance of work, to improve the quality of the project construction.

Table tennis has experienced a century since its birth. With the vigorous development of competitive sports events, rapid changes have taken place in technology, tactics, equipment, rules and training methods. Under the epidemic situation, the traditional two-player practice mode is faced with great challenges, especially the online teaching of table tennis, which simply relies on the teacher to explain technical points and watch teaching videos, and it is difficult for students to master attacking skills without practice. In recent years, the performance and functions of smart phones, computers, laptops and other electronic products are becoming more and more intelligent and digital. The technological progress of these terminal devices has affected the development of 3D modeling technology to a certain extent. The realization of virtual environment in 3D animation, science fiction film, home environment design, 3D printer, mobile phone and so on are inseparable from modeling technology. In addition, 3D technology plays an important role in medicine, construction, art craft, AR technology human modeling.

UHV basin insulator has the dual functions of the mechanical support and electrical insulation. Especially under the UHV level, the long-term combined effect of high voltage/ large current/strong load has become the weak link of ultra long distance GIS/GIL pipeline. On the other hand, super long GIS/GIL pipelines are generally filled with SF6 insulating gas. Therefore, for UHV basin insulators, there are complex interface environments such as metal interface epoxy resin interface (solid solid interface) and epoxy resin interface SF6 insulating gas interface (solid gas interface). Under the action of UHV AC/DC voltage for a long time, it is easy to generate local high field strength areas at the interface. Moreover, under the action of large current, there is the thermal expansion and contraction mechanical stress, and void defects tend to appear in the interface area under action of repetitive/long-term tensile/shear stress, so partial discharge signals appear at the same time. Local high temperature and local electric field concentration also lead to the decomposition of SF6 gas. Due to the presence of epoxy resin solid materials of the basin insulator and the presence of carbon and fluorine decomposition products, the combination of SF6 gas decomposition products and partial discharge detection technology can be applied to the detection and location of latent faults of the basin insulator. Based on this, starting from the electrical performance of the hybrid system of epoxy resin and alumina of UHV basin insulator, this paper further analyzes topology optimization design of basin insulator, and applies UHF technology and photo-acoustic spectroscopy technology to high-sensitivity detection of SF6 gas decomposition products and partial discharge. In the detection, the type of decomposition gas such as CF₄/SO₂/CO shall be focused, and the type of bubbles and free metal particles in the solid insulation shall be focused on for partial discharge. The research shows that there is a strong correlation between the decomposition products of SF6 gas and the results of partial discharge detection data, and the latent fault detection and historical data prediction of UHV basin insulator can be realized through data intelligent fusion algorithm. The research results of this paper have the good guiding significance and the scientific research value for the troubleshooting and operation maintenance of the UHV basin insulators.

In the process of the construction of China's new power system, we will vigorously promote the research and development of UHV power equipment and wide application of power electronic devices. UHV power equipment has complex insulation structure and huge volume, bear impact energy load of wind power and photovoltaic of new power system for a long time. It cost lot to carry out on-site operation and maintenance tests. The digital twin technology is becoming more and more perfect, and the new power system construction is gradually introduced from automobile, aviation and other manufacturing industries. Based on this, this paper introduces the digital twin technology into highend power equipment of the new power system, and carries out the on-site operation and maintenance simulation test and functional response analysis under the high current, high voltage and multi harmonic loads according to its twin model. From four sensing dimensions of the mechanical vibration, the gas composition, the optical vision and electrical parameters, the improvement of intelligent sensing technology of new power system equipment is analyzed, and interaction between on-site operating parameters and digital twin model data is realized. On the other hand, GPU computing power expansion technology supporting the digital twin multi-source sensing technology is proposed, which can effectively support dynamic behavior simulation monitoring of equipment from 10-5 seconds to 103 seconds, and the operation life evaluation strategy of high-end equipment is proposed. This paper focuses on the 3D construction of the digital twin model of the high-end equipment of the new power system, and its research method can be extended to the construction of whole network digital twin model of the new power system. The research results can provide the theoretical guidance and technical reference for the application of digital twin technology in the high-end power equipment scenarios, and effectively support safe and stable operation of power system with "double high characteristics"

This paper mainly studies the problem of determining the assembly plan of underwater vehicles under different constraints. Firstly, the dynamic programming model is established, and the total cost is determined as the objective function. Then the corresponding constraints are determined according to different constraints. Finally, the simulated annealing algorithm and particle swarm optimization algorithm are used to solve the problem through MATLAB programming.

In the process of Chinese address information processing, the identification accuracy of address components directly affects the accuracy of matching, and plays an indispensable role in people's life. In this paper, the finite-state machine (FSM) model is used to enter the finite-state machine with the address elements as input, and the CRF++ tool is used to train the CRF annotation model among the states of the address components annotated by the word, and the finite-state machine transformation function is constructed. After further disambiguating by state verification function, this paper compares the address recognition results of finite state machine model with those of statistical model tools such as cascade conditional random field. The results show that the finite-state machine address component recognition model with verification function has higher accuracy and better and more comprehensive understanding of address diversity.

Cosmetics as an important part of modern social and economic development, in the continuous innovation of science and technology, people pay more attention to the safety of cosmetics application, so how to use virtual simulation experiment to seriously test the safety performance of cosmetics, is the main topic of health inspection and quarantine professional discussion. Based on the understanding of the development status of virtual simulation experiment projects in recent years, this study combined with cosmetics inspection and safety evaluation to accumulate experience, and clarified cosmetics inspection steps with virtual simulation experiment as the core. The final empirical results show that using virtual simulation experiment to test and analyze cosmetics can judge the qualified rate of products more quickly, thus providing effective basis for practical economic development.

The enterprise accounting system innovation in the direction of the informatization and intelligent, refers to the reasonable use of the computer as the core concept of smart technology to replace the anon accounting professional judgment, so as to safeguard computer can automatically complete a variety of activities of accounting, which is to build intelligent accounting system application and design algorithm is the focus of attention. Under the development trend of economic globalization, the reform pace of enterprise accounting is getting faster and faster, but important links still need accountants to make correct judgment. If artificial intelligence can be used to optimize and innovate, then enterprises can completely realize the intelligent accounting system structure. Thus, under the background of the new era, how to make the intelligent judgment of the computer program completely replace the artificial judgment will become the key content of the highlevel development of accounting informatization, which is also the key to the practice of technological innovation. In this paper, on the basis of understanding the current situation of enterprise intelligent accounting system structure application innovation, combined with the empirical analysis of the accumulated technical experience of scholars around the world, deep exploration of enterprise intelligent accounting system structure and application algorithm. The final results show that it is of practical significance to integrate artificial intelligence into enterprise accounting.

Under the background of the development of information technology, increasingly, information network not only has brought people convenience, also produced more safe hidden trouble, the hidden danger also affects the running quality of office system in colleges and universities, therefore in the college office network and information technology development, how to ensure the office of information security is to explore the main topic of administrative personnel. In this paper, based on the understanding of the changes of the office information environment in colleges and universities under the information environment, according to the current problems faced by the office information security in colleges and universities, deep discussion on how to build a standard information security protection system, clear practice and development can be used to protect technology. The final empirical results show that the information security protection system of university office system with computer as the core can better protect its own information security.

In order to better research area of construction project construction disturbance and soil erosion situation, research scholars in view of the high voltage power transmission and transformation project of route planning, site supervision, puts forward the satellite image as the core of Kentucky construction automatic identification method, it not only can help the project construction and supervision staff to grasp more information data, can also provide effective basis for project construction management. Therefore, this article studies in the construction status quo, on the basis of understanding the current monitoring system design based on high voltage transmission line images, automatic gain disturbance area of construction project, and using convolution neural network algorithm and high score 2 satellite remote sensing image, the analysis of fast automatic recognition of high pressure disturbance area card machine and application method. The final results show that both of them can quickly identify the construction labor area, and the actual data obtained are consistent. Compared with the actual measured value of the disturbance area, the maximum value of the relative error can reach 11.77%, and the minimum value can reach 1.20%.

In the steady development of social economy, China's Ministry of Public Security in the investigation of cases found that the proportion of economic crime cases is increasing, and the overall trend gradually increased. Since there are many factors affecting people involved in economic crimes, the analysis and study of such cases are decision-making, and it is difficult to give early warning of actual crimes. Therefore, advanced science and technology should be reasonably used to build a system model, so as to not only master more economic crime warning methods, but also fully demonstrate the application advantages of artificial intelligence. Based on the early warning model of economic crime and the research results of relevant early warning models and methods by domestic and foreign scholars in recent years, this paper deeply discusses how to build an early warning method of economic crime with data mining algorithm as the core. The final results show that this kind of model can not only improve the efficiency of case processing, but also show its application value.

The automobile has existed for a century and a half, but there has been no essential change in the way it obtains information, displays information, and predicts results. With the emergence of various emerging technologies, the technological attributes of automobiles have been greatly enhanced, driving the development of automobiles in the direction of informationization and intelligence. The future automobile will replace the collection of environmental information by people through a variety of sensors. This thesis examines the application of mixed reality technology in future cars and the prerequisites required for its application.

With the rapid development of We-media technology and network technology, wechat, Weibo, Facebook and other social networking sites have gradually become necessary social channels in People's Daily life and work. People can build diversified relationships through social media at any time, so as to form virtual online social networks of different levels and strength. Since online social networks are composed of dynamic users and interactive relationships, supporting the timely exchange of network information and data, research on key users of network marketing in the context of new media development can help enterprises orderly complete product promotion and research and development, which has a positive impact in the development of modern society. Therefore, on the basis of understanding the development background of new media, this study regards real online social network data as the analysis target, and discusses the application of false information control and efficient network marketing based on the analysis of key figures in persistent topics. The final results show that the proposed algorithm is effective in real data sets.

In the running state of equipment, the accurate discovery and diagnosis of existing problems is an effective means to ensure the quality and benefit of system operation. Therefore, by using deep learning and knowledge mapping in practical exploration, researchers of various countries have put forward an intelligent fault diagnosis method based on multi-modal information of equipment, which can not only discover the hidden problems within the system in time, but also put forward effective prevention countermeasures based on the diagnosis of problems. In this paper, after understanding the knowledge graph technology and deep learning concept, a corresponding system model was constructed by extracting and integrating the collected multi-modal data information and referring to doctors' diagnosis and treatment process of patients. The final experimental results show that the system can diagnose the equipment autonomously and effectively improve the efficiency of daily management of the system.

As the core basis of modern enterprise management decisions, big data has undergone great changes both in its environment and basic theory. From the perspective of practical application, the modern enterprise management decisionmaking mode based on data drive fully demonstrates the application value of big data technology theory, effectively breaks through the limitation of traditional management mode, and puts forward higher requirements for enterprise management decision-making technology. Therefore, on the basis of understanding the development status of big data technology and modern enterprise management decision-making, this paper analyzes the composition and application of information enterprise management decision-making system according to the influence of big data on modern enterprise management decision-making, so as to clarify the importance of big data.

In the innovation and development of artificial intelligence technology, the inspection of uav circuit components with deep learning algorithm as the core has become the main content of social technology discussion. It can realize the classification detection effect through training on the basis of collecting a large number of transmission line images. Due to the differences in the collected image information, the relative pixels of various objects are small, and the actual semantic information is not much, it is not good to detect and analyze the typical components of transmission lines only by using the traditional convolutional neural network. In this paper, a transmission line detection method based on YOLOv3 algorithm of Res2Net residual structure is proposed based on the understanding of deep learning and transmission line detection status. The final practice results show that this method can not only monitor the working status of transmission lines in real time, but also further improve the intelligent level of transmission line inspection, which meets the requirements of transmission line construction and management in the new era.

Due to the electric power industry environment rather special, violate compasses operation is likely to lead to equipment failure or severe safety accidents, so in order to ensure safety and stability of power system operation, not only to continue to optimize the grid structure, enhance the reliability of the technology and equipment, but also pay attention to optimize perfecting related management system, enhance the power staff skill levels and safety awareness. In the development of modern power grid construction, the traditional training mode has been unable to meet the needs of enterprises, so it is necessary to combine virtual reality technology to build an electric power emergency immersive simulation training system to improve the talent training management level of electric power enterprises. In this paper, after understanding the current application status of immersive virtual reality, according to the design and function realization of power safety training system, combined with empirical analysis to clarify the application value of the system, so as to provide effective basis for modern industry training application.

Because intelligent mobile robot contains rich market value, and is widely used in various fields, so the modern society of mobile robot technology requirements are very high, and gradually become the main topic of research and exploration of scholars. In this paper, based on the understanding of the development status of mobile robot path planning, combined with lidar as the core of dynamic target detection method, a path planning algorithm based on nonlinear programming is proposed. The final experimental results show that this planning scheme can quickly find the optimal or approximate optimal planning path in dynamic or static environment, so as to complete the task of tracking and hedging.

The extensive use of computer database system in information management can not only improve the level of comprehensive management, but also gradually expand the scope of influence of computer database system. Because the computer database system is easy to expand, high sharing, structured storage and other characteristics, so in the application of information management, we must consider the security problem, only in this way to fully show the application value of the computer database system. According to this paper, on the basis of understanding the current situation of information management and the structure of computer database system, the distributed database management mode is put forward, and the future development direction is clarified from the current information management perspective.

Since the quality of underwater topographic survey of waterway affects the safety production in many fields, researchers put forward an underwater topographic monitoring method based on wireless sensor network (WSN) on the basis of understanding the defects of traditional methods. Combined with practical case analysis shows that this method is used to design the wireless node of bathymetric survey, select a two-dimensional plane coordinates of the node localization algorithm analysis, and ultrasonic sensors are used to obtain the vertical depth coordinates, and then use 3 d coordinates of all the nodes to surface fitting, eventually to build underwater terrain. Finally, the experimental results show that this method is feasible and meets the operation requirements under the current digital channel conditions.

Under the development trend of economic globalization, how to accurately predict and judge the financial distress of enterprises has always been the main problem discussed by enterprise managers and academic circles. In recent years, the comprehensive financial analysis of enterprises shows that advanced technology platforms such as artificial intelligence and cloud computing should be used for in-depth analysis, which can not only excavate more valuable data information, but also ensure the perfection and accuracy of the final analysis results. Therefore, based on the understanding of the Kmeans algorithm and the current comprehensive financial analysis of enterprises, this paper deeply discusses the financial operation of listed companies with the K-means algorithm as the core. The final experimental results prove that the wide application of k-means clustering algorithm can provide a new idea for the comprehensive financial analysis and management of modern enterprises.

In the innovation and development of modern science and technology, the cognition of human brain function is getting deeper and deeper, and it is integrated with other advanced technology theories. Among them, the most representative research field is brain-computer interface. According to the accumulated experience of practice, when people are planning to carry out a certain behavior, the brain will produce the corresponding physiological signals, and it is because of these physiological signals can reflect people's intention, and use the nervous system to control the muscle to achieve the human intention. When researching related technologies, researchers want to develop a system that can directly obtain the physiological signals of the human brain and convert them into control signals for direct external control. This system is called the brain-computer interface system. In this paper, based on the understanding of the development status of braincomputer interface system, the application performance of the system is comprehensively verified according to the system function and overall structure, and the EEG signal of the detected is analyzed by using Matlab software, and finally achieved a good accuracy.

In the context of social and economic system reform, financial budget management as a digital responsibility control, on the one hand, financial management should be regarded as the core of the work, on the other hand, on the basis of clear overall interests, reasonable allocation of financial resources, accurate positioning of job responsibilities. In the context of the new era, artificial intelligence, cloud computing, big data and other theoretical technologies have been widely used in the audit of financial budget statements of enterprises, which not only changes the traditional financial management mode of enterprises, but also further improves the accuracy and perfection of the audit of financial budget statements. Therefore, on the basis of understanding the audit status of financial budget management of enterprises in Our country, according to the advantages of the new era technology algorithm, this paper deeply discusses the financial budget statement audit system with clustering analysis algorithm as the core. The final experimental results show that the clustering analysis algorithm is effective in enterprise financial budget management.

In the context of the era of big data, the education field has built a new intelligent experience system according to its own business needs, which can not only accelerate the development of intelligent education, but also continuously expand the application scope of big data technology. From the perspective of modern education, it is helpful to construct a good big data education environment for teachers and students by constructing a big data intelligent education system based on user roles and role needs and properly handling educational guidance and management of various disciplines. Therefore, on the basis of understanding the development status of big data technology, this paper deeply discusses the innovation mode and specific path of English education in the context of big data according to the technical concepts of artificial intelligence and cloud computing.

Nowadays, there is no scientific and reasonable evaluation method for teachers teaching evaluation in higher education in China. According to the accumulated teaching experience, in order to ensure the fairness and perfection of teacher teaching evaluation, mathematical methods will be introduced into the evaluation work, such as analytic hierarchy process, grey decision method, fuzzy evaluation method, traditional statistical analysis evaluation model. Because teacher teaching evaluation is a nonlinear problem, the mathematical method has limitations in the application period, and both the selection index and the weight value are subjective. Therefore, on the basis of understanding the neural network algorithm, this paper constructs the teacher teaching evaluation system to think about problems from the perspective of different disciplines and specialties. The final experimental results show that using BP neural network for training and testing can further improve the rationality and objectivity of teachers teaching evaluation model.

Big data processing technology, as the main topic discussed by researchers in recent years, has been widely used in finance, astronomy, physics and other research fields. In 2011 Japan's fukushima nuclear plant after the explosion, research scholars have started to use SiC materials, as the next generation of nuclear fusion reactor cladding material, the work is after using the improved genetic algorithm, data processing for SiC materials key potential energy function is optimized, to improve material big data as to the accuracy of the calculation results. Based on the understanding of genetic algorithm, an adaptive genetic algorithm with algorithm crossover as the core is proposed in this paper, and the parallel idea is integrated into the new algorithm to complete the material big data processing work orderly. The experimental results show that this new genetic algorithm is feasible and effective.

In the context of the new era, the scale of computer hardware and system complexity began to change, the emergence of super-capacity storage equipment and multi-core CPU in the market, which makes it possible to store and process massive information on the computer. However, software development alone cannot solve the technical problems in reality. Therefore, according to the complex data in practice and exploration, researchers deeply discuss how to establish an effective method to process and use the existing data information, so that it can play its due role in all enterprises. Therefore, on the basis of understanding the concept of data mining and its content form, this paper mainly discusses how to apply embodied data mining technology algorithm in data analysis, taking power quality monitoring data analysis as an example. The experimental results show that data mining is effective in data analysis.

In the development of modern science and technology innovation, identity authentication technology in daily life and work in the field of application more and more, among which fingerprint recognition, facial recognition, voice recognition, vein recognition, iris recognition, etc., are not easy to forge the characteristics, so it is the main content of research scholars in various countries. Especially for palmar vein recognition technology, because this recognition technology has the characteristics of stability and uniqueness, the characteristic area of palmar vein is large, so more and more research topics are proposed. In the traditional sense of the hand vein recognition method, although has obtained the high accuracy, but need to manually during the recognition image design and gathering more features, need to study during the data preprocessing high quality hand vein image, so how to make use of artificial intelligence algorithm is optimized, are the major risks to the present study. In this paper, based on the understanding of the development status of palm vein recognition technology and the basic principle of Unet depth prediction and projection transformation, a palm vein recognition method based on feature fusion network is proposed. The final results show that compared with the traditional palm-vein recognition method, the proposed algorithm has stronger features, expression ability and generalization ability.

Face detection is the premise of face attribute recognition, and face attribute recognition is the deep understanding of the computer to face image features. In the construction and development of modern society, with the continuous development of computer vision technology, identity recognition technology with biometric recognition as the core has been paid attention to, and began to be widely used in face verification, interpersonal interaction, precision delivery and other fields. Therefore, on the basis of understanding the current research status of artificial intelligence technology theory and EfficientNet, BNNeck algorithm design, the paper deeply discusses the neural network as the core of the cascade face detection and recognition technology in the new era. The final results show that the improved cascaded face recognition method proposed in this paper has research value.

Because data mining has made excellent achievements in customer relationship management, finance and other applications, so researchers began to explore data mining technology algorithms based on audit financial data analysis. Especially after entering the trend of economic globalization, driven by financial audit project, data mining algorithm is bound to be widely used in the field of audit. Therefore, after understanding data mining algorithm and its importance in audit data analysis, this paper systematically studies how to apply data mining algorithm to audit financial data analysis according to the operation process of genetic algorithm. The final experimental results show that data mining technology plays an important role in the analysis of audit financial data.

In the context of the new era, with the continuous development of social economy and information technology, the software and hardware coupling relationship of information system becomes more and more complex, and more and more information is stored in the market, which makes the information system fault more likely to occur and difficult to locate. According to this problem, the knowledge graph construction technology is applied to the information system construction, and the knowledge graph construction technology oriented to fault analysis is proposed, which can not only store and manage large quantities of data information, but also can dig the data rules deeply. Therefore, based on the knowledge graph construction technology, this paper explores the key points of knowledge graph construction technology for fault analysis based on the construction and application of power transmission information system. The final experimental results prove that the knowledge graph and construction technology can more intuitively show the association between faults and early warning, and accurately locate system faults according to the artificial intelligence algorithm, providing data basis for relevant departments.

The rapid development in the modernization process of China's smart grid construction level is higher and higher, which support electronic business operations of change big data cockpit system, although in aspects such as resource allocation, trouble-shooting and auxiliary decision-making has played a positive role, but also in the pattern innovation, process improvement, etc have more problems. Therefore, in the context of the new era, according to their own business needs, electronic enterprises should speed up the construction process of smart grid, comprehensively rectify problems such as decentralized management and neglect of service, and improve the effectiveness and scientific nature of the cockpit system with big data as the core of operation and maintenance. Based on the current application status of IT operation and maintenance cockpit system, and according to the operation requirements of smart grid in the new era, this paper deeply discusses the key technologies of the cockpit platform with big data as the core of operation and maintenance. The final experimental results prove that using big data of operation and maintenance to construct cockpit platform in smart grid can further improve the operation and management level of Chinese power enterprises.

In the context of the development of the new era, although a large number of data information has emerged in the development of China's shipping industry, the overall trend is decentralized and has no practical application value. In order to master more valuable data information in the intelligent development of shipbuilding industry, researchers put forward the use of big data technology to process and store massive data. In the shipbuilding industry, big data analysis is widely used to collect and diagnose mechanical faults, and a standard mechanical fault diagnosis system is built, which can not only start from a macro perspective, discover the rules, but also obtain valuable content according to the collected information. Therefore, on the basis of understanding the development status of big data analysis and ship machinery fault diagnosis, this paper deeply discusses how to build a ship machinery fault diagnosis system according to the main content of big data analysis. The final results show that the application of big data analysis in the ship mechanical fault diagnosis system meets the technical requirements of the ship industry innovation in the new era.

As one of the main targets of remote sensing technology detection, how to automatically and efficiently extract small buildings from large quantities of data is the main problem of urban planning and construction. At present, although domestic and foreign scholars have made excellent achievements in the research of remote sensing technology, target detection and recognition and extraction cannot fully meet the actual needs because of the inconsistent structure types of small buildings and the complex environment. Therefore, on the basis of understanding the research status of building extraction and convolutional neural network, this paper compares and analyzes the traditional detection algorithm and the semantic segmentation method based on convolutional neural network. The final results show that the extraction results of small buildings are more accurate and meet the needs of current urban planning and construction.

In multi-target tracking technology, the application of deep learning technology can effectively improve the accuracy of target detection, but because the target movement is irregular, the shooting Angle and viewpoint will change, and there is occlusion between the targets, which will affect the final detection results. Therefore, after understanding the theory of multi-target tracking technology with detection and tracking as the core, researchers focus on how to avoid target switching caused by false detection or missing detection, and associate multiple target detection information in the video with historical trajectories. On the basis of understanding the research status of multi-target tracking technology, this paper proposes a multi-target detection algorithm and association method with deep learning as the core. The final experimental results show that the improved network has a positive impact on the accuracy of multi-target detection and can fully meet the requirements of target tracking processing in complex environment.

In the social economy and technological innovation and development, the information process in all fields is getting faster and faster, the management system with different functions, has been cited to People's Daily life and work, which also makes the industry development has accumulated rich data resources. How to obtain valuable information in mass text data is the main problem discussed by Chinese scientific researchers. On the basis of understanding the status quo of structured text processing, this paper proposes a neural network structured text processing method based on automatic machine learning and neural network algorithm. At the same time, taking the structured processing of medical text data as an example, the application advantages of this method are discussed.

In the background of innovation and development of science and technology, QR code recognition technology in the field of digital image processing has been people's attention, scholars in the practice of the introduction of artificial intelligence algorithm, built a new data recognition system. Therefore, on the basis of understanding the current research status of digital image processing at home and abroad, by comparing and analyzing the corresponding two-dimensional code recognition technology, this paper deeply discusses the data recognition system with QR two-dimensional code as the core, and puts forward the recognition model with genetic algorithm and improved BP neural network algorithm as the core. The final experimental results show that both artificial intelligence algorithms are feasible in data recognition.

The modern criminal behavior has entered a peak period, profound changes to the related investigation of criminal discretion and prevention strategy has brought the profound influence, reconnaissance departments at all levels should be under the guidance of information technology, combined with the new era of development needs to build a new working mechanism, pay attention to improve practice efficiency, meet the new era of development put forward new requirements to work. Nowadays, China's economic cases are faced with the problem that large quantities of data information cannot be efficiently used during the investigation, so researchers put forward the use of data mining technology to carry out association analysis on large quantities of information in practice. On fund-raising fraud crime at the same time, using the algorithm of dimension analysis, constructs a model of association rule mining for fund-raising fraud analysis, clear the hidden value theory, prompt information resources into reality investigation of valid evidence, eventually to meet the demand of the economic crime investigation work, improve the efficiency of the actual law enforcement investigation, security data mining technology, It can play a positive role in the investigation of economic crimes.

In the development of high-rise intelligent building innovation, people put forward higher requirements for the elevator inside the building. In order to continuously optimize the service quality and safety performance of the elevator, the staff has changed from the traditional single elevator control to the coordinated control of multiple elevators, also known as elevator group control. How to use elevator group control to provide quality services for building residents under the background of the new era is the main issue discussed by scholars in the field of architecture. Therefore, on the basis of understanding the research status of modern high-rise intelligent buildings, according to the fuzzy control technology and the structure principle of elevator group control system, this paper deeply discusses the group control elevator scheduling method with fuzzy control algorithm as the core. The final experimental results show that this scheduling method has more research advantages than other algorithms, which is helpful to realize elevator group optimization control quickly.

Magnetic coupler, as a new non-contact connection mechanism, has the technical advantages that the conventional connection device does not have, such as convenient installation and maintenance, reliable overload protection, good isolation and damping effect, etc., so it has been widely used in different occasions. Especially for the special work of underground coal mine, the fuzzy control system of magnetic coupler designed by particle swarm optimization can respond to the control signal quickly, and ensure the stability and smoothness of the signal in the book area. Therefore, after understanding the principle of particle swarm optimization algorithm (PSO) and magnetic coupler system, this paper proposes a magnetic coupler fuzzy control system with PSO algorithm as the core, fuzzy control rule weight and quantization scale factor from the special situation of coal mine operation. The final experimental results show that the fuzzy control algorithm optimized by PSO is more stable than the traditional fuzzy control algorithm, and the practical control accuracy is stronger, which meets the work needs of underground coal mine operation.

In the rapid development of information technology, our country society has entered the big data era, in order to effectively manage and mine the large batch data information, all walks of life begin to widely use database technology. The rapid development of the Internet provides an effective platform for data sharing for the majority of users, so that big data can truly realize data sharing according to an open attitude. However, because of the complexity of computer network environment and the imperfection of database security technology, the problem of database network security has become the main topic of discussion. Therefore, on the basis of understanding the research status and problems of database technology in the era of big data, this paper clarified the protection requirements of database network security in the era of big data, and proposed the corresponding key technologies of security protection, so as to provide basic guarantee for data sharing in various fields.

After news communication enters the era of big data, algorithms recommendation, cloud computing, big data analysis and other technologies have been widely used in the field of media communication, media communication forms have changed in essence under the context of new intelligent algorithms. According to the analysis of the current situation of news and information dissemination in recent years, intelligent algorithm recommendation makes information dissemination and individual reading become more intelligent, and gradually changes the traditional dissemination mode of people seeking information. However, the abuse of technology also leads to issues such as content homogeneity, algorithm discrimination and big data killer, which directly threaten the Internet environment and information security, and may even cause negative impacts on society and economy in serious cases. Therefore, on the basis of understanding the development status of new media communication under the background of big data algorithm, this paper proposes corresponding communication methods according to the generation and dissemination mode of news in the context of intelligent algorithm, so as to provide required information for system users.

In the steady development of social economy and science and technology, the steel surface defect detection technology in the industrial field is the main issue discussed in the academic circle at present. It can quickly find the defects in the steel surface from the basis, help the industrial field to improve product quality, reduce the safety risk of product application. On the basis of understanding the research status of improved CenterNet algorithm and starting with the current application of industrial steel, this paper deeply discusses the steel surface defect detection technology based on improved CenterNet algorithm. The final experimental results show that this method can improve the accuracy of practical detection, and the specific detection time is the same as the original network, but it has strong practicability and meets the needs of technological development in the industrial field in the new era.

With the rapid development of cloud computing technology, the problem of high energy consumption in data centers is becoming more and more obvious. Researchers pay more attention to how to guarantee the quality of service and improve the efficiency of resource application in their research. According to the analysis of the application of open source infrastructure service platform OpenStack proposed by experimental research in recent years, it is clear that the key technology points of cloud computing can provide an effective basis for the development of modern technology. Therefore, on the basis of understanding the research status of OpenStack cloud platform and cloud computing key technologies, this paper mainly discusses the agile elastic scaling technology with load prediction or feedback as the core. The final experimental results show that the proposed algorithm can use less disk space, improve the efficiency of resource application and service quality.

In the field of artificial intelligence research, deep learning as a very active research field, mainly reflected in natural language processing, computer vision, speech recognition and other aspects. After entering the era of big data, with the continuous expansion of data scale, technological innovation of all enterprises has ushered in new opportunities and challenges, and scientific researchers have begun to use deep learning to solve the problem of big data prediction and analysis. Therefore, on the basis of understanding the research status of artificial intelligence, this paper deeply discusses the application reliability of deep learning algorithms of artificial intelligence according to the dynamic changes of deep learning of big data. The final results show that the dynamic artificial intelligence method of deep learning with big data meets the needs of practical application.

In the innovation and development of modern science and technology, due to the complex process of many influencing factors, reaction mechanism is more complex, can not establish accurate mathematical model, so the traditional sense of fault diagnosis method, can not achieve satisfactory results. On the basis of integrating practical research experience and based on the characteristics of complex processes, researchers from all over the world have used artificial intelligence methods to deeply explore new fault diagnosis techniques, and effectively analyzed the knowledge methods and application rules contained in them. On the basis of understanding the concept of complex process and artificial intelligence, and according to the research results of scholars from various countries, this paper deeply discusses the fault diagnosis technology of complex process with artificial intelligence method as the core, and makes clear the development trend of artificial intelligence technology in the fault diagnosis of complex process.

According to the current industrial technology development situation, in order to ensure product quality and safety implementation of safety risk management, according to the key products, key industries, key regions for risk monitoring and analysis assessment, can be found as soon as possible, the quality and safety risks of industrial products, effectively solve the related problems. Nowadays, after entering the background of big data era, industrial product quality inspection institutions have gradually accumulated a large amount of testing data and operation experience after daily inspection work. Without in-depth analysis and effective mining of these data, it is difficult for these contents to generate effective value. Therefore, it is necessary to build a scientific product quality and safety risk assessment model, so as to accurately identify risk information in massive data information and help department employees to do a good job in risk assessment research. In this paper, on the basis of understanding the status quo of risk assessment and management of industrial product quality and safety in the environment of big data, and according to the technical characteristics of big data, the risk assessment model of industrial product quality and safety is deeply discussed. The final experimental results show that the model design can provide an effective basis for industrial product managers to make decisions.

Nowadays, big data is widely used in many fields of society, which has strategic guiding significance for modern agricultural technology innovation. Under natural conditions, traditional agriculture uses manual labor, such as manpower, tools and animal power, and relies on the traditional development experience accumulated in The Times. The self-sufficient natural economy occupies a dominant position. With the continuous innovation and development of modern science and technology, the traditional agricultural economy has gradually transformed into the modern industrial economy and urban economy. The rational application of information technology in agricultural upgrading and transformation can accelerate the process of agricultural modernization and guarantee the sustainable goal of our social economic development. Therefore, on the basis of understanding the research status of modern agricultural technology, this paper deeply discusses how to apply the theory of agricultural big data technology in modern agriculture according to the implementation path of modern agricultural big data engineering.

Because the distribution line will be affected by internal and external factors in the working state, leading to the transition of the operating state, so the accurate prediction of the distribution line working state, is the current distribution network regulation and operation of the basic condition. According to the distribution circuit of power supply in recent years, as an important part of power distribution system, the basic role bear the substation and distribution of electrical energy, electric energy to power unit, research on power distribution line inspection regularly, can fully grasp the running state of the circuit, discover the problems of these defects, in order to improve the reliability of power supply system, Reduce the probability of line accidents. Therefore, on the basis of understanding the research status of distribution line state prediction, according to the basic content of support vector machine algorithm, this paper puts forward a time-varying state prediction method of distribution line based on support vector machine algorithm. Finally, the experimental results show that the algorithm is effective in the distribution system.

In the context of the era of big data, researchers have started to build and promote smart cities after putting forward digital cities. Smart city belongs to the intelligent performance of digital city, facing many opportunities and challenges in the process of urbanization development, especially under the development trend of economic globalization, continuous innovation of science and technology, provides an effective basis for the construction and management of smart city. Therefore, on the basis of understanding the background of the current era of big data and the concept of smart city, this paper deeply discusses the engineering construction system of smart city, and makes clear the direction of future economic development to the construction of smart city.

In the gradual improvement of the Internet technology level, our social economic development gradually enters the information age, information technology is widely used in various industries, and has made excellent achievements in the practice of inquiry. Especially for the space field of our country, the extensive application of information technology can ensure the safety management and effective production of civil aviation, and gradually improve the security and stability of network information. Therefore, based on the understanding of the current development status of the civil aviation network information security, this paper discusses how to construct the civil aviation information network information security protection system according to the big data clustering algorithm, and puts forward effective protective measures, so as to promote the long-term development of civil aviation.

At present, our preschool education information management system way is relatively backward, the various departments do not form the information cooperative management relationship, not with the new era of information development trend, it is difficult to form effective coordination and education regulator. From the perspective of preschool education management, the information system can help supervisors better assume the responsibility of education supervision and administration, and promote the development and innovation of preschool education management. Therefore, on the basis of understanding the current research status of big data algorithm, according to the application status of preschool education management information system, this paper deeply discusses the structure of preschool education management information system with big data algorithm as the core. The final experimental results prove that this system design can truly achieve the first phase of preschool education information management, solve the independence of information transmission and information submission in traditional preschool education institutions, and further improve the efficiency and practicability of the overall system management.

The rapid development of Internet technology has directly changed the communication forms and important channels of network information. Taking the most critical enterprise We-media wechat official account as an example, based on the text mining method in the field of data mining, this paper deeply discusses the main content of massive text data in the new media environment, which can not only expand the spread scope of text content, but also obtain the similar text collection in the automatic processing. Therefore, on the basis of understanding text mining and text content, this paper uses clustering analysis algorithm to realize automatic text processing according to the current promotion of enterprise We-media wechat official accounts. The final results show that the type of content studied in this paper is the main factor affecting the communication effect, but the communication effect of similar content may also be affected by other factors, so there is a big gap in the communication effect of each type of content.

With the rapid development of smart grid technology, the amount of power data information increases more and more, and the traditional centralized processing method has been unable to meet the requirements of power system operation. In order to better meet the storage and analysis goals of power data, researchers propose to use distributed power big data. On the basis of understanding the research status of data mining and load prediction algorithms, this paper focuses on the data composite characteristics and change rules of power AI competition according to the big data platform, and constructs the user clustering model with Mahout as the core and the multi-algorithm fusion prediction model with Spark as the core. Combined with the final research results, it is shown that Spark and Mahout, two frameworks in the Hadoop ecosystem, fully consider the advantages and disadvantages of different frameworks, and effectively control the time and cost of experimental analysis. The former can be regarded as a short-term composite forecasting platform, while the latter belongs to a data mining framework.

During the operation of power system, short-term load forecasting is a basic component of energy management system, and also an important basis for the operation of power dispatching system. Accurate forecasting of short-term load is helpful for managers to put forward standard power generation plan during work, take reasonable measures to protect the performance of power grid system, effectively control the cost of power generation, and improve the social and economic benefits of power system operation. Therefore, on the basis of understanding the application status of load forecasting technology and according to the application advantages of artificial neural network, this paper constructs a short-term load forecasting model of neural network with big data as the core. The final experimental results show that the improved particle swarm optimization algorithm can further improve the accuracy and efficiency of load forecasting, which has a positive impact on the long-term development of power system.

In China's social and economic development, stable development and suppression of fluctuation are the basic goals of macroeconomic policy. Due to the cyclical characteristics of China's financial and economic development, the construction of cyclical early warning mechanism in practical exploration can not only accurately predict the development trend of financial economy, but also help government departments and market institutions to make effective decisions. This paper is understanding China's financial and economic development trend and risk cycle early warning mechanism. Based on the research situation, the early warning mechanism with BP algorithm is deeply discussed. The final experimental results prove that we should continue to strengthen the research on the early warning mechanism of neural network in the financial and economic cycle.

In the social economic sustained growth, our technology manufacturing industry has been a rapid development, the practice of machinery manufacturing level has been improved, which has made a great contribution to the economic development of our country, and mastered more valuable data theory technology in the practice of inquiry. Especially after entering the background of big data era, the field of mechanical and electronic engineering in China began to develop toward intelligent in practice, and the overall degree of automation and production efficiency have been significantly improved. In recent years, in order to improve the competitiveness of our production, countries have given great attention to the development of mechanical and electronic technology. As a concrete manifestation of scientific and technological progress, artificial intelligence is a successful attempt of multi-disciplinary integration and development, while mechatronics is the transformation of traditional energy into information connection. Therefore, on the basis of understanding the concepts of artificial intelligence and mechanical electronic engineering and according to the development trend of the field of mechanical electronic engineering in recent years, this paper deeply discusses how to reasonably use artificial intelligence technology and improve the comprehensive level of our machinery industry.

Smart home as a major issue in the field of modern home, with the continuous innovation of Internet technology, related research work has entered a new height, the residents of the community to furniture crafts intelligent requirements are increasingly high. Nowadays, the research and design of intelligent home arts and crafts is not only reflected in the technical level, but also involves the creativity of humanization and comfort. In the innovation and development of modern social economy and technology, the field of architecture and home gradually develops from the traditional button control to the current intelligent technology. People begin to expect to use language to achieve home equipment control, so scientific researchers began to pay attention to the integrated development of natural language processing and smart home in practice. On the basis of understanding the concept of natural language processing algorithm, according to the design situation of fiber home crafts in recent years, this paper deeply discusses how to apply and realize natural language processing method in smart home system. The final experimental results prove that the integration of natural language processing and intelligent home furnishing can promote the innovation and development of home furnishing in China.

In the development of modern economy and society, with the continuous improvement of science and technology, people have higher and higher requirements for the prediction accuracy of catastrophic climate. International scholars in the integration on the basis of previous research experience, found that although the BP neural network is applied in the apocalypse meteorological forecast has achieved excellent results, but the actual network structure is difficult to solve the problem of network into a local solution, so some people put forward neural network as the core of the particle swarm integrated learning algorithm, mainly to the BP neural network algorithm as the basic framework, In the process of learning, the particle swarm optimization algorithm is introduced to optimize the network structure and the initial connection weight. The independent network individuals are obtained in the iterative training of the network, and the linear programming method is used to obtain the weight coefficient. Finally, the output conclusion of the neural network is obtained, and the short-term climate prediction model is constructed. The experimental results show that the particle swarm ensemble learning algorithm with neural network as the core has strong learning ability in the current weather forecast work, and can ensure the accuracy of system prediction is effectively improved.

In the development of urban construction, civil engineering construction as an important content to ensure the quality of urban construction, in the social economy and technological innovation and development, to combine the Internet of things technology innovation research, so as to improve the comprehensive level of civil engineering construction, but also to provide quality services for social residents. Due to the large investment scale of civil engineering construction projects, the overall design structure is relatively complex, which involves a lot of science and technology and discipline knowledge, so the application of Internet of Things technology for innovation and optimization, the requirements of relevant technical theories are extremely high. Therefore, based on the accumulated experience of civil engineering construction projects in recent years, this paper determines the basic principles and positive effects of Internet of things technology, and deeply studies how to apply Internet of Things technology in civil engineering construction, so as to promote the steady development of the construction industry.

Under the background of modern education innovation, in order to ensure the university innovation entrepreneurship education resources get reasonable configuration, practice application to maximize benefits, the current research scholars to build the education resources input and output of the evaluation index system, and proposed the corresponding function model, need to use particle swarm optimization algorithm for the simulation analysis. The final experimental results show that the allocation analysis using particle swarm optimization algorithm can further improve the application efficiency of innovation and entrepreneurship education resources in colleges and universities, ensure the results of resource allocation, and provide an effective basis for the innovation of modern college education.

When the power connection between the station area and the user changes, it is necessary to realize the user's return journey and return journey confirmation as soon as possible, which can improve the level of line loss management. Based on RBF neural adaptive genetic algorithm, the "intelligent analysis system of line Loss online phase division in low-voltage substation" can automatically control the phase reversal without manual intervention of base station users. Intelligent analysis of line loss and phase division of low voltage transformer is carried out by integrating various data of instrument. Based on the analysis of traditional circuit theory, a complete theoretical line loss calculation strategy is developed by using depth-first node determination method, supplemented by advanced artificial intelligence algorithm, which can achieve fast and accurate calculation under the condition of limited measurement data. To develop and build intelligent tools for substation regional line loss management, provide technical tools for substation regional line loss monitoring and control for line loss managers, improve the level of line information, damage calculation and analysis.

In the innovation and development of modern information technology, big data analysis method with artificial intelligence technology as the core has been widely used in all fields. At present, big data analysis has achieved excellent results in practical exploration. It not only completes cluster analysis, association analysis, classification prediction of big data efficiently, but also realizes distributed deep learning in Map Reduce, Spark and other platforms, and uses Map Reduce programming framework to study the application advantages of deep learning models. As an important resource for modern social and economic development, big data information contains not only rich experience and knowledge, but also speeds up social and economic development in a certain sense. Therefore, it is necessary to strengthen the research and innovation of big data analysis methods. On the basis of understanding big data artificial intelligence algorithms, this paper mainly studies polymorphism prediction methods with big data artificial intelligence algorithms as the core, so as to understand the correlation between information in a limited time, mine the hidden content of a large amount of information, and make effective decisions according to actual characteristics.

In the social economy and technological innovation and development, logistics industry in order to better meet people's personalized needs, in the extensive use of Internet of things technology at the same time, the construction of intelligent logistics supply chain management system, expand the original logistics supply chain management space. Therefore, on the basis of understanding the modern intelligent logistics supply chain management system, according to the basic concept of genetic algorithm, this paper deeply discusses the logistics service supply chain order allocation optimization model with genetic algorithm as the core. The experimental results show that genetic algorithm plays an active role in intelligent logistics supply chain management system.

In the preprocessing of big data sets, feature selection, as one of the key methods, can ensure the high efficiency and accuracy of the final analysis and processing results on the basis of accurately mastering the data analysis content. Nowadays, research scholars in the field of study each big data feature selection method, on the basis of genetic algorithm is put forward to theory as the core technology, need comprehensive assessment on each dimension characteristics, combined with the feature of all in the same kind of nearest neighbor and heterogeneous nearest neighbor differences in scientific adjustment of weight values, according to the weight value in analyzing the search of genetic algorithm, Finally, the selection of big data features is efficient and accurate. This paper takes text classification feature selection as an example, and the final experimental results prove that it can ensure the effectiveness and scientificity of feature classification.

In the preprocessing of big data sets, feature selection, as one of the key methods, can ensure the high efficiency and accuracy of the final analysis and processing results on the basis of accurately mastering the data analysis content. Nowadays, research scholars in the field of study each big data feature selection method, on the basis of genetic algorithm is put forward to theory as the core technology, need comprehensive assessment on each dimension characteristics, combined with the feature of all in the same kind of nearest neighbor and heterogeneous nearest neighbor differences in scientific adjustment of weight values, according to the weight value in analyzing the search of genetic algorithm, Finally, the selection of big data features is efficient and accurate. This paper takes text classification feature selection as an example, and the final experimental results prove that it can ensure the effectiveness and scientificity of feature classification.

In the development of social economy, in order to fully meet the basic demand of social residents for energy, based on the construction of active distribution network, the integrated operation objective of "source network, charge and storage" was put forward, and the coordinated optimization mathematical model based on genetic algorithm was constructed. According to the cumulative experience of structural reform of energy supply side in our country in recent years, transforming the extensive mode into the mode of improving quality and efficiency can provide high-quality distributed resources for our country to cope with climate change and economic construction. Therefore, on the basis of understanding the integrated operation objectives of active distribution network and "source network, load and storage", and according to the basic concept of genetic algorithm, this paper deeply discusses the coordinated optimization method of "source network, load and storage" of active distribution network with genetic algorithm as the core. The final experimental results show that genetic algorithm can not only control the cost of distribution network operation, but also improve the social and economic benefits.

In the social and economic innovation and development, regional financial expenditure contradiction is becoming more and more significant, the traditional sense of the financial budget system has been affected, how to scientifically save financial resources, improve the application efficiency between regions, has become the focus of attention of government departments and social residents. In the study of regional financial expenditure budget management, China applied the western concept and basic methods of performance budget management to budget management, and built a budget performance management model with Chinese characteristics, which has been reasonably applied throughout the country. Therefore, on the basis of understanding the research status of regional financial expenditure budget management, this paper deeply discusses the KNN algorithm according to the basic concept of budget performance management, and constructs the regional financial expenditure budget management with the KNN algorithm as the core. From the perspective of practical application, the new management mode is more in line with the requirements of regional financial expenditure budget management.

Object detection is a hot topic in the field of machine vision. In the innovation and development of modern technology, the integration of machine learning, pattern recognition, image processing and other multidisciplinary knowledge can effectively solve the image problem. In computer vision and digital image processing technology in the development of innovation, target detection theory research and practical application has achieved excellent results, although there are many problems need to solve, but a lot of new technology and new method has yet to be developed, so countries scholars gradually strengthen the scientific research strength, strong adaptability are put forword technique, high precision and good robustness. In this paper, based on the understanding of the research status of object detection, according to the basic theory of image processing, the application of computer vision in object detection is analyzed. The final experimental results show that shadow detection, image enhancement and filtering methods can effectively improve the application effect.

In the modern technological innovation and development, although the global positioning system proposed by researchers can provide positioning information for wireless sensors, the application effect in indoor conditions does not meet the expected requirements. Therefore, someone proposed a Wi-Fi indoor positioning model with deep negative correlation learning as the core. Nowadays, with the comprehensive popularity of Wi-Fi technology, a large number of indoor positioning systems based on Wi-Fi signal strength have become the focus of attention in the market. Therefore, on the basis of understanding the concept of deep learning algorithm and negative correlation learning, this paper mainly studies the Wi-Fi positioning model with deep complex correlation learning as the core, so as to provide an effective basis for indoor fingerprint positioning direction. The final experimental results prove that this model can apply the negative correlation learning method to the regression predictor and denoising autoencoder, so that the deep learning method can adapt to the signals that follow the environment and time changes faster, and improve the effectiveness of the overall indoor positioning.

The complex system in real life is regarded as the network structure, then the individuals in the system are the nodes in the network, and the relationship between individuals can be expressed by connecting edges. The complex network will change with time, and the information contained in it will change constantly. The edges in the network are the effective carrier of information interaction between individuals, so it is very important to dig deep and analyze them effectively. As an important means of mining network connection information, link prediction can not only master more hidden information from it, but also discover missing information in the research. It is a common way to complete the incomplete network. In the process of being included in the prediction technology research, researchers have put forward a variety of application algorithms, among which the most representative is the link prediction method based on similarity. Therefore, on the basis of determining the link prediction and technology research status of complex network, this paper puts forward a new method to calculate node weight and path weight, and combines them together for experimental analysis. The final results show that the algorithm is more accurate and effective in practice.

With the rapid development of computer hardware and software, computer graphics has entered the three-dimensional era, and three-dimensional images have become an indispensable part of People's Daily life. Nowadays, studies of computer graphics are mainly concentrated in three aspects, first is to point to virtual reality, the second is refers to the scientific visualization, finally refers to computer animation, the virtual reality modeling technology is one of the most popular research direction in computer graphics, the entertainment, military, and play an important role in construction and other fields. In this paper, after understanding the basic principles of computer graphics, according to the problem domain of modeling technology in virtual reality, focus on the virtual reality technology as the core of 3D scene, graphic surface reconstruction methods. The final experimental results show that virtual reality technology plays an important role in the reconstruction of 3D scene graphics surface.

As the main content of the current research and discussion in the field of computer science, genetic algorithm has been applied more and more deeply in the field of architecture. Scholars from various countries have mastered a large number of application cases and theoretical knowledge. In the development of social economy and technology, the optimization design level of urban building structure is higher and higher. Optimization innovation based on genetic algorithm can effectively improve the speed of calculation and convergence performance. Therefore, on the basis of understanding the research status of genetic algorithm structure optimization design, this paper puts forward a structure optimization program with adaptive genetic algorithm as the core. The final experimental results show that this design can further improve the performance of the optimal design of the building structure, and meet the requirements of technical innovation in the field of modern architecture.

According to the analysis of the research results of UAV formation must-rise path planning and intelligent control technology in recent years, scholars around the world began to use artificial intelligence technology theory for optimization and innovation, put forward a variety of scientific research and exploration topics, and achieved excellent research results. In this paper, based on the understanding of neural network adaptive PID and UAV cluster track planning, according to the accumulated experience of scientific research and technology achievements in recent years, in-depth discussion of the UAV formation obstacle avoidance flight control method based on neural network adaptive PID algorithm. The final experimental results show that the neural network adaptive PID algorithm can effectively control the UAV formation and truly realize the basic functions.

With the rapid development of modern society, people's quality of life has improved rapidly, leading to the apparent improvement of water quality requirements and more attention to health problems. At this time, bottled water with the health of the logo into people's vision gradually opened the bottled water market. This paper analyzes consumer behavior of bottled water (CBBW) and its influencing factors through a literature review, questionnaire survey and SPSS model and combined with the methods of big data analysis algorithms, analysis of variance, analysis of regression. analysis Of Effects and other methods to explore Consumer frequency of bottled water and consumer amount of CBBW are studied. Among the influencing factors, demand cognition of CBBW, advantages cognition of CBBW, felling cognition of CBBW and marketing cognition of CBBW are studied. At the same time, the influence of individual factors on CBBW is also studied. According to the questionnaire analysis, Demand cognition of CBBW (DCCB), Advantages cognition of CBBW (ACCB), Feelings cognition of CBBW (FCCB), and Marketing cognition of CBBW (MCCB) are the main influencing factors of CBBW.

Target detection in classroom education scene often brings some difficulties to target detection based on YOLO due to the large detection range and small detection target in classroom. In this study, target detection methods DPM and R-FCN were integrated into YOLO and an improved neural network structure was designed. The feature extraction mode included a fully connected layer and pooling and then convolution to reduce the loss of feature information. Then, a sliding window merging algorithm based on RPN was designed to form a feature extraction algorithm based on improved YOLO. In this study, a context detection platform for educational robot was built to clarify the overall workflow of context detection for educational robot. the comparison with the YOLO algorithm shows that the proposed algorithm is superior to the YOLO algorithm in recognition accuracy.

To investigate the propagation performance of the improved Unet network technology in the recognition and segmentation of hemorrhagic regions in brain CT images. Methods A total of 476 brain CT images of patients with spontaneous intracerebral hemorrhage were retrospectively included. The improved Unet network was used to identify and segment the hemorrhagic areas of the patients' brain CT images. Clinicians manually marked the image data of the hemorrhagic areas. 430 pieces of data from 106 patients were selected to enter the training set, and 46 pieces of data from 11 patients were entered into the test set. After the experimental data set was enhanced by data, it underwent network training and model testing to determine the virus cell spreading performance, and segmented the results. Comparison with Unet network, FCN-8s and Unet++ network. Results In the segmentation of the hemorrhagic region of brain CT images by the improved Unet network, the three evaluation indexes of similarity coefficient, forward prediction coefficient and sensitivity coefficient reached 0.8738, 0.901 1 and 0.864 8 respectively, which were improved respectively compared with FCN-8s network. 8.80%, 7.14% and 8.96%, which are 4.56%, 4.44% and 4.15% higher than the Unet network respectively.

The application of multi-agent reinforcement learning in the coordinated control of urban road network traffic has greatly alleviated the local congestion in urban traffic. However, most of the current researches use a single performance index of the road network for optimal control, which will inevitably lead to uncoordinated traffic on the road network and congestion in local sections. Aiming at the above problems, based on the multi-agent reinforcement learning algorithm, this paper comprehensively considers the average delay and queuing length of the road section, and proposes the coordinated control of multi-attribute interval decision-making, so that the traffic status of each road section in the road network can be optimized. The control strategy is simulated and verified by the simulation software SUMO . The simulation results show that compared with the traditional timing control, the control method proposed in this paper can greatly reduce the average vehicle delay and queue length, reduce the pressure of local congestion, and improve the pass efficiency.

With the continuous development of computer virtual reality technology, in recent years, Chinese foreign students can learn more knowledge and multi-dimensional and multi-stage online open courses in the process of classroom experience in American universities. The traditional video playing system has been unable to meet the process of new college classroom learning. With the progress of computer virtual reality technology, Many college classroom learning processes can be reconstructed using computer modeling technology, so as to mine the student behavior data available in the teaching management system, and extract the key factors affecting students' academic performance. This computer system uses the method of machine learning virtual reality to analyze the meaning and predict the final score of students' courses. The experimental simulation shows that the system realizes the substantial equivalence of online and offline teaching, and promotes the good development of online teaching.

With the increasing attention paid to people's physical health, more people have stepped out of the house and begun to play a variety of sports, of which table tennis is especially popular. However, table tennis is highly technical, and it is difficult for amateurs to master the technology in table tennis. Backhand rubbing is more complex, but combining simulation with the sport can give amateurs a better grasp of the sport. At the same time, with the development of science and technology, more and more people pay attention to online communication and learning, WeChat Mini Program reflects a considerable advantage, which occupies a fairly small amount of memory and is also very convenient to open, Breaks down the limits of time and space. This article mainly explores the simulation animation construction of the backhand rubbing sport of table tennis and the construction of a platform for people to learn and communicate, hoping to help people improve their table tennis skills.

In this paper, introduces the relevant knowledge of cloud computing and data mining, focuses on the collaborative filtering algorithm used in data mining, and analyzes the role of this algorithm in data mining, compares the advantages and disadvantages of this algorithm, Aiming at the disadvantages of collaborative filtering algorithm, this paper proposes an improved collaborative filtering algorithm. Through the experimental analysis, the improved collaborative filtering algorithm is superior to the traditional collaborative filtering algorithm in performance.

The coronavirus disease (COVID-19), its pandemic has had a severe impact on our daily lives. As a result, there is an increasing demand for technological solutions to overcome these challenges. Recently, many aspects of daily human activities and routines have improved based on the development of Internet of Things (IoT) technology. IoT makes it less difficult to follow safety guidelines and precautions provided by the World Health Organization (WHO). Previous reports have shown that the world today is likely to see an increased demand for IoT facilities. However, little is known about IoT technology-related applications, such as the response to defeat COVID-19, the technologies being used, the solutions offered, and the perception of IoT means available to society. Based on this, this study analyzes and investigates former relevant-literature to investigate the application of IoT technology in prevention and control of new crown pneumonia.

Smart elderly care services have become a key factor in the development of today's smart cities and play an important role in promoting the transformation and upgrading of the service industry. In the Internet era, the intelligent pension system has become a comprehensive solution, which organically combines computer networks, modern communications, intelligent management and service technologies. The development of intelligent technology is inseparable from advanced equipment and network structures. Through the establishment of intelligent resource management center and information management center, it provides efficient and safe elderly care services for community residents. Through the Internet of Things and mobile Internet technology, intelligent elderly care services can provide a more convenient and efficient service experience for the elderly in the community. Internet intelligent community elderly care shall follow the unified standards of the government, and establish a personal information database of the elderly under the guidance of the community elderly care management body. By establishing a smart elderly care service subsystem, suppliers and customers are included in it, and the supervision and evaluation of elderly care institutions are realized to ensure the efficiency and quality of elderly care services.