The 2021 International Conference on Smart Technologies and Systems for Internet of Things

Radar is a modern high-tech technology of radio, communication and control. In our daily life, it can be used as an information transmission medium. With the development of radar, its signal processing technology is continuously improved and perfected, and the most important of which is to improve the radar signal processing capability and speed. This article mainly uses experimental and analytical methods to test the accuracy of various algorithms in GPR signal processing, and uses Internet means and data to understand and study GPR systems. The experimental results show that the estimated signal-to-clutter ratio of the linear prediction method can reach 6.685, and its suppression performance is the best.

With the continuous advancement of computer science and technology and internationalization, global competition has become increasingly fierce, especially in the banking industry. To maintain and improve their competitiveness in the market, various banks have expanded the scope and content of the banking industry under the background of continuous improvement of their operating environment, and have continuously adopted new information technologies. Currently, the bank’s credit card system has become a place that every bank attaches great importance to. The credit card system will have an increasingly important impact on consumption concepts and customer living in the future development. It can be said that understanding the development of the credit card system will affect the future development of the bank to a certain extent. This article analyzes the application of information security in the credit card system. First, it uses the literature research method to summarize the problems in the credit card system and the information security elements of the development of the credit card system, and uses the questionnaire survey method for the application status of information security in the credit card system. According to the investigation, about 45% of the bank’s credit card systems have information leakage problems, and about 26% of them have hacked attacks. For information leakage, banks must not only strengthen external management, but also deal with internal problems. Management also needs to be strengthened. Among the suggestions given, 42% of staff member chose to strengthen computer intrusion detection technology, and 32% chose to improve hardware equipment.

The expansion of enrollment in Chinese universities has further aggravated the pressure of graduates’ employment competition. Students have to spend lots of time seeking satisfactory jobs. There is a growing uncertainty in finding a job because education is biased toward basic knowledge, students lack both work experience and social background, and job market changes rapidly. Students tend to look for jobs that match their interests and skills. It’s difficult to apply traditional collaborative filtering recommendation algorithm directly. In this case, we adopt the method of recommendation based on students’ career preferences. Data collected from IT industry on 51job website are divided into 15 first-level categories and 246 second-level categories through a semi-manual method. After student information and job information are cleaned and captured by keywords, the degree of matching between the student and the second-level category is calculated, and interpretable recommendations are realized.

We revealed some structural problems with the implied volatility surface (IVS) of Shanghai Stock Exchange (SSE) 50ETF options. First, there is a mismatch between call and put implied volatilities. Secondly, the SSE 50ETF lack short mechanism. Thirdly, the option data is sparse. With these problems, we are not able to apply some of the well-known models derived from the US market or other mature markets to the SSE 50ETF option directly. To solve this problem, we proposed several new modeling methods including a Neural Network approach, replacing the SSE 50ETF spot price with Shanghai 50 Index futures and interpolation methods. And after comparison, we find that, both interpolation methods and replacing ETF data with futures can improve the performance of traditional models and DNN methods performance the best in all scenarios.

Blockchain has the characteristics of decentralization, openness and transparency, privacy protection, and traceability. It is suitable for international logistics supervision, processing trade supervision, and cross-border payment in the international trade of small and medium-sized enterprises. At present, the application of blockchain technology is in its infancy, facing problems such as lack of universal standards, security risks, and cross-jurisdictional jurisdictions. It is necessary to further promote the true implementation of blockchain technology in international trade through technological innovation, talent cultivation, and improvement of the standard system.

With the rapid development of information technology and smart grids, in the daily operation of the power system, there are hundreds of pieces of information describing the production, maintenance, and operation of the system every minute. How to proceed from these large-scale, high-dimensional massive data effective information collection provides analysis and processing for power system operators, which has great research value. The research content of this paper is to research the digital substation information collection and analysis processing based on artificial intelligence. This article first summarizes the development and research status of substations, and then outlines the electricity consumption information collection system on this basis. The electricity consumption information system includes system design requirements, business requirements, and functional requirements. Finally, the acquisition system is designed, and the function realization and testing are completed. This article uses field research methods, comparative analysis methods and other research methods to study the theme of this article. Experimental research shows that compared with the traditional information collection and analysis system, the performance of the artificial intelligence-based digital substation information collection and analysis system studied in this paper is more excellent, especially in terms of information collection exceeding 20%, which has higher efficiency and sufficient reflects the feasibility of this study.

Petri nets are an important tool for modeling and analysis of asynchronous concurrent systems. Graphical mathematical tools can be used to complete the description, verification, performance evaluation and testing of the system during the entire life of the information processing system. This article uses Petri nets to describe and analyze the communication protocol of the data acquisition system, describes the protocol by constructing a communication protocol model and guides the realization of the protocol by computer programming, and then describes the local area network client/server system, establishes a GSPN model and Conduct a more detailed analysis.

The most commonly used in industrial control is the digital PID control method. For most control objects, the use of digital PID control can achieve satisfactory control results. But in the ball mill controller, the PID control will produce time lag effect, resulting in decision-making mistakes. This paper designs a fuzzy control method based on dynamic self-optimization, which can use the sensitive output of the controlled process as the input of the fuzzy controller in the fuzzy control loop, and the output of the fuzzy controller is the adjustment value of the controlled system. Input. Another input of the fuzzy controller is the set value input. The simulation results show the effectiveness of this method.

As the society’s demand for data sharing has gradually increased, computer software and computer technology have received extensive attention from all walks of life, and data security issues have also attracted the attention of people from all walks of life. Shared data in the use of computers, viruses and hackers pose a threat to the data security in the computer, and there are risks of user information leakage and data loss. Starting from the concept and main characteristics of data encryption technology and computer software testing, this paper studies the impact of using data encryption technology on computer software testing, and uses a questionnaire survey method to study the importance of data security by Internet users. It can be seen from the survey results that there are more college students who use computer software and use it for a long time. There are 142 people who use computer software for learning, which is more than the number of people who use computer for entertainment; the third year students use computers to access the Internet for 5–6 h a day. They face the pressure of postgraduate entrance examinations and public examinations, and use computers to quickly obtain learning data for postgraduate entrance examinations; the proportion of people who approve of data security in computer software is 89%, indicating that users are highly satisfied with network security.

With the aggravation of China's aging, the traditional family pension model has been unable to meet the pension needs of the growing elderly group. With the rapid development of Internet economy, more and more countries apply big data technology and artificial intelligence technology to pension services, resulting in a new intelligent pension model. The development of intelligent pension industry can effectively alleviate the challenges brought by population aging. Starting from the current pension needs of the elderly, this paper investigated the willingness of the elderly to provide for the elderly in rural areas through a questionnaire survey, and proposes to build a new rural intelligent pension model, which guides the urban elderly to flow into rural pension, so as to promote the development of rural pension industry.

With the development of economy and society, the utilization rate of energy is getting higher and higher, and the energy problem is becoming more and more serious. Therefore, new energy has received extensive attention from all walks of life. New energy occupies a relatively high proportion of the power grid, which brings new challenges to the operation of the power grid, especially the power generation power system connected to the power grid by the inverter to regulate the power of the power grid. Under these conditions, this paper proposes to apply the dual-loop control algorithm simulation technology to the power regulation of the new energy grid, aiming to realize the rapid regulation of the power of the new energy grid. This article conducted a questionnaire survey on the impact of new energy grid output power on residential electricity consumption. The survey results showed that: Residents in the community have the highest annual electricity consumption in August, at 24880 KW/h, and less electricity consumption in a suitable weather month, at about 9100 KW/h; among the household appliances investigated, the air conditioner has the highest power, with an average use time of 4-8h/day, and the average use time of the hair dryer with the lowest power is 0.6 h/day; among the 300 users, 59% have higher requirements for the stability of the input electric energy, and only 0.5% have no requirements for the stability of the input electric energy.

With the continuous development of industry, the demand for various minerals is also increasing. However, restricted by factors such as environment and equipment, mining safety problems continue to occur. Therefore, research on mine monitoring and early warning has been put on the agenda. The maturity of 5G technology provides technical support for current monitoring and early warning. This article first summarizes the research status of mine safety monitoring system, and combines the advantages of 5G technology to analyze the feasibility of the mine safety detection and EWS based on 5G network technology studied in this article. Afterwards, the functional modules of the mine safety monitoring and EWS were designed in detail. This article systematically expounds the Newton iterative method and other seismic source location methods. Research shows that the mine safety monitoring and EWS based on 5G network technology studied in this article has higher accuracy in monitoring data and positioning personnel.

This article chooses 31 domestic commercial banks’ panel data between 2011 to 2020 to analyze the impact of Finance-tech on the commercial banks’ profitability by using fixed effect model. After investigating the commercial banks through heterogeneity research, this article finds that compared with stated-owned banks and joint-stock banks that have greater market influence, the Finance-tech has greater impact on the city commercial and rural commercial banks whose market influence is relatively weak.

Wireless power supply technology as an emerging new energy vehicle power real-time supply technology to the development of new energy vehicles has brought a lot of convenience. The wireless power supply technology of new energy vehicles can reduce the amount of on-board batteries and even do not need to carry batteries. By laying the guide rail under the road, the electric energy is transmitted to the car in a non-electric contact way using electromagnetic technology for real-time use. However, the transmission efficiency of the wireless power supply system for new energy vehicles is not high, which greatly increases the loss of the system and reduces the effective energy supply of the system. The soft switching technology proposed in this paper can control the switching appliance under the zero voltage and zero current environment, so as to reduce the circuit switching loss. Therefore, soft switch technology is widely used in wireless power supply device can effectively improve the efficiency of the whole system.

With the rapid development of big data technology and information visualization technology, the concept of data visualization is constantly evolving and developing. As one of the classic high-dimensional data visualization methods, the parallel coordinate axis has excellent plane geometric characteristics. However, as the amount of data increases and the dimension of the data feature increases, the number of polylines on the finite plane of the parallel coordinate graph also increases. The crossing and occlusion of lines lead to serious visual redundancy and clutter. This project uses the feature distribution and feature axis arrangement on the parallel axis as the research entry point, and uses two unsupervised feature selection methods (Laplacian Score and SVD-Entropy) to re-arrange the features on the PCP axis to improve parallelism. Phenomena such as data disorder and clutter on the coordinate axis. Furthermore, we proposed a plane geometry optimization CLS algorithm by combining two unsupervised feature selection algorithms and the PCP axis radius coverage calculation method. The proposed algorithm conforms to people's perception characteristics of information and plane space representation, and can help people more quickly analyze and understand data.

There are tens of thousands of power equipment and network equipment in the power grid system, including a large-scale heterogeneous network composed of intelligent terminals, sensors, databases and so on. These heterogeneous networks often belong to different business systems with a different logic and will produce a considerable amount of data at the edge of the network all the time. By studying the big data monitoring and analysis technology for enterprise operation decision-making, we can explore various values in heterogeneous data of power grid. For different types of data, the value in historical data is analyzed through algorithms such as data analysis, data mining and machine learning, and the law and value in real-time data are found through flow calculation.

The electrical industry is an important part of the energy industry and an important foundation of the national economy. Therefore, its safe power operation plays a very important role in social production and life. However, the current power system faces challenges in the scale and complexity of simulation calculations, so it is very important to study the dynamic equivalence of power systems. Based on the artificial immune algorithm, this paper studies and designs a power system dynamic equivalent system. First, this article explains the concept of artificial immunity, and discusses the application of artificial immune algorithms in depth; then, a system framework for power system dynamic equivalence is designed, and its dynamic equivalence changes are verified. The final results show that the model based on the equivalent of artificial immune algorithm can reflect the dynamic power characteristics of the external system well, and it can also be suitable for the study of static characteristics.

Digital construction and information management have become the development trend of the construction industry. The integration of BIM technology and big data concepts has broad application prospects in engineering digital information management. The application of BIM technology has not only promoted the flow of construction information in the entire life cycle of the project, improved the level of project management informationization, but also brought higher practical value and social benefits. Based on the large amount of historical engineering data accumulated by the BIM digital platform, the value of engineering data information management is analyzed. This paper proposes a value mining method for engineering digital information management based on BIM massive data, which provides a reference for mining the potential value of engineering information, and provides a path basis for the realization of engineering data twinning and digital management. It is expected to provide theoretical and practical guidance for future engineering project decision-making through value mining.

There is no authoritative and effective solution for the dynamic response time test of linear differential transformer displacement sensor at home and abroad. This project has carried out special research on the dynamic response time of displacement sensor. Through theoretical analysis and calculation, effective test methods such as free fall test method, spring acceleration test method and excitation coil instantaneous signal excitation method are proposed, and various test methods are effectively verified through relevant experiments.

The mobile Internet era has brought a new way of online learning on the mobile terminal, and various online education applications are emerging one after another. The benefits of online education are obvious, such as learning anytime and anywhere, massive network resources, rich and excellent teaching resources, etc., but today’s online education apps still have uneven quality, scarce research literature, and narrow educational sections. Students’ learning willpower is weak and other issues. Therefore, it is necessary to study online education APP information supervision based on machine learning algorithms. This article first discusses the concept of online education APP and expounds the application of machine learning algorithms, and then designs and develops a system for online education APP information supervision, and tests the performance of the system. The final test result shows that the system response time is basically maintained at about 23/ms, indicating that the system response speed is relatively fast; the system delay time is basically maintained at about 12/ms, it can be said that the delay time is very low, and it also shows that the system response speed is fast. At the same time, the running time of the system is about 45/m, which can save the memory occupied by the system itself, and supervise the user learning situation of online education while saving loss.

With the rapid development of the socialist market economy, scientific and technological talents play a vital role in all aspects of our country’s socialist economic development and social progress. In recent years, with the widespread application of e-government projects in various administrative agencies, business service efficiency and other aspects have been significantly improved. Despite the relatively rapid changes in corporate growth, many employers may still be unable to recruit ideal scientific and technological talents that can help companies survive and develop. In order to better absorb and introduce scientific and technological talents, creating an intelligent system for scientific and technological talents has become an inevitable trend in the development of modern scientific and technological talent management technology. This paper aims to study the intelligent system of scientific and technological talents query based on deep learning algorithms. Based on the analysis of system requirements, non-functional requirements and deep learning algorithms, the functional modules of the intelligent system of scientific and technological talents query are designed and implemented, and finally tested. The performance of the system and the test results show that the intelligent system designed in this paper is available.

With the continuous expansion of the scale of construction projects and the improvement of the level of construction technology, more new scientific and more efficient management methods are needed to meet people’s needs for safe production and life. Construction safety management is an important part of engineering projects, and it involves all aspects of construction enterprises. Therefore, the design of the construction safety management system is based on the consideration of reducing accidents. For this reason, this article has carried on corresponding research to this system based on BIM technology and genetic algorithm. This article first applies the data analysis method to research BIM technology and related theories, and then explores the application of genetic algorithms in safety management. Subsequently, experimental and investigation methods were used to conduct in-depth research on the series of problems and designs of the construction intelligent management system, and finally the investigation results and conclusions were drawn. The survey results show that more than 50% of safety accidents are caused by falling from a high altitude. Therefore, in the design of the safety intelligent management system, special attention should be paid to the monitoring and prevention of guardrails.

Meteorological monitoring and forecasting system is a new type of work equipment with real-time, high effectiveness, high degree of automation, convenient equipment maintenance, low economic cost, wide range of use, and long-term operation in harsh environments. Meteorological disaster monitoring and early warning systems have become an important part of modern meteorological services. Therefore, for the design of the weather monitoring and forecasting system, this article discusses from the distributed network. This article mainly uses experimental methods, data analysis methods and other methods to conduct an in-depth discussion on the research of system construction. Experimental results show that the system designed in this paper has a certain effect on weather monitoring and forecasting, and its accuracy can reach 98%.

With the maturity of artificial intelligence and Internet of Things technology, the research on robots has also become one of the hotspots of artificial intelligence. Indoor mobile educational robots are an important part of machine intelligence. Research on the path of indoor mobile educational robots has become a key point in machine research. The purpose of this paper is to study the path planning of indoor mobile educational robots to improve deep reinforcement learning. This article first summarizes the research status of mobile educational robots at home and abroad. On this basis, the kinematics model of the indoor mobile educational robot is researched and analyzed. This article systematically elaborates the path planning based on the Actor-Critic algorithm and the deep reinforcement learning training model based on the minimum depth of field information. And use comparative analysis method, observation method and other research methods to carry out experimental research on the theme of this article. Research shows that the Actor-Critic algorithm proposed in this paper is shorter in path planning time and path distance than traditional algorithms.

With the rapid development of Internet technology, the network information data presents an explosive growth trend, and the society has also entered an era of big data. The era of big data facilitates people’s life and work, but it also brings many technical challenges. In order to solve the problems of various formats, form complexity and huge challenges brought to traditional computing technology, this paper optimizes the network information system under the background of big data, analyzes the optimization needs of the system, optimizes the functional module according to the demand content, and finally compares the optimization function. This paper finds that the start and implementation of the information management system is a very important and critical link in the construction of the enterprise information system, and the intelligent network information management system realizes the effect of information security.

Spatial indexing is an important research in the field of spatial databases, and plays a key role in how to efficiently perform spatial data retrieval and query. In this paper, a new hierarchical clustering algorithm RHCA is proposed, and accordingly, a Hilbert R-tree index based on RHCA clustering algorithm is proposed. This clustering algorithm is improved in the split stage and merge stage of hierarchical clustering. First, the sample distribution is counted in the split stage to find the appropriate split position, and then the merge strategy with label detection is used in the merge stage, which reduces the amount of calculation and overcome the shortcomings of the traditional hierarchical clustering algorithm that the intermediate results cannot be traced back. The experimental results show that the Hilbert R-tree index based on the RHCA clustering algorithm reduces the execution and query time by about 25%, the coverage and overlap area is reduced by 27%, the performance of the index is greatly improved compared with Hilbert R-tree.

With the development of the times, computer technology is booming, and the degree of association between modern society and computer is higher and higher. Therefore, the rapid development of computer technology has led to earth shaking changes in the whole society. However, due to the rapid development of network information technology, there are unstable factors, hiding their identity in the network world, calling the wind and rain, doing whatever they want, which has caused a certain turbulence to the society. Therefore, in order to solve these potential dangers, we hope to use cloud computing technology with various algorithms to develop a set of network communication signal tracking technology to track everyone’s IP address in real time, so as to ensure the security of the network, which is the purpose of this paper. After we reported the project to the school and obtained approval, we borrowed the school’s laboratory, used the school’s internal network data and the concealment of the school’s website, consulted the literature on cloud computing technology and network communication signal tracking technology, and used the improved particle swarm optimization algorithm and K-means algorithm to model and analyze it to determine the data effectiveness of the experiment. The experimental results show that there is a certain correlation between cloud computing technology and network communication signal tracking technology. Because of the complex network, the network communication signal tracking technology needs huge data and computing resources to build an effective network communication signal tracking system. Compared with before, it has increased the speed by about 189% and is more accurate. This experiment is relatively successful.

This paper studies the enhancement of the architecture based on the optimization of the user plane service architecture, and proposes to extend the service concept from the 5G control plane to the user plane, so that the 5G system provides higher flexibility and better modularity, so as to make it easier and easier. Good support for the automation and high reliability of network functions and services.

With the continuous integration and application of the Internet of Things and cloud computing, more and more devices need to connect to the Internet of Things cloud platform for remote access and control. At the same time, the access of illegal devices and abuse of access rights have brought many data security problems. IoT devices have application characteristics such as low power consumption, low cost, small storage, and heterogeneity, which are different from the structure of terminal devices in computer networks. Therefore, applying traditional identity authentication technology to IoT devices will no longer be applicable. This paper adopts the digital identity certificate and public key encryption technical scheme based on cryptographic technology to construct a credible security architecture for identity authentication of Internet of Things devices. Design the initial identity registration and activation process and technical route of IoT devices centered on the trusted identity registration agency, and then propose the network access verification process and security management of IoT devices based on the challenge-response mechanism. This solution uses cryptographic technology to ensure the confidentiality and non-repudiation of information during the identity verification process, which can better meet the actual needs of Internet of Things devices to access the network, and ensure secure device authentication connections, authorized access and identity management.

Modern society is an information society. With the continuous popularization of informatization, people have realized the importance of information literacy. As the talent reserve force of our country, college students have good information literacy has become the requirement of the times. Cloud computing (C C G) provides technical support for information literacy teaching due to its high cost performance, good scalability, and on-demand services. Based on this, the purpose of this article is to design and research a university information literacy teaching platform based on C C G technology. This article first summarizes the current situation of information literacy research, and then extends the current problems and shortcomings of information literacy teaching in my country’s universities. On the basis of it, design and analyze the information literacy teaching platform of colleges and universities (C A U), and analyze the strategy of information literacy teaching. This paper systematically expounds the design of the scheduling algorithm of the teaching platform based on C C G, and uses the field research method, questionnaire survey method and other research forms to carry out experimental research on the theme of this article. Research shows that the university information literacy teaching platform based on C C G technology studied in this paper has higher feasibility.

With the advancement of video target positioning technology, the amount of image data captured by people is increasing rapidly, which puts forward more efficient and automated requirements for data processing methods. As the most popular artificial intelligence technology, deep intelligence has become a research hotspot and has shown potential in the field of mobile image target search. This paper focuses on the research of track and field image target detection based on feature learning. Aiming at some of the problems in video target detection, we use feature learning to solve related problems, so as to improve the accuracy of target detection, and carry out experimental verification. As a result, the target detection method based on feature learning proposed in this paper is 5% more accurate than the traditional target detection method.

On the basis of traditional mechanical manufacturing, through the effective combination with computer technology, intelligent algorithm, CAM technology and other technologies, NC machining technology has been formed. Compared with traditional machining, numerical control technology has great advantages and has become an indispensable process technology in contemporary machinery manufacturing industry. The purpose of this paper is to study the application of NC technology in mechanical manufacturing under the analysis of integrated intelligent algorithm. This paper takes the CNC machine tool control system as the research object, combined with the integrated intelligent algorithm, designs the system. This paper analyzes the functional requirements of the application of NC machine tool control system in mechanical manufacturing, then designs the overall hardware circuit of NC system, and explains in detail the module circuit design and its working principle, mainly including power circuit design and input-output interface circuit design. Finally, the NC system is tested. The control effect of NC machine tool on workpiece contour error is as follows: the minimum value of contour error CEA is 0.0124 mm and the maximum error is 0.141 mm; the minimum linear error is 0.0113 mm and the maximum error is 0.0146 mm. It can be seen that under the NC system, the contour error of the machine tool is small and the precision is high.

With the advent of the Internet age and the rapid development of global information technology, people’s life has entered the electronic age. Among them, electronic account not only improves service efficiency, but also has important practical significance for protecting the environment, saving resources and reducing carbon emissions. The electronic bill data collection system supports fast data retrieval and automatically uploads it to the platform during the storage service to realize experimental data collection and data exchange. This paper aims to study the electronic bill data information collection based on network printing simulation. Based on the analysis of system functional requirements, system non-functional requirements, network printing simulation system and data information collection algorithm, the functional modules in the electronic bill data information collection system are designed, and finally the performance of the system is tested. The test results show that the electronic bill data information collection system designed in this paper has achieved good performance and meets the needs of this paper.

With the rapid development of market econoour and the continuous deepening of market-oriented reforms of the power system, power supply companies should actively integrate into the tide of “Internet +”, build a marketing service system that meets the current market competition environment and the Internet background, and satisfy power customers demand, in order to achieve the goal of common development of power supply companies and power customers. At present, the marketing management of electric power enterprises seems to be an important work for enterprises, which is closely related to the development and growth of the company. Therefore, this article mainly studies the MS of the digital transformation of the electric power market in the Internet era. First of all, the electricity sales of company A in recent years and the average time limit for customers of various voltage levels comprehensively reflect the current status of EM (electricity marketing). The results show that the electricity sales from 2017 to 2020 are gradually increasing, and the electricity sales in 2020 will be 567.71 million kWh. 220 V customers are the main customers of company A, accounting for 72.85% of the total number of customers. Secondly, it briefly introduced the transformation effect of the digital MS of the electric power market in the Internet era. 41% of the people believed that the EM strategy after the digital transformation would help the electric power company provide more efficient power services. The rest of the people also pointed out that the power marketing system supported by the Internet can enable customers to control their electricity consumption well.

With the development of society and the progress of science and technology, people’s demand for electric energy is becoming stronger and stronger. In order to adapt to the increasing demand for electric energy, smart grids are gradually popularized. It not only changed the development mode of our country’s power grid, but also brought new opportunities and challenges for our country’s power companies. Among them, the electricity bill information management system based on network micro-service technology ensures the accuracy and stability of users’ electricity consumption data. The purpose of this paper is to study the construction of electricity bill information management system based on network microservice technology. This article takes the electricity bill information management system as the research object, combined with the network micro-service technology, analyzes the functional characteristics of the system, and details the functional modules of the system’s user management, meter reading management, electricity bill collection management, and electricity monitoring management. This article briefly introduces the system test environment, and conducts main functional module tests and stress tests on the system. The test results show that the shortest response time of each function of the system is 2.016 s, and the longest is 2.341 s. And the success rate of system function operation is above 98%. It can be seen that this system has good performance and meets the performance requirements of the system.

The purpose of this paper is to design an abnormal sound detection system to detect abnormal sound during mechanical operation. The system uses the modulation spectrogram as a feature of the sound signal to train a dense autoencoder. Using the development set provided by DCASE2021 to compare 7 machine types with its baseline system, the result is better than the baseline system. Among the 7 machine types, the effects of Fan, Gearbox, Pump, Slider and Valve are significantly better than the baseline system provided by DCASE2021. By comparison, we believe that the method of feature extraction has an impact on the training of the neural network. In addition, the number of layers of the neural network should not be too large.

Speech Emotion Recognition (SER) is a difficulty of deep learning algorithms. The difficulty is that people’s own understanding of emotions is not absolute. Different people may also have different judgments on the same speech. And speech emotion recognition plays a huge role in many real-time applications. With the continuous development of deep learning in recent years, many people use convolutional neural networks (CNN) to extract high-dimensional features in speech from speech spectrograms, thereby improving the accuracy of speech emotion recognition. In contrast, we propose a new model of speech emotion recognition. The model uses the eGeMAPS feature set extracted through the openSMILE toolkit to input into our model. The model learns the correlation and timing between features. In addition, we perform intra-class normalization on the input features to ensure more accurate recognition and faster data fitting. In our model, the key speech segments can be selected through the characteristics of convolutional neural network (CNN), so that the recognition accuracy of the model can achieve a better effect. Our model was evaluated experimentally in the IEMOCAP dataset. Experimental results show that our unweighted accuracy (UA) and weighted accuracy (WA) on the test set reached 60.9% and 63.0%.

With the rapid development of information technology, computers have been applied to all walks of life. Computer technology has been widely used in the construction industry. If CAD is to free people from the heavy labor of hand-painting engineering drawings, then BIM technology is another major change in the construction industry, which is called a “revolution” technology. Now BIM technology has been applied in various fields of the construction industry. For example, it plays an irreplaceable role in heating engineering management, structural construction drawings and green building design. The application in the industry is introduced in detail.

In order to solve the problem that the evaporation sensor can only be verified manually at present, an automatic evaporation sensor verification equipment is developed. By simulating different liquid level heights and equipped with a high-precision acquisition module to collect the signal of the detected sensor, the liquid level height and the sensor output are compared to determine whether the accuracy of the sensor meets the requirements, so as to realize the automatic verification of the evaporation sensor and improve the verification efficiency.

The construction of urban rail transit is one of the important ways to solve traffic congestion, drive employment as well as drive economic development. By the end of 2020, China’s rail transit has opened 1083 km of operation, while there are about 45 approved cities. The development of intelligent system and the construction of smart urban rail is the necessary way to realize the development of urban rail transportation from high speed to high quality, and it is also the premise and guarantee to promote the construction of a strong transportation country. In this paper, firstly, the system architecture and functional modules are designed according to the actual requirements. Secondly, the system database is designed based on the system development environment. The system provides intelligent inspection function and leaky cable monitoring function, realizing good equipment visualization effect, complete asset management data and high vehicle electrical system overhaul efficiency, etc. Finally, through the test of the platform, the results show that the accuracy of fault diagnosis module for turnout fault diagnosis reaches more than 95%, especially for the troubleshooting of uncommon faults, the effect of on-site guidance is better, which improves the efficiency of on-site troubleshooting and fault location.

In recent years, with the rapid development of information technology, the combination of mobile communication and Internet technology appears in all walks of life. The concept of smart city has gradually entered the daily life of community residents. Smart city is a new concept proposed by combining Internet of Things, cloud computing and mobile Internet technologies to achieve a safe, comfortable, convenient and efficient living experience. By investigating the current actual situation of smart city, this paper analyzes some specific requirements and overall development direction of smart city, and makes a detailed analysis of the functional requirements of the system. A smart city management system based on Java programming language and B/S architecture is designed. The design of the back-end management terminal of the system is based on B/S architecture and adopts the MVC framework. The whole system is divided into three basic levels: interface display layer, logical control layer, data access and data model layer. Through the lightweight Web container management work provided by the framework to achieve the management of smart city, greatly improve the management efficiency of managers. This paper conducts a pressure test on the smart city management system, and the test results show that both the system concurrency and the system resource occupation can meet the daily use requirements.

With the rapid growth of people’s living standards and national economic levels, the increase of per capita vehicles leads to the exponential boost in urban traffic congestion. To solve the existing problems of traffic congestion, a deep learning architecture based on yolov4 was proposed to realize monitoring of vehicles, which is used for the real-time detection and statistics of traffic stream information. The result shows that the mean average precision (mAP) of vehicle detection can reach 85% under different occasions of light, traffic flow and vehicle speed. The method has strong environmental adaptability and broad applicability.

In recent years, light pollution has become a very serious problem, and many nighttime traffic accidents are caused by rapid changes in the intensity of car headlights. As the rapid change of external light intensity can reduce people's ability to observe objects, cause human discomfort, and even damage the naked eye. Therefore, trying to achieve the brightness switching of car headlights by means of breathing lights is an important way to reduce traffic accidents. In this paper, we use microcontroller as the main control device and PWM technology to design a set of headlight state switching by breathing. Finally, the safety and security of night travelers are improved and the traffic accident rate is reduced.

The development of the mobile Internet is an important trend today, and the development of all areas of life is based on the mobile Internet. Integrated marketing is the integration of certain different resources into a powerful, competitive product package, which can produce synergy and aim to maximize the value of trade. Integrated marketing is a company that integrates independent marketing into a whole according to its own development needs, business goals, and its ability to achieve synergy and maximize the company’s profits. The company’s integrated marketing communication means that the company integrates all communication activities related to marketing. This article takes mobile internet as the theoretical basis of the research, and analyzes and studies the influence of its important content on the integrated marketing model. This article takes the classic mobile Internet as the research object and separately optimizes and improves the integrated marketing model. The technology in the mobile Internet can be used to construct a variety of integrated MLM models. The experimental results show that this research is very useful for the use of mobile Internet to influence the integrated marketing communication model, and it has better results in the study of the influence of integrated marketing communication methods observed from the mobile Internet.

With the emergence of intelligent buildings, as stadiums, the design of stadiums is also developing rapidly towards the direction of intelligence. Computer technology can not only help Venue Managers to complete their work, but also realize the information exchange between systems. Control technology can realize the automatic operation of various equipment. This paper discusses the design points of intelligent weak current system for stadiums. The results show that there are at most seven badminton fields and at least two volleyball fields in a city’s functional core area.

With the fast advancement of science and technology, internet and information technology are increasingly being integrated into people’s professional and personal lives, bringing ease. The environment, or landscape, is a vital link in the urbanization process; it not only creates a pleasant living environment for people, but it also contributes considerably to the restoration of ecological balance, which is necessary for future urban expansion. This article examines the features and growth trend of computer augmented reality (AR) technology, as well as its potential use in landscape architectural design, with the goal of providing other perspectives for China's urban development.

With the development of power transmission Internet of Things communication technology, higher requirements are put forward for data transmission speed, and various terminal protocols are proposed according to different needs. There is an increasing demand for communication between devices using different protocols. Therefore, it is necessary to seek an efficient and universal protocol adaptive conversion method to achieve this goal. The purpose of this article is to study the protocol adaptive conversion method of power transmission IoT terminal based on protocol matching. This article starts from the protocol grammar matching, gives a multi-pattern matching algorithm, and gives a detailed description of the overall design scheme and interface design of the adaptive conversion system. Finally, this paper tests the methods proposed in the research, introduces the implementation of simulation software NS-2, NS-2 and its simulation process, and uses adaptive mechanism, PCF mechanism and DCF mechanism to perform delay and packet loss rate performance for different services. The simulation results show that the delay time of the conversion service under the PCF mechanism is within 50 ms; before the simulation time is 55 s, the delay time of the conversion service under the DCF mechanism is within 50 ms and within 60 s; the conversion service delay time of the adaptive mechanism is all within 20 ms. It can be seen that the adaptive conversion method proposed in this paper shows better QoS performance than the standard PCF and DCF mechanisms.

In this paper, the model of two 1000 kV UHV AC transmission lines of B - J is built on PSCAD, and set the type of fault on the model. By observing the voltage and current at the bus and the voltage change at the fault point, the fault point can be located quickly, and the reliability of the model can be verified by simulation. At the same time, this paper introduces in detail the calculation method of basic electrical parameters of power system transmission line, the function of PSCAD and the modeling method. In addition, the influence of the installation location of shunt reactance and the distribution parameters of transmission line is considered in the model, which reduces the influence of shunt reactance and line parameters on the model accuracy.

As an important part of the smart grid, the smart distribution network is an important link between power companies and power users. Cloud computing technology has the advantages of distributed computing, strong fault tolerance and easy expansion, which can solve the problem of processing and storing massive data in smart distribution networks. The purpose of this paper is to study the intelligent distribution automation fault location system based on cloud computing. This article mainly uses cloud computing technology to build an intelligent power distribution network based on cloud computing, designs a distribution automation fault location system, and finally tests the system. The system test results show that the average value of the avalanche test results is 0.52, and the ideal value is 0.5. No matter how many digits of the plaintext are changed, even if one bit changes the smallest change, nearly half of the ciphertext digits change. It is very close to the ideal avalanche effect. That is, the AES encryption algorithm designed in this paper has good avalanche characteristics and can effectively resist linear analysis attacks launched by criminals.

In the 1990s, it became easier for people to obtain digital information and to spread the information through the Internet. Against this background, machine learning has begun to develop vigorously, focusing more on solving practical problems. The phenomenon of radial fluid enhanced diffusion has always been the focus of attention in the field of fluid mechanics. However, due to the limitations of various actual physical conditions, convection dominates the radial fluid enhanced diffusion problem. The classic solution method will cause serious non-physical oscillations when solving the problem, and no more accurate numerical results can be obtained. Based on this, this paper proposes a radial fluid-enhanced diffusion technology based on machine learning. The logarithmic increment method is proposed to improve the non-parametric estimation of the drift coefficient. Experiments show that in the two typical models, the mean value of the logarithmic increment method fluctuates between 0.4–0.6, which is closer to the actual value of 0.495, indicating that the mean value of the logarithmic increment method is closer to the true value and the variance is smaller. The effect is better than that of the direct incremental method.

With the rapid development of our country’s national economic system, the further improvement of domestic power reforms, the improvement of corporate social responsibility, the increase in environmental pollution and natural disasters, power grid companies are facing huge impacts from economic, social, technological, natural and other fields and challenges. In order to reduce the impact of energy and environmental factors on the national economy, it is necessary to improve energy utilization efficiency, strengthen environmental protection, and create a model for scientific and technological development. State Grid operation risk management is critical to whether the State Grid can operate safely and stably. Therefore, this article aims to study the State Grid operation management risk model and decision support system based on big data. Based on the model construction and system design principles, the functional modules of the decision support system based on the State Grid operation management risk model were designed, including the system management module, project module, risk evaluation module and risk control module, and finally tested the performance of the system. The test results show that the maximum response time of the system is 140 ms, which meets the requirements of this article.

In order to reduce the energy consumption of WSN data fusion and improve the reliability of data fusion, we propose a hybrid data fusion algorithm based on improved data ARMA prediction model and compressed sensing technology (HDFAC). By analyzing the characteristics of the temporal and spatial correlation of monitoring data, the excess valued elimination mechanism is used to remove redundant invalid data, and then a prediction model is established to estimate the monitoring value. The predicted value of high credibility is uploaded to the cluster head, the cluster head node compresses the data, and then the original data is reconstructed at the Sink node to reduce the overall energy consumption of the network. Experimental results show that the HDFAC algorithm can effectively balance the load between nodes, reduce the amount of nodes sent, and extend the network life.

With the increasing development of life aesthetics, the forms of guide system design are becoming more and more diversified. It is no longer a single design and production project defined by materials, shapes, or processing types, but a “people-oriented” system design that integrates into People’s Daily life to meet the aesthetic requirements and combines with the space environment. In modern city life, the information between people and the environment is more and more closely related to its requirements are also more and more high, small to an office building, large to a region or even a city are required to have a scientific and humane guide system design.

With the leap-forward development of power grid, the number of transmission line equipment has increased significantly. Traditional operation and maintenance methods can no longer meet the needs of comprehensively controlling equipment status and accurately assessing power grid risks. It is urgent to accelerate the gradual integration of modern information and communication technology and traditional inspection technology. This paper mainly studies the design of smart grid operation and maintenance management command platform based on computer distributed network. The docker-based distributed Web platform designed and implemented in this paper will explore continuous integration, performance monitoring, gray publishing and log retrieval. The platform developed in this paper realizes the functions of remote monitoring and analysis, fault prediction, intelligent personnel scheduling and so on. It can greatly shorten the emergency response time and solve the problems such as low efficiency of transmission line operation and maintenance management and lack of intelligent level.

Electrical automation technology is a comprehensive technology that optimizes the production process through computers, information technology and control theory, and realizes the improvement of production efficiency. Hardware automation system and software system are the prerequisites for realizing electrical automation technology. In addition to the hardware part of this platform, electrical automation systems are also used in it. Man-machine interface refers to industrial control computers and operating screens. Touch screens are widely used devices in recent years. They have the characteristics of instant information interaction, easy operation, and high reliability, and are widely used in the field of industrial control. The advantage of this automatic control function is that it is separated from the traditional manual control pendulum, which greatly reduces the cost of supervision and improves the quality of supervision at the same time. From this perspective, the study of electrical automation control systems based on computer algorithms has certain practical significance. The purpose of this paper is to study the electrical automation control system based on the improved KMP algorithm. For electrical automation control systems, the kmp mode distribution algorithm is used on the basis of the bf mode distribution algorithm, and the kmp algorithm is further improved on the basis of the kmp mode distribution algorithm. In practical applications, the feasibility of the improved kmp algorithm is studied. The PID control model algorithm and the improved kmp algorithm control experiment verify the efficiency and practicability of the improved kmp calculation and transmission, and the experimental results are in line with expectations. The experimental results show that the improved algorithm proposed in this paper is based on the kmp algorithm, and the error does not exceed 0.1 in actual experiments compared with the experimental results of the standard control group, which proves that the algorithm is feasible in practical applications.

Transmission line engineering have a wide range of regions and many uncertain factors, which have brought great difficulties to the design, construction and operation of transmission lines. Cost management of transmission line engineering is crucial. The cost prediction is a key link in the cost management. This paper takes the transmission line construction cost data as the research object, analyzes it by using the multiple regression analysis theory. By selecting the research index, a reasonable multiple linear regression equation was established and the statistical test was carried out. The results show that the equation is in line with the reality and can be used to predict the transmission line construction cost. It provides ideas and suggestions for cost prediction of power enterprises.

Faced with a complex network environment, network security issues are getting more and more serious. Cyber attacks will not only leak user privacy, but also cause huge economic losses. In the face of massive network data, decision trees have become an effective method for detecting abnormal network data. The decision tree method trains a model on a large amount of data, classifies normal data and abnormal data, and detects network attacks more efficiently and accurately. This article aims to study DLP network data security detection and positioning technology. Based on the analysis of DLP trends, the development direction of intrusion detection, abnormal data classification algorithms and positioning technology, the KDD CUP1999 data set is selected as the experimental data set. These three methods, namely, decision tree, support vector machine, are used to detect the data set. The detection results show that the data detection rate and false alarm rate of the decision tree algorithm perform better among the three algorithms, and are suitable for network data security detection.

With the rapid popularization and development of the mobile Internet, the issue of network data security has received more and more attention. In order to effectively guarantee Internet data security, an advanced, efficient and reliable intrusion monitoring and detection system is bound to be indispensable. This article mainly focuses on the research of the Internet data security detection management system based on data mining algorithms. Based on the collection of relevant literature materials, it summarizes the actual needs of the Internet data security detection management system, and then analyzes the Internet data mining in the Internet data security detection application research in the management system. Based on these technologies, the Internet data security detection management system of the Internet data mining algorithm is designed, and the designed system is tested. The detection results are obtained. The system after the algorithm is improved. It is lower than the traditional system, and as the amount of data increases, the system saves more time. The detection rate of the improved system is 4.44% higher than the traditional detection rate.

Intelligent semantic analysis is based on the user’s security protection needs, according to the current system application system problems, using artificial intelligence technology and information theory and other related knowledge to realize data mining, screening and sorting. Intelligent semantic retrieval technology is used to realize the identification and judgment of the dangerous factors in the user’s environment and the information that may cause accident consequences. This is a new safety protection technology. The significance of studying this technology in this paper is to optimize the safety system through the understanding of the technology. This article mainly uses experimental method, data method and data analysis method to gain an in-depth understanding of intelligent semantic analysis technology and security protection technology, and conduct experiments. The experimental results show that the accuracy rate of the safety protection system designed in this paper can reach 99%, and the real-time performance is relatively strong.

With the growth of scientific and technological information technology and the rapid popularization of the Internet, network big data and information technology are also growing rapidly. Information technology provides people with more information, and it also significantly increases the operating and management costs of my country’s Internet companies. In order to solve this problem thoroughly, people propose a new type of it development, research and business model, namely BP neural network technology. At present, BP neural network technology has been widely used in various application fields such as network storage, search engines, distributed computers, e-commerce, social networks, and has achieved rapid growth. This article mainly adopts the method of organically combining theoretical exploration and empirical research, and systematically analyzes the data collected through research based on the views and research contents of some scholars in recent years. Combining with the analysis of the data of intelligent semantic analysis algorithm, some relevant characteristics of BP neural network are summarized. This article mainly focuses on the research of an intelligent algorithm for image semantic analysis for image processing. The semantic analysis intelligent algorithm can well change the situation of target detection difficulties. This article uses an intelligent algorithm based on BP neural network to automatically analyze and distinguish differences. The final results of the research show that this paper uses the attention model and proposes a semantic analysis algorithm combined with graphic target detection through a multi-scale segmentation network. The experiment shows that the three performances of attention are 71.6, 56.5 and 49.3, which can be learned this algorithm is better than the same comparison algorithm in terms of three performance evaluation indexes.

Science popularization is the popularization of science and technology. In the post-epidemic era, in response to the popular science issues exposed during the epidemic, the creation and dissemination of popular science animations urgently require in-depth reflection and research. The research object of this topic is the creation and dissemination of popular science animation in the post-epidemic era, and the purpose is to use popular science animation to conduct scientific and rational publicity and guidance to the public. The main methods used in this article are questionnaire survey and interview method. The survey results show that about 45% of people think that dynamic design is very important, and 30% think that animation duration is also the key. Therefore, the innovation of popular science animation should focus on these two aspects.

Distribution network is an important part of power system and the last link of power system facing customers. Distribution lines mostly cover urban and rural areas with complex operation environment, which are vulnerable to different types of faults such as bad weather, vegetation growth and equipment failure. With the rapid development of distribution automation technology, people have higher and higher requirements for the reliability of distribution network. The purpose of this paper is to study an efficient distribution network fault recovery and reconstruction platform to solve the problems of time-consuming, labor-consuming, inconvenient management and low degree of automation in the current distribution network fault recovery and reconstruction. In this paper, the distribution network automation is not high in the area as the research object, when the distribution network fault occurs, using the available measurement information of the distribution network, using the method of state estimation to solve the problem of distribution network fault location, put forward the distribution network fault recovery model based on differential evolution algorithm to speed up the location search ability. In this paper, a mathematical model is proposed to solve the problem of fault recovery and reconfiguration. By studying the collaborative work mechanism and process optimization of distribution network fault repair, the demand analysis and design of functional modules of distribution network fault platform are carried out. After the application of the platform, the fault processing efficiency of the distribution network is effectively improved, the fault location time is shortened by 19 min, and the repair time is shortened by 15 min; simplify the personnel allocation of emergency repair and reduce the staffing by 31%; the operation reliability of distribution network is improved by 1.02%.

Due to the continuous development and in-depth promotion of smart grid construction in China, the amount of information accumulated has increased exponentially. The technical method of extracting “treasure” from these important historical materials has gradually become an urgent need for building a powerful intelligent power grid, and the rise of big data storage and processing technology has also opened up a new road for data mining. This paper studies the high-capacity data processing of main distribution network based on information processing cluster framework. After understanding the relevant theories, a high-capacity data processing system of main distribution network based on information processing cluster framework is designed and tested. According to the accuracy test results of the system, the accuracy of the system is about 70%, which basically meets the needs of the system, and then the system management efficiency is good. The effectiveness test results show that the parallel test time of the system is greatly reduced compared with the serial time, so the system has good parallel processing efficiency.

With the expansion of power system scale and more frequent operation adjustments, people are increasing the need for real-time analysis and calculations. Graph data model is a new type of data model derived from parallel and analytical processing of a large number of data information on the mobile Internet that has appeared in recent years. Its data model provides users with an intuitive expression of grid topology, and can easily realize the parallel query of the data. This paper aims to study the power grid CIM model modeling method based on the graph data model. On the basis of analyzing the classes and relationships in the graph data model, CIM model and CIM model, a power grid CIM model based on the graph data model is constructed, which mainly includes the power grid equipment asset model and the power grid topology model are two parts, and then the parallel network topology analysis algorithm is proposed and implemented. Finally, the test verification shows that the model and algorithm in this paper can speed up the network topology analysis speed and improve the power grid calculation efficiency.

In recent years, with the vigorous development of wireless communication technology, devices such as information acquisition DTU are becoming more and more common in our daily life. DTU is widely favored by the public because of its small size, low cost, high transmission accuracy and convenient networking. Aiming at the problems of single transmission mode, multi scene and poor applicability of traditional DTU, a multi-protocol transmission information acquisition DTU is designed, which takes STM32 single chip microcomputer as the core and integrates WiFi, Lora, 433 M and other wireless transmission modules as well as the corresponding receiver and LCD module. The DTU can complete the functions of information acquisition, wireless transmission mode switching, data receiving and display, etc.; The software and hardware design of information acquisition adaptive multi-protocol transmission DTU is introduced in detail, and the system test is carried out. The test results show that the DTU can collect information in real time and switch a variety of wireless transmission modes, can meet the needs of a variety of scenarios, and has great market application value.

In order to complete the transmission circuit inorganic and automatic search, shrink deviation hand control, this paper is devoted to the complete type of uav autonomous cruise system based on laser point precise positioning, through the high precision 3 d laser spot time data to complete the course of independent planning, automatically generated, and then complete the inorganic and the whole flow of automatic cruise work. Experiment results shows that, given the precise positioning of the laser point cloud drones in autonomous cruise phase, with space collision testing and automatic blocking function, high efficiency to ensure the safety of the unmanned aerial vehicle (uav) navigation, reduce the latent threat to power grid, improve power transmission cable inspection results and the safety of the operation, provide strategies for the development of power transmission cable inspection to explore in the late.

The rapid development of blockchain technology and the construction of MOOC communication platform have a positive role in promoting higher education, the problems we have to think about and solve up to now are how to effectively build a MOOC communication platform in higher vocational education and how to use blockchain technology to build a MOOC curriculum system, and how to deal with the development of various MOOC platform contradictions, the preliminary proposed corresponding solutions. In accordance with the trend of education reform, this paper focuses on the sustainable development of MOOC education production, teaching, learning, research and use integrated block chain ecosystem, and the development and implementation of MOOC communication platform, so as to evaluate its development prospects.

The selection of image focus discrimination function is the basis for obtaining high-quality images in automatic image scene measurement. The performance of several digital image processing algorithms for automatic image focus discrimination is compared comprehensively, and the calculation speed, uniqueness, accuracy and sensitivity of different algorithms are analyzed quantitatively. Firstly, this paper briefly summarizes the imaging principle and focusing principle of digital image processing automatic image focusing, and then from the image information entropy function, gray gradient function, frequency domain evaluation function, and other evaluation functions. Finally, the area selection and focus search algorithm of digital image processing window are described from the aspects of depth of field and focal depth, algorithm selection and algorithm improvement direction. The above analysis results of the characteristics of image focusing discriminant function have guiding significance for the automatic focusing control required by image automatic measurement.

With the rapid development of social economy and the acceleration of urbanization in China, the scale of urban road network is expanding day by day, and the total mileage of all levels of roads is growing rapidly. As the road with the highest level of technology and service in the urban road network, urban expressway plays the role of the skeleton road network in the urban road system, bearing the traffic demand of high flow and high speed. Once a traffic incident occurs, it is easy to produce traffic congestion. If it is not handled in time, it will also cause secondary accidents, which will have a serious impact on the operation of the whole urban expressway network. In this paper, an algorithm design based on Intelligent Transportation nonlinear dynamic control and automatic accident detection algorithm is proposed to improve the coverage and uncertainty of expressway incident detection.

With the rapid development of social economy and the acceleration of urbanization in China, the scale of urban road network is expanding day by day, and the total mileage of all levels of roads is growing rapidly. As the road with the highest level of technology and service in the urban road network, urban expressway plays the role of the skeleton road network in the urban road system, bearing the traffic demand of high flow and high speed. Once a traffic incident occurs, it is easy to produce traffic congestion. If it is not handled in time, it will also cause secondary accidents, which will have a serious impact on the operation of the whole urban expressway network. In this paper, an algorithm design based on Intelligent Transportation nonlinear dynamic control and automatic accident detection algorithm is proposed to improve the coverage and uncertainty of expressway incident detection.

As a government management evaluation system, it must be relatively independent and integrated. However, the existing research results lack the research on the technical platform of performance, which makes the design limitations and the repair of the index system, can not connect the index design with the performance evaluation, and can not make full use of the existing relevant data and information, resulting in the weak practicability of the designed index. This paper mainly introduces the econometric analysis method of extracting conceptual data from a large number of data by using the decision tree method of data mining technology in the process of public utility management performance evaluation, so as to improve the reliability and effectiveness of evaluation and save evaluation time.

The technology of the system is .Net. It has the advantages of low requirement for client and good expansibility. The whole system is flexible and efficient. The main work of this paper is the system analysis of the financial management system, and based on the system analysis, puts forward the overall business objectives of the financial management system; gives the overall design of the system, as well as the detailed design of the main functional modules. It includes voucher management, salary management, fixed assets management, account book management, statement management, period end management, business transaction management, cashier management and financial analysis. On the basis of detailed analysis of each functional module of the system, coding is carried out to achieve the expected function.

Horizontal well increases the relative contact area of underground reservoir. Horizontal well has the advantages of large controlled reserves, large oil drainage area and high production. Horizontal well development technology is suitable for the whole process of oilfield development, and is an important technology to improve oil well production, oilfield recovery and development benefits.

In the big data environment, the innovation of music communication path should not only pay attention to the powerful function of new media, but also can not ignore the important role of traditional media such as radio, television, newspapers and periodicals. The two should be combined to achieve coordinated development, so as to better promote the development of music industry.

The design of excellent geography research and study plan is conducive to the efficient development of research and study activities, the improvement of students’ comprehensive quality and the professional growth of geography teachers. Taking a research and study activity as an example, this paper discusses the design strategy of research and study plan preparation and research theme, goal, content, form and process.

This paper analyzes some key problems in the design of neural network economic alarm system, and puts forward an improved Liao Chuan algorithm. On this basis, it introduces a hybrid algorithm neural network economic prediction system based on the improved genetic algorithm and BP algorithm. In order to optimize BP neural network comprehensively and make it have better generalization performance, a genetic algorithm is improved and designed. The comparison test shows that the improved genetic algorithm reduces the memory consumption, ensures the diversity of population, and improves the running speed and convergence effect of the algorithm.

Software testing can guarantee the quality of software products, but it also takes up nearly half of the cost and resources of the entire software development cycle. The traditional test data acquisition requires manual design, but as the scale and complexity of software increases, manual design of test data can no longer meet the requirements of testing, therefore, automatic test data generation has become a hot spot and focus of many scholars’ research. In this paper, we will study and analyse the automatic generation of computer software test data based on intelligent optimisation algorithms.

The triadic compound flooding test of three types of oil layers in the North East block of Lamadian oilfield is a key test project of tertiary oil production in Daqing Oilfield. The test area is planned to be put into operation in 2011. Through the analysis of logging data of 32 newly drilled oil wells in the experimental area, the heterogeneity of the layers, interlayer and plane and the influence on the remaining oil are studied. According to the water flooded condition of the reservoir in the test area, the types and distribution of remaining oil are analyzed, The results show that the reservoir conditions of high II 1–18 in polymer flooding test area of three types of reservoirs are compared. Combined with the design and experience of polymer flooding test parameters, it can provide important guidance for the design of three-dimensional test parameters of three types of oil layers.

Fast data processing is one of the important technologies and development directions of big data processing, which fully embodies the characteristics of big and fast big data. The digital reading recommendation model based on fast data processing technology can make full use of the advantages of big data, and further improve the digital reading experience of readers.

With the deepening of China’s opening to the outside world, China’s demand for English talents is gradually increasing. As a compulsory subject for high school students, it is not objective for high school students to take office in the field of English on the premise of large market demand and sufficient talent reserve. Higher vocational colleges have always emphasized the education goal of taking employment as the center. On this basis, this paper hopes to cultivate high-quality talents through the analysis of English education in Higher Vocational Colleges in the cloud computing environment.

Financial management is an important part of enterprise management, which directly affects the operation and development of enterprises. Attention must be paid to financial management. Industrial financial management data processing can effectively improve the quality and efficiency of enterprise financial management and ensure the long-term and stable development of enterprises. Firstly, this paper introduces the development direction of enterprise financial management, and discusses the specific application of big data in enterprise financial management.

With a large number of digital resources as the carrier, mobile learning breaks through the shortage of resources and the limitation of time and space under the current learning mode. However, its rich resources also bring information overload, which greatly affects the learning efficiency. The mobile English learning platform based on collaborative filtering algorithm not only makes full use of the advantages of mobile learning, but also recommends learning resources according to the learning needs of different learners to meet the learning needs of different learners, saving learners’ time and effort to a certain extent all. It has certain practical significance.

This paper studies the optimization model and algorithm of the hybrid transportation network design problem considering sustainable development. The bi level programming model is used to describe the problem, and the link level decision variables are used to discretize the problem, and the algorithm is solved by the model. The example shows that the traffic congestion of the optimized transportation network is significantly alleviated, Moreover, the reduction of vehicle emissions in the road network is also very obvious. All these prove that the bi level programming model and algorithm proposed in this paper is an effective method to study the traffic network design problem in the environment of sustainable development.

The current wave of artificial intelligence is mainly the performance of the prosperity of deep learning algorithm based on big data. The process of artificial intelligence acting on public management practice roughly needs to go through three processes: intelligent infrastructure construction → intelligent algorithm design → intelligent application landing, and correspondingly forms three levels: infrastructure layer → algorithm layer → application layer. This paper analyzes the corresponding data security risks of the three levels, technical accuracy, algorithm bias and algorithm supervision risks, as well as the risk of intelligent transformation of public sector, and discusses the general logic of risk transmission from the bottom to the surface, which provides a holistic perspective for analyzing the risk of artificial intelligence application in public management.

With the rapid development of IT industry, network sharing has been unable to meet the current resource management. Cloud computing has become an important tool for the development of various software systems. Using cloud computing to manage resources can effectively solve the current massive resource management problems. This paper analyzes the cloud computing technology, and then designs the resource management system on the basis of the technology, divides the whole system into three layers, and finally gives the core code of some modules.

Distance education management platform is the core of network education, which undertakes most of the functions of teaching resources, organization and implementation of educational activities. Since 2010, more and more students have been served by the distance education management platform, which eventually causes the distance education management platform to face the situation that at a certain moment, many users access the system service together, and the system service is very easy to collapse. In recent years, in this case of multi-user and high concurrency, a variety of emerging technologies emerge in endlessly. Based on K-means algorithm technology, this paper aims to achieve a high-performance distance education platform.

Through the combination of artificial intelligence technology and distributed technology, this paper puts forward a design scheme of Taijiquan action teaching online assistant system based on deep learning, which can be applied to Taijiquan teaching, broadcast gymnastics, fitness action and other fields, and provide intelligent action coach for users in the environment of no guidance. The platform adopts the front-end and back-end separation development, and uses the distributed and cluster technology to solve the problems of high concurrency and availability. Through tensorflow.js framework, the platform can identify, analyze and guide actions in real time on the front end, which can avoid privacy leakage and server overload in the front and back end data interaction. Through the scoring and comparison algorithm, users can constantly correct their actions in the process of using, and achieve the positive feedback effect of action learning.

This paper is based on the Apriori algorithm to obtain more efficient data mining methods, analysis of Eucommia Leaf prevention and treatment of diabetes and mechanism. The classic Apriori algorithm of association rule mining is used to analyze the medical data with the characteristics of privacy, polymorphism, incompleteness, timeliness and redundancy. Firstly, Apriori algorithm is used to find the frequent itemsets of data in the database, and then strong association rules are generated according to the frequent itemsets to find useful association relationships or patterns between itemsets in massive data. The final purpose is to analyze the application of association rules mining in clinical disease monitoring, evaluation of drug treatment effect and prevention of diabetes by Eucommia ulmoides leaves.

Based on 549 literatures with the theme of “sports artificial intelligence” and other keywords in web of science database since 1995, this paper uses CiteSpace V software for visualization processing and analysis, and combs the country, discipline distribution, research hotspots and evolution trend of sports artificial intelligence research in recent 25 years by means of visual knowledge mapping, and discusses its research progress and development direction. 1) The research area of sports artificial intelligence is widely distributed, among which the United States, China and Germany are in the leading position. 2) Sports artificial intelligence research involves many disciplines, mainly using and learning from the research methods and theoretical perspectives of computer science, engineering, sports science and other disciplines. 3) The frequency and centrality of keywords confirm that machine learning is the main direction in the field of sports artificial intelligence, artificial neural network is the main algorithm, and data mining is the basis of practice and research. 4) Research hotspots include simple activity recognition and energy consumption research based on wearable accelerometer technology; action analysis and damage prevention and control research based on wearable sensor; computer vision scene classification research based on convolution neural network algorithm; analysis and prediction of physical fitness and technology and tactics based on computer vision; human posture recognition technology based on computer Deep learning.

Aiming at the problems of many types of sand bodies, poor reservoir physical properties, low recovery degree, scattered residual oil, large adsorption capacity, and difficult chemical flooding in the development process of the third type reservoirs in Sazhong Development Zone, the “fracturing oil displacement” technology is proposed, which can form a high-speed channel by fracturing long fractures, and quickly send high-efficiency oil displacement agent to the remaining oil enrichment position through fractures, It can reduce the contact time and distance between the chemical agent and the formation, reduce the loss of chemical agent performance along the injection process, improve the utilization efficiency and enhance the oil displacement effect.