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Splicing the shredded paper means the technology that, according the paper, which has been shredded to design a particular algorithm to splice and recover the original paper. This paper introduced the algorithm of splicing the shredded paper based on the matching to texture feature. By means of this algorithm, we modeled for the problem of splicing the shredded paper and solved it. And, we used the algorithm to splice both pieces of English shredded paper and Chinese shredded paper. The recovered paper had proved the accuracy of the algorithm.

We consider a one-dimensional cutting stock problem (CSP) in which the stock widths are not used to fulfill the order but kept for use in the future for the industrial-use paper production. We present a new model based on the flexible stock allocation and trim loss control to determine the production quantity. We evaluate our approach using a real data and show that we are able to solve industrial-size problems, while also addressing common cutting considerations such as aggregation of orders, multiple stock widths, and cutting different patterns on the same machine. In addition, we compare our model with others, including trim loss minimization problem (TLMP) and cutting stock problem (CSP). The results show that the proposed model outperforms the other two models regarding total flexibility and trim loss ratio.

Pulp washing process has the features of multivariate, time delay, nonlinearity. Considering the difficulties of modeling and optimal control in pulp washing process, a data-driven operational-pattern optimization method is proposed to model and optimize the pulp washing process in this paper. The most important quality indexes of pulp washing performance are residual soda in the washed pulp and Baume degree of extracted black liquor. Considering the difficulties of modeling, online measurement of these indexes, two-step neural networks, and multivariate logistic regression are used to establish the prediction models of residual soda and Baume degree. The mathematical model of the washing process can be identified, and the indexes can meet the production requirements. In the target of better product quality, low cost, and low energy consumption, a multiobjective problems is solved by ant colony optimization algorithm based on the optimized operational-pattern database. It shows that the theoretical analyses are correct and the practical applications are feasible, optimization control system has been designed for the pulp washing process, and the practical results show that pulp production increased by 20% and water consumption decreased by nearly 30%. This method is effective in the pulp washing process.

In recent years, although coal mine accidents in China have decreased, they still occur frequently. Most previous studies on the evolutionary game of safety mining are limited to a focus on system dynamics and two-party game problems and lack a spatial graphic analysis of strategy evolution. The parameters adopted are too few, and the influencing factors considered are too simple. The purpose of the paper is to introduce more parameters to study which will have an important impact on the strategy choices of participants and the evolution path of the strategy over time. We construct a tripartite evolutionary game model of coal mining enterprises, local governments, and central governments. As our method, a payment matrix of participants and replicated dynamic equations is established, and we also implement parameter simulation in MATLAB. In summary, we found that the reward and punishment mechanism plays an important role in safe coal mining. Specifically, (1) intensifying rewards and penalties for coal mining enterprises and local governments will help encourage coal mining enterprises to implement safe production measures and local governments to implement central government safety supervision policies. However, increased rewards will reduce central government’s willingness to adopt incentive strategies. (2) The central government’s reward for coal mining enterprises’ safe production must be greater than the increased cost of safe production to encourage enterprises to implement such production. Economic incentives for local governments must be greater than the benefits of rent-seeking; only then will local governments choose to strictly implement supervision policies. (3) Increasing sales revenue and rent-seeking costs of coal mining enterprises can also encourage them to implement safe production. Therefore, a well-designed reward and punishment mechanism will change the behaviour of coal enterprises and improve the probability of safe production. The research presented in this paper further works on improving safe coal mining production and designing reasonable reward and punishment mechanisms.

Categorizing national football teams by level is challenging because there is no standard of reference. Therefore, the self-organizing feature mapping network is used to solve this problem. In this paper, appropriate sample data were collected and an appropriate self-organizing feature mapping network model was built. After training, we obtained the classification results of 4 grades of 16 major Asian football national teams. As for the classification results, it is different to normalize the input data and not to normalize the input data. The classification results accord with our subjective cognition, which indicates the rationality of self-organizing feature mapping network in solving the classification problem of national football teams. In addition, the paper makes a detailed analysis of the classification results of the Chinese team and compares the gap between the Chinese team and the top Asian teams. It also analyses the impact of the normalization of input data on the classification results, taking Saudi Arabia as an example.

In order to improve the accuracy of sports combination training action recognition, a sports combination training action recognition model based on SMO algorithm optimization model and artificial intelligence is proposed. In this paper, by expanding the standard action data, the standard database of score comparison is established, and the system architecture and the key acquisition module design based on 3D data are given. In this paper, the background subtraction method is used to process the sports video image to obtain the sports action contour and realize the sports action segmentation and feature extraction, and the artificial intelligence neural network is used to train the feature vector to establish the sports action recognition classifier. This paper mainly uses a three-stream CNN artificial intelligence deep learning framework based on convolutional neural network and uses a soft Vlad representation algorithm based on data decoding to learn the action features. Through the data enhancement of the existing action database, it uses support vector machine to achieve high-precision action classification. The test results show that the model improves the recognition rate of sports action and reduces the error recognition rate, which can meet the online recognition requirements of sports action.

To realize real-time detection of the operating environment of bridge cranes, this paper presents a three-dimensional mapping method using a binocular camera and laser ranging sensor. First, the left and right images are obtained by using the binocular camera, and the block matching is used for stereo correspondence to obtain the disparity image. Then, according to the reprojection matrix and the disparity image, the depth value of each pixel is obtained, and the depth image can be transformed into a three-dimensional point cloud of the current frame. Relying on the camera position data obtained by the laser ranging sensor, the three-dimensional point cloud generated from different positions is registered to obtain the three-dimensional point cloud of the craneʼs global operating environment. Finally, an octree map is used to represent the operating environment. To simulate the construction process of the operating environment of the bridge crane, an experimental platform was built, and experiments were carried out using the method proposed in this paper. The experimental results verified the feasibility of the method proposed in this paper.

After the market-oriented reform of China’s financial industry, there have been some problems in financial risk assessment. In recent years, commercial bank finance has made rapid development, but on the whole, the financial risk assessment of commercial banks is still the weakest link in the Chinese financial system. This experiment selects data from state-owned commercial banks and foreign-funded commercial banks. Through the analysis and deconstruction of the macroenvironment, participants, and business models, this paper systematically combines the factors influencing the financial risk of commercial banks, which can identify the main sources of financial risk in this complex way of financing and clarify the effects of the transfer of financial risk between different participants. Based on this, the paper studies the differences between the assets and liabilities between banks on the risk-taking of banks and the reform of the organizational evolution of fuzzy system. According to the application scenarios and actual needs of commercial banks’ financial risks, the entropy weight analysis method is used to reflect the weight of indicators by the difference degree of observed index values. The information quantity of indicators is measured to ensure that the established indicators can reflect most of the initial information. The experimental results show that, compared with state-owned banks, the proportion of foreign banks’ assets in 2018 is very small. The highest value of public debt assets is 9.2 billion yuan, followed by financial institutions with 2.58 billion yuan, and deposit institutions with 280 million yuan. The central bank has no debt amount.

The powerful advanced manufacturing industry is the most powerful driving force for economic development and growth, and it is also the main source of environmental pollution. Artificial intelligence and blockchain technology are recognized as a breakthrough technology that can be widely used, changing the way the entire society and economy operate. The main constraints affecting the sustainable development of the manufacturing industry are ecological deterioration and resource shortage, but the development of artificial intelligence and blockchain technology provides new ideas for solving manufacturing problems. Based on this, this paper proposes a research on the sustainable development performance of green manufacturing technology innovation based on artificial intelligence and blockchain technology. This paper deeply grasps the essence and connotation of artificial intelligence and blockchain technology, analyzes its specific application form and research background, searches for the effective effect of manufacturing technology innovation and green manufacturing performance path, and clarifies the mechanism between the two. All measurement items in the questionnaire used in this article use Likert5 scale and use 1–5 options to indicate the degree of conformity with the actual situation of the enterprise. The results show that the average green manufacturing capacity of each measurement item is between 3.18∼3.97, indicating that the company's green manufacturing capacity is relatively high. Among them, the ability of green technology innovation is relatively high, indicating that most companies have noticed that the improvement of the ability of green technology innovation plays a vital role in the future sustainable development of enterprises. Moreover, more and more companies are paying attention to the latest applications of blockchain technology, which can effectively promote the development of enterprises.

The Simultaneous Localization and Mapping (SLAM) method of mobile robots has the problem of low accuracy in complex environments with dense clutter and various map features, such as complex indoor environments and underwater environments. This problem is mainly embodied in estimating the location and number of feature points on the map and the position of the robot itself. In order to solve this problem, a new method based on the probability hypothesis density (PHD) SLAM is proposed in this paper, a PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information (AI-MBMI-PHD-SLAM). Firstly, this paper proposes a PHD-SLAM method based on amplitude information (AI-PHD-SLAM). The method uses the amplitude information of map features to obtain more precise map features. Then, the clutter likelihood function is used to improve the estimation accuracy of the feature map in the SLAM process. Meanwhile, this paper studies the performance of the PHD-SLAM method with the amplitude information under the condition of the known signal-to-noise ratio or the unknown signal-to-noise ratio. Secondly, aiming at the problem that PHD-SLAM lacks a priori information in the prediction stage, an AI-PHD-SLAM-based mixed birth map information method is added. In this method, map information that has been detected before the previous moment is added to the observation information in the map prediction phase as a new map information set in the prediction phase. This can increase the prior information and improve the problem of insufficient prior information in the prediction stage. The results of the experiments show that the proposed method and the improved method outperform the RB-PHD-SLAM method in estimating the number and location accuracy of map features and have higher computational efficiency.

When the outbreak of COVID-19 began, people could not go out. It was not allowed to provide agricultural machinery services in different places across regions to reduce the flow and gathering of people. Improvement of utilization efficiency of agricultural machinery resources is required through scientific scheduling of agricultural machinery. With seizing the farming season and stabilizing production as the goal, this paper studied the scientific scheduling of tractors within the scope of town and established agricultural machinery operation scheduling model with the minimization of total scheduling cost as the optimization objective. Factors such as farmland area, agricultural machinery, and farmland location information and operating time window are considered in this model to improve the accuracy of the agricultural machinery operation scheduling model. The characteristics of multiple scheduling algorithms are analyzed comprehensively. The scheduling requirements of agricultural machinery operation to ensure spring ploughing are combined to design the agricultural machinery scheduling algorithm based on the SA algorithm. With Hushu Street, Jiangning District, Nanjing City, as an example, a comparative experiment is conducted on the simulated annealing algorithm (SA) designed in this paper and the empirical algorithm and genetic algorithm (GA). The results suggest that the total cost of the scheduling scheme generated by the SA algorithm is 19,042.07 yuan lower than that by the empirical scheduling algorithm and 779.19 yuan lower than that by the genetic algorithm on average. Compared with the GA algorithm, the transfer distance, waiting cost, and delay cost of the SA algorithm are reduced by 11.6%, 100%, and 98.1% on average, indicating that the transfer distance of agricultural machinery in the scheduling scheme generated by the SA algorithm is shorter, so is the waiting and delay time. Meanwhile, it can effectively obtain the near-optimal solution that meets the time window constraint, with good convergence, stability, and adaptability.

The carbon footprint of the cold chain logistics system refers to the greenhouse gas emissions directly or indirectly caused in each link of the cold chain logistics activities. Because cold chain logistics is the main carbon emitter in the field of logistics, research on how to reduce carbon emissions in the field of cold chain logistics plays an important role in energy conservation and emission reduction. Based on the in-depth analysis of the carbon footprint of cold chain logistics, this paper introduces the distance coefficient and freshness parameters into the optimization model innovatively and uses the life cycle assessment method and input-output method to determine the calculation range of the carbon footprint of fresh products of each link in the cold chain logistics. The system calculates the carbon emissions generated by the production and operation activities of each place of origin, distribution center, retailer, and waste disposal during the circulation of fresh products. This paper establishes a carbon footprint optimization model to discuss how to balance carbon constraints and minimized costs. Through the analysis of the simulation results, from the perspective of the government and enterprises, corresponding countermeasures are put forward to more effectively achieve the goal of energy conservation and emission reduction and guide the cold chain logistics industry to sustainable development.

Accompaniment production is one of the most important elements in music work, and chord arrangement is the key link of accompaniment production, which usually requires more musical talent and profound music theory knowledge to be competent. In this article, the machine learning model is used to replace manual accompaniment chords’ arrangement, and an automatic computer means is provided to complete and assist accompaniment chords’ arrangement. Also, through music feature extraction, automatic chord label construction, and model construction and training, the whole system finally has the ability of automatic accompaniment chord arrangement for the main melody. Based on the research of automatic chord label construction method and the characteristics of MIDI data format, a chord analysis method based on interval difference is proposed to construct chord labels of the whole track and realize the construction of automatic chord labels. In this study, the hidden Markov model is constructed according to the chord types, in which the input features are the improved theme PCP features proposed in this paper, and the input labels are the label data set constructed by the automated method proposed in this paper. After the training is completed, the PCP features of the theme to be predicted and improved are input to generate the accompaniment chords of the final arrangement. Through PCP features and template-matching model, the system designed in this paper improves the matching accuracy of the generated chords compared with that generated by the traditional method.

In recent years, China’s cross-border e-commerce has flourished, and the transaction volume has increased year by year. Cross-border e-commerce has become a favorable breakthrough point for China's foreign trade. This article mainly studies the action mechanism and model of cross-border e-commerce green supply chain based on customer behavior. Green supply chain partners select 24 secondary indicators of the evaluation system as the input vector. The historical data of each index is collected by field investigation as sample data and brought into the neural network for training. The output vector of the only output layer of the network is used as the evaluation result of the supplier. This paper divides the operation mode of green supply chain into four stages and puts forward improvement tools for the functional modules in each stage. Enterprises can use the tools in the modules to improve the operation efficiency of green supply chain. According to the green level evaluation demand of green supply chain, this paper uses the hierarchical method to evaluate it. According to the survey results, this paper uses arithmetic average method to deal with the operation and establishes a secondary index after decomposition. Finally, this paper uses confirmatory factor analysis to test the measurement model and further uses analysis of variance to test the relationship between the two types of social cues and behavioral willingness. The data shows that the orderly fluctuation range of the east, middle, and west cross-border logistics subsystems basically remains at around 0.2 to 0.6. The results show that the establishment of the green supply chain model has a very positive significance for the implementation and development of the green supply chain in China’s manufacturing industry. Through the research of profit and profit distribution in the green supply chain, it provides guidance for the green supply chain to effectively select the supply chain members to cooperate and calculate and distribute the profit reasonably, so that the green supply chain management can be widely used in reality.

Nowadays, electronic book vendors are increasingly proactive in trying to strategically capitalize on online big data generated by consumers. It will bring great profit for vendors if they can make the most of online reviews to figure out the impact of online data on the selling prices of e-books. In this paper, we complement an emerging body of research to explore how e-book prices could be affected by online information via analyzing the sheer volume of online data from e-book websites, namely, we first employ a domain ontology-based method to select the most discriminative features that may affect e-book prices. Then, the topic modeling method latent Dirichlet allocation and aspect-oriented sentiment analysis methods are applied as a supplement. Using the multiple regression method, we identify the key features that may have effects on the prices of e-books and give the related regression equation. In our results, some factors including paper book prices, paper book pages corresponding to the e-book, and e-book content have significant effects on the price of e-books. The managerial implication is that e-book firms can obtain a reference price for an e-book and may dynamically adjust the price to increase e-book sales according to our data analysis results.

The traditional social work services are mainly visits which have some problems such as inconvenient information circulation, unreasonable resource allocation, and low service efficiency. To improve these problems, Internet plus is used to reform social work services and form an Internet plus social work service mode. Although this model has a very good improvement effect on social work service, with the rapid increase of the number of social work services and the rapid growth of the number of volunteers, this model has limitations in the arrangement of social work services and volunteer management. Therefore, based on this model, with the help of machine learning, the Internet plus social work service mode can be deepened by using machine learning to manage social services and volunteers. Internet plus social work service is the main problem in this paper. The Internet plus social work service mode is formed. Then, the deepening role of machine learning in Internet + social work service is discussed, and some problems in Internet plus social work service mode are improved. Internet plus social work service mode can better improve the problems in traditional social work service. The paper also uses machine learning to further optimize the mode of Internet plus social work service, which has a good application in social work service prospects.

Limited by traditional construction project management ideas and systems, the implementation of the PMC model in China still has serious problems such as opposition and frequent conflicts. How to reveal the causes of organizational conflicts and explore the key mechanism of the implementation of the PMC model from the system perspective are urgent problems to be solved. Based on the idea of engineering system view, this paper abstracts the PMC project participants with self-organizing characteristics of the organizational management system, in which the internal structure is closely related, and defines the connotation of synergy and synergistic evolution of the PMC project organizational management system. Using the Cucker–Smale model to describe the group movement, the hierarchical system and the acceleration efficiency function of the project legal person’s free will are constructed, and the structure, movement, and development law of the system itself are emphasized to simulate the ordered evolution trend of PMC project organizational management system and reveal the intrinsic causes of conflicts in PMC project and the key mechanisms of the PMC model application. The results show that first, the intensity of information communication between PMC subjects has a significant positive contribution to the orderliness of the organizational management system; second, too much acceleration of the project legal person’s free will causes group chaos in the system, while too little slows down the group stabilization time, which has a negative impact on cost and schedule; third, the more the organizational structure of PMC contractors tends to the whole-process integrated control, the more it can drive the group to gather in an orderly manner and form a synergistic control mode combining self-organization and other organizations; and fourth, the implementation of the PMC model should focus on eliminating the traditional institutional and conceptual barriers, forming a project management model with integrated control of the whole process of the PMC project contractor and effective macro supervision of the project legal person. The research results of this paper revealed the intrinsic causes of conflicts in PMC projects and the key mechanisms of PMC model application; it can help solve the confrontational situation of PMC project participants, promote the development of the PMC model, and give full play to the investment benefits.

This paper conducts an in-depth analysis and research on the automatic selection and parameter configuration of the core components of Big Data software by using the retention model and the automatic selection of Big Data components by establishing a standardized requirement index and using the decision tree model to solve the problem of component selection in Big Data application development. By establishing standardized demand indicators and based on the retention model, a data transmission intermediate platform for bidirectional data detection is proposed based on the three demands of user input: storage, computation, and analysis, as well as the problem of undetectable packet loss in data transmission of existing IoT and Web service platforms. The data communication module of the data transmission intermediate platform enables mutual monitoring and detection of data interaction between IoT smart terminals and cloud platforms. The retention mode is built separately to realize the automatic selection of Big Data components. In this paper, we start from several mainstream distributed storage systems and use Cassandra as an example for experiments and tests. We use the multiple regression fitting method to build a corresponding performance model for hardware parameters, take user requirements as input, and use the performance model to configure system hardware parameters; by studying its system principle, architecture, features, and application scenarios, we build a software parameter configuration knowledge base to guide the software. This solves the difficult problem of selecting, deploying, and configuring parameters for Big Data applications.

This article uses a multiple regression model to evaluate the extent to which financial resources in various regions can be put into the most appropriate direction through the financial system or financial market. This paper uses the Tobit model to conduct empirical analysis on the input and output data of technology finance in multiple provinces and cities and explores the impact of various factors on technology finance efficiency from the perspective of three technology finance entities: high-tech enterprises, governments, and venture capital companies. Based on the DEA model and Malmquist index model in the data envelopment analysis method, this paper uses deap software to calculate the efficiency of agricultural science and technology resource allocation in Gansu Province and the efficiency of agricultural science and technology operation in many provinces in the western region. This paper divides the allocation of scientific and technological innovation resources into the ability to allocate scientific and technological innovation resources and the allocation efficiency of scientific and technological innovation resources. By constructing a performance evaluation system for the allocation of scientific and technological innovation resources, it is found that there are obvious differences in the ability and efficiency of scientific and technological innovation resource allocation in various regions. This paper proposes the selected efficiency evaluation method, the multiple regression model, which uses the data from 2012 to 2018 to evaluate the efficiency of the allocation of scientific and technological financial resources in my country, calculates the efficiency value of each region, and conducts a comparative analysis of provinces and regions. Research shows that, through multiple regression model analysis, the total technical efficiency of technology finance in Northeast China rose from 0.759 fluctuations in 2012 to 0.922 in 2018, gradually reducing the difference with the eastern and western regions. The overall promotion plays a significant role. The innovation of this article lies in the use of multiple regression models to analyze the factors affecting the allocation of regional scientific and technological financial resources, aiming to improve the efficiency of financial resource allocation.

In recent years, the Internet of Things technology can effectively innovate applications and services. The Internet of Things technology has become more and more popular. It provides an effective and direct bridge between the physical world and virtual objects in cyberspace. With the increase in the intensity of dragon boat training and the increasingly fierce competition, the possibility of injury is increasing. Dragon boat racing is a noncontact team sport based on strength and technology. The purpose of this paper is to solve the problem of people's lack of understanding of the sports injuries and causes of dragon boat athletes. We used the data fusion algorithm and cluster maintenance optimization algorithm to study the application of Internet of Things technology in the cause of dragon boat sports injury. In order to save energy, extend the network life cycle, shorten service interruption time, and increase data packet transmission, the cluster maintenance optimization algorithm in this paper mainly improves and optimizes the startup time of cluster maintenance, which depends on the maintenance cost. The experiment result shows that the etiological detection system proposed in this paper matches the actual sports injury results well. The experiment result shows that the research on the cause of injury in dragon boat sports based on Internet of Things technology can detect the damage law well and can have a more comprehensive understanding for the cause of injury, which helps to prevent injuries better and take effective treatments. In the analysis part, it can be concluded that the detection system is very accurate in detecting the cause, and the accuracy rate is basically 100%.

As a special symbolic cultural carrier that reflects people’s material life and spiritual state, jewelry plays an increasingly important role in life. How can we accelerate brand promotion, promote jewelry product sales, and establish a rapid market response mechanism? High efficiency, high quality, and low cost to meet consumers’ increasingly personalized and diversified needs are the problems we currently need to solve. The purpose of this article is to explore the application of VR technology in jewelry displays and provide a brand new idea for jewelry display. In order to realize the virtualization of the jewelry design process, this article uses the Cult 3D VR platform to complete the design and realization of the interactive function of the jewelry virtual model, uses the Photoshop software to design the jewelry virtual display system interface, and finally completes the jewelry virtual display system in the Dreamweaver software integration and release. Through detailed example application, the feasibility of the viewpoint of this subject was effectively verified. In this paper, the two algorithms BRSK and SURF are used in conjunction, and the multiscale expression characteristics of BRISK in space and the rotation-invariant characteristics of SURF are used. Studies have shown that the experimental results of the rotation performance of the method in this paper show that the accuracy is improved by 60%, and the time-consuming is relatively less. Therefore, under the premise of ensuring the rapidity, the method in this paper can guarantee the accuracy and time cost of control matching.

Small and microenterprises are most affected during the COVID-19 epidemic period. Despite the government introducing many preferential policies, financing for small and microenterprises is still difficult. Based on evolutionary game theory and Matlab r2017b software, this paper discusses the causes of financing problems from two aspects. By taking loan amount, loan interest rate, guarantee value, and intermediary business income as variables, the research shows that banks not only pay attention to the comprehensive return of small and microenterprises but also pay more attention to the coverage of loan principal and interest by the value of collateral. Relying on collateral for credit is still the main way, and the lack of collateral causes financing difficulties of small and microenterprises. Accordingly, this paper puts forward the countermeasures and suggestions to improve the guarantee compensation mechanism and innovate the financing mode.

With the analysis of mobile clients and artificial intelligence technology, e-commerce is growing faster and faster. In today’s daily life, the e-commerce model has even been integrated into human life and has become an indispensable part. In the past few years, in the face of such a large business opportunity, e-commerce companies have sprung up in the capital market. Along with the rise of e-commerce, there have been many problems that have never arisen. The purpose of this paper is to analyze the status of development of e-commerce and its enterprise. This paper focuses on combing the current marketing overview and problems of e-commerce companies and proposes positive countermeasures and some practical solutions. Using the specific analysis method of the specific problem, the data comparison is made to draw conclusions. The results show that national policies focus on the rise of small and microenterprises, from the perspective of current business development, and developing e-commerce is conducive to enterprises to expand business breadth, expand the market scope of enterprises, develop business needs and brand products of enterprise by using Internet technology, and can break the constraints of time and space effectively. Strengthened communication and communication between enterprises and customers reduced the costs of enterprise, saved time, and improved utilization rate of resource. Therefore, solving the marketing problems which is most urgent at present is the primary goal of developing e-commerce companies.

With the rapid development of information science today, multifunctional and intelligent applications have gradually become the focus of attention. In the data management system, the first consideration is the reliability of the data source, followed by the intelligent processing after the data are collected. Due to the upgrade of the Internet to the Internet of Things, the way of network information transmission has also become a problem that people need to think about. The transmission mode of network information services will be converted from the passive transmission of information by traditional servers to the form of actively pushing information. The application of intelligent push technology in the field of the Internet of Things is a prominent and important direction in the development of the Internet of Things. This article mainly introduces the research on the intelligent music push and data analysis system based on the 5G Internet of Things, with the intention of providing some ideas and directions for the research of the music intelligent push and play and data analysis system. This paper proposes a research method for music intelligent push playback and data analysis system based on 5G Internet of Things, including current intelligent push related technologies, music evaluation matrix, user dissimilarity matrix, and music feature similarity calculation. The experimental results in this paper show that with the increase in the number of users, the accuracy of the recommended results of the system under the Hadoop framework gradually stabilizes, eventually reaching 91.2%.

The automobile industry serves as the primary industry for national development and also enhances manufacturing development strategies. Its source of power comes from the maturity and operational efficiency of the technology innovation network within the industry. Firstly, this paper takes the automobile industry of the Yangtze River Economic Belt as the research object and depicts the topological structure of the 1985–2015 Industry-University-Research Cooperation Innovation Network (IURCIN) from the perspective of space-time evolution. Then, based on the 2015 industrial network characteristic data, the paper analyses the impact of individual network characteristics such as intermediate centrality, structural hole limitation, and cooperation intensity on the innovation performance of the respective network. At the same time, it adds innovation activity, per capita gross regional product (GRP), and research and development (R&D) fund input intensity as control variables in the quantitative analysis. The results show that the centrality and structural hole limitation significantly and positively affect the subject innovation performance. The cooperation intensity and the subject innovation performance have an inverted U-shaped relationship. The innovation activity has a positive effect on the subject’s innovation performance. Furthermore, per capita GRP and R&D expenditure intensity on the main innovation performance is not significant. Finally, the countermeasures and suggestions are put forward to promote the innovation performance of each subject in the IURCIN.

With the development of social economy and the improvement of material level, people are paying more and more attention to their own health, and they are also constantly improving their fitness awareness. As far as the current sports equipment is concerned, the boring exercise methods can no longer satisfy people’s pursuit of higher quality fitness exercises. The embedding of the distributed virtual environment can incorporate its perceptual, immersive, and interactive characteristics, which increases the realization of multiuser interactive behavior in the field of sports equipment in the virtual scene. Therefore, this paper proposes the research and development of basketball sports equipment embedded system based on distributed virtual environment. First of all, this article adopts the literature method to learn the application of distributed virtual environment and embedded system in depth and, secondly, designs the embedded system architecture of basketball sports equipment suitable for distributed virtual environment and the software and hardware framework of the embedded system. Finally, the performance, delay comparison, system optimization test, and other aspects of the embedded system are analyzed. The average delay of interactive functions of embedded systems based on distributed virtual environment is 1.4 min, which saves 4 min compared with traditional interactive methods. And, the throughput rate of the system has also been greatly improved. The research on the embedded system of sports equipment based on the distributed virtual environment proposed in this paper will be beneficial to the development of society and economy, and it can also help more people to enrich the way of sports and enhance the fun of sports.

The position of mechanical arm in people’s life is getting higher and higher. It replaces the function of human arm, moving and moving in space. Generally, the structure is composed of mechanical body, controller, servo mechanism, and sensor, and some specified actions are set to complete according to the actual production requirements. The manipulator has flexible operation, good stability, and high safety, so it is widely used in industrial automation production line. With the development of science and technology, many practical production requirements for the function of the manipulator are more and more refined, especially in the high-end research field. For example, medical devices, automobile manufacturing, deep-sea submarines, and space station maintenance put forward higher requirements for it. In terms of miniaturization and precision, it can meet the needs of scientific research and actual production. But these are inseparable from the motion control system technology. This paper mainly introduces the research of manipulator control system based on AI wearable acceleration sensor, aiming to provide some ideas and directions for the research of wearable manipulator. This paper presents the research method of manipulator control system based on AI wearable acceleration sensor, including the establishment of manipulator kinematics model, common filtering algorithm, and PI algorithm of speed control system. It is used for the research and experiment of manipulator control system based on AI wearable acceleration sensor. The experimental results show that the average matching rate of the manipulator control system based on AI wearable acceleration sensor is as high as 88.89%, and the stability of the feature descriptor is high.

With the advent of the information age, the way people obtain information has changed profoundly. The wave of informationization in higher education has also come with it, and the teaching mode, teaching content, and teaching form are constantly innovated. How to organically integrate information technology into education teaching in order to care for learners’ learning experience and promote the cultivation of new talents is an issue that current educational technology researchers need to pay great attention to. This paper first builds a complete blended teaching model of public English for higher education, but its application effect needs to be further examined. This paper is an investigation in the background of the current era to build a blended teaching model. Based on the continuous development of the era, the ideology and application technology of this field will keep upgrading, so the teaching model also needs to be changed and updated according to the characteristics of the development of the era. The investigation of mixed teaching modes is not permanent. The investigation of the mixed teaching mode is not permanent. At present, only a few courses apply the blended teaching mode. On the basis of the continuous updating of teaching concepts and the latest technologies, it is foreseen that the focus of subsequent investigations will be on the individualized development of the blended teaching mode.

A scientific and comprehensive measurement of the efficiency of scientific and technological innovation activities is the basis and prerequisite for evaluating the existing environment and conditions of universities and is conducive for improving the efficiency and promoting the development of provincial economy. The paper selects the DEA (Data Envelopment Analysis) method to measure the efficiency of scientific and technological innovation in universities of China from 2007 to 2019 and studies its spatial differences. Then, the paper uses Markov chain estimation to describe the dynamic evolution process of the efficiency, and finally uses system GMM (generalized method of moments) model to identify the key influencing factors of its efficiency. The conclusions obtained are as follows: (1) The scientific and technological innovation efficiency in universities presents the highest distribution characteristics in the eastern part, the western part of China has the lowest, and the central part is midway of the two regions. There are significant differences between regions. (2) During the entire observation period, the efficiency in each region showed a path-dependent characteristic. After 2016, the mobility of different levels of technological innovation efficiency of universities in eastern part of China has increased, while in the central and western parts of China it tends to be stable. (3) Economic advantages, location advantages, government support, research and development foundation, and the efficiency of scientific and technological innovation in universities have a significant relationship, and the results are different in different regions.

With the rapid development of coal mine intelligent technology, the complexity of coal mine equipment has been continuously improved and the equipment maintenance resources have been continuously enriched. The traditional coal mine equipment maintenance knowledge management technology can no longer meet the current needs of equipment maintenance knowledge management, and the problems of low utilization rate, poor interoperability, and serious loss of knowledge have gradually emerged. It is urgent to study new knowledge system construction and knowledge management application technology for large-scale coal mine equipment maintenance resources. Knowledge graph is a technical method to describe the relationship between things in the objective world by using a graph model. It can effectively solve the problem of knowledge dynamic mining and management under large-scale data. Therefore, this paper focuses on the establishment of a coal mine equipment maintenance knowledge graph system by using knowledge graph technology. The main research contents are as follows: Firstly, based on the current situation that there is no unified basic knowledge system in the field of coal mine equipment maintenance, this paper establishes the coal mine equipment maintenance ontology (CMEMO) to effectively solve the problem that there are no unified representation, integration, and sharing of coal mine equipment maintenance knowledge in this field and provide support for the construction of coal mine equipment maintenance knowledge graph. Then, aiming at the problem that the traditional named-entity recognition method has a poor recognition effect and relies too much on artificial feature design, this paper proposes a named-entity recognition model for coal mine equipment maintenance based on neural network (BERT-BiLSTM-CRF) and applies the model to the coal mine equipment maintenance data set for verification. The experimental results show that, under the same data set, the entity recognition effect of this model is more leading than that of other models. Finally, through demand analysis and architecture design, combined with the constructed ontology model of coal mine equipment maintenance field, the entity identification of coal mine equipment maintenance is completed based on the BERT-BiLSTM-CRF model and the Django application framework is used to build the coal mine equipment maintenance knowledge graph system to realize the functions of each module of the knowledge graph system.

Beam-shaped components in large mechanical structures such as propellers, gas turbine blades, engine turbines, rotating railway bridges, and so on, when operating, usually engage in rotational movement around the fixed axis. Studying the mechanical behavior of these structures has great significance in engineering practice. Therefore, this paper is the first investigation on the static bending of rotating functionally graded material (FGM) beams with initial geometrical imperfections in thermal environments, where the higher-order shear deformation theory and the finite element method (FEM) are exercised. The material properties of beams are assumed to be varied only in the thickness direction and changed by the temperature effect, which increases the correctness and proximity to technical reality. The numerical results of this work are compared with those of other published papers to evaluate the accuracy of the proposed theory and mechanical model used in this paper. A series of parameter studies is carried out such as geometrical and material properties, especially the rotational speed and temperature, to evaluate their influences on the bending responses of structures.

Based on the upper bound theorem of limit analysis (UBLA) combined with the pseudostatic methods, this paper elaborates on a calculated procedure for evaluating fissured slope stability under seismic conditions reinforced with prestressed anchor cables. An existing simple slope case is presented as a case study in this work. The comparison is given to verify that the solution derived from this study is correct and feasible. By means of a numerical optimization procedure, the critical location of the crack is determined from the best upper bound solutions. The results demonstrate a significant influence of the depth of crack and seismic acceleration coefficient on the critical location distribution of the cracks. Meanwhile, the axial force of anchor cables is investigated via parametric studies. It is shown that the variation of the crack depth has little effect on the axial force of anchor cables. Moreover, this paper also illustrates the variation in the axial force of anchor cables under the impact of five marked factors (crack depth, anchor arrangement, anchor inclination angle, slope angle, and seismic conditions). Finally, the required critical length of the free section of anchor cables is determined to ensure the stability of fissured slopes subjected to seismic action.

With the development of computational simulation technology, the need for stable and immersive display effects of space scene animation in the field of life experience and visual art has gradually increased. In this case, the requirements for immersive characteristics of space scene animation have also been strengthened. The existing 3D space scene animation has a limited degree of stereoscopic display model and data visualization. However, the current space scene interactive animation adopts a plane layout as a whole, and the size of the view interface is generally fixed, including the size of virtual elements. However, the immersion in this paper interactive animation of spatial scene can effectively solve the problems of incomplete display of 3D effects and unstable view simulation in 3D effects. This paper takes immersive space scene animation as the research object and studies the 3D and characteristics of space scene animation based on computer parallel computing in different immersive space scenes and different virtual space technologies, as well as the animation effects of different scene transformations and art forms. The result of research shows that, with the continuous increase of the degree of virtuality within a certain range, the immersive effect of spatial scene interactive animation gradually becomes better. When the color of the space scene animation is below 15, the virtual immersive effect changes less. When the space scene is in the range of 15–20, the space scene will make people feel the atmosphere of a beautiful and mysterious illusion.

Accuracy and efficiency are essential topics in the current biometric feature recognition and security research. This paper proposes a deep neural network using bidirectional feature extraction and transfer learning to improve finger-vein recognition performance. Above all, we make a new finger-vein database with the opposite position information of the original one and adopt transfer learning to make the network suitable for our overall recognition framework. Next, the feature extractor is constructed by adjusting the unidirectional database’s parameters, capturing vein features from top to bottom and vice versa. Correspondingly, we concatenate the above two features to form the finger-veins’ bidirectional features, which are trained and classified by Support Vector Machines (SVM) to realize recognition. Experiments are conducted on the Malaysian Polytechnic University’s published database (FV-USM) and finger veins of Signal and Information Processing Laboratory (FV-SIPL). The accuracy of our proposed algorithm reaches 99.67% and 99.31%, which is significantly higher than the unidirectional recognition under each database. Compared with the algorithms cited in this paper, our proposed model based on bidirectional feature enjoys higher accuracy, faster recognition speed than the state-of-the-art frameworks, and excellent practical value.

The two most important factors that must be considered in the wireless sensor networks are energy efficiency and fault tolerance. Multipath routing is an effective method to improve the fault tolerance in wireless sensor networks. By taking the energy consumption into consideration, in this paper, a multipath routing algorithm for the wireless sensor networks based on the genetic algorithm is proposed. The proposed algorithm computes the fitness function by using the distance between nodes in the network and then generates the routing scheme at the base station. The routing scheme is shared with all the nodes of the entire network, to realize the multipath routing for each node. Finally, the simulation experiment is used to verify the validity of our method, and the results show that the routing method in this paper has a better effect.

We design in this paper a virtual English teaching system based on a wireless sensor network using the features of microservice architecture such as low coupling, lightweight, and high autonomy. This paper explains the characteristics of microservice architecture, uses the wireless sensor network as a carrier, uses the technology provided by Spring Cloud ecology to practice microservices, and makes a specific design and implementation of microservice architecture. The system allows teachers and students to upload and download English course resources, adopts the knowledge map to classify and manage course resources, realizes the diversified search and sorting function of course resources, greatly facilitates users to query and manage course resources, and improves the utilization rate. The internal functional modules of the platform are reasonably divided with the microservice design principle and are developed and deployed independently. For the security of the system, the authentication mechanism of the device and the authentication and authentication strategy of the application interface are designed, respectively. Finally, functional and nonfunctional tests were conducted on the system through the built test environment. The test results show that the virtual English teaching system designed and implemented in this paper has good feasibility and scalability. Through the research of this paper, it brings great help to the wireless sensor network virtual English teaching system and also improves the learning efficiency of students.

With the development of society and the improvement of urban economic level, people are no longer satisfied with the simple material and functional requirements of the city; thus, the spiritual requirements of city beauty, environmental quality, place atmosphere, and so on need to be improved. Based on the above background, the purpose of this paper is to analyze the landscape planning of Guangzhou’s digital city based on spatial information from the perspective of smart cities. Based on the relevant theoretical research, this paper combs the ideas of intelligent urban road landscape design. This paper analyzes the concept of urban road and smart road and puts forward the definition and characteristics of intelligent urban road landscape; according to the research on the development status of urban road and the connotation of smart city, combined with the concept and principle of urban road landscape design, it summarizes the design method of smart city road landscape. This paper, taking the innovative design of urban landscape as the research object and using the research methods of literature analysis and field investigation, innovatively combines the urban landscape design with digital information technology, changes the traditional landscape design ideas, constructs the urban landscape innovative design model, realizes the personalization of the urban landscape design, as well as the intelligent, digital, diversified, and humanized service and function. The experimental results show that nearly 60% of people are satisfied with the Guangzhou digital urban landscape planning based on spatial information in the smart city perspective.

Automatic and accurate segmentation of brain tumors plays an important role in the diagnosis and treatment of brain tumors. In order to improve the accuracy of brain tumor segmentation, an improved multimodal MRI brain tumor segmentation algorithm based on U-net is proposed in this paper. In the original U-net, the contracting path uses the pooling layer to reduce the resolution of the feature image and increase the receptive field. In the expanding path, the up sampling is used to restore the size of the feature image. In this process, some details of the image will be lost, leading to low segmentation accuracy. This paper proposes an improved convolutional neural network named AIU-net (Atrous-Inception U-net). In the encoder of U-net, A-inception (Atrous-inception) module is introduced to replace the original convolution block. The A-inception module is an inception structure with atrous convolution, which increases the depth and width of the network and can expand the receptive field without adding additional parameters. In order to capture the multiscale features, the atrous spatial pyramid pooling module (ASPP) is introduced. The experimental results on the BraTS (the multimodal brain tumor segmentation challenge) dataset show that the dice score obtained by this method is 0.93 for the enhancing tumor region, 0.86 for the whole tumor region, and 0.92 for the tumor core region, and the segmentation accuracy is improved.

Human face recognition has been widely used in many fields, including biorobots, driver fatigue monitoring, and polygraph tests. However, the end-to-end models fit by most of the existing algorithms perform poorly in interpretation because complex classifiers are constructed using facial images directly. In addition, in some of the models, dynamic characteristics of subjects as individuals are not fully considered, so dynamic information is not extracted. In order to solve these problems, this paper proposes an action unit intensity prediction model. The three-dimensional coordinates of 68 landmarks of human faces are obtained based on the convolutional experts constrained local model (CE-CLM), which enables the construction of dynamic facial features. Based on the error analysis of the CE-CLM algorithm, dimension reduction of the constructed features is performed by the principal components analysis (PCA). The radial basis function (RBF) neural network is also constructed to train the action unit prediction models. The proposed method is verified by the experiments, and the overall mean square error (MSE) of the proposed method is 0.01826. Lastly, the network construction process is optimized, so that for the same training samples, the models are fitted using fewer iterations. The number of iterations is decreased by 27 on average. In summary, this paper provides a method to rapidly construct action unit (AU) intensity prediction models and constructs automatic AU intensity estimation models for facial images.

Today people are witnessing the rapid evolvement in every area. This is because of the emerging trends in communication technology and autonomous unmanned vehicles. These trends have led us towards the high standards of health, energy, transportation, monitoring, and surveillance of huge domestic and industrial projects. Thus, this review paper presents the integration of the latest trend in communication technology, i.e., Internet of things (IoT) with unmanned aerial vehicles (UAVs). This manuscript not only reviews the use of IoT-enabled unmanned aerial vehicles for inspecting the several construction sites but also emphasizes the utilization of such IoT-enabled autonomous aerial vehicles for ensuring the health and safety measures at the site. It discusses the major limitations and shortcomings of state-of-the-art techniques for the same purpose, i.e., optimization issues in path planning, lightweight artificial intelligence (AI) and computer vision algorithms, coordination in communication using IoT, and scalability of IoT network. Thus, this paper shall help the reader to explore different open research problems in-depth.

Public opinion is a crucial factor for government policy making, implementation, evaluation, and social development. This paper analyzes the public opinion perception of intellectual property subject based on microblog data and puts forward the concept and calculation model for the breadth, depth, and comprehensive value of public perception. Then, taking the opinion evaluation in microblog platform as an example, this paper studies the public perception of intellectual property subject and illustrates the changing trend of public perception through related events in microblog. The results show that the four topics of trademark, intellectual property, copyright, and patent are the hot topics of public perception, the breadth, depth, and comprehensive value of public perception for the four topics fluctuated greatly, on the whole. In addition, when the content of microblog is related to personal benefits, business benefits, and personal interests, it can cause widespread public concern. Additionally, the public mainly participate in the dissemination of public opinion on intellectual property subject through forwarding. Finally, this paper puts forward some suggestions from the perspective of how to maximize the effect of intellectual property policy.

This paper represents the recognition of group activity in public areas, considering personal actions and interactions between people from the field of computer vision. Modeling the interaction relationships between multiple people is essential for recognizing group activity in the video scene. In artificial intelligence applications, identifying group activities based on human interaction is often a challenging task. This paper proposed a model that formulates a group action context (GAC) descriptor. The descriptor was developed by integrating the focal person action descriptor and interaction joint context descriptor of nearby people in the video frame. The model used an efficient optimization principle based on machine learning to learn the discriminative interaction context relations between multiple persons. The proposed novel group action context descriptor is classified by support vector machine (SVM) to recognize group activity. The proposed technique effectiveness is evaluated for group activity recognition by performing experiments on a publicly available collective activity dataset. The proposed approach infers a group action class when multiple persons are together in the video sequence, especially when the interaction between people is confusing. The overall group action recognition model is interrelated with a baseline model to estimate the performance of interaction context information. The experimental result of the proposed group activity recognition model is comparable and outperforms the previous methods.

This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment based on fine-grained sentiment analysis evaluation model. Then, an algorithm for behavior prediction and course recommendation based on emotional change trends is proposed, and the established multiple linear regression equation is solved with an improved algorithm. Finally, the method in this paper is verified by a comprehensive example with algorithm comparison analysis and cross-validation evaluation method. The research method proposed in this article provides new research ideas for evaluating and predicting the learning behavior of e-learners, which is conducive to timely discovering learners’ dropout tendency and recommending relevant courses of interest to improve their graduation rate, so as to optimize the learning experience of learners, promote the development of personalized education and effective teaching of the e-learning teaching platform, and provide a certain reference value for accelerating the reform process of education informatization. In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds the best parameters which also achieved the goal from academic expression to academic performance.

With the rapid growth of Internet technology, the blockchain has been developed fast in recent years with the wide expansions in finance, medicine, public welfare, and other fields. At the same time, the blockchain technology has also attracted researchers’ increasing attention, leading to a large number of scientific outputs that made significant contributions to blockchain research. However, few attempts can be found to investigate the development of blockchain based on the literature in this area. Therefore, this paper focuses on blockchain research by introducing bibliometric methods and comprehensively analyzes its status quo, emerging trends, and development path. Based on the above research, this paper draws two main conclusions. First, we made a comprehensive investigation of blockchain research in the past five years and then found that the main research directions in this area are Bitcoin, digital currency, and Fintech. Second, the development of blockchain research generally presents a trend of π-transformation based on the main path analysis. The above studies provide a brief view for beginners who want to learn blockchain research in a direct way and contribute to extend the theoretical foundation of blockchain research by giving a comprehensive bibliometric analysis.

Radar Emitter Individual Identification is a key technology in modern electronic radar systems. This paper will focus on Radar Emitter Individual Identification (REII). Based on the advantages of Empirical Mode Decomposition (EMD) and bispectrum in signal processing, we propose an REII method based on the CNN. Firstly, the radar emitter signal is preprocessed. Secondly, the Hilbert–Huang Transform (HHT) spectrum and bispectrum are combined to form an image of the signal. Finally, in order to avoid loss of information and achieve the potential identification performance improvement, the signal image obtained is identified by the optimized CNN. Experimental results based on the measured signals show that the proposed method has high identification accuracy and is capable of meeting real-time identification requirements. The deep-learning-based identification method proposed in this paper has strong generalization ability and adaptability, which provides a new way for REII.

With the development of global urbanization and the construction of regional urbanization, residents around urban cities are increasingly making demands on urban public transportation system. A new kind of modern public transportation vehicle named Multi-Articulated Guided Vehicle based on Virtual Track (MAAV-VT) with the advantages of beautiful, smart energy conservation and environmental protection is proposed in this paper, which aims at optimizing the public transportation system between and within urban areas. Therefore, concentrating on the general design and control strategy, the main contents of this paper are as follows. At first, the design concepts and key technologies of MAAV-VT are introduced. It is the fusion of urban rail transit operation mode and advanced automotive technologies, which have the characteristics of 100% low-floor, medium to high velocity, medium to big capacity, and low construction cost. Then, as the core subsystem, to guarantee the properties of self-guiding and trajectory tracking of the new vehicle, this paper is focused on the control system based on the dynamics and kinematics model of the whole multi-articulated vehicle. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform is built considering the mechanical properties of each vehicle element and the characteristic of articulated mechanism. Finally, the function of control system is validated. The tracking error of each vehicle elements would be reduced to make sure the whole multi-articulated vehicle moves along the preset virtual track.

With the rapid development and maturity of unconnected communication technology, effector technology, embedded computing technology, and distributed information processing technology, as well as the rapid advancement of digital processing and computing capabilities, unconnected effector Internet has received more and more attention. It is a new type of self-organizing unconnected multihop Internet. With the development and progress of technology and society, the development of unconnected effector Internet is also advancing by leaps and bounds, and it has a wide range of applications in many fields such as military, civil, environmental, medical, and industrial. At present, the research on unconnected effector Internet mainly focuses on the communication protocol, but there is almost no research on input review of unconnected effector Internet. Due to the limited energy of the effector and the limitation of the transmission signal bandwidth, the use of limited resources for input review research in effector Internet is of great significance to the development of unconnected effector Internet. Therefore, this paper studies the input review of unconnected effector Internet based on the L-A model on the on-chip embedded tune system. Because the classic low-power adaptive cluster layering protocol (LEACH) has the problems of unbalanced energy consumption and short node life cycle, this paper uses the embedded tune technology based on the L-A model and analyzes the remaining energy and location of the unconnected effector Internet node. Parameters are tested and studied. Through the research on the input review of unconnected effector Internet, the simulation results obtained show that the research in this paper is feasible and reasonable.

The development of customized service is an important way to transform and upgrade China’s mining industry. However, in practice, there remain problems, such as the slow market response speed of service providers and the contradiction between the large-scale development of service providers and the personalized service needs of service demanders. This paper uses the theory and method of service modular design to solve these problems and explores the process-based service modular design method. Service modular design depends largely on the determination of the relationship between service activities and the reasonable division of modules. However, previous research has rarely made use of modular design methods and modeling tools in the mining service context. At the same time, evaluations of the relationship between service activities relying on knowledge and those relying on experience have been inconclusive. Therefore, this paper proposes a service modularization design method based on the fuzzy relation analysis of a design structure matrix (DSM) that solves the optimal module partition scheme. Triangular fuzzy number and fuzzy evidence theory are used to evaluate and fuse the multidimensional and heterogeneous relationship between service activities, and the quantitative processing of the comprehensive relationship between service activities is carried out. On this basis, the service module structure is divided, followed by the construction of the mathematical programming model with the maximum sum of the average cohesion degree in the module and the average coupling degree between modules as the driving goal. The genetic algorithm is used to solve the problem, and the optimal module division result is obtained. Finally, taking the service modular design of SHD coal production enterprises in China as an example, the feasibility of the proposed method is verified.