As school is the main carrier to promote social development and individual development, the construction of an ‘intelligent’ campus has gradually become an important link in constructing an intelligent city in the macro context of social informatisation [1] while the diversity of campus safety characteristics is also one of the main concerns in the process of intelligent campus construction. Meanwhile, the campus security problems of different universities are characterised by dense personnel, complex and diverse environments, and openness, which bring difficulties to the security defence, decisionmaking, and governance of campuses of colleges and universities. At present, the traditional management mode can no longer meet the needs of constructing an intelligent campus. Following the development of new technologies, although advanced artificial intelligence, security game, digital twin and other technologies are gradually specifically applied in different types of public safety governance issues in smart parks, there are few related studies conducted from the comprehensive perspective of the development of intelligent campus security issues. As a consequence, this paper has unfolded the three aspects subsequently, namely, the identification and detection of accidents when they are occurring on campus, the tracking of abnormal targets, together with the security resource path planning.
In the current study regarding abnormal behaviour identification, the feature extraction methods based on human appearance and movement information require to extract the specific features, while the preprocessing of data is also complicated. Behaviour identification based on deep images asks for deep data, and its acquisition cost is relatively high. Comparatively speaking, behaviour identification based on a convolutional neural network is more suitable for this study Since defining specific extracted features is unnecessary in this method, it is better to directly learn useful features from the raw data through less preprocessing and hidden nodes. Although C3D [2], P3D [3], and LSTM [4] networks generate good results, C3D and P3D fail to notice local information, and the training of LSTM itself is difficult. Moreover, strict iterative comparison in sequence order affects the training efficiency. As a result, this paper plans to design a network to learn the spatiotemporal information in the video added with the attention mechanism to focus on the local information, thus improving the network performance, and realising the identification of common abnormal behaviours on campus.
With the development of artificial intelligence, in current research, the influence generated by the interagent interactions is gradually applied to the decisionmaking process, and network structures are designed based on this. The purpose of introducing multiagent reinforcement learning to form Lenient DQN is also to accelerate the speed of cooperative learning. From another perspective, it is to design the corresponding networks to solve the problem of multiagent learning. For instance, in 2018, Deepmind proposed a VDN network. The VDN network wishes to allocate the global reward signals so that each agent can learn faster. The RFM proposed by Tacchetti et al. is to use a pretrained graph network to predict the behaviour of other teammates, anticipating learning to be a better feature expression, which will be used to learn more efficiently about learning speed and cooperation.
In terms of applying reinforcement learning, current research are still constrained by the instability of the environment where the intelligent agent is perceived by the setting of the reward function framework, etc. In the NLP task, training the model mainly relies on reducing the gap between the expected value and the real label while in the intelligent agent, agent performance mostly depends on the designer's accurate understanding and design of the reward signal function and environmental instability, which reflect the accuracy degree and the stability degree of modelling. Accordingly, the key to applying reinforcement learning to unstable environments is the setting of the reward function and unstable environments. We can inject the idea of game theory into the reinforcement learning methods, attempt to remove the obstacles brought about by the unstable environment, and build a bridge between the applications. Starting from the two perspectives of environmental modelling and solving process, we reduce the performance decline caused by environmental instability. Reward design based on the attention mechanism can be used to explain the agent behaviour of reinforcement learning and improve the interpretability of reinforcement learning. The concept of attention can be utilised to encode a strategy tendency of interactive behaviour in the environment so that we can code the behaviour data produced by various participants, and map them to the motivation space. Then, we can differentiate strategy styles through methods like Cluster, employ the evolutionary strategy to encourage to the generation of a new strategy style different from the strategy base, obtain a diversified strategy base, and also inspire more environmentally adaptive methods and practical applications of reinforcement learning consequently.
Path planning is one of the main research contents of motion planning, which consists of path planning and trajectory planning. The sequence point or curve connecting the starting and ending positions is called a path. There are many studies on path planning, and the main research contents can be summarised as [5]: (1) Intelligent search algorithm; (2) Artificial intelligence algorithm; (3) Geometric model algorithm; (4) Local obstacle avoidance algorithm.
Shortest path planning has always been a hot issue and plays a significant role, especially in resource allocation, path planning and other directions. Recently, the Dijkstra algorithm has been extensively used in numerous fields, such as optimisation, image processing and grid processing. In pace with the rapid development and change of traffic, new requirements are put forward for the efficient operation of the Dijkstra algorithm, and the algorithm optimisation for solving the shortest path problem has always been a research hotspot of experts and scholars. For instance, Liu et al. [6] studied the ‘intersection path’ and ‘loop path’ problems in the Dijkstra algorithm and proposed an improved Dijkstra algorithm method for the above problems. Furthermore, in studying choosing the most feasible path at least cost or time, Tamatjita and Mahastama [7] applied the case using the Dijkstra algorithm on the graph representing the street paths with two possible digraphs, to name just a few.
Considering the characteristics of campus roads, this paper takes optimising Dijkstra algorithm as the core to design and implement the path planning system. The research ideas of this paper are as follows: (1) Data processing, preprocessing the data set to make the research go more smoothly; (2) Data analysis, studying the characteristics of the data, is the basis for selecting appropriate research methods; (3) Strategy selection, when anomalies are identified During the behaviour, choose the best tracking strategy, and plan the optimal path to transport the material to the accident site; (4) Set problem optimisation, analyse the remaining problems and optimise them.
The dataset used in this paper is obtained from the CASIA behaviour analysis dataset of the Chinese Academy of Sciences, which consists of 422 video data with a frame rate of 25 fps. The dataset is shot from three angles, that is, horizontal angle, oblique angle and overlooking angle with singleperson behaviour and multiperson interaction behaviour. Each video is recorded between 5 s and 30 s, depending on different behaviours.
There are other behaviours unrelated to this behaviour in the video. To avoid affecting the model performance by the irrelevant behaviour, it is initially needed to delete these behaviours in the video. Since there are too many overlaps between adjacent frames, there is a lot of overlapping information between them. Thus, a lot of redundant calculations will be encountered when extracting features, which affects the performance of the model. To reduce redundant information and improve operational efficiency, this paper employs the method of extracting Nframe images at the same time interval.
The process of training a model is very complex and requires a lot of calculations. If all kinds of behavioural video data are insufficient, it is easy to cause model overfitting. In general, the larger the data volume, the more robust the model is. To increase the generalisation ability of the model, we use the method of data augmentation to increase the data amount Before training the model, the data is augmented. We use six augmentation methods to augment and save the augmented results, that is, the upper right corner cutting, upper left corner cutting, lower right corner cutting, lower left corner cutting, centre cutting, and horizontal flip of the above enhancement results and the original image, the final data set is enlarged 11 times than the original data set size, which can increase the robustness of the model, and provide enough data set for training. In each training round, the pictures are randomly cut to 112×112 and fed into the network.
Convolutional neural networks (CNN) is one of the representative algorithms of deep learning, which has the characteristics like reducing the data preprocessing work, requiring no manual extraction of the feature information, and possessing excellent learning ability, etc In this paper, considering that the datasets are a characteristic of a video, a network structure that can learn the spatiotemporal information in a video is designed, which is shown in Figure 1. After the input image passes through the 3DCNN convolutional layer, the obtained features are entered into the improved attention mechanism. After the obtained result passes through the fully connected layer, we obtain the classification results by softmax.
The 3DCNN in this paper are made up of overlapping convolutional and pooling layers. The 3DCNN, different from 2DCNN, can not only extract spatial features in the video but also learn temporal information in it. The pooling layer downsamples, removes redundant features and presses features to reduce computation. The network takes the 16frame pictures of 16×112×112×3 as the input and obtains the feature vectors through eight convolution layers and five pooling layers. Whereinto, the convolution core size is 3×3×3 and the step size is 1. In the pooling layer, all the pooling window is 2×2×2 with 2×2×2 step size except the first pool layer which is 1×2×2 in pooling window and 1×2×2 in step size.
The CBAM [8] attention mechanism model contains two independent submodules, namely, the channel attention module (CAM) and the spatial attention module (SAM). It is a lightweight attention module, with negligible cost when added to other models, and can be trained in an endtoend manner together with the original model. Woo et al. [8] added it to ResNet and MobileNet, significantly improving the model performance. For a given feature map, CBAM will compute the attention map along the path attention channel and the spatial attention channel, and then multiply the attention map by the input feature map to conduct feature learning. In order that the 3DCNN can learn features better, the CBAM model can be improved to be combined with 3DCNN. The specific refined model is shown in Figure 2.
After entering the input feature F_{3D} ∈ R^{T}^{*}^{W}^{*}^{H}^{*}^{C}, and passing the path attention module M_{c3D} ∈ R^{1}^{*}^{1}^{*}^{1}^{*}^{C}, the convolution result multiplies the input feature to obtain the output of the path attention module, which is input to the SAM M_{s3D} ∈ R^{T}^{*}^{W}^{*}^{H}^{*}^{1}. By multiplying the result with the input feature, the final output result of the improved attention model is obtained. The formula is as follows:
CAM mainly focuses on which paths contribute to the final classification. It adopts global average pooling and global maximum pooling. The two pooling are more conducive to extracting richer highlevel features. The specific formula is as follows:
First, the feature F_{3D} is entered, and two features of 1 × 1×1×C are obtained by the two pooling, and sent to the MLP network. After summing the corresponding elements of the output of MLP and passing the sigmoid activation function, the final feature is obtained, which is multiplied with the input feature F_{3D} to obtain the feature required by the CAM.
SAM mainly focuses on which positions in the image can affect the final classification. It also adopts global average pooling and global maximum pooling. The specific formula is as follows:
Whereinto
It is supposed that both the manchasers and the runaway are rational [9] participants, that is, both the manchaser and the runaway choose their own strategies according to the principle of maximising their own returns.
Define 1 multiagent mobile target tracking strategy selected model MultiAgent Deep Deterministic Policy Gradient (MADDPG), described by sixagent group (E, A, D, P,
E= {Ea, Ed} is the action space. Whereinto, Na is set as the manchaser and Nd as the runaway. The experiment grids the map, that is, the movement space of the manchaser and the runaway is seen as five movements: up, down, left, right and static, that is, in the hunting process, the runaway is first given a random orientation probability, and the manchaser makes his own strategy according to the position information of the runaway.
A= {a1, a2,…, am} is the hunting strategy set, where m ≥ 1.
D= {d1, d2,…, dk} is the escape strategy set, where k ≥ 1.
P is the set of prior probabilities of the manchaser to the movements of the runaway, representing the initial judgement of the direction position of the runaway by the manchaser, where P= {p1, p2,…, pn}, pi > 0.
U = {Ua, Ud} is the set of revenue functions of the runaway and manchaser, and different hunting strategies have different revenue values.
Optimal active hunting strategy selection algorithm for the hunting game

(Φa={ 
//Initialize the escape action space and the hunting action space 
(P={p}1,p2,…,pn); 
//Initialize the escape action prior probability 
(A={a}1,a2, …,am, D={d1,d2, …,dk}); 
//Initialize the policy set 
While (aj??Adh??D) //Calculate the proceeds 
{ 
Bayes′

//Calculate the posterior probabilities 
Ua( 
Ud(a)j,dh, 
for(i=1;i≤ s;i++) 
//The s is the number of stages in the game process 
{ 

a*( 
//Calculate the optimal escape and hunting strategies 
Bayes′

// Use the Bayes’ rule to calculate the posterior probability of the escape action 
Create

//EQ Construct a refined Bayes’ equilibrium solution EQ 
OutPu (td*(a)); 
//Output the optimal hunting strategy in this stage 
} 

The runaway action is first assigned a prior probability that can be obtained by historical experience or equally assigned probability [10]. The manchaser adjusts his tracking strategy by observing the escape behaviour and constantly correcting the judgement of the runaway's position. The game order is as follows:
Select an action Ei from the action space Ea of the runaway Na at a certain probability, where Ei∈ Ea. The runaway Na knows Ei and manchaser Nd does not, but the manchaser Nd has the inference on Ei, that is to say, the manchaser knows the prior probability of the runaway's action.
The runaway Na selects an escape strategy Dw from his strategy space D after observing Ei.
The manchaser Nd observes that the escape strategy is aj and then applies the Bayes’ method to obtain the posterior probability from the prior probability, and selects a chase strategy Ah from his strategy space A.
The proceeds of both the manchaser and the runaway are obtained after calculating by Ua and Ud, respectively. The game ends in two cases [11, 12]: the hunting strategy of the manchaser can completely block each escape path; the runaway has succeeded to escape.
The calculation steps of the refine Bayes’ equilibrium solution are as follows:
The manchaser establishes posterior probability inference
The manchaser deduces the optimal hunting strategy d * (aj). When the manchaser collects the escape strategy a of the runaway, the escape optimal strategy d * (a) is selected based on the posterior judgement of the action
The runaway infers the optimal escape strategy a * (
Refined Bayes’ equilibrium solution (
The subgame refined Nash equilibrium (a * (
According to the game theory [13], the strategy in equilibrium is the optimal choice for both sides, so the manchaser believes that d * (a) is chosen as the optimal hunting strategy.
The shortest path refers to the shortest path from the starting point to the end point and the smallest sum of path weights. However, after the emergency occurring on the campus, besides requiring the shortest path of demanded material arriving at the demanded point, restrictions such as the effective time of delivery of materials are added. Comprehensively analysed, the socalled security resources scheduling ‘optimal path’ refers to that when a security event happens at one point on the campus, and within the required time scope, it ensures the supplies are delivered in the shortest path, hoping to reduce more unnecessary losses generated in the delivery process. Since there is not only one site for campus materials to be stored, thus it involves more material points of the incampus security material scheduling ‘optimal path’. Therefore, it is necessary to improve according to the actual situation of the campus based on the existing refined Dijkstra [14].
Whereinto,
We first label each class of behaviours to distinguish between different behaviours, and divide 75% of the dataset into the training set and 25% into the test set. When training at 250 Epoch, the model accuracy and loss values are nearly stationary, and all the parameters of the model are approximately converged. Figure 3 reflects the changes in accuracy and loss values during training, with the network losing values around 0.1 after training over 250 Epoch.
To verify the advantages of the network in the paper, the present network and several other network structures are compared. The method used by Karpthy et al. [15] achieved an average accuracy of 79.9% on the CASIA dataset and the method used by Koesdwiady et al. [16] tested on the CASIA dataset obtained 84.2%. Wang and Gao [17] proposed the method of dualstream convolutional neural network fusion with spatiotemporal space, which achieved 88.3% accuracy on CASIA. However, our proposed method achieves an accuracy of 89.6% on the CASIA dataset, as shown in details in Table 1.
Accuracy comparison of several methods
Karpthy et al. [15]  79.9 
Koesdwiady et al. [16]  84.2 
Wang and Gao [17]  88.3 
The method in this paper  89.6 
To verify the effect of the added attention mechanism, we compare the effect of the 3DCNN network without the attention mechanism, and also compare the added positions, both adding the attention mechanism after five convolution layers and in the last convolution layer. The experimental results are shown in Table 2. The added attention mechanism is better for identifying the classification. Although the attention model helps the identification, the excessive number of layers makes the generalisation performance of the model worse.
Effect comparison of the attention mechanism
No increase  85.2 
5  87.6 
1  89.6 
In this paper, we demonstrate the improved algorithm while introducing game ideas based on the existing algorithms, and building our own hunting game test environment. We first chose to improve the DQN series algorithm and compare it with the original algorithm. The experience playback method of the algorithm adopts the priority experience playback (PER). In the subsequent supplementary experiments, we improve and compare the algorithms of the strategy gradient class to further prove the reliability and superiority of the algorithm.
In the experimental environment, n manchasers must cooperate with each other to chase a runaway in a 2dimensional discrete k×k grid environment. We assume that the grid is bounded by the wall, that is, if moving in a certain direction causes the manchaser or the runaway to hit the wall, the movement fails, and the agent remains in the same grid. The environmental state includes the positions of all manchasers d1t, d2t,…, dnt and the location of the runaway gt. This ambient diversely runs, and for each episode, the manchaser's initial locations d10, d20,…, dn0 and the runaway's initial position g0 are uniformly sampled, so that no two locations are the same. Each round is composed of many time steps. At each time step, the manchaser performs one of the five possible actions: up, down, left, right, or static. If there is more than one manchaser moving into the same grid, the movement fails and the manchasers remain in their own grids. For models of the reward function, we consider the following settings of the reward function:
Sparse team reward: At each time step, all manchasers are punished by 1 for delaying capturing the runaway, and zero punishment if successful.
Full information team reward: At each time step, all agents are punished by
Joint team and personal reward: At each time step, the manchaser a gets two rewards,
The combinatorial space of the hyperparameters is too large for an exhaustive search. Due to high computational costs, we do not perform a systematic grid search. Instead, we only conduct informal searches. We linearly anneal from 1.0 to 0.01 during the exploration phase (100,000 time step) and fix it at 0.01 thereafter. For the first environment, we train each agent's network for a total of 50,000 sets, while for the second environment, each network is trained for 20,000 sets. We use replay memory with 10,000 recent time steps. The RMSProp algorithm that we use has a learning rate of 0.00005. Furthermore, we use a discount factor of 0.99 and perform a learning update every 4 time steps using a small batch of 32 transformations. For replay priority, we use the priority index
During training, the manchaser and the runaway move for five movements like up and down, left and right, or static. The algorithm is trained 500 times, and the cumulative return of each time is recorded. The results are shown in the Figure. It can be seen from Figure 4 that the algorithm starts to converge when trained about 100 times.
Through analysing the attack and defence game model and analysing the proceeds of the algorithm, the general law of the hunting strategy of the algorithm can be obtained: the accuracy of the detection of the escape strategy has a significant impact on the game trend of the hunting and the equilibrium solution. When the manchaser may mistake the escape strategy, introducing the minimum risk strategy benefit is closer to the actual hunting situation so that the algorithm can choose a better hunting strategy. Therefore, the proposed algorithmic strategy should be deployed together with the hunting detection strategy to achieve optimal hunting effects.
Based on the results in Section 4.2, the traffic topology network on the campus of Xinjiang Normal University is planned, and the application scenario and practical value of the planning path are mainly studied.
In the research process of various safety issues, the campus, as one of the types with a crowded population, has a typical risk amplification and social response characteristics, making it have a very important safety significance. In this paper taking Xinjiang Normal University as an example, the campus map is shown in Figure 5a). MATLAB software is used to abstract important sites as nodes and numbers them, and abstract the roads between important sites as edges. The obtained campus network map is shown in Figure 5b), which consists of 44 nodes and 64 edges.
Based on the experimental content in Section 4.2, the planning platform is developed to set the initial position of attacker escape g0 (point 39 as shown in Figure 5b) as the attack point, requiring a certain amount of security resources, and the material storage in the real campus background is the starting point of path planning, points 2, 22 and 31 as shown in Figure 5b). The simulation results are shown in Figure 6 (bold blue line is the acquired optimal path):
Under the demand of constructing intelligent campus, to improve the ability in campus security management, we target the campus security events in three stages, namely, abnormal behaviour identification, abnormal behaviour mark tracking, security resource scheduling path planning of the attacked point after the safety event happens, which helps college and university campus reduce human resources cost as far as possible. This paper fully applies advanced technology, fuses it with a game idea, designs and develops a path planning platform, and combines it with the real scene of Xinjiang Normal University for simulation.
Some limitations in this model remain to simplify the model and to ease the solution. First, the combined platform for campus realscene design has boundedness, such as the universal impact of scene scenarios. Second, there is still room for the models and methods in this paper still have room to be improved. Therefore, future research can be turned to break the above bottlenecks, take the budget into the model, and further relax the premise of sufficient facility capacity, to better fit the real situation.
Effect comparison of the attention mechanism
No increase  85.2 
5  87.6 
1  89.6 
Optimal active hunting strategy selection algorithm for the hunting game

(Φa={ 
//Initialize the escape action space and the hunting action space 
(P={p}1,p2,…,pn); 
//Initialize the escape action prior probability 
(A={a}1,a2, …,am, D={d1,d2, …,dk}); 
//Initialize the policy set 
While (aj??Adh??D) //Calculate the proceeds 
{ 
Bayes′

//Calculate the posterior probabilities 
Ua( 
Ud(a)j,dh, 
for(i=1;i≤ s;i++) 
//The s is the number of stages in the game process 
{ 

a*( 
//Calculate the optimal escape and hunting strategies 
Bayes′

// Use the Bayes’ rule to calculate the posterior probability of the escape action 
Create

//EQ Construct a refined Bayes’ equilibrium solution EQ 
OutPu (td*(a)); 
//Output the optimal hunting strategy in this stage 
} 

Accuracy comparison of several methods
Karpthy et al. [ 
79.9 
Koesdwiady et al. [ 
84.2 
Wang and Gao [ 
88.3 
The method in this paper  89.6 
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Computer Art Design Model Based on Nonlinear Fractional Differential Equations The Optimization Model of Public Space Design Teaching Reform Based on Fractional Differential Equations The Approximate Solution of Nonlinear Vibration of Tennis Based on Nonlinear Vibration Differential Equation Graphical Modular Power Technology of Distribution Network Based on Machine Learning Statistical Mathematical Equation Employment and Professional Education Training System of College Graduates Based on the Law of Large Numbers Economic Research on Multiple Linear Regression in Fruit Market inspection and Management Nonlinear Differential Equations in Preventing Financial Risks Lagrange’s Mathematical Equations in the Sports Training of College Students Simulation Research of Electrostatic Precipitator Power Supply Voltage Control System Based on Finite Element Differential Equation Research on the effect of generative adversarial network based on wavelet transform hidden Markov model on face creation and classification Research on Lightweight Injection Molding (CAE) and Numerical Simulation Calculate of New Energy Vehicle Power Flow Calculation in Smart Distribution Network Based on Power Machine Learning Based on Fractional Differential Equations Demonstration of application program of logistics public information management platform based on fuzzy constrained programming mathematical model Basketball Shooting Rate Based on Multiple Regression LogicalMathematical Algorithm The Optimal Application of Lagrangian Mathematical Equations in Computer Data Analysis Similarity Solutions of the Surface Waves Equation in (2+1) Dimensions and Bifurcation Optimal decisions and channel coordination of a green supply chain with marketing effort and fairness concerns Game theoretic model for low carbon supply chain under carbon emissions reduction sensitive random demand Limit cycles of a generalised Mathieu differential system Influence of displacement ventilation on the distribution of pollutant concentrations in livestock housing Application of data mining in basketball statistics The nonlinear effects of ageing on national savings rate – An Empirical Study based on threshold model Design of fitness walker for the elderly based on ergonomic SAPAD model AtanKA New SVM Kernel for Classification Mechanical behaviour of continuous girder bridge with corrugated steel webs constructed by RW Study of a linearphysicalprogrammingbased approach for web service selection under uncertain service quality The Relationship Between College Students’ Taekwondo Courses and College Health Based on Mathematical Statistics Equations Analysis and countermeasures of cultivating independent learning ability in colleges teaching English based on OBE theory A mathematical model of plasmidcarried antibiotic resistance transmission in two types of cells Fractional Differential Equations in the Exploration of Geological and Mineral Construction AdaBoost Algorithm in Trustworthy Network for Anomaly Intrusion Detection Projection of Early Warning Identification of Hazardous Sources of Gas Explosion Accidents in Coal Mines Based on NTM Deep Learning Network Burnout of frontline city administrative lawenforcing personnel in new urban development areas: An empirical research in China Enterprise Financial Risk Early Warning System Based on Structural Equation Model A Study on the Application of Quantile Regression Equation in Forecasting Financial Value at Risk in Financial Markets Fractional Differential Equations in the Model of Vocational Education and Teaching Practice Environment Information transmission simulation of Internet of things communication nodes under collision free probability equation Image denoising model based on improved fractional calculus mathematical equation Random Fourier Approximation of the Kernel Function in Programmable Networks The Complexity of Virtual Reality Technology in the Simulation and Modeling of Civil Mathematical Models University Library Lending System Model Based on Fractional Differential Equations Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features Intelligent Matching System of Clauses in International Investment Arbitration Cases Based on Big Data Statistical Model Evaluation and Verification of Patent Value Based on Combination Forecasting Model Financial Institution Prevention Financial Risk Monitoring System Under the Fusion of Partial Differential Equations Prediction and Analysis of ChiNext Stock Price Based on Linear and Nonlinear Composite Model Calculus Logic Function in Tax Risk Avoidance in Different Stages of Enterprises The Psychological Memory Forgetting Model Based on the Analysis of Linear Differential Equations Optimization Simulation System of University Science Education Based on Finite Differential Equations The Law of Large Numbers in Children's Education Optimization System of Strength and Flexibility Training in Aerobics Course Based on Lagrangian Mathematical Equation Data structure simulation for the reform of the teaching process of university computer courses RETRACTION NOTE Research on the mining of ideological and political knowledge elements in college courses based on the combination of LDA model and Apriori algorithm Research on nonlinear visual matching model under inherent constraints of images Good congruences on weakly Uabundant semigroups Can policy coordination facilitate unimpeded trade? An empirical study on factors influencing smooth trade along the Belt and Road Research on the processing method of multisource heterogeneous data in the intelligent agriculture cloud platform Internal control index and enterprise growth: An empirical study of Chinese listedcompanies in the automobile manufacturing industry Research on design of customer portrait system for Ecommerce Research on rule extraction method based on concept lattice of intuitionistic fuzzy language FedUserPro: A user profile construction method based on federated learning A multifactor Regression Equationbased Test of Fitness Maximal Aerobic Capacity in Athletes Design and evaluation of intelligent teaching system on basic movements in PE Garment Image Retrieval based on Grab Cut Auto Segmentation and Dominate Color Method Financial Risk Prediction and Analysis Based on Nonlinear Differential Equations Constructivist Learning Method of Ordinary Differential Equations in College Mathematics Teaching Multiple Effects Analysis of Hangzhou Issuing Digital Consumer Coupons Based on Simultaneous Equations of CDM Model Response Model of Teachers’ Psychological Education in Colleges and Universities Based on Nonlinear Finite Element Equations A Hybrid Computational Intelligence Method of Newton's Method and Genetic Algorithm for Solving Compatible Nonlinear Equations Pressure Image Recognition of Lying Positions Based on Multifeature value Regularized Extreme Learning Algorithm English Intelligent Question Answering System Based on elliptic fitting equation Precision Machining Technology of Jewelry on CNC Machine Tool Based on Mathematical Modeling Application Research of Mathematica Software in Calculus Teaching Computer Vision Communication Technology in Mathematical Modeling Skills of Music Creation Based on Homogeneous FirstOrder Linear Partial Differential Equations Mathematical Statistics Technology in the Educational Grading System of Preschool Students Music Recommendation Index Evaluation Based on Logistic Distribution Fitting Transition Probability Function Children's Educational Curriculum Evaluation Management System in Mathematical Equation Model Query Translation Optimization and Mathematical Modeling for EnglishChinese CrossLanguage Information Retrieval The Effect of Children’s Innovative Education Courses Based on Fractional Differential Equations Fractional Differential Equations in the Standard Construction Model of the Educational Application of the Internet of Things Optimization research on prefabricated concrete frame buildings based on the dynamic equation of eccentric structure and horizontaltorsional coupling Optimization in Mathematics Modeling and Processing of New Type Silicate Glass Ceramics Green building considering image processing technology combined with CFD numerical simulation Research on identifying psychological health problems of college students by logistic regression model based on data mining Abnormal Behavior of Fractional Differential Equations in Processing Computer Big Data Mathematical Modeling Thoughts and Methods Based on Fractional Differential Equations in Teaching Research on evaluation system of crossborder Ecommerce platform based on the combined model A mathematical model of PCNN for image fusion with nonsampled contourlet transform Nonlinear Differential Equations in ComputerAided Modeling of Big Data Technology The Uniqueness of Solutions of Fractional Differential Equations in University Mathematics Teaching Based on the Principle of Compression Mapping Financial customer classification by combined model Influence of displacement ventilation on the distribution of pollutant concentrations in livestock housing Recognition of Electrical Control System of Flexible Manipulator Based on Transfer Function Estimation Method Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm Real Estate Economic Development Based on Logarithmic Growth Function Model Design of Tennis Mobile Teaching Assistant System Based on Ordinary Differential Equations Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis High Simulation Reconstruction of Crowd Animation Based on Optical Flow Constraint Equation Construction of Intelligent Search Engine for Big Data Multimedia Resource Subjects Based on Partial Least Squares Structural Equation 3D Animation Simulation of Computer Fractal and Fractal Technology Combined with DiamondSquare Algorithm Analysis of the Teaching Quality of Physical Education Class by Using the Method of Gradient Difference The Summation of Series Based on the Laplace Transformation Method in Mathematics Teaching Optimal Solution of the Fractional Differential Equation to Solve the Bending Performance Test of Corroded Reinforced Concrete Beams under Prestressed Fatigue Load Animation VR scene mosaic modeling based on generalized Laplacian equation Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure Optimal Model Combination of Crossborder Ecommerce Platform Operation Based on Fractional Differential Equations The influence of accounting computer information processing technology on enterprise internal control under panel data simultaneous equation Research on Stability of Timedelay Force Feedback Teleoperation System Based on Scattering Matrix BIM Building HVAC Energy Saving Technology Based on Fractional Differential Equation Construction of comprehensive evaluation index system of watersaving irrigation project integrating penman Montei the quation Human Resource Management Model of Large Companies Based on Mathematical Statistics Equations Data Forecasting of AirConditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression Analysis of technical statistical indexes of college tennis players under the winlose regression function equation Automatic extraction and discrimination of vocal main melody based on quadratic wave equation Analysis of wireless English multimedia communication based on spatial state model equation Optimization of Linear Algebra Core Function Framework on Multicore Processors Application of hybrid kernel function in economic benefit analysis and evaluation of enterprises Research on classification of ecommerce customers based on BP neural network The Control Relationship Between the Enterprise's Electrical Equipment and Mechanical Equipment Based on Graph Theory Mathematical Modeling and Forecasting of Economic Variables Based on Linear Regression Statistics Nonlinear Differential Equations in Crossborder Ecommerce Controlling Return Rate 3D Mathematical Modeling Technology in Visualized Aerobics Dance Rehearsal System Fractional Differential Equations in Electronic Information Models BIM Engineering Management Oriented to Curve Equation Model Leakage control of urban water supply network and mathematical analysis and location of leakage points based on machine learning Analysis of higher education management strategy based on entropy and dissipative structure theory Prediction of corporate financial distress based on digital signal processing and multiple regression analysis Mathematical Method to Construct the Linear Programming of Football Training Multimedia sensor image detection based on constrained underdetermined equation The Size of Children's Strollers of Different Ages Based on Ergonomic Mathematics Design Application of Numerical Computation of Partial Differential Equations in Interactive Design of Virtual Reality Media Stiffness Calculation of Gear Hydraulic System Based on the Modeling of Nonlinear Dynamics Differential Equations in the Progressive Method Knowledge Analysis of Charged Particle Motion in Uniform Electromagnetic Field Based on Maxwell Equation Relationship Between Enterprise Talent Management and Performance Based on the Structural Equation Model Method Term structure of economic management rate based on parameter analysis of estimation model of ordinary differential equation Influence analysis of piano music immersion virtual reality cooperation based on mapping equation Chinese painting and calligraphy image recognition technology based on pseudo linear directional diffusion equation Label big data compression in Internet of things based on piecewise linear regression Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation Design of language assisted learning model and online learning system under the background of artificial intelligence Study on the influence of adolescent smoking on physical training vital capacity in eastern coastal areas Application of machine learning in stock selection Comparative analysis of CR of ideological and political education in different regions based on improved fuzzy clustering Action of Aut( G ) on the set of maximal subgroups ofp groupsResearch on loyalty prediction of ecommerce customer based on data mining Algebraic Equations in Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on ecommerce platform Evaluation and prediction of regional human capital based on optimised BP neural network Study on inefficient land use determination method for cities and towns from a city examination perspective A study of local smoothnessinformed convolutional neural network models for image inpainting Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Computational Algorithm to Solve Two–Body Problem Using Power Series in Geocentric System Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Crossborder Ecommerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training Nonlinear Channel Estimation for Internet of Vehicles Some Necessary Conditions for Feedback Functions of de Bruijn Sequences The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model An empirical research on economic growth from industrial structure optimisation in the Three Gorges Reservoir area The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Study of agricultural finance policy information extraction based on ELECTRABiLSTMCRF Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Innovative research of vertical video creation under the background of mobile communication Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model ChineseEnglish Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithmbased congestion control optimisation for mobile data network