Measurement and verification play a crucial role in flexible production, and with the development of technology, advanced measurement systems in flexible production systems gradually integrate fault diagnosis and prediction techniques to improve production efficiency. In this paper, a deep confidence neural network model, combined with the ISSA-VMD feature fusion model, is used to model fault diagnosis and prediction in flexible production of power systems. The training effect, prediction performance, feature extraction and fault diagnosis of this paper’s model in flexible production are evaluated and analysed through simulation experiments. The Loss value of this paper’s model converges to about 0.05 after 15 rounds of training, and has a good fitting effect on the training and test sets. The RMSE, MAE and R² of the model in this paper are 0.613, 0.371 and 0.988, respectively, which show good prediction performance. And the prediction results in the measurement system of power generation in flexible production are also more close to the real results. In addition, the DBN model incorporating ISSA-VMD feature fusion can completely separate the five fault signals, and the overall fault identification accuracy reaches 98.53% for the fault test set selected in this paper, which has strong diagnostic effect. This study provides more scientific and effective technical support for metrological verification in flexible production.

With the rise of major e-commerce, how to make more customer groups choose to buy items in their own websites is the goal that major e-commerce platforms have been relying on. Therefore, a set of personalised recommendation system that can intelligently explore customers’ needs comes into being. In this paper, a graph neural network model is used to sort out the multi-path fusion neighbourhood relationship among three objects: user, product and query. The utility matrix is established and the collaborative filtering algorithm is used to derive the user’s preference situation for commodities. Subtractive clustering is combined with fuzzy C-means to obtain the clustering centre of gravity and cluster e-commerce users. Graph neural network is introduced to ensure that the data sparsity of the user dataset is within a reasonable range. The practical application effect of the model is evaluated through simulation experiments and empirical analysis, respectively. In this paper, according to the age of the users, the users are clustered and analysed, and three clustering centres of gravity are obtained, which are (3.16, 32.73), (45.35, 40.25), and (14.03, 52.89), so the users are classified into three clusters, and the analysis of simulation experiments is carried out. The training effect of this paper’s model is fitted, and the adjusted R² = 0.8292, which shows that the accuracy of personalised recommendation is high. Meanwhile, comparing with other algorithms, this paper’s method reaches a recommendation satisfaction level of 100% when the number of learning times is 60, which is significantly better than other algorithms.

Supply chain finance innovation has a significant impact on regional economy. In this paper, blockchain technology is applied to supply chain finance business to improve the technology and security of traditional supply chain finance business. Drawing on relevant research results, we construct a blockchain-based supply chain financial innovation efficiency evaluation index system and measure the supply chain financial innovation efficiency using Malmquist index. A spatial econometric model is used to test the spillover effect and spatial synergy between supply chain financial innovation and regional economic growth, and to demonstrate the promotional effect of blockchain-based supply chain financial innovation on regional economic growth.The centres of the distribution curves of the kernel density function of the logarithmic value of GDP and supply chain financial innovation of the 30 provinces and regions are all shifted to the right, and the height of the main peak rises gradually.The 2013-2023 regional Moran’s index of economic growth and supply chain financial innovation are both significantly positive. The regression coefficients of supply chain financial innovation under the two spatial weights are significant at the 1% level, which provides strong data support for the view that supply chain financial innovation can promote regional economic growth in this paper.

The load of power supply has been increasing in recent years, and the scale of the power grid has been expanding. The impact of electromagnetic radiation on the lives of residents is also increasingly visible, and the electromagnetic environment around high-voltage AC transmission equipment has attracted great attention. Based on the principle of electromagnetic induction and Gauss theorem, this paper proposes the calculation method of electromagnetic radiation to evaluate the distribution law of spatial electromagnetic field around high-voltage AC transmission lines. Then the risk analysis of the electromagnetic environment around the high-voltage AC transmission line is carried out from the height from the ground and the presence of woods according to the measured data. Finally, according to the electromagnetic law of high-voltage transmission lines, the safety control technology to reduce the environmental impact of electromagnetic fields is proposed, mainly by raising the vertical height of the arc of the transmission line from the ground and reasonably designing the distribution of forest planting in the vicinity of the transmission line. When the vertical height of the conductor’s arc height from the ground was increased from 10m to 40m, the electric field strength and magnetic induction strength were reduced by 2.9kV/m and 2.35µT correspondingly, and at the same time, the electric field strength in the vicinity of the building was reduced by 71% at the most. The study proposes measures to effectively mitigate the electromagnetic impact by reasonably analysing the electromagnetic environment in the area where the UHV transmission line is located.

Aiming at the demand for scientific training of athletes in college sports education, this paper integrates data mining technology to propose athlete training and optimisation methods, and constructs an athlete training quality monitoring system and intelligent recovery assessment system. The traditional Apriori algorithm is improved by using multidimensional association rules, and multidimensional attribute mining is carried out on the collected data of athletes’ training data to search for frequent item sets and output strong association rules, so as to achieve the monitoring of training quality and adjustment of training programmes. Using the improved fuzzy decision-making method to filter out the optimal feature subset, and integrating the improved whale algorithm and random forest to achieve intelligent recovery effect evaluation. By carrying out the practice of training and recovery optimisation, it can be seen that the total score of physical fitness test of track and field athletes increased from 18.19 to 19.8 before the experiment, and the training quality was significantly improved. Various health indicators such as heart rate, blood lactate, serum creatine kinase, etc. gained significant improvement in adopting the recovery optimisation method of athletes in this paper. The mean values of training status, coaching factors, and personal situation satisfaction evaluation dimensions were 4.35, 4.425, and 4.38, respectively, and the training and recovery plan of this experiment was well received by the subject athletes.

Through the examination and calculation of each link of the dairy industry chain, we analyze the benefit distribution pattern of the dairy industry chain and highlight the necessity of optimizing the benefit distribution strategy of the dairy industry chain. The Shapley value method of the equilibrium of interests in game theory is chosen to study the benefit distribution strategy of each subject in the dairy industry chain under the cooperative game, and the model is revised by using the input factor, the risk factor and the correction factor, so as to further improve the rationality of the benefit distribution strategy. The research data were obtained by visiting the dairy industry chain in Xilingol League through field investigation, and the modified Shapley values of the herdsmen, middlemen, milk processors and retailers were finally obtained as 3976.43 yuan, 3839.31 yuan, 4175.53 yuan, and 3977.47 yuan after the modeling calculation, respectively. The comprehensive cost profit margin of each subject after correction is 2.17%, 1.82%, 7.43%, 7.68%, respectively, and herdsmen and milk processors are compensated in the benefit distribution strategy of this paper, and the amount of benefit distribution and the comprehensive profit margin of all the subjects in the dairy industry chain have been improved compared with that before the cooperation.

In this study, we construct an unmanned vehicle path optimization model based on fast extended random tree, and after kinematic modeling of unmanned vehicles, we introduce the artificial potential field method to improve the fast extended random tree algorithm, and apply it to the path optimization of unmanned vehicles. According to the swarm intelligence perception decision-making algorithm, the end-to-end unmanned vehicle decision-making model based on vehicle-circuit collaboration is constructed. The effectiveness of this paper’s driverless path optimization and decision-making model based on vehicle-circuit collaboration is examined. The waiting time for red light of this paper’s model is shorter than other path planning schemes, and the vehicle passing benefit at intersections is the highest. The passing benefit values of this paper’s model are 70.3% and 46.8% higher than Maxband scheme and Synchro scheme, respectively. In the right-turn simulation experiments, the main vehicle speed change shows a tendency to accelerate and the path is basically overlapped with the edge of the lane without offsetting the center of the lane. In the normal driving speeds of [14,38], the fuel consumption of the driverless vehicle shows an up and down trend, and the carbon dioxide emission varies with the fuel consumption. The total cost of traveling decreases with increasing speed.

High-fidelity modeling of complex surfaces is the basis for accurate characterization of surface quality and realistic analysis of performance in the fields of digital process design of products and digital twin. This paper proposes to improve the new polynomial interpolation algorithm to improve the effect of the polynomial interpolation algorithm fitting in complex surface modeling through the center variable, and combines the moving least squares approximation function with the new polynomial interpolation algorithm to further optimize the effect of the complex surface modeling through the regular moving construction of the fitting surface by the local approximation method. It is found that the overall average error and standard deviation between the turbine blade surface roughness modeled based on the new polynomial interpolation algorithm and the roughness meter measurements are within 1.7 μm (0.7580-1.6715 μm), and the error is within the acceptable range. It is also found that using the method of this paper can save a lot of time and realize the rapid modeling of complex surfaces of the body. It also has good smoothness, which provides convenience for the subsequent processing of complex surface modeling. The new polynomial interpolation algorithm proposed in this paper provides a new idea for the research in the field of complex surface modeling, and can be applied to the actual production to assist the design and production of related products.

Container and cargo matching is a key issue to realize the construction of container and cargo supply and demand matching platform, through the intelligent matching of cargo and container information, improve the efficiency of container and cargo matching, which is conducive to the integration of resources, and improve the platform professional services. In this paper, we analyze the process of container cargo matching and transportation distribution center operation, put forward the two-stage container cargo model assumption in accordance with the basic principle of distribution optimization, and complete the establishment of container cargo matching model under the demand of cargo owners. Optimize the container and cargo matching and vehicle path model respectively, derive the optimized combination mathematical model, and solve the combination optimization model through genetic algorithm. Simulation experiments are designed to analyze the effectiveness of the model. The results of the analyses of the algorithms show that when the crossover probability is increased from 0.6 to 0.8, the average value of the RV value decreases from 1078.76 to 915.76, and the recommended value of the crossover probability is obtained as 0.8. After optimization, the average vehicle load and average loading volume of the recommended scheme of the combined model reach 98.436% and 87.963%, respectively, with a total mileage of 23.456km for distribution, and the total cost of distribution in the region is 1246.489 yuan, which achieves the optimal container-cargo matching and path scheduling scheme.

In this paper, the weights of different risks in the management process of e-commerce platforms are calculated on the basis of hierarchical analysis. After that, with the help of fuzzy comprehensive assessment algorithm, the risk level is divided. Finally, with the assistance of decision tree, simulation is carried out to simulate the risk of the first-level indicators affecting the risk control of e-commerce platform. According to the survey results, reasonable countermeasures are given to the management of e-commerce platform risks. Among the first-level indicators of the five major risk categories, the business model risk belongs to the high-risk category of Class I, with a fuzzy comprehensive evaluation score of >4.5. The rest belong to the risk category of Class II, with fuzzy comprehensive evaluation scores ranging from 3.5 to 4.5. Among the Level II indicators, there are 6, 6 and 3 Level II indicators rated as high risk category, medium risk and low risk respectively, with their fuzzy composite scores ranging from 4.7495-5.6370, 3.6807-4.4988 and 3.1356-3.2435 respectively Between. In the comprehensive risk simulation prediction of the case-based e-commerce platform, only the logistics model risk belongs to the medium risk control strategy with a risk value of 4.8614 (day 60). The simulation results for the remaining four risk types were all low risk, and their risk values decreased (3.5 points) when the simulation time was day 60. The experimental results provide a prediction for the change of risk and provide reasonable countermeasures and suggestions for the risk control of ecommerce platforms.