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In general, technological innovations have transformed sports training or physical education classrooms constructively. Physical education instructors can develop personalized and realistic expectations for their students using software, streaming videos, cameras, and trackers. However, the intelligent physical education models seek efficient assessment to improve its real-time acceptance. This paper proposes Enhanced Cat Swarm Optimization (ECSO) algorithm to create a physical education system assessment model and increase the evaluation impact of sports training methods. This research identifies and explains various factors to design a multi-functional physical education monitoring device based on visual sensing that meets the real needs of users. To find out how sensitive the parameters are, this study uses the local sensitivity analysis theory. It incorporates real-world needs to suggest an optimized CSO algorithm with modified searching and tracking modes. Subsequently, the proposed methodology implements a randomized experiment to assess the model’s success. The proposed ECSO algorithm employing modified searching and tracing modes gives the best assessment of 98.8%.

Conventional financial risk assessment is not accurate and its adaptive assessment ability is low. In order to solve this problem, a financial risk assessment model based on big data is proposed. In this method, the quantitative analysis method is adopted to analyze the explanatory variable model and the control variable model of financial risk assessment. The market-to-book ratio, asset–liability ratio, cash flow ratio and financing structure model are adopted as constraint parameters to construct a big data analysis model for financial risk assessment. On this basis, the adaptive fuzzy weighted control method is adopted for information fusion of financial risk assessment data and big data classification, and the asset income control and innovative evaluation model are adopted for linear planning and square fitting during financial risk assessment. Based on the intervention factors of financial market participants, quantitative regression analysis is performed, and according to the economic game theory, big data analysis and prediction of financial risk assessment are performed through the regression analysis method. Then the big data fusion and clustering algorithms are adopted for financial risk assessment. The simulation results show that this method can provide a relatively high accuracy in financial risk assessment, and has relatively strong adaptive evaluation capability to the risk coefficient, so it has a good application value in the prevention and control of risk factors in financial systems.

Environmental pollution and energy consumption caused by automobiles have facilitated green design technology. The key step of green design is to conduct the scientific assessment of its consequent effects. Based on the method of Life Cycle Assessment (LCA), the comprehensive assessment model developed in this paper aims to save energy and reduce emission. Two design schemes of dust-proof covers in the automobile steering system were evaluated by using the developed model. The results demonstrate that the scheme of the plastic dust-proof cover is better than the rubber type. This research is useful for guiding the implementation of the green design in the automobile enterprises in China.

This paper aims at the buffing technology development of high-speed train bogies. Requirements of buffing welded joints for the bogie were presented. The adaptability of plane weld, dovetail fillet weld and part of girth weld by using different grinding dynamic tools and cutters were studied, looking for grinding wheel and abrasive belt which are suitable for fine grinding. Existing problems in the buffing process of bogie welded joints at different positions were analyzed. The results showed that different cutters should be applied to different welded joints according to actual situation. Due to the complexity of buffing position, off-line programming technology for robot automatic buffing are necessary to be developed, and buffing efficiency, cost and feasibility should be considered.

Transformer district line loss rate is a comprehensive technical and economic indicator to assess the management level of a power company. Previous studies on residential district line loss rate assessment were mainly based on theory algorithms, but this paper is based on large amounts of marketing data supplied by Power Company of Jiangsu province. Relationships between normal line loss rate ranges and marketing data of the districts were identified to establish a line loss assessment model. Besides, district line loss rate were divided into excellent, normal and poor three levels by using the statistical methods namely classification, meshing, taking class centers and multivariate regression, etc., and a model based on marketing data like user number and load rate was established to realize the abnormal warning of line loss rate. This method provides a new direction for line loss management.