The early warning system of College Students’ target course achievement is an important part of the educational administration system in Colleges and universities. This paper proposes to use some techniques of association principle to mine a large amount of data in the performance system to a certain extent, and obtain available rules from the data. Based on the characteristics and shortcomings of Apriori algorithm, an improved Apriori is proposed. The algorithm can process and mine the data in the early warning system of College Students’ scores, and finally obtain the management principles, thus forming an effective early warning for the course learning. In order to promote the improvement of students’ academic performance and achieve the ultimate goal of cultivating excellent talents in Colleges and universities.

Aiming at the low recognition rate, low recognition efficiency, poor anti-interference and high missing detection rate of current traffic sign recognition methods, a fast recognition algorithm based on SURF for static traffic sign information of highway is proposed. The expansion of the digital morphological method is used to connect the cracks in the traffic sign. Traffic sign images are corroded according to the corrosion, and the connected areas are contracted or refined. Regions of interest are detected by region filling. According to the result of traffic sign image processing, the scale of traffic sign image is normalized by bilinear interpolation method, and the SURF feature points of traffic sign image are extracted. The FLANN algorithm is used to realize feature point matching, and the threshold is set to determine the best matching point. The matching result is output and the traffic sign information is recognized. Experimental results show that the algorithm has high recognition rate and recognition efficiency, strong anti-interference, and can control the rate of missing detection in a certain range.

No abstract

Motor end cover mounting fracture is a problem recently encountered by novel pure electric vehicles. Regarding the study of the traditional vehicle engine mount bracket and on the basis of the methods of design and optimisation available, we have analysed and optimised the pure electric vehicle end cover mount system. Multi-body dynamic software and finite element software have been combined. First, we highlight the motor end cover mount bracket fracture engineering problems, analyse the factors that may produce fracture, and propose solutions. By using CATIA software to establish a 3D model of the power train mount system, we imported it into ADAMS multi-body dynamic software, conducted 26 condition analysis, obtained five ultimate load conditions, and laid the foundations for subsequent analysis. Next, a mount and shell system was established by the ANSYS finite element method, and modal, strength, and fatigue analyses were performed on the end cover mount. We found that the reason for fracture lies in the intensity of the end cover mount joint, which leads to the safety factor too small and the fatigue life not being up to standard. The main goal was to increase the strength of the cover mount junction, stiffness, safety coefficient, and fatigue life. With this aim, a topology optimisation was conducted to improve the motor end cover. A 3D prototype was designed accordingly. Finally, stiffness, strength, modal, and fatigue were simulated. Our simulation results were as follows. The motor end cover suspension stiffness increases by 20%, the modal frequency increases by 2.3%, the quality increases by 3%, the biggest deformation decreases by 52%, the maximum stress decreases by 28%, the minimum safety factor increases by 40%, and life expectancy increases 50-fold. The results from sample and vehicle tests highlight that the component fracture problem has been successfully solved and the fatigue life dramatically improved.

Real time prediction of energy consumption is the basis of energy conservation and emission reduction. Aiming at the problems of large prediction error and poor effect, a real-time prediction method of energy consumption of geothermal system of public buildings based on wavelet neural network is proposed. Firstly, the energy consumption of geothermal system in public buildings is analyzed, the wavelet neural network is designed, the neural network is optimized and solved by genetic algorithm, and the necessity of constructing the real-time prediction model of energy consumption based on wavelet neural network is established. Then it introduces the basic principle of model establishment, wavelet analysis, and shows the role of wavelet analysis in prediction model. Finally, based on the distribution structure of public buildings, this paper analyzes the energy consumption system of geothermal system, constructs the energy consumption prediction method, analyzes the over?all temperature regulation energy consumption prediction principle of building geothermal system, and realizes the real-time prediction of energy consumption of geothermal system of public buildings. The experimental results show that the energy consumption real-time prediction results of the designed method are basically similar to the actual prediction values, and the prediction efficiency is high, which can effectively reduce the energy consumption of the geothermal system of public buildings.