With the continuous innovation of artificial intelligence technology, the teaching level of mechanical education courses in colleges and universities is also constantly improving. In the context of the era of big data, the teaching of mechanical education courses in colleges and universities has become more intelligent and informatized. This article mainly introduces BP neural network method and hill climbing algorithm. This paper uses BP neural network method to analyze the application of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, and establishes a potential mathematical model of BP neural network method. The model is solved by BP neural network method, and the current situation analysis and application status of the teaching mode of mechanical education courses in colleges and universities are analyzed, and the model is revised using historical data to improve the application research of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities Accuracy. The experimental results of this paper show that the BP neural network method has increased the effect of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities by 33%. Finally, by comparing the value analysis of the application of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, and the data analysis of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, the system shows that artificial intelligence technology is used in mechanical education courses in universities. Application in teaching.
With the slow rise of the construction industry, cranes, as indispensable mechanical equipment in construction projects, are widely used in the lifting and handling of specific space ranges of construction projects. The quality of crane booms is particularly important for safety. This paper uses image measurement methods including filter processing, mean filtering, and Gaussian filtering to detect the quality of the crane boom material. The image is processed by the wavelet transform and Fourier transform. The grayscale transformation stretching method is applied to the surface image analysis of the boom material to obtain the final inspection. The research results show that the use of image measurement methods can effectively measure the thickness of the crane boom material, the geometric information of the boom material, and the surface roughness of the material and obtain effective image information. The detection accuracy reaches 98.1%. The error can be controlled better. The inspection and research on the quality of crane jib materials can ensure the quality and performance of crane jib materials, reduce the potential safety hazards of cranes during operation, and improve safety. This article organically combines workpiece surface roughness detection with digital image processing technology to preprocess the surface picture of the arm tube material. On this basis, the texture features in the image are extracted and programmed to calculate, and the final workpiece is obtained by the surface roughness value, which proves the feasibility of this method. The research results have very important practical significance for the detection of the quality of the arm tube material and the improvement of the quality level of the arm tube material.
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