To understand the operating status of the road network and measure the traffic congestion problem, an intelligent calculation method for the intelligent traffic flow index based on big data mining is proposed. According to the error data discriminating rules, the error data in the traffic flow data is discriminated, all lanes are detected according to the data discriminating result, the traffic data of each lane are recorded in chronological order, and the traffic data is converted. Fuzzy data mining technology is used to predict the converted traffic flow, combined with traffic flow sequence segmentation and BP neural network model to realize the intelligent calculation of the smart traffic flow index. Experimental results show that the method can achieve accurate calculation of daily and weekly smart traffic index, and the calculation time is short, indicating that it can provide a reliable data basis for traffic operation state estimation and traffic early warning mechanism formulation.
Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that
Using computer vision technology to obtain and analyze biomechanical information is an important research direction in recent years. However, the linear model in the computer vision system cannot accurately describe the geometric relationship of the camera imaging, so it is difficult to realize human posture recognition in high-precision mechanics information. Therefore, how to improve the recognition accuracy is very important. In this paper, we apply nonlinear differential equations to stereo computer vision (SCV) information systems. And based on the median theorem, a nonlinear posture recognition and error compensation algorithm based on BP neural network is proposed to reduce the recognition error. The test set uses the Leeds Motion Pose (LSP) dataset to verify the performance of the algorithm. Experimental results show that the compensated median filter of BP neural network can eliminate glitches in attitude data. Superimposing the output attitude error compensation value with the attitude estimation value can greatly reduce the root-mean-square error of the attitude angle. The result of gesture recognition is closer to reality. Compared with traditional algorithms, the cyclomatic complexity of the proposed BP neural network algorithm has a much lower growth rate in high-order calculations, which indicates that the proposed BP neural network algorithm is more concise and scalable.
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