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 the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

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.

Due to the complex structure of cytopathological images, data loss and low transmission efficiency may occur in the transmission of cytopathological images by common data stream transmission methods. To ensure the stable transmission of the data stream of the cytopathology image storage system and maintain the safe operation of the cytopathology image storage system, a safe transmission method of the data stream of the cytopathology image storage system was designed. The security threats faced by the data flow of the cytopathology image storage system were analyzed from the aspects of information network and control network, and the risk indexes of data flow and attack loss were constructed. The security risk indexes were quantified by the general vulnerability scoring system, and the data flow security transmission model of the cytopathology image storage system was constructed. Different transmission nodes and cytopathological image storage system devices were set as attack graph nodes to collect and configure data and optimize data flow transmission path. Deploy the network node equilibrium state, control the time slot window interval equilibrium, output transmission delay allocation, and ensure the confidentiality and integrity of the data stream transmission of the cytopathological image storage system. The simulation results show that the proposed method has a higher transmission efficiency of about 11.76 Mb/s. It is highly practical and can realize the safe transmission of data stream in the cytopathological image storage system.