A visual mechanical signal detection and loading platform with super-resolution based on deep learning is designed to improve the detection accuracy of mechanical signals. The visual mechanical signal detection and loading platform with super-resolution include three-dimensional (3D) biological force quantitative detection platform and the mechanical signal loading platform with 3D magnetic distortion and ultrahigh resolution. In the 3D biological force quantitative detection platform, four 3D force sensors are used to collect mechanical signals, and the improved fuzzy clustering fusion method is used to fuse the mechanical signals collected by 3D force sensors to improve the detection accuracy of mechanical signals. The mechanical signal loading platform of 3D magnetic distortion and ultrahigh resolution technology connects the 3D magnetic distortion instrument and microscope, collects images through high-speed scanning components and distorted magnetic field, reconstructs the collected images by deep learning method, obtains ultrahigh-resolution mechanical signal visual images, and triggers mechanical signal loading and release by synchronous interactive system. The consequences of the experiment demonstrate that the designed platform can display the super-resolution mechanical signals through the visual interface. The mechanical signals are loaded in different directions, and the detection accuracy of mechanical signals is higher than 99.5%.

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.

To accurately detect and identify whether there are abnormalities in the information of the expressway networking system, an information security event reasoning and traceability method based on deep learning is proposed to build a data security protection system that includes the data life periodicity of the expressway networking system. In this system, the information security event model based on intrusion detection message exchange format is established. The model uses the information risk event reasoning method based on a deep convolution neural network to infer the risk event during data sharing of the expressway networking system, reorganize and standardize the risk event information according to the format of the information security event standardization model, and store it in the risk event database in the form of Extensible Markup Language data document. The information risk event traceability method based on the electronic fingerprint takes all risk events in the database as the target. After designing the electronic fingerprint of risk events, the original network attack tree is constructed to realize risk event traceability combined with fingerprint information. Testing indicates that the reasoning and traceability results of this method to the information security events of the expressway networking system are consistent with those in reality and our method has good usability.

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.

To ensure the reliability and safety of expressway networking systems, this paper designs a data flow risk monitoring system for expressway networking systems based on deep learning. The monitoring system is composed of data flow risk analysis, formulation of safety strategy, real-time monitoring, and disaster recovery. Data flow risk analysis is the basis for the operation of each part of the system. Meanwhile, indexes such as network management, data assets, and network resources are selected to build a data flow risk monitoring index system. The deep convolution neural network model is constructed, and the data flow risk monitoring index data are input into the deep convolution neural network to extract the index data features through the convolution and pooling process. Based on this, feature mapping is realized with a multilayer perceptron, and the index data risk classification results of data flow risk monitoring are output by the SoftMax classifier. The experimental results show that the monitoring system can obtain accurate data flow risk analysis results which effectively reduces the data loss rate, alleviate the impact of different types of malicious attacks, and ensure the stability and security of the experimental object.