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

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-

The cellular automation traffic flow model of roundabout with inner-roundabout-lane, injection-roundabout-lane and extraction-roundabout-lane is used to study the traffic flow of single loop roundabout under open boundary conditions. Through computer simulation, the relationships between the entrance probability, exit probability, vehicle braking probability and system flow, the average speed of each lane and system in the open boundary conditions are studied.Besides, the space-time diagram of vehicles on the injection-roundabout-lane with different entrance exit probabilities and the system phase diagram are established. In the system phase diagram, the critical entry probability and exit probability values of each part of the phase diagram under different braking probability are given. The results show that the system traffic flow can be divided into three phases: the phase in which the system flow changes with the entrance probability, the phase in which the system flow changes with the exit probability, and the phase in which the system flow has nothing to do with the entrance probability and exit probability. This will provide some guidance for road management.

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