Design of data acquisition and analysis system for deep vertical shaft

At present, the cage guide inspection robot used for data acquisition of deep vertical shaft can only carry a small number of sensors. There is no safety protection device, so there are potential safety hazards. In addition, most of the deep vertical shaft data visualization programs use 3D GIS for rendering and display. It is difficult to transplant and has long development cycle. In order to solve the above problems, a data acquisition and analysis system for deep vertical shaft is designed. The cage guide inspection robot is improved by adding a wheel lock device to ensure the safety of the robot during operation. The cage guide inspection robot with wheel lock is used as mobile platform. The data acquisition of deep vertical shaft is realized by infrared camera, photoelectric encoder, ultrasonic ranging module and various sensors on the robot. The cloud server plus front-end visualization panel is used for data processing and display. The cloud server receives various data sent by the cage guide inspection robot and classifies and processes the data. The data of temperature and humidity sensor and the gas sensor is directly stored in the corresponding folder. The convolutional neural network (CNN) is applied to process the video data and analyze whether there are cracks or deformation in the cage guide and the shaft wall. The analysis results are stored in the corresponding folder. The front-end visual panel is lightweighted. Asynchronous JavaScript and XML (Ajax) are used to read data from the cloud server regularly. And JavaScript is used to write the host computer interface display program to improve the portability of the system. The test results show that the cage guide inspection robot with wheel lock used as the data acquisition device can improve the reliability and safety of the system. The data processing and display method of the visualization panel and cloud server reduces the memory occupied by the host computer software to less than 5 MB. The page refreshes quickly and the portability is strong.