Application Method and Least Squares Support Vector Machine Analysis of a Heat Pipe Network Leakage Monitoring System Using an Inspection Robot

With the maturity of the Internet and big data technAology, heat supply intelligence has become a development trend, and the traditional heat pipe network management mode is gradually transitioning to an "intelligent heat pipe network". It has become a hot spot for research and development at home and abroad. Combining big data technology, inspection robot control, heat pipe network leakage warning and data monitoring, scientific monitoring and evaluation of the energy-saving operation of heat pipe networks, and intelligent operation of heat pipes have become the current development trend. Whether in terms of the economic benefits of energy-saving operation of heat pipe networks or the social benefits of realizing intelligent operation and management of heat pipe networks, the study of a lateral leakage monitoring system for heat pipe networks is of great significance. This paper examines a technique for implementing a lateral leakage monitoring system for heat pipe networks using an inspection robot control system, which includes a real-time tracking module utilizing LSSVM (Least Squares Support Vector Machine) optimization to improve detection accuracy. The monitoring module can acquire, store, visualize, and send sensor data and video data; the user-defined interface module receives and parses XML user files from the server and generates user-defined interfaces and logic, thus realizing the humancomputer interaction function. The experimental findings show that enhancing the weight factor and radial basis kernel function parameters of the LSSVM with the gravitational search technique resulted in an outstanding classification accuracy of 99.99% with a classification time of only 55.938 seconds, surpassing other optimization techniques.