Research on Digital Analysis Space Construction Technology for Ultra High Voltage Dense Transmission Channels

Ultra high voltage transmission channels are important carriers for long-distance power transmission, but overly dense transmission channels pose challenges to the important carriers for long-distance power transmission, but overly dense transmission channels pose challenges to the safety of power entities. Therefore, it is necessary to extract the spatial safety boundaries of transmission channels and urgently analyze the spatial occupancy of power equipment. In urgently analyze the spatial occupancy of power equipment. In response to this issue, the article proposes a safety analysis method for ultra-high voltage dense transmission channels method for ultra-high voltage dense transmission channels based on digital space construction technology. Based on the information database of historical accidents, faults, hidden dangers, and operational status of ultra-high voltage dense dangers, and operational status of ultra-high voltage dense transmission channels, obtain practical data on the failure of the channel body in multiple disaster risks and service the channel body in multiple disaster risks and service environments. Design boundary condition partitioning rules for multiple risks and scenarios using techniques such as clustering analysis and Bayesian networks. Then analyze the for multiple risks and scenarios using techniques such as clustering analysis and Bayesian networks. Then analyze the composition of the power grid business and its main changing characteristics, and establish a multidimensional correlation model between environment risk and business based on the correlation data of environment risk business. In risk analysis and the operation of ultra-high voltage dense transmission channels, it is necessary to update the safety definition rules of and the operation of ultra-high voltage dense transmission channels, it is necessary to update the safety definition rules of the regional channel body in real time according to different the regional channel body in real time according to different purposes and needs. By using machine learning technology to deeply analyze real-time collected channel failure data, key indicators and corresponding judgment criteria can be deeply analyze real-time collected channel failure data, key indicators and corresponding judgment criteria can be adjusted to provide data support for channel emergency response and safety maintenance.