Methods and systems for managing a pipe network of natural gas

A system for managing a pipe network of natural gas, comprising:

• at least one storage medium storing a set of instructions;
• and
• at least one processor in communication with the at least one storage medium to execute the set of instructions to perform operations comprising:
• obtaining pipe network information of natural gas in at least one area, the pipe network information including a running time of a system of the pipe network of the natural gas and gas leakage information of the pipe network;
• extracting feature information based on the running time and the gas leakage information;
• and
• predicting a maintenance time of the pipe network by inputting the feature information into a maintenance time prediction model, wherein an input of the maintenance time prediction model further comprises a pipe network maintenance value, and the pipe network maintenance value is obtained through a maintenance value prediction model based on pipe network maintenance information and pipe network environment information;
• wherein
• the maintenance value prediction model is a Graph Neural Network model, a plurality of nodes of the Graph Neural Network model include a plurality of historical maintenance locations of the pipe network and historical pipe network environmental information, a plurality of edges of the Graph Neural Network model include one or more pipes between the plurality of historical maintenance locations of the pipe network, the features of the nodes include replacement pipe material, maintenance time, a maintenance location, gas leakage after maintenance, a vibration detection result, vibration frequency of the pipe network, and natural gas usage environment information, and features of edges include pipe material, diameter, connection manner, and relationship between the pipe network environment information and maintenance locations of the pipe network;
• and
• the input of the maintenance time prediction model further comprises a vibration fatigue factor of the pipe network;
• the vibration fatigue factor of the pipe network is obtained by inputting the vibration frequency of the pipe network, vibration time of the pipe network, and pipe material strength into a second model, the second model is a Deep Neural Network model, and the second model is obtained by training based on historical vibration frequency of the pipe network, historical vibration time of the pipe network, and historical pipe material strength.

A non-transitory computer readable medium storing instructions, when executed by at least one processor, causing the at least one processor to implement a method comprising:

• obtaining pipe network information of natural gas in at least one area, the pipe network information including a running time of a system of the pipe network of the natural gas and gas leakage information of the pipe network;
• extracting feature information based on the running time and the gas leakage information;
• and
• predicting a maintenance time of the pipe network by inputting the feature information into a maintenance time prediction model, wherein an input of the maintenance time prediction model further comprises a pipe network maintenance value, and the pipe network maintenance value is obtained through a maintenance value prediction model based on pipe network maintenance information and pipe network environment information;
• wherein
• the maintenance value prediction model is a Graph Neural Network model, a plurality of nodes of the Graph Neural Network model include a plurality of historical maintenance locations of the pipe network and historical pipe network environmental information, a plurality of edges of the Graph Neural Network model include one or more pipes between the plurality of historical maintenance locations of the pipe network, the features of the nodes include replacement pipe material, maintenance time, a maintenance location, gas leakage after maintenance, a vibration detection result, vibration frequency of the pipe network, and natural gas usage environment information, and features of edges include pipe material, diameter, connection manner, and relationship between the pipe network environment information and maintenance locations of the pipe network;
• and
• the input of the maintenance time prediction model further comprises a vibration fatigue factor of the pipe network;
• the vibration fatigue factor of the pipe network is obtained by inputting the vibration frequency of the pipe network, vibration time of the pipe network, and pipe material strength into a second model, the second model is a Deep Neural Network model, and the second model is obtained by training based on historical vibration frequency of the pipe network, historical vibration time of the pipe network, and historical pipe material strength.