Method for evaluating pipeline cleaning solution based on smart gas and internet of things system thereof

An Internet of Things system for evaluating a pipeline cleaning solution based on smart gas, comprising:

• a smart gas user platform, a smart gas service platform, a smart gas equipment management platform, a smart gas sensing network platform, and a smart gas object platform, wherein
• the smart gas object platform is used to obtain an operation feature of at least one gas pipeline and inspection data of the at least one gas pipeline;
• the smart gas sensing network platform is used to transmit the operation feature of the at least one gas pipeline and the inspection data of the at least one gas pipeline to the smart gas equipment management platform;
• the smart gas equipment management platform is used to:
• generate at least one candidate pipeline cleaning solution based on the area feature of the area to be cleaned;
• process each of the at least one candidate pipeline cleaning solution and the area feature through a pipeline cleaning effect prediction model to determine a pipeline cleaning effect of each of the at least one candidate pipeline cleaning solution;
• determine a pipeline cleaning solution based on the pipeline cleaning effect of each of the at least one candidate pipeline cleaning solution;
• and
• evaluate a pipeline cleaning effect of the pipeline cleaning solution based on an implementation of the pipeline cleaning solution;
• wherein
• the pipeline cleaning effect prediction model includes a future feature prediction layer and a pipeline cleaning effect prediction layer, the future feature prediction layer and the pipeline cleaning effect prediction layer are obtained by joint training based on training samples and labels, wherein the training samples include a sample historical cleanliness sequence, a sample area feature, and a sample pipeline cleaning solution, the sample area feature includes sample cleanliness at a current moment and a sample pipeline feature, and the sample pipeline cleaning solution includes a sample pipeline cleaning time and other sample parameters;
• and the labels include a sample pipeline cleaning effect of the sample pipeline cleaning solution, and the joint training includes:
• inputting the sample historical cleanliness sequence, the sample cleanliness at the current moment and the sample pipeline feature in the sample area feature, and the sample pipeline cleaning time in the sample pipeline cleaning solution into an initial future feature prediction layer and obtaining cleanliness at a future moment output from the initial future feature prediction layer;
• inputting the cleanliness at the future moment and the other sample parameters in the sample pipeline cleaning solution into an initial pipeline cleaning effect prediction layer and obtaining a pipeline cleaning effect output from the initial pipeline cleaning effect prediction layer;
• constructing a loss function based on the sample pipeline cleaning effect and the pipeline cleaning effect output from the initial pipeline cleaning effect prediction layer;
• updating parameters of the initial future feature prediction layer and the initial pipeline cleaning effect prediction layer simultaneously;
• and
• obtaining a trained future feature prediction layer and a trained pipeline cleaning effect prediction layer through updating the parameters;
• and
• the smart gas service platform is used to transmit the pipeline cleaning solution and the pipeline cleaning effect of the pipeline cleaning solution to the smart gas user platform.