Methods and internet of things systems for determining safety inspection plans based on smart gas geographic information systems

1. A method for determining a safety inspection plan based on a smart gas geographic information system (GIS), wherein the method is implemented by a smart gas pipeline network safety management platform of an Internet of Things system for determining the safety inspection plan based on the smart gas GIS, wherein the Internet of Things system further includes a smart gas user platform, a smart gas service platform, a smart gas pipeline network device sensing network platform, and a smart gas pipeline network device object platform, the smart gas user platform includes a terminal device, the smart gas service platform includes a first server, the smart gas pipeline network safety management platform includes a second server, the smart gas pipeline network device sensing network platform includes a communication network and a gateway, the smart gas pipeline network device object platform includes various types of gas pipeline network devices and monitoring devices, and the method comprises:

generating an obtaining instruction, wherein the obtaining instruction is used to obtain a geographical feature, a pipeline feature, and pipeline network historical data of a gas pipeline network from the smart gas GIS and upload the geographical feature, the pipeline feature, and the pipeline network historical data of the gas pipeline network to the smart gas pipeline network safety management platform;

obtaining a count of gas pipeline network grades based on the smart gas user platform through the smart gas pipeline network device sensing network platform;

generating a grading instruction through a preset grading algorithm based on the geographical feature, the pipeline feature, the pipeline network historical data, and the count of gas pipeline network grades, wherein the grading instruction is used to perform grading processing on the gas pipeline network to determine a grading result;

determining an inspection plan through a first preset algorithm based on the grading result, wherein the inspection plan includes an inspection time and an inspection route, wherein the inspection plan further includes a required inspection area and an optional inspection area, the required inspection area refers to an inspection area that is required to be inspected in the inspection plan, the optional inspection area refers to an inspection area that is not required to be inspected in the inspection plan, and the determining the required inspection area and the optional inspection area based on the grading result comprises:

determining a gridding result through performing gridding processing on the gas pipeline network, wherein the ridding processing refers to a process in which the inspection area of the gas pipeline network is divided by ridding based on a preset gridding standard;

determining the required inspection area and the optional inspection area based on the gridding result, wherein the determining the required inspection area and the optional inspection area based on the gridding result comprises:

determining a grid safety failure possibility by processing an inspection time interval, an accident time interval, the geographical feature, and a gas density in a grid based on a possibility prediction layer, the grid safety failure possibility refers to a failure possibility of the gas pipeline in each grid area, wherein the possibility prediction layer is a machine learning model and the gas density in the grid refers to a ratio of a gas pipeline volume contained in a grid area to a total volume of the grid;

determining a grid safety score by retrieving in a score preset table through a safety score evaluation layer based on the grid safety failure possibility, wherein the score preset table refers to a table storing a plurality different sets of grid safety failure possibilities and grid safety scores corresponding to the grid safety failure possibilities; and

determining whether the grid area is the required inspection area or the optional inspection area by retrieving in an area preset table through a judgment layer based on the grid safety score and the gas pipeline network grade;

generating at least one candidate inspection route based on the required inspection area and the optional inspection area;

determining a target inspection route based on the at least one candidate inspection route; and

determining the inspection plan based on the target inspection route; and

determining an inspection instruction through a preset generation algorithm based on the inspection plan, and sending the inspection instruction to the smart gas user platform through the smart gas pipeline network device sensing network platform;

obtaining feedback data from the user terminal based on the smart gas user platform through the smart gas pipeline network device sensing network platform;

performing an adjustment periodically on the inspection plan through a second preset algorithm based on the feedback data, wherein the adjustment includes determining at least one required inspection area as an optional inspection area or determining at least one optional inspection area as a required inspection area, wherein the performing an adjustment periodically on the inspection plan through a second preset algorithm based on the feedback data comprises:

updating a gas pipeline grid graph based on inspection data after inspecting each inspection area; wherein the gas pipeline grid graph is determined based on the gridding result, the gas pipeline grid graph refers to a graph used to represent a connection relationship of the gas pipeline network, a node feature of the gas pipeline grid graph includes a geographical feature of the grid area, the grid safety score, whether the gas pipeline has been inspected, the gas pipeline grid graph is determined or updated based on the gridding result, and the gas pipeline grid graph includes at least one node and at least one edge;

determining at least one failure possibility of the at least one node and the at least one edge through a failure possibility determination model based on an updated gas pipeline grid graph, wherein the failure possibility determination model is a graph neural network model, the failure possibility determination model is obtained by training with a second training sample and a second label, a second training sample includes a sample gas pipeline grid graph, and a second label is an actual failure possibility of each node or edge in the sample gas pipeline grid graph, wherein the second training sample and the second label are determined based on historical data;

determining the grid safety score by retrieving in the score preset table based on the at least one failure possibility of the at least one node or the at least one edge through the safety score evaluation layer, and

determining an updated inspection plan based on the grid safety score.

2. The method according to claim 1 , wherein the generating a grading instruction through a preset grading algorithm based on the geographical feature, the pipeline feature, the pipeline network historical data, and a count of gas pipeline network grades, wherein the grading instruction is used to perform grading processing on the gas pipeline network to determine a grading result, comprises:

determining the gas pipeline network grade through a grading preset table based on the geographical feature, the pipeline feature, the pipeline network historical data, and the count of the gas pipeline network grades.

3. The method according to claim 1 , wherein the determining a gridding result through performing gridding processing on the gas pipeline network comprises:

performing the gridding processing on a gas pipeline network space through a gridding algorithm based on a preset period.

4. An Internet of Things system for determining a safety inspection plan based on a smart gas geographic information system (GIS), wherein the Internet of Things system includes a smart gas user platform, a smart gas service platform, a smart gas pipeline network safety management platform, a smart gas pipeline network device sensing network platform, and a smart gas pipeline network device object platform, the smart gas user platform including a terminal device, the smart gas service platform including a first server, the smart gas pipeline network safety management platform including a second server, the smart gas pipeline network device sensing network platform including a communication network and a gateway, the smart gas pipeline network device object platform including various types of gas pipeline network devices and monitoring devices;

the smart gas user platform includes a plurality of smart gas user sub-platforms;

the smart gas service platform includes a plurality of smart gas service sub-platforms;

the smart gas pipeline network safety management platform includes a plurality of smart gas pipeline network safety management sub-platforms and a smart gas data center;

the smart gas pipeline network device sensing network platform is configured to interact with the smart gas data center and the smart gas pipeline network device object platform;

the smart gas pipeline network device object platform is configured to obtain a geographical feature, a pipeline feature, and pipeline network historical data of a gas pipeline network from the smart gas GIS and upload the geographical feature, the pipeline feature, and the pipeline network historical data of a gas pipeline network to the smart gas pipeline network safety management platform based on an obtaining instruction;

the smart gas pipeline network safety management platform is configured to obtaining a count of gas pipeline network grades based on the smart gas user platform through the smart gas pipeline network device sensing network platform;

the smart gas pipeline network safety management platform is configured to obtain the geographical feature, the pipeline feature, and the pipeline network historical data from the smart gas data center; generate a grading instruction through a preset grading algorithm based on the geographic feature, the pipeline feature, the pipeline network historical data, and a count of gas pipeline network grades, and perform grading processing on the gas pipeline network based on the grading instruction to determine a grading result; determine an inspection plan through a first preset algorithm based on the grading result, wherein the inspection plan includes an inspection time and an inspection route; and determine an inspection instruction through a preset generation algorithm based on the inspection plan, and send the inspection instruction to the smart gas service platform through the smart gas data center, wherein the inspection plan further includes a required inspection area and an optional inspection area, the required inspection area refers to an inspection area that is required to be inspected in the inspection plan, the optional inspection area refers to an inspection area that is not required to be inspected in the inspection plan, and the smart gas pipeline network safety management platform is further configured to:

determine a gridding result through performing gridding processing on the gas pipeline network, wherein the ridding processing refers to a process in which an inspection area of the qasr pipeline network is divided by gridding based on a preset gridding standard;

determine the required inspection area and the optional inspection area based on the gridding result, wherein to determine the required inspection area and the optional inspection area based on the gridding result, the smart gas pipeline network safety management platform is further configured:

determine the grid safety failure possibility by processing an inspection time interval, an accident time interval, the geographical feature, and a gas density in a grid based on the possibility prediction layer, wherein the grid safety failure possibility refers to a failure possibility of the qas pipeline in each grid area, wherein the possibility prediction layer is a machine learning model and the gas density in the grid refers to a ratio of a gas pipeline volume contained in a grid area to a total volume of the grid;

determine a grid safety score by retrieving in a score preset table through a safety score evaluation layer based on the grid safety failure possibility, wherein the score preset table refers to a table storing a plurality different sets of grid safety failure possibilities and grid safety scores corresponding to the grid safety failure possibilities; and

determine whether the grid area is the required inspection area or the optional inspection area by retrieving in an area preset table through a judgment layer based on the grid safety score and the gas pipeline network grade;

generate at least one candidate inspection route based on the required inspection area and the optional inspection area;

determine a target inspection route based on the at least one candidate inspection route; and

determine the inspection plan based on the target inspection route; and

the smart gas service platform is configured to upload the inspection instruction to the smart gas user platform through the smart gas pipeline network device sensing network platform;

the smart gas pipeline network device object platform is configured to obtain feedback data from the user terminal based on the smart gas user platform through the smart gas pipeline network device sensing network platform;

the smart gas pipeline network safety management platform is configured to periodically perform an adjustment on the inspection plan through a second preset algorithm based on the feedback data, wherein the adjustment includes determining at least one required inspection area as an optional inspection area or determining at least one optional inspection area as a required inspection area, wherein the periodically performing an adjustment on the inspection plan through a second preset algorithm based on the feedback data comprises:

the smart gas pipeline network safety management platform is configured to update a gas pipeline grid graph based on inspection data after inspecting each inspection area, wherein the gas pipeline grid graph is determined based on the gridding result, the gas pipeline grid graph refers to a graph used to represent a connection relationship of the gas pipeline network, a node feature of the gas pipeline grid graph includes a geographical feature of the grid area, the grid safety score, whether the gas pipeline has been inspected, the gas pipeline grid graph is determined or updated based on the gridding result, and the gas pipeline grid graph includes at least one node and at least one edge;

the smart gas pipeline network safety management platform is configured to determine at least one failure possibility of the at least one node or the at least one edge through a failure possibility determination model based on an updated gas pipeline grid graph, wherein the failure possibility determination model is a graph neural network model, the failure possibility determination model is obtained by training with a second training sample and a second label separately, a second training sample of the failure possibility determination model includes a sample gas pipeline grid graph, and a second label is an actual failure possibility of each node or edge in the sample gas pipeline grid graph, the second training sample and the second label is determined based on historical data;

the smart gas pipeline network safety management platform is configured to determine a grid safety score by retrieving in a score preset table based on the at least one failure possibility of the at least one node or the at least one edge through a safety score evaluation layer, and,

the smart gas pipeline network safety management platform is configured to determine an updated inspection plan based on the grid safety score.

5. The Internet of Things system according to claim 4 , wherein the smart gas pipeline network safety management platform is further configured to:

determine the gas pipeline network grade through a grading preset table based on the geographical feature, the pipeline feature, the pipeline network historical data, and the count of the gas pipeline network grades.