Methods and Internet of Things (IoT) systems for smart gas pipeline maintenance based on human-machine linkage

1. An Internet of Things (IoT) system for smart gas pipeline maintenance based on human-machine linkage, comprising a smart gas user platform, a smart gas service platform, a smart gas pipeline network safety management platform, a smart gas pipeline network sensor network platform, and a smart gas pipeline network object platform, wherein the smart gas pipeline network safety management platform is configured to:

determine a first cycle based on data of a pipeline to be maintained, a feature of a maintainer, or a feature of a maintenance robot;

obtain, through a maintainer terminal or the maintenance robot, first feedback data based on the first cycle;

determine, based on the first feedback data and the data of the pipeline to be maintained, a maintenance parameter and send the maintenance parameter to the maintainer terminal; wherein the maintenance parameter refers to work that the maintainer needs to complete and work that needs to be completed by controlling the maintenance robot; the maintenance parameter is related to second feedback data, the second feedback data being obtained by the maintenance robot according to a second cycle; and

the smart gas pipeline network safety management platform is further configured to:

determine a maintenance risk degree based on the first feedback data and the second feedback data; wherein the maintenance risk degree refers to a risk change value of a maintenance parameter in a current maintenance process compared with before the maintenance process;

determine the maintenance parameter based on the maintenance risk degree and the data of the pipeline to be maintained; and

generate, based on the maintenance parameter, a control instruction and send the control instruction to the maintenance robot.

2. The IoT system according to claim 1 , wherein the smart gas pipeline network safety management platform is further configured to:

determine a fault level based on the data of the pipeline to be maintained; and

determine a pre-maintenance risk based on the fault level and the first feedback data.

3. The IoT system according to claim 1 , wherein the smart gas pipeline network safety management platform is further configured to:

determine a candidate maintenance parameter based on the data of the pipeline to be maintained;

assess a post-maintenance risk based on the data of the pipeline to be maintained and the candidate maintenance parameter; and

determine a target maintenance parameter as the maintenance parameter based on the maintenance risk degree and the post-maintenance risk.

4. The IoT system according to claim 3 , wherein the smart gas pipeline network safety management platform is further configured to:

determine the post-maintenance risk based on the data of the pipeline to be maintained, gas pipeline data, and the candidate maintenance parameter.

5. A method for smart gas pipeline maintenance based on human-machine linkage, implemented by a smart gas pipeline network safety management platform of an Internet of Things (IoT) system for smart gas pipeline maintenance based on human-machine linkage, wherein the method comprises:

determining a first cycle based on data of a pipeline to be maintained, a feature of a maintainer, or a feature of a maintenance robot;

obtaining, through a maintainer terminal or the maintenance robot, first feedback data based on the first cycle;

determining, based on the first feedback data and the data of the pipeline to be maintained, a maintenance parameter and sending the maintenance parameter to the maintainer terminal; wherein the maintenance parameter refers to work that the maintainer needs to complete and work that needs to be completed by controlling the maintenance robot; the maintenance parameter is related to second feedback data, the second feedback data being obtained by the maintenance robot according to a second cycle; and

the method further comprises:

determining a maintenance risk degree based on the first feedback data and the second feedback data; wherein the maintenance risk degree refers to a risk change value of a maintenance parameter in a current maintenance process compared with before the maintenance process;

determining the maintenance parameter based on the maintenance risk degree and the data of the pipeline to be maintained; and

generating, based on the maintenance parameter, a control instruction and sending the control instruction to the maintenance robot.

6. The method according to claim 5 , further comprising:

determining a fault level based on the data of the pipeline to be maintained; and

determining a pre-maintenance risk based on the fault level and the first feedback data.

7. The method according to claim 5 , wherein the determining the maintenance parameter based on the maintenance risk degree and the data of the pipeline to be maintained includes:

determining a candidate maintenance parameter based on the data of the pipeline to be maintained;

assessing a post-maintenance risk based on the data of the pipeline to be maintained and the candidate maintenance parameter; and

determining a target maintenance parameter as the maintenance parameter based on the maintenance risk degree and the post-maintenance risk.

8. The method according to claim 7 , wherein the assessing a post-maintenance risk based on the data of the pipeline to be maintained and the candidate maintenance parameter includes:

determining the post-maintenance risk based on the data of the pipeline to be maintained, gas pipeline data, and the candidate maintenance parameter.