US 12,236,768 B2
Method, Internet of Things system, and storage medium for generating inspection scheme based on smart gas
Zehua Shao, Chengdu (CN); Yong Li, Chengdu (CN); Feng Wang, Chengdu (CN); and Quan Wang, Chengdu (CN)
Assigned to CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Chengdu (CN)
Filed by CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Sichuan (CN)
Filed on Jun. 2, 2024, as Appl. No. 18/731,332.
Application 18/731,332 is a continuation of application No. 18/360,845, filed on Jul. 28, 2023, granted, now 12,051,315.
Claims priority of application No. 202310765316.9 (CN), filed on Jun. 27, 2023.
Prior Publication US 2024/0321078 A1, Sep. 26, 2024
Int. Cl. G08B 21/16 (2006.01); G16Y 10/35 (2020.01); G16Y 40/10 (2020.01)
CPC G08B 21/16 (2013.01) [G16Y 10/35 (2020.01); G16Y 40/10 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A method for generating an inspection scheme for smart gas pipeline network inspection, wherein the method is implemented based on a smart gas management platform of an Internet of Things system for generating an inspection scheme for smart gas pipeline network inspection, comprising:
obtaining, based on first monitoring data at a first location and distance data, second monitoring data at a second location by controlling a drive module to drive a gas leakage inspection device to move; the first monitoring data being obtained by the gas leakage inspection device, wherein the first monitoring data includes at least one of a combustible gas concentration, a wind direction, a wind speed, or an abnormal sound; the distance data being related to a distance between the gas leakage inspection device and an obstacle; and the second location being a location other than the first location during movement of the gas leakage inspection device;
determining, based on the second monitoring data, a necessary inspection point and an inspection frequency of the necessary inspection point; the necessary inspection point being a location point that the gas leakage inspection device need to inspect; wherein:
the inspection frequency of the necessary inspection point is determined by processing a necessary inspection map using an inspection frequency determination model, the inspection frequency determination model being a machine learning model; wherein the necessary inspection map is constructed based on information related to the necessary inspection point, a node of the necessary inspection map corresponds to the necessary inspection point, and node features of the node include at least one of a necessary inspection point type, historical monitoring data, historical maintenance data, and an initial inspection frequency, an edge of the necessary inspection map corresponds to an inspection route between two nodes, and edge features of the edge at least include a movement distance corresponding to the inspection route;
determining, based on the necessary inspection point and the inspection frequency of the necessary inspection point, a first inspection scheme; and
controlling the drive module to drive the gas leakage inspection device to inspect a gas pipeline network based on the first inspection scheme.