Patent ID: 11879599
Assignee: CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.
Field: Measurement (Instruments)
Classification: CPC F  G | IPC F  G

Claim 3:
4. An Internet of Things (IoT) system for assessing electrochemical corrosion of a smart gas pipeline, 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 object platform is configured to obtain environmental data of at least one position of a gas pipeline at a first time, and transmit the environmental data to the smart gas pipeline network safety management platform through the smart gas pipeline network sensor network platform; and
the smart gas pipeline network safety management platform is configured to:
determine an electrochemical corrosion degree of each of the at least one position of the gas pipeline at a second time based on the environmental data of the at least one position at the first time, wherein the first time is before the second time; and
determine, based on the electrochemical corrosion degree, a protection scheme of the gas pipeline;
wherein the determining, based on the electrochemical corrosion degree, a protection scheme of the gas pipeline includes:
obtaining a difference between a thickness of the gas pipeline and the electrochemical corrosion degree at the second time;
in response to a determination that the difference is smaller than a first threshold, determining the protection scheme of the gas pipeline based on a first scheme; or
in response to a determination that the difference is smaller than a second threshold, determining the protection scheme of the gas pipeline based on a second scheme, wherein the second threshold is smaller than the first threshold, and the second scheme includes determining a site selection scheme of at least one cathode protection station, comprising:
determining a plurality of candidate site selection schemes, each of the plurality of candidate site selection schemes comprising a set of site selection coordinates of the at least one cathode protection station; and
determining a target site selection scheme based on a preset evaluation function by performing at least one round of iterative updating on the plurality of candidate site selection schemes, wherein the preset evaluation function is related to the electrochemical corrosion thickness at the second time, and the at least one round of iterative updating on the plurality of candidate site selection schemes includes:
for the site selection scheme of at least one cathode protection station, updating a corresponding multi-dimensional increment to be processed based on a relationship between the site selection scheme of the at least one cathode protection station and a site selection scheme of an optimal cathode protection station in history, and
updating the site selection scheme of at least one candidate cathode protection station based on an updated multi-dimensional increment;

wherein to determine an electrochemical corrosion degree of each of the at least one position of the gas pipeline at a second time based on the environmental data of the at least one position at the first time, the smart gas pipeline network safety management platform is further configured to:
determine an electrochemical corrosion thickness of each of the at least one position of the gas pipeline at the second time by processing the environmental data of the at least one position at the first time based on a corrosion thickness prediction model, wherein the corrosion thickness prediction model is a machine learning model, and the corrosion thickness prediction model comprises a potential prediction layer and a corrosion thickness prediction layer;

wherein the corrosion thickness prediction model is obtained through a training process by the processor of the smart gas pipeline network safety management platform of the IoT system, comprising:
generating a plurality of sets of first training samples with first labels, wherein the first training sample includes sample historical environmental data of a plurality of positions of the gas pipeline at the first time, and the first label is the electrochemical corrosion thickness of a corresponding plurality of positions of the gas pipeline obtained by a monitoring device at the second time;
inputting each group of the sample historical environmental data into an initial corrosion thickness prediction model;
processing each group of the sample historical environmental data through the corrosion thickness prediction model;
outputting the electrochemical corrosion thickness;
constructing a loss function based on a label of each set of the sample historical environmental data and an output of the corrosion thickness prediction model;
generating a trained corrosion thickness prediction model by iteratively updating parameters of the initial corrosion thickness prediction model based on the loss function until a preset condition is satisfied; and
to determine an electrochemical corrosion thickness of each of the at least one position of the gas pipeline at the second time by processing the environmental data of the at least one position at the first time based on a corrosion thickness prediction model, the smart gas pipeline network safety management platform is further configured to:
determine potential of each of the at least one position of the gas pipeline at the second time by processing the environmental data of the at least one position at the first time based on the potential prediction layer; and
determine the electrochemical corrosion thickness of each of the at least one position of the gas pipeline at the second time by processing the potential of each of the at least one position based on the corrosion thickness prediction layer; and

the smart gas service platform is configured to feed back the protection scheme of the gas pipeline to the smart gas user platform.