| CPC H04L 67/12 (2013.01) [G06Q 50/06 (2013.01); G16Y 10/35 (2020.01); G16Y 20/20 (2020.01)] | 11 Claims |
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1. A method for determining a gas abnormity for a safe gas use, wherein the method is implemented by a smart gas Internet of Things (IoT) system, the system comprises a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor device sensing network platform, and a smart gas indoor device object platform, wherein
the smart gas user platform is configured as a terminal device, and is configured to interact with the smart gas service platform to send request information of a target user to the smart gas service platform, and receive analysis information of a gas abnormity cause uploaded by the smart gas service platform;
the smart gas service platform is configured to receive and transmit data and information, interact with the smart gas safety management platform to send the request information of the target user to the smart gas safety management platform, and receive the analysis information of the gas abnormity cause uploaded by the smart gas safety management platform;
the smart gas indoor device sensing network platform is configured as a plurality of groups of gateway servers or a plurality of groups of intelligent routers, and is configured to receive information related to the gas abnormity uploaded by the smart gas indoor device object platform and upload the information related to the gas abnormity to the smart gas safety management platform;
the smart gas indoor device object platform is configured as indoor gas devices and gas safety detection devices, and is configured to obtain the information related to the gas abnormity, upload the information to the smart gas indoor device sensing network platform, and receive an instruction for obtaining the information related to the gas abnormity issued by the smart gas indoor device sensing network platform;
the smart gas safety management platform includes a smart gas indoor safety management sub-platform and a smart gas data center, the smart gas indoor safety management sub-platform interacts with the smart gas data center in two directions; and
the method is performed by the smart gas safety management platform, comprising:
receiving the request information of the target user, the request information including a request of the target user for analyzing the gas abnormity cause input by the target user via the smart gas user platform;
extracting user data based on the request information, and extracting gas data based on the user data, wherein the gas data includes a gas balance, gas pipeline data of all levels involved by the target user, gas usage data, and gas abnormity data of the target user; and
determining, based on the user data and the gas data, analysis information of the gas abnormity cause; and
generating a control signal when the analysis information of the gas abnormity cause contains an abnormity cause whose abnormity score exceeds the safety threshold, and sending the control signal to a safety valve control device object sub-platform so as to close gas valves, wherein the safety valve control device object sub-platform is a sub-platform of the smart gas indoor device object platform;
wherein the determining the analysis information of the gas abnormity cause comprises:
extracting a preset rule based on a rule base, and determining a candidate gas abnormity cause and a certainty level of the candidate gas abnormity cause through a rule determination engine; wherein
the rule base refers to a knowledge base composed of various preset rules;
the preset rule refers to a rule that is artificially set in advance and is used to determine whether the user data and the gas data satisfy a condition, and the preset rule includes thresholds related to determining a type of the gas abnormity cause and a certainty level thereof;
the certainty level refers to a level of certainty of the gas abnormity cause, which is expressed as a percentage or a grade; the certainty level of 100% indicates that the gas abnormity cause is directly determined; the certainty level being less than 100% indicates that the gas abnormity cause is concluded through data analysis; and
the preset rule or the rule determination engine is used to determine whether the user data and the gas data satisfy the preset rule;
determining whether the certainty level satisfies a first preset condition, wherein the first preset condition refers to a condition that is set in advance for determining whether to obtain additional data and continue analyzing the gas abnormity cause, and the first preset condition includes that the certainty level is greater than or equal to 90%; and
in response to a determination that the certainty level does not satisfy the first preset condition, determining, based on pipeline information and user terminal information, the analysis information of the gas abnormity cause through a preset algorithm, wherein the pipeline information and the user terminal information are determined based on the user data and the gas data, the pipeline information includes pipeline gas information and pipeline terminal information, and the preset algorithm denotes an algorithm that is preset for analyzing and processing an image structure, wherein the preset algorithm comprises:
constructing, based on the pipeline information and the user terminal information, the image, a node of the image including a pipeline terminal node and a user terminal node, an edge of the image including a gas pipeline between nodes, and a direction of the edge is a gas delivery direction; attributes of the pipeline terminal node include an abnormity score, gas use data, and gas abnormity data of the pipeline terminal node; attributes of the user terminal node include an abnormity score, the gas usage data, user image data, and the gas abnormity data of the user terminal node; and attributes of the edge include a weight value and gas flow information; and
analyzing, based on the preset algorithm, the image to determine the abnormity score of the node of the image; and
updating, through a continuous iteration, the abnormity score of the node of the image until the abnormity score satisfies a second preset condition; and
using a last updated abnormity score of the node of the image as a final score of the node.
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