# US 12,190,696 B2 · Methods for indoor gas leakage disposal of smart gas and internet of things systems thereof > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,190,696 B2 - Document ID: US12190696B2 - Status: Granted - Kind: B2 - Application number: 18/446,461 - Filed: 2023-08-08 - Published: 2023-11-30 - Granted: 2025-01-07 - Earliest claimed: 2022-10-20 - Assignees: Chengdu Qinchuan Iot Technology Co., Ltd. - Applicants: Chengdu Qinchuan Iot Technology Co., Ltd. - Inventors: Zehua Shao; Yaqiang Quan; Bin Liu; Xiaojun Wei; Lei Zhang - Web page: https://patentgrep.com/patent/US12190696B2 ## CPC - G01M 3/26: by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors - G08B 17/10: Actuation by presence of smoke or gases {, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means} - G16Y 20/10: relating to the environment, e.g. temperature; relating to location - Y02P 90/02: Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS] - G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS - G16Y: INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT] - Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS ## Abstract The present disclosure provides a method for indoor gas leakage disposal of smart gas and an Internet of Things system. The method is implemented by a smart gas indoor safety management sub platform. The method includes obtaining monitoring information and user description information from a smart gas data center, the monitoring information including alarm information and gas terminal monitoring data, the monitoring information being obtained from a smart gas object platform through a smart gas sensor network platform, the user description information including user-defined alarm information uploaded by a user, and the user description information being obtained from a smart gas user platform through a smart gas service platform; determining a cause of gas leakage based on the monitoring information and the user description information; and determining a plurality of candidate solutions based on the cause of gas leakage. ## Claims ### Claim 1 A method for indoor gas leakage disposal of smart gas, wherein the method is implemented by a smart gas indoor safety management sub platform, including: obtaining monitoring information and user description information from a smart gas data center, wherein the monitoring information includes alarm information and gas terminal monitoring data, the monitoring information is obtained from a smart gas object platform through a smart gas sensor network platform by the smart gas data center, the user description information includes user-defined alarm information uploaded by a user, and the user description information is obtained from a smart gas user platform through a smart gas service platform by the smart gas data center; determining a cause of gas leakage based on the monitoring information and the user description information; determining a plurality of candidate solutions based on the cause of gas leakage, including: determining a feature vector based on the monitoring information, the user description information, a probability of misreporting, and the cause of gas leakage, wherein the probability of misreporting is determined based on a misreporting probability determination model, and the misreporting probability determination model is a machine learning model; and determining the plurality of candidate solutions based on a similarity between the feature vector and a reference vector in a database, wherein the database includes a plurality of the reference vectors and candidate solutions corresponding to the plurality of reference vectors; and determining an effective solution rate of the candidate solutions, and determining a target solution among the candidate solutions according to the effective solution rate. ### Claim 2 (depends on 1) The method for indoor gas leakage disposal of smart gas of claim 1, further including: the smart gas indoor safety management sub platform uploading the cause of gas leakage and the target solution to the smart gas data center; the smart gas data center sending the cause of gas leakage and the target solution to the smart gas service platform; and the smart gas service platform sending the cause of gas leakage and the target solution to the smart gas user platform. ### Claim 3 (depends on 1) The method for indoor gas leakage disposal of smart gas of claim 1, wherein the determining a cause of gas leakage based on the monitoring information and the user description information includes: determining the cause of gas leakage by a gas leakage cause prediction model, wherein an input of the gas leakage cause prediction model includes the monitoring information and the user description information, and an output of the gas leakage cause prediction model includes the cause of gas leakage. ### Claim 4 (depends on 3) The method for indoor gas leakage disposal of smart gas of claim 3, wherein the input of the gas leakage cause prediction model also includes a historical feature vector; and the historical feature vector is obtained based on historical data through an embedded layer. ### Claim 5 (depends on 4) The method for indoor gas leakage disposal of smart gas of claim 4, wherein the historical data includes historical monitoring information of a gas system and historical disposal information of the gas system, and the historical monitoring information includes historical alarm information of the gas system. ### Claim 6 (depends on 3) The method for indoor gas leakage disposal of smart gas of claim 3, wherein the gas leakage cause prediction model includes a preprocessing layer and a prediction layer, and the preprocessing layer includes a first processing layer and a second processing layer; the first processing layer is configured to process the monitoring information to obtain a system feature vector; and the second processing layer is configured to process the user description information to obtain a user feature vector. ### Claim 7 (depends on 6) The method for indoor gas leakage disposal of smart gas of claim 6, wherein the prediction layer includes a first prediction layer and a second prediction layer; an input of the first prediction layer includes the system feature vector, the user feature vector and the historical feature vector, and an output of the first prediction layer includes a gas leakage cause vector; and an input of the second prediction layer includes the gas leakage cause vector, and an output of the second prediction layer includes the cause of gas leakage. ### Claim 8 (depends on 7) The method for indoor gas leakage disposal of smart gas of claim 7, wherein the input of the second prediction layer includes the probability of misreporting. ### Claim 9 (depends on 8) The method for indoor gas leakage disposal of smart gas of claim 8, further comprising: in response to a determination that the probability of misreporting is greater than a probability threshold, supplementing system monitoring data, wherein the supplementing system monitoring data includes supplementing types and quantities of the system monitoring data; and updating the system feature vector based on supplemented system monitoring data. ### Claim 10 (depends on 1) The method for indoor gas leakage disposal of smart gas of claim 1, wherein an input of the misreporting probability determination model includes a system feature vector, a user feature vector, a historical feature vector, and a user reliability, and an output of the misreporting probability determination model includes the probability of misreporting; and the user reliability is determined by historical misreporting frequency and user alarm frequency. ### Claim 11 (depends on 1) The method for indoor gas leakage disposal of smart gas of claim 1, wherein the determining an effective solution rate of the candidate solutions includes: processing the cause of gas leakage and the candidate solutions based on an effective solution rate determination model to determine the effective solution rate, wherein the effective solution rate determination model is a machine learning model. ### Claim 12 (depends on 10) The Internet of Things system for indoor gas leakage disposal of smart gas of claim 10, wherein the smart gas indoor safety management sub platform is further configured to: in response to a determination that the probability of misreporting is greater than a probability threshold, supplement system monitoring data, wherein the supplementing system monitoring data includes supplementing types and quantities of the system monitoring data; and update the system feature vector based on supplemented system monitoring data. ### Claim 13 (depends on 1) A non-transitory computer-readable storage medium storing computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes the method for indoor gas leakage disposal of smart gas of claim 1. ### Claim 14 An Internet of Things system for indoor gas leakage disposal of smart gas, wherein the Internet of Things system includes a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas sensor network platform, and a smart gas object 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 is configured to perform operations including: obtaining monitoring information and user description information from a smart gas data center, wherein the monitoring information includes alarm information and gas terminal monitoring data, the monitoring information is obtained from a smart gas object platform through a smart gas sensor network platform by the smart gas data center, the user description information includes user-defined alarm information uploaded by a user, and the user description information is obtained from a smart gas user platform through a smart gas service platform by the smart gas data center; determining a cause of gas leakage based on the monitoring information and the user description information; determining a plurality of candidate solutions based on the cause of gas leakage, including: determining a feature vector based on the monitoring information, the user description information, a probability of misreporting, and the cause of gas leakage, wherein the probability of misreporting is determined based on a misreporting probability determination model, and the misreporting probability determination model is a machine learning model; and determining the plurality of candidate solutions based on a similarity between the feature vector and a reference vector in a database, wherein the database includes a plurality of the reference vectors and candidate solutions corresponding to the plurality of reference vectors; and determining an effective solution rate of the candidate solutions, and determining a target solution among the candidate solutions according to the effective solution rate. ### Claim 15 (depends on 14) The Internet of Things system for indoor gas leakage disposal of smart gas of claim 14, wherein the smart gas indoor safety management sub platform is configured to upload the cause of gas leakage and the target solution to the smart gas data center; the smart gas data center is configured to send the cause of gas leakage and the target solution to the smart gas service platform; and the smart gas service platform is configured to send the cause of gas leakage and the target solution to the smart gas user platform. ### Claim 16 (depends on 14) The Internet of Things system for indoor gas leakage disposal of smart gas of claim 14, wherein the smart gas indoor safety management sub platform is further configured to: determine the cause of gas leakage by a gas leakage cause prediction model, wherein an input of the gas leakage cause prediction model includes the monitoring information and the user description information, and an output of the gas leakage cause prediction model includes the cause of gas leakage. ### Claim 17 (depends on 16) The Internet of Things system for indoor gas leakage disposal of smart gas of claim 16, wherein the input of the gas leakage cause prediction model also includes a historical feature vector; and the historical feature vector is obtained based on historical data through an embedded layer. ### Claim 18 (depends on 17) The Internet of Things system for indoor gas leakage disposal of smart gas of claim 17, wherein the historical data includes historical monitoring information of a gas system and historical disposal information of the gas system, and the historical monitoring information includes historical alarm information of the gas system. ### Claim 19 (depends on 16) The Internet of Things system for indoor gas leakage disposal of smart gas of claim 16, wherein the gas leakage cause prediction model includes a preprocessing layer and a prediction layer, and the preprocessing layer includes a first processing layer and a second processing layer; the first processing layer is configured to process the monitoring information to obtain a system feature vector; and the second processing layer is configured to process the user description information to obtain a user feature vector. ### Claim 20 (depends on 19) The Internet of Things system for indoor gas leakage disposal of smart gas of claim 19, wherein the prediction layer includes a first prediction layer and a second prediction layer; an input of the first prediction layer includes the system feature vector, the user feature vector and the historical feature vector, and an output of the first prediction layer includes a gas leakage cause vector; and an input of the second prediction layer includes the gas leakage cause vector, and an output of the second prediction layer includes the cause of gas leakage.