# US 2025/0146908 A1 · Methods and internet of things (iot) systems for abnormality troubleshooting at refueling stations based on safety supervision

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- Number: US 2025/0146908 A1
- Document ID: US20250146908A1
- Status: Published application
- Kind: A1
- Application number: 19/016,041
- Filed: 2025-01-10
- Published: 2025-05-08
- Earliest claimed: 2024-12-03
- Assignees: Chengdu Qinchuan Iot Technology Co., Ltd.
- Applicants: Chengdu Qinchuan Iot Technology Co., Ltd.
- Inventors: Zehua SHAO; Bin LIU; Feng WANG; Yuefei WU
- Web page: https://patentgrep.com/patent/US20250146908A1

## CPC

- G01M 99/00: Subject matter not provided for in other groups of this subclass
- G06Q 10/20: Administration of product repair or maintenance
- G06Q 50/06: Energy or water supply
- G06Q 50/26: Government or public services (business processes related to the transportation industry G06Q50/40)
- G16Y 10/40: Transportation
- G16Y 40/10: Detection; Monitoring
- 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
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- 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

Provided is a method for abnormality troubleshooting at a gas refueling station based on safety supervision, comprising: generating a refueling anomaly warning based on refueling error anomaly information and refueled vehicle anomaly information; generating a device inspection instruction based on the refueling anomaly warning and a device characteristic of a refueling device; determining an additional monitoring parameter corresponding to each of one or more devices to be inspected based on the device inspection instruction and uploading the additional monitoring parameter to a smart gas device object platform; generating a monitoring instruction and sending the monitoring instruction to a monitoring device associated with the one or more devices to be inspected; generating an initial inspection result based on an additional monitoring result and a routine monitoring result, uploading the initial inspection result to a smart gas government safety supervision management platform, and storing the initial inspection result in a database.

## Claims

### Claim 1

A method for abnormality troubleshooting at a refueling station based on safety supervision, performed by a gas company management platform of an Internet of Things (IoT) system for abnormality troubleshooting at a refueling station based on safety supervision, the method comprising:
collecting refueling information of at least one refueled vehicle during a predetermined time period through a smart gas device object platform;
processing the refueling information to generate refueling error anomaly information;
receiving, through a smart control center, refueled vehicle anomaly information uploaded by a gas user object platform;
generating a refueling anomaly warning based on the refueling error anomaly information and the refueled vehicle anomaly information and sending the refueling anomaly warning to a console of a refueling system of a refueling station corresponding to the refueling anomaly warning and to a smart gas government safety supervision management platform, wherein the refueling information collected in the predetermined time period is related with at least one of an abnormal vehicle and a refueling time corresponding to the refueled vehicle anomaly information;
generating a device inspection instruction based on the refueling anomaly warning and a device characteristic of a refueling device obtained from the refueling system;
controlling a monitoring device to determine an additional monitoring parameter corresponding to each of one or more devices to be inspected based on the device inspection instruction and uploading the additional monitoring parameter to the smart gas device object platform, wherein the additional monitoring parameter includes at least one of an additionally turned-on and an additionally turned-on monitoring device;
obtaining aggregated additional monitoring parameters corresponding to the one or more devices to be inspected through the smart gas device object platform;
generating a first monitoring instruction and sending, through the smart gas device object platform, the first monitoring instruction to a first monitoring device associated with the one or more devices to be inspected, wherein the first monitoring instruction is configured to adjust an existing monitoring parameter of the first monitoring device, and the first monitoring device is a monitoring device that is currently performing monitoring;
generating a second monitoring instruction and sending, through the smart gas device object platform, the second monitoring instruction to a second monitoring device associated with the one or more devices to be inspected, wherein the second monitoring instruction is configured to initiate new monitoring, and the second monitoring device is a monitoring device that has not initiated monitoring; and
generating an initial inspection result based on an additional monitoring result and a routine monitoring result, uploading the initial inspection result to the smart gas government safety supervision management platform, and storing the initial inspection result in a database.

### Claim 2 (depends on 1)

The method of claim 1, wherein the processing the refueling information to generate refueling error anomaly information further includes:
conducting a metering fluctuation determination at a predetermined judgment frequency, the metering fluctuation determination including:
determining an analysis interval of a current metering fluctuation determination based on a current moment, and designating the analysis interval of the current metering fluctuation determination as a current analysis interval;
executing a first judgment instruction within the current analysis interval; the first judgment instruction including: selecting a first predetermined count of refueling intensive periods from the current analysis interval and obtaining refueling information in the refueling intensive periods;
performing a metering fluctuation analysis on the refueling information in the refueling intensive periods to determine whether the refueling intensive periods include target refueling information;
in response to determining that the refueling intensive periods include the target refueling information, generating a metering fluctuation set based on the target refueling information; and
generating the refueling error anomaly information based on the metering fluctuation set, and storing the refueling error anomaly information in a storage device of the smart control center.

### Claim 3 (depends on 2)

The method of claim 2, wherein the processing the refueling information to generate refueling error anomaly information further includes:
in response to determining that the first predetermined count of refueling intensive periods do not include the target refueling information, executing one or more second judgment instructions, each of the one or more second judgment instructions including: selecting a target interval as an expansion interval pending evaluation from the current analysis interval, wherein the target interval is a time interval that is not selected by the first judgment instruction and not selected by one or more second judgment instructions that have been executed;
performing the metering fluctuation analysis on refueling information in the expansion interval pending evaluation to determine target refueling information in the expansion interval pending evaluation;
generating the refueling error anomaly information for the target refueling information in the current analysis interval based on the target refueling information obtained by at least one of the first judgment instruction and the one or more second judgment instructions and the metering fluctuation set corresponding to the target refueling information; wherein a count of the one or more second judgment instructions does not exceed a second predetermined count, the second predetermined count being determined based on a time feature and the predetermined judgment frequency.

### Claim 4 (depends on 3)

The method of claim 3, wherein the first predetermined count is determined based on a concentration degree of different refueling intensive periods in the analysis interval, and the refueling information distributed in the first predetermined count of refueling intensive periods is not less than a predetermined proportion.

### Claim 5 (depends on 2)

The method of claim 2, wherein the processing the refueling information to generate refueling error anomaly information further includes:
generating the refueling error anomaly information based on the refueled vehicle anomaly information and metering fluctuation data corresponding to the refueling information; and
the generating a refueling anomaly warning based on the refueling error anomaly information and the refueled vehicle anomaly information and sending the refueling anomaly warning to a console of a refueling system of a refueling station corresponding to the refueling anomaly warning and to a smart gas government safety supervision management platform further includes:
sending the refueling anomaly warning to a refueling user terminal corresponding to the abnormal vehicle.

### Claim 6 (depends on 5)

The method of claim 5, wherein the generating the refueling error anomaly information based on the refueled vehicle anomaly information and metering fluctuation data corresponding to the refueling information includes:
determining, through a refueling model, the refueling error anomaly information based on the refueled vehicle anomaly information, the metering fluctuation set including the metering fluctuation data, and an environmental parameter; the refueling model being a machine learning model; wherein
the refueling error anomaly information includes at least one of an anomalous time and an anomalous amount.

### Claim 7 (depends on 6)

The method of claim 6, wherein the refueling model is obtained through training based on a plurality of sets of labeled training samples; the training samples are determined based on historical monitoring data or generated based on a platform simulation; wherein the training samples generated based on the platform simulation include training samples generated by simulating target historical data, the target historical data includes historical monitoring data including the refueling error anomaly information.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
obtaining an environmental parameter through an environmental monitoring device that is connected to the smart gas device object platform via a signal;
generating a correction check result based on the environmental parameter, the additional monitoring result, and the routine monitoring result, and sending the correction check result to a government safety supervision management platform;
obtaining a refueling quality ranking generated by the government safety supervision management platform, the refueling quality ranking being generated based on correction check results of refueling stations corresponding to a same gas source type; and
generating a device self-inspection parameter corresponding to each of the refueling stations based on the refueling quality ranking, and sending the device self-inspection parameter to the corresponding refueling station, the device self-inspection parameter including a self-inspection cycle and a self-inspection device.

### Claim 9 (depends on 8)

The method of claim 8, wherein the generating a correction check result based on the environmental parameter, the additional monitoring result, and the routine monitoring result includes:
determining a permissible value of each of the one or more devices to be inspected based on the environmental parameter and a check score corresponding to the device inspection instruction; and
generating the correction check result by correcting the initial inspection result based on the permissible value, the additional monitoring result, and the routine monitoring result.

### Claim 10 (depends on 8)

The method of claim 8, wherein device self-inspection parameters correspond to refueling stations corresponding to different gas source types.

### Claim 11

An Internet of Things (IoT) system for abnormality troubleshooting at a refueling station based on safety supervision, comprising: a smart gas government safety supervision management platform, a smart gas government safety supervision sensor network platform, a smart gas government safety supervision object platform, a gas company sensor network platform, and a gas user object platform and a smart gas device object platform interacting with the gas company sensor network platform, wherein
the smart gas government safety supervision object platform includes a gas company management platform; the smart gas government safety supervision sensor network platform interacts with the smart gas government safety supervision management platform and the gas company management platform, respectively;
the gas company management platform is disposed in a smart control center of a gas company, the smart control center including a server, a storage device, and a data transmission component, and the gas company management platform is connected to the smart gas device object platform at the refueling station via a signal based on a communication network, the communication network being operated under the control of the gas company sensor network platform; and
the gas company sensor network platform includes a sensor network sub-platform corresponding to the refueling station, the sensor network sub-platforms being configured on a communication server of the refueling station; the smart gas device object platform is disposed in a control center of the refueling station and connected to a refueling system corresponding to the refueling station via a signal, the refueling system including a console and a plurality of refueling devices.

### Claim 12 (depends on 11)

The IoT system of claim 11, wherein the gas company management platform is configured to:
collect refueling information of at least one refueled vehicle during a predetermined time period through a smart gas device object platform;
process the refueling information to generate refueling error anomaly information;
receive, through the smart control center, refueled vehicle anomaly information uploaded by the gas user object platform;
generate a refueling anomaly warning based on the refueling error anomaly information and the refueled vehicle anomaly information and send the refueling anomaly warning to the console of the refueling system of a refueling station corresponding to the refueling anomaly warning and to the smart gas government safety supervision management platform, wherein the refueling information collected in the predetermined time period is related with at least one of an abnormal vehicle and a refueling time corresponding to the refueled vehicle anomaly information;
generate a device inspection instruction based on the refueling anomaly warning and a device characteristic of a refueling device obtained from the refueling system;
control a monitoring device to determine an additional monitoring parameter corresponding to each of one or more devices to be inspected based on the device inspection instruction and upload the additional monitoring parameter to the smart gas device object platform, wherein the additional monitoring parameter includes at least one of an additionally turned-on and an additionally turned-on monitoring device;
obtain aggregated additional monitoring parameters corresponding to the one or more devices to be inspected through the smart gas device object platform;
generate a first monitoring instruction and send, through the smart gas device object platform, the first monitoring instruction to a first monitoring device associated with the one or more devices to be inspected, wherein the first monitoring instruction is configured to adjust an existing monitoring parameter of the first monitoring device, and the first monitoring device is a monitoring device that is currently performing monitoring;
generate a second monitoring instruction and send, through the smart gas device object platform, the second monitoring instruction to a second monitoring device associated with the one or more devices to be inspected, wherein the second monitoring instruction is configured to initiate new monitoring, and the second monitoring device is a monitoring device that has not initiated monitoring; and
generate an initial inspection result based on an additional monitoring result and a routine monitoring result, upload the initial inspection result to the smart gas government safety supervision management platform, and store the initial inspection result in a database.

### Claim 13 (depends on 12)

The IoT system of claim 12, wherein the gas company management platform is further configured to:
conduct a metering fluctuation determination at a predetermined judgment frequency, wherein to conduct the metering fluctuation determination, the gas company management platform is further configured to:
determine an analysis interval of a current metering fluctuation determination based on a current moment, and designate the analysis interval of the current metering fluctuation determination as a current analysis interval;
execute a first judgment instruction within the current analysis interval; the first judgment instruction including: selecting a first predetermined count of refueling intensive periods from the current analysis interval and obtaining refueling information in the refueling intensive periods;
perform a metering fluctuation analysis on the refueling information in the refueling intensive periods to determine whether the refueling intensive periods include target refueling information;
in response to determining that the refueling intensive periods include the target refueling information, generate a metering fluctuation set based on the target refueling information; and
generate the refueling error anomaly information based on the metering fluctuation set, and store the refueling error anomaly information in a storage device of the smart control center.

### Claim 14 (depends on 13)

The IoT system of claim 13, wherein the gas company management platform is further configured to:
in response to determining that the first predetermined count of refueling intensive periods do not include the target refueling information, execute one or more second judgment instructions, each of the one or more second judgment instructions including: selecting a target interval as an expansion interval pending evaluation from the current analysis interval, wherein the target interval is a time interval that is not selected by the first judgment instruction and not selected by one or more second judgment instructions that have been executed;
perform the metering fluctuation analysis on refueling information in the expansion interval pending evaluation to determine target refueling information in the expansion interval pending evaluation;
generate the refueling error anomaly information for the target refueling information in the current analysis interval based on the target refueling information obtained by at least one of the first judgment instruction and the one or more second judgment instructions and the metering fluctuation set corresponding to the target refueling information; wherein a count of the one or more second judgment instructions does not exceed a second predetermined count, the second predetermined count being determined based on a time feature and the predetermined judgment frequency.

### Claim 15 (depends on 14)

The IoT system of claim 14, wherein the first predetermined count is determined based on a concentration degree of different refueling intensive periods in the analysis interval, and the refueling information distributed in the first predetermined count of refueling intensive periods is not less than a predetermined proportion.

### Claim 16 (depends on 13)

The IoT system of claim 13, wherein the gas company management platform is further configured to:
generate the refueling error anomaly information based on the refueled vehicle anomaly information and metering fluctuation data corresponding to the refueling information; and
to generate the refueling anomaly warning based on the refueling error anomaly information and the refueled vehicle anomaly information and send the refueling anomaly warning to the console of the refueling system of the refueling station corresponding to the refueling anomaly warning and to the smart gas government safety supervision management platform, the gas company management platform is further configured to:
send the refueling anomaly warning to a refueling user terminal corresponding to the abnormal vehicle.

### Claim 17 (depends on 16)

The IoT system of claim 16, wherein the gas company management platform is further configured to:
determine, through a refueling model, the refueling error anomaly information based on the refueled vehicle anomaly information, the metering fluctuation set including the metering fluctuation data, and an environmental parameter; the refueling model being a machine learning model; wherein
the refueling error anomaly information includes at least one of an anomalous time and an anomalous amount.

### Claim 18 (depends on 17)

The IoT system of claim 17, wherein the refueling model is obtained through training based on a plurality of sets of labeled training samples; the training samples are determined based on historical monitoring data or generated based on a platform simulation; wherein the training samples generated based on the platform simulation include training samples generated by simulating target historical data, the target historical data includes historical monitoring data including the refueling error anomaly information.

### Claim 19 (depends on 12)

The IoT system of claim 12, wherein the gas company management platform is further configured to:
obtain an environmental parameter through an environmental monitoring device that is connected to the smart gas device object platform via a signal;
generate a correction check result based on the environmental parameter, the additional monitoring result, and the routine monitoring result, and send the correction check result to the government safety supervision management platform;
obtain a refueling quality ranking generated by the government safety supervision management platform, the refueling quality ranking being generated based on correction check results of refueling stations corresponding to a same gas source type; and
generate a device self-inspection parameter corresponding to each of the refueling stations based on the refueling quality ranking, and send the device self-inspection parameter to the corresponding refueling station, the device self-inspection parameter including a self-inspection cycle and a self-inspection device.

### Claim 20 (depends on 1)

A non-transitory computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer executes the method for abnormality troubleshooting at a refueling station based on safety supervision of claim 1.