Granted Patent B2

US 12,644,569 · App. 18/624,076 · Granted Jun 2, 2026

Inventors: Zehua Shao (Chengdu, CN); Yong Li (Chengdu, CN); Yaqiang Quan (Chengdu, CN)

Assignee: CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.

F17D5/02G01F1/66G01F15/063

Abstract

Claims (78)

1 . A method for determining abnormity of an ultrasonic metering device and remote adjustment of the ultrasonic metering device, wherein the method is implemented by a smart gas device management platform of an Internet of Things (IoT) system for determining abnormity of an ultrasonic metering device and remote adjustment of the ultrasonic metering device, and the method comprises:

obtaining metering data of at least one ultrasonic metering device;

determining any one of the at least one ultrasonic metering device as a current metering device;

determining an accuracy of the current metering device through verifying the current metering device based on metering data of the current metering device and metering data of a related metering device, wherein the related metering device includes at least one of an upper metering device, a lower metering device, and a parallel metering device of the current metering device;

determining a target metering device based on the accuracy of at least one current metering device corresponding to the at least one ultrasonic metering device, wherein an accuracy of the target metering device is lower than an accuracy threshold;

determining a plurality of historical accuracies of the target metering device based on the historical metering data of the target metering device;

determining variation parameters of the plurality of historical accuracies, wherein the variation parameters include a magnitude and a direction of changes between adjacent historical accuracies, and a count of times of the historical accuracies below the accuracy threshold;

determining an abnormality type of the target metering device based on the variation parameters and a current accuracy of the target metering device; and

determining a data upload frequency instruction based on the abnormality type and sending the data upload frequency instruction to the target metering device.

2 . The method according to claim 1 , wherein the accuracy threshold is related to a verification error.

3 . The method according to claim 1 , wherein the determining an accuracy of the current metering device through verifying the current metering device based on the metering data of the current metering device and metering data of a related metering device comprises:

performing upstream verification using metering data of the upper metering device, metering data of the current metering device, and metering data of the parallel metering device during a same time period;

performing downstream verification using metering data of the lower metering device and the metering data of the current metering device during the same time period; and

determining the accuracy of the current metering device based on an upstream verification result and a downstream verification result.

4 . The method according to claim 3 , wherein the upstream verification refers to verifying if a difference between upstream metering data and same level metering data during the same time period exceeds a verification error of the current metering device, wherein the upstream metering data is the metering data of the upper metering device, and the same level metering data is a sum of the metering data of the current metering device and the metering data of the parallel metering device;

the upstream verification refers to verifying if a difference between current metering data and downstream metering data during the same time period exceeds the verification error, wherein the current metering data is the metering data of the current metering device, and the downstream metering data is a sum of the metering data of a plurality of lower metering devices;

the method further comprising:

determining the verification error by an error evaluation model based on gas density and gas pressure between the upper metering device and the current metering device, and gas density and gas pressure between the upper metering device and the parallel metering device, wherein the error evaluation model is a machine learning model; or

determining the verification error by the error evaluation model based on gas density and gas pressure between the lower metering device and the current metering device.

5 . The method according to claim 3 , wherein the determining the accuracy of the current metering device based on an upstream verification result and a downstream verification result includes:

in response to the upstream verification result and the downstream verification result satisfying a preset condition, performing verification on the at least one of the upper metering device, the lower metering device, or the parallel metering device; and

determining the accuracy of the current metering device based on a verification result of the at least one of the upper metering device, the lower metering device, or the parallel metering device.

6 . The method according to claim 5 , wherein a count of verification steps of the upper metering device or the lower metering device is determined by a process including:

determining the count of verification steps based on a current data upload frequency and a variation parameter of a historical accuracy of the upper metering device or the lower metering device.

7 . The method according to claim 1 , wherein the method further comprises:

determining metering parameter instruction of the target metering device, including:

in response to the abnormality type being a non-sporadic abnormality, obtaining an error direction of the target metering device;

predicting a suspicious abnormal parameter based on the error direction, the historical metering data, pipeline parameters, and current metering parameters of the target metering device; and

determining the metering parameter instruction of the target metering device based on the suspicious abnormal parameter.

8 . The method according to claim 7 , wherein the predicting a suspicious abnormal parameter based on the error direction, the historical metering data, pipeline parameters, and current metering parameters of the target metering device includes:

constructing a metering parameter analysis graph based on a gas pipeline network, wherein the metering parameter analysis graph includes a plurality of subgraphs, and nodes of the metering parameter analysis graph include the ultrasonic metering devices, and features of the nodes include models of the ultrasonic metering devices, the accuracy, the error direction, and the current metering parameters, the plurality of subgraphs having different current metering parameters, and edges of the metering parameter analysis graph represent gas pipelines between the ultrasonic metering devices, and features of the edges include pipeline lengths, gas density in the pipelines, gas pressure, and gas temperatures;

predicting, based on the metering parameter analysis graph, abnormal probabilities of a plurality of current metering parameters through a parameter analysis model, the parameter analysis model being a machine learning model; and

determining the suspicious abnormal parameter based on the abnormal probabilities.

9 . The method according to claim 7 , wherein the metering parameter instruction includes a probing parameter instruction and a target parameter instruction, and the determining the metering parameter instruction of the target metering device based on the suspicious abnormal parameter includes:

performing a probing adjustment on the target metering device based on the probing parameter instruction; and

determining the target parameter instruction based on an adjustment effect of the target metering device.

10 . A non-transitory computer-readable storage medium, wherein the storage medium stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer implements the method of claim 1 .

11 . An Internet of Things (IoT) system for determining abnormality of an ultrasonic metering device and remote adjustment of the ultrasonic metering device, wherein the IoT system includes a smart gas user platform, a smart gas service platform, a smart gas device management platform, a smart gas sensing network platform, and a smart gas object platform;

the smart gas user platform includes a plurality of smart gas user sub-platforms;

the smart gas service platform includes a plurality of smart gas service sub-platforms;

the smart gas device management platform includes a plurality of smart gas device management sub-platforms and a smart gas data center, the smart gas device management platform being configured to transmit an adjustment instruction to the smart gas sensing network platform via the smart gas data center;

the smart gas sensing network platform is configured to interact with the smart gas data center and the smart gas object platform and send the adjustment instruction to the smart gas object platform;

the smart gas object platform is configured to obtain metering data of at least one ultrasonic metering device;

the smart gas device management platform is configured to:

determine any one of the at least one ultrasonic metering device as a current metering device;

determine an accuracy of the current metering device through verifying the current metering device based on metering data of the current metering device and metering data of a related metering device, wherein the related metering device includes at least one of an upper metering device, a lower metering device, and a parallel metering device of the current metering device; and

determine a target metering device based on the accuracy of at least one current metering device corresponding to the at least one ultrasonic metering device, wherein an accuracy of the target metering device is lower than an accuracy threshold;

determine a plurality of historical accuracies of the target metering device based on the historical metering data of the target metering device;

determine variation parameters of the plurality of historical accuracies, wherein the variation parameters include a magnitude and a direction of changes between adjacent historical accuracies, and a count of times of the historical accuracies below the accuracy threshold;

determine an abnormality type of the target metering device based on the variation parameters and a current accuracy of the target metering device; and

determine a data upload frequency instruction based on the abnormality type and send the data upload frequency instruction to the target metering device.

12 . The IoT system according to claim 11 , wherein the accuracy threshold is related to a verification error.

13 . The IoT system according to claim 11 , wherein the smart gas device management platform is further configured to:

perform upstream verification using metering data of the upper metering device, metering data of the current metering device, and metering data of the parallel metering device during a same time period;

perform downstream verification using metering data of the lower metering device and the metering data of the current metering device during the same time period; and

determine the accuracy of the current metering device based on an upstream verification result and a downstream verification result.

14 . The IoT system according to claim 13 , wherein the upstream verification refers to verifying if a difference between upstream metering data and same level metering data during the same time period exceeds a verification error of the current metering device, wherein the upstream metering data is the metering data of the upper metering device, and the same level metering data is a sum of the metering data of the current metering device and the metering data of the parallel metering device;

the upstream verification refers to verifying if a difference between current metering data and downstream metering data during the same time period exceeds the verification error, wherein the current metering data is the metering data of the current metering device, and the downstream metering data is a sum of the metering data of a plurality of lower metering devices;

the smart gas device management platform is further configured to:

determine the verification error by an error evaluation model based on gas density and gas pressure between the upper metering device and the current metering device, and gas density and gas pressure between the upper metering device and the parallel metering device, wherein the error evaluation model is a machine learning model; or

determine the verification error by the error evaluation model based on gas density and gas pressure between the lower metering device and the current metering device.

15 . The IoT system according to claim 13 , wherein the smart gas device management platform is further configured to:

in response to the upstream verification result and the downstream verification result satisfying a preset condition, perform verification on at least one of the upper metering device, the lower metering device, or the parallel metering device; and

determine the accuracy of the current metering device based on a verification result of the at least one of the upper metering device, the lower metering device, or the parallel metering device.

16 . The IoT system according to claim 15 , wherein the smart gas device management platform is further configured to:

determine a count of verification steps based on a current data upload frequency and a variation parameter of a historical accuracy of the upper metering device or the lower metering device.

17 . The IoT system according to claim 11 , wherein the smart gas device management platform is further configured to determine a metering parameter instruction;

wherein to determine the metering parameter instruction, the smart gas device management platform is further configured to:

in response to the abnormality type being a non-sporadic abnormality, obtain an error direction of the target metering device;

predict a suspicious abnormal parameter based on the error direction, the historical metering data, pipeline parameters, and current metering parameters of the target metering device; and

determine the metering parameter instruction of the target metering device based on the suspicious abnormal parameter.

18 . The IoT system according to claim 17 , wherein the smart gas device management platform is further configured to:

construct a metering parameter analysis graph based on a gas pipeline network, wherein the metering parameter analysis graph includes a plurality of subgraphs, and nodes of the metering parameter analysis graph include the ultrasonic metering devices, and features of the nodes include models of the ultrasonic metering devices, the accuracy, the error direction, and the current metering parameters, the plurality of subgraphs having different current metering parameters, and edges of the metering parameter analysis graph represent gas pipelines between the ultrasonic metering devices, and features of the edges include pipeline lengths, gas density in the pipelines, gas pressure, and gas temperatures;

predict, based on the metering parameter analysis graph, abnormal probabilities of a plurality of current metering parameters through a parameter analysis model, the parameter analysis model being a machine learning model; and

determine the suspicious abnormal parameter based on the abnormal probabilities.

19 . The IoT system according to claim 17 , wherein the metering parameter instruction includes a probing parameter instruction and a target parameter instruction, and the smart gas device management platform is further configured to:

perform a probing adjustment on the target metering device based on the probing parameter instruction; and

determine the target parameter instruction based on an adjustment effect of the target metering device.

Assignments (1)

Priority Claims (1)

Continuity (2)

References Cited (18)