A method, device and equipment for monitoring abnormality of residential gas data

Abstract

The application belongs to the technical field of gas management, and particularly discloses a monitoring method and device and equipment for civil gas data anomaly, which comprises a data acquisition unit, a gas use frequency detection unit, a gas use time detection unit, a gas use amount detection unit, a gas use instability detection unit, a data integration unit, a normal standard vector establishment unit and a result determination unit. Compared with the prior art, the application can analyze civil gas data, analyze the gas use behavior of users and the accuracy of the metering value of gas meters, effectively curb the behavior of users stealing gas, and monitor the gas meters to timely find the state of inaccurate metering anomaly of the gas meters.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas management, in particular to a method and device and equipment for monitoring abnormal domestic gas data. BACKGROUND

[0002] At present, with the acceleration of coal-to-gas projects, natural gas has replaced coal as the main fuel. The scale of natural gas pipe networks in central urban areas is gradually expanding, and the construction of pipeline natural gas is rapidly developing, improving the natural gas penetration rate. At the same time, in relatively remote rural areas and areas not suitable for pipe network construction, canned liquefied petroleum gas is used to replace pipeline natural gas.

[0003] Domestic gas data is relatively regular, but there may be cases of domestic user gas theft, so user behavior needs to be monitored. User gas behavior anomalies mainly include:

[0004] 1. Abnormal user gas consumption:

[0005] The actual gas consumption of the user is significantly different from the historical data or the same type of user.

[0006] The gas consumption time does not conform to the normal life or production rules, for example, there is a large amount of gas consumption during off-peak hours.

[0007] 2. Abnormal device state:

[0008] The gas meter or related equipment is damaged, the lead seal is missing, or it is privately disassembled.

[0009] The gas meter is disturbed, such as magnetic interference, tilting, or data anomalies.

[0010] 3. Abnormal data transmission:

[0011] The smart gas meter data cannot be normally returned or displayed abnormally.

[0012] The gas consumption data does not match the gas purchase data.

[0013] Currently, measures to prevent gas theft include:

[0014] 1. Promote the use of Internet of Things smart gas meters, which have automatic reporting functions and can detect abnormal behaviors such as magnetic interference and disassembly. This measure can only detect whether the metering is accurate and cannot monitor whether the gas consumption data is abnormal.

[0015] 2. Use big data analysis and intelligent monitoring systems to monitor and detect user gas behavior in real time, but the existing method has inaccurate monitoring results and a complex method. SUMMARY

[0016] To address the aforementioned technical problems, this invention provides a method, apparatus, and equipment for monitoring abnormal gas consumption data of residential users.

[0017] To achieve the above objectives, the present invention is implemented according to the following technical solution:

[0018] One of the technical solutions of the present invention provides a method for monitoring abnormal gas consumption data of residential users, comprising the following steps:

[0019] S1. Obtain the number of users of remote gas meters and collect gas consumption data of the gas meters, including hourly gas consumption, daily gas consumption, monthly gas consumption, and annual gas consumption.

[0020] S2. Analyze the gas consumption data collected from the remote gas meter, and perform gas consumption frequency detection, gas consumption time detection, gas consumption volume detection, and gas consumption instability detection respectively.

[0021] S3. Based on the detection results of step S2, the vector β = (a, b, c, d, e, f, g, h, j) is obtained after integration.

[0022] S4. Sort the annual gas consumption data of all users for a given year, and use x as the index. i Let {x} represent the sorted dataset, where i ranges from 1 to i, and only the middle 95% of the data is taken to mask erroneous or unrepresentative data. The corrected dataset is {x}. j |x 0.1i+1 ≤j≤x 0.9i The corrected dataset is segmented to classify users into those with wall-hung boilers and those without.

[0023] S5. Randomly select N users without wall-hung boilers, and perform step S2 on the gas usage data of the N users without wall-hung boilers. Calculate the average value of each of the N parameters to obtain the normal standard vector α = (A, B, C, D, E, F, G, H, J).

[0024] S6. Subtract vectors α and β;

[0025] Δ = α - β;

[0026] And find the magnitude of the vector:

[0027]

[0028]

[0029] when At that time, among them If the threshold is reached, the user's gas usage behavior is considered abnormal.

[0030] when At that time, among them If the threshold is reached, then this user will be the focus of attention;

[0031] when At that time, among them If the threshold is reached, the user's gas usage behavior is considered to be normal.

[0032] Furthermore, in step S2, the gas usage count detection specifically includes:

[0033] 1) Suppose that the user's remote transmission data is the cumulative gas consumption for a certain day, and its function is y = Q(x), where x represents 0-24 hours and y is the cumulative gas consumption;

[0034] Calculate the number of gas usages and the duration based on the following conditions:

[0035] If there exists a range (m, n) such that 0 ≤ m <n≤24,

[0036] exist

[0037] And for all x0∈(m,n),

[0038] If the above conditions are met, it is recorded as one gas consumption, and the gas consumption time is t = nm;

[0039] For the heating season, the above formula becomes:

[0040] If there exists a range (m, n) such that 0 ≤ m <n≤24,

[0041] exist

[0042] And for all x0∈(m,n),

[0043] If the above conditions are met, it is recorded as one gas consumption, and the gas consumption time is t = nm;

[0044] 2) The detection cycle is one week or M days. The cumulative number of gas consumptions in the detection cycle is recorded. If the number of gas consumptions in the current cycle is 50% of the number of gas consumptions in the previous cycle, the number of gas consumptions a in the current cycle is output.

[0045] 3) The detection cycle is based on a month, year, or heating season. For cases where the daily gas consumption is 0, if the cumulative number of days with 0 daily gas consumption is greater than 5 days, it is counted as one instance. If the total number of times within the detection cycle is greater than 10, the number of times b in this cycle is output.

[0046] Furthermore, in step S2, the gas usage time detection specifically includes:

[0047] 1) The detection cycle is weekly or M days. The cumulative gas consumption time of the detection cycle is calculated. If the gas consumption time of the current cycle is 50% of the gas consumption time of the previous cycle, the gas consumption time c of the current cycle is output.

[0048] 2) The detection cycle is based on a month, year, or heating season. For days with zero gas consumption, if the cumulative number of days with zero gas consumption is greater than 5, it is counted as one time. If the total number of times in the detection cycle is greater than 10, the number of times d in this cycle is output.

[0049] Furthermore, in step S2, the gas volume detection specifically includes:

[0050] 1) The detection cycle is weekly or M days. The gas consumption in the detection cycle is recorded as once if the gas consumption in the current cycle is 50% of the gas consumption in the previous cycle. The number of gas consumptions e is output.

[0051] 2) The monthly gas consumption is used as the detection cycle. The ratio of the monthly gas consumption to the monthly gas consumption of the community is recorded as one instance when the ratio is lower than 50% of the tolerance. The number of gas consumptions f is then output.

[0052] 3) The detection cycle is based on a month, year, or heating season. For cases where the daily gas consumption is 0, if the cumulative number of days with 0 daily gas consumption is greater than 5 days, it is counted as one instance, and the number of gas consumptions g is output.

[0053] Furthermore, in step S2, the gas instability detection specifically includes:

[0054] 1) Using a monthly monitoring period and a weekly or M-day monitoring cycle, calculate the cumulative gas consumption count X within each month and each monitoring cycle. i And cumulative gas consumption Y i Calculate the average number of gas usages and the average gas consumption for each monthly monitoring cycle, and calculate the variance and standard deviation. If:

[0055]

[0056] When a user's gas usage frequency and cumulative gas consumption during a monitoring period are data x0 and y0, if:

[0057] x0-μ1>2×σ1;

[0058] y0-μ2>2×σ2;

[0059] The standard deviations σ1, σ2, σ1, and σ2 of the cumulative number of gas uses and the cumulative gas consumption are output as h and j.

[0060] Preferably, in step S4, the corrected dataset is divided into 5 levels: large, relatively large, medium, relatively small, and small, with each level having the same length.

[0061] The second technical solution of the present invention provides a monitoring device for abnormal gas consumption data of residential users, comprising:

[0062] The data acquisition unit is used to obtain the number of users of the remote gas meter and collect the gas consumption data of the gas meter, including hourly gas consumption, daily gas consumption, monthly gas consumption, and annual gas consumption.

[0063] The gas consumption detection unit is used to: 1) detect the cumulative gas consumption in a week or M days as the detection cycle; if the gas consumption in the current cycle is 50% of the gas consumption in the previous cycle, output the gas consumption a in the current cycle.

[0064] 2) The detection cycle is based on a month, year, or heating season. For cases where the daily gas consumption is 0, if the cumulative number of days with 0 daily gas consumption is greater than 5 days, it is counted as one instance. If the total number of times within the detection cycle is greater than 10, the number of times b in this cycle is output.

[0065] The gas usage time detection unit is used for: 1) detecting the cumulative gas usage time in a week or M days as the detection cycle; if the gas usage time in the current cycle is 50% of the gas usage time in the previous cycle, outputting the gas usage time c of the current cycle.

[0066] 2) The detection cycle is based on a month, year, or heating season. For days with zero gas consumption, if the cumulative number of days with zero gas consumption is greater than 5, it is counted as one instance. If the total number of times within the detection cycle is greater than 10, the number of times d in this cycle is output.

[0067] The gas consumption detection unit is used to: 1) detect the gas consumption of a week or M days as the detection cycle. If the gas consumption of the current cycle is 50% of the gas consumption of the previous cycle, it is recorded as one time and the number of gas consumptions e is output.

[0068] 2) The monthly gas consumption is used as the detection cycle. The ratio of the monthly gas consumption to the monthly gas consumption of the community is recorded as one instance when the ratio is lower than 50% of the tolerance. The number of gas consumptions f is then output.

[0069] 3) The detection cycle is based on a month, year, or heating season. For cases where the daily gas consumption is 0, and the cumulative number of days with 0 daily gas consumption is greater than 5 days, it is counted as one instance, and the number of gas consumptions g is output.

[0070] The gas usage instability detection unit is used to: 1) calculate the cumulative number of gas usages X within each month and each detection cycle, with a monthly detection range and a weekly or M-day detection period. i And cumulative gas consumption Y i Calculate the average number of gas usages and the average gas consumption for each monthly monitoring cycle, and calculate the variance and standard deviation. If:

[0071]

[0072] When a user's gas usage frequency and cumulative gas consumption during a monitoring period are data x0 and y0, if:

[0073] x0-μ1>2×σ1;

[0074] y0-μ2>2×σ2;

[0075] The standard deviations σ1, σ2, σ1, σ2 are output as h, j, respectively, representing the cumulative number of gas usages and the cumulative gas consumption.

[0076] The data integration unit is used to integrate the results of gas usage frequency detection, gas usage time detection, gas consumption volume detection and gas usage instability detection to obtain the vector β = (a, b, c, d, e, f, g, h, j);

[0077] The normal standard vector establishment unit is used to randomly select N users without wall-hung boilers and perform gas usage frequency detection, gas usage time detection, gas consumption detection, and gas usage instability detection on the gas usage data of the N users without wall-hung boilers. The average value of each of the N parameters is calculated to obtain the normal standard vector α = (A, B, C, D, E, F, G, H, J).

[0078] The result determination unit is used to subtract vectors α and β.

[0079] Δ = α - β;

[0080] And find the magnitude of the vector:

[0081]

[0082] when At that time, among them If the threshold is reached, the user's gas usage behavior is considered abnormal.

[0083] when At that time, among them If the threshold is reached, then this user will be the focus of attention;

[0084] when At that time, among them If the threshold is reached, the user's gas usage behavior is considered to be normal.

[0085] The third technical solution of the present invention provides an electronic device, including: a memory for storing executable instructions; and a processor including the aforementioned monitoring device for abnormal gas consumption data of residential users, for communicating with the memory to execute the executable instructions to complete the operation of the aforementioned monitoring method for abnormal gas consumption data of residential users.

[0086] Compared with existing technologies, this invention can effectively curb gas theft by analyzing gas usage data of residential users, analyzing users' gas usage behavior and the accuracy of gas meter readings, and monitoring gas meters to promptly detect inaccurate or abnormal gas meter readings. Detailed Implementation

[0087] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0088] Example 1

[0089] This embodiment exemplifies a method for monitoring abnormal gas consumption data of residential users, including the following steps:

[0090] S1. Obtain the number of users of remote gas meters and collect gas consumption data of the gas meters, including hourly gas consumption, daily gas consumption, monthly gas consumption, and annual gas consumption.

[0091] S2. Analyze the gas consumption data collected from the remote gas meter, and perform gas consumption frequency detection, gas consumption time detection, gas consumption volume detection, and gas consumption instability detection respectively.

[0092] The gas usage frequency detection specifically includes:

[0093] 1) Suppose that the user's remote transmission data is the cumulative gas consumption for a certain day, and its function is y = Q(x), where x represents 0-24 hours and y is the cumulative gas consumption;

[0094] Calculate the number of gas usages and the duration based on the following conditions:

[0095] If there exists a range (m, n) such that 0 ≤ m <n≤24,

[0096] exist

[0097] And for all x0∈(m,n),

[0098] If the above conditions are met, it is recorded as one gas consumption, and the gas consumption time is t = nm;

[0099] For the heating season, the above formula becomes:

[0100] If there exists a range (m, n) such that 0 ≤ m <n≤24,

[0101] exist

[0102] And for all x0∈(m,n),

[0103] If the above conditions are met, it is recorded as one gas consumption, and the gas consumption time is t = nm;

[0104] 2) The detection cycle is a week or M days (e.g., 5 days, which can be modified as needed). The cumulative number of gas consumptions in the detection cycle is counted. If the number of gas consumptions in the current cycle is 50% of the number of gas consumptions in the previous cycle, the number of gas consumptions a in the current cycle is output.

[0105] 3) The detection cycle is based on a month, year, or heating season. For cases where the daily gas consumption is 0, if the cumulative number of days with 0 daily gas consumption is greater than 5, it is counted as one instance. If the total number of times within the detection cycle is greater than 10, the number of times b in this cycle is output.

[0106] Gas usage time detection specifically includes:

[0107] 1) The detection cycle is a week or M days (e.g., 5 days, which can be modified as needed). The cumulative gas consumption time of the detection cycle is calculated. If the gas consumption time of the current cycle is 50% of the gas consumption time of the previous cycle, the gas consumption time c of the current cycle is output.

[0108] 2) The detection cycle is based on a month, year, or heating season. For days with zero gas consumption, if the cumulative number of days with zero gas consumption is greater than 5, it is counted as one time. If the total number of times in the detection cycle is greater than 10, the number of times d in this cycle is output.

[0109] Gas volume detection specifically includes:

[0110] 1) The detection cycle is a week or M days (e.g., 5 days, which can be modified as needed). The gas consumption in the detection cycle is recorded. If the gas consumption in the current cycle is 50% of the gas consumption in the previous cycle, it is counted as one time and the number of gas consumptions e is output.

[0111] 2) The monthly gas consumption is used as the detection cycle. The ratio of the monthly gas consumption to the monthly gas consumption of the community is recorded as one instance when the ratio is lower than 50% of the tolerance. The number of gas consumptions f is then output.

[0112] 3) The detection cycle is based on a month, year, or heating season. For cases where the daily gas consumption is 0, if the cumulative number of days with 0 daily gas consumption is greater than 5 days, it is counted as one instance, and the number of gas consumptions g is output.

[0113] Gas instability testing specifically includes:

[0114] 1) The detection range is monthly, and the detection cycle is weekly or M days (e.g., 5 days, which can be modified as needed). Calculate the cumulative gas consumption X times within each detection cycle of each month. i And cumulative gas consumption Y iCalculate the average number of gas usages and the average gas consumption for each monthly monitoring cycle, and calculate the variance and standard deviation. If:

[0115]

[0116] When a user's gas usage frequency and cumulative gas consumption during a monitoring period are data x0 and y0, if:

[0117] x0-μ1>2×σ1;

[0118] y0-μ2>2×σ2;

[0119] The standard deviations σ1, σ2, σ1, and σ2 of the cumulative number of gas uses and the cumulative gas consumption are output as h and j.

[0120] In some embodiments, the corrected dataset is divided into five levels: large, larger, medium, smaller, and small, with each level having the same length.

[0121] S3. Based on the detection results of step S2, the vector β = (a, b, c, d, e, f, g, h, j) is obtained after integration.

[0122] S4. Sort the annual gas consumption data of all users for a given year, and use x as the index. i Let {x} represent the sorted dataset, where i ranges from 1 to i, and only the middle 95% of the data is taken to mask erroneous or unrepresentative data. The corrected dataset is {x}. j |x 0.1i+1 ≤j≤x 0.9i The corrected dataset is segmented to classify users into those with wall-hung boilers and those without.

[0123] S5. Randomly select N users without wall-hung boilers, and perform step S2 on the gas usage data of the N users without wall-hung boilers. Calculate the average value of each of the N parameters to obtain the normal standard vector α = (A, B, C, D, E, F, G, H, J).

[0124] S6. Subtract vectors α and β;

[0125] Δ = α - β;

[0126] And find the magnitude of the vector:

[0127]

[0128] when At that time, among them If the threshold is reached, the user's gas usage behavior is considered abnormal.

[0129] when At that time, among them If the threshold is reached, then this user will be the focus of attention;

[0130] when At that time, among them If the threshold is reached, the user's gas usage behavior is considered to be normal.

[0131] By implementing this embodiment, it is possible to monitor in a timely manner whether there are any abnormalities in the user's gas usage behavior.

[0132] Example 2

[0133] This embodiment exemplarily illustrates a monitoring device for abnormal gas consumption data of residential users, including:

[0134] The data acquisition unit is used to obtain the number of users of the remote gas meter and collect the gas consumption data of the gas meter, including hourly gas consumption, daily gas consumption, monthly gas consumption, and annual gas consumption.

[0135] The gas usage detection unit is used to: 1) use a week or M days (e.g., 5 days, which can be modified as needed) as the detection period, detect the cumulative gas usage in the period, and if the gas usage in the current period is 50% of the gas usage in the previous period, output the gas usage a in the current period.

[0136] 2) The detection cycle is based on a month, year, or heating season. For cases where the daily gas consumption is 0, if the cumulative number of days with 0 daily gas consumption is greater than 5 days, it is counted as one instance. If the total number of times within the detection cycle is greater than 10, the number of times b in this cycle is output.

[0137] The gas usage time detection unit is used for: 1) using a week or M days (e.g., 5 days, which can be modified as needed) as the detection cycle, detecting the cumulative gas usage time of the cycle; if the gas usage time of the current cycle is 50% of the gas usage time of the previous cycle, outputting the gas usage time c of the current cycle.

[0138] 2) The detection cycle is based on a month, year, or heating season. For days with zero gas consumption, if the cumulative number of days with zero gas consumption is greater than 5, it is counted as one instance. If the total number of times within the detection cycle is greater than 10, the number of times d in this cycle is output.

[0139] The gas consumption detection unit is used to: 1) detect the gas consumption in a week or M days (e.g., 5 days, which can be modified as needed) as the detection cycle. If the gas consumption in the current cycle is 50% of the gas consumption in the previous cycle, it is counted as one time and the number of gas consumptions e is output.

[0140] 2) The monthly gas consumption is used as the detection cycle. The ratio of the monthly gas consumption to the monthly gas consumption of the community is recorded as one instance when the ratio is lower than 50% of the tolerance. The number of gas consumptions f is then output.

[0141] 3) The detection cycle is based on a month, year, or heating season. For cases where the daily gas consumption is 0, and the cumulative number of days with 0 daily gas consumption is greater than 5 days, it is counted as one instance, and the number of gas consumptions g is output.

[0142] The gas usage instability detection unit is used to: 1) calculate the cumulative number of gas usages X within each month and each detection cycle, with a monthly detection range and a weekly or M-day (e.g., 5 days, which can be modified as needed) detection period. i And cumulative gas consumption Y i Calculate the average number of gas usages and the average gas consumption for each monthly monitoring cycle, and calculate the variance and standard deviation. If:

[0143]

[0144] When a user's gas usage frequency and cumulative gas consumption during a monitoring period are data x0 and y0, if:

[0145] x0-μ1>2×σ1;

[0146] y0-μ2>2×σ2;

[0147] The standard deviations σ1, σ2, σ1, σ2 are output as h, j, respectively, representing the cumulative number of gas usages and the cumulative gas consumption.

[0148] The data integration unit is used to integrate the results of gas usage frequency detection, gas usage time detection, gas consumption volume detection and gas usage instability detection to obtain the vector β = (a, b, c, d, e, f, g, h, j);

[0149] The normal standard vector establishment unit is used to randomly select N users without wall-hung boilers and perform gas usage frequency detection, gas usage time detection, gas consumption detection, and gas usage instability detection on the gas usage data of the N users without wall-hung boilers. The average value of each of the N parameters is calculated to obtain the normal standard vector α = (A, B, C, D, E, F, G, H, J).

[0150] The result determination unit is used to subtract vectors α and β.

[0151] Δ = α - β;

[0152] And find the magnitude of the vector:

[0153]

[0154] when At that time, among them If the threshold is reached, the user's gas usage behavior is considered abnormal.

[0155] when At that time, among them If the threshold is reached, then this user will be the focus of attention;

[0156] when At that time, among them If the threshold is reached, the user's gas usage behavior is considered to be normal.

[0157] Example 3

[0158] This embodiment exemplarily provides an electronic device, including: a memory for storing executable instructions; and a processor, including the monitoring device for abnormal gas consumption data of residential users in Embodiment 2 above, for communicating with the memory to execute the executable instructions to complete the operation of the monitoring method for abnormal gas consumption data of residential users in Embodiment 1 above.

[0159] In summary, this invention can effectively curb gas theft by analyzing residential users' gas consumption data, analyzing users' gas consumption behavior and the accuracy of gas meter readings, and monitoring gas meters to promptly detect inaccurate or abnormal gas meter readings.

[0160] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A method for monitoring abnormal gas consumption data of residential users, characterized in that, Includes the following steps: S1. Obtain the number of users of remote gas meters and collect gas consumption data of the gas meters, including hourly gas consumption, daily gas consumption, monthly gas consumption, and annual gas consumption. S2. Analyze the gas consumption data collected from the remote gas meter, and perform gas consumption frequency detection, gas consumption time detection, gas consumption volume detection, and gas consumption instability detection respectively. The gas usage frequency detection specifically includes: 1) Suppose the user's remote transmission data is the cumulative gas consumption for a certain day, and its function is: y = Q ( x ),in x Indicates 0-24 hours. y This represents the cumulative gas consumption. Calculate the number of gas usages and the duration based on the following conditions: If a range exists ( m , n ), 0≤ m < n ≤24, exist , ; And for all , ; If the above conditions are met, it is recorded as one gas consumption, and the gas consumption time is t= n - m ; For the heating season, the above formula becomes: If a range exists ( m , n ), 0≤ m < n ≤24, exist ; , And for all , ; If the above conditions are met, it is recorded as one gas consumption, and the gas consumption time is t= n - m ; 2) The detection cycle is weekly or M days. The cumulative gas consumption within the detection cycle is recorded. If the gas consumption in the current cycle is 50% of the gas consumption in the previous cycle, the gas consumption for the current cycle is output. a ; 3) The detection cycle is monthly, yearly, or heating season. For instances where the daily gas consumption is 0, if the cumulative number of days with 0 daily gas consumption is greater than 5, it is counted as one instance. If the total number of instances within the detection cycle is greater than 10, the number of instances for this cycle is output. b ; Gas usage time detection specifically includes: 1) The detection cycle is weekly or M days. The cumulative gas consumption time of the detection cycle is recorded. If the gas consumption time of the current cycle is 50% of the gas consumption time of the previous cycle, the gas consumption time of the current cycle is output. c ; 2) The detection cycle is based on a month, year, or heating season. For days with zero gas consumption, if the cumulative number of days with zero gas consumption is greater than 5, it is counted as one instance. If the total number of times within the detection cycle is greater than 10, the number of times in this cycle is output. d ; Gas volume detection specifically includes: 1) The detection cycle is weekly or M days. The gas consumption in the detection cycle is recorded. If the gas consumption in the current cycle is 50% of the gas consumption in the previous cycle, it is counted as one instance, and the number of gas consumptions is output. e ; 2) Using a monthly monitoring cycle, the ratio of monthly gas consumption to the total monthly gas consumption of the residential area is used. When the ratio is lower than 50% of the tolerance, it is counted as one instance, and the number of gas consumptions is output. f ; 3) The detection cycle is monthly, yearly, or heating season. For any day with zero gas consumption, if the cumulative number of days with zero gas consumption is greater than 5 days, it is counted as one instance, and the number of gas consumptions is output. g ; Gas instability testing specifically includes: 1) Using a monthly monitoring period and a weekly or M-day monitoring cycle, calculate the cumulative number of gas consumptions within each month and each monitoring cycle. X i and cumulative gas consumption Y i Calculate the average number of gas usages and the average gas consumption for each monthly monitoring cycle, and calculate the variance and standard deviation. If: ； ； ； ； When a user's gas usage frequency and cumulative gas consumption within a detection period are data... ,like: ； ； It also outputs the standard deviation of the cumulative number of gas usages and the cumulative gas consumption. Recorded as h , j ; S3. Based on the detection results of step S2, the vector is obtained after integration. ; S4. Sort the annual gas consumption data of all users for a given year, and use... This represents the sorted dataset. The sorted data is then corrected to only take the middle 95% of the data to filter out erroneous or unrepresentative data. The corrected dataset is then segmented to classify users into wall-hung boiler users and non-wall-hung boiler users. S5. Randomly select N users without wall-hung boilers, and perform step S2 on the gas usage data of these N users. Calculate the average value of each of the N parameters to obtain the normal standard vector. ; S6, Transform the vector , Subtraction; ； And find the magnitude of the vector: = ； when At that time, among them If the threshold is reached, the user's gas usage behavior is considered abnormal. when At that time, among them If the threshold is reached, then this user will be the focus of attention; when At that time, among them If the threshold is reached, the user's gas usage behavior is considered to be normal.

2. The method for monitoring abnormal gas consumption data of residential users according to claim 1, characterized in that, In step S4, the corrected dataset is divided into 5 levels: large, relatively large, medium, relatively small, and small, with each level having the same length.

3. A monitoring device for abnormal gas consumption data of residential users, used to implement the monitoring method for abnormal gas consumption data of residential users as described in claim 1 or 2, characterized in that, include: The data acquisition unit is used to obtain the number of users of the remote gas meter and collect the gas consumption data of the gas meter, including hourly gas consumption, daily gas consumption, monthly gas consumption, and annual gas consumption. The gas consumption detection unit is used to: 1) detect the cumulative gas consumption within a week or M-day period; if the gas consumption in the current period is 50% of the gas consumption in the previous period, output the gas consumption for the current period. a ; 2) The detection cycle is based on a month, year, or heating season. For instances where the daily gas consumption is 0, if the cumulative number of days with 0 daily gas consumption is greater than 5, it is counted as one instance. If the total number of instances within the detection cycle is greater than 10, the number of instances for this cycle is output. b ; The gas consumption time detection unit is used for: 1) detecting the cumulative gas consumption time in a weekly or M-day period; if the gas consumption time in the current period is 50% of the gas consumption time in the previous period, outputting the gas consumption time for the current period. c ; 2) The detection cycle is based on a month, year, or heating season. For days with zero gas consumption, if the cumulative number of days with zero gas consumption is greater than 5, it is counted as one instance. If the total number of times within the detection cycle is greater than 10, the number of times in this cycle is output. d ; The gas consumption detection unit is used for: 1) detecting gas consumption in a weekly or M-day cycle. If the gas consumption in the current cycle is 50% of the gas consumption in the previous cycle, it is counted as one instance, and the number of gas consumptions is output. e ; 2) Using a monthly monitoring cycle, the ratio of monthly gas consumption to the total monthly gas consumption of the residential area is used. When the ratio is lower than 50% of the tolerance, it is counted as one instance, and the number of gas consumptions is output. f ; 3) The detection cycle is monthly, yearly, or heating season. For any day with zero gas consumption, if the cumulative number of days with zero gas consumption is greater than 5 days, it is counted as one instance, and the number of gas consumptions is output. g ; The gas usage instability detection unit is used to: 1) calculate the cumulative number of gas usages within each month and each detection cycle, with a monthly detection range and a weekly or M-day detection period. X i and cumulative gas consumption Y i Calculate the average number of gas usages and the average gas consumption for each monthly monitoring cycle, and calculate the variance and standard deviation. If: ； ； ； ； When a user's gas usage frequency and cumulative gas consumption within a detection period are data... , ,like: ； ； It also outputs the standard deviation of the cumulative number of gas usages and the cumulative gas consumption. Recorded as h , j ; The data integration unit is used to integrate the results of gas usage frequency detection, gas usage time detection, gas consumption volume detection, and gas usage instability detection to obtain a vector. ; The normal standard vector establishment unit is used to randomly select N users without wall-hung boilers and perform gas usage frequency detection, gas usage time detection, gas consumption detection, and gas usage instability detection on the gas usage data of these N users. The average value of each of the N parameters is then calculated to obtain the normal standard vector. ; The result determination unit is used to determine the vector. , Subtraction; ； And find the magnitude of the vector: = ； when At that time, among them If the threshold is reached, the user's gas usage behavior is considered abnormal. when At that time, among them If the threshold is reached, then this user will be the focus of attention; when At that time, among them If the threshold is reached, the user's gas usage behavior is considered to be normal.

4. An electronic device, characterized in that, include: Memory, used to store executable instructions; And a processor, including the monitoring device for abnormal gas consumption data of residential users as described in claim 3, for communicating with the memory to execute the executable instructions to complete the operation of the monitoring method for abnormal gas consumption data of residential users as described in claim 1 or 2.

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