A method and system for detecting abnormal meter code entry of an electric energy meter

Abstract

This invention provides a method and system for detecting abnormal meter readings on electricity meters. The method includes: collecting meter removal data and determining whether abnormal meter readings exist based on preset anomaly diagnosis rules; if abnormal readings are found, assessing the risk level based on the removal data and recording it as a risk event; quantifying the surface anomaly according to preset quantification to obtain accuracy evaluation parameters, and verifying the cause of the meter reading anomaly and identifying inaccurate electricity metering based on risk warning feedback results, recording it as a responsibility event; and performing work quality analysis and electricity metering accuracy detection based on the recorded risk event data and responsibility event data. This invention improves the efficiency of identifying the accuracy of meter readings on removed electricity meters, thereby improving the accuracy level of electricity metering.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, and in particular to a method and system for detecting abnormal meter readings. Background Technology

[0002] In the process of electricity metering, abnormal meter readings are a common problem, which can lead to inaccurate electricity measurement and affect the accuracy of electricity trading and user consumption. Existing methods mostly involve manual verification or a combination of manual and equipment verification, but these are inefficient and lack automated and systematic solutions. Summary of the Invention

[0003] The purpose of this invention is to propose a method and system for detecting abnormal meter readings in electricity meters, and to solve the technical problem of how to improve the detection efficiency of abnormal meter readings in electricity meters.

[0004] On the one hand, a method for detecting abnormal meter readings is provided, including:

[0005] Collect data on the removal of electricity meters and determine whether there are any abnormalities in the meter code entry for removal based on preset anomaly diagnosis rules;

[0006] If there are any abnormalities in the dismantling form entries, a risk level assessment will be conducted based on the dismantling data, and the event will be recorded as a risk event.

[0007] The surface anomalies are quantified according to the preset quantification to obtain accuracy evaluation parameters. Combined with the risk warning feedback results, the causes of meter reading anomalies are verified and the phenomenon of inaccurate electricity metering is identified and recorded as a responsibility event.

[0008] Based on recorded risk event data and liability event data, we conduct work quality analysis and power metering accuracy testing.

[0009] Preferably, the dismantling data of the electricity meter includes at least the electricity meter asset number, work order number, work order type, work order unit, electricity meter type, electricity meter dismantling date and reason, information of the person in charge of the dismantling work and the unit to which it belongs, dismantled meter code, on-site meter code photo, and the last meter reading date and meter code information of the metering automation system.

[0010] Preferably, the dismantling data of the electricity meter includes at least the electricity meter asset number, work order number, work order type, work order unit, electricity meter type, electricity meter dismantling date and reason, information of the person in charge of the dismantling work and the unit to which it belongs, dismantled meter code, on-site meter code photo, and the last meter reading date and meter code information of the metering automation system.

[0011] Preferably, when a risk event is recorded, at least the operation and maintenance unit, event type, and risk level are recorded.

[0012] Preferably, when the event is recorded as a responsibility event, at least the operation and maintenance unit, event type, responsibility level, error amount, error cause, and responsible person shall be recorded.

[0013] Preferably, determining whether there is an abnormality in the meter reading during dismantling includes comparing the meter reading in the acquired dismantling data, the meter reading shown in the on-site photos, the meter reading collected by the metering automation system, and the actual meter reading. If the two do not match, it is determined that there is an abnormality in the meter reading during dismantling.

[0014] Preferably, the risk level assessment based on the demolition data includes matching the severity of the consequences caused by the abnormal entry of the demolition code with the corresponding level in the preset risk level based on the probability of the code occurring within a preset time period, and sending the level and corresponding handling requirements to the unit where the risk occurred based on the level.

[0015] Preferably, the preset quantification is to assign a corresponding score based on the ratio of the number of anomalies to the total number.

[0016] Preferably, the surface anomalies include at least system checks, file checks, and on-site checks.

[0017] On the other hand, a detection system for abnormal electricity meter code entry is also provided, which is used in the aforementioned method for detecting abnormal electricity meter code entry, including,

[0018] The anomaly detection module is used to collect the removal data of the electricity meter and determine whether there is an anomaly in the meter code entry of the removal according to the preset anomaly diagnosis rules.

[0019] If there are any abnormalities in the dismantling form entries, a risk level assessment will be conducted based on the dismantling data, and the event will be recorded as a risk event.

[0020] The anomaly verification module is used to quantify surface anomalies according to preset quantification, obtain accuracy evaluation parameters, and, in conjunction with risk warning feedback results, verify the causes of meter reading anomalies, identify inaccurate electricity metering phenomena, and record them as responsibility events.

[0021] Based on recorded risk event data and liability event data, we conduct work quality analysis and power metering accuracy testing.

[0022] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0023] The present invention provides a method and system for detecting abnormal meter readings in electricity meters, which improves the efficiency of identifying the accuracy of meter readings in electricity meters and thus improves the accuracy of electricity metering. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0025] Figure 1 This is a schematic diagram of the main process of a method for detecting abnormal meter code entry in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0027] like Figure 1 The diagram shown is an embodiment of a method for detecting abnormal meter readings in electricity meters provided by the present invention. In this embodiment, the method includes the following steps:

[0028] Step S1: Collect the removal data of the electricity meters and determine whether there are any abnormalities in the meter code entry for removal based on preset anomaly diagnosis rules. The removal data includes at least the following: electricity meter asset number, work order number, work order type, work order unit, electricity meter type, removal date and reason, information of the person in charge of the removal work and their unit, removal meter code, on-site meter code photo, and the last meter reading date and code information from the metering automation system.

[0029] Step S2: If there is an anomaly in the meter reading entry, a risk level assessment is performed based on the removal data, and the event is recorded as a risk event. Understandably, diagnostic analysis is used to determine if there are any anomalies in the meter reading entry. If an anomaly is found, a risk level assessment is performed, and the event is recorded as a risk event, triggering the risk warning process. The diagnostic analysis is based on discrepancies between the meter readings recorded by the business management system, the meter readings shown in on-site photos, and the meter readings collected by the metering automation system. The risk warning process is based on the risk assessment results, whereby the risk assessment results and handling requirements are sent manually or systematically to the unit where the risk occurred. The risk event recording operation facilitates timely access to historical data on abnormal events for power company personnel.

[0030] In one embodiment, determining whether there is an abnormality in the meter reading during removal includes comparing the meter reading in the acquired removal data, the meter reading shown in the on-site photo, the meter reading collected by the metering automation system, and the actual meter reading. If the two do not match, it is determined that there is an abnormality in the meter reading during removal. When a risk event is recorded, at least the maintenance unit, event type, and risk level are recorded. That is, the record of risk events includes information such as the maintenance unit, event type, and risk level.

[0031] In one embodiment, the risk level assessment based on demolition data includes matching the severity of the consequences of an abnormal entry in the demolition form with the probability of the form code occurring within a preset time period, matching it with the corresponding risk level in a preset risk level hierarchy, and sending the risk level and corresponding handling requirements to the unit where the risk occurred based on the risk level. In other words, the risk level assessment is a comprehensive judgment based on the severity of the consequences and the probability of occurrence in a recent period.

[0032] Step S3 involves quantifying surface anomalies according to preset quantification criteria to obtain accuracy evaluation parameters. Combined with risk warning feedback results, the causes of meter reading anomalies are verified and energy metering inaccuracies are identified, and recorded as responsibility events. This process involves quantifying surface anomalies according to preset quantification criteria to obtain accuracy evaluation parameters and conducting assessments. Combined with risk warning feedback results, the causes of meter reading anomalies are verified and analyzed, energy metering inaccuracies are identified, and recorded as responsibility events, triggering the problem correction process. The problem correction process involves confirming and handling erroneous energy consumption based on the cause analysis results, and determining and pursuing responsibility. The responsibility event recording operation facilitates timely access to historical data on anomalies for power company personnel.

[0033] The preset quantification involves assigning a score based on the ratio of the number of anomalies to the total number of cases. In other words, the quantification is based on the ratio of the number of anomalies to the total number of cases (i.e., the anomaly rate), with a certain score assigned according to the magnitude of the anomaly rate. The anomaly rate is calculated as shown in the table below:

[0034] The rate of abnormal meter readings after removal = (total number of abnormal meter readings / total number of loosely installed meters) * 100%.

[0035] Score = Base number + ((Abnormality rate - Maximum abnormality rate) / (- Maximum abnormality rate)) * Total score.

[0036] Operation and maintenance unit Total number of scattered removed electricity meters Total number of table code errors Abnormality rate Score ↓ Unit 1 417 0 0％ 100 Unit 2 350 20 5.71％ 90.74 …… 640 79 12.34％ 80.00

[0037] Step S4: Based on the recorded risk event data and liability event data, perform work quality analysis and power metering accuracy testing.

[0038] Embodiments of the present invention also provide a detection system for abnormal electricity meter code entry, used to implement the aforementioned method for detecting abnormal electricity meter code entry, including,

[0039] The anomaly detection module is used to collect the removal data of the electricity meter and determine whether there is an anomaly in the meter code entry of the removal according to the preset anomaly diagnosis rules.

[0040] If there are any abnormalities in the dismantling form entries, a risk level assessment will be conducted based on the dismantling data, and the event will be recorded as a risk event.

[0041] The anomaly verification module is used to quantify surface anomalies according to preset quantification, obtain accuracy evaluation parameters, and, in conjunction with risk warning feedback results, verify the causes of meter reading anomalies, identify inaccurate electricity metering phenomena, and record them as responsibility events.

[0042] Based on recorded risk event data and liability event data, we conduct work quality analysis and power metering accuracy testing.

[0043] It should be noted that the system described in the above embodiments corresponds to the method described in the above embodiments. Therefore, the parts of the system described in the above embodiments that are not described in detail can be obtained by referring to the content of the method described in the above embodiments, and will not be repeated here.

[0044] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0045] The present invention provides a method and system for detecting abnormal meter readings in electricity meters, which improves the efficiency of identifying the accuracy of meter readings in electricity meters and thus improves the accuracy of electricity metering.

[0046] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for detecting abnormality of meter code entry for an electric energy meter, characterized by, include: Collect the dismantling data of the electricity meters and determine whether there is an abnormality in the dismantling code entry of the electricity meters according to the preset abnormality diagnosis rules. Specifically, compare the electricity meter readings in the acquired dismantling data, the electricity meter readings shown in the on-site photos, the electricity meter readings collected by the metering automation system, and the actual readings. If the two do not match, it is determined that there is an abnormality in the dismantling code entry. If there is an anomaly in the dismantling code entry, a risk level assessment will be conducted based on the dismantling data, and it will be recorded as a risk event. Specifically, based on the severity of the consequences caused by the anomaly in the dismantling code entry and the probability of the code occurring within a preset time period, it will be matched with the corresponding level in the preset risk level, and the level and corresponding handling requirements will be sent to the unit where the risk occurred based on the level. The surface anomalies are quantified according to the preset quantification to obtain accuracy evaluation parameters. Combined with the risk warning feedback results, the causes of meter reading anomalies are verified and the phenomenon of inaccurate electricity metering is identified and recorded as a responsibility event. Based on recorded risk event data and liability event data, we conduct work quality analysis and power metering accuracy testing.

2. The method of claim 1, wherein, The data for removing the electricity meter shall include at least the following: electricity meter asset number, work order number, work order type, work order unit, electricity meter type, electricity meter removal date and reason, information of the person in charge of the removal work and the unit to which it belongs, removal meter code, on-site meter code photo, and the last meter reading date and meter code information of the metering automation system.

3. The method of claim 2, wherein, Also includes: When a risk event is recorded, at least the operation and maintenance unit, event type, and risk level should be recorded.

4. The method as described in claim 3, characterized in that, Also includes: When a case is recorded as a responsibility incident, at least the following information should be recorded: the operations and maintenance unit, the type of incident, the level of responsibility, the amount of the error, the cause of the error, and the person responsible.

5. The method of claim 4, wherein, The preset quantification is to assign a corresponding score based on the ratio of the number of anomalies to the total number.

6. The method of claim 5, wherein, The surface anomalies include at least system checks, file checks, and on-site checks.

7. A system for detecting meter code entry anomalies for an electric energy meter, for implementing the method according to any one of claims 1 to 6, characterized in that, include: The anomaly detection module is used to collect the removal data of the electricity meter and determine whether there is an anomaly in the meter code entry of the removal according to the preset anomaly diagnosis rules. If there are any abnormalities in the dismantling form entries, a risk level assessment will be conducted based on the dismantling data, and the event will be recorded as a risk event. The anomaly verification module is used to quantify surface anomalies according to preset quantification, obtain accuracy evaluation parameters, and, in conjunction with risk warning feedback results, verify the causes of meter reading anomalies, identify inaccurate electricity metering phenomena, and record them as responsibility events. Based on recorded risk event data and liability event data, we conduct work quality analysis and power metering accuracy testing.

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