Acquisition abnormity diagnosis method based on electricity consumption information data

Abstract

The invention discloses an acquisition abnormity diagnosis method based on electricity consumption information data, and belongs to the technical field of electric power acquisition. The diagnosis method comprises the following steps: uploading electric meter data in an area to a background server; setting an abnormal item judgment threshold value for the ammeter data; and the computer system compares the ammeter data with an abnormal matter judgment threshold value, and reports the data exceeding the threshold value. Compared with the prior art, the method has the beneficial effects that aiming at the conditions that the electric energy meter fails to communicate with the master station for a long time and the terminal is frequently online and offline, a large model technology is utilized to analyze data acquired by the master station, equipment which is not in communication with the master station for a large time in a monitoring period is marked, and a long-term failure abnormal field operation list is generated; accurate acquisition abnormity type determination and abnormity closed-loop management and control are realized, electric energy acquisition quality is guaranteed, and company benefits are improved.

Description

Technical Field

[0001] The invention relates to a collection abnormality diagnosis method based on power consumption information data, and belongs to the technical field of power collection. Background Art

[0002] At present, in the process of digital transformation of the power industry at home and abroad, large model technology has been applied in multiple professions and has achieved certain application effects. However, the current application in the field of data collection and measurement is still not in-depth enough, and the applicable scenarios have not been analyzed to give full play to the application results of large model technology. Electric energy collection is the basic supporting technology for conducting spot power trading. Only by providing accurate electric energy data can the execution of electricity and electricity fee related businesses of spot power trading business be ensured. Abnormalities such as missing electric energy data cannot be avoided in the transmission of high-frequency collected metering data. There is currently no very effective method for screening and identifying abnormal data. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned problems existing in the background technology and to provide a collection abnormality diagnosis method based on electricity consumption information data.

[0004] The present invention achieves the above-mentioned purpose by adopting the following technical solutions:

[0005] A method for diagnosing abnormalities in the collection of electricity consumption information data, the method comprising the following steps:

[0006] Step 1: Upload the meter data in the area to the backend server;

[0007] Step 2: Set an abnormal event judgment threshold for the meter data;

[0008] Step 3: Compare the meter data with the abnormal event judgment threshold through the computer system, and report the data that exceeds the threshold.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: in response to the situation where the electric energy meter fails to communicate with the main station for a long time and the terminal frequently goes online and offline, the present invention uses the big model technology to analyze the data collected by the main station, marks the devices that have lost communication with the main station for a long time during the monitoring period, and generates a long-term failure abnormal on-site work order, so as to accurately determine the type of collection abnormality, implement abnormal closed-loop control, ensure the quality of electric energy collection, and improve the company's benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a schematic diagram of a collection abnormality diagnosis method based on power consumption information data of the present invention;

[0011] Figure 2 It is a flow chart of the abnormality diagnosis method based on the power consumption information data of the present invention;

[0012] Figure 3 It is a schematic diagram of a monitoring system of the present invention based on the collection abnormality diagnosis method of electricity consumption information data. DETAILED DESCRIPTION

[0013] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0014] Specific implementation method: Figure 1-3 As shown, this embodiment records a collection abnormality diagnosis method based on power consumption information data, and the diagnosis method includes the following steps:

[0015] Step 1: Upload the meter data in the area to the backend server;

[0016] Step 2: Set an abnormal event judgment threshold for the meter data;

[0017] Step 3: Compare the meter data with the abnormal event judgment threshold through the computer system, and report the data that exceeds the threshold.

[0018] The abnormal events include abnormal terminal restart or login and abnormal meter reading success rate.

[0019] The terminal restart or login abnormality includes the number of restarts or logins within a specified time being greater than the first threshold K2 or the second threshold K4. The acquisition of the terminal restart or login abnormality data should exclude non-operating devices.

[0020] The data obtained by abnormal judgment includes terminal type, observation days, and the number of consecutive logins. (If there is a null value, the setting is invalid)

[0021] If the number of consecutive logins in K1 is greater than that in K2, the corresponding terminal warning data is generated;

[0022] If the number of consecutive logins within K3 days is greater than that of K4, the corresponding terminal failure data is generated;

[0023] For example, when K1 is set to 1, the first threshold K2 is 3, and the data with the number of logins greater than 3 generates the corresponding terminal warning data;

[0024] For example, when K3 is set to 1, the first threshold K2 is 5, and the data with the number of logins greater than 6 generates the corresponding terminal fault data;

[0025] The abnormal meter reading success rate includes that within a specified time period, the number of times that the number of times the forward active power is successfully collected is 0 exceeds a third threshold value K6 times every month.

[0026] The acquisition of abnormal data on meter reading success rate should exclude terminal types that do not perform monitoring.

[0027] The terminal types that do not perform monitoring include non-operating electricity meters, terminals that have no communication with the main station, no data under the collector, no data under the concentrator, and electricity meters with unstable terminal meter reading.

[0028] Obtain the equipment collection details table to see whether the number of times the monthly forward active power (daily frozen) has been successfully collected and is 0 exceeds K2 times in the past K1 months, such as 101010 (3 times), 1000101 (2 times), 100000001 (1 time); for data that meets the above conditions, generate the corresponding meter fault data.

[0029] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other forms of assembly without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and range of equivalents of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0030] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for diagnosing abnormalities based on the collection of electricity consumption information data, characterized in that: The diagnostic method comprises the following steps: Step 1: Upload the meter data in the area to the backend server; Step 2: Set an abnormal event judgment threshold for the meter data; Step 3: Compare the meter data with the abnormal event judgment threshold through the computer system, and report the data that exceeds the threshold.

2. The method for diagnosing abnormalities based on the collection of electricity consumption information data according to claim 1, characterized in that: The abnormal events include abnormal terminal restart or login and abnormal meter reading success rate.

3. The method for diagnosing abnormalities based on the collection of electricity consumption information data according to claim 2, characterized in that: The terminal restart or login abnormality includes that the number of restarts or logins within a specified time is greater than the first threshold K2 or the second threshold K4.

4. The method for diagnosing abnormalities based on the collection of electricity consumption information data according to claim 3 is characterized in that: The acquisition of abnormal terminal restart or login data should exclude non-operating equipment.

5. The method for diagnosing abnormalities based on the collection of electricity consumption information data according to claim 3 is characterized in that: The abnormal meter reading success rate includes that within a specified time period, the number of times that the number of times the forward active power is successfully collected is 0 exceeds a third threshold value K6 times every month.

6. The method for diagnosing abnormalities based on the collection of electricity consumption information data according to claim 5, characterized in that: The acquisition of abnormal data on meter reading success rate should exclude terminal types that do not perform monitoring.

7. The method for diagnosing abnormalities based on the collection of electricity consumption information data according to claim 6, characterized in that: The terminal types that do not perform monitoring include non-operating electricity meters, terminals that have no communication with the main station, no data under the collector, no data under the concentrator, and electricity meters with unstable terminal meter reading.

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