A method and system for rapid testing of electric energy meter measurement errors based on ant colony algorithm

Abstract

The present invention belongs to the field of power equipment testing, and relates to a method and system for rapid testing of metering errors of electric energy meters based on ant colony algorithms. The testing method first connects multiple electric energy meters in series and then connects them to the same test node, performs measurements on the same load, calculates the entropy deviation and discreteness deviation of each electric energy meter, and analyzes the relationship between the entropy deviation and discreteness deviation of each electric energy meter and their respective theoretical thresholds, thereby obtaining a judgment result on whether the measurement error of each electric energy meter is qualified, and performs a new round of retesting on the electric energy meter initially marked as abnormal, and confirms again whether it is qualified. The present invention effectively detects abnormalities and unqualified individuals in a group of smart meters, and there is no need to use test equipment to test individual meters one by one, thereby effectively improving the test efficiency and helping to improve the operation and maintenance efficiency of power grid personnel. In addition, the testing method is suitable for laboratory or field testing, has fewer restrictions on test conditions, and has a wider range of applications.

Description

Technical Field

[0001] The present invention belongs to the field of power equipment testing, and in particular relates to a method and system for quickly testing metering errors of electric energy meters based on an ant colony algorithm. Background Art

[0002] Smart electricity meters are metering terminals for customers' electricity consumption. Smart electricity meters are one of the basic devices for data collection in smart grids, especially smart distribution networks. They are responsible for collecting, measuring and transmitting original electricity data, and are the basis for information integration, analysis and optimization, and information presentation. They are also an important and indispensable part of smart distribution systems.

[0003] The measurement results of smart energy meters are directly related to the safety of the power grid and whether the trade settlement between the two parties is fair and reasonable. Therefore, it is particularly important to determine the measurement error of smart energy meters. At present, one of the main ways to detect the measurement error of energy meters is for power grid workers to use instruments and equipment to conduct small-scale spot checks on some meters. Although this method is more accurate for the results of a single energy meter, the detection steps are cumbersome and the detection range is not universal enough, which is easy to "slip through the net" and cause certain safety hazards to the power grid. Therefore, it is urgent to provide a monitoring and positioning technology that can efficiently detect abnormal equipment in a large number of energy meters. Summary of the invention

[0004] In order to solve the technical problem of low test efficiency when detecting the metering error of the smart energy meter, the present invention provides a method, system and device for testing the communication reliability of the smart energy meter.

[0005] The present invention is implemented by the following technical solutions:

[0006] The present invention discloses a method for quickly testing the measurement error of an electric energy meter based on an ant colony algorithm, which comprises the following steps:

[0007] S1: Connect multiple electric energy meters to be tested in series and connect them to the same test node to measure the same load;

[0008] S2: Obtain the electricity metering value m of all electricity meters in a test cycle i , and calculate the average electricity measurement value μ of all electric energy meters in the current measurement cycle:

[0009]

[0010] In the above formula, i represents the number of the electric energy meter; n is the number of electric energy meters in the current measurement cycle;

[0011] S3: Generate a corresponding histogram based on the electricity meter value in the current test cycle, and then estimate the probability distribution p(mi ) and the average distribution of all electric energy meters p(m avg );

[0012] S4: According to the probability distribution p(m i ) Calculate the entropy value H of each electric energy meter measurement result i , and the average entropy value H′ of all electric energy meters;

[0013] S5: Calculate the entropy deviation δ of the measurement results of each electric energy meter in the current test cycle using the following formula i , the calculation formula is as follows:

[0014]

[0015] S6: According to the probability distribution p(m i ) and the average distribution p(m avg ) Calculate the discreteness K of each electric energy meter measurement result i , and the average dispersion K′ of all electric energy meters;

[0016] S7: Use the following formula to calculate the discreteness deviation ε of the measurement results of each electric energy meter in the current test cycle i , the calculation formula is as follows:

[0017]

[0018] S8: Analyze the entropy deviation δ of each electric energy meter i and the dispersion deviation ε i The relationship between the respective theoretical thresholds C and E is used to determine whether the measurement error of each electric energy meter is qualified. The decision-making process is as follows:

[0019] (1) When δ i ≤C and ε i When ≤E, the measurement error of the electric energy meter is judged to be qualified and marked;

[0020] (2) When δ i >C and ε i >E, the measurement error of the electric energy meter is judged to be unqualified and marked;

[0021] (3) When δ i >C and ε i ≤E, or, δ i ≤C and ε i >E, the measurement error of the electric energy meter is judged to be abnormal and marked;

[0022] S9: retain all the electric energy meters marked as abnormal in the current test cycle, adjust the adjacent positions of the electric energy meters when they are connected in series, and then retest in the next round of test tasks: when any electric energy meter is still judged to be in an abnormal measurement error state during the retest process, the electric energy meter is judged to be unqualified.

[0023] As a further improvement of the present invention, in step S4, the entropy value H of the measurement result of each electric energy meter is i The calculation formula is as follows:

[0024] H i =-∑p(m i )log(p(m i ));

[0025] The calculation formula of the average entropy value H′ of all electric energy meter measurement results in the current test cycle is as follows:

[0026] H′=-∑p(m ci )log(p(m ci )).

[0027] In the formula, m ci is the mean of the probability distribution of a single electric energy meter in the group.

[0028] As a further improvement of the present invention, in step S6, the discreteness K of the measurement result of each electric energy meter is i The calculation formula is as follows:

[0029]

[0030] The calculation formula of the average dispersion K′ of all electric energy meters is as follows:

[0031]

[0032] In the formula, m ci is the mean of the probability distribution of a single electric energy meter in the group.

[0033] As a further improvement of the present invention, after the test is completed, for the unqualified electric energy meter, the entropy deviation δ i and the dispersion deviation ε i The error values ​​are written into the error register of the electric energy meter respectively and serve as one of the indicators for the subsequent calibration of the electric energy meter.

[0034] The present invention also discloses a rapid test system for metering errors of electric energy meters based on an ant colony algorithm, which uses the aforementioned rapid test method for metering errors of electric energy meters based on an ant colony algorithm to rapidly test the metering errors of multiple electric energy meters to be calibrated, and determines whether the metering accuracy of the electric energy meters is qualified; the test system includes: a test machine, a position allocation module, a measurement information acquisition module, an entropy deviation calculation module, a discreteness deviation calculation module, and a metering accuracy calibration module.

[0035] The test machine includes multiple installation sites for electric energy meters. The electric energy meters at each installation site are connected to the same measurement site and measure the power information of the same load.

[0036] The location allocation module is used to communicate with the electric energy meter and obtain the meter address of the electric energy meter connected to each installation site; and after the meter address is successfully obtained, a location allocation table is generated to characterize the association between the electric energy meter device identification code and the installation site.

[0037] The measurement information acquisition module is used to obtain the power measurement value m of each electric energy meter in the current measurement cycle. i , and calculate the average electricity metering value μ of all electricity meters for the current test site; then generate the corresponding histogram based on the electricity metering value of each electricity meter in the current test cycle, and then estimate the probability distribution p(m i ) and the average distribution of all electric energy meters p(m avg ).

[0038] The entropy deviation calculation module is used to first calculate the probability distribution p(m i ) Calculate the entropy value H of each electric energy meter measurement result i And the average entropy value H' of all electric energy meters; then use the formula: Calculate the entropy deviation δ of the measurement results of each electric energy meter in the current test cycle i .

[0039] The discreteness deviation calculation module is used to first calculate the probability distribution p(m i ) and the average distribution p(m avg ) Calculate the discreteness K of each electric energy meter measurement result i And the average dispersion K' of all electric energy meters; then use the formula: Calculate the discreteness deviation ε of the measurement results of each electric energy meter in the current test cycle i .

[0040] The metering accuracy verification module is used to generate a test result of whether the metering accuracy of each electric energy meter is qualified according to the calculation results of the entropy deviation calculation module and the discreteness deviation calculation module; when the entropy deviation and discreteness deviation of the electric energy meter both exceed the preset threshold value, it is deemed unqualified; when both do not exceed the threshold value, it is deemed qualified; when only one of the two exceeds the threshold value, it is deemed abnormal.

[0041] As a further improvement of the present invention, the test system further comprises a dynamic memory, in which the device identification codes of the electric energy meters determined to be abnormal during each round of testing are stored.

[0042] As a further improvement of the present invention, the test system also includes an abnormality review module. The abnormality review module is used to first obtain the device identification code of the electric energy meter that is initially identified as abnormal after each round of testing; then match the device identification code of each abnormal electric energy meter with each device identification stored in the dynamic memory, and finally make the following decisions based on the matching results:

[0043] (1) When the device identification code of any electric energy meter is matched successfully, the mark representing the test result of the electric energy meter is modified to unqualified, and the corresponding device identification code stored in the dynamic memory is deleted.

[0044] (2) When the device identification code of any electric energy meter fails to match, the device identification code of the electric energy meter is added to the data stored in the dynamic memory, and the test result of the current electric energy meter is marked as abnormal.

[0045] As a further improvement of the present invention, the test system also includes a position update module, which is used to randomly generate a new position allocation table for each electric energy meter identified as having metering abnormalities in the current round of test tasks after any round of test tasks is completed. In the new position allocation table, the installation site to which each electric energy meter with metering abnormalities is planned to be connected is changed.

[0046] As a further improvement of the present invention, the test system also includes a display module, which includes indicator lights corresponding to multiple electric energy meters respectively, and each indicator light is used to display the test result of the corresponding electric energy meter: green for qualified, red for unqualified, and yellow for abnormal.

[0047] The present invention also discloses a test device for measuring errors of electric energy meters, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The functional modules in the test device are deployed in the manner of the aforementioned rapid test system for measuring errors of electric energy meters based on an ant colony algorithm, and are electrically connected to the electric energy meters to be tested. The processor then executes the computer program to implement the steps of the aforementioned rapid test method for measuring errors of electric energy meters based on an ant colony algorithm, thereby completing the work of evaluating the measuring errors of the electric energy meters.

[0048] The technical solution provided by the present invention has the following beneficial effects:

[0049] The method for rapid testing the measurement error of electric energy meters based on the ant colony algorithm provided by the present invention obtains the electric energy measurement values ​​and the average electric energy measurement values ​​of all electric energy meters in a test cycle by connecting multiple electric energy meters to the same test node, thereby simultaneously obtaining the entropy deviation and the discreteness deviation of each electric energy meter, and then judging the measurement error of each electric energy meter accordingly, and marking the electric energy meters with different measurement error states after periodic testing. The measurement method calculates the deviation and determines the key pheromone of the ant colony algorithm by comparing the overall state value and the mean, thereby effectively screening out abnormalities and unqualified individuals in the group of smart meters, without the need to use test equipment to test individual electric meters one by one, thereby effectively improving the test efficiency and helping to improve the operation and maintenance efficiency of power grid personnel. In addition, the test method is suitable for laboratory or field testing, has fewer restrictions on test conditions, and has a wider range of applications.

[0050] The ant colony algorithm-based electric energy meter measurement error rapid testing system and equipment provided by the present invention can be electrically connected to a large number of electric energy meters, and then obtain the measurement error levels of different electric energy meters, and finally screen multiple electric energy meters. Therefore, the present invention can support the synchronous testing of a large number of electric energy meters, thereby reducing the labor cost during the testing process and effectively improving the detection efficiency of electric energy meter measurement errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0052] Figure 1 This is a flow chart of a method for quickly testing measurement errors of electric energy meters based on ant colony algorithm in Example 1 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] Example 1

[0055] This embodiment provides a method for quickly testing the measurement error of an electric energy meter based on an ant colony algorithm, which includes the following steps, namely steps S1 to S9.

[0056] S1: Connect multiple electric energy meters to be tested in series to the same test node and measure the same load. That is, connect all n electric energy meters in the batch to be tested in series to the same circuit and load the same load for measurement.

[0057] S2: Obtain the electricity metering value m of all electricity meters in a test cycle i , and calculate the average electricity measurement value μ of all electric energy meters in the current measurement cycle:

[0058]

[0059] In the above formula, i represents the number of the electric energy meter; n is the number of electric energy meters in the current measurement cycle.

[0060] S3: Generate a corresponding histogram based on the electricity meter value in the current test cycle, and then estimate the probability distribution p(m i ) and the average distribution of all electric energy meters p(m avg ).

[0061] S4: According to the probability distribution p(m i ) Calculate the entropy value H of each electric energy meter measurement result i , and the average entropy value H′ of all electricity meters.

[0062] In this embodiment, the entropy value H of the measurement result of each electric energy meter is i It can be expressed by the following calculation formula:

[0063] H i =-∑p(m i )log(p(m i ))

[0064] The average entropy value H′ of all the calculation results of the electric energy meters in the current test cycle can be expressed by the following calculation formula:

[0065] H′=-∑p(m ci )log(p(m ci ))

[0066] In the formula, m i As the electricity quantity measurement value in each meter test cycle, it can characterize the probability distribution of a single electric energy meter in the group, and m ci is the mean of the probability distribution of a single electric energy meter in the group.

[0067] S5: Calculate the entropy deviation δ of the measurement results of each electric energy meter in the current test cycle using the following formula i , the calculation formula is as follows:

[0068]

[0069] S6: According to the probability distribution p(m i ) and the average distribution p(m avg ) Calculate the discreteness K of each electric energy meter measurement result i , and the average dispersion K′ of all electric energy meters.

[0070] In this embodiment, the discreteness K of the measurement result of each electric energy meter is i It can be expressed by the following calculation formula:

[0071]

[0072] Among them, the average dispersion K′ of all electric energy meters can be expressed by the following calculation formula:

[0073]

[0074] S7: Use the following formula to calculate the discreteness deviation ε of the measurement results of each electric energy meter in the current test cycle i , the calculation formula is as follows:

[0075]

[0076] S8: Analyze the entropy deviation δ of each electric energy meter i and the dispersion deviation ε i The relationship between the respective theoretical thresholds C and E is used to determine whether the measurement error of each electric energy meter is qualified. The decision-making process is as follows:

[0077] (1) When δ i ≤C and ε i When ≤E, the measurement error of the electric energy meter is judged to be qualified and marked;

[0078] (2) When δ i >C and ε i >E, the measurement error of the electric energy meter is judged to be unqualified and marked;

[0079] (3) When δ i>C and ε i ≤E, or, δ i ≤C and ε i >E, the measurement error of the electric energy meter is judged to be abnormal and marked.

[0080] After the above marking operation, all the meters in the test cycle have a mark of qualified, unqualified or abnormal.

[0081] S9: retain all the electric energy meters marked as abnormal in the current test cycle, adjust the adjacent positions of the electric energy meters when they are connected in series, and then retest in the next round of test tasks: when any electric energy meter is still judged to be in an abnormal measurement error state during the retest process, the electric energy meter is judged to be unqualified.

[0082] In this embodiment, after the test is completed, for the unqualified electric energy meter, the entropy deviation δ i and the dispersion deviation ε i The error values ​​are written into the error register of the electric energy meter respectively and serve as one of the indicators for the subsequent calibration of the electric energy meter.

[0083] Example 2

[0084] On the basis of Example 1, this embodiment further provides a rapid test system for metering errors of electric energy meters based on ant colony algorithm, which adopts the rapid test method for metering errors of electric energy meters based on ant colony algorithm in Example 1 to rapidly test the metering errors of multiple electric energy meters to be calibrated, and determine whether the metering accuracy of the electric energy meters is qualified. The test system may include: a test machine, a position allocation module, a measurement information acquisition module, an entropy deviation calculation module, a discreteness deviation calculation module, and a metering accuracy verification module. Of course, in this embodiment, the test system may also include a dynamic memory, an abnormality review module, a position update module, and a display module.

[0085] The test machine includes multiple installation sites for electric energy meters. The electric energy meters at each installation site are connected to the same measurement site and measure the power information of the same load.

[0086] The location allocation module is used to communicate with the electric energy meter and obtain the meter address of the electric energy meter connected to each installation site; and after the meter address is successfully obtained, a location allocation table is generated to characterize the association between the electric energy meter device identification code and the installation site.

[0087] The measurement information acquisition module is used to obtain the power measurement value m of each electric energy meter in the current measurement cycle. i, and calculate the average electricity metering value μ of all electricity meters for the current test site; then generate the corresponding histogram based on the electricity metering value of each electricity meter in the current test cycle, and then estimate the probability distribution p(m i ) and the average distribution of all electric energy meters p(m avg ).

[0088] The entropy deviation calculation module is used to first calculate the probability distribution p(m i ) Calculate the entropy value H of each electric energy meter measurement result i And the average entropy value H' of all electric energy meters; then use the formula: Calculate the entropy deviation δ of the measurement results of each electric energy meter in the current test cycle i .

[0089] The discreteness deviation calculation module is used to first calculate the probability distribution p(m i ) and the average distribution p(m avg ) Calculate the discreteness K of each electric energy meter measurement result i And the average dispersion K' of all electric energy meters; then use the formula: Calculate the discreteness deviation ε of the measurement results of each electric energy meter in the current test cycle i ;as well as

[0090] The metering accuracy verification module is used to generate a test result of whether the metering accuracy of each electric energy meter is qualified according to the calculation results of the entropy deviation calculation module and the discreteness deviation calculation module; when the entropy deviation and discreteness deviation of the electric energy meter both exceed the preset threshold value, it is deemed unqualified; when both do not exceed the threshold value, it is deemed qualified; when only one of the two exceeds the threshold value, it is deemed abnormal.

[0091] The dynamic memory may store the device identification codes of the electric energy meters determined to be abnormal during each round of testing.

[0092] The abnormality review module can be used to obtain the device identification code of the electric energy meter that is initially identified as abnormal after each round of testing; then match the device identification code of each abnormal electric energy meter with each device identification stored in the dynamic memory, and finally make the following decisions based on the matching results:

[0093] (1) When the device identification code of any electric energy meter is matched successfully, the mark representing the test result of the electric energy meter is modified to unqualified, and the corresponding device identification code stored in the dynamic memory is deleted.

[0094] (2) When the device identification code of any electric energy meter fails to match, the device identification code of the electric energy meter is added to the data stored in the dynamic memory, and the test result of the current electric energy meter is marked as abnormal.

[0095] The location update module can be used to randomly generate a new location allocation table for each electric energy meter identified as having abnormal metering in the current round of test tasks after any round of test tasks is completed. In the new location allocation table, the installation location to be connected for each electric energy meter with abnormal metering is changed.

[0096] The display module may include indicator lights corresponding to the plurality of electric energy meters respectively, and each indicator light may be used to display the test result of the corresponding electric energy meter: green for qualified, red for unqualified, and yellow for abnormal.

[0097] Example 3

[0098] This embodiment provides a test device for measuring errors of electric energy meters, which belongs to a computer device. The test device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. Each functional module in the test device can be deployed in the manner of a rapid test system for measuring errors of electric energy meters based on an ant colony algorithm as in Embodiment 2, and electrically connected to each electric energy meter to be tested; then the processor can execute the computer program, implement the steps of the rapid test method for measuring errors of electric energy meters based on an ant colony algorithm as in Embodiment 1, and then complete the work of evaluating the measuring errors of each electric energy meter.

[0099] The computer device may be a smart phone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server or cabinet server (including an independent server or a server cluster composed of multiple servers) capable of executing a program. The computer device of this embodiment includes at least but is not limited to: a memory and a processor that can be interconnected through a system bus.

[0100] In this embodiment, the memory (i.e., readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Of course, the memory may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed on the computer device. In addition, the memory may also be used to temporarily store various types of data that have been output or are to be output.

[0101] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data to implement the processing of a method for testing the communication reliability of a smart electric energy meter in Example 1, and synchronously perform various communication reliability test tasks on a large number of electric energy meters according to specific needs; and obtain the communication reliability evaluation results of each electric energy meter.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A rapid test method for electric energy meter measurement error based on ant colony algorithm; characterized in that: The steps include: S1: Connect multiple electric energy meters to be tested in series and connect them to the same test node to measure the same load; S2: Obtain the electricity metering value m of all electricity meters in a test cycle i , and calculate the average electricity measurement value μ of all electric energy meters in the current measurement cycle: In the above formula, i represents the number of the electric energy meter; n is the number of electric energy meters in the current measurement cycle; S3: Generate a corresponding histogram based on the electricity meter value in the current test cycle, and then estimate the probability distribution p(m i ) and the average distribution of all electric energy meters p(m avg ); S4: According to the probability distribution p(m i ) Calculate the entropy value H of each electric energy meter measurement result i , and the average entropy value H′ of all electric energy meters; H′=-∑p(m ci )log(p(m ci )), where m ci is the probability distribution mean of a single electric energy meter in the group; S5: Calculate the entropy deviation δ of the measurement results of each electric energy meter in the current test cycle using the following formula i , the calculation formula is as follows: S6: According to the probability distribution p(m i ) and the average distribution p(m avg ) Calculate the discreteness K of each electric energy meter measurement result i , and the average dispersion K′ of all electric energy meters; S7: Use the following formula to calculate the discreteness deviation ε of the measurement results of each electric energy meter in the current test cycle i , the calculation formula is as follows: S8: Analyze the entropy deviation δ of each electric energy meter i and the dispersion deviation ε i The relationship between the respective theoretical thresholds C and E is used to determine whether the measurement error of each electric energy meter is qualified. The decision-making process is as follows: (1) When δ i ≤C and ε i When ≤E, the measurement error of the electric energy meter is judged to be qualified and marked; (2) When δ i >C and ε i >E, the measurement error of the electric energy meter is judged to be unqualified and marked; (3) When δ i >C and ε i ≤E, or, δ i ≤C and ε i >E, the measurement error of the electric energy meter is judged to be abnormal and marked; S9: retain all the electric energy meters marked as abnormal in the current test cycle, adjust the adjacent positions of the electric energy meters when they are connected in series, and then retest in the next round of test tasks: when any electric energy meter is still judged to be in an abnormal measurement error state during the retest process, the electric energy meter is judged to be unqualified.

2. The method for rapid testing of electric energy meter measurement errors based on ant colony algorithm according to claim 1, characterized in that: In step S4, the entropy value H of the measurement result of each electric energy meter is i The calculation formula is as follows: H i =-∑p(m i )log(p(m i ))。 3. The method for rapid testing of electric energy meter measurement errors based on ant colony algorithm according to claim 1, characterized in that: In step S6, the discreteness K of the measurement result of each electric energy meter is i The calculation formula is as follows:

4. The method for rapid testing of electric energy meter measurement errors based on ant colony algorithm according to claim 1, characterized in that: After the test, for the unqualified electric energy meter, the entropy deviation δ i and the dispersion deviation ε i The error values ​​are written into the error register of the electric energy meter respectively and serve as one of the indicators for the subsequent calibration of the electric energy meter.

5. A rapid test system for electric energy meter measurement error based on ant colony algorithm, characterized by: The method for quickly testing the metering error of an electric energy meter based on an ant colony algorithm as claimed in any one of claims 1 to 4 is used to quickly test the metering errors of a plurality of electric energy meters to be tested, so as to determine whether the metering accuracy of the electric energy meter is qualified; The test system comprises: A test machine, which includes multiple installation sites for electric energy meters, and the electric energy meters at each installation site are connected to the same measurement site and measure the power information of the same load; A location allocation module is used to communicate with the electric energy meter to obtain the meter address of the electric energy meter connected to each installation site; and after the meter address is successfully obtained, a location allocation table is generated to characterize the association relationship between the electric energy meter device identification code and the installation site; The measurement information acquisition module is used to obtain the power measurement value m of each electric energy meter in the current measurement cycle. i , and calculate the average power measurement value μ of all power meters for the current test site; then generate the corresponding histogram based on the power measurement value of each power meter in the current test cycle, and then estimate the probability distribution p(m i ) and the average distribution of all electric energy meters p(m avg ); The entropy deviation calculation module is used to first calculate the probability distribution p(m i ) Calculate the entropy value H of each electric energy meter measurement result i And the average entropy value H' of all electric energy meters; then use the formula: Calculate the entropy deviation δ of the measurement results of each electric energy meter in the current test cycle i ; The discreteness deviation calculation module is used to first calculate the probability distribution p(m i ) and the average distribution p(m avg ) Calculate the discreteness K of each electric energy meter measurement result i And the average dispersion K' of all electric energy meters; then use the formula: Calculate the discreteness deviation ε of the measurement results of each electric energy meter in the current test cycle i ;as well as The metering accuracy verification module is used to generate a test result of whether the metering accuracy of each electric energy meter is qualified according to the calculation results of the entropy deviation calculation module and the discreteness deviation calculation module; wherein, when the entropy deviation and the discreteness deviation of the electric energy meter both exceed the preset threshold value, it is determined to be unqualified; when both do not exceed the threshold value, it is determined to be qualified; when only one of the two exceeds the threshold value, it is determined to be abnormal.

6. The electric energy meter measurement error rapid testing system based on ant colony algorithm as claimed in claim 5, characterized in that: The test system further comprises a dynamic memory, in which the device identification codes of the electric energy meters determined to be abnormal during each round of testing are stored.

7. The electric energy meter measurement error rapid testing system based on ant colony algorithm as claimed in claim 6, characterized in that: The test system also includes an abnormality review module; The abnormality review module is used to first obtain the device identification code of the electric energy meter that is initially identified as abnormal after each round of testing; then match the device identification code of each abnormal electric energy meter with each device identification stored in the dynamic memory, and finally make the following decisions based on the matching results: (1) When the device identification code of any electric energy meter is matched successfully, the mark representing the test result of the electric energy meter is modified to unqualified, and the corresponding device identification code stored in the dynamic memory is deleted; (2) When the device identification code of any electric energy meter fails to match, the device identification code of the electric energy meter is added to the data stored in the dynamic memory, and the test result of the current electric energy meter is marked as abnormal.

8. The electric energy meter measurement error rapid testing system based on ant colony algorithm according to claim 7, characterized in that: The test system also includes a position update module, which is used to randomly generate a new position allocation table for each electric energy meter that is identified as having metering abnormalities in the current round of test tasks after any round of test tasks is completed. In the new position allocation table, the installation site to be connected for each electric energy meter with metering abnormalities is changed.

9. The electric energy meter measurement error rapid testing system based on ant colony algorithm according to claim 7, characterized in that: The test system also includes a display module, which includes indicator lights corresponding to multiple electric energy meters respectively, and each indicator light is used to display the test result of the corresponding electric energy meter: green for qualified, red for unqualified, and yellow for abnormal.

10. A test device for measuring error of an electric energy meter, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: Each functional module in the test equipment is deployed in the form of a rapid test system for metering errors of electric energy meters based on an ant colony algorithm as described in any one of claims 6 to 9, and is electrically connected to each electric energy meter to be tested; then the processor executes the computer program to implement the steps of the rapid test method for metering errors of electric energy meters based on an ant colony algorithm as described in any one of claims 1-4, thereby completing the work of evaluating the metering errors of each electric energy meter.

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