An electric energy meter data analysis method, an electric energy meter, and a storage medium

Abstract

The application discloses an electric energy meter data analysis method. The electric energy meter data analysis method comprises the following steps: acquiring a data analysis instruction; determining a corresponding data analysis time sequence according to the data analysis instruction and executing the data analysis time sequence; and generating a corresponding test report according to an execution result of the data analysis time sequence. The above scheme solves the technical problem that the data analysis capability of an existing electric energy meter is weak.

Description

Technical Field

[0001] This application relates to the technical field of electricity meters, and in particular to a method, apparatus, and storage medium for analyzing electricity meter data. Background Technology

[0002] Currently, with the development of intelligence and automation, more requirements are being placed on the functions of electricity meters. In existing technologies, there are two main methods for testing the parameters or related performance of electricity meters:

[0003] (1) Manual testing: This requires highly professional testers. The testers set an interval time and mode word for the load curve of the meter, then start the meter and let it record data on its own. Then, at the set time, the meter is turned off and the data of various parameters of the meter is collected by the data acquisition device. The manual process is complicated and cannot achieve the most comprehensive test. It is only a sampling method. In the later stage, the collected meter parameter data is analyzed one by one by manual calculation or by using relevant instruments to obtain the results. This is very time-consuming and laborious.

[0004] (2) Manufacturers with relevant technologies: When inspecting the various indicators of the load curve of the electricity meter, they use software to inspect them one by one. In a single test, for example, if it is necessary to determine the increment of the current of an electricity meter, the current needs to be detected and saved separately for a period of time before it is obtained. When the data to be tested changes or the amount of data increases, the amount of data to be tested and the complexity of the calculation will change. In the existing technology, the electricity meter is generally tested by instantaneous data. After the data is detected and the increment or difference is obtained, the detected data is not further analyzed, and it is impossible to reveal the further hidden meaning of these data.

[0005] Application content

[0006] The main purpose of this application is to propose a method, device, and storage medium for analyzing electricity meter data, aiming to solve the technical problem of the weak data analysis capabilities of existing electricity meters.

[0007] To achieve the above objectives, this application proposes a method for analyzing electricity meter data, the method comprising:

[0008] Get data analysis instructions;

[0009] The corresponding data analysis sequence is determined according to the data analysis instruction and the data analysis sequence is executed.

[0010] A corresponding inspection report is generated based on the execution results of the data analysis time series.

[0011] Optionally, the data analysis instructions include instructions for determining the growth trend type, instructions for determining the current value type, and instructions for determining the growth step size type.

[0012] Optionally, the data analysis timing sequence includes a meter data quiescent timing sequence and a load curve verification timing sequence; the step of determining the corresponding data analysis timing sequence according to the data analysis instruction and executing the data analysis timing sequence includes:

[0013] Set the meter parameters;

[0014] The corresponding target settling time, meter data settling sequence, and load curve verification sequence are determined according to the data analysis instructions.

[0015] The meter data is kept in a static sequence until the real-time freeze duration meets the target static duration.

[0016] The load curve verification sequence is executed to generate the execution results.

[0017] Optionally, the data analysis timing sequence includes meter data freezing timing sequence and load curve verification timing sequence; the step of determining the corresponding data analysis timing sequence according to the data analysis instruction and executing the data analysis timing sequence includes:

[0018] Set the meter parameters;

[0019] The corresponding target number of freezes, the timing sequence of meter data freezes, and the timing sequence of load curve verification are determined according to the data analysis instructions.

[0020] Execute the meter data freeze sequence until the real-time freeze count meets the target freeze count;

[0021] The load curve verification sequence is executed to generate the execution results.

[0022] Optionally, the meter data freeze sequence includes instantaneous freeze sequence and hourly freeze sequence.

[0023] Optionally, the load curve verification sequence includes the load curve verification sequence for increasing values ​​and the load curve verification sequence for the current value;

[0024] When the data analysis instruction is a growth trend type instruction, the meter data freeze sequence is an instantaneous freeze sequence, and the load curve verification sequence is a growth value load curve verification sequence.

[0025] Optionally, before the step of determining the corresponding data analysis timing according to the data analysis instruction and executing the data analysis timing, the method further includes:

[0026] Execute the meter reading parameter sequence to obtain the first meter parameter data group;

[0027] After the step of determining the corresponding data analysis timing according to the data analysis instruction and executing the data analysis timing, the method further includes:

[0028] The timing sequence for reading the electricity meter parameters is executed to obtain the second set of electricity meter parameter data.

[0029] The accuracy of the test report is determined by comparing the first set of meter parameter data with the second set of meter parameter data.

[0030] Optionally, the parameters of the load curve verification timing include the reading method, object attribute descriptor, reading start condition, reading end condition, data interval, freeze period, number of readings per time, reading item OAD, and reading item OAD comparison conditions.

[0031] Optionally, the execution result includes the theoretical starting sequence number, the theoretical sequence number step size, the theoretical starting time, and the theoretical time step size.

[0032] To achieve the above objectives, this application also proposes an electricity meter, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the electricity meter data analysis method as described above.

[0033] To achieve the above objectives, this application also proposes a storage medium storing at least one executable instruction, which, when executed on an electronic device, causes the electronic device to perform the operation of the electricity meter data analysis method described above.

[0034] The technical solution of this application obtains data analysis instructions; determines the corresponding data analysis timing sequence according to the data analysis instructions and executes the data analysis timing sequence; and generates a corresponding inspection report based on the execution result of the data analysis timing sequence. Through the above solution, the corresponding data analysis timing sequence can be selected and executed according to different instructions that need to be analyzed, that is, different data analysis instructions, thereby realizing the diversification and diversity of detection data. It can not only realize the analysis of real-time data, but also judge the growth of quantities related to time changes, thereby further predicting the stability of the tested electricity meter, strengthening the data analysis capability of existing electricity meters, and thus avoiding the problem of weak data analysis capability of existing electricity meters. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating the electricity meter data analysis method of this application;

[0037] Figure 2 This is a schematic diagram of an inspection report for the electricity meter data analysis method of this application;

[0038] Figure 3 This is a schematic diagram of another test report for the electricity meter data analysis method of this application;

[0039] Figure 4 This is a flowchart illustrating one of the electricity meter data analysis methods described in this application;

[0040] Figure 5 This is another flowchart illustrating the electricity meter data analysis method of this application;

[0041] Figure 6 This is another flowchart illustrating the electricity meter data analysis method of this application;

[0042] Figure 7 This is a flowchart illustrating the electricity meter data analysis method of this application;

[0043] Figure 8 This is a circuit diagram of the power supply circuit of the energy meter detected in the energy meter data analysis method of this application;

[0044] Figure 9 This is a circuit diagram of the power supply circuit of the energy meter detected in the energy meter data analysis method of this application;

[0045] Figure 10 This is a circuit diagram of the power supply circuit of the energy meter detected in the energy meter data analysis method of this application;

[0046] Figure 11 This is a schematic diagram of the storage medium module of this application;

[0047] Figure 12 This is a schematic diagram of the module of the electricity meter frozen data storage device of this application.

[0048] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. If the embodiments of this application involve descriptions such as "first" or "second", such descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features.

[0050] This application proposes a data analysis method for electricity meters, aiming to solve the technical problem of the weak data analysis capabilities of existing electricity meters.

[0051] In one embodiment, such as Figure 1 As shown, the electricity meter data analysis method includes:

[0052] S1. Obtain data analysis instructions;

[0053] Optionally, the data analysis instructions include instructions for determining the growth trend type, instructions for determining the current value type, and instructions for determining the growth step size type.

[0054] Among them, the instruction to determine the growth trend type is: simulate the data of the electricity meter through the frozen time point, focusing on trend-type growth parameters, such as electricity consumption and power, which have a certain growth trend over time.

[0055] The freeze time point can be set as needed, with an emphasis on time progression.

[0056] Determine the current value type: You can refer to the command to determine the growth trend type, focusing on constant parameters and only comparing the changes of parameters before and after, such as parameters that are generally stable within a certain range, like current and power factor.

[0057] Instructions for determining the growth step type: Focus on parameters that increase in step size, such as the meter's usage time, which increases over time.

[0058] It should be noted that there can also be some special instructions at this time, such as instructions to judge the performance of the electricity meter, for example, to simulate the changes that would occur if the electricity meter were to run or be left idle for a long time by running or leaving it idle for a short time.

[0059] S2. Determine the corresponding data analysis sequence according to the data analysis instruction and execute the data analysis sequence;

[0060] Data analysis sequence refers to the process of collecting and analyzing data from electricity meters. This includes various data acquisition schemes and analysis schemes. At this time, the corresponding data acquisition scheme and analysis scheme are selected according to the data analysis instructions, so that various data analyses can be performed as needed.

[0061] S3. Generate a corresponding inspection report based on the execution results of the data analysis time series.

[0062] The above scheme allows for the selection and execution of appropriate data analysis sequences based on the specific instructions required for analysis, i.e., different data analysis commands. This enables the diversification and variety of test data. Subsequently, corresponding inspection reports can be generated based on the execution results. Figure 2 as well as Figure 3 As shown, this allows users to easily view the analysis results. Through this scheme, the matching data analysis sequence can be changed at any time according to different user instructions, resulting in different analysis results. This enables comprehensive and customized operation of electricity meter performance testing, allowing personnel without any technical background to perform comprehensive testing on factory-produced electricity meters without relying on complex testing circuit connections and software operations. By inputting instructions, the program can automatically generate test reports, reducing the complexity of factory electricity meter performance testing. When electricity meters need to be inspected after production, this scheme can inspect a large number of meters in a short time. One electricity meter data analysis method can cover a large number of meter parameters, greatly reducing the redundancy and complexity of the inspection scheme, shortening the inspection time, and achieving unattended operation. Therefore, this method, equipment, or system can greatly improve inspection efficiency and reduce labor costs.

[0063] The above solution not only enables the analysis of real-time data, but also allows for the assessment and prediction of changes over time, thereby further predicting the stability of the tested electricity meter and enhancing its data analysis capabilities. This avoids the problem of the weak data analysis capabilities of existing electricity meters.

[0064] In an alternative embodiment, reference Figure 4 As shown, the data analysis timing sequence includes a meter data quiescent timing sequence and a load curve verification timing sequence; the step of determining the corresponding data analysis timing sequence according to the data analysis instruction and executing the data analysis timing sequence includes:

[0065] S21. Set the meter parameters;

[0066] At this point, the meter parameters include the meter time and date, the basic data cache period, and the number of valid data entries. The meter time and date generally refer to the time and date after the meter has been synchronized to the current time and date. Based on the basic data cache period and the number of valid data entries, a new OAD (Object Attribution Descriptor) is generated and saved to the meter storage area every data cache period after the current time. The meter storage area stores at most the number of valid data entries for this OAD; if the number exceeds this limit, the first OAD entry is overwritten, and so on.

[0067] S22. Determine the corresponding target settling time, meter data settling sequence, and load curve verification sequence according to the data analysis instructions.

[0068] The target settling time is typically set based on real-time data analysis commands.

[0069] The meter data settling sequence refers to the meter settling sequence executed according to the target settling time, including the start time, end time, the number of OADs to be stored during the natural settling time, and the number of storage times. The specific values ​​are also preset according to the data analysis instructions.

[0070] Load curve verification timing: Some execution parameters set include: copying method, object attribute descriptor, copying start condition, copying end condition, data interval, freeze period, number of copies per copy, copying item OAD, and copying item OAD comparison conditions. The comparison condition values ​​are configured in the format X_Y, X_Y, ..., X_Y for each column, separated by commas. X represents the comparison method; different comparison methods correspond to different judgment rules. Y represents the column index. If it's a single-column index, the column number (e.g., 1) represents column 1. If it's a multi-column index, the expression is start index - end index number (e.g., in example 9-10, column 9 and column 10). Example: N2_1, T1_2, W1_3-4, U1_5, I1_6, P1_7-8, C1_9-10. The column index and the selected comparison method must match the actual copied content.

[0071] S23. Execute the meter data static timing sequence until the real-time freeze duration meets the target static duration;

[0072] S24. Execute the load curve verification sequence to generate execution results.

[0073] Based on the above scheme, the execution of the load curve verification sequence refers to reading the values ​​of each OAD item of the load curve reading item from the meter data storage area. Multiple OAD values ​​constitute a data entry, and multiple data entries are arranged in a queue according to time sequence. The reading results are obtained and compared in real time. The judgment rule is as follows: parameters KZ00010C and KZ000A02 are calculated based on the reading parameters and assigned by the system during the reading process. Reading conditions can be divided into reading by time and reading by sequence number. BeginTime = KZ00010C is the reading start time, and Interval = KZ000A02 is the reading time interval. Judgment rule: The system will judge the reading results row by row and column by column. Current value = the reading value of the corresponding column in the current row. Increase value = the difference between the current row value and the previous row value. The unit of difference is different for different data types. The theoretical starting number, theoretical number step size, theoretical starting time, and theoretical time step size are calculated based on the load curve reading parameters and reading results. The specific judgment process can be referred to Table 1.

[0074] In the above embodiments, this scheme is mainly used to determine the performance instructions of the electricity meter, such as simulating the changes that would occur if the electricity meter were to run or remain idle for a short period of time. At this time, a longer period can be simulated by allowing the electricity meter to remain idle or run for a period of time, and a shorter simulation period can be used to reasonably predict the changes of the current electricity meter over a longer period. This enables effective testing of trend-type parameters and setpoint parameters, avoiding the shortcomings of being unable to measure such parameters due to short measurement times.

[0075] Optionally, the parameters in the load curve verification sequence include the reading method, object attribute descriptor, reading start condition, reading end condition, data interval, freeze period, number of readings per time, reading item OAD, and reading item OAD comparison conditions.

[0076] In an alternative embodiment, refer to Figure 5 As shown, the data analysis timing sequence includes meter data freezing timing sequence and load curve verification timing sequence; the step of determining the corresponding data analysis timing sequence according to the data analysis instruction and executing the data analysis timing sequence includes:

[0077] S25. Set the meter parameters;

[0078] At this point, the meter parameters include the meter time and date, the basic data cache period, and the number of valid data entries. The meter time and date generally refer to the time and date after the meter has been synchronized to the current time and date. Based on the basic data cache period and the number of valid data entries, a new OAD (Object Attribution Descriptor) is generated and saved to the meter storage area every data cache period after the current time. The meter storage area stores at most the number of valid data entries for this OAD; if the number exceeds this limit, the first OAD entry is overwritten, and so on.

[0079] S26. Determine the corresponding target number of freezes, the timing sequence of meter data freezes, and the timing sequence of load curve verification according to the data analysis instructions.

[0080] The number of times the target is frozen is set according to the actual command requirements.

[0081] Meter data freeze sequence types: such as instantaneous freeze, hourly freeze, time synchronization, etc. Parameter settings include freeze type OAD, freeze time interval (any positive integer, the same as the current freeze cycle of the meter), number of freezes, etc. Preferably, this sequence type is a key feature; by repeatedly looping this sequence type, it creatively simulates the verification of meter parameters that need to meet trends over several months or years, such as energy and time-related verifications. When testers only care whether the energy and time growth trends meet expectations, the meter data freeze sequence type is executed. The meter is synchronized forward according to the meter time to t1 seconds before the most recent point to be frozen. After t1 seconds, the meter generates a frozen data record in the meter storage area. This process is then repeated looping, with the most recent point to be frozen as the starting freeze time. After recursively calculating the freeze interval, the timer moves to the freeze point closest to the current meter time, and the meter parameter data at the frozen time point is copied to the meter data storage area, and so on until the loop is complete. This timing sequence can shorten the time it takes for the meter to pass through natural days, months, and years, thus achieving the purpose of testing trend-type parameters and constant-value parameters.

[0082] S27. Execute the meter data freeze sequence until the real-time freeze count meets the target freeze count;

[0083] S28. Execute the load curve verification sequence to generate execution results.

[0084] When performing load curve verification timing, the comparison condition values ​​are configured in the format X_Y, X_Y, ..., X_Y for each column, separated by commas. X represents the comparison method; different comparison methods correspond to different judgment rules. Y represents the column index. If it's a single-column index, the column number (e.g., 1) indicates column 1. If it's a multi-column index, the expression is the start index minus the end index number (e.g., in example 9-10, column 9 and column 10). Example: N2_1, T1_2, W1_3-4, U1_5, I1_6, P1_7-8, C1_9-10. The column index and the selected comparison method must match the actual content being copied. (Refer to...) Figure 6 As shown, the values ​​of each OAD item in the load curve are read from the meter's data storage area. Multiple OAD values ​​are combined into one data entry, and multiple data entries are arranged in a queue according to time sequence. The reading results are obtained and compared in real time. The judgment rule is that parameters KZ00010C and KZ000A02 are calculated based on the reading parameters and assigned by the system during the reading process. The reading conditions can be divided into reading by time and reading by sequence number. BeginTime = KZ00010C is the reading start time, and Interval = KZ000A02 is the reading time interval. Judgment rule: The system will judge the reading results row by row and column by column. Current value = the reading value of the corresponding column in the current row. Increase value = the difference between the current row value and the previous row value. The difference unit is different for different data types. The theoretical starting sequence number, theoretical sequence number step size, theoretical starting time, and theoretical time step size are calculated based on the load curve reading parameters and the reading results. The specific judgment scheme is shown in Table 1 below:

[0085]

[0086]

[0087] Table 1

[0088] The following example illustrates the specific judgment process for different parameters or different judgment targets:

[0089] Data 1:

[0090] 0,2020-01-1617:15:00,0.0350,2.0790,0.0000,0.0190,0.0000,0.0070,0.0120,0.0000,0.0000,0.0000,220.0,220.0,220.1,5.000,5.000,5.000,0.000,7.500,3301.8,1100.4,1100.3,1100.7,3.0,0.5,3.0,-0.5,0.999,0.999,0.999,0.999;

[0091] Data 2:

[0092] 1,2020-01-1617:30:00,0.0400,2.0790,0.0010,0.0190,0.0010,0.0070,0.0120,0.0000,0.0033,0.0041,220.3,220.2,220.3,5.000,-5.005,-4.998,0.000,0.023,-3.3,1101.5,-549.6,-555.3,4.3,-0.4,955.6,-951.2,-0.609,0.999,-0.499,-0.505;

[0093] Data 3:

[0094] 2,2020-01-1617:45:00,0.0410,2.0790,0.0020,0.0190,0.0020,0.0070,0.0120,0.0000,0.0031,0.0040,220.4,220.3,220.3,5.001,-5.006,-5.000,0.000,0.023,-3.2,1102.1,-549.5,-555.6,4.2,-0.5,956.3,-951.5,-0.618,0.999,-0.499,-0.505.

[0095] At this point, data 1 is the first data entry. Following the first data entry comparison rule, if the comparison condition is N1, it is judged as qualified according to the rule that the current value equals the theoretical starting sequence number. If it is not the first data entry, it is judged as qualified according to the rule that the increment value equals the theoretical sequence number step size. The data returned by each column of OAD is then judged sequentially, such as voltage phase A, voltage phase B, voltage phase C, and current phase A. If a value is unqualified, the reason for the unqualification is explained. For the specific execution process, refer to [reference needed]. Figure 7 As shown.

[0096] Optionally, the meter data freeze sequence includes instantaneous freeze sequence and hourly freeze sequence.

[0097] At this point, the specific scheme for freezing time series can also be set by the user according to the different purposes of the measurement.

[0098] In an optional embodiment, the load curve verification sequence includes a load curve verification sequence for increasing values ​​and a load curve verification sequence for the current value.

[0099] When the data analysis instruction is a growth trend type instruction, the meter data freeze sequence is an instantaneous freeze sequence, and the load curve verification sequence is a growth value load curve verification sequence.

[0100] In an alternative embodiment, reference Figure 8 As shown, before the step of determining the corresponding data analysis timing according to the data analysis instruction and executing the data analysis timing, the method further includes:

[0101] S4. Execute the meter reading parameter sequence to obtain the first meter parameter data group;

[0102] refer to Figure 9 As shown, the first meter parameters include multiple OADs (Freeze-Associated Objects), and the first meter parameter data group at this time is the specific reading value of each OAD. After the step of determining the corresponding data analysis sequence according to the data analysis instruction and executing the data analysis sequence, the following is also included:

[0103] S5. Execute the meter reading parameter sequence to obtain the second meter parameter data group;

[0104] refer to Figure 10 As shown, the timing sequence for reading meter parameters after inspection is generally placed after the load curve inspection timing sequence, appearing in pairs with the timing sequence for reading meter parameters before inspection. The correctness of the load curve timing sequence is verified by comparing the associated object attributes of the frozen data of the load curve with those of the first meter parameter data group before inspection through rereading the associated object attributes.

[0105] S6. Compare the first set of meter parameter data with the second set of meter parameter data to determine the accuracy of the inspection report.

[0106] The above method can verify the correctness of the previous load curve timing execution results. When the data ranges of the first meter parameter data group and the second meter parameter data group are the same, the accuracy of the issued inspection report is high. If the data ranges of the first meter parameter data group and the second meter parameter data group differ significantly, the accuracy of the issued inspection report is low.

[0107] Optionally, the execution result includes the theoretical starting sequence number, the theoretical sequence number step size, the theoretical starting time, and the theoretical time step size.

[0108] Optionally, after the step of generating the corresponding inspection report based on the execution results of the data analysis time series, the method further includes:

[0109] A load curve reading comparison result report is generated based on the execution results of the data analysis time sequence.

[0110] The results of the load curve reading and comparison are saved to the database in Excel format.

[0111] At this point, the information can be displayed on the interface or printed out, providing the reason for non-compliance and the time of non-compliance for inspection personnel to verify. The scheme value is to read the data content of each component of the OAD from the load curve, and the return value is the detailed verification result of the data content. If it is non-compliant, the non-compliance information is updated, and the conclusion value records the overall conclusion of the compliance verification of the data.

[0112] This application also proposes an electricity meter, with reference to Figure 12 As shown, the electricity meter includes:

[0113] Detection module 10 is used to acquire data analysis instructions;

[0114] Control module 30: Determines the corresponding data analysis sequence according to the data analysis instruction and executes the data analysis sequence, and generates a corresponding inspection report based on the execution result of the data analysis sequence.

[0115] Optionally, the data analysis instructions include instructions for determining the growth trend type, instructions for determining the current value type, and instructions for determining the growth step size type.

[0116] Optionally, the control module 30 is also used for

[0117] Set the meter parameters;

[0118] The corresponding target settling time, meter data settling sequence, and load curve verification sequence are determined according to the data analysis instructions.

[0119] The meter data is kept in a static sequence until the real-time freeze duration meets the target static duration.

[0120] The load curve verification sequence is executed to generate the execution results.

[0121] Optionally, the control module 30 is also used to set the meter parameters;

[0122] The corresponding target number of freezes, the timing sequence of meter data freezes, and the timing sequence of load curve verification are determined according to the data analysis instructions.

[0123] Execute the meter data freeze sequence until the real-time freeze count meets the target freeze count;

[0124] The load curve verification sequence is executed to generate the execution results.

[0125] Optionally, the meter data freeze sequence includes instantaneous freeze sequence and hourly freeze sequence.

[0126] Optionally, the load curve verification sequence includes the load curve verification sequence for increasing values ​​and the load curve verification sequence for the current value;

[0127] When the data analysis instruction is a growth trend type instruction, the meter data freeze sequence is an instantaneous freeze sequence, and the load curve verification sequence is a growth value load curve verification sequence.

[0128] Optionally, the control module 30 is also used for

[0129] Execute the meter reading parameter sequence to obtain the first meter parameter data group;

[0130] After the step of determining the corresponding data analysis timing according to the data analysis instruction and executing the data analysis timing, the method further includes:

[0131] The timing sequence for reading the electricity meter parameters is executed to obtain the second set of electricity meter parameter data.

[0132] The accuracy of the test report is determined by comparing the first set of meter parameter data with the second set of meter parameter data.

[0133] Optionally, the parameters of the load curve verification timing include the reading method, object attribute descriptor, reading start condition, reading end condition, data interval, freeze period, number of readings per time, reading item OAD, and reading item OAD comparison conditions.

[0134] Optionally, the execution result includes the theoretical starting sequence number, the theoretical sequence number step size, the theoretical starting time, and the theoretical time step size.

[0135] The above scheme allows for the selection and execution of appropriate data analysis sequences based on the specific instructions required for analysis, i.e., different data analysis commands. This enables the diversification and variety of test data. Subsequently, corresponding inspection reports can be generated based on the execution results. Figure 2 as well as Figure 3 As shown, this allows users to easily view the analysis results. Through the above scheme, the matching data analysis sequence can be changed at any time according to different user instructions, thereby obtaining different analysis results. This enables comprehensive and customized operation of electricity meter performance testing, allowing personnel with no prior knowledge of the field to conduct comprehensive testing on electricity meters produced in factories without relying on complex testing circuit connections and software operations. By inputting instructions, the program can automatically generate test reports, reducing the complexity of electricity meter performance testing in factories.

[0136] The above solution not only enables the analysis of real-time data, but also allows for the assessment and prediction of changes over time, thereby further predicting the stability of the tested electricity meter and enhancing its data analysis capabilities. This avoids the problem of the weak data analysis capabilities of existing electricity meters.

[0137] This application also proposes a storage medium storing at least one executable instruction, which, when executed on an electronic device, causes the electronic device to perform the operation of the energy meter data analysis method as described above.

[0138] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0140] This application also proposes an electricity meter, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the electricity meter data analysis method described above.

[0141] Reference Figure 11As shown, in some embodiments, the memory 21 can be an internal storage unit of the terminal device 2, such as a hard disk or memory of the terminal device 2. In other embodiments, the memory 21 can also be an external storage device of the terminal device 2, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device 2. Furthermore, the memory 21 can include both internal and external storage units of the terminal device 2. The memory 21 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 21 can also be used to temporarily store data that has been output or will be output.

[0142] Corresponding to the electricity meter data analysis method described in the above embodiments, Figure 12 A structural block diagram of an energy meter provided in an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0143] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0144] In addition, optionally, in one embodiment, the electricity meter is provided with a byte-modifiable storage medium EEPROM and a sector-modifiable storage medium FLASH for performing electricity meter data analysis methods, and is also configured with a metering interruption communication interface to realize signal interaction and perform data calculations required by the microcontroller, i.e., the processor.

[0145] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0146] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0147] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0148] The above are merely optional embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made based on the content of the specification and drawings of this application under the concept of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A method for analyzing data of an electric energy meter, characterized in that, The electric energy meter data analysis method comprises: acquiring a data analysis instruction; determining a corresponding data analysis time sequence according to the data analysis instruction and executing the data analysis time sequence; generating a corresponding test report according to the execution result of the data analysis time sequence; the data analysis instruction comprises a judgment growth trend type instruction, a judgment current value type instruction and a judgment growth step type instruction; the data analysis time sequence comprises an electric meter data freezing type time sequence and a load curve test time sequence; the step of determining a corresponding data analysis time sequence according to the data analysis instruction and executing the data analysis time sequence comprises: setting an electric meter parameter; determining a corresponding target freezing number, an electric meter data freezing type time sequence and a load curve test time sequence according to the data analysis instruction; executing the electric meter data freezing type time sequence until the real-time freezing number meets the target freezing number; executing the load curve test time sequence to generate an execution result; the test parameter of the load curve test time sequence comprises a comparison condition value, a current value and a growth value, wherein the comparison condition value comprises a theoretical starting sequence number, a theoretical sequence number step, a theoretical starting time and a theoretical time step, and the test rule followed by the load curve test time sequence is a first data comparison rule; the first data comparison rule is that if the comparison condition is N1, the current value is judged to be qualified according to the rule that the current value is the theoretical starting sequence number, and if it is not the first data, the growth value is judged to be qualified according to the rule that the growth value is the theoretical sequence number step; the electric meter data freezing type time sequence comprises an instantaneous freezing time sequence and an integral freezing time sequence; the data type frozen by the instantaneous freezing time sequence comprises a sequence number type, a time type, an electric energy type, a voltage type, a current type, a power type and a frequency type; when the data type is the electric energy type, the qualified condition of the growth value load curve test time sequence comprises 0 ≤ growth value ≤ Un * 1.3 * Max * 1.3 *(number of phases) / 60 * step.

2. The electric energy meter data analysis method of claim 1, wherein, the data analysis time sequence comprises an electric meter data static time sequence and a load curve test time sequence; the step of determining a corresponding data analysis time sequence according to the data analysis instruction and executing the data analysis time sequence comprises: setting an electric meter parameter; determining a corresponding target static time length, an electric meter data static time sequence and a load curve test time sequence according to the data analysis instruction; executing the electric meter data static time sequence until the real-time freezing time length meets the target static time length; executing the load curve test time sequence to generate an execution result.

3. The electric energy meter data analysis method of claim 1, wherein, the load curve test time sequence comprises a growth value load curve test time sequence and a current value load curve test time sequence; when the data analysis instruction is a judgment growth trend type instruction, the electric meter data freezing type time sequence is an instantaneous freezing time sequence, and the load curve test time sequence is a growth value load curve test time sequence.

4. The electric energy meter data analysis method of claim 1, wherein, before the step of determining a corresponding data analysis time sequence according to the data analysis instruction and executing the data analysis time sequence, there further comprises: executing a copy reading electric meter parameter time sequence to acquire a first electric meter parameter data group; after the step of determining a corresponding data analysis time sequence according to the data analysis instruction and executing the data analysis time sequence, there further comprises: performing the meter reading parameter sequence to obtain a second meter parameter data set; comparing the first meter parameter data set with the second meter parameter data set to determine the accuracy of the verification report.

5. The electric energy meter data analysis method of claim 1, wherein, The parameters of the load curve verification sequence include a reading mode, an object attribute descriptor, a reading start condition, a reading end condition, a data interval, a freeze period, a single reading number, a reading item OAD, and a reading item OAD comparison condition.

6. An electric energy meter, characterized by The electric energy meter comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the electric energy meter data analysis method according to any one of claims 1 to 5 when executing the computer program.

7. A storage medium, characterized by The storage medium stores at least one executable instruction, and the executable instruction, when executed on the electronic device, causes the electronic device to perform the operations of the electric energy meter data analysis method according to any one of claims 1 to 5.

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