An automatic analysis method for electric energy meter reading data

Abstract

The present invention provides an automated analysis method for meter reading data of an electric energy meter, comprising: obtaining meter reading data of an electric energy meter; performing splitting processing on the meter reading data, splitting the meter reading data according to preset interval symbols, and obtaining split data; integrating the split data according to preset capture object parameters, and obtaining integrated data; calculating and judging the integrated data according to preset calculation methods and judgment conditions, and obtaining calculation results and judgment results; if the judgment result is abnormal, determining that the data corresponding to the calculation result is abnormal data; obtaining the location information of the abnormal data, and generating an abnormal data summary table. The automated analysis method for meter reading data of the present invention reduces manual time consumption, reduces the error rate of manual inspection, and provides a guarantee for the reliability test results of electric energy meters.

Description

Technical Field

[0001] The present invention is applied to the analysis field of batch reliability test data of electric energy meters, and in particular, relates to an automatic analysis method for meter reading data of electric energy meters. Background Art

[0002] As a key device for power measurement, the reliability of the energy meter is directly related to the accurate measurement and stable operation of the power system. In practical applications, the energy meter needs to work stably for a long time under various complex environmental conditions, so its reliability requirements are extremely high.

[0003] Currently, the functions of smart meters are becoming more and more complex, and we are constantly updating the use cases for reliability testing of electricity meters. As the number of use cases continues to increase, the data supported by electricity meters is becoming more and more complex. When manually checking the test data of batch meters, we will face the problems of incomplete test data inspection and time-consuming inspection. Especially for some complex data, it is easy to miss or check the test data when checking manually, resulting in uncontrollable quality of test results. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of low efficiency and poor accuracy of manual data analysis, and propose an automated analysis method for electric energy meter reading data, and analyze and process relatively complex electric energy meter reading data; this method is realized by a data inspection tool generated by VB compilation. The tool integrates the functions of data splitting and integration, data search, data calculation and judgment, abnormal summary, and parameter saving.

[0005] The technical solution of the present invention is:

[0006] The present invention provides an automatic analysis method for electric energy meter reading data, the method comprising the following steps:

[0007] Obtain meter reading data of electric energy meter;

[0008] Splitting the meter reading data according to a preset interval symbol to obtain split data;

[0009] Integrating the split data according to preset capture object parameters to obtain integrated data;

[0010] Calculating and judging the integrated data according to a preset calculation method and judgment condition to obtain a calculation result and a judgment result;

[0011] If the judgment result is abnormal, determining that the data corresponding to the calculation result is abnormal data;

[0012] The location information of the abnormal data is obtained, and an abnormal data summary table is generated.

[0013] Furthermore, the split data is integrated, including:

[0014] Get the name and number of the preset target capture objects;

[0015] Searching for the name of the target captured object in the split data to obtain target data;

[0016] According to the number of the target captured objects, the target data are arranged and combined to obtain integrated data.

[0017] Furthermore, the calculation and judgment of the integrated data according to the preset calculation method and judgment condition includes:

[0018] Get the preset target calculation parameters;

[0019] According to the target calculation parameters, corresponding data is obtained from the integrated data, and a preset calculation formula is executed to obtain a calculation result;

[0020] It is determined whether the calculation result meets a preset threshold condition. If not, the calculation result is determined as abnormal data.

[0021] Further, the obtaining the location information of the abnormal data includes: obtaining the row number and column number of the abnormal data in the integrated data.

[0022] Furthermore, generating the abnormal data summary table includes:

[0023] Create a new abnormal data summary table, and fill the location information of the abnormal data into the abnormal data summary table;

[0024] A jump link is set in the abnormal data summary table, and the jump link is used to jump to the position of the abnormal data in the integrated data.

[0025] Furthermore, the method further comprises:

[0026] Obtaining configuration parameters input by a user, wherein the configuration parameters include a spacing symbol, a capture object, a calculation method, and a judgment condition;

[0027] The configuration parameters are saved to obtain a configuration parameter file;

[0028] When the method is executed again, the configuration parameter file is read and the read configuration parameters are used as preset parameters.

[0029] Furthermore, the method further includes judging the consistency of the calculation results between different electric meters, specifically:

[0030] Match according to the time recorded by the electric energy meter, and organize the data collected at the same time together;

[0031] Compare the electricity data, demand data, clock data, voltage and current value data of different meters collected at the same time point to determine their consistency;

[0032] If the difference between the electricity data, demand data, clock data, and voltage and current value data of different meters collected at the same time point exceeds a preset threshold, it is judged to be inconsistent, and the data corresponding to the calculation result is determined to be abnormal data.

[0033] Furthermore, the calculation method includes calculating the total forward active power rate, calculating the total reverse active power rate, calculating the total forward reactive power rate, calculating the total reverse reactive power rate, calculating the absolute value and total rate, calculating the absolute value difference total rate, calculating the forward apparent total rate, calculating the reverse apparent total rate, calculating the total electricity increment, calculating the interval electricity, calculating the average value, calculating the average power and interval electricity, calculating the maximum demand consistency, calculating the reactive power one quadrant total rate, calculating the reactive power two quadrant total rate, calculating the reactive power three quadrant total rate and calculating the reactive power four quadrant total rate.

[0034] Furthermore, the calculation of the total forward active power rate is specifically as follows:

[0035] Search the data whose captured objects are named total forward active power, forward active rate 1 power, and forward active rate 2 power in the data;

[0036] Copy the found data and store it in the total rate electricity calculation page;

[0037] Subtract the electricity of each rate from the total electricity, and judge the value after the subtraction. When the value is not equal to 0, it is judged as abnormal, and the data field where the data is located is marked in red.

[0038] Beneficial effects of the present invention:

[0039] The automated analysis method for meter reading data of the present invention reduces manual time consumption, reduces the error rate of manual inspection, provides a guarantee for the reliability test results of the electric energy meter, and can effectively cope with the continuously added functions and continuously added data records of the electric energy meter.

[0040] The method of the present invention solves the problem of difficulty in analyzing a large amount of data by continuously updating the data checking tool, greatly improving the control of the test results and improving the test quality.

[0041] The intelligent analysis method for electric energy meter reading data of the present invention flexibly sets analysis parameters through parameter configuration files, splits, integrates and classifies meter reading data, uses preset formulas to calculate and compare with thresholds, automatically marks abnormal data; generates an abnormal data summary table, and realizes rapid positioning through hyperlinks, which greatly improves the efficiency and accuracy of data analysis.

[0042] The present invention also uses a parameter configuration file in the form of an electronic spreadsheet, which is edited through a graphical interface to achieve flexible configuration and storage and reading of parameters, thereby enhancing the applicability and scalability of the analysis. This intelligent data processing and anomaly detection method provides power companies with an efficient and accurate meter reading data analysis tool, which helps to timely discover and handle abnormal situations and improve the level of power management.

[0043] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0045] Figure 1 A flow chart of an automated analysis method for electric energy meter reading data is shown. DETAILED DESCRIPTION

[0046] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0047] like Figure 1 As shown, the present invention provides an automatic analysis method for electric energy meter reading data, comprising:

[0048] Step S1, obtaining the electric energy meter reading data file and parameter configuration file to be analyzed, wherein the parameter configuration file includes analysis parameters such as data type, capture object, calculation method, etc.

[0049] Step S2, using different interval symbols to split the meter reading data, extracting the data and storing them in an array, integrating them according to the capture object quantity parameter, and arranging the same type of data into the same column.

[0050] Step S3, according to the calculation method selected in the parameter configuration file, the corresponding calculation data is searched from the integrated data by using the data name matching method, and the corresponding calculation data is classified and stored according to the calculation method requirements.

[0051] Step S4, using a preset calculation formula to calculate the classified data, comparing the calculation result with a preset threshold, determining whether the result is abnormal, and marking the cell containing the abnormal data in red.

[0052] Step S5, searching each calculation data table, obtaining the cell address information of the abnormal data marked in red, generating an abnormal data summary table, and using a hyperlink method to achieve rapid location of the abnormal data.

[0053] Step S6, the parameter configuration file is in the form of a spreadsheet and is edited through a graphical interface. Analysis parameters of different data types can be set to achieve flexible configuration and storage and reading of parameters, thereby improving the applicability of the analysis.

[0054] In one example, the step S4 further includes judging the consistency of the calculation results between different electricity meters, specifically:

[0055] Match according to the time recorded by the electric energy meter, and organize the data collected at the same time together;

[0056] Compare the electricity data, demand data, clock data, voltage and current value data of different meters collected at the same time point to determine their consistency;

[0057] If the difference between the electricity data, demand data, clock data, and voltage and current value data of different meters collected at the same time point exceeds a preset threshold, it is judged to be inconsistent, and the data corresponding to the calculation result is determined to be abnormal data.

[0058] In one example, the tool interface mainly includes configuration parameters input by the user, including interval symbols, capture objects, calculation methods and judgment conditions; determine the data type to be processed, select multiple calculation methods or only perform one operation, summarize the error data after the operation is completed, and finally complete the entire batch data analysis process.

[0059] The configuration parameters have the functions of editing, saving and reading. The parameter configuration file adopts the xlsx format. It supports the tool interface to save the parameters and store them in the corresponding configuration file. The button control can be used to execute the reading function at the next execution, avoiding repeated editing of the same parameters.

[0060] In one example, the calculation method includes calculating the total forward active power rate, calculating the total reverse active power rate, calculating the total forward reactive power rate, calculating the total reverse reactive power rate, calculating the absolute value and total rate, calculating the absolute value difference total rate, calculating the forward apparent total rate, calculating the reverse apparent total rate, calculating the total electricity increment, calculating the interval electricity, calculating the average value, calculating the average power and the interval electricity, calculating the maximum demand consistency, calculating the reactive power total rate for the first quadrant, calculating the reactive power total rate for the second quadrant, calculating the reactive power total rate for the third quadrant, and calculating the reactive power total rate for the fourth quadrant.

[0061] The specific implementation process is explained based on the functional design of the smart table curve data analysis tool:

[0062] 1. Select the configuration file and data file: After running the .exe executable file, use the interface buttons to select the target parameter configuration file and the address of the curve file to be processed. After selecting, click OK to jump to the curve capture object parameter configuration interface and pass the file address parameter to the textbox control of the curve parameter configuration interface.

[0063] 2. Data processing: After clicking the execution button on the interface, open the curve file we selected previously through the workbooks.open method.

[0064] After the file is opened, we start processing the data. According to the curve data saving format, we use "," as a delimiter to split the data and store the required data in an array. Then, according to the actual number of captured objects, we read the values ​​in the array one by one to wrap the data.

[0065] After the data is sorted, we calculate the data according to the calculation method selected on the configuration interface. For example, we calculate the total rate of forward active power. By searching the sorted data, we can capture the objects named forward active total power, forward active rate 1 power, forward active rate 2 power, etc. and other supported rate power, copy and store them in the total rate power calculation page, subtract the power of each rate from the total power, and judge the value after the subtraction. When the value is not equal to 0, it is judged as abnormal and the data field is marked in red.

[0066] After all calculations are completed, search for the data marked in red on each calculation page. After finding them, obtain the row and column numbers of the cells where the abnormal results are located and summarize them in the abnormal summary table in turn. Finally, determine whether there are any abnormalities in this data analysis through the abnormal summary.

[0067] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. An automated analysis method for electric energy meter reading data, characterized in that The method comprises the following steps: Obtain meter reading data of electric energy meter; Splitting the meter reading data according to a preset interval symbol to obtain split data; Integrating the split data according to preset capture object parameters to obtain integrated data; Calculating and judging the integrated data according to a preset calculation method and judgment condition to obtain a calculation result and a judgment result; If the judgment result is abnormal, determining that the data corresponding to the calculation result is abnormal data; The location information of the abnormal data is obtained, and an abnormal data summary table is generated.

2. The automatic analysis method of electric energy meter reading data according to claim 1 is characterized in that: Integrating the split data, including: Get the name and number of the preset target capture objects; Searching for the name of the target captured object in the split data to obtain target data; According to the number of the target captured objects, the target data are arranged and combined to obtain integrated data.

3. The automatic analysis method of electric energy meter reading data according to claim 1 is characterized in that: The step of calculating and judging the integrated data according to the preset calculation method and judgment condition includes: Get the preset target calculation parameters; According to the target calculation parameters, corresponding data is obtained from the integrated data, and a preset calculation formula is executed to obtain a calculation result; It is determined whether the calculation result meets a preset threshold condition. If not, the calculation result is determined as abnormal data.

4. The automatic analysis method for electric energy meter reading data according to any one of claims 1 to 3, characterized in that: The obtaining the location information of the abnormal data includes: obtaining the row number and column number of the abnormal data in the integrated data.

5. The automatic analysis method of electric energy meter reading data according to claim 1, characterized in that: The generating of the abnormal data summary table includes: Create a new abnormal data summary table, and fill the location information of the abnormal data into the abnormal data summary table; A jump link is set in the abnormal data summary table, and the jump link is used to jump to the position of the abnormal data in the integrated data.

6. The automatic analysis method of electric energy meter reading data according to claim 1, characterized in that: The method further comprises: Obtaining configuration parameters input by a user, wherein the configuration parameters include a spacing symbol, a capture object, a calculation method, and a judgment condition; The configuration parameters are saved to obtain a configuration parameter file; When the method is executed again, the configuration parameter file is read and the read configuration parameters are used as preset parameters.

7. The automatic analysis method of electric energy meter reading data according to claim 1, characterized in that: The method further includes judging the consistency of the calculation results between different electric energy meters, specifically: matching according to the time recorded by the electric energy meter, and collating the data collected at the same time together; Compare the electricity data, demand data, clock data, voltage and current value data of different meters collected at the same time point to determine their consistency; If the difference between the electricity data, demand data, clock data, and voltage and current value data of different meters collected at the same time point exceeds a preset threshold, it is judged to be inconsistent, and the data corresponding to the calculation result is determined to be abnormal data.

8. The automatic analysis method of electric energy meter reading data according to claim 1, characterized in that: The calculation method includes calculating the total forward active power rate, calculating the total reverse active power rate, calculating the total forward reactive power rate, calculating the total reverse reactive power rate, calculating the absolute value and total rate, calculating the absolute value difference total rate, calculating the forward apparent total rate, calculating the reverse apparent total rate, calculating the total electricity increment, calculating the interval electricity, calculating the average value, calculating the average power and the interval electricity, calculating the maximum demand consistency, calculating the reactive power one quadrant total rate, calculating the reactive power two quadrant total rate, calculating the reactive power three quadrant total rate and calculating the reactive power four quadrant total rate.

9. The automatic analysis method of electric energy meter reading data according to claim 8, characterized in that: The calculation of the total forward active power rate is specifically as follows: Search the data whose captured objects are named total forward active power, forward active rate 1 power, and forward active rate 2 power in the data; Copy the found data and store it in the total rate electricity calculation page; Subtract the electricity of each rate from the total electricity, and judge the value after the subtraction. When the value is not equal to 0, it is judged as abnormal, and the data field where the data is located is marked in red.

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