Return sorting management system based on digital twin

Abstract

The present invention discloses a return sorting management system based on digital twins, which relates to the technical field of sorting management and includes a sorting detection unit, a feature collection unit, a twin model library, a data analysis unit, and a result display unit. The present invention compares detection and classification analysis with a benchmark electric energy meter, comprehensively considers multiple electric energy metering parameters, and accurately determines whether the returned electric energy meter is faulty; combines the twin model library to perform secondary classification analysis based on appearance and weight to determine whether it is damaged, and the fault judgment calculation steps are rigorous, including the calculation of metering parameter error, error analysis value and fault assessment value, and compares them with preset thresholds; the appearance damage coefficient is calculated using a series of processing such as image similarity algorithm, and the weight damage coefficient calculation is concise and effective; the result display unit provides a clear decision-making basis for detection personnel, improves sorting efficiency, saves manpower and time costs, improves resource utilization, and repairs and reuses repairable electric energy meters.

Description

Technical Field

[0001] The present invention relates to the technical field of sorting management, and in particular to a return sorting management system based on digital twins. Background Art

[0002] With the continuous development of the power industry, the number of electricity meters in use is increasing. During their lifecycle, meters may be returned for various reasons. Returned meters require sorting and management to determine which meters can be put back into use after repair and which need to be scrapped. Traditional return sorting management relies primarily on manual inspection and empirical judgment, resulting in low efficiency and accuracy.

[0003] At the same time, with the continuous development of digital twin technology, its application in various fields is becoming increasingly widespread. By establishing a correspondence between virtual models and physical entities, digital twin technology can achieve real-time monitoring, analysis, and optimization of physical entities. Applying digital twin technology to return sorting management systems can improve the efficiency and accuracy of sorting management, reduce costs, and provide strong support for the sustainable development of the power industry.

[0004] Therefore, a return sorting and management system based on digital twins came into being. The system realizes accurate classification and judgment of returned electricity meters through comparative detection and feature collection of returned electricity meters, combined with data analysis and result display functions, and provides a scientific basis for the subsequent processing of returned electricity meters. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a return sorting management system based on digital twins.

[0006] The return sorting management system based on digital twin includes:

[0007] The sorting and testing unit is used to select an electric energy meter with good performance as a reference electric energy meter, then compare and test each returned electric energy meter with the reference electric energy meter, and collect the electric energy measurement parameters corresponding to each returned electric energy meter and the reference electric energy meter;

[0008] A feature collection unit, used to collect feature data of each returned electric energy meter;

[0009] Twin model library, used to store factory feature information of all types of electricity meters;

[0010] A data analysis unit is used to classify and analyze the electric energy metering parameters obtained by the sorting and detection unit, and determine whether the returned electric energy meter is faulty based on the classification analysis results. It is also used to combine the feature data of the returned electric energy meter with the twin model library for secondary classification analysis, and determine whether the returned electric energy meter is damaged based on the secondary classification analysis results;

[0011] The result display unit is used to display the judgment results of the data analysis unit to relevant testing personnel.

[0012] As a further solution of the present invention: the comparative detection method is as follows:

[0013] Under the same load conditions, the reference electric energy meter and the returned electric energy meter are connected respectively, and the electric energy measurement parameters of the two electric energy meters are recorded during the specified period.

[0014] As a further solution of the present invention: the electric energy metering parameters include but are not limited to attribute parameters corresponding to electric quantity, power, voltage, and current.

[0015] As a further solution of the present invention: the characteristic data includes the model, current appearance photo, and actual weight of the returned electricity meter; the factory characteristic information includes factory photos of each surface of the electricity meter and its factory weight.

[0016] As a further solution of the present invention: the classification analysis method is as follows:

[0017] Step 1.1, select a returned electric energy meter as an example;

[0018] Extract the energy measurement parameters corresponding to the returned energy meter and mark them as Ti; and extract the energy measurement parameters corresponding to the reference energy meter and mark them as Di;

[0019] Wherein, i=1, 2, ... n, n represents the number of attribute parameters in the electric energy metering parameters, and i represents the number of attribute parameters;

[0020] Step 1.2, let the value of i be 1, 2, ... n;

[0021] And the measurement parameter error Ci between the returned electric energy meter and the reference electric energy meter is calculated by Ci=|Di-Ti|;

[0022] Step 1.3, calculate the absolute value of the difference between the metering parameter error Ci between the returned electric energy meter and the reference electric energy meter and the preset allowable error threshold ui, and then record the result as the error analysis value Wi;

[0023] Wherein, ui represents the preset allowable error threshold according to the attribute parameter number;

[0024] Step 1.4. Calculate the fault assessment value P of the returned electricity meter by P=W1×α1+W2×α2+…Wn×αn;

[0025] Among them, αi is the corresponding preset weight value;

[0026] Step 1.5: Calculate the fault assessment value of each returned energy meter according to the method from Step 1.1 to Step 1.4;

[0027] Then, the fault assessment value of each returned electric energy meter is compared with a preset fault assessment threshold, and the returned electric energy meter that exceeds the fault assessment threshold is determined to be a faulty electric energy meter; otherwise, it is determined to be a normal electric energy meter.

[0028] As a further solution of the present invention: the secondary classification analysis method is as follows:

[0029] Step V1: Select a returned electricity meter and, based on the model of the returned electricity meter, obtain the factory photo and factory weight of the same model of electricity meter from the twin model library;

[0030] Step V2: Compare the current appearance photo and actual weight of the returned electric energy meter with the factory photo and factory weight of the same model electric energy meter, and obtain the appearance damage coefficient and weight damage coefficient of the returned electric energy meter;

[0031] Step V3: Multiply the weight damage coefficient and the appearance damage coefficient by the corresponding preset weight coefficients respectively, and then add the results to obtain the damage assessment coefficient;

[0032] Step V4: Compare the damage assessment coefficient with the preset damage assessment threshold. When the damage assessment coefficient exceeds the damage assessment threshold, the returned electricity meter is determined to be a damaged electricity meter. Otherwise, the returned electricity meter is determined to be a normal electricity meter.

[0033] As a further solution of the present invention: the comparison processing method in Step V2 is as follows:

[0034] Step 0.1: Take a current appearance photo and combine it with the image similarity algorithm to extract the factory photo with the highest similarity to the current appearance photo from all factory photos;

[0035] Step 0.2, adjust the size and resolution of the main body of the extracted factory photo and the current appearance photo to be consistent, and the total number of pixels of the main body of the electric energy meter in the factory photo and the current appearance photo after adjustment are consistent;

[0036] Step 0.3, convert the factory photo and current appearance photo of the electric energy meter into grayscale images;

[0037] Step 0.4, select the pixel values ​​of all pixel coordinates from the two grayscale images, then select the pixel value of the same pixel coordinate from the two grayscale images, and calculate the pixel difference of the same pixel coordinate;

[0038] Step 0.5. Obtain the pixel difference of all the points with the same pixel coordinates in the two grayscale images and mark them as Xj, where j = 1, 2, ..., m, and m represents the total number of pixels of the subject in the photo.

[0039] Step 0.6, calculate the average value of the pixel differences corresponding to all the same pixel coordinate points;

[0040] Then, the standard deviation of the pixel differences corresponding to all the same pixel coordinate points is calculated based on the average value;

[0041] Step 0.7, calculate the sum of the mean and standard deviation, then compare the pixel difference values ​​of each pixel coordinate point with the sum of the mean and standard deviation one by one, and obtain the number of pixel differences at the same pixel coordinate point that exceed the sum of the mean and standard deviation, and then mark it as an abnormal pixel v;

[0042] Step 0.8. Calculate the proportion of v in m and record it as the appearance damage coefficient;

[0043] Step 0.9. Extract the factory weight and actual weight of the same model of electricity meter, calculate the absolute value of the difference between them, then divide the result by the factory weight, and then mark the result as the weight loss coefficient.

[0044] As a further solution of the present invention: in Step 0.7, each pixel is arranged in a 9-square grid;

[0045] When the pixel difference of the same pixel coordinate point exceeds the sum of the mean and standard deviation, and the pixel differences of its eight adjacent pixel coordinates do not exceed the sum of the mean and standard deviation, the v value is reduced by one; otherwise, the v value remains unchanged.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention accurately determines whether a returned energy meter is faulty by comparing it with a benchmark meter and performing a classification analysis. This comprehensive assessment utilizes multiple energy metering parameters, taking into account multiple attribute parameters such as energy consumption, power, voltage, and current, improving the accuracy of fault determination.

[0048] This invention combines a twin model library with a secondary classification analysis of returned electricity meters to determine whether the returned meters are damaged. The evaluation is based on both appearance and weight, and the physical condition of the meter is comprehensively considered by comparing it with factory photos and weight.

[0049] This invention employs detailed calculation steps to determine if an electric energy meter is faulty. By calculating metering parameter errors, error analysis values, and fault assessment values ​​and comparing them with preset thresholds, the determination process is scientific and objective. Furthermore, the preset weights can be adjusted based on actual conditions, increasing the flexibility of the method.

[0050] This invention calculates the appearance loss coefficient using a series of scientific processing methods, including image similarity algorithms, image adjustment, grayscale conversion, and pixel difference calculation, accurately reflecting the degree of change in the meter's appearance. The weight loss coefficient is also simple and straightforward to calculate, effectively measuring the meter's weight change by comparing it with the factory weight.

[0051] In the present invention, the result display unit displays the determination result of the data analysis unit to relevant inspection personnel, providing them with a clear basis for decision-making, and facilitating rapid sorting and processing of returned electric energy meters.

[0052] The present invention has a high degree of automation of the entire system, can greatly improve the sorting efficiency of returned electric energy meters, and save manpower and time costs.

[0053] The present invention can repair and reuse repairable electric energy meters and reasonably dispose of electric energy meters that cannot be repaired by accurately determining the fault and damage status of returned electric energy meters, thereby improving resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a system block diagram of the present invention;

[0055] Figure 2 Schematic diagram of the process of the data analysis unit of the present invention Figure 1 ;

[0056] Figure 3 Schematic diagram of the process of the data analysis unit of the present invention Figure 2 ;

[0057] Figure 4 Schematic diagram of the comparison process in the data analysis unit of the present invention. DETAILED DESCRIPTION

[0058] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] Example 1

[0060] See also Figure 1 and Figure 2 As shown, the present invention is a return sorting management system based on digital twin, including:

[0061] The sorting and testing unit is used to select an electric energy meter with good performance as a reference electric energy meter, then compare and test each returned electric energy meter with the reference electric energy meter, and collect the electric energy measurement parameters corresponding to each returned electric energy meter and the reference electric energy meter;

[0062] The comparative test method is as follows:

[0063] Under the same load conditions, connect the reference energy meter and the returned energy meter respectively, and then record the energy measurement parameters of the two energy meters within the specified period;

[0064] Electric energy metering parameters include but are not limited to attribute parameters corresponding to electricity, power, voltage, and current;

[0065] Data analysis unit, used for classifying and analyzing electric energy metering parameters;

[0066] The classification analysis method is as follows:

[0067] Step 1.1, parameter extraction and marking

[0068] Take a returned electric energy meter as an example;

[0069] Extract the energy measurement parameters corresponding to the returned energy meter and mark them as Ti; and extract the energy measurement parameters corresponding to the reference energy meter and mark them as Di;

[0070] Wherein, i=1, 2, ... n, n represents the number of attribute parameters in the electric energy metering parameters, and i represents the number of attribute parameters;

[0071] Step 1.2. Calculation of measurement parameter errors

[0072] Let the value of i be 1, 2, ... n;

[0073] And the measurement parameter error Ci between the returned electric energy meter and the reference electric energy meter is calculated by Ci=|Di-Ti|;

[0074] Step 1.3. Calculation of error analysis value

[0075] Calculate the absolute value of the difference between the measurement parameter error Ci between the returned electric energy meter and the reference electric energy meter and the preset allowable error threshold ui, and then record the result as the error analysis value Wi;

[0076] Wherein, ui represents the preset allowable error threshold according to the attribute parameter number;

[0077] Step 1.4. Calculation of fault assessment value

[0078] The fault assessment value P of the returned electric energy meter is calculated by P=W1×α1+W2×α2+…Wn×αn;

[0079] Among them, αi is the corresponding preset weight value;

[0080] Step 1.5, Fault determination

[0081] Then the fault assessment value of the returned electric energy meter is compared with the preset fault assessment threshold Py:

[0082] When P>Py, the returned electric energy meter is determined to be a faulty electric energy meter;

[0083] When P≤Py, the returned electric energy meter is determined to be a normal electric energy meter;

[0084] Finally, according to the method from Step 1.1 to Step 1.5, determine whether each returned electric energy meter is a faulty electric energy meter;

[0085] The result display unit is used to display the judgment results of the data analysis unit to relevant testing personnel.

[0086] This embodiment provides a return sorting management system based on digital twins. By selecting a benchmark electricity meter with good performance and comparing it with the returned electricity meter, it can accurately collect the electricity metering parameters of the returned electricity meter, providing reliable data for subsequent analysis. The electricity metering parameters are classified and analyzed, and by calculating the metering parameter error, fault assessment value, etc., it can accurately determine whether the returned electricity meter is a faulty electricity meter, providing a scientific basis for sorting the returned electricity meters and improving sorting efficiency and accuracy. The judgment results are displayed to relevant inspection personnel, allowing them to promptly understand the status of the returned electricity meter and perform subsequent processing.

[0087] Example 2

[0088] See also Figure 1 、 Figure 3 、 Figure 4 As shown, as the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the technical solution of this embodiment is different from that of the first embodiment only in that this embodiment further includes:

[0089] A feature collection unit, used to collect feature data of each returned electric energy meter;

[0090] The characteristic data includes the model, current appearance photo, and actual weight of the returned electricity meter;

[0091] Twin model library, used to store factory photos of all surfaces of all models of electricity meters and their factory weights;

[0092] The data analysis unit is also used to combine the characteristic data of the returned electricity meters with the twin model library for secondary classification analysis:

[0093] The secondary classification analysis method is as follows:

[0094] Step V1. Get factory information

[0095] Select a returned electricity meter.

[0096] According to the model of the returned electric energy meter, obtain the factory photos and factory weight of the electric energy meter of the same model from the twin model library;

[0097] Step V2, compare the coefficients

[0098] Then, the current appearance photo and actual weight of the returned electric energy meter are compared with the factory photo and factory weight of the same model electric energy meter, and the appearance damage coefficient and weight damage coefficient of the returned electric energy meter are obtained;

[0099] The comparison is processed as follows:

[0100] Step 0.1. Extract similar photos

[0101] A current appearance photo, combined with an image similarity algorithm, and then extracting the factory photo with the highest similarity to the current appearance photo from all factory photos;

[0102] Step 0.2. Adjust photo parameters

[0103] Adjust the size and resolution of the main body of the extracted factory photo and the current appearance photo to be consistent, and the total number of pixels of the main body of the electric energy meter in the factory photo and the current appearance photo after adjustment are consistent;

[0104] Step 0.3, convert to grayscale image

[0105] Convert the factory photos and current appearance photos corresponding to the electric energy meter into grayscale images;

[0106] Step 0.4. Calculate pixel difference

[0107] The pixel values ​​of all pixel coordinate points are obtained from the two grayscale images respectively, and then the pixel value of the same pixel coordinate point is selected from the two grayscale images, and the pixel difference of the same pixel coordinate point is calculated;

[0108] Step 0.5: Mark pixel differences

[0109] Get the pixel difference of all the same pixel coordinates in the two grayscale images and mark it as Xj, j = 1, 2, ... m, m represents the total number of pixels of the subject in the photo;

[0110] Step 0.6. Calculate the mean and standard deviation

[0111] pass: , calculate the average value XP of the pixel differences corresponding to all the same pixel coordinate points;

[0112] pass: , calculate the standard deviation XB of the pixel differences corresponding to all the same pixel coordinate points;

[0113] Step 0.7. Determine the number of abnormal pixels

[0114] By XH=XP+XB, the sum of the mean and standard deviation XH is calculated, and then the pixel difference values ​​of each pixel coordinate point are compared with XH one by one, and the number of pixel differences exceeding XH at the same pixel coordinate point is obtained, and then it is marked as the abnormal pixel quantity v;

[0115] Step 0.8. Calculate the appearance damage coefficient

[0116] By K0=v / m, calculate the proportion of v in m, K0, and record K0 as the appearance damage coefficient;

[0117] Step 0.9, calculate the weight loss coefficient

[0118] Extract the factory weight and actual weight of the same model of electric energy meter;

[0119] Then, the weight loss coefficient F0 is calculated through F0= (E0-E1) / E0;

[0120] Where, E0 is the factory weight of the same model of electric energy meter, and E1 is the actual weight of the same model of electric energy meter;

[0121] Step V3. Calculate the damage assessment coefficient

[0122] The damage assessment coefficient H of the returned electric energy meter is calculated by H=F0×γ1+K0×γ2;

[0123] Where F0 is the weight loss coefficient, K0 is the appearance loss coefficient, γ1 and γ2 are the corresponding preset weight coefficients;

[0124] Step V4, damage determination

[0125] Compare the damage assessment coefficient with the preset damage assessment threshold Hy:

[0126] When H>Hy, the returned electric energy meter is determined to be a damaged electric energy meter;

[0127] When H≤Hy, the returned electricity meter is determined to be a normal electricity meter.

[0128] In this embodiment, this embodiment can directly evaluate and determine all returned electric energy meters, or, based on the first embodiment, perform a secondary evaluation and determination on all normal electric energy meters obtained in the first embodiment.

[0129] Based on the first embodiment, this embodiment adds a feature acquisition unit and a twin model library, which can collect feature data of returned electricity meters and perform secondary classification analysis in combination with the twin model library, thereby improving the comprehensiveness and accuracy of the judgment of the status of returned electricity meters; by comparing the current appearance photo and actual weight of the returned electricity meter with the factory photo and factory weight of the electricity meter of the same model, the appearance damage coefficient and weight damage coefficient are obtained, and then the damage assessment coefficient is calculated, which can accurately determine whether the returned electricity meter is a damaged electricity meter, and provide support for the refined sorting of returned electricity meters.

[0130] Example 3

[0131] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments. The technical solution of this embodiment is different from the first and second embodiments only in that: on the basis of the second embodiment, this embodiment removes outliers from the abnormal pixel quantity;

[0132] The method is as follows:

[0133] Among them, each pixel is arranged in the form of a 9-square grid;

[0134] When the pixel difference of the same pixel coordinate point exceeds the sum of the mean and standard deviation, and the pixel differences of its eight adjacent pixel coordinates do not exceed the sum of the mean and standard deviation, the v value is reduced by one; otherwise, the v value remains unchanged.

[0135] Based on the second embodiment, this embodiment removes abnormal values ​​from abnormal pixels, thereby improving the accuracy of the calculation of the appearance damage coefficient, thereby improving the accuracy of the damage assessment coefficient, and making the judgment of the status of the returned electricity meter more reliable.

[0136] Example 4

[0137] As the fourth embodiment of the present invention, when this application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second and third embodiments.

[0138] This embodiment combines the solutions of the first, second and third embodiments, and can comprehensively and accurately sort and manage returned electricity meters, thereby improving sorting efficiency and accuracy and providing a scientific basis for the reuse or disposal of returned electricity meters.

[0139] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0140] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The return sorting management system based on digital twin is characterized by: include: The sorting and testing unit is used to select an electric energy meter with good performance as a reference electric energy meter, then compare and test each returned electric energy meter with the reference electric energy meter, and collect the electric energy metering parameters corresponding to each returned electric energy meter and the reference electric energy meter obtained from the comparison test; A feature collection unit, used to collect feature data of each returned electric energy meter; Twin model library, used to store factory feature information of all types of electricity meters; A data analysis unit is used to classify and analyze the electric energy metering parameters obtained by the sorting and detection unit, and determine whether the returned electric energy meter is faulty based on the classification analysis results. It is also used to combine the feature data of the returned electric energy meter with the twin model library for secondary classification analysis, and determine whether the returned electric energy meter is damaged based on the secondary classification analysis results; The classification analysis method is as follows: Step 1.1, select a returned electric energy meter as an example; Extract the energy measurement parameters corresponding to the returned energy meter and mark them as Ti; and extract the energy measurement parameters corresponding to the reference energy meter and mark them as Di; Wherein, i=1, 2, ... n, n represents the number of attribute parameters in the electric energy metering parameters, and i represents the number of attribute parameters; Step 1.2, calculate the absolute value of the difference between the corresponding electric energy measurement parameters of the returned electric energy meter and the reference electric energy meter, and mark it as the measurement parameter error Ci; Step 1.3, calculate the absolute value of the difference between the metering parameter error Ci between the returned electric energy meter and the reference electric energy meter and the preset allowable error threshold ui, and then mark the result as the error analysis value Wi; Wherein, ui represents the preset allowable error threshold according to the attribute parameter number; Step 1.

4. Calculate the fault assessment value P of the returned electricity meter by P=W1×α1+W2×α2+…Wn×αn; Among them, αi is the corresponding preset weight value; Step 1.5: Calculate the fault assessment value of each returned energy meter according to the method from Step 1.1 to Step 1.4; Then, the fault assessment value of each returned energy meter is compared with the preset fault assessment threshold, and the returned energy meter that exceeds the fault assessment threshold is determined to be a faulty energy meter; otherwise, it is determined to be a normal energy meter; Among them, the secondary classification analysis method is as follows: Step V1: Select a returned electricity meter and, based on the model of the returned electricity meter, obtain the factory photo and factory weight of the same model of electricity meter from the twin model library; Step V2: Compare the current appearance photo and actual weight of the returned electric energy meter with the factory photo and factory weight of the same model electric energy meter, and obtain the appearance damage coefficient and weight damage coefficient of the returned electric energy meter; Step V3: Multiply the weight damage coefficient and the appearance damage coefficient by the corresponding preset weight coefficients respectively, and then add the results to obtain the damage assessment coefficient; Step V4: Compare the damage assessment coefficient with the preset damage assessment threshold. When the damage assessment coefficient exceeds the damage assessment threshold, the returned electricity meter is determined to be a damaged electricity meter. Otherwise, the returned electricity meter is determined to be a normal electricity meter.

2. The return sorting management system based on digital twin according to claim 1 is characterized in that: The comparison process in StepV2 is as follows: Step 0.1: Take a current appearance photo and combine it with the image similarity algorithm to extract the factory photo with the highest similarity to the current appearance photo from all factory photos; Step 0.

2. Adjust the subject size and resolution of the extracted factory photo and the current appearance photo to be consistent; Step 0.3, convert the factory photo and current appearance photo of the electric energy meter into grayscale images; Step 0.4, select the pixel values ​​of all pixel coordinates from the two grayscale images, then select the pixel value of the same pixel coordinate from the two grayscale images, and calculate the pixel difference of the same pixel coordinate; Step 0.5, obtain the pixel difference of all the same pixel coordinate points in the two grayscale images; Step 0.6, calculate the average value of the pixel differences corresponding to all the same pixel coordinate points; Then, the standard deviation of the pixel differences corresponding to all the same pixel coordinate points is calculated based on the average value; Step 0.7, calculate the sum of the mean and standard deviation, then compare the pixel difference values ​​of each pixel coordinate point with the sum of the mean and standard deviation one by one, and obtain the number of pixel differences at the same pixel coordinate point that exceed the sum of the mean and standard deviation, and then mark it as an abnormal pixel v; Step 0.

8. Calculate the proportion of v in m and record it as the appearance damage coefficient; Where m represents the total number of pixels of the subject in the photo; Step 0.

9. Extract the factory weight and actual weight of the same model of electricity meter, calculate the absolute value of the difference between them, then divide the result by the factory weight, and then mark the result as the weight loss coefficient.

3. The return sorting management system based on digital twin according to claim 1 is characterized in that: The comparative test method is as follows: Under the same load conditions, the reference electric energy meter and the returned electric energy meter are connected respectively, and the electric energy measurement parameters of the two electric energy meters are recorded during the specified period.

4. The return sorting management system based on digital twin according to claim 1 is characterized in that: Electric energy metering parameters include attribute parameters corresponding to electricity, power, voltage, and current.

5. The return sorting management system based on digital twin according to claim 1 is characterized in that: The characteristic data includes the model, current appearance photo, and actual weight of the returned electricity meter; The factory characteristic information includes factory photos of each surface of the electricity meter and its factory weight.

6. The return sorting management system based on digital twin according to claim 2 is characterized in that: After adjustment in Step 0.2, the total number of pixels of the electricity meter body is consistent in the factory photo and the current appearance photo.

7. The return sorting management system based on digital twin according to claim 2 is characterized in that: In Step 0.7, each pixel is arranged in a 9-square grid; When the pixel difference of the same pixel coordinate point exceeds the sum of the mean and standard deviation, and the pixel differences of its eight adjacent pixel coordinates do not exceed the sum of the mean and standard deviation, the v value is reduced by one; otherwise, the v value remains unchanged.

8. The return sorting management system based on digital twin according to claim 1 is characterized in that: Also includes: The result display unit is used to display the judgment results of the data analysis unit to relevant testing personnel.

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