A smart electricity meter state perception evaluation method and system based on digital twin technology

Abstract

The present application relates to a method and system for state perception and evaluation of smart electricity meters based on digital twin technology, which relates to the technical field of metering equipment in the power industry. It solves the time-consuming problem that regardless of whether there is a problem with the electricity meter, staff need to conduct on-site calibration to determine its operating status. The method includes: converting equipment data into action data corresponding to the mechanism model, and combining the action data and the mechanism model analysis to obtain the working model matched by the smart electricity meter in each time period; comparing the working model matched by the electricity meter in each time period with the digital twin model corresponding to the preset electricity meter in different time periods; if the comparison is consistent, the electricity meter state is determined to be normal; if the comparison is inconsistent, the electricity meter state is determined to be abnormal, and the abnormal situation is sent to the terminal held by the person in charge. The present application has the following effects: improving the efficiency of discovering problems with electricity meters and the efficiency of handling problems.

Description

Technical Field

[0001] The present application relates to the technical field of metering equipment in the electric power industry, and in particular to a method and system for state perception and evaluation of smart electricity meters based on digital twin technology. Background Art

[0002] The electric energy meter is an important component of the power grid and has the closest relationship with users. Due to the requirements of smart grid interoperability, users' expectations for electric energy meters are constantly increasing, which has led to the emergence of smart electric energy meters. Smart electric energy meters are one of the basic devices for smart grid data collection. They are responsible for the collection, measurement and transmission of raw electric energy data. They are the basis for realizing information integration, analysis optimization and information display. As a result, the existing dual-core smart electric energy meter can no longer meet the application needs.

[0003] At present, power companies mainly analyze the status of electric energy meters running on site by having their staff conduct on-site verification to determine their operating status.

[0004] Regarding the above-mentioned related technologies, the inventors believe that there are the following defects: regardless of whether there is a problem with the electricity meter, staff are required to conduct on-site verification to determine its operating status, which is time-consuming. Summary of the Invention

[0005] In order to improve the efficiency of discovering and handling problems of electricity meters, the present application provides a method and system for intelligent electricity meter state perception and evaluation based on digital twin technology.

[0006] In the first aspect, the present application provides a method for state perception and evaluation of smart electricity meters based on digital twin technology, which adopts the following technical solutions:

[0007] A method for state perception and evaluation of smart electricity meters based on digital twin technology includes:

[0008] Obtaining device data of a target electric energy meter and a mechanism model corresponding to the target electric energy meter;

[0009] Convert device data into action data corresponding to the mechanism model, and combine the action data and mechanism model analysis to obtain the working model matched by the smart energy meter in each time period;

[0010] Compare the working model matched by the electric energy meter in each time period with the digital twin model corresponding to the preset electric energy meter in different time periods;

[0011] If the comparison is consistent, it is determined that the status of the electric energy meter is normal;

[0012] If the comparison is inconsistent, the electricity meter status is determined to be abnormal, and the abnormal situation is sent to the terminal held by the person in charge.

[0013] Optionally, obtaining the device data of the target electric energy meter and the mechanism model corresponding to the target electric energy meter includes:

[0014] Obtaining the acquisition period preset by the person to whom the target electric energy meter belongs, wherein the acquisition period includes periodic and real-time;

[0015] If the acquisition period preset by the person to whom the target electric energy meter belongs is real-time, the device data of the target electric energy meter and the mechanism model corresponding to the target electric energy meter are acquired in real time;

[0016] If the acquisition period preset by the person to whom the target electric energy meter belongs is periodic, the average number of times the target electric energy meter is in abnormal state per month is obtained;

[0017] If the average number of times the target electric energy meter is in an abnormal electric energy meter state per month is less than a first preset number, acquiring device data of the target electric energy meter and a mechanism model corresponding to the target electric energy meter according to a first period;

[0018] If the average number of times the target electric energy meter is in an abnormal electric energy meter state per month exceeds a second predetermined number, obtaining device data of the target electric energy meter and a mechanism model corresponding to the target electric energy meter according to a second periodic period, the second periodic period being shorter than the first periodic period;

[0019] If the average number of times the target electricity meter is in an abnormal electricity meter state per month is greater than or equal to the first preset number of times and less than or equal to the second preset number of times, then based on the average number of times the other target electricity meters in the preset area are in an abnormal electricity meter state per month and the corresponding periodic period, and the average number of times the target electricity meter is in an abnormal electricity meter state per month, analyze and obtain the third periodic period, and obtain the equipment data of the target electricity meter and the mechanism model corresponding to the target electricity meter according to the third periodic period.

[0020] Optionally, the analysis for the third period is obtained as follows:

[0021] Obtain the ratio of the average number of times each month that the remaining target electric energy meters in the preset area are in an abnormal electric energy meter state to the average number of times each month that the target electric energy meter is in an abnormal electric energy meter state;

[0022] Obtain statistics on the average period of time corresponding to the remaining target electric energy meters in the preset area;

[0023] The third period is obtained by analysis based on the ratio and the statistically obtained average period corresponding to the remaining target electric energy meters in the preset area.

[0024] Optionally, the analysis and acquisition of the preset area includes:

[0025] Obtain the external temperature and humidity information and time nodes corresponding to the abnormal state of the target electricity meter;

[0026] Analyze and obtain the difference between the parameters corresponding to the external temperature and humidity information of the remaining electric energy meters when the target electric energy meter is in an abnormal state and the parameters corresponding to the external temperature and humidity information corresponding to the target electric energy meter when the target electric energy meter is in an abnormal state;

[0027] The remaining electric energy meters whose difference values ​​are within the preset difference range are selected as reference electric energy meters, and all reference electric energy meters are included in the electric energy meters of the preset area.

[0028] Optionally, the method further includes steps after determining that the electric energy meter state is abnormal and before sending the abnormality to the terminal held by the person in charge, specifically as follows:

[0029] Compare the working model matched by the electric energy meter in each period with the preset digital twin model corresponding to the smart electric energy meter under different periods and under the influence of different factor combinations, including environmental temperature and humidity factors, equipment component failure factors

[0030] If the comparison is consistent, the corresponding external factor combination is analyzed as the factor combination that causes the abnormal state of the electric energy meter;

[0031] Analyze and determine the treatment plan based on the correspondence between the combination of factors and the treatment plan, and record it in the notification information sent to the terminal held by the person in charge;

[0032] If the comparison is inconsistent, continue with the next step.

[0033] Optionally, if the factor combination is a factor causing the failure of some equipment components, then based on the corresponding relationship between the factor combination and the treatment plan, the treatment plan is analyzed and determined to include:

[0034] Analyze the specific locations of equipment parts that need to be replaced;

[0035] Analyze and determine replacement precautions based on the correspondence between the specific location of the equipment parts to be replaced and the replacement precautions;

[0036] The specific location of the equipment parts that need to be replaced and the replacement precautions will be used as the solution.

[0037] Optionally, if the factor combination is an environmental factor, then based on the corresponding relationship between the factor combination and the treatment plan, the treatment plan is analyzed and determined to include:

[0038] Obtain the ambient temperature and humidity of the target electric energy meter within a preset time period in the future;

[0039] If the total proportion of the parameters corresponding to the ambient temperature and humidity information of the target electric energy meter within the preset time period in the future that fall within the parameter range corresponding to the preset ambient temperature and humidity information is less than the preset proportion, then after confirming the processing plan, the person in charge closest to the target electric energy meter will be designated as the processing person, and the specific processing plan will be sent to the terminal held by the corresponding processing person;

[0040] If the total proportion of the parameters corresponding to the ambient temperature and humidity information of the target electricity meter within the preset time period in the future that fall within the parameter range corresponding to the preset ambient temperature and humidity information is greater than or equal to the preset proportion, then after confirming the processing plan, the person in charge who is within the preset distance range from the target electricity meter and has the highest processing success rate will be selected as the processing personnel, and the specific processing plan will be sent to the terminal held by the corresponding processing personnel.

[0041] Optionally, the person in charge closest to the target electric energy meter is designated as the processing person, and a specific processing plan is sent to the terminal held by the corresponding processing person, including:

[0042] Analyze the attention items corresponding to the historical errors of the person in charge closest to the target electricity meter;

[0043] When the person in charge who is closest to the target electric energy meter arrives at the target electric energy meter, the attention item is sent to the terminal held by the person in charge as a reminder.

[0044] In the second aspect, the present application provides a smart electricity meter state perception and evaluation system based on digital twin technology, which adopts the following technical solutions:

[0045] A smart electricity meter state perception and evaluation system based on digital twin technology includes a memory, a processor, and a program stored in the memory and executable on the processor. When the program is loaded and executed by the processor, the smart electricity meter state perception and evaluation method based on digital twin technology as described in the first aspect is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0047] Figure 1 This is a schematic diagram of the overall process of a smart electricity meter state perception and evaluation method based on digital twin technology in an embodiment of the present application.

[0048] Figure 2 It is a schematic diagram of the process of obtaining the device data of a target electric energy meter and the mechanism model corresponding to the target electric energy meter in another embodiment of the present application.

[0049] Figure 3 This is a schematic diagram of the analysis and acquisition process for the third period of another embodiment of the present application.

[0050] Figure 4It is a flowchart of analyzing and obtaining a preset area according to another embodiment of the present application.

[0051] Figure 5 This is a flowchart of another embodiment of the present application, which is located after determining that the status of the electric energy meter is abnormal and before sending the abnormal situation to the terminal held by the person in charge.

[0052] Figure 6 In another embodiment of the present application, if the factor combination is a factor causing the failure of some parts of the equipment, a flowchart of the processing solution is analyzed and determined based on the correspondence between the factor combination and the processing solution.

[0053] Figure 7 In another embodiment of the present application, if the factor combination is an environmental factor, a flowchart of a treatment solution is analyzed and determined based on the correspondence between the factor combination and the treatment solution.

[0054] Figure 8 Another embodiment of the present application is to use the person in charge who is closest to the target electricity meter as the processing person, and send a specific processing plan to the terminal process diagram held by the corresponding processing person. DETAILED DESCRIPTION

[0055] The present application is further described in detail below with reference to the accompanying drawings.

[0056] Reference Figure 1 , which is a smart electricity meter state perception and evaluation method based on digital twin technology disclosed in this application, including:

[0057] Step S100: Acquire device data of a target electric energy meter and a mechanism model corresponding to the target electric energy meter.

[0058] Among them, the equipment data of the target electricity meter may include static data and dynamic data. The static data includes equipment appearance, equipment structure, etc., and the dynamic data may include equipment operation data, equipment energy consumption, etc. The mechanism model corresponding to the target electricity meter refers to an accurate mathematical model established based on the internal mechanism of the object, production process, or the transfer mechanism of material flow.

[0059] The target meter's device data and corresponding mechanism model can be retrieved from a pre-set database storing the target meter's device data. The mechanism model can be obtained from the industrial mechanism platform at the BaaS layer, or the IoT management platform can first obtain it from the industrial mechanism platform and then from the IoT management platform. The higher the match between the mechanism model and the industrial equipment, the more accurately it can simulate the equipment's lifecycle, thereby improving the accuracy of the digital twin.

[0060] In step S200 , the device data is converted into action data corresponding to the mechanism model, and the action data and the mechanism model are combined to analyze and obtain the working model matched by the smart energy meter in each time period.

[0061] Because device data is static parameter data, it needs to be converted into action data corresponding to the mechanism model to facilitate simulation of the target device. For example, if the device data is a rotational speed of 300 revolutions per second, it needs to be converted into corresponding rotational action data. The analysis and acquisition of the operating model that matches the smart energy meter in each time period is formed by integrating the mechanism model and action data.

[0062] In step S300 , the working model matched by the electric energy meter in each time period is compared with the digital twin model corresponding to the electric energy meter in different preset time periods.

[0063] In step S400, if the comparison is consistent, it is determined that the state of the electric energy meter is normal.

[0064] In step S500, if the comparison is inconsistent, the state of the electric energy meter is determined to be abnormal, and the abnormal situation is sent to the terminal held by the person in charge.

[0065] exist Figure 1 In step S100, it is further considered that in the process of formulating the equipment data of the target electric energy meter and the mechanism model period corresponding to the target electric energy meter, it is necessary to consider that the personnel of the target electric energy meter will also consider regular settings. Therefore, it is necessary to further analyze the acquisition of the equipment data of the target electric energy meter and the mechanism model corresponding to the target electric energy meter. For details, refer to Figure 2 The illustrated embodiment is described in detail.

[0066] Reference Figure 2 , the acquisition of the device data of the target electric energy meter and the mechanism model corresponding to the target electric energy meter includes:

[0067] Step S110 , obtaining an acquisition period preset by a person who owns the target electric energy meter, wherein the acquisition period includes periodic and real-time.

[0068] Step S120 : If the acquisition deadline preset by the person to whom the target electric energy meter belongs is real-time, then the device data of the target electric energy meter and the mechanism model corresponding to the target electric energy meter are acquired in real time.

[0069] Step S130 : If the acquisition period preset by the person to whom the target electric energy meter belongs is periodic, the average number of times the target electric energy meter is in an abnormal state per month is acquired.

[0070] Step S140 : if the average number of times the target electric energy meter is in an abnormal state per month is less than a first preset number, then obtaining device data of the target electric energy meter and a mechanism model corresponding to the target electric energy meter according to a first period.

[0071] Step S150: If the average number of times the target electric energy meter is in an abnormal state per month exceeds a second predetermined number, the device data of the target electric energy meter and the mechanism model corresponding to the target electric energy meter are obtained according to a second period, and the second period is shorter than the first period.

[0072] Step S160: If the average number of times the target electricity meter is in an abnormal electricity meter state per month is greater than or equal to the first preset number of times and less than or equal to the second preset number of times, then according to the average number of times the other target electricity meters in the preset area are in an abnormal electricity meter state per month and the corresponding periodic period, and the average number of times the target electricity meter is in an abnormal electricity meter state per month, analyze and obtain the third periodic period, and obtain the equipment data of the target electricity meter and the mechanism model corresponding to the target electricity meter according to the third periodic period.

[0073] exist Figure 2 In step S160, further disclosure of the third period is considered, with specific reference to Figure 3 The illustrated embodiment is described in detail.

[0074] Reference Figure 3 , the analysis of the third period is obtained as follows:

[0075] Step S161 , obtaining the ratio of the average number of times that the remaining target electric energy meters in the preset area are in abnormal electric energy meter status each month to the average number of times that the target electric energy meter is in abnormal electric energy meter status each month.

[0076] Step S162: Statistically obtain the average periodic periods corresponding to the remaining target electric energy meters in the preset area.

[0077] Step S163 , analyzing and obtaining a third periodic period based on the ratio and the statistically obtained average periodic periods corresponding to the remaining target electric energy meters in the preset area.

[0078] exist Figure 3 In step S161, it is further considered that the preset area is not necessarily a place, but may also be a collection of similar environments. Therefore, it is necessary to further analyze the acquisition of the preset area. For details, refer to Figure 4 The illustrated embodiment is described in detail.

[0079] Reference Figure 4 , the acquisition of the preset area includes:

[0080] Step S162.1, obtaining the external temperature and humidity information and the time node corresponding to the abnormal state of the target electricity meter.

[0081] Step S162.2, analyzing and obtaining the difference between the parameters corresponding to the external temperature and humidity information of the remaining electric energy meters when the target electric energy meter is in an abnormal state and the parameters corresponding to the external temperature and humidity information corresponding to the target electric energy meter when the target electric energy meter is in an abnormal state.

[0082] Step S162.3: The remaining electric energy meters whose differences are within the preset difference range are selected as reference electric energy meters, and all reference electric energy meters are included in the electric energy meters in the preset area.

[0083] exist Figure 1 In step S500, it is further considered that after determining that the state of the electric energy meter is abnormal and before sending the abnormal situation to the terminal held by the person in charge, the abnormal situation should be further analyzed to accurately analyze and determine the abnormality, so as to better ensure that when notifying the person in charge, the notification can be more accurate. For details, refer to Figure 5 The illustrated embodiment is described in detail.

[0084] Reference Figure 5 A method for evaluating the state of a smart electric energy meter based on digital twin technology also includes the following steps after determining that the electric energy meter state is abnormal and before sending the abnormality to a terminal held by a person in charge:

[0085] In step S510, the working model matched by the electric energy meter in each time period is compared with the preset digital twin model corresponding to the smart electric energy meter in different time periods and under the influence of different factor combinations, including environmental temperature and humidity factors and equipment component failure factors.

[0086] In step S520, if the comparison is consistent, the corresponding external factor combination is analyzed to determine whether it is a factor combination that causes the abnormal state of the electric energy meter.

[0087] In step S530, a processing solution is determined based on the correspondence between the combination of factors and the processing solution, and is recorded in a notification message sent to the terminal held by the person in charge.

[0088] Step S540: If the comparison is inconsistent, continue with the subsequent steps.

[0089] Reference Figure 6 If the factor combination is the failure factor of some parts of the equipment, then according to the corresponding relationship between the factor combination and the treatment plan, the treatment plan is analyzed and determined to include:

[0090] Step S53a: Analyze the specific location of the equipment parts that need to be replaced.

[0091] Step S53b: Analyze and determine replacement precautions based on the correspondence between the specific location of the equipment parts to be replaced and the replacement precautions.

[0092] Step S53c: The specific location of the equipment parts to be replaced and the replacement precautions are used as a processing solution.

[0093] Reference Figure 7 If the factor combination is an environmental factor, then according to the corresponding relationship between the factor combination and the treatment plan, the treatment plan is analyzed and determined to include:

[0094] Step S53A: obtaining the ambient temperature and humidity of the target electric energy meter within a future preset time period.

[0095] Step S53B, if the total proportion of the parameters corresponding to the ambient temperature and humidity information of the target electricity meter within the preset time period in the future that fall into the parameter interval range corresponding to the preset ambient temperature and humidity information is less than the preset proportion, then after confirming the processing plan, the person in charge closest to the target electricity meter will be designated as the processing personnel, and the specific processing plan will be sent to the terminal held by the corresponding processing personnel.

[0096] Step S53C, if the total proportion of the parameters corresponding to the ambient temperature and humidity information of the target electricity meter within the preset time period in the future that fall into the parameter interval range corresponding to the preset ambient temperature and humidity information is greater than or equal to the preset proportion, then after confirming the processing plan, the person in charge who is within the preset distance range from the target electricity meter and has the highest processing success rate will be selected as the processing personnel, and the specific processing plan will be sent to the terminal held by the corresponding processing personnel.

[0097] Reference Figure 8 , the person in charge closest to the target electricity meter is designated as the processing person, and the specific processing plan is sent to the terminal held by the corresponding processing person.

[0098] Step S53B.1, analyzing the attention items corresponding to the historical error situations of the person in charge closest to the target electric energy meter.

[0099] Step S53B.2: When the person in charge who is closest to the target electricity meter arrives at the target electricity meter, the attention item is sent to the terminal held by the person in charge as a reminder.

[0100] Based on the same inventive concept, an embodiment of the present invention provides a smart electric energy meter state perception and evaluation system based on digital twin technology, including a memory and a processor, wherein the memory stores data that can be executed on the processor to implement the following Figures 1 to 8 Procedure for either method.

[0101] The examples of this specific implementation are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, any equivalent changes made based on the structure, shape, and principle of this application should be included in the scope of protection of this application.

Claims

1. A method for state perception and evaluation of smart electric energy meters based on digital twin technology, characterized in that: include: Obtaining device data of a target electric energy meter and a mechanism model corresponding to the target electric energy meter; Convert device data into action data corresponding to the mechanism model, and combine the action data and mechanism model analysis to obtain the working model matched by the smart energy meter in each time period; Compare the working model matched by the electric energy meter in each time period with the digital twin model corresponding to the preset electric energy meter in different time periods; If the comparison is consistent, the electric energy meter is judged to be in normal state; If the comparison is inconsistent, the electricity meter status is determined to be abnormal, and the abnormal situation is sent to the terminal held by the person in charge; The acquisition of the device data of the target electric energy meter and the mechanism model corresponding to the target electric energy meter includes: Obtaining the acquisition period preset by the person to whom the target electric energy meter belongs, wherein the acquisition period includes periodic and real-time; If the acquisition period preset by the person to whom the target electric energy meter belongs is real-time, the device data of the target electric energy meter and the mechanism model corresponding to the target electric energy meter are acquired in real time; If the acquisition period preset by the person to whom the target electric energy meter belongs is periodic, the average number of times the target electric energy meter is in an abnormal state each month is obtained; If the average number of times the target electric energy meter is in an abnormal state per month is less than a first preset number, acquiring device data of the target electric energy meter and a mechanism model corresponding to the target electric energy meter according to a first period; If the average number of times the target electric energy meter is in an abnormal electric energy meter state per month exceeds a second predetermined number, obtaining device data of the target electric energy meter and a mechanism model corresponding to the target electric energy meter according to a second periodic period, the second periodic period being shorter than the first periodic period; If the average number of times the target electricity meter is in an abnormal electricity meter state per month is greater than or equal to the first preset number of times and less than or equal to the second preset number of times, then based on the average number of times the other target electricity meters in the preset area are in an abnormal electricity meter state per month and the corresponding periodic period, and the average number of times the target electricity meter is in an abnormal electricity meter state per month, analyze and obtain the third periodic period, and obtain the equipment data of the target electricity meter and the mechanism model corresponding to the target electricity meter according to the third periodic period.

2. The method for state perception and evaluation of a smart electric energy meter based on digital twin technology according to claim 1 is characterized in that: The analysis of the third period is obtained as follows: Obtain the ratio of the average number of times each month that the remaining target electric energy meters in the preset area are in abnormal electric energy meter status to the average number of times each month that the target electric energy meter is in abnormal electric energy meter status; Obtain statistics on the average period of time corresponding to the remaining target electric energy meters in the preset area; The third period is obtained by analysis based on the ratio and the statistically obtained average period corresponding to the remaining target electric energy meters in the preset area.

3. The method for state perception and evaluation of a smart electric energy meter based on digital twin technology according to claim 2 is characterized in that: The analysis and acquisition of the preset area includes: Obtain the external temperature and humidity information and time nodes corresponding to the abnormal state of the target electricity meter; Analyze and obtain the difference between the parameters corresponding to the external temperature and humidity information of the remaining electric energy meters when the target electric energy meter is in an abnormal state and the parameters corresponding to the external temperature and humidity information corresponding to the target electric energy meter when the target electric energy meter is in an abnormal state; The remaining electric energy meters whose difference values ​​are within the preset difference range are selected as reference electric energy meters, and all reference electric energy meters are included in the electric energy meters of the preset area.

4. The method for state perception and evaluation of a smart electric energy meter based on digital twin technology according to claim 3 is characterized in that: The method also includes the following steps after determining that the electric energy meter is in an abnormal state and before sending the abnormal state to the terminal held by the person in charge: Compare the working model of the electricity meter in each time period with the preset digital twin model corresponding to the smart electricity meter at different time periods and under different combinations of factors, including environmental temperature and humidity factors and equipment component failure factors; If the comparison is consistent, the corresponding external factor combination is analyzed as the factor combination that causes the abnormal state of the electric energy meter; Analyze and determine the treatment plan based on the correspondence between the combination of factors and the treatment plan, and record it in the notification information sent to the terminal held by the person in charge; If the comparison is inconsistent, continue with the next step.

5. The method for state perception and evaluation of smart electric energy meters based on digital twin technology according to claim 4 is characterized in that: If the factor combination is the cause of failure of some parts of the equipment, then according to the corresponding relationship between the factor combination and the treatment plan, the treatment plan is analyzed and determined to include: Analyze the specific locations of equipment parts that need to be replaced; Analyze and determine replacement precautions based on the correspondence between the specific location of the equipment parts to be replaced and the replacement precautions; The specific location of the equipment parts that need to be replaced and the replacement precautions will be used as the solution.

6. The method for state perception and evaluation of smart electric energy meters based on digital twin technology according to claim 4 is characterized in that: If the factor combination is environmental factors, then according to the corresponding relationship between the factor combination and the treatment plan, the treatment plan is analyzed and determined to include: Obtain the ambient temperature and humidity of the target electric energy meter within a preset time period in the future; If the total proportion of the parameters corresponding to the ambient temperature and humidity information of the target electric energy meter within the preset time period in the future that fall within the parameter range corresponding to the preset ambient temperature and humidity information is less than the preset proportion, then after confirming the processing plan, the person in charge closest to the target electric energy meter will be designated as the processing person, and the specific processing plan will be sent to the terminal held by the corresponding processing person; If the total proportion of the parameters corresponding to the ambient temperature and humidity information of the target electricity meter within the preset time period in the future that fall within the parameter range corresponding to the preset ambient temperature and humidity information is greater than or equal to the preset proportion, then after confirming the processing plan, the person in charge who is within the preset distance range from the target electricity meter and has the highest processing success rate will be selected as the processing personnel, and the specific processing plan will be sent to the terminal held by the corresponding processing personnel.

7. The method for state perception and evaluation of a smart electric energy meter based on digital twin technology according to claim 6 is characterized in that: The person who is closest to the target electricity meter is designated as the processing person, and a specific processing plan is sent to the terminal held by the corresponding processing person, including: Analyze the attention items corresponding to the historical errors of the person in charge closest to the target electricity meter; When the person in charge who is closest to the target electric energy meter arrives at the target electric energy meter, the attention item is sent to the terminal held by the person in charge as a reminder.

8. A smart electricity meter state perception and evaluation system based on digital twin technology, characterized by: include: It includes a memory, a processor, and a program stored in the memory and executable on the processor. When the program is loaded and executed by the processor, a smart electricity meter state perception and evaluation method based on digital twin technology as described in any one of claims 1 to 7 can be implemented.

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