Metering box electric energy abnormity determination method and system based on big data evaluation

Abstract

The invention discloses a metering box electric energy abnormity determination method and system based on big data evaluation, and belongs to the field of electric energy abnormity determination. Based on the comparison of future environment data and current environment data, the influence of the future environment on the metering equipment statistics is analyzed, according to the influence of the future environment on the metering equipment statistics, metering equipment abnormity early warning is carried out, and the metering accuracy of the metering equipment is evaluated based on the influence of the environment change on the precision of the metering equipment. According to the method, the metering abnormity of the metering equipment in the next period is accurately analyzed according to the environmental change influence, the influence of signal fluctuation and signal strength on the analysis result is avoided while the environmental influence analysis is performed, and the analysis accuracy is further improved.

Description

Technical Field

[0001] The present invention belongs to the field of electric energy anomaly determination, and specifically relates to a method and system for determining electric energy anomaly in a meter box based on big data evaluation. Background Art

[0002] With the development of social economy and the continuous growth of electricity demand, the accuracy and stability of the operating status of the electricity meter box, as an important part of the power system, is crucial to the power supply and user power safety. Traditional electricity meter boxes mainly rely on manual inspections and simple monitoring methods to detect and handle abnormal situations. However, this method has many limitations, such as low work efficiency, long inspection cycles, low accuracy, and the inability to monitor in real time. In this case, a meter box power anomaly determination system is needed to determine the power anomaly of the meter box.

[0003] However, the existing meter box power anomaly determination system cannot evaluate the metering accuracy of the metering equipment based on the impact of environmental changes on the accuracy of the metering equipment during operation, resulting in an inability to accurately analyze the metering anomaly of the metering equipment in the next cycle based on the impact of environmental changes. At the same time, during the environmental impact analysis process, signal fluctuations and signal strength have a serious impact on the analysis results, resulting in a low analysis accuracy rate. Most of the existing technologies have the above problems;

[0004] In order to solve the problems raised by this background technology, the present application designs a method and system for determining power anomalies in a meter box based on big data evaluation. Summary of the Invention

[0005] In response to the deficiencies in the prior art, the present invention proposes a method and system for determining electric energy anomalies in meter boxes based on big data evaluation. The present invention analyzes the statistical impact of the environment on the metering equipment based on the analysis of the statistical accuracy of the metering equipment and historical environmental data. It analyzes the statistical impact of the future environment on the metering equipment based on a comparison of future environmental data with current environmental data. It issues an abnormality warning for the metering equipment based on the impact of the future environment on the statistics of the metering equipment. It evaluates the metering accuracy of the metering equipment based on the impact of environmental changes on the precision of the metering equipment. It accurately analyzes the metering anomalies of the metering equipment in the next period based on the comprehensive impact of environmental changes. While conducting the environmental impact analysis, it avoids the influence of signal fluctuations and signal strength on the analysis results, thereby further improving the accuracy of the analysis.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for determining anomalies in electric energy of a meter box based on big data evaluation, which comprises the following specific steps:

[0007] S1. Obtain historical power statistics and environmental data from the metering equipment in the meter box, and also obtain future environmental data;

[0008] S2. Analyze the statistical difficulty of fluctuations in historical power statistics based on data from metering equipment, and analyze the statistical accuracy based on statistical difficulty weighting;

[0009] S3. Based on the statistical accuracy of the measuring equipment and historical environmental data obtained through analysis, analyze the impact of the environment on the statistical accuracy of the measuring equipment;

[0010] S4. Analyze the statistical impact of the future environment on the measuring equipment based on the comparison of future environmental data with current environmental data;

[0011] S5. Provide abnormal warning for measuring equipment based on the impact of future environment on the statistics of measuring equipment.

[0012] It should be noted that, as a preferred technical solution for the method of determining power anomalies in a meter box based on big data evaluation, obtaining historical power statistics and environmental data of the metering equipment data in the meter box, and simultaneously obtaining future environmental data, includes the following specific steps:

[0013] S11. The power data acquisition module in the data acquisition terminal acquires historical power statistical data and actual power usage data from the metering device in the meter box, and simultaneously acquires grid voltage fluctuations, and stores the data in a first storage component;

[0014] S12. The environment collection module in the data collection terminal collects environmental data of the metering device when collecting electric energy, wherein the environmental data includes environmental types that affect the normal operation of the metering device, such as ambient temperature, ambient humidity, and environmental pollution, and stores the collected environmental data in a second storage component;

[0015] S13. Obtain environmental data at future times through weather forecasts and store the data in a third storage component.

[0016] It should be noted that as a preferred technical solution for the method of determining meter box power anomalies based on big data evaluation, the statistical difficulty analysis of fluctuations in historical power statistical data based on metering equipment data includes the following specific steps:

[0017] S21. Obtain a curve of actual power usage data change, and perform power fluctuation analysis based on the actual power usage fluctuations between the corresponding moment and the previous moment of the actual power usage data. The power fluctuation analysis formula at moment t is: Where Nt is the actual power usage data at time t, N(t-1) is the actual power usage data at time t-1, Nmax is the maximum safe power change that the power statistics table can identify, and Nmin is the minimum safe power change that the power statistics table can identify;

[0018] S22. Obtain a grid voltage variation region, and perform voltage fluctuation analysis based on the grid voltage variation fluctuation at the corresponding moment and the previous moment. The voltage fluctuation analysis formula at moment t is: Wherein, Ut is the grid voltage at the corresponding location at time t, U(t-1) is the grid voltage at the corresponding location at time t-1, and Um is the maximum voltage for safe operation of the electric energy statistics table;

[0019] S23. Perform weighted summation on the acquired power fluctuation analysis result and voltage fluctuation analysis result at the corresponding moment to obtain the statistical difficulty of the metering device at the corresponding moment.

[0020] In this step, since voltage fluctuations and power fluctuations will have a negative impact on the accuracy of power calculation when the metering equipment is measuring, the negative impact of voltage fluctuations and power fluctuations on the accuracy of power calculation needs to be removed when performing environmental impact analysis.

[0021] It should be noted that as a preferred technical solution for the method of determining meter box power anomalies based on big data evaluation, the statistical accuracy analysis based on statistical difficulty weighting includes the following specific steps:

[0022] S24. Obtain the statistical difficulty of the metering device at the corresponding moment, and simultaneously obtain the electric energy statistical collection data and the actual electric energy usage data at the corresponding moment;

[0023] S25. Calculate the statistical standardization accuracy at the corresponding moment based on the statistical difficulty at the corresponding moment, the electric energy statistical collection data at the corresponding moment, and the actual electric energy usage data. In this formula, the standardization of accuracy not only eliminates the impact of electric energy fluctuations, but also eliminates the impact of electric energy changes that are too large or too small to be statistically analyzed.

[0024] It should be noted that as a preferred technical solution for the method of determining meter box power anomalies based on big data evaluation, the analysis of the statistical accuracy of metering equipment and historical environmental data, and the analysis of the impact of the environment on the statistical accuracy of metering equipment, include the following specific contents:

[0025] S31. Obtain historical environmental data, and calculate the impact value of the environment on the measuring equipment based on the historical environmental data;

[0026] S32. Obtain the environmental impact value at the corresponding moment and the standardized accuracy at the corresponding moment, and substitute the environmental impact value at the corresponding moment of the statistical period and the standardized accuracy at the corresponding moment into the accuracy environmental impact analysis value calculation formula to calculate the accuracy environmental impact analysis value.

[0027] It should be noted that as a preferred technical solution for the method of determining meter box power anomalies based on big data evaluation, the statistical analysis of the future environment's impact on the metering equipment based on the comparison of future environmental data with current environmental data includes the following specific steps:

[0028] Obtain environmental data for future periods, calculate the impact value of the environment on the measuring equipment at each moment through the environmental data for future periods, substitute the impact value of the environment on the measuring equipment at each moment in the future period and the accuracy environmental impact analysis value into the calculation formula for the statistical impact value of the future environment on the measuring equipment to calculate the statistical impact value of the future environment on the measuring equipment.

[0029] It should be noted that as a preferred technical solution for the method of determining meter box power anomalies based on big data evaluation, the metering equipment anomaly warning based on the impact of future environmental factors on metering equipment statistics includes the following specific contents:

[0030] Obtain the calculated statistical impact value of the future environment on the metering equipment, and compare the calculated statistical impact value of the future environment on the metering equipment with the equipment statistical impact threshold. If the statistical impact value of the environment on the metering equipment in the future period is greater than or equal to the equipment statistical impact threshold, it means that the metering of the metering equipment in the next period is normal and there is no need to replace the metering equipment. If the statistical impact value of the environment on the metering equipment in the future period is less than the equipment statistical impact threshold, it means that the metering of the metering equipment in the next period is abnormal and the metering equipment needs to be replaced.

[0031] The meter box power anomaly determination system based on big data evaluation is implemented based on the above-mentioned meter box power anomaly determination method based on big data evaluation, and specifically includes the following modules:

[0032] Acquisition module: acquires historical power statistics and environmental data of the metering equipment in the meter box, and also acquires future environmental data;

[0033] Statistical accuracy analysis module: Analyzes the statistical difficulty of fluctuations in historical power statistics based on metering equipment data, and analyzes statistical accuracy based on statistical difficulty weighting;

[0034] Equipment statistical impact analysis module: Based on the statistical accuracy of measuring equipment and historical environmental data obtained through analysis, the module analyzes the impact of the environment on the statistical performance of measuring equipment.

[0035] Future environmental statistical impact analysis module: Analyze the statistical impact of the future environment on the metering equipment based on the comparison of future environmental data with current environmental data;

[0036] The equipment abnormality warning module provides abnormal warning for measuring equipment based on the impact of future environment on the statistics of measuring equipment.

[0037] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0038] The processor executes the above-mentioned method for determining electric energy anomaly of the meter box based on big data evaluation by calling the computer program stored in the memory.

[0039] A computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer is caused to execute the above-mentioned method for determining electric energy anomaly of a meter box based on big data evaluation.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention analyzes the statistical difficulty of fluctuations in historical electric energy statistical data based on the data of the metering equipment, analyzes the statistical accuracy based on the weighting of the statistical difficulty, analyzes the statistical impact of the environment on the metering equipment based on the statistical accuracy of the metering equipment and the historical environmental data obtained by analysis, analyzes the statistical impact of the future environment on the metering equipment based on a comparison between future environmental data and current environmental data, performs an abnormal warning of the metering equipment according to the impact of the future environment on the statistics of the metering equipment, evaluates the metering accuracy of the metering equipment based on the impact of environmental changes on the precision of the metering equipment, accurately analyzes the metering anomaly of the metering equipment in the next period based on the impact of environmental changes, and avoids the influence of signal fluctuations and signal strength on the analysis results while performing the environmental impact analysis, thereby further improving the accuracy of the analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the overall process of an embodiment of the method of the present invention;

[0043] Figure 2 This is a schematic flow chart of step S2 of an embodiment of the method of the present invention;

[0044] Figure 3 This is a schematic flow chart of step S3 of an embodiment of the method of the present invention;

[0045] Figure 4 Schematic diagram of the overall framework of the system embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0047] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0048] Example 1

[0049] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1-Figure 3 As shown, the method for determining the abnormality of electric energy in a meter box based on big data evaluation includes the following specific steps:

[0050] S1. Obtain historical power statistics and environmental data from the metering equipment in the meter box, and also obtain future environmental data;

[0051] In this embodiment, obtaining historical power statistics and environmental data of the metering device data in the meter box and simultaneously obtaining future environmental data includes the following specific steps:

[0052] S11. The power data acquisition module in the data acquisition terminal acquires historical power statistical data and actual power usage data from the metering device in the meter box, and simultaneously acquires grid voltage fluctuations, and stores the data in a first storage component;

[0053] S12. The environmental collection module in the data collection terminal collects environmental data of the metering device when collecting electric energy. The environmental data includes environmental temperature, environmental humidity, environmental pollution and other environmental types that affect the normal operation of the metering device. Among them, environmental factors have a significant impact on the operation of the electric energy metering device. Temperature changes will affect the performance of the electronic components inside the electric energy metering device. For example, the parameters of resistors, capacitors and semiconductor devices will change with temperature, thereby affecting the accuracy of measurement; high temperature may cause the device to overheat, accelerate component aging, and even cause equipment failure; low temperature may cause the device to respond slowly, the LCD display to display unclearly, and other problems; high humidity environment may cause the internal circuit board of the electric energy metering device to become damp, causing short circuit or corrosion, affecting the normal operation and life of the device; humidity may also cause frost inside the device, affecting the performance of sensors and circuits; dust and other pollutants in the environment may block the ventilation holes of the device, causing the internal temperature to rise; after long-term exposure to a polluted environment, the device may accumulate dust, affecting heat dissipation and electrical connections, and reducing the reliability of the device. The collected environmental data is stored in the second storage component;

[0054] S13, obtaining environmental data at a future time through weather forecast, and storing the data in a third storage component;

[0055] S2. Analyze the statistical difficulty of fluctuations in historical power statistics based on data from metering equipment, and analyze the statistical accuracy based on statistical difficulty weighting;

[0056] In this embodiment, the analysis of statistical difficulty based on fluctuations in historical power statistical data of the metering device includes the following specific steps:

[0057] S21. Obtain a curve of actual power usage data change, and perform power fluctuation analysis based on the actual power usage fluctuations between the corresponding moment and the previous moment of the actual power usage data. The power fluctuation analysis formula at moment t is: Where Nt is the actual electricity usage data at time t, N(t-1) is the actual electricity usage data at time t-1, Nmax is the maximum safe electricity energy variation that the electricity statistics table can identify, and Nmin is the minimum safe electricity energy variation that the electricity statistics table can identify. The safe electricity energy variation range is the production performance data of the electricity meter, which can be obtained by querying the production specifications of the corresponding electricity meter;

[0058] S22. Obtain a grid voltage variation region, and perform voltage fluctuation analysis based on the grid voltage variation fluctuation at the corresponding moment and the previous moment. The voltage fluctuation analysis formula at moment t is: Wherein, Ut is the grid voltage at the corresponding location at time t, U(t-1) is the grid voltage at the corresponding location at time t-1, and Um is the maximum voltage for safe operation of the electric energy statistics table;

[0059] S23. Perform weighted summation on the acquired power fluctuation analysis result and voltage fluctuation analysis result at the corresponding moment to obtain the statistical difficulty of the metering device at the corresponding moment.

[0060] In this step, since voltage fluctuations and power fluctuations will have a negative impact on the accuracy of power calculation when the metering equipment is measuring, the negative impact of voltage fluctuations and power fluctuations on the accuracy of power calculation needs to be removed when performing environmental impact analysis;

[0061] At the same time, the analysis of statistical accuracy based on statistical difficulty weighting includes the following specific steps:

[0062] S24. Obtain the statistical difficulty of the metering device at the corresponding moment, and simultaneously obtain the electric energy statistical collection data and the actual electric energy usage data at the corresponding moment;

[0063] S25. Calculate the statistical standardization accuracy of the corresponding moment based on the statistical difficulty at the corresponding moment, the electric energy statistical collection data at the corresponding moment, and the actual electric energy usage data. The calculation formula for the standardization accuracy of the corresponding moment at time t is: Where Ntc is the energy statistics data collected at time t, mt is the statistical difficulty at time t, Ntm is the value closest to Nt within the safe range of energy fluctuations that the energy statistics table can identify, b is the weight of the energy safety ratio, and exp() is the power of the natural constant e. Standardizing the accuracy in this formula not only eliminates the impact of energy fluctuations, but also eliminates the impact of energy changes that are too large or too small to be counted.

[0064] S3. Based on the statistical accuracy of the measuring equipment and historical environmental data obtained through analysis, analyze the impact of the environment on the statistical accuracy of the measuring equipment;

[0065] In this embodiment, based on the analysis of the statistical accuracy of the metering equipment and the historical environmental data, the analysis of the impact of the environment on the metering equipment statistics includes the following specific contents:

[0066] S31. Obtain historical environmental data, and calculate the impact value of the environment on the metering equipment based on the historical environmental data. The environmental impact value calculation formula at the corresponding moment t is: Where M is the type of environmental data, vi is the impact weight of the i-th environmental data, cit is the specific value of the i-th environmental data at time t, and cim is the median of the safety range of the i-th environmental data;

[0067] S32. Obtain the environmental impact value at the corresponding moment and the standardized accuracy at the corresponding moment, and substitute the environmental impact value at the corresponding moment of the statistical period and the standardized accuracy at the corresponding moment into the accuracy environmental impact analysis value calculation formula to calculate the accuracy environmental impact analysis value, wherein the accuracy environmental impact analysis value calculation formula is: Where T is the detection cycle length, dt is the time integral;

[0068] S4. Analyze the statistical impact of the future environment on the measuring equipment based on the comparison of future environmental data with current environmental data;

[0069] In this embodiment, analyzing the statistical impact of the future environment on the metering equipment based on the comparison between the future environment data and the current environment data includes the following specific steps:

[0070] Obtain environmental data for the future period, calculate the impact value of the environment on the measuring equipment at each moment through the environmental data for the future period, substitute the impact value of the environment on the measuring equipment at each moment in the future period and the accuracy environmental impact analysis value into the calculation formula for the statistical impact value of the future environment on the measuring equipment to calculate the statistical impact value of the future environment on the measuring equipment. The calculation formula for the statistical impact value of the future environment on the measuring equipment is: Where Htc is the environmental impact value at time tc in the future cycle, and ΔT is the duration of the future cycle;

[0071] S5. Provide abnormal warning of measuring equipment based on the impact of future environment on the statistics of measuring equipment;

[0072] In this embodiment, the abnormal warning of metering equipment based on the impact of the future environment on the metering equipment statistics includes the following specific contents:

[0073] Obtain the calculated future environmental impact value on the metering device, and compare the calculated future environmental impact value on the metering device with the device statistical impact threshold. If the future period's environmental impact value on the metering device is greater than or equal to the device statistical impact threshold, it indicates that the metering device in the next period is measuring normally and does not need to be replaced. If the future period's environmental impact value on the metering device is less than the device statistical impact threshold, it indicates that the metering device in the next period is measuring abnormally and needs to be replaced.

[0074] It should be emphasized here that the value of the setting parameters in this embodiment is: the preferred value selection method is: obtain historical power statistical data and environmental data of historical metering equipment data, determine whether the metering accuracy of the metering equipment in the next cycle meets the requirements, and at the same time substitute the historical data into each step of this embodiment to calculate the statistical impact value of the future environment on the metering equipment, and then import the calculation results and judgment results into the fitting software to fit the data, and output the value of the setting parameter that meets the maximum judgment accuracy;

[0075] The advantages of this embodiment over the prior art are as follows: performing statistical difficulty analysis based on fluctuations in historical electric energy statistical data of metering equipment data, performing statistical accuracy analysis based on statistical difficulty weighting, performing statistical impact analysis of the environment on the metering equipment based on the statistical accuracy of the metering equipment and historical environmental data obtained through analysis, performing statistical impact analysis of the future environment on the metering equipment based on a comparison between future environmental data and current environmental data, performing abnormal warning of metering equipment based on the impact of the future environment on the metering equipment statistics, evaluating the metering accuracy of the metering equipment based on the impact of environmental changes on the precision of the metering equipment, accurately analyzing the metering abnormalities of the metering equipment in the next period based on the comprehensive impact of environmental changes, avoiding the influence of signal fluctuations and signal strength on the analysis results while performing environmental impact analysis, and further improving the accuracy of the analysis.

[0076] Example 2

[0077] like Figure 4 As shown, a meter box power anomaly determination system based on big data evaluation is implemented based on the above-mentioned meter box power anomaly determination method based on big data evaluation, and specifically includes: an acquisition module: acquiring historical power statistical data and environmental data of the metering equipment data in the meter box, and acquiring future environmental data at the same time; a statistical accuracy analysis module: performing statistical difficulty analysis based on the fluctuation of the historical power statistical data of the metering equipment data, and performing statistical accuracy analysis based on statistical difficulty weighting; an equipment statistical impact analysis module: performing statistical impact analysis of the environment on the metering equipment based on the statistical accuracy and historical environmental data of the metering equipment obtained by analysis; a future environmental statistical impact analysis module: analyzing the statistical impact of the future environment on the metering equipment based on the comparison of future environmental data with current environmental data; an equipment anomaly warning module, performing an anomaly warning of the metering equipment according to the impact of the future environment on the metering equipment statistics. The specific steps of the above modules in this embodiment are all specifically described in the above-mentioned method embodiment, and will not be repeated in this embodiment.

[0078] Example 3

[0079] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0080] The processor executes the above-mentioned method for determining electric energy anomaly of the meter box based on big data evaluation by calling the computer program stored in the memory.

[0081] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the method for determining electric energy anomalies in a meter box based on big data evaluation provided by the above method embodiment, which includes the following specific steps:

[0082] S1. Obtain historical power statistics and environmental data from the metering equipment in the meter box, and also obtain future environmental data;

[0083] S2. Analyze the statistical difficulty of fluctuations in historical power statistics based on data from metering equipment, and analyze the statistical accuracy based on statistical difficulty weighting;

[0084] S3. Based on the statistical accuracy of the measuring equipment and historical environmental data obtained through analysis, analyze the impact of the environment on the statistical accuracy of the measuring equipment;

[0085] S4. Analyze the statistical impact of the future environment on the measuring equipment based on the comparison of future environmental data with current environmental data;

[0086] S5. Providing an abnormal warning for the metering device based on the impact of the future environment on the metering device statistics. The electronic device may also include other components for implementing the device's functions. For example, the electronic device may also include components such as a wired or wireless network interface and an input / output interface for data input and output. This embodiment is not further described here.

[0087] Example 4

[0088] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0089] When the computer program is executed on a computer device, the computer device is caused to execute the above-mentioned method for determining anomaly of electric energy in a meter box based on big data evaluation.

[0090] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0091] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

Claims

1. A method for determining anomaly of electric energy in a meter box based on big data evaluation, characterized in that: It includes the following specific steps: S1. Obtain historical power statistics and environmental data from the metering equipment in the meter box, and also obtain future environmental data; S2. Analyze the statistical difficulty of fluctuations in historical power statistics based on data from metering equipment, and analyze the statistical accuracy based on statistical difficulty weighting; S3. Based on the statistical accuracy of the measuring equipment and historical environmental data obtained through analysis, analyze the impact of the environment on the statistical accuracy of the measuring equipment; S4. Analyze the statistical impact of the future environment on the measuring equipment based on the comparison of future environmental data with current environmental data; S5. Provide abnormal warning for measuring equipment based on the impact of future environment on the statistics of measuring equipment.

2. The method for determining anomaly of electric energy in a meter box based on big data evaluation according to claim 1, characterized in that: The statistical difficulty analysis of fluctuations in historical electric energy statistical data based on data from metering equipment includes the following specific steps: S21. Obtain a curve of actual power usage data change, and perform power fluctuation analysis based on the actual power usage fluctuation at a corresponding moment and a previous moment of the actual power usage data; S22. Obtain a grid voltage change region, and perform voltage fluctuation analysis based on the grid voltage change fluctuation at a corresponding moment and the previous moment; S23. Perform weighted summation on the acquired power fluctuation analysis result and voltage fluctuation analysis result at the corresponding moment to obtain the statistical difficulty of the metering device at the corresponding moment.

3. The method for determining anomaly of electric energy in a meter box based on big data evaluation according to claim 2, characterized in that: The statistical accuracy analysis based on statistical difficulty weighting includes the following specific steps: S24. Obtain the statistical difficulty of the metering device at the corresponding moment, and simultaneously obtain the electric energy statistical collection data and the actual electric energy usage data at the corresponding moment; S25. Calculate the statistical standardization accuracy of the corresponding moment based on the statistical difficulty at the corresponding moment, the electric energy statistical collection data at the corresponding moment, and the actual electric energy usage data. The calculation formula for the standardization accuracy of the corresponding moment at time t is: Among them, Ntc is the electric energy statistical collection data corresponding to time t, mt is the statistical difficulty at time t, Ntm is the value closest to Nt within the safe range of electric energy changes that can be identified by the electric energy statistics table, b is the weight of the electric energy safety ratio, and exp() is the power of the natural constant e.

4. The method for determining anomaly of electric energy in a meter box based on big data evaluation according to claim 3, characterized in that: The analysis of the impact of the environment on the statistical accuracy of the measuring equipment and the historical environmental data obtained by analysis includes the following specific contents: S31. Obtain historical environmental data, and calculate the impact value of the environment on the measuring equipment based on the historical environmental data; S32. Obtain the environmental impact value at the corresponding moment and the standardized accuracy at the corresponding moment, and substitute the environmental impact value at the corresponding moment of the statistical period and the standardized accuracy at the corresponding moment into the accuracy environmental impact analysis value calculation formula to calculate the accuracy environmental impact analysis value.

5. The method for determining anomaly of electric energy in a meter box based on big data evaluation according to claim 4, characterized in that: The statistical analysis of the future environment's impact on the metering equipment based on the comparison between the future environment data and the current environment data includes the following specific steps: Obtain environmental data for future periods, calculate the impact value of the environment on the measuring equipment at each moment through the environmental data for future periods, substitute the impact value of the environment on the measuring equipment at each moment in the future period and the accuracy environmental impact analysis value into the calculation formula for the statistical impact value of the future environment on the measuring equipment to calculate the statistical impact value of the future environment on the measuring equipment.

6. The method for determining anomaly of electric energy in a meter box based on big data evaluation according to claim 5, characterized in that: The abnormal warning of measuring equipment based on the impact of future environment on the statistics of measuring equipment includes the following specific contents: Obtain the calculated statistical impact value of the future environment on the metering equipment, and compare the calculated statistical impact value of the future environment on the metering equipment with the equipment statistical impact threshold. If the statistical impact value of the environment on the metering equipment in the future period is greater than or equal to the equipment statistical impact threshold, it means that the metering of the metering equipment in the next period is normal and there is no need to replace the metering equipment. If the statistical impact value of the environment on the metering equipment in the future period is less than the equipment statistical impact threshold, it means that the metering of the metering equipment in the next period is abnormal and the metering equipment needs to be replaced.

7. The method for determining anomaly of electric energy in a meter box based on big data evaluation according to claim 6, characterized in that: The method of obtaining historical power statistics and environmental data of the metering equipment data in the meter box and simultaneously obtaining future environmental data includes the following specific steps: The electric energy data acquisition module in the data acquisition terminal obtains the historical electric energy statistical data and the actual electric energy usage data of the metering equipment in the meter box, and obtains the voltage fluctuation of the power grid, and stores the data in the first storage component; The environment collection module in the data collection terminal collects the environment data of the metering device when collecting electric energy, and stores the collected environment data in the second storage component; Environmental data at future times is obtained through weather forecasts and stored in a third storage component.

8. A system for determining anomaly of electric energy in a meter box based on big data evaluation, which is implemented based on the method for determining anomaly of electric energy in a meter box based on big data evaluation according to any one of claims 1 to 7, and is characterized in that: It specifically includes the following modules: Acquisition module: acquires historical power statistics and environmental data of the metering equipment in the meter box, and also acquires future environmental data; Statistical accuracy analysis module: Analyzes the statistical difficulty of fluctuations in historical power statistics based on metering equipment data, and analyzes statistical accuracy based on statistical difficulty weighting; Equipment statistical impact analysis module: Based on the statistical accuracy of measuring equipment and historical environmental data obtained through analysis, the module analyzes the impact of the environment on the statistical performance of measuring equipment. Future environmental statistical impact analysis module: Analyze the statistical impact of the future environment on the metering equipment based on the comparison of future environmental data with current environmental data; The equipment abnormality warning module provides abnormal warning for measuring equipment based on the impact of future environment on the statistics of measuring equipment.

9. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the method for determining electric energy anomaly of a meter box based on big data evaluation as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the method for determining electric energy anomaly of a meter box based on big data evaluation as described in any one of claims 1 to 7.

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