A kind of anti-electricity theft protection system and method of electric energy meter

Abstract

The present invention relates to the technical field of electricity theft protection management, and specifically discloses an anti-electricity theft protection system and method for an electric energy meter. The system can comprehensively analyze and obtain a data collection deviation factor of the electric energy meter by comprehensively acquiring the operating environment parameters and operating parameters of the electric energy meter in a target area, and then compare it with a preset data collection deviation threshold, which is helpful to accurately manage the operating parameters of the electric energy meter. At the same time, based on the operating parameters of the electric energy meter and its data collection deviation factor, the abnormal power use risk index of the target area is evaluated, and compared with the abnormal power use risk threshold, so as to effectively identify abnormal power conditions and provide strong support for subsequent processing measures. On this basis, the electricity theft related parameters of the target area are obtained, and the abnormal electricity theft factor of the electric energy meter is determined, and then according to the result of the abnormal electricity theft factor, it is decided whether to start the anti-electricity theft protection measures of the electric energy meter, which is helpful to maintain the stability and safety of power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity theft protection management, and in particular to an anti-electricity theft protection system and method for an electric energy meter. Background Art

[0002] At present, with the continuous advancement of power technology and the deepening of smart grid construction, the anti-theft protection technology of electricity meters is facing new challenges and opportunities. Traditional means of stealing electricity, such as illegally changing the polarity of current and voltage coils, removing voltage hooks, changing current and voltage phases, etc., have gradually been replaced by more covert and complex ways of stealing electricity. These new ways of stealing electricity often use high-tech means. In order to cope with this situation, the current electricity meter anti-theft protection system is constantly being upgraded and improved.

[0003] For example, the invention patent with publication number CN117808624A discloses a line loss and electricity theft detection and analysis method based on wireless synchronous sampling. It randomly selects some users under the high line loss line as detection users, and then collects data samples through the installed data acquisition device; then uses the computer of the analysis center to analyze the data samples, and uses the possible common trends among the users under the high line loss line to establish a forward stepwise regression linear regression model, which can effectively reflect the functional relationship between the calculated power, power supply, line loss power and users; thereby efficiently screening out abnormal users with high line losses.

[0004] For example, the invention patent with announcement number CN106373025B announces a real-time anti-electricity theft monitoring method for an electricity consumption information collection system based on outlier detection. It calculates the line loss of each substation based on the collected electricity data of each substation and preliminarily determines whether there is abnormal electricity consumption in any substation. If the line loss of some substations is abnormal, the electricity consumption data of the corresponding substations will be called for, including the user's real-time electricity consumption data and abnormal event data. At the same time, reasonable evaluation indicators are selected and combined with outlier detection and data statistics methods to build a comprehensive electricity theft suspicion analysis model. Then, the data called by the main station is used as the input of the model, and the user data is processed through the analysis model to obtain the user's electricity theft suspicion coefficient, thereby locating the suspicious electricity theft users.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above technology has at least the following technical problems: in the process of identifying and analyzing electricity theft, the prior art directly uses the data collected by the electricity meter. Although this direct use method is simple and quick in operation, the collected data often has certain errors. If it is directly used for electricity theft identification and analysis, it may cause false alarms or omissions, affecting the accuracy of the analysis. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an anti-electricity theft protection system and method for an electric energy meter, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, the present invention provides an anti-electricity theft protection system for an electric energy meter, including: a data collection and analysis module, used to obtain the operating environment parameters of the electric energy meters belonging to the target area and the operating parameters of the electric energy meters belonging to the target area, and comprehensively analyze to obtain the data collection deviation factor of the electric energy meters belonging to the target area; an abnormal risk assessment module, used to compare the data collection deviation factor of the electric energy meters belonging to the target area with the data collection deviation threshold, if the data collection deviation factor of the electric energy meters belonging to the target area is greater than the data collection deviation threshold, then the operating parameters of the electric energy meters belonging to the target area are managed, if the electric energy meters If the data collection deviation factor of the energy meter is less than or equal to the data collection deviation threshold, the abnormal power usage risk index of the target area is evaluated according to the operating parameters of the energy meter and the data collection deviation factor of the energy meter; the abnormal risk comparison module is used to compare the abnormal power usage risk index of the target area with the abnormal power usage risk threshold. If the abnormal power usage risk index of the target area is greater than the abnormal power usage risk threshold, the power theft protection determination module is executed; the power theft protection determination module is used to determine the power theft abnormal factor of the energy meter belonging to the target area, thereby determining whether to perform anti-power theft protection of the energy meter according to the power theft abnormal factor of the energy meter belonging to the target area.

[0008] As a further solution, the operating parameters of the electric energy meters belonging to the target area are managed, and the specific management process is: according to the data collection deviation factor of the electric energy meters belonging to the target area, a parameter processing set is matched from the power database, and the operating parameters of the electric energy meters belonging to the target area are processed according to the parameter processing set, thereby updating the operating parameters of the electric energy meters belonging to the target area.

[0009] As a further solution, the abnormal electricity usage risk index of the target area is compared with the abnormal electricity usage risk threshold. The specific comparison process is: if the abnormal electricity usage risk index of the target area is less than or equal to the abnormal electricity usage risk threshold, the operating environment parameters of the electric energy meters belonging to the target area and the operating parameters of the electric energy meters belonging to the target area are continuously collected, and the data collection deviation factor of the electric energy meters belonging to the target area is analyzed.

[0010] As a further solution, the determination of whether to perform anti-theft protection for the electric energy meter is based on the electric energy meter's electric energy meter's electric energy meter in the target area according to its electric energy meter's electric energy meter's electric energy meter abnormality factor. The specific determination process is as follows: comparing the electric energy meter's electric energy meter's electric energy meter in the target area with the electric energy meter's electric energy meter abnormality threshold value. If the electric energy meter's electric energy meter in the target area has an electric energy meter abnormality factor greater than the electric energy meter's electric energy meter abnormality threshold value, the electric energy meter's electric energy meter is protected against theft. If the electric energy meter's electric energy meter in the target area has an electric energy meter abnormality factor less than or equal to the electric energy meter abnormality threshold value, the electric energy meter is not protected against theft. A power protection plan for the target area is matched from a power database based on the target area's electric energy meter abnormality usage risk index. The electric energy in the target area is protected based on the power protection plan for the target area.

[0011] The second aspect of the present invention provides a method for the anti-electricity theft protection system applied to the electric energy meter, characterized in that it includes: step 1, obtaining the operating environment parameters of the electric energy meter belonging to the target area and the operating parameters of the electric energy meter belonging to the target area, and comprehensively analyzing and obtaining the data collection deviation factor of the electric energy meter belonging to the target area; step 2, comparing the data collection deviation factor of the electric energy meter belonging to the target area with the data collection deviation threshold, if the data collection deviation factor of the electric energy meter belonging to the target area is greater than the data collection deviation threshold, managing the operating parameters of the electric energy meter belonging to the target area, if the data collection deviation factor of the electric energy meter is less than or equal to the data collection deviation threshold, evaluating the abnormal power use risk index of the target area according to the operating parameters of the electric energy meter and the data collection deviation factor of the electric energy meter; step 3, comparing the abnormal power use risk index of the target area with the abnormal power use risk threshold, if the abnormal power use risk index of the target area is greater than the abnormal power use risk threshold, executing step 4; step 4, determining the abnormal power theft factor of the electric energy meter belonging to the target area, thereby determining whether to perform anti-electricity theft protection of the electric energy meter according to the abnormal power theft factor of the electric energy meter belonging to the target area.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0013] (1) The present invention provides an anti-electricity theft protection system and method for an electric energy meter. By comprehensively acquiring the operating environment parameters and operating parameters of the electric energy meters in the target area, the data collection deviation factor of the electric energy meters can be comprehensively analyzed and obtained, and then compared with the preset data collection deviation threshold. This not only helps to accurately manage the operating parameters of the electric energy meters and improve management efficiency, but also ensures the accuracy and reliability of the data. At the same time, based on the operating parameters of the electric energy meters and their data collection deviation factors, the abnormal power usage risk index of the target area is further evaluated and compared with the abnormal power usage risk threshold, so as to effectively identify abnormal power conditions. This step can not only timely discover potential power problems, but also provide strong support for subsequent processing measures. On this basis, the electric power theft-related parameters of the target area are obtained, and the electric power theft abnormality factor of the electric energy meter is determined. Then, according to the result of the electric power theft abnormality factor, it is decided whether to start the anti-electricity theft protection measures of the electric energy meter, which helps to maintain the stability and safety of the power supply.

[0014] (2) The present invention obtains the data collection deviation factor of the electric energy meters in the target area by analyzing the operating environment parameters and the operating parameters of the electric energy meters in the target area, so as to carry out subsequent processing on the data collected by the electric energy meters in a targeted manner, effectively ensuring the authenticity and practicality of the analysis data, providing solid data support for the optimization management of the electric energy meters, fault prediction and formulation of anti-electricity theft measures, and further improving the stability and safety of the power system.

[0015] (3) By acquiring the electricity theft-related parameters in the target area, the present invention can make in-depth judgments on power anomalies, scientifically identify and accurately locate areas where electricity theft may exist, and reduce the one-sidedness of directly judging electricity theft simply because of the risk of abnormal electricity use. This not only greatly improves the detection efficiency of electricity theft, but also reduces the tediousness and cost of manual investigation, thereby facilitating the rapid adoption of countermeasures and effectively curbing the occurrence of electricity theft. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0018] Figure 2 The figure is a schematic flow chart of the method steps of the present invention.

[0019] Figure 3 It is a schematic diagram of monitoring current curve of electric energy meters in the target area involved in the present invention during an abnormal monitoring period.

[0020] Reference numerals: 1. the highest point of the curve; 2. the lowest point of the curve. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 As shown, the first aspect of the present invention provides an anti-electricity theft protection system for an electric energy meter, comprising: a data collection and analysis module, an abnormal risk assessment module, an abnormal risk comparison module and an electric power theft protection determination module.

[0023] A first aspect of the present invention provides an anti-electricity theft protection system for an electric energy meter, which also includes an electric energy database, wherein the electric energy database is used to store a total thermal stress limit value, an average humidity reference value, a data collection deviation weight factor corresponding to a unit value of an average data update time, a parameter processing set, a weight factor corresponding to a data collection deviation factor, a monitoring voltage average offset limit value, a reference monitoring current fluctuation maximum value, a reference monitoring current and voltage average phase difference, a reference average speed of an electric energy meter belonging to a target area within an abnormal monitoring period, a weight factor corresponding to an abnormal electric energy usage risk index, a monitoring average power factor reference value, an abnormal electric energy theft weight factor corresponding to a unit value of a monitoring electric energy consumption deviation average value, an abnormal electric energy theft weight factor corresponding to a unit value of a speed deviation value, an electric energy protection plan for the target area, a data collection deviation threshold, an abnormal electric energy usage risk threshold, and an abnormal electric energy theft threshold.

[0024] The data collection and analysis module is connected to the abnormal risk assessment module, the abnormal risk assessment module is connected to the abnormal risk comparison module, the abnormal risk comparison module is connected to the power theft protection determination module, and the data collection and analysis module, the abnormal risk assessment module, the abnormal risk comparison module and the power theft protection determination module are all connected to the power database.

[0025] The data collection and analysis module is used to obtain the operating environment parameters of the electric energy meters belonging to the target area and the operating parameters of the electric energy meters belonging to the target area, and comprehensively analyze to obtain the data collection deviation factor of the electric energy meters belonging to the target area.

[0026] In a specific embodiment, the present invention obtains the data collection deviation factor of the electric energy meters belonging to the target area by analyzing the operating environment parameters of the electric energy meters belonging to the target area and the operating parameters of the electric energy meters belonging to the target area, so as to perform subsequent processing on the data collected by the electric energy meters in a targeted manner, effectively ensuring the authenticity and practicality of the analysis data, providing solid data support for the optimization management of electric energy meters, fault prediction and formulation of anti-electricity theft measures, and further improving the stability and safety of the power system.

[0027] Specifically, the operating environment parameters of the electric energy meters belonging to the target area and the operating parameters of the electric energy meters belonging to the target area, wherein the operating environment parameters of the electric energy meters belonging to the target area include the temperature difference of the target area in each historical monitoring sub-period and the average humidity of the target area in the historical monitoring period; the above-mentioned historical monitoring period refers to a historical time period for monitoring and data analysis of the performance of the electric energy meters belonging to the target area, and its specific duration is determined by the power management department; the above-mentioned historical monitoring sub-periods refer to dividing the historical monitoring period into a number of sub-periods of the same length, named as historical monitoring sub-periods; the temperature difference of the target area in each historical monitoring sub-period represents the target area. The difference between the maximum temperature and the minimum temperature of the target area in each historical monitoring sub-period can be obtained by measuring the real-time temperature of the target area in each historical monitoring sub-period through the temperature sensor of the electric energy meter, and extracting the maximum temperature and the minimum temperature corresponding to each historical monitoring sub-period for difference processing, so as to obtain the temperature difference of the target area in each historical monitoring sub-period; the average humidity of the above-mentioned target area in the historical monitoring period represents the average value of the ambient humidity of the electric energy meter in the target area in the historical monitoring period. The real-time humidity of the electric energy meter in the historical monitoring period can be measured by the humidity sensor of the electric energy meter, and the average humidity of the target area in the historical monitoring period can be obtained by mean processing.

[0028] It should be explained that the target area may refer to a large factory or the like.

[0029] The operating parameters of the electric energy meters belonging to the target area include the data update time points of the electric energy meters belonging to the target area in the historical monitoring cycle, the monitoring voltage curves of the electric energy meters belonging to the target area in the abnormal monitoring cycle, the monitoring current curves of the electric energy meters belonging to the target area in the abnormal monitoring cycle, the average phase difference of the monitoring current and voltage of the electric energy meters belonging to the target area in the abnormal monitoring cycle, and the electricity theft related parameters of the target area.

[0030] Furthermore, the analysis obtains the data collection deviation factor of the electric energy meter in the target area, and the specific analysis process is as follows:

[0031] The material parameters of the electric energy meter belonging to the target area are obtained, and the material parameters of the electric energy meter belonging to the target area include the elastic modulus of the electric energy meter belonging to the target area and the thermal expansion coefficient of the electric energy meter belonging to the target area; the elastic modulus is a physical quantity that describes the elasticity of a material, and represents the proportional coefficient of the stress and strain of an object in the elastic deformation stage. For the electric energy meter, it contains elastic elements or structures, so the elastic modulus of these elements or structures will affect its overall mechanical properties and stability; the thermal expansion coefficient refers to the regularity coefficient of the geometric properties of a material changing with the change of temperature under the effect of thermal expansion and contraction. For the electric energy meter, the thermal expansion coefficient mainly affects the dimensional stability and electrical properties of its internal elements; the elastic modulus of the electric energy meter belonging to the target area and the thermal expansion coefficient of the electric energy meter belonging to the target area can be extracted from the production specification manual of the electric energy meter. It should be explained that the elastic modulus of the electric energy meter belonging to the target area and the thermal expansion coefficient of the electric energy meter belonging to the target area will not change significantly with the use time of the electric energy meter. Therefore, in this embodiment, the data recorded in the production specification manual of the electric energy meter is used for analysis.

[0032] The temperature difference of the target area in each historical monitoring sub-period, the elastic modulus of the electric energy meter to which the target area belongs, and the thermal expansion coefficient of the electric energy meter to which the target area belongs are processed to obtain the thermal stress of the electric energy meter to which the target area belongs in each historical monitoring sub-period, and the data are accumulated, and the processing result is marked as the total thermal stress of the electric energy meter to which the target area belongs in the historical monitoring period; the thermal stress of the electric energy meter to which the target area belongs in each historical monitoring sub-period, the specific data processing process is: the temperature difference of the target area in a certain historical monitoring sub-period multiplied by the elastic modulus of the electric energy meter to which the target area belongs and then multiplied by the thermal expansion coefficient of the electric energy meter to which the target area belongs, is the thermal stress of the target area in the historical monitoring sub-period, and the data processing process of the thermal stress of the target area in the remaining historical monitoring sub-periods is consistent with the above data processing process; the total thermal stress of the electric energy meter to which the target area belongs in the historical monitoring period refers to the sum of all thermal stresses accumulated due to temperature changes in the electric energy meter (especially the key components and materials inside it) in a specific time period (such as the historical monitoring period).

[0033] The adjacent time points of the data update time points of the electric energy meters belonging to the target area within the historical monitoring period are subjected to difference processing to obtain the data update duration of the electric energy meters belonging to the target area within the historical monitoring period, and the average processing is performed, and the processing result is marked as the average data update duration of the electric energy meters belonging to the target area within the historical monitoring period; the above-mentioned data update time points of the electric energy meters belonging to the target area within the historical monitoring period refer to the specific moments marked by the timestamps of the data recorded by the electric energy meters belonging to the target area within the historical monitoring period, which can be extracted from the timestamp log of the electric energy meters; the above-mentioned adjacent time points refer to the historical adjacent data update time points of a data update time point.

[0034] The total thermal stress of the electric energy meter in the target area during the historical monitoring period, the average humidity of the target area during the historical monitoring period, and the average data update time of the electric energy meter in the target area during the historical monitoring period are comprehensively analyzed to obtain the data collection deviation factor of the electric energy meter in the target area. In this embodiment, the data collection deviation factor of the electric energy meter in the target area represents the value of the data collection deviation degree of the electric energy meter in the target area. The specific analysis method is:

[0035] ;

[0036] In the formula, is the data collection deviation factor of the electric energy meter in the target area, is the total thermal stress of the electric energy meters in the target area during the historical monitoring period, is the average humidity of the target area during the historical monitoring period, The average data update time of the electric energy meters in the target area during the historical monitoring period. The total thermal stress limit value preset in the power database, The average humidity reference value preset in the power database, It is the data collection deviation weighting factor corresponding to the unit value of the average data update time preset in the power database, and e is a natural constant.

[0037] The above-mentioned total thermal stress limit value indicates the maximum allowable total thermal stress of the electric energy meters in the target area during the historical monitoring period; the above-mentioned average humidity reference value indicates the reference value of the average humidity in the target area during the historical monitoring period.

[0038] The data collection deviation weight factor corresponding to the above-mentioned unit value of the average data update time represents the degree of influence of the unit value of the average data update time on the data collection deviation factor of the electric energy meter belonging to the target area. The corresponding relationship between the average data update time and its corresponding data collection deviation weight factor is stored in the power database. For example, when the average data update time is input into the power database, the power database can match the data collection deviation weight factor corresponding to the unit value of the average data update time.

[0039] It needs to be explained that the increase in total thermal stress will have an adverse effect on the induction efficiency of the parts inside the electricity meter, resulting in deviations in data collection. This is because thermal stress will change the material properties of the electricity meter, affecting the normal operation of the sensor and circuit, and thus causing errors in the data collection process of the electricity meter. Secondly, a significant deviation in average humidity will also affect the data collection accuracy of the electricity meter. Too high or too low humidity may cause corrosion, oxidation or performance degradation of the internal components of the electricity meter, thereby increasing the deviation in data collection. Especially in an environment with extreme humidity changes, the stability and reliability of the electricity meter will be seriously challenged. At the same time, a significant change in average humidity will also change the moisture content on the surface of the material, further The material parameters that affect the electricity meter increase the total thermal stress level of the electricity meter. The combined effect of environmental humidity and temperature (manifested through thermal stress) will also affect the data update speed of the electricity meter. Changes in humidity and temperature may cause delays or failures in the internal circuit of the electricity meter, thereby extending the average data update time and causing lags in data collection. This lag will not only reduce the real-time accuracy of the data, increase the degree of data collection deviation of the electricity meters in the target area, but also affect the accurate judgment of electricity usage. Therefore, the above parameters are integrated and analyzed, and the degree of data collection deviation of the electricity meters in the target area is analyzed from multiple dimensions to reduce the one-sidedness of single-dimensional analysis.

[0040] In this exemplary embodiment, a table showing a change in the data collection deviation factor and its corresponding parameter of the electric energy meter in the target area is shown in Table 1:

[0041] Table 1. Data collection deviation factors and corresponding parameter changes of electric energy meters in the target area

[0042]

[0043] In this example embodiment, the total thermal stress limit value is set to 15000 Pascal, the average humidity reference value is set to 45%, and the value of the data collection deviation weight factor corresponding to the unit value of the average data update time is set to 0.23. It can be seen from Table 1 that when the total thermal stress on the electric energy meter is small (5000 Pascal), and there is a certain deviation between the average humidity and the average humidity reference value (that is, there is a 20% deviation between 65% and 45%), the performance of the electric energy meter remains intact, the average data update time is short, and the power situation in the target area can be accurately captured. Therefore, the electric energy meter has a high level of accuracy in collecting power from the target area, which is manifested in that the data collection deviation factor is at a low level, specifically 101%.

[0044] The abnormal risk assessment module is used to compare the data collection deviation factor of the electric energy meter belonging to the target area with the data collection deviation threshold. If the data collection deviation factor of the electric energy meter belonging to the target area is greater than the data collection deviation threshold, the operating parameters of the electric energy meter belonging to the target area are managed; if the data collection deviation factor of the electric energy meter is less than or equal to the data collection deviation threshold, the abnormal electricity usage risk index of the target area is assessed based on the operating parameters of the electric energy meter and the data collection deviation factor of the electric energy meter.

[0045] The above-mentioned data collection deviation threshold represents the maximum value of the reasonable range of the data collection deviation factor of the electric energy meter in the target area, which is extracted from the electric energy database; if the data collection deviation factor of the electric energy meter in the target area is less than or equal to the data collection deviation threshold, the abnormal electricity usage risk index of the target area is evaluated directly based on the operating parameters of the electric energy meter and the data collection deviation factor of the electric energy meter.

[0046] Specifically, the operating parameters of the electric energy meters belonging to the target area are managed, and the specific management process is: according to the data collection deviation factor of the electric energy meters belonging to the target area, a parameter processing set is matched from the power database, and the operating parameters of the electric energy meters belonging to the target area are processed according to the parameter processing set, thereby updating the operating parameters of the electric energy meters belonging to the target area; it should be explained that the power database stores the parameter processing set corresponding to each data collection deviation factor interval. In an example embodiment, if the data collection deviation factor of the electric energy meters belonging to a certain target area belongs to a certain data collection deviation factor interval stored in the power database, the parameter processing set corresponding to the data collection deviation factor interval includes: multiplying the operating parameters of the electric energy meters belonging to the target area by a correction coefficient of 1.0526, and then using a statistical method (such as the 3σ principle) to detect and process abnormal values ​​in the operating parameters of the electric energy meters belonging to the target area. The specific steps are: calculating the average value of parameters of the same category in the operating parameters of the electric energy meters belonging to the target area (for example, the monitored real-time voltage data is a category, and ... The measured real-time current data is another category), detect outliers: compare each data point with the mean of the corresponding category. If it exceeds the range of ±3 times the mean, it is regarded as an outlier. Process outliers: for detected outliers, choose to replace them with the average value of adjacent normal values; therefore, in this example embodiment, the parameter processing set obtained by matching the data collection deviation factor of the electric energy meter belonging to the target area includes: multiplying the operating parameters of the electric energy meter belonging to the target area by the correction coefficient 1.0526, and then using a statistical method (such as the 3σ principle) to detect and process the outliers in the operating parameters of the electric energy meter belonging to the target area. The specific steps are: calculating the average value of the same category parameters in the operating parameters of the electric energy meter belonging to the target area (for example, the monitored real-time voltage data is one category, and the monitored real-time current data is another category), detect outliers: compare each data point with the mean of the corresponding category. If it exceeds the range of ±3 times the mean, it is regarded as an outlier. Process outliers: for detected outliers, choose to replace them with the average value of adjacent normal values.

[0047] It needs to be explained that after the parameter processing set adjusts the operating parameters of the electric energy meters in the target area, the operating parameters of the electric energy meters in the target area can be made more in line with the actual status. Therefore, the abnormal electricity usage risk index of the target area can be evaluated based on the processed operating parameters of the electric energy meters and the data collection deviation factor of the electric energy meters.

[0048] Specifically, the abnormal power usage risk index of the target area is evaluated by:

[0049]

[0050] In the formula, The power abnormality usage risk index for the target area, is the data collection deviation factor of the electric energy meters in the target area, is the average monitoring voltage offset of the electric energy meter in the target area during the abnormal monitoring period, is the maximum value of the monitoring current fluctuation of the electric energy meter in the target area during the abnormal monitoring period. is the average phase difference of the monitoring current and voltage of the electric energy meter in the target area during the abnormal monitoring period, It is the weight factor corresponding to the data collection deviation factor preset in the power database. The average offset limit value of the monitored voltage preset in the power database, The maximum value of the reference monitoring current fluctuation preset in the power database. is the average phase difference of reference monitoring current and voltage preset in the power database, and e is a natural constant.

[0051] The above-mentioned monitoring voltage average offset limit value indicates the maximum allowable value of the monitoring voltage average offset of the electric energy meters in the target area during the abnormal monitoring period; the above-mentioned reference monitoring current fluctuation maximum value indicates the reference value of the monitoring current fluctuation maximum value of the electric energy meters in the target area during the abnormal monitoring period; the above-mentioned reference monitoring current and voltage average phase difference indicates the reference value of the monitoring current and voltage average phase difference of the electric energy meters in the target area during the abnormal monitoring period.

[0052] The weighting factor corresponding to the above-mentioned data collection deviation factor represents the proportion of the data collection deviation factor of the electric energy meters in the target area to the abnormal electricity usage risk index of the target area. The electric energy database stores the correspondence between the data collection deviation factor of the electric energy meters in the target area and the corresponding weighting factor. For example, when the data collection deviation factor of the electric energy meters in the target area is input into the electric energy database, the electric energy database can match the weighting factor corresponding to the data collection deviation factor.

[0053] In order to accurately assess the risk of abnormal electricity usage in the target area, it is crucial to ensure that the degree of data collection deviation is maintained at a low level. The magnitude of the data collection deviation is directly reflected by the data collection deviation factor of the electric energy meter in the target area. When the factor is large, it means that the operating parameters of the electric energy meter may be abnormal, which in turn causes the abnormal electricity usage risk index of the target area to deviate greatly from the actual value during the analysis process. Therefore, the data collection deviation factor of the electric energy meter and the abnormal electricity usage risk index of the target area are comprehensively considered. At the same time, during the abnormal monitoring period, if the electric energy meter detects a significant increase in the average voltage offset, this signal is often a strong warning that there may be abnormal electricity usage in the target area. This abnormal change in voltage will first directly affect the current state in the circuit, resulting in an increase in the current fluctuation. Specifically, the voltage offset will break the original voltage stability, causing the current in the circuit to deviate greatly. Significant fluctuations occur instantly or over a period of time. This fluctuation is manifested as a significant increase in the maximum current fluctuation value through the monitoring of the electric energy meter. Furthermore, the phase relationship between voltage and current will also be affected by voltage anomalies. Under normal circumstances, a certain phase difference is maintained between current and voltage, which is determined by the load characteristics and power supply properties in the circuit. However, when the voltage shifts, this phase relationship will change, causing the average phase difference between current and voltage to deviate significantly from its normal reference value. This phase difference deviation not only reflects the change in the load characteristics of the circuit, but may also indicate abnormal power usage in the circuit, such as power theft and equipment failure. These combined factors jointly aggravate the potential risk of abnormal power usage in the target area. In summary, there is a close and complex interaction between the above parameters, which jointly affect the assessment of the risk level of abnormal power usage in the target area.

[0054] Furthermore, the abnormal power usage risk index of the target area is evaluated, and the specific evaluation process is as follows:

[0055] The monitoring voltage curves of the electric energy meters in the target area in the corresponding historical abnormal monitoring periods are obtained, and the monitoring voltage curves of the electric energy meters in the target area in the abnormal monitoring period are respectively compared with the monitoring voltage curves of the electric energy meters in the target area, and the voltage offsets of the electric energy meters in the target area in the abnormal monitoring period are analyzed and averaged, and the processing results are marked as the average monitoring voltage offsets of the electric energy meters in the target area in the abnormal monitoring period; the above-mentioned abnormal monitoring period refers to the time period for monitoring the power usage in the target area, and its specific duration is determined by the power management department; the monitoring voltage of the electric energy meters in the target area in the abnormal monitoring period The curve refers to a graphical representation of the voltage change recorded by the electric energy meter in the target area during the abnormal monitoring period. The real-time voltage monitored by the electric energy meter in the abnormal monitoring period is denoised by the matrix laboratory software and connected into a smooth curve, which is the monitoring voltage curve of the electric energy meter in the target area during the abnormal monitoring period; the above-mentioned historical abnormal monitoring period refers to a series of time period sets corresponding to the specific abnormal monitoring period of current concern (for example, 12 noon to 2 pm every day) on the past time axis. These time period sets are repeated in history at a fixed time interval (24 hours in this embodiment), so that It constitutes a continuous and ordered time series; the monitoring voltage curve of the electric energy meter belonging to the above-mentioned target area in the corresponding historical abnormal monitoring period is obtained in the same way as the monitoring voltage curve of the electric energy meter belonging to the target area in the abnormal monitoring period; the voltage offsets of the electric energy meter belonging to the above-mentioned target area in the abnormal monitoring period are specifically compared as follows: the monitoring voltage curve of the electric energy meter belonging to the target area in the corresponding historical abnormal monitoring period and the monitoring voltage curve of the electric energy meter belonging to the target area in the abnormal monitoring period are input into the matrix laboratory software, and the curve comparison function provided by the matrix laboratory software is used to compare the abnormal monitoring time. Point below, the vertical offset between the monitoring voltage curve in each historical abnormal monitoring period and the monitoring voltage curve in the abnormal monitoring period are respectively accumulated to obtain the voltage offset between the monitoring voltage curve of the electric energy meter in the target area in the abnormal monitoring period and the monitoring voltage curve of the electric energy meter in the target area in the corresponding historical abnormal monitoring periods, which are marked as the voltage offsets of the electric energy meter in the target area in the abnormal monitoring period, among which the average monitoring voltage offset of the electric energy meter in the target area in the abnormal monitoring period indicates the average offset of the voltage monitored by the electric energy meter in the target area relative to the voltage in each historical abnormal monitoring period during the abnormal monitoring period.

[0056] The highest point and the lowest point of the curve are located on the monitoring current curve of the electric energy meter belonging to the target area during the abnormal monitoring period, the current at the highest point of the curve and the current at the lowest point of the curve are obtained and difference processing is performed, and the processing result is marked as the maximum value of the monitoring current fluctuation of the electric energy meter belonging to the target area during the abnormal monitoring period; the above-mentioned monitoring current curve of the electric energy meter belonging to the target area during the abnormal monitoring period refers to a graphical representation of the current changes recorded by the electric energy meter belonging to the target area during the abnormal monitoring period, and the real-time current monitored by the electric energy meter during the abnormal monitoring period is denoised by the matrix laboratory software and connected into a smooth curve, which is the monitoring current curve of the electric energy meter belonging to the target area during the abnormal monitoring period. In an example embodiment, the monitoring current curve of the electric energy meter belonging to the target area during the abnormal monitoring period is as follows Figure 3 As shown, the horizontal axis is the abnormal monitoring time point in minutes, and the vertical axis is the current in amperes. It clearly shows the current changes monitored by the ammeters in the target area, obtains the current at the highest point 1 of the curve and the current at the lowest point 2 of the curve and performs difference processing, and the processing result is marked as the maximum value of the monitored current fluctuation of the electric energy meter in the target area during the abnormal monitoring period, wherein the maximum value of the monitored current fluctuation of the electric energy meter in the target area during the abnormal monitoring period represents the difference between the highest current value and the lowest current value in the change curve formed by the current values ​​monitored by the electric energy meters installed in the target area within the set abnormal monitoring time period.

[0057] The average monitoring voltage offset of the electric energy meters in the target area during the abnormal monitoring period, the maximum monitoring current fluctuation of the electric energy meters in the target area during the abnormal monitoring period, the average monitoring current and voltage phase difference of the electric energy meters in the target area during the abnormal monitoring period, and the data collection deviation factor of the electric energy meters in the target area are comprehensively evaluated to obtain the abnormal power usage risk index of the target area. The abnormal power usage risk index of the target area refers to a numerical value for quantitatively evaluating the risk of abnormal power usage in the target area; the average monitoring current and voltage phase difference of the electric energy meters in the target area during the abnormal monitoring period refers to the average phase angle difference between the current signal and the voltage signal measured by the electric energy meters during the abnormal monitoring period. The current at each abnormal monitoring time point on the monitoring current curve during the abnormal monitoring period and the voltage at the corresponding abnormal monitoring time point on the monitoring voltage curve are obtained, marked as pairs of current and voltage, and the phase difference calculation formula is used to obtain the phase difference of each pair of current and voltage. Then, the obtained phase differences are averaged to obtain the average monitoring current and voltage phase difference of the electric energy meters in the target area during the abnormal monitoring period.

[0058] The abnormal risk comparison module is used to compare the abnormal power usage risk index of the target area with the abnormal power usage risk threshold. If the abnormal power usage risk index of the target area is greater than the abnormal power usage risk threshold, the power theft protection determination module is executed.

[0059] Specifically, the abnormal power usage risk index of the target area is compared with the abnormal power usage risk threshold, and the specific comparison process is:

[0060] If the abnormal electricity usage risk index of the target area is less than or equal to the abnormal electricity usage risk threshold, the operating environment parameters of the electric energy meters in the target area and the operating parameters of the electric energy meters in the target area are continuously collected, and the data collection deviation factors of the electric energy meters in the target area are analyzed; if the abnormal electricity usage risk index of the target area is less than or equal to the abnormal electricity usage risk threshold, it indicates that the electricity usage in the target area is good and there is no usage risk, so the electricity usage in the target area can be continuously monitored; the abnormal electricity usage risk threshold represents the maximum value of the reasonable range of the abnormal electricity usage risk index of the target area, which is extracted from the power database.

[0061] The electricity theft protection determination module is used to determine the electricity theft abnormality factor of the electric energy meter belonging to the target area, thereby determining whether to perform anti-electricity theft protection on the electric energy meter according to the electricity theft abnormality factor of the electric energy meter belonging to the target area.

[0062] In a specific embodiment, the present invention can deeply determine the power anomaly situation by acquiring the power theft-related parameters of the target area, scientifically identify and accurately locate the area where power theft may exist, and reduce the one-sidedness of directly determining it as power theft just because the risk of abnormal power use is identified. It not only greatly improves the detection efficiency of power theft, but also reduces the tediousness and cost of manual investigation, thereby facilitating the rapid adoption of countermeasures and effectively curbing the occurrence of power theft.

[0063] Specifically, the process of determining whether to perform anti-electricity theft protection on the electric energy meter according to the abnormal factor of the electric energy meter in the target area is as follows:

[0064] The power theft abnormality factor of the electric energy meter in the target area is compared with the power theft abnormality threshold. If the power theft abnormality factor of the electric energy meter in the target area is greater than the power theft abnormality threshold, the electric energy meter is protected against power theft. The above-mentioned power theft abnormality threshold represents the maximum value of the reasonable range of the power theft abnormality factor of the electric energy meter in the target area, which is extracted from the power database. In an example embodiment, the specific anti-power theft protection process is: automatically control the power consumption of the target area to 0.8 times the existing power to prevent the occurrence of overload, and generate an early warning log for the target area, which contains the location of the target area, the model of the electric energy meter, the code of the suspected power theft device, and the power supply unit to which the target area belongs, etc. The early warning log of the target area is notified to relevant personnel (such as power company administrators) by SMS or email, and the relevant personnel can check according to the early warning log to perform anti-power theft protection.

[0065] If the power theft abnormality factor of the electric energy meter belonging to the target area is less than or equal to the power theft abnormality threshold, the anti-power theft protection of the electric energy meter is not performed, and the power protection plan of the target area is matched from the power database according to the power abnormal use risk index of the target area, and the power of the target area is protected according to the power protection plan of the target area; it should be explained that if the power theft abnormality factor of the electric energy meter belonging to the target area is less than or equal to the power theft abnormality threshold, it indicates that although there is an abnormal power use condition in the target area, it is not caused by power theft. In this situation, according to the degree of abnormal power use risk of the target area, that is, the abnormal power use risk index of the target area, the personalized power protection plan is accurately matched; the power database stores the power protection plan corresponding to each abnormal power use risk index interval; in an example embodiment, the abnormal power use risk index of a certain target area belongs to a certain abnormal power use risk index interval stored in the power database, and the power protection plan corresponding to the abnormal power use risk index interval includes: generating a fault warning log of the target area, the fault warning log includes the target area location and the possible circuit failure cause of the target area; then the power protection plan of the target area includes: generating a fault warning log of the target area, the fault warning log includes the target area location and the possible circuit failure cause of the target area.

[0066] The above-mentioned protecting the power of the target area according to the power protection plan of the target area means notifying the relevant personnel (such as the power company administrator) of the fault warning log of the target area by SMS or email, and the relevant personnel can check according to the fault warning log to carry out power protection.

[0067] Furthermore, the specific determination process of the power theft abnormality factor of the electric energy meter in the target area is as follows:

[0068] The power theft related parameters of the target area include the monitored power consumption of the power meters in the target area during the abnormal monitoring period, the monitored average power factor of the power meters in the target area during the abnormal monitoring period, and the average speed of the power meters in the target area during the abnormal monitoring period; the monitored power consumption of the power meters in the target area during the abnormal monitoring period and the monitored average power factor of the power meters in the target area during the abnormal monitoring period are extracted from the data recorded by the power meters, wherein the monitored power consumption of the power meters in the target area during the abnormal monitoring period refers to the power consumption of the power meters installed in the target area during the set abnormal monitoring period. The total amount of electric energy consumed in the area measured and recorded by the meter; the average power factor monitored by the electric energy meters in the target area during the abnormal monitoring period refers to the average value of the power factor measured by the electric energy meters installed in the target area during the set abnormal monitoring time period. The power factor is the ratio of active power to apparent power, which reflects the degree to which electric energy is effectively utilized in the power grid; the average speed of the electric energy meters in the above-mentioned target area during the abnormal monitoring period refers to the average rotation speed of the rotating parts (such as turntable) related to electric energy measurement inside the electric energy meter during the set abnormal monitoring time period, which can be monitored by the counting sensor.

[0069] According to the monitored power consumption of the electric energy meter belonging to the target area during the abnormal monitoring period, the reference average speed of the electric energy meter belonging to the target area during the abnormal monitoring period is matched from the power database, and the difference processing is performed with the average speed of the electric energy meter belonging to the target area during the abnormal monitoring period, and the processing result is marked as the speed deviation value of the electric energy meter belonging to the target area during the abnormal monitoring period; the above-mentioned reference average speed of the electric energy meter belonging to the target area during the abnormal monitoring period is matched from the power database, and the specific matching process is: the reference average speed corresponding to each monitored power consumption interval is stored in the power database, and the monitored power consumption interval stored in the power database to which the monitored power consumption of the electric energy meter belonging to the target area during the abnormal monitoring period belongs is queried, and the reference average speed corresponding to the monitored power consumption interval stored in the power database is the reference average speed of the electric energy meter belonging to the target area during the abnormal monitoring period; the speed deviation value of the electric energy meter belonging to the target area during the abnormal monitoring period refers to the absolute value of the difference between the actual average speed of the rotating parts (such as a turntable or a metering mechanism) inside the electric energy meter and the reference average speed within the set abnormal monitoring time period. This deviation value reflects whether the working state of the internal metering mechanism of the electric energy meter is stable and accurate when measuring electric energy.

[0070] The monitored power consumption of the electric energy meters belonging to the target area in the corresponding historical abnormal monitoring periods is obtained, and the monitored power consumption of the electric energy meters belonging to the target area in the abnormal monitoring period is subjected to difference processing to obtain the deviation values ​​of the monitored power consumption of the electric energy meters belonging to the target area in the abnormal monitoring period, and the average processing is performed, and the processing result is marked as the average value of the deviation of the monitored power consumption of the electric energy meters belonging to the target area in the abnormal monitoring period; the monitored power consumption of the electric energy meters belonging to the target area in the corresponding historical abnormal monitoring periods is consistent with the acquisition method of the monitored power consumption of the electric energy meters belonging to the target area in the abnormal monitoring period; the average value of the deviation of the monitored power consumption of the electric energy meters belonging to the target area in the abnormal monitoring period refers to the average value of the difference between the monitored power consumption of each period when comparing the monitored power consumption of the electric energy meters belonging to the target area in the current abnormal monitoring period with the monitored power consumption in multiple historical abnormal monitoring periods, and then the average of these differences (i.e., the deviation values ​​of the monitored power consumption) is obtained, and this average value reflects the change in power consumption in the target area in the current abnormal monitoring period relative to the historical periods.

[0071] The power abnormal use risk index of the target area, the average value of the monitored power consumption deviation of the electric energy meter in the target area during the abnormal monitoring period, the speed deviation value of the electric energy meter in the target area during the abnormal monitoring period, and the monitored average power factor of the electric energy meter in the target area during the abnormal monitoring period are comprehensively determined to obtain the power theft abnormality factor of the electric energy meter in the target area. In this embodiment, the power theft abnormality factor of the electric energy meter in the target area is a numerical indicator used to quantify and characterize the possibility of power theft in the target area. The specific determination method is:

[0072] ;

[0073] In the formula, is the power theft anomaly factor of the electric energy meter in the target area, The power abnormality usage risk index for the target area, The weight factor corresponding to the power abnormality usage risk index preset in the power database, is the average value of the monitored power consumption deviation of the electric energy meters in the target area during the abnormal monitoring period. is the speed deviation value of the electric energy meter in the target area during the abnormal monitoring period, is the average power factor of the electric energy meter in the target area during the abnormal monitoring period. It is the monitoring average power factor reference value preset in the power database. It is the power theft abnormality weight factor corresponding to the unit value of the average value of the monitored power consumption deviation preset in the power database. It is the power theft abnormality weight factor corresponding to the unit value of the speed deviation value preset in the power database, and e is a natural constant.

[0074] The above-mentioned monitoring average power factor reference value represents the reference value of the monitoring average power factor of the electric energy meter belonging to the target area during the abnormal monitoring period.

[0075] The weight factor corresponding to the above-mentioned abnormal electricity usage risk index represents the proportion of the abnormal electricity usage risk index of the target area to the abnormal electricity theft factor of the electric energy meter belonging to the target area. The power database stores the correspondence between the abnormal electricity usage risk index of the target area and its corresponding weight factor. For example, the abnormal electricity usage risk index of the target area is input into the power database, and the power database can match the weight factor corresponding to the abnormal electricity usage risk index.

[0076] The electricity theft abnormality proportion factor corresponding to the unit value of the above-mentioned monitored electricity consumption deviation average value represents the degree of influence of the unit value of the monitored electricity consumption deviation average value on the electricity theft abnormality factor of the electric energy meter in the target area. The correspondence between the monitored electricity consumption deviation average value and its corresponding electricity theft abnormality proportion factor is stored in the power database. For example, the monitored electricity consumption deviation average value is input into the power database, and the power database can match the electricity theft abnormality proportion factor corresponding to the unit value of the monitored electricity consumption deviation average value.

[0077] The electricity theft abnormality weight factor corresponding to the unit value of the above-mentioned speed deviation value represents the degree of influence of the unit value of the speed deviation value on the electricity theft abnormality factor of the electric energy meter belonging to the target area. The corresponding relationship between the speed deviation value and its corresponding electricity theft abnormality weight factor is stored in the power database. For example, the speed deviation value is input into the power database, and the power database can match the electricity theft abnormality weight factor corresponding to the unit value of the speed deviation value.

[0078] When the risk level of abnormal power usage in the target area is at a high level, it often indicates that the area may face a high risk of abnormal power theft. In order to more accurately assess this risk, the abnormal power usage risk index of the target area and the abnormal power theft factor of the electric energy meter in the target area are integrated and analyzed, so as to comprehensively and systematically grasp the actual situation of power usage in the area, timely discover and respond to potential power theft. At the same time, the average value of the monitored power consumption deviation reflects the difference between the power consumption recorded by the electric energy meter during the abnormal monitoring period and the actual power consumption. When there is power theft, the power thief may change the internal structure or external wiring of the electric energy meter to slow down the speed of the electric energy meter during measurement, thereby causing the recorded power consumption to be less. This slowdown in speed is directly reflected in the increase in the speed deviation value, which becomes an intuitive manifestation of power theft on the electric energy meter. At the same time, since electricity theft may change the internal structure or external wiring of the electricity meter and thus change the load characteristics in the circuit, interfering with the normal metering of the electricity meter, it leads to abnormal changes in the power factor, which is manifested as the average power factor monitored by the electricity meter significantly deviates from the corresponding reference value. The abnormal changes in these parameters together provide an important basis for judging the abnormal risk of electricity theft in the target area, significantly enhancing the accuracy and comprehensiveness of the assessment. Compared with the practice of judging electricity theft based on a single parameter, this comprehensive analysis method greatly reduces the risk of misjudgment and one-sidedness. By monitoring the coordinated changes of multiple parameters such as the average power consumption deviation, the speed deviation value, and the average power factor, we can have a deeper understanding of the impact mechanism of electricity theft on the electricity meter metering system, thereby more accurately identifying potential electricity theft and providing strong support for the formulation of targeted anti-electricity theft strategies.

[0079] It should also be explained that in the act of electricity theft, the active power metering may be reduced by changing the electrical characteristics of the power grid. For example, capacitors may be connected in parallel to compensate for the reactive power consumption of inductive loads, and the reactive power consumption may be increased by reducing the power factor, thereby concealing the electricity theft. This is because reactive power is usually provided by the power grid company and is not included in the user's electricity bill. In summary, when the power consumption remains unchanged and the power factor decreases, the possibility of electricity theft increases because some measures may have been taken to artificially increase the reactive power consumption, thereby reducing the power factor, thereby concealing the electricity theft. Therefore, in order to accurately determine whether electricity theft has occurred, multiple parameters need to be integrated and analyzed.

[0080] In a specific embodiment, the present invention provides an anti-electricity theft protection system for an electric energy meter. By comprehensively acquiring the operating environment parameters and operating parameters of the electric energy meters in the target area, the data collection deviation factor of the electric energy meter can be comprehensively analyzed and obtained, and then compared with the preset data collection deviation threshold. This not only helps to accurately manage the operating parameters of the electric energy meter and improve management efficiency, but also ensures the accuracy and reliability of the data. At the same time, based on the operating parameters of the electric energy meter and its data collection deviation factor, the abnormal power usage risk index of the target area is further evaluated and compared with the abnormal power usage risk threshold, so as to effectively identify abnormal power conditions. This step can not only timely discover potential power problems, but also provide strong support for subsequent processing measures. On this basis, the power theft related parameters of the target area are obtained, and the power theft abnormal factor of the electric energy meter is determined. Then, according to the result of the power theft abnormal factor, it is decided whether to start the anti-electricity theft protection measures of the electric energy meter, which helps to maintain the stability and safety of the power supply.

[0081] Reference Figure 2 As shown, the second aspect of the present invention provides a method for the anti-electricity theft protection system applied to the electric energy meter, including: step one, obtaining the operating environment parameters of the electric energy meters belonging to the target area and the operating parameters of the electric energy meters belonging to the target area, and comprehensively analyzing to obtain the data collection deviation factor of the electric energy meters belonging to the target area.

[0082] Step 2: Compare the data collection deviation factor of the electric energy meter in the target area with the data collection deviation threshold. If the data collection deviation factor of the electric energy meter in the target area is greater than the data collection deviation threshold, manage the operating parameters of the electric energy meter in the target area. If the data collection deviation factor of the electric energy meter is less than or equal to the data collection deviation threshold, evaluate the abnormal power usage risk index of the target area based on the operating parameters of the electric energy meter and the data collection deviation factor of the electric energy meter.

[0083] Step 3: Compare the abnormal power usage risk index of the target area with the abnormal power usage risk threshold. If the abnormal power usage risk index of the target area is greater than the abnormal power usage risk threshold, execute step 4.

[0084] Step 4: determine the power theft abnormality factor of the power meter in the target area, and determine whether to perform anti-power theft protection on the power meter according to the power theft abnormality factor of the power meter in the target area.

[0085] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An anti-electricity theft protection system for an electric energy meter, characterized in that: include: A data collection and analysis module is used to obtain the operating environment parameters of the electric energy meters in the target area and the operating parameters of the electric energy meters in the target area, and to obtain the data collection deviation factors of the electric energy meters in the target area through comprehensive analysis; The abnormal risk assessment module is used to compare the data collection deviation factor of the electric energy meter in the target area with the data collection deviation threshold. If the data collection deviation factor of the electric energy meter in the target area is greater than the data collection deviation threshold, the operating parameters of the electric energy meter in the target area are managed. If the data collection deviation factor of the electric energy meter is less than or equal to the data collection deviation threshold, the abnormal power use risk index of the target area is assessed based on the operating parameters of the electric energy meter and the data collection deviation factor of the electric energy meter. The abnormal risk comparison module is used to compare the abnormal power usage risk index of the target area with the abnormal power usage risk threshold. If the abnormal power usage risk index of the target area is greater than the abnormal power usage risk threshold, the power theft protection determination module is executed; The power theft protection determination module is used to determine the power theft abnormality factor of the power meter belonging to the target area, thereby determining whether to perform anti-power theft protection of the power meter according to the power theft abnormality factor of the power meter belonging to the target area.

2. The anti-electricity theft protection system for an electric energy meter according to claim 1, characterized in that: The operating environment parameters of the electric energy meters belonging to the target area and the operating parameters of the electric energy meters belonging to the target area, wherein the operating environment parameters of the electric energy meters belonging to the target area include the temperature difference of the target area in each historical monitoring sub-period and the average humidity of the target area in the historical monitoring period; The operating parameters of the electric energy meters belonging to the target area include the data update time points of the electric energy meters belonging to the target area in the historical monitoring cycle, the monitoring voltage curves of the electric energy meters belonging to the target area in the abnormal monitoring cycle, the monitoring current curves of the electric energy meters belonging to the target area in the abnormal monitoring cycle, the average phase difference of the monitoring current and voltage of the electric energy meters belonging to the target area in the abnormal monitoring cycle, and the electricity theft related parameters of the target area.

3. The anti-electricity theft protection system for an electric energy meter according to claim 2, characterized in that: The analysis results in the data collection deviation factor of the electric energy meter in the target area. The specific analysis process is as follows: Acquire material parameters of the electric energy meter belonging to the target area, wherein the material parameters of the electric energy meter belonging to the target area include an elastic modulus of the electric energy meter belonging to the target area and a thermal expansion coefficient of the electric energy meter belonging to the target area; The temperature difference of the target area in each historical monitoring sub-period is processed with the elastic modulus of the electric energy meter to which the target area belongs and the thermal expansion coefficient of the electric energy meter to which the target area belongs to obtain the thermal stress of the electric energy meter to which the target area belongs in each historical monitoring sub-period, and the thermal stress is accumulated, and the processing result is marked as the total thermal stress of the electric energy meter to which the target area belongs in the historical monitoring period; Perform difference processing on adjacent time points of each data update time point of the electric energy meter belonging to the target area in the historical monitoring period to obtain each data update duration of the electric energy meter belonging to the target area in the historical monitoring period, and perform average processing, and the processing result is marked as the average data update duration of the electric energy meter belonging to the target area in the historical monitoring period; The total thermal stress of the electric energy meters in the target area during the historical monitoring period, the average humidity in the target area during the historical monitoring period, and the average data update time of the electric energy meters in the target area during the historical monitoring period are comprehensively analyzed to obtain the data collection deviation factor of the electric energy meters in the target area.

4. The anti-electricity theft protection system for an electric energy meter according to claim 1, characterized in that: The operation parameters of the electric energy meters in the target area are managed, and the specific management process is as follows: According to the data collection deviation factor of the electric energy meter belonging to the target area, a parameter processing set is matched from the power database, and the operating parameters of the electric energy meter belonging to the target area are processed according to the parameter processing set, thereby updating the operating parameters of the electric energy meter belonging to the target area.

5. The anti-electricity theft protection system for an electric energy meter according to claim 1, characterized in that: The specific evaluation process of evaluating the abnormal power usage risk index of the target area is as follows: Obtain the monitoring voltage curves of the electric energy meters in the target area in the corresponding historical abnormal monitoring cycles, and compare the curves with the monitoring voltage curves of the electric energy meters in the target area in the abnormal monitoring cycle, compare and analyze the voltage offsets of the electric energy meters in the target area in the abnormal monitoring cycle, and perform average processing. The processing result is marked as the average monitoring voltage offset of the electric energy meters in the target area in the abnormal monitoring cycle; Locate the highest point and the lowest point of the curve on the monitoring current curve of the electric energy meter belonging to the target area within the abnormal monitoring period, obtain the current at the highest point of the curve and the current at the lowest point of the curve and perform difference processing, and mark the processing result as the maximum value of the monitoring current fluctuation of the electric energy meter belonging to the target area within the abnormal monitoring period; The average monitoring voltage offset of the electric energy meters in the target area during the abnormal monitoring period, the maximum monitoring current fluctuation of the electric energy meters in the target area during the abnormal monitoring period, the average monitoring current and voltage phase difference of the electric energy meters in the target area during the abnormal monitoring period, and the data collection deviation factor of the electric energy meters in the target area are comprehensively evaluated to obtain the abnormal electricity usage risk index of the target area.

6. The anti-electricity theft protection system for an electric energy meter according to claim 5, characterized in that: The specific evaluation method of the abnormal power usage risk index of the target area is as follows: ； In the formula, The power abnormality usage risk index for the target area, is the data collection deviation factor of the electric energy meter in the target area, is the average monitoring voltage offset of the electric energy meter in the target area during the abnormal monitoring period, is the maximum value of the monitoring current fluctuation of the electric energy meter in the target area during the abnormal monitoring period. is the average phase difference of the monitoring current and voltage of the electric energy meter in the target area during the abnormal monitoring period, It is the weight factor corresponding to the data collection deviation factor preset in the power database. The average offset limit value of the monitored voltage preset in the power database, The maximum value of the reference monitoring current fluctuation preset in the power database. is the average phase difference of reference monitoring current and voltage preset in the power database, and e is a natural constant.

7. The anti-electricity theft protection system for an electric energy meter according to claim 1, characterized in that: The abnormal power usage risk index of the target area is compared with the abnormal power usage risk threshold. The specific comparison process is: If the abnormal electricity usage risk index of the target area is less than or equal to the abnormal electricity usage risk threshold, the operating environment parameters of the electric energy meters in the target area and the operating parameters of the electric energy meters in the target area are continuously collected, and the data collection deviation factors of the electric energy meters in the target area are analyzed.

8. The anti-electricity theft protection system for an electric energy meter according to claim 1, characterized in that: The specific determination process of the power theft abnormality factor of the electric energy meter in the target area is as follows: The electricity theft related parameters of the target area include the monitored power consumption of the electric energy meter in the target area during the abnormal monitoring period, the monitored average power factor of the electric energy meter in the target area during the abnormal monitoring period, and the average speed of the electric energy meter in the target area during the abnormal monitoring period; According to the monitored power consumption of the electric energy meter in the target area during the abnormal monitoring period, the reference average speed of the electric energy meter in the target area during the abnormal monitoring period is matched from the power database, and the reference average speed of the electric energy meter in the target area during the abnormal monitoring period is processed with the average speed of the electric energy meter in the target area during the abnormal monitoring period, and the processing result is marked as the speed deviation value of the electric energy meter in the target area during the abnormal monitoring period; Obtain the monitored power consumption of the electric energy meter in the target area in the corresponding historical abnormal monitoring periods, perform difference processing with the monitored power consumption of the electric energy meter in the target area in the abnormal monitoring period, obtain the deviation values ​​of each monitored power consumption of the electric energy meter in the target area in the abnormal monitoring period, perform average processing, and mark the processing result as the average value of the monitored power consumption deviation of the electric energy meter in the target area in the abnormal monitoring period; The abnormal power usage risk index of the target area, the average power consumption deviation of the electric energy meters in the target area during the abnormal monitoring period, the speed deviation of the electric energy meters in the target area during the abnormal monitoring period, and the average power factor monitored by the electric energy meters in the target area during the abnormal monitoring period are comprehensively determined to obtain the abnormal power theft factor of the electric energy meters in the target area.

9. The anti-electricity theft protection system for an electric energy meter according to claim 1, characterized in that: The specific determination process of whether to perform anti-electricity theft protection on the electric energy meter according to the abnormal factor of the electric energy meter in the target area is as follows: The power theft abnormality factor of the power meter in the target area is compared with the power theft abnormality threshold. If the power theft abnormality factor of the power meter in the target area is greater than the power theft abnormality threshold, the power meter is protected against power theft. If the electricity theft abnormality factor of the electric energy meter in the target area is less than or equal to the electricity theft abnormality threshold, the electric energy meter will not be protected against electricity theft. The electric energy protection plan for the target area will be matched from the electric energy database according to the abnormal power usage risk index of the target area, and the electric energy in the target area will be protected according to the electric energy protection plan of the target area.

10. A method for the anti-electricity theft protection system of the electric energy meter according to any one of claims 1 to 9, characterized in that: include: Step 1: Obtain operating environment parameters of the electric energy meters in the target area and operating parameters of the electric energy meters in the target area, and comprehensively analyze and obtain data collection deviation factors of the electric energy meters in the target area; Step 2: Compare the data collection deviation factor of the electric energy meter in the target area with the data collection deviation threshold. If the data collection deviation factor of the electric energy meter in the target area is greater than the data collection deviation threshold, manage the operating parameters of the electric energy meter in the target area. If the data collection deviation factor of the electric energy meter is less than or equal to the data collection deviation threshold, evaluate the abnormal power use risk index of the target area based on the operating parameters of the electric energy meter and the data collection deviation factor of the electric energy meter. Step 3: compare the abnormal power usage risk index of the target area with the abnormal power usage risk threshold. If the abnormal power usage risk index of the target area is greater than the abnormal power usage risk threshold, execute step 4; Step 4: determine the power theft abnormality factor of the power meter in the target area, and determine whether to perform anti-power theft protection on the power meter according to the power theft abnormality factor of the power meter in the target area.

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