Method and system for anti-electricity-stealing of real estate based on guide rail table and user portrait

Abstract

The application discloses a kind of based on guide rail table and user portrait's real estate community anti-stealing electricity method and system, including the key node of distribution network is deployed guide rail type electric energy meter and forms monitoring network, obtains electric quantity data by guide rail type electric energy meter, based on the network executes topological section monitoring: according to the topological relationship of the distribution network, the electric quantity data of guide rail type electric energy meter at upstream and downstream key nodes is compared gradually, the loss electric quantity of each cable section is calculated, to locate the target cable section that there is abnormal loss electric quantity, obtains the electric data of all users corresponding to the target cable section, based on the pre-set electricity stealing user portrait index system, calculates the electricity stealing suspicion score of each user to user and carries out investigation, exports electricity stealing high-risk user list.The application realizes that electricity stealing behavior is accurately positioned from area to specific cable section gradually, and user screening is carried out using interpretable business index, which significantly improves the precision and efficiency of anti-stealing electricity inspection.

Description

Technical Field

[0001] This invention relates to the field of anti-electricity theft technology in power systems, and in particular to a method and system for anti-electricity theft in residential communities based on guide rail meters and user profiles. Background Technology

[0002] With the widespread adoption of smart meters, analyzing electricity theft based on user electricity consumption data has become a research hotspot. Existing technologies typically employ purely data-driven methods, such as comparing the total power supply on the transformer side with the sum of all users' electricity consumption to locate power supply areas with abnormal line losses, and then using complex machine learning models (such as clustering and classification algorithms) to screen users within the area.

[0003] However, such methods have significant shortcomings: First, their anomaly localization is limited to the macro level of "transformer power supply range," failing to pinpoint specific abnormal cable segments within the complex low-voltage distribution networks of residential buildings. This results in a still large on-site investigation scope and low efficiency. Second, the machine learning models relied upon (such as fuzzy C-means clustering and random forests) are mostly "black box" models, lacking interpretability in their analysis process and results. This hinders understanding and application by grassroots inspectors, and the model performance heavily depends on training with large amounts of labeled data.

[0004] For example, prior art document CN121030552A discloses a method for detecting electricity theft based on smart meter data. This method determines suspected electricity theft areas (i.e., transformer substations) by comparing the total meter readings on the transformer side with the total readings of user meters. Then, it performs a series of complex data processing steps on user electricity consumption data, including ratio profile calculation, frequency domain feature extraction, fuzzy clustering, and random forest classification, to ultimately identify the electricity thief. While this method improves the detection accuracy at the algorithm level, it still fails to solve the problem of "precisely locating electricity theft points" on the physical network, and its technical solution is complex and has poor interpretability.

[0005] Therefore, there is an urgent need for an anti-electricity theft technology solution that can directly and accurately locate the location of electricity theft in low-voltage distribution networks, and has a clear screening logic and strong interpretability. Summary of the Invention

[0006] To address the problems in related technologies, this application provides a method and system for preventing electricity theft in residential communities based on guide rail meters and user profiles, thus solving the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for preventing electricity theft in residential communities based on guide rail meters and user profiles, comprising the following steps: Step S1: Deploy multiple rail-mounted energy meters at key nodes of the power distribution network in the target area to form a monitoring network. Obtain power data through the rail-mounted energy meters. The key nodes include the transformer outgoing end, the cable branch box incoming and outgoing ends, and the cable incoming end of the meter box. Step S2: Based on the monitoring network, perform topology segment monitoring: According to the topology of the power distribution network, compare the power data of the rail-mounted energy meters at key upstream and downstream nodes level by level, calculate the power loss of each cable section, and locate the target cable section with abnormal power loss. Step S3: Obtain the electricity consumption data of all users corresponding to the target cable section, and calculate the electricity theft suspicion score of each user based on the preset electricity theft user profile index system; the electricity theft user profile index system includes a primary index for evaluating the stability of user electricity consumption behavior and a secondary index for the correlation of transformer area line loss. Step S4: Sort users according to the suspected electricity theft score and output a list of high-risk electricity theft users.

[0008] Furthermore, the specific process for calculating the lost electricity is as follows: The topology of the power distribution network is analyzed, and a voltage topology diagram of the low-voltage power distribution network is drawn. Based on the electricity data collected by the deployment of multiple rail-mounted energy meters in step S1, the comparison is performed level by level to obtain the comparison difference between the upstream and downstream electricity at each monitoring point. Based on the electricity data of low-voltage household meters and public transformer assessment meters in the electricity information collection system, and on the premise of clarifying the voltage topology of the low-voltage distribution network, the electricity data of low-voltage household meters and public transformer assessment meters are calculated based on the comparison difference between the upstream and downstream electricity of each level of monitoring points, and the power loss of the rail-mounted energy meter monitoring line in each cable section is calculated.

[0009] Furthermore, the primary indicators for assessing the stability of users' electricity consumption behavior include electricity consumption data and electricity consumption habit indicators.

[0010] Furthermore, the secondary indicators of line loss correlation in transformer areas include annual electricity fluctuation rate, meter reading deviation rate, 15-minute electricity correlation rate, and line loss electricity inflection point rate.

[0011] Furthermore, the specific process for calculating the electricity theft suspicion score is as follows: Based on four secondary indicators—annual electricity fluctuation rate, meter code deviation rate, 15-minute electricity correlation rate, and line loss electricity inflection point rate—these indicators are categorized into two primary indicator dimensions: electricity consumption data and electricity consumption habits. The two primary indicator dimensions are then weighted and summed to obtain the electricity theft suspicion score.

[0012] A residential community anti-electricity theft system based on guide rail meters and user profiles, applied to an anti-electricity theft method for residential communities based on guide rail meters and user profiles, includes: The deployment module is used to deploy multiple rail-mounted energy meters at key nodes of the power distribution network in the target area to form a monitoring network and obtain power data through the rail-mounted energy meters. The key nodes include the transformer outgoing end, the cable branch box incoming and outgoing ends, and the cable incoming end of the meter box. The abnormal power loss calculation module is used to perform topology segment monitoring based on the monitoring network: according to the topology of the power distribution network, the power data of the rail-mounted energy meters at the upstream and downstream key nodes are compared level by level to calculate the power loss of each cable section, so as to locate the target cable section with abnormal power loss. The scoring calculation module is used to obtain the electricity consumption data of all users corresponding to the target cable section, and calculate the electricity theft suspicion score of each user based on the preset electricity theft user profile index system; the electricity theft user profile index system includes a primary index for evaluating the stability of user electricity consumption behavior and a secondary index for the correlation of transformer area line loss. The sorting module is used to sort users according to the suspected electricity theft score and output a list of high-risk electricity theft users.

[0013] An electronic device includes a processor, a memory, and a bus, wherein the processor and the memory are connected via the bus, wherein the memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory to execute a method for preventing electricity theft in residential communities based on guide rail tables and user profiles.

[0014] A non-volatile computer storage medium storing computer-executable instructions that execute a method for preventing electricity theft in residential communities based on guide rail meters and user profiles.

[0015] Compared with existing technologies, the present invention has the following advantages: This invention deploys rail-mounted energy meters at key nodes within the power distribution network and employs a topology-based segmented power comparison method. This method can gradually narrow down the scope of electricity theft from the entire distribution area to a specific cable branch box and the cable segment between the meter boxes, greatly reducing the scope of on-site investigation and significantly improving inspection efficiency.

[0016] This invention organically combines segmented monitoring of rail-mounted energy meters with a user profiling index system to form a technical solution for combating electricity theft in complex power distribution topologies in residential communities. The solution includes topology analysis, installation of rail-mounted energy meters, analysis of target cable sections with abnormal power loss, analysis of the user profiling index system, and on-site investigation analysis. It effectively solves the pain points of existing pure data solutions, such as vague positioning and obscure models, as well as the lack of clear targets in extensive manual inspections. The invention boasts unique technical methods and significant results. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention.

[0018] Figure 2 This is a schematic diagram illustrating the installation sequence of the rail-mounted energy meter of the present invention.

[0019] Figure 3 This is a schematic diagram of the primary and secondary indicators of the electricity theft user profiling indicator system of the present invention.

[0020] Figure 4 This is a schematic diagram of the topology of a typical transformer substation according to the present invention. Detailed Implementation

[0021] like Figure 1 As shown, the present invention provides a technical solution: a method for preventing electricity theft in residential communities based on guide rail meters and user profiles, comprising: Step S1: Deploy multiple rail-mounted energy meters at key nodes of the power distribution network in the target area to form a monitoring network. Obtain power data through the rail-mounted energy meters. The key nodes include the transformer outgoing end, the cable branch box incoming and outgoing ends, and the cable incoming end of the meter box. Step S2: Based on the monitoring network, perform topology segment monitoring: According to the topology of the power distribution network, compare the power data of the rail-mounted energy meters at key upstream and downstream nodes level by level, calculate the power loss of each cable section, and locate the target cable section with abnormal power loss. Step S3: Obtain the electricity consumption data of all users corresponding to the target cable section, and calculate the electricity theft suspicion score of each user based on the preset electricity theft user profile index system; the electricity theft user profile index system includes a primary index for evaluating the stability of user electricity consumption behavior and a secondary index for the correlation of transformer area line loss. Step S4: Sort users according to the suspected electricity theft score and output a list of high-risk electricity theft users.

[0022] The specific process for defining and installing a DIN rail-mounted energy meter is as follows: The installation sequence of DIN rail type energy meters is as follows: Figure 2As shown, a first rail-mounted energy meter and a fifth rail-mounted energy meter are respectively installed at the transformer output terminals; The outgoing end of the first rail-mounted energy meter is sequentially connected to the first cable branch box, the second rail-mounted energy meter, the second cable branch box, the third rail-mounted energy meter, the third cable branch box, the fourth cable branch box, and the fourth rail-mounted energy meter. The fourth rail-mounted energy meter is connected to the cable inlet end of the fourth meter box. The outgoing end of the first cable branch box is also connected to the first meter box, the outgoing end of the second cable branch box is also connected to the second meter box, and the outgoing end of the third cable branch box is also connected to the third meter box. The outgoing terminals of the fifth rail-mounted energy meter are sequentially connected to the fifth cable branch box, the sixth rail-mounted energy meter, the sixth cable branch box, the seventh rail-mounted energy meter, the seventh cable branch box, the eighth cable branch box, and the eighth rail-mounted energy meter; the eighth rail-mounted energy meter is connected to the cable inlet terminal of the eighth meter box. The outgoing end of the fifth cable branch box is also connected to the fifth meter box, the outgoing end of the sixth cable branch box is also connected to the sixth meter box, and the outgoing end of the seventh cable branch box is also connected to the seventh meter box.

[0023] Among them, the first to fourth rail-mounted energy meters constitute the upstream monitoring link; the fifth to eighth rail-mounted energy meters constitute the downstream monitoring link.

[0024] A DIN rail-mounted energy meter is an energy meter installed on a standard DIN rail in industrial settings. It is generally used for monitoring data such as voltage, current, and power in industrial automation processes, and works in conjunction with auxiliary equipment (i.e., functional modules connected via expansion interfaces such as RJ45 interfaces) to achieve functions such as abnormal state monitoring and alarms. Compared to user-end metering energy meters, DIN rail-mounted energy meters typically use puncture equipment (puncture clamps) to obtain voltage data at key nodes and Rogowski coil current transformers to obtain current data at key nodes. Based on the voltage and current data, they then calculate the energy consumption data.

[0025] The specific process for calculating the lost electricity is as follows: The power distribution network topology of residential complexes is complex, with cable branch boxes and meter boxes interconnected in series and parallel. Therefore, clarifying the power distribution network topology is a primary task. Common methods include checking the cable lug types and using lug dimensions to initially determine if they belong to the same cable; additionally, personnel at both ends of the cable use clamp meters to monitor the conductors of the same cable. If the current in each phase conductor of the same cable is approximately the same at any given time, it can be determined that it is the same cable. Finally, based on the data indicating that they are the same cable, a low-voltage distribution network topology diagram is drawn.

[0026] By verifying meters on-site and retrieving data from the marketing system, a low-voltage user ledger was compiled to identify the users monitored by each DIN rail type energy meter. Based on the installation and deployment of the rail-mounted energy meters in step S1, the energy data collected by the first to eighth rail-mounted energy meters in step S1 are compared step by step to obtain the comparison difference between the upstream and downstream energy of each monitoring point. Based on the 15-minute electricity consumption data of low-voltage household meters and public transformer assessment meters in the electricity consumption information collection system, and on the premise of clarifying the voltage topology of the low-voltage power distribution network, the 15-minute electricity consumption data of low-voltage household meters and public transformer assessment meters are calculated based on the comparison difference between the upstream and downstream electricity consumption of each level of monitoring points. The power loss of the monitoring lines of the guide rail type energy meters in each cable section is calculated.

[0027] Once it is determined that the DIN rail-mounted energy meter has power loss, the DIN rail-mounted energy meter with no abnormal power loss is moved to the back-end equipment of the DIN rail-mounted energy meter with power loss (such as the downstream cable branch box and the cable section between the meter box) for synchronous monitoring, and the power loss is recalculated. When the power loss is abnormal, it means that the electricity theft is within the range of the back-end equipment, that is, the target cable section with abnormal power loss is obtained.

[0028] The specific process for constructing the user profile indicator system for electricity theft is as follows: After identifying the target cable section, a list of users for that section is compiled using the low-voltage user ledger. However, verifying the users who steal electricity and their methods of theft is quite challenging. Therefore, a user profiling indicator system is constructed, such as... Figure 3 As shown in Table 1, the user profile indicator system for electricity theft includes two primary indicators to assess the stability of user electricity consumption behavior and four secondary indicators related to line loss in distribution areas. The primary indicators are electricity consumption data. Electricity consumption habits indicators The secondary indicators specifically cover the annual electricity fluctuation rate. Electricity meter base code deviation rate 15-minute battery correlation rate Line loss power inflection point rate Subsequently, based on two primary indicators and four secondary indicators, the electricity theft suspicion score of each user on the target cable section user list is calculated using the user profile indicator system (for ease of calculation, each indicator value is rounded to the nearest integer), and the users are ranked according to the electricity theft suspicion score.

[0029] Table 1. Explanation of User Profile Indicators for Electricity Theft .

[0030] Annual electricity fluctuation rate The calculation formula is: ; ; ; ; ; In the formula, This represents the average monthly electricity consumption. For the first Monthly electricity consumption; This represents the sample standard deviation of monthly electricity consumption. For the first Monthly composite fluctuation value; This is the weighting coefficient for monthly fluctuations; For the first Monthly electricity consumption; The coefficient of variation; The weights for the coefficient of variation; The weight for extreme value deviation; This represents the highest monthly electricity consumption throughout the year. This represents the minimum monthly electricity consumption for the entire year. The weighting of the monthly comprehensive fluctuation value; , , .

[0031] Electricity meter base code deviation rate The calculation formula is: ; ; ; In the formula, The average annual increment of the base code of user's electricity meter; For the first User ID; For the first User's electricity meter service life; This represents the total number of electricity meters of the same type in the distribution area; This represents the average annual increment of the base code for similar users' electricity meters.

[0032] 15-minute battery correlation rate =The Pearson correlation between the meter's 15-minute electricity consumption and the area's 15-minute electricity loss indicates: ; ; ; In the formula, This represents the average daily electricity consumption of 15 minutes for each user. For users in the first Electricity consumption for a 15-minute time period; The average daily power loss in the transformer area is 15 minutes. For the Taiwan region in the first Power loss over a 15-minute period .

[0033] Line loss power inflection point rate The calculation method is as follows: ; ; ; In the formula, This represents the real-time change in line loss rate. For high loss Time-based line loss rate; For high loss Time-based line loss rate; For high loss Historical baseline loss rate at any given time; For high loss Historical baseline loss rate at that time; This represents the change in the historical baseline loss.

[0034] The specific process for calculating the electricity theft suspicion score is as follows: Based on annual electricity fluctuation rate Electricity meter base code deviation rate 15-minute battery correlation rate Line loss power inflection point rate These four secondary indicators are respectively categorized into electricity consumption data. Electricity consumption habits indicators The two primary indicator dimensions are used to obtain the electricity theft suspicion score by weighted summation.

[0035] Users are ranked according to their suspected electricity theft score, resulting in a list of high-risk users. Investigations are then conducted on users in the target cable section, focusing on the following two key aspects in addition to routine equipment and wiring checks: First, temperature load consistency: Observe whether there is a significant difference between the temperature inside the user's room and the ambient temperature through direct observation or infrared thermometry. If there is a significant difference, it indicates that the user has temperature control equipment (air conditioner, oil heater, heater, etc.) in operation. Further observe whether the user's electricity meter load matches the load generated by the temperature control equipment. If a mismatch is observed, the user is listed as a key focus.

[0036] Second, consistency of electricity consumption with living traces: For users who have not generated electricity recently, check whether there are any signs of living in the user's house (lighting status, clothes drying status). If there are signs of living in the user's house but no electricity is generated, the user will be listed as a key monitoring target.

[0037] Finally, users who were the focus of the investigation on the consistency of temperature load and the consistency of electricity consumption records were identified as suspected electricity thieves. These suspected users were then ranked to form a final list of high-risk electricity thieves.

[0038] To verify the effectiveness of the on-site investigation strategies in steps S1, S2 and S4 in actual residential communities, a typical transformer area with abnormal daily power loss was selected as the analysis object. Analysis of a typical transformer substation exhibiting abnormal daily power loss revealed a recent sharp increase in daily power loss. Considering the absence of new customers and no recent power outages causing meter reading abnormalities, the analysis preliminarily determined that the abnormal power loss was caused by electricity theft. Subsequently, the staff reviewed the substation's network topology. After analysis, the typical distribution transformer in the area has a capacity of 630kVA, serving 200 households across 8 buildings and 16 units. There are 4 transformer outgoing lines, 16 low-voltage cable branch boxes, and 16 meter boxes. The specific topology diagram is shown below. Figure 4 As shown, Figure 4 In the diagram, DF stands for cable branch box, BX stands for meter box, 01#DF stands for cable branch box DF, and 01#BX stands for meter box 01; # represents the number.

[0039] like Figure 4 As shown in Table 2, rail-mounted energy meters were installed at the 01#DF incoming line, 05#DF incoming line, 09#DF incoming line, and 13#DF incoming line, and named monitoring points 1#, 2#, 3#, and 4# respectively. After a first round of 6-hour monitoring (i.e., 6 hours of rail meter power consumption (kWh) and 6 hours of user power consumption (kWh)) at monitoring points 1#, 2#, 3#, and 4#, the power consumption data of the rail-mounted energy meters at each monitoring point was obtained, which is the first monitoring data. After calculation based on the power consumption data of the rail-mounted energy meters at each monitoring point, monitoring point 2#, i.e., downstream of the 05#DF incoming line, showed abnormal power loss, which is suspected to be electricity theft.

[0040] Table 2 First Monitoring Data .

[0041] Subsequently, the rail-mounted energy meters installed at the 01#DF inlet, 09#DF inlet, and 13#DF inlet were moved to the 06#DF inlet, 07#DF inlet, and 08#DF inlet, and named monitoring points 5#, 6#, and 7#. A second round of monitoring was then conducted for 6 hours to obtain the energy consumption data of the rail-mounted energy meters at each monitoring point, which is the second monitoring data, as shown in Table 3.

[0042] Table 3 Second Monitoring Data .

[0043] After obtaining the electricity consumption data from the rail-mounted energy meters at each monitoring point, it was found that the electricity consumption data of the rail-mounted energy meter on the 06#DF incoming line was normal. This determined that the electricity theft point was located in the cable section from 05#DF to 06#DF, or from 05#DF to 05#BX. On-site investigation confirmed no abnormalities inside the meter box BX, but a significant difference in the current of phase A and the neutral wire at both ends of the cable from 05#DF to 06#DF indicated electricity theft. The method of theft was to damage the cable and illegally connect a new wire, bypassing the metering device to consume electricity.

[0044] The location of the electricity theft point was determined to be at the positions of phase A and the neutral conductor at both ends of the cable. Users in three buildings, totaling 14 households, were identified as having the potential to steal electricity near the cable. Of these, 12 were low-voltage residential users and 2 were metered users. Data from these 14 households was retrieved, and a user profile and suspicion score for electricity theft were calculated, as shown in Table 4.

[0045] Table 4. User Profiles and Suspicion Scoring Table for Electricity Theft .

[0046] Calculations showed that users #01, #04, and #10 scored highly and required focused investigation. After conducting an on-site electricity inspection, staff discovered the electricity theft occurred at the #06 DF inlet line, and the thief was identified as user #10. The user on the first floor of building #3 had damaged the cable during home renovations to steal electricity. This situation matched the results of the electricity theft user profile indicators.

[0047] A residential community anti-electricity theft system based on guide rail meters and user profiles, applied to an anti-electricity theft method for residential communities based on guide rail meters and user profiles, includes: The deployment module is used to deploy multiple rail-mounted energy meters at key nodes of the power distribution network in the target area to form a monitoring network and obtain power data through the rail-mounted energy meters. The key nodes include the transformer outgoing end, the cable branch box incoming and outgoing ends, and the cable incoming end of the meter box. The abnormal power loss calculation module is used to perform topology segment monitoring based on the monitoring network: according to the topology of the power distribution network, the power data of the rail-mounted energy meters at the upstream and downstream key nodes are compared level by level to calculate the power loss of each cable section, so as to locate the target cable section with abnormal power loss. The scoring calculation module is used to obtain the electricity consumption data of all users corresponding to the target cable section, and calculate the electricity theft suspicion score of each user based on the preset electricity theft user profile index system; the electricity theft user profile index system includes a primary index for evaluating the stability of user electricity consumption behavior and a secondary index for the correlation of transformer area line loss. The sorting module is used to sort users according to the suspected electricity theft score and output a list of high-risk electricity theft users.

[0048] An electronic device includes a processor, a memory, and a bus, wherein the processor and the memory are connected via the bus, wherein the memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory to execute a method for preventing electricity theft in residential communities based on guide rail tables and user profiles.

[0049] A non-volatile computer storage medium storing computer-executable instructions that execute a method for preventing electricity theft in residential communities based on guide rail meters and user profiles.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for preventing electricity theft in residential communities based on guide rail meters and user profiles, characterized in that, Includes the following steps: Step S1: Deploy multiple rail-mounted energy meters at key nodes of the power distribution network in the target area to form a monitoring network. Obtain power data through the rail-mounted energy meters. The key nodes include the transformer outgoing end, the cable branch box incoming and outgoing ends, and the cable incoming end of the meter box. Step S2: Based on the monitoring network, perform topology segment monitoring: According to the topology of the power distribution network, compare the power data of the rail-mounted energy meters at key upstream and downstream nodes level by level, calculate the power loss of each cable section, and locate the target cable section with abnormal power loss. Step S3: Obtain the electricity consumption data of all users corresponding to the target cable section, and calculate the electricity theft suspicion score of each user based on the preset electricity theft user profile index system; the electricity theft user profile index system includes a primary index for evaluating the stability of user electricity consumption behavior and a secondary index for the correlation of transformer area line loss. Step S4: Sort users according to the suspected electricity theft score and output a list of high-risk electricity theft users.

2. The method for preventing electricity theft in residential communities based on guide rail meters and user profiles as described in claim 1, characterized in that: The specific process for calculating the lost electricity is as follows: The topology of the power distribution network is analyzed, and a voltage topology diagram of the low-voltage power distribution network is drawn. Based on the electricity data collected by the deployment of multiple rail-mounted energy meters in step S1, the comparison is performed level by level to obtain the comparison difference between the upstream and downstream electricity at each level of monitoring point. Based on the electricity data of low-voltage household meters and public transformer assessment meters in the electricity information collection system, and on the premise of clarifying the voltage topology of the low-voltage distribution network, the electricity data of low-voltage household meters and public transformer assessment meters are calculated based on the comparison difference between the upstream and downstream electricity of each level of monitoring points, and the power loss of the rail-mounted energy meter monitoring line in each cable section is calculated.

3. The method for preventing electricity theft in residential communities based on guide rail meters and user profiles as described in claim 2, characterized in that: The primary indicators for assessing the stability of users' electricity consumption behavior include electricity consumption data and electricity consumption habits.

4. The method for preventing electricity theft in residential communities based on guide rail meters and user profiles as described in claim 3, characterized in that: The secondary indicators of line loss correlation in transformer areas include annual electricity fluctuation rate, meter reading deviation rate, 15-minute electricity correlation rate, and line loss electricity inflection point rate.

5. The method for preventing electricity theft in residential communities based on guide rail meters and user profiles as described in claim 4, characterized in that: The specific process for calculating the electricity theft suspicion score is as follows: Based on four secondary indicators—annual electricity fluctuation rate, meter code deviation rate, 15-minute electricity correlation rate, and line loss electricity inflection point rate—these indicators are categorized into two primary indicator dimensions: electricity consumption data and electricity consumption habits. The two primary indicator dimensions are then weighted and summed to obtain the electricity theft suspicion score.

6. A residential community anti-electricity theft system based on guide rail meters and user profiles, applied to the residential community anti-electricity theft method based on guide rail meters and user profiles as described in any one of claims 1-5, characterized in that, include: The deployment module is used to deploy multiple rail-mounted energy meters at key nodes of the power distribution network in the target area to form a monitoring network and obtain power data through the rail-mounted energy meters. The key nodes include the transformer outgoing end, the cable branch box incoming and outgoing ends, and the cable incoming end of the meter box. The abnormal power loss calculation module is used to perform topology segment monitoring based on the monitoring network: according to the topology of the power distribution network, the power data of the rail-mounted energy meters at the upstream and downstream key nodes are compared level by level to calculate the power loss of each cable section, so as to locate the target cable section with abnormal power loss. The scoring calculation module is used to obtain the electricity consumption data of all users corresponding to the target cable section, and calculate the electricity theft suspicion score of each user based on the preset electricity theft user profile index system. The user profile index system for electricity theft includes a primary index for assessing the stability of user electricity consumption behavior and a secondary index for the correlation of line loss in the transformer area. The sorting module is used to sort users according to the suspected electricity theft score and output a list of high-risk electricity theft users.

7. An electronic device, characterized in that, The system includes a processor, a memory, and a bus. The processor and the memory are connected via the bus. The memory stores a set of program code, and the processor calls the program code stored in the memory to execute the anti-electricity theft method for residential communities based on guide rail tables and user profiles as described in any one of claims 1-5.

8. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer can execute instructions to perform the anti-electricity theft method for residential communities based on guide rail meters and user profiles as described in any one of claims 1-5.

Images