Multi-role cooperative anti-stealing electricity simulation countermeasure training method

Abstract

The application discloses a multi-role cooperative anti-electricity-stealing simulation confrontation training method, which comprises the following steps: constructing a typical electricity-stealing scene case library and a simulation power grid simulation model; assigning control, inspection and electricity-stealing confrontation roles to a teacher end, an inspector end and a user end respectively; in the simulation confrontation, the user end secretly sets electricity-stealing behaviors, the inspector end inspects based on real-time simulation data, and the teacher end dynamically injects interference events and evaluates the operation of each role; and the system performs multi-dimensional comprehensive evaluation on the training process based on a preset scoring rule and an AI behavior analysis model, and generates a personalized training report. Through immersive confrontation drills and multi-role cooperation, the application effectively improves the actual combat skills, strategy coordination ability and abnormal judgment level of anti-electricity-stealing personnel, and solves the problem of lack of interactivity and practicality in traditional training.

Description

Technical Field

[0001] This invention relates to the field of power system training technology, specifically to a multi-role collaborative anti-electricity theft simulation training method. Background Technology

[0002] Electricity theft seriously endangers power grid security and economic benefits. Anti-theft work demands extremely high levels of professional skills, on-site judgment, and legal evidence awareness from frontline personnel. Currently, anti-theft training primarily employs theoretical lectures, case studies, or simple simulated equipment operation, which has the following shortcomings: First, it lacks a realistic sense of confrontation, making it difficult for trainees to experience the stealth and suddenness of electricity theft methods; second, the training roles are singular, failing to cultivate the collaborative combat capabilities of inspectors and personnel from marketing, metering, and public security departments; third, it lacks a dynamic and variable simulation environment, making it difficult to cover constantly evolving electricity theft techniques; and fourth, the evaluation of training effectiveness is highly subjective, lacking quantitative and multi-dimensional capability assessment. Therefore, there is an urgent need for an anti-theft training method that can simulate real confrontation, support multi-role collaboration, and possess intelligent evaluation functions. Summary of the Invention

[0003] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0004] In view of the problems existing in the above and / or existing multi-role collaborative anti-electricity theft simulation training methods, the present invention is proposed.

[0005] Therefore, the purpose of this invention is to provide a multi-role collaborative anti-electricity theft simulation training method to solve the problems of poor training interactivity, insufficient practicality, and inaccurate evaluation in the prior art.

[0006] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution: A multi-role collaborative anti-electricity theft simulation training method includes the following steps: Step S1: Construct a basic training platform, which includes a typical electricity theft scenario case library, an electricity load simulation model, a metering circuit simulation model, and a user electricity consumption behavior simulation engine; Step S2: Create training roles, including at least: instructor role, inspector role, and user role; Step S3: Enter the adversarial training mode. The system initializes the power grid simulation model and user load curves based on the scenario case selected by the instructor. The user role conceals one or more simulated electricity theft behaviors within a specified time and stores the settings information in encrypted form, making them invisible to the inspector. The inspector role investigates based on the simulation data and submits an analysis report. Step S4: The instructor dynamically injects interference events during the confrontation process and observes the operational behavior in real time; Step S5: After the countermeasures are completed, the system retrieves the electricity theft setting log and the inspection operation log and compares them with the preset standards; Step S6: Based on the preset scoring rules and AI behavior analysis model, conduct a comprehensive score for each party; Step S7: Generate a personalized training report. As a preferred embodiment of the multi-role collaborative anti-electricity theft simulation training method described in this invention, the typical electricity theft scenario case library in step S1 includes at least five types of electricity theft methods: voltage circuit abnormality, current circuit abnormality, internal meter tampering, high-frequency interference, and remote control electricity theft; the metering circuit simulation model supports electricity metering simulation under single-phase, three-phase three-wire, and three-phase four-wire connection methods.

[0007] As a preferred embodiment of the multi-role collaborative anti-electricity theft simulation training method described in this invention, in step S2, the instructor role has the authority to control the system, select scenarios, inject interference events, and set scoring weights; the inspector role has the functions of simulating on-site inspection, measuring instruments, comparing parameters, and collecting evidence; and the user role has the function of concealing electricity theft behavior within the authorized scope.

[0008] As a preferred embodiment of the multi-role collaborative anti-electricity theft simulation training method described in this invention, the user-end roles in step S2 are further subdivided into everyday electricity theft imaging roles and high-tech electricity theft imaging roles, respectively corresponding to electricity theft methods of different complexity and concealment levels, and operated by different trainees.

[0009] As a preferred embodiment of the multi-role collaborative anti-electricity theft simulation training method described in this invention, the concealment setting function in step S3 provides a graphical interactive interface for user roles, allowing the simulation of electricity theft wiring by dragging and dropping components on virtual meter boxes, meter tails, or secondary circuit nodes, and the system automatically calculates the resulting electrical quantity deviation.

[0010] As a preferred embodiment of the multi-role collaborative anti-electricity theft simulation training method described in this invention, the interference events dynamically injected in step S4 include short-term voltage drops, load changes, weather effects, or user harassment tactics, and the interference events synchronously affect the operating data of the power grid simulation model.

[0011] As a preferred embodiment of the multi-role collaborative anti-electricity theft simulation training method described in this invention, the AI ​​behavior analysis model in step S6 is an LSTM network model trained based on historical anti-electricity theft expert operation logs, used to identify whether the operation sequence of the inspection end conforms to the optimal investigation path.

[0012] As a preferred embodiment of the multi-role collaborative anti-electricity theft simulation training method described in this invention, the method further includes a debriefing and discussion sub-mode. In this mode, the instructor can synchronously replay the screen recording of the confrontation process, key data nodes, and operational intentions, and support online annotation and discussion.

[0013] As a preferred embodiment of the multi-role collaborative anti-electricity theft simulation training method described in this invention, the scoring rules include five dimensions: basic score, timeliness score, strategy score, evidence score, and safety compliance score. The safety compliance score is used to assess whether safety procedures such as power outage, voltage verification, and labeling are followed.

[0014] Compared with existing technologies, the beneficial effects of this invention are: by constructing a digital model containing various typical electricity theft methods and an electricity load simulation engine, it simulates a near-real power grid operating environment, thereby effectively improving the ability to respond to emergencies and judge anomalies.

[0015] By introducing a three-way collaborative mechanism involving instructors, inspectors, and users, a multi-role collaboration can be formed, which can effectively improve the teamwork, division of labor, and strategy selection capabilities of inspectors when facing electricity theft methods of varying complexity.

[0016] The instructor's end can inject events such as voltage drops, load changes, and user interference in real time during the confrontation process, simulating emergencies in real inspection sites, which significantly enhances the challenge and coverage of the training.

[0017] It adopts a scoring rule that includes five dimensions: basic score, timeliness score, strategy score, evidence score, and safety compliance score. Combined with an AI behavior analysis model based on LSTM, it can objectively identify whether the operation sequence conforms to the expert path and avoid the subjective bias of traditional scoring. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below.

[0019] Example 1: I. Training Preparation The instructor logged into the system via the instructor's terminal and selected a typical scenario from the case library: a commercial user remotely controlling electricity theft. The system automatically loaded the user's typical daily load curve. This user was a 10kV dedicated transformer user in a commercial complex, and its peak load period was from 6 PM to 9 PM. The system also loaded a three-phase four-wire electronic energy meter model and a secondary circuit model. This training session included one instructor, two inspectors, and two user-side trainees. The two inspectors served as the primary and secondary inspectors, respectively; of the two user-side trainees, one simulated a regular electricity thief, and the other simulated a technical interference perpetrator.

[0020] II. Implementation of Countermeasures User-side trainees are given a five-minute setup session. Technical interference trainees use a drag-and-drop interface on a virtual metering screen to set up a wireless remote control module that periodically short-circuits the secondary circuit of the current transformer, causing the meter to record only 70% of the actual electricity consumption during peak load periods. Ordinary electricity theft trainees create simulated damage to the lead seal and place a disguised signal transmitter near the meter.

[0021] After setup, the system synchronously runs the simulation model and pushes real-time data to the inspection team, including load curves, three-phase voltage and current values, active power, and meter readings. The inspection team observed that the user's registered capacity was 400kVA, and the peak current should have been high, but the meter reading was stable and low, while the power factor was abnormally high, close to 1.0. The lead inspector suspected electricity theft via a current loop and used a virtual clamp meter to measure the estimated primary current of the current transformer (provided by the simulation system) and the secondary current. The secondary current was found to be significantly lower than the equivalent primary current. Further inspection of the junction box revealed no obvious short-circuit connections, leading to suspicion of a remote control device.

[0022] At this point, an interference event is injected into the instructor's end: a simulated call from a user claiming there's a large promotional event that evening, that electricity usage is normal, and asking them not to disturb the system; simultaneously, the system voltage drops by 5% for a short period. The trainees on the inspection end need to eliminate the interference and insist on inspecting the metering cabinet. In the virtual scenario, they disassemble the metering cabinet panel and find an external wireless remote control switching device. After manually switching it, the current returns to normal. The trainees then take photos as evidence, fill out an on-site inspection work order, and identify the method of electricity theft as remotely short-circuiting the current transformer circuit in the report.

[0023] III. Behavioral Assessment After the training ends, the system will automatically perform a comparison.

[0024] In the electricity theft setup log, the user terminal was set to remotely short-circuit the current transformer circuit and forge the lead seal. This kind of behavior belongs to the category of high-tech electricity theft.

[0025] In the inspection log, the trainee performed the following steps in sequence: visual inspection, finding an anomaly in the lead seal; current measurement, revealing an abnormal current transformer ratio; subsequent inspection of the junction box, finding no abnormalities; and finally, an internal inspection of the metering cabinet, locating the remote control device. This sequence of operations showed a high degree of matching with the optimal path in the expert database.

[0026] Final report: The method and location of electricity theft were correctly identified, but the evidence collection process omitted a photo of the remote control device's model nameplate and failed to simulate filling out a safety measures form. The system scores trainees based on its scoring rules, including basic score, timeliness score, strategy score, evidence score, and safety compliance score. The report also scores the concealment of the electricity theft methods used by trainees at the user end.

[0027] IV. Review and Improvement The instructor initiated a debriefing and discussion sub-mode, synchronously replaying the operational trajectories, data change curves, and decision-making nodes of both parties. The focus was on the error of the inspector failing to promptly take photos after discovering the remote control device, and a standard evidence chain collection process was demonstrated online, including panoramic photos, close-up photos, nameplate photos, and wiring photos. Finally, the system generated personalized reports for the two inspectors, and based on the weaknesses identified in their reports, corresponding micro-learning tasks and simulation exercises were pushed to each. The reports were stored in their individual electronic files for future training follow-up.

[0028] Example 2: Remote Distributed Collaborative Training In another implementation, the training employs a remote, distributed deployment. The instructor is located at the company's training center, while three trainees from different power supply stations access the system via client applications. Two user-side trainees operate the system remotely. The system synchronizes and encrypts data via the internet to ensure information isolation during the training. The training process is similar to that in Implementation Example 1, and all operation logs are automatically aggregated and scored by the central server after completion.

[0029] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A multi-role collaborative anti-electricity theft simulation training method, characterized in that, Includes the following steps: Step S1: Construct a basic training platform, which includes a typical electricity theft scenario case library, an electricity load simulation model, a metering circuit simulation model, and a user electricity consumption behavior simulation engine; Step S2: Create training roles, including at least: instructor role, inspector role, and user role; Step S3: Enter the adversarial training mode. The system initializes the power grid simulation model and user load curves based on the scenario case selected by the instructor. The user role conceals one or more simulated electricity theft behaviors within a specified time and stores the settings information in encrypted form, making them invisible to the inspector. The inspector role investigates based on the simulation data and submits an analysis report. Step S4: The instructor dynamically injects interference events during the confrontation process and observes the operational behavior in real time; Step S5: After the countermeasures are completed, the system retrieves the electricity theft setting log and the inspection operation log and compares them with the preset standards; Step S6: Based on the preset scoring rules and AI behavior analysis model, conduct a comprehensive score for each party; Step S7: Generate a personalized training report.

2. The multi-role collaborative anti-electricity theft simulation training method according to claim 1, characterized in that, The typical electricity theft scenario case library in step S1 includes at least five types of electricity theft methods: voltage circuit abnormality, current circuit abnormality, internal meter tampering, high frequency interference, and remote control electricity theft. The metering circuit simulation model supports electricity metering simulation under single-phase, three-phase three-wire, and three-phase four-wire connection methods.

3. The multi-role collaborative anti-electricity theft simulation training method according to claim 2, characterized in that, In step S2, the instructor role has permissions for system control, scene selection, interference event injection, and scoring weight setting; the inspector role has functions for simulating on-site inspection, instrument measurement, parameter comparison, and evidence collection; and the user role has the function of concealing electricity theft behavior within the authorized scope.

4. The multi-role collaborative anti-electricity theft simulation training method according to claim 3, characterized in that, In step S2, the user-end roles are further subdivided into everyday eavesdropping roles and high-tech eavesdropping roles, each corresponding to different levels of complexity and concealment in electricity theft methods, and operated by different trainees.

5. The multi-role collaborative anti-electricity theft simulation training method according to claim 4, characterized in that, The concealment setting function in step S3 provides a graphical interactive interface for the user terminal, allowing the simulation of electricity theft wiring by dragging and dropping components on virtual meter boxes, meter tails, or secondary circuit nodes. The system automatically calculates the resulting electrical quantity deviation.

6. The multi-role collaborative anti-electricity theft simulation training method according to claim 5, characterized in that, The interference events dynamically injected in step S4 include short-term voltage drops, load changes, weather effects, or user harassment, and these interference events synchronously affect the operating data of the power grid simulation model.

7. The multi-role collaborative anti-electricity theft simulation training method according to claim 6, characterized in that, The AI ​​behavior analysis model in step S6 is an LSTM network model trained based on historical anti-electricity theft expert operation logs, used to identify whether the operation sequence of the inspection end conforms to the optimal investigation path.

8. The multi-role collaborative anti-electricity theft simulation training method according to claim 7, characterized in that, The method also includes a debriefing and discussion sub-mode, in which the instructor can synchronously replay the screen recording of the confrontation process, key data nodes, and operational intentions, and supports online annotation and discussion.

9. The multi-role collaborative anti-electricity theft simulation training method according to claim 8, characterized in that, The scoring rules include five dimensions: basic score, timeliness score, strategy score, evidence score, and safety compliance score. The safety compliance score is used to assess whether safety procedures such as power outage, voltage testing, and labeling have been implemented.