Low-voltage electricity stealing behavior dynamic monitoring device and method

Abstract

The invention relates to a low-voltage electricity stealing behavior dynamic monitoring device and method. The device comprises a data acquisition module, a data processing module and an artificial intelligence identification module. The data acquisition comprises a current transformer; the current transformer is electrically connected with a background; the background has a data center; the background carries a big data model; the device is reasonable in design, compact in structure and convenient to use.

Description

Technical Field

[0001] This invention relates to a device and method for dynamic monitoring of low-voltage electricity theft. Background Technology

[0002] With economic development and increased electricity consumption, electricity theft has become increasingly prominent, not only hindering the development of power companies but also seriously affecting national economic construction and social stability, causing huge economic losses to the country. Traditional methods of investigating and dealing with electricity theft mainly rely on manpower to conduct irregular on-site inspections, which involves an element of "luck" and often results in "futile" investigations. This is clearly incompatible with the requirements of lean line loss management. Furthermore, electricity theft methods are gradually developing towards technological sophistication, concealment, complexity, and diversification, leading to a continuous increase in demand for monitoring and detecting electricity theft, time-saving and labor-saving investigations, and covert investigation operations.

[0003] In addition, existing clamps are inconvenient to operate during use. They cannot easily clamp wires near walls or trees, the clamp opening is small, the angle cannot be adjusted, and they can only be operated vertically, not at an angle. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a dynamic monitoring device and method for low-voltage electricity theft.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] A dynamic monitoring device for low-voltage electricity theft includes data acquisition, data processing, and artificial intelligence recognition modules;

[0007] Data acquisition includes current transformers;

[0008] A current transformer includes a main body component and a clamping part that cooperate with each other;

[0009] A battery module electrically connected to the mutual inductance coil is provided on the main body;

[0010] A mounting plate is provided on the main body;

[0011] A fan-shaped tooth is provided on the back of the movable claw part;

[0012] Side guide arcs are provided on both sides of the movable claw part;

[0013] A tilted connector is attached to the lower end of the main body component;

[0014] A hexagonal prism A is obliquely connected to the lower end of the tilted head connector; a coaxially arranged hexagonal prism B is connected to the lower end of hexagonal prism A via a hinge shaft that is rotatably set.

[0015] Movable sleeves are fitted on hexagonal prism A, hinge shaft, and hexagonal prism B;

[0016] The hexagonal prism A is longer than the hexagonal prism B; the hexagonal prism A has the same shape as the hexagonal prism B;

[0017] The hinge shaft has a smaller shape than the hexagonal prism A;

[0018] The movable sleeve has an inner hexagonal hole that is adapted to the shape of the hexagonal prism A and the hexagonal prism B;

[0019] The lower end of the hexagonal prism B is connected to a fixed connecting seat with a downwardly directed lower end;

[0020] A guide sleeve body with an inlet and an outlet is arranged on the main body;

[0021] Fixed guide arc portions are arranged on the two inner side walls of the guide sleeve body;

[0022] The side guide arc is arranged in matching with the fixed guide arc portions;

[0023] The lower part of the movable claw portion is arranged in the guide sleeve body through the inlet and the outlet;

[0024] A bottom opening is arranged at the lower part of the guide sleeve body;

[0025] A drive shaft that engages with the fan-shaped tooth portion is arranged below the inlet and the outlet.

[0026] As a further improvement of the above technical solution:

[0027] The caliper portion includes a movable claw portion; a plurality of process perforations A and process perforations B are distributed on the movable claw portion;

[0028] A positioning taper hole is arranged on the end face of the caliper portion;

[0029] A positioning taper head is arranged on the end face of the movable claw portion, which is used for inserting into the positioning taper hole;

[0030] A resilient steel ball is arranged on the upper end of the hexagonal prism A;

[0031] When the movable sleeve abuts against the resilient steel ball, the movable sleeve is separated from the hexagonal prism B;

[0032] In a natural state, the movable sleeve is in positioning contact with the hexagonal prism A and the hexagonal prism B at the same time.

[0033] The background carries out data information processing, and artificial intelligence algorithm recognition;

[0034] The big data model includes a preprocessing module, a feature extraction module, and a data processing module in sequence;

[0035] The data processing module is electrically connected to the early warning module;

[0036] The early warning module includes a bell alarm, a light alarm, or a short message alarm;

[0037] The method comprises the following steps:

[0038] Step one, the system collects and transmits data;

[0039] The sensor is installed on the power grid equipment, which can collect power parameters and environmental parameters in real time and transmit parameter data to the data center;

[0040] Step two, the data center pre-processes the data;

[0041] First, pre-processing, including data cleaning, denoising, and normalization;

[0042] Then, the system extracts features; the pre-processed parameter data will be sent to the feature extraction module, from which the features related to electricity stealing behavior will be extracted as the basis for subsequent data processing module identification;

[0043] The data processing module identifies and warns of the behavior;

[0044] Among them, after extracting the features, the data processing module uses artificial intelligence algorithms to learn and identify the features to determine whether there is electricity stealing behavior;

[0045] When there is electricity stealing behavior, the data processing module will notify the warning and generate a corresponding report for the user's reference;

[0046] The warning mechanism is an important part of the low-voltage electricity stealing behavior dynamic monitoring device.

[0047] The warning mechanism is based on a database, which sets the threshold value of the parameter data through normalization and overall algorithm, and triggers the notification when the collected real-time data is not within the threshold value;

[0048] The current transformer includes a main body and a clamp data collection, data processing, and artificial intelligence identification module;

[0049] Data collection includes a current transformer;

[0050] The current transformer is electrically connected to a background; the background has a data center;

[0051] The background is equipped with a big data model;

[0052] The current transformer serves as a sensor;

[0053] An antenna is provided on the main body for wireless connection with the background or host;

[0054] An indicator light is provided on the main body;

[0055] A battery module is provided on the main body and electrically connected to the mutual inductor;

[0056] The battery module is electrically connected with the processor;

[0057] The processor is electrically connected with the WiFi module and the GPRS communication module;

[0058] When the electricity stealing behavior is determined,

[0059] The line loss rate is considered, and the line loss rate = loss of electricity / power supply = (power supply - electricity sales) / power supply);

[0060] The difference between the total meter (A) electricity of the transformer area and the sum of the total electricity of each electricity meter (E) of the transformer area is compared, the line loss rate of the transformer area is calculated and analyzed, so as to determine whether the line loss of the transformer area is within the set reasonable range;

[0061] Secondly, if the line loss rate of the transformer area exceeds the reasonable range, the metering data of the transformer side outgoing line and the distribution box (C) are calculated, if the branch current of the transformer side outgoing line is not equal to the sum of the branch currents of the distribution box (C), the branch of the transformer side outgoing line is the loop of line loss, and if the branch current of the distribution box (C) is not equal to the sum of the branch currents of the electricity meter (E), the branch of the distribution box (C) is the loop of line loss, and so on, the abnormal line section of line loss is located step by step;

[0062] The technical benefits of the present application are prominent, mainly embodied in accurately grasping the clues of illegal and criminal behavior and the situations of the small areas, villages and towns that need to be centralized and rectified, and in eliminating the objects of key crackdown, comprehensive investigation and centralized rectification, so as to crack down on electricity stealing behavior from the source and effectively curb the occurrence of electricity stealing behavior.

[0063] The present application realizes dynamic monitoring of electricity stealing behavior, accurate locking of electricity stealing range and alarm function of electricity stealing behavior. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 It is a system working structure schematic diagram of the present application.

[0065] Figure 2 It is an experimental structure schematic diagram of the present application.

[0066] Figure 3 It is a current transformer structure schematic diagram of the present application.

[0067] Figure 4 It is a monitoring principle diagram A of the present application.

[0068] Figure 5 It is a monitoring principle diagram B of the present application.

[0069] Figure 6 It is a communication transmission principle diagram of the present application.

[0070] Figure 7It is the main body structure schematic diagram of the application.

[0071] Figure 8 It is the process perforation A structure schematic diagram of the application.

[0072] Figure 9 It is the elastic steel ball structure schematic diagram of the application.

[0073] Figure 10 It is the hinge shaft structure schematic diagram of the application.

[0074] Figure 11 It is the drive shaft structure schematic diagram of the application.

[0075] Figure 12 It is the guide sleeve body internal structure schematic diagram of the application.

[0076] Among them, 1, antenna part; 2, indicator light; 3, main body; 4, caliper part; 5, table area; 6, main current transformer; 7, branch current transformer; 8, electric energy meter; 9, electricity stealing line; 10, platform main station.

[0077] 11, card table part; 12, movable jaw part; 13, process perforation A; 14, process perforation B; 15, fan-shaped tooth part; 16, side guide arc; 17, positioning cone hole; 18, positioning cone head; 19, skew head connecting seat; 20, hexagonal prism A; 21, elastic steel ball; 22, hinge shaft; 23, hexagonal prism B; 24, movable sleeve; 25, fixed connecting seat; 26, import and export; 27, fixed guide arc part; 28, drive shaft; 29, guide sleeve body; 30, bottom opening. DETAILED DESCRIPTION

[0078] As Figures 1-12 , the core technology of the low-voltage electricity stealing behavior dynamic monitoring device of the application mainly includes data acquisition, data processing and artificial intelligence recognition module;

[0079] As Figures 7-12 shown, the low-voltage electricity stealing behavior dynamic monitoring device of the embodiment includes data acquisition, data processing and artificial intelligence recognition module;

[0080] Data acquisition includes current transformer;

[0081] As Figure 7 , the current transformer includes a main body 3 and a caliper part 4 that cooperate with each other;

[0082] On the main body 3, a battery module is arranged in electrical connection with the mutual inductance coil;

[0083] As Figure 8 , a card table part 11 is arranged on the main body 3;

[0084] A sector tooth part 15 is arranged on the back of the movable jaw part 12;

[0085] Side guide arcs 16 are arranged on both sides of the movable jaw part 12;

[0086] A skew head connecting seat 19 is connected to the lower end of the main body 3;

[0087] As Figure 9 , 10 A hexagonal prism A 20 is connected to the lower end of the skew head connecting seat 19 obliquely; a coaxial hexagonal prism B 23 is connected to the lower end of the hexagonal prism A 20 through a hinge shaft 22 arranged by rotation;

[0088] A movable sleeve 24 is sleeved on the hexagonal prism A 20, the hinge shaft 22 and the hexagonal prism B 23;

[0089] The hexagonal prism A 20 is longer than the hexagonal prism B 23; the outer shape of the hexagonal prism A 20 is the same as that of the hexagonal prism B 23;

[0090] The outer shape of the hinge shaft 22 is smaller than that of the hexagonal prism A 20;

[0091] The movable sleeve 24 has an inner hexagonal hole which is adapted to the outer shape of the hexagonal prism A 20 and the hexagonal prism B 23;

[0092] A fixed connecting seat 25 with a lower end head facing downward is connected to the lower end of the hexagonal prism B 23;

[0093] A guide sleeve body 29 with an inlet and outlet 26 is arranged on the main body 3;

[0094] Fixed guide arc parts 27 are arranged on both inner side walls of the guide sleeve body 29;

[0095] The side guide arcs 16 are arranged in matching with the fixed guide arc parts 27;

[0096] The lower part of the movable jaw part 12 is arranged in the guide sleeve body 29 through the inlet and outlet 26;

[0097] As Figure 12 A bottom opening 30 is arranged at the lower part of the guide sleeve body 29;

[0098] A driving shaft 28 which engages with the sector tooth part 15 is arranged below the inlet and outlet 26.

[0099] As Figure 11 The caliper part 4 includes the movable jaw part 12; a plurality of process perforations A 13 and process perforations B 14 are distributed on the movable jaw part 12;

[0100] A positioning taper hole 17 is arranged on the end face of the caliper base part 11;

[0101] A positioning taper head 18 is arranged on the end face of the movable jaw part 12 for inserting into the positioning taper hole 17;

[0102] The elastic steel ball 21 is arranged on the upper end of the hexagonal prism A20;

[0103] When the movable sleeve 24 abuts against the elastic steel ball 21, the movable sleeve 24 is separated from the hexagonal prism B23;

[0104] In the natural state, the movable sleeve 24 is positioned and contacted with the hexagonal prism A20 and the hexagonal prism B23.

[0105] The caliper of the application can increase the opening degree, realize automatic operation, realize automatic position control through the Hall sensor, reduce the operation amount of the high caliper, realize the combination of the closed or open separation of the caliper shaft 11 and the movable caliper jaw 12, and the process perforation A13 and the process perforation B14 can reduce the weight and also be threading, and the structure is not limited to the structure in the figure.

[0106] The driving shaft 28 and the sector tooth part 15 realize driving opening and closing, the side guide arc 16 realizes guiding and avoids deflection, the positioning taper hole 17 and the positioning taper head 18 facilitate positioning after closing, the skew head connecting seat 19 is designed for easy side or inclined operation, the elastic steel ball 21 realizes clamping and fixing, the hinge shaft 22 realizes rotation angle, realizes adjustment of the caliper opening skew head, utilizes the inclined setting, and thus satisfies the angle adjustment of the caliper opening.

[0107] The guide sleeve body 29 realizes the storage or opening of the movable caliper jaw part. The fixed guide arc part 27 is an example and can be a roller or a guide column or other structures.

[0108] As Figures 1-6 Firstly, data acquisition is the basis of the whole system;

[0109] By installing sensors on power grid equipment, the system can collect real-time power parameters such as current and power. These data provide an important basis for subsequent analysis and identification;

[0110] Sensor data acquisition:

[0111] Sensor data acquisition is the cornerstone of the low-voltage electricity stealing behavior dynamic monitoring device. This technology relies on high-precision sensors and receiving devices to realize real-time online monitoring and remote transmission of various data information of measuring instruments. In this way, relevant personnel can quickly obtain monitoring data, providing strong support for subsequent data processing and analysis.

[0112] Secondly, data processing is the key link of the system;

[0113] After receiving the collected data, the data center processes it using big data analysis techniques to extract features related to electricity theft behavior. These features may include abnormal electricity consumption such as current loss and current mutation. Through analysis of these features, the system can preliminarily determine whether there is electricity theft behavior.

[0114] Processing of data information

[0115] In-depth analysis and mining of monitoring data information are achieved.

[0116] Finally, artificial intelligence recognition.

[0117] After extracting the features, the system uses artificial intelligence algorithms to learn and recognize these features to determine whether there is electricity theft behavior. Through continuous learning and optimization, the recognition accuracy of the system will gradually improve. In practical application, significant results have been achieved.

[0118] The working mode of the research content of the present application can be divided into the following steps:

[0119] Firstly, the system performs data collection and transmission.

[0120] Through the sensors installed on the power grid equipment, real-time collection of power parameters and environmental parameters is performed, and these data are transmitted to the data center. In this process, high-quality sensors and stable communication networks are used to ensure the accuracy and real-time performance of the data.

[0121] Secondly, the data center performs data preprocessing. After receiving the data,

[0122] Firstly, preprocessing is performed, including data cleaning, denoising, normalization and other operations, to improve data quality and analysis effect. Through this step, we can filter out effective data to provide more valuable information for subsequent analysis and recognition. Then, the system performs feature extraction. The preprocessed data will be sent to the feature extraction module to extract features related to electricity theft behavior. These features will serve as the basis for subsequent recognition. In the feature extraction process, we will use big data analysis techniques to deeply mine the potential rules in the data to provide strong support for subsequent recognition. Finally, the system performs behavior recognition and early warning.

[0123] After extracting the features, the system uses artificial intelligence algorithms to learn and recognize the features to determine whether there is electricity theft behavior. Once electricity theft behavior is found, the system will immediately issue a warning and generate a corresponding report for user reference. In this process, we will use artificial intelligence algorithms and deep learning techniques to improve the recognition accuracy and adaptability of the system.

[0124] The monitoring system of the application is based on the design of an intelligent anti-electricity-stealing monitoring device based on ARM, which completes the software and hardware design of the electricity-stealing monitoring device.

[0125] The current transformer, the device is connected in series with the current transformer (CT) on the secondary side of the CT to test the CT, and the test results of the current transformer are as follows: the reliability of the current transformer is high.

[0126] In summary, the reliability of the detection system and the current transformer is high through theoretical verification, and the value of the theoretical verification of the multiplication of the two is greater than 90%, and the target can be achieved.

[0127] The early warning mechanism is an important part of the low-voltage electricity-stealing behavior dynamic monitoring device.

[0128] When abnormal data is found in the monitoring system, the early warning mechanism can start in time and notify relevant personnel to take measures, thereby effectively preventing and cracking down on electricity-stealing behavior. The early warning mechanism has various implementation methods, such as SMS notification, background system reminder pop-up window, etc. These methods can convey abnormal information to relevant personnel in the first time so that they can quickly respond and take effective measures.

[0129] As Figure 2 , the collected data are tested through line branch inspection simulation boxes, the field test current is 1.86A, the background display data is 1.867A, the current transformer is accurately tested in the field, and the data communication returns well. In the multi-branch test, the current transformer can accurately measure the branch current and calculate the lost power.

[0130] After theoretical speculation, the target is feasible.

[0131] As Figures 3-5 , the self-powered wireless transmission current transformer is developed, which uses a self-powered induction coil as a power supply, and is free from maintenance;

[0132] There are two networking modes: general mode and relay mode. In the general mode, the element directly interacts with the data acquisition terminal for data;

[0133] In the relay mode, the wireless clamp-on ammeter is relayed through the relay terminal to form a network, which ensures that the current transformer and the data acquisition terminal can be normally connected wirelessly in the case of large environmental signal interference and too long distance.

[0134] In combination Figures 4-6 , the district 5 is provided with a main current transformer 6, and the main current transformer 6 outputs a plurality of branches, each of which is electrically connected with a branch current transformer 7, an electric energy meter 8 and a electricity-stealing line 9.

[0135] In Figure 5 , a platform master station 10 is electrically connected between the branch current transformer 7 and the electricity-stealing line 9.

[0136] The device is arranged at each branch line and household line of the abnormal high-loss transformer area, effectively forming monitoring for electricity stealing behavior of the transformer area. The device is integrated with power consumption data measurement, concentrator communication meter reading, WiFi and GPRS communication and other functions with a high-performance processor as a control core, and realizes electrical parameter measurement of low-voltage power distribution installation points.

[0137] The device focuses on monitoring abnormal high-loss transformer areas, and through branch and section measurement and statistics, realizes multi-branch node loss calculation and positioning of line loss abnormal line sections. The device can measure current data of each branch line in real time, converts electrical quantities into analog quantities through high-frequency data acquisition, and remotely and wirelessly transmits transformer power consumption information to a low-voltage electricity stealing behavior dynamic monitoring device platform master station.

[0138] According to Kirchhoff's current law, the branch line current balance relationship is calculated, and then the power loss is calculated to determine the current loss interval and accurately locate the electricity stealing range. The device greatly improves the timeliness and accuracy of low-voltage power distribution line power consumption supervision, saves manpower and resources, and improves the intelligent level of low-voltage transformer area management and the safe and economic operation ability of the power grid.

[0139] The calculation principle is as follows:

[0140] (Note: Line loss rate = power loss / power supply = (power supply - power sales) / power supply)

[0141] 1. Compare the difference between the total meter (A) of the transformer area and the sum of the total power of each electricity meter (E) of the transformer area, calculate and analyze the line loss rate of the transformer area, and determine whether the line loss of the transformer area is within a reasonable range.

[0142] 2. If the line loss rate of the transformer area exceeds the reasonable range, the metering data of the transformer side outgoing line and the distribution box (C) can be further calculated. If the current of a branch of the transformer side outgoing line is not equal to the sum of the currents of the branches of the distribution box (C), the branch of the transformer side outgoing line is the loop where the line loss occurs. If the current of a branch of the distribution box (C) is not equal to the sum of the currents of the branches of the electricity meter (E), the branch of the distribution box (C) is the loop where the line loss occurs. In this way, the line loss abnormal line section is located step by step.

[0143] The device realizes the function of electricity stealing behavior alarm, collects all branch line current collection data of the monitored transformer area, dynamically determines the current balance relationship in real time, and realizes alarm when the balance is unbalanced. The device pushes electricity stealing information to the mobile phone of the power supply personnel, timely feeds back the abnormality to the staff, carries out offline management, and accurately strikes. Online and offline, complement each other. The device can be specific to transformer area number, user address, user number, and realize long-term storage of power consumption information for 180 days.

[0144] Without considering the electricity stealing mode, any electricity stealing behavior of destroying, changing and bypassing the metering device can be fully covered and monitored.

[0145] The present application is fully described in order to more clearly disclose the present application, and the prior art is not listed one by one.

[0146] Finally, it should be noted that: the above examples are used to illustrate the technical solutions of the present application, but not limited to them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; as it is obvious for those skilled in the art to combine the technical solutions of the present application. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. The technical contents not described in detail in the present application are all known technologies.

Claims

1. A dynamic monitoring device for low-voltage electricity theft, characterized in that: Includes data acquisition, data processing, and artificial intelligence recognition modules; Data acquisition includes current transformers; The current transformer includes a main body (3) and a clamping part (4) that cooperate with each other; A battery module electrically connected to the mutual inductance coil is provided on the main body (3); A card plate (11) is provided on the main body (3); A fan-shaped toothed part (15) is provided on the back of the movable claw part (12); Side guide arcs (16) are provided on both sides of the movable claw part (12); A tilted head connector (19) is connected to the lower end of the main body (3); A hexagonal prism A (20) is obliquely connected to the lower end of the tilted head connector (19); a coaxially arranged hexagonal prism B (23) is connected to the lower end of the hexagonal prism A (20) via a hinge shaft (22) that is rotatably set. Movable sleeves (24) are fitted on hexagonal prism A (20), hinge shaft (22), and hexagonal prism B (23); Hexagonal prism A(20) is longer than hexagonal prism B(23); the shape of hexagonal prism A(20) is the same as that of hexagonal prism B(23); The shape of the hinge shaft (22) is smaller than that of the hexagonal prism A (20); The movable sleeve (24) has an internal hexagonal hole that fits the shape of hexagonal prism A (20) and hexagonal prism B (23); The lower end of the hexagonal prism B (23) is connected to a fixed connecting seat (25) with the lower end facing downward; A guide sleeve (29) with an inlet and outlet (26) is provided on the main body (3); Fixed guide arcs (27) are provided on the two inner side walls of the guide sleeve (29); The side guide arc (16) is matched with the fixed guide arc (27); The lower part of the movable claw part (12) is set in the guide sleeve (29) through the inlet and outlet (26); A bottom opening (30) is provided at the lower part of the guide sleeve (29); A drive shaft (28) that meshes with the sector teeth (15) is provided below the inlet / outlet (26).

2. The low-voltage electricity theft dynamic monitoring device according to claim 1, characterized in that: The caliper part (4) includes a movable jaw part (12); a plurality of process through holes A (13) and process through holes B (14) are distributed on the movable jaw part (12); The end face of the card plate part (11) is provided with a positioning cone hole (17); The end face of the movable claw part (12) is provided with a positioning cone (18) for insertion into the positioning cone hole (17); An elastic steel ball (21) is provided at the upper end of the hexagonal prism A (20); When the movable sleeve (24) comes into contact with the elastic steel ball (21), the movable sleeve (24) separates from the hexagonal prism B (23); In its natural state, the movable sleeve (24) is simultaneously in positional contact with hexagonal prism A (20) and hexagonal prism B (23).

3. The low-voltage electricity theft dynamic monitoring device according to claim 1 or 2, characterized in that: The current transformer has a backend connection; the backend has a data center. The backend is equipped with a big data model; The background processes data and uses artificial intelligence algorithms for identification. The big data model includes a preprocessing module, a feature extraction module, and a data processing module that are sequentially linked.

4. The low-voltage electricity theft dynamic monitoring device according to claim 1, characterized in that: The data processing module is electrically connected to the early warning module; The early warning module includes a ringtone alarm, a light alarm, or an SMS alarm.

5. A method for dynamic monitoring of low-voltage electricity theft, characterized in that: By means of the monitoring device as described in claim 1; The method includes the following steps: Step one: The system collects and transmits data; Step two: The data center performs data preprocessing.

6. The method for dynamic monitoring of low-voltage electricity theft according to claim 5, characterized in that: In step one, sensors are installed on the power grid equipment to collect power and environmental parameters in real time and transmit the parameter data to the data center.

7. The method for dynamic monitoring of low-voltage electricity theft according to claim 5, characterized in that: In step two, the first step is to perform preprocessing, including data cleaning, noise reduction, and normalization. Then, the system performs feature extraction; the preprocessed parameter data is sent to the feature extraction module to extract features related to electricity theft, which serve as the basis for identification by the subsequent data processing module. The data processing module performs behavior recognition and early warning; After extracting the features, the data processing module uses artificial intelligence algorithms to learn and identify the features to determine whether there is any electricity theft. When electricity theft is detected, the data processing module will issue an alert and generate a corresponding report for the user's reference. The early warning mechanism is an important component of the dynamic monitoring device for low-voltage electricity theft. The early warning mechanism is based on a database. Through normalization and overall planning algorithms, thresholds for parameter data are set. When the collected real-time data is outside the threshold, a notification is triggered.

8. The method for dynamic monitoring of low-voltage electricity theft according to claim 5, characterized in that: An antenna section (1) is provided on the main body (3) for wireless connection with the back-end or host. An indicator light (2) is provided on the main body (3).

9. The method for dynamic monitoring of low-voltage electricity theft according to claim 5, characterized in that: Battery module, electrically connected to processor; The processor is electrically connected to the WiFi module and the GPRS communication module.

10. The method for dynamic monitoring of low-voltage electricity theft according to claim 5, characterized in that: Step three, when determining the act of electricity theft, Considering the line loss rate, the line loss rate = power loss / power supply = (power supply - power sales) / power supply; By comparing the difference between the total electricity consumption of the distribution area's main meter (A) and the sum of the total electricity consumption of all meters (E) in the distribution area, the line loss rate of the distribution area is calculated and analyzed to determine whether the line loss of the distribution area is within the set reasonable range. Secondly, when the line loss rate of the transformer area exceeds the reasonable range, calculations are performed based on the metering data of the transformer side outgoing line and the junction box (C). If the current of a certain branch of the transformer side outgoing line is not equal to the sum of the branch currents of the junction box (C), then the branch of the transformer side outgoing line is the circuit where the line loss occurs. Then, the analysis is performed level by level. If the current of a certain branch of the junction box (C) is not equal to the sum of the branch currents of the electricity meter (E), then the branch of the junction box (C) is the circuit where the line loss occurs. This process is repeated to locate the abnormal line loss line section level by level.

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