Cable anti-theft monitoring method and system, computer equipment and storage medium

Abstract

The invention discloses a cable anti-theft monitoring method and system, computer equipment and a storage medium, and relates to the technical field of power system monitoring, and the method comprises the steps: employing a camera to shoot an image of an anti-theft area, and obtaining video information; key images are intercepted from the video information, objects in the key images are analyzed, displacement data are obtained through analysis, and the moving track and speed are determined; the tensile force borne by the cable is measured, and the change of the tensile force is detected through deformation of the resistance strain gauges; and comparing the moving track and speed of the target object with the tension parameter of the cable in real time, and judging whether the data meet a preset theft behavior standard or not. According to the cable anti-theft monitoring method provided by the invention, the cable anti-theft efficiency and the response speed can be remarkably improved. The state of the cable can be monitored in real time from multiple dimensions, the change of the position of the cable can be detected, and early warning of potential theft behaviors is achieved. And the monitoring accuracy and efficiency are improved. Normal operation and theft behaviors are effectively distinguished, and the false alarm rate is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system monitoring, in particular to a cable anti-theft monitoring method and system, computer equipment and storage medium. BACKGROUND

[0002] In the process of social and economic development in China, the supply of various metal resources is becoming increasingly tight. Due to the driving of interests, many unscrupulous people steal power equipment, thus embarking on the road of crime. Power cables play an important role in the power system. Because the value of the cable itself is high, there is a problem of theft in the use process of the cable. Once the cable is stolen, it will cause economic losses much higher than the value of the cable itself. The cable has high reliability in the power supply process and occupies less area, and can be integrated with the environment, so it is widely used in urban power distribution networks. However, due to the large number of floating population in cities, the situation of public security is severe, so high-voltage cables are often stolen, which seriously affects related business enterprises and power supply departments. And after the cable line is damaged, the power performance will gradually decrease, causing equipment failure or power failure.

[0003] At present, with the improvement of technology, the existing cable device usually implants a sensor to accurately determine the stolen position when stolen. When the cable is detected to be cut, the cut cable position information is immediately sent to the microcomputer host. However, this method can only report after failure and cannot give early warning.

[0004] In view of the above problems, a cable anti-theft monitoring method is designed to solve the problem of single cable monitoring mode and lack of reasonable early warning in the prior art. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is that the existing cable monitoring method has the problems of single monitoring mode and lack of effective early warning, and how to optimize real-time data analysis and dynamic response.

[0007] To solve the above technical problems, the present application provides the following technical scheme: a cable anti-theft monitoring method, comprising:

[0008] capturing images of the anti-theft area by using a camera and obtaining video information;

[0009] cutting key images from the video information, analyzing objects in the key images, and analyzing displacement data to determine moving tracks and speeds;

[0010] The tension of the cable is measured by detecting the change of the tension through the deformation of the resistance strain gauge.

[0011] The movement trajectory and speed of the target object are compared with the tension parameter of the cable in real time, and it is judged whether the data meets the preset theft behavior standard.

[0012] As a preferred scheme of the cable anti-theft monitoring method, the video information is obtained by arranging a night vision camera, which automatically captures two video frames per second; the captured video frames are automatically adjusted in resolution and contrast enhancement.

[0013] The analysis of the object in the key image includes applying a scene change detection algorithm to automatically extract key frames from the video stream, and running a Faster R-CNN algorithm in each key frame to identify and locate the object in the image.

[0014] The DeepSORT tracking algorithm is applied to the object identified in the key frame to extract the position coordinate sequence of each object from the tracking data provided by the DeepSORT algorithm, and the movement trajectory and speed of the object are output by calculating the change of the continuous coordinates.

[0015] As a preferred scheme of the cable anti-theft monitoring method, the change of the tension is detected by the deformation of the resistance strain gauge, which includes installing a resistance strain gauge at a key stress point of the cable, connecting the resistance strain gauge to a measurement circuit through a wire, calibrating the installed strain gauge and measurement circuit, monitoring the tension of the cable in real time, recording the resistance change data from the strain gauge, and converting the resistance value change to tension parameter.

[0016] As a preferred scheme of the cable anti-theft monitoring method, the real-time comparison includes feature extraction of the movement trajectory and speed data and the tension parameter data of the cable, time synchronization and format unification of the movement trajectory and speed and the tension parameter data, extraction of the average value, maximum value and change rate of the moving speed from the movement trajectory and speed data, and extraction of the peak value and change trend of the tension from the tension parameter.

[0017] The change trend is obtained by linear regression analysis, and is quantified by the slope, where a positive slope represents an increasing trend and a negative slope represents a decreasing trend.

[0018] As a preferred scheme of the cable anti-theft monitoring method, the judgment of whether the data meets the preset theft behavior standard includes constructing a cable anti-theft monitoring model by the data obtained by feature extraction, comparing the model output result Y with the preset threshold value, and if Y is greater than or equal to the preset threshold value, it is judged that a theft behavior has occurred, and if Y is less than the preset threshold value, it is judged that no theft behavior has occurred.

[0019] As a preferred scheme of the cable theft monitoring method, the cable theft monitoring model is represented as,

[0020]

[0021] wherein Y represents the comprehensive score, represents the average speed of the object movement, v max represents the maximum speed of the object recorded in the measurement time window, Δv represents the change rate of the object movement speed, F max represents the maximum pulling force value recorded in the measurement time window, β F represents the slope of the pulling force change trend, a, b, c, d, and e respectively represent the weight coefficients, tanh(·) represents the hyperbolic tangent function, represents the natural logarithmic conversion of the average speed, e c·Δv represents the exponential function with e as the base, sigmoid(β F ) represents the S-shaped logic function, and abs(β F ) represents the absolute value function.

[0022] As a preferred scheme of the cable theft monitoring method, if it is judged that a theft behavior occurs, a data pop-up window of the theft behavior information is triggered, a short message is sent to inform the staff, an alarm prompt sound broadcast is performed in the anti-theft area, and the light is controlled to flash.

[0023] If it is judged that no theft behavior occurs, the identity of the personnel in the anti-theft area is verified and the behavior is analyzed.

[0024] Another object of the present application is to provide a cable theft monitoring system, which can solve the problems of real-time monitoring and early warning response by constructing a cable theft monitoring system.

[0025] To solve the above technical problems, the present application provides the following technical solutions: a cable anti-theft monitoring system, comprising: a target object recognition module, a tension sensor, and an alarm module; the target object recognition module shoots image information of an anti-theft area at a preset time interval through a camera, obtains video information of the anti-theft area, intercepts the image to obtain the image information of the anti-theft area, identifies the objects in the image information to obtain displacement data for identifying the moving track thereof; the tension sensor obtains tension parameter data of the cable through the tension sensor; the elastic element of the tension sensor produces elastic deformation under external force, so that the resistance strain gauges pasted on the surface thereof also produce deformation, the resistance value of the resistance changes with the deformation, the resistance change is converted into a measurable electrical signal through a measurement circuit, so as to obtain the tension parameter data of the cable, and the moving track of the target object is compared with the tension parameter value of the cable; the alarm module matches the comparison result of the tension parameter data and the moving data with a standard for judging a theft behavior, if the comparison result meets the standard of the theft behavior, it is determined that the theft behavior occurs, the alarm module sends a data pop-up window or a short message containing the theft behavior information to a client to timely inform the staff, and at the same time, an alarm prompt sound is broadcasted in the anti-theft area and the light is controlled to flash to deter cable theft personnel.

[0026] A computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the cable anti-theft monitoring method as described above when executing the computer program.

[0027] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the cable anti-theft monitoring method as described above.

[0028] The cable anti-theft monitoring method provided by the present application can significantly improve the cable anti-theft efficiency and response speed. By integrating video monitoring and tension sensing technology, the cable state can be monitored in real time from multiple dimensions, not only the change of the cable position can be detected, but also the change of the cable force can be perceived, so that early warning of potential theft behavior is realized. By using image recognition and object tracking technology, suspicious individuals in the anti-theft area can be accurately identified and tracked, improving the accuracy and efficiency of monitoring. By comparing and analyzing the moving track of the target object and the cable tension data, normal operation and theft behavior can be effectively distinguished, and the false alarm rate can be reduced. The timely response mechanism can quickly notify security personnel and intervene on site through sound and light alarm when confirming the occurrence of theft behavior. Not only the safety of the cable is improved, but also the stable operation of the power system is maintained, and the loss caused by cable theft is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0030] Figure 1 A whole flow chart of a cable anti-theft monitoring method provided by an embodiment of the present application is shown in FIG. 1.

[0031] Figure 2 A whole structure diagram of a cable anti-theft monitoring system provided by an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0032] In order to make the above objectives, features and advantages of the present application more apparent, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative effort should fall within the protection scope of the present application.

[0033] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details, other than those described herein, and it is understood that the present application is not limited to the embodiments described herein and can be practiced with or without other apparatuses, systems, structures, methodologies, procedures, components, materials and so on. Therefore, the present application is not limited to the specific embodiments disclosed below, but includes any alterations, modifications, and improvements within the scope of the present application.

[0034] Embodiment 1

[0035] Reference Figure 1 For an embodiment of the present application, a cable anti-theft monitoring method is provided, which comprises:

[0036] An image of the anti-theft area is captured by a camera, and video information is obtained;

[0037] A key image is intercepted from the video information, and an object in the key image is analyzed to obtain displacement data to determine a moving track and a speed;

[0038] A tension of the cable is measured, and a change of the tension is detected by deformation of a resistance strain gauge;

[0039] The moving track and the speed of the target object are compared with the tension parameter of the cable in real time, and it is judged whether the data conforms to a preset theft behavior standard.

[0040] Acquiring video information involves deploying night vision cameras to automatically capture two frames of video per second; automatically adjusting the resolution and contrast of the captured video frames, and implementing noise reduction algorithms to ensure that the image quality meets the requirements of subsequent processing.

[0041] Analyzing objects in key images includes applying scene change detection algorithms, automatically extracting keyframes from the video stream, running the Faster R-CNN algorithm in each keyframe, and identifying and locating objects in the image.

[0042] The scene change detection algorithm is based on the amount of pixel change between image frames. When the change exceeds a set threshold, it is identified as a key frame. This threshold is set according to the environment and test data to ensure that only frames containing important events are selected.

[0043] The DeepSORT tracking algorithm is applied to the objects identified in the keyframes. The position coordinate sequence of each object is extracted from the tracking data provided by the DeepSORT algorithm. By calculating the changes in continuous coordinates, the movement trajectory and speed of the object are output.

[0044] Faster R-CNN can process data in real time and provide accurate object bounding boxes. DeepSORT can effectively track multiple objects in a video frame sequence, maintaining tracking continuity even when objects are occluded or temporarily leave the frame.

[0045] Faster R-CNN is used to identify and locate dynamic objects (potential thieves) in a video and to provide the bounding boxes of the objects.

[0046] Applying DeepSORT: Tracking a well-located object, extracting its movement trajectory, and using the output of Faster R-CNN to track the object, outputting a sequence of the object's position coordinates in the video sequence.

[0047] The object's movement speed and rate of change of speed are calculated. Based on the position coordinate sequence, the object's movement distance per frame is calculated to obtain the speed. The rate of change of speed is obtained by differencing and smoothing the speed sequence.

[0048] Detecting tensile force changes by measuring the deformation of resistance strain gauges involves installing strain gauges at critical stress points in the cable, connecting the strain gauges to a measuring circuit via wires. This circuit includes a signal amplifier and an analog-to-digital converter to ensure that the physical deformation signal is converted into a usable electronic signal. The installed strain gauges and measuring circuit are calibrated to ensure they accurately reflect tensile force changes under different loads. This typically involves applying a known load in a controlled environment, recording the strain gauge response, monitoring the cable tension in real time, recording the resistance change data from the strain gauges, and converting the resistance change into a tensile force parameter.

[0049] The key stress point is the middle section of the cable, which is most likely to exhibit significant deformation under external force.

[0050] Real-time comparison includes feature extraction of movement trajectory and speed data with cable tension parameter data, synchronizing movement trajectory and speed data with tension parameter data in time and unifying the format, extracting the average value, maximum value, and rate of change of movement speed from movement trajectory and speed data, and extracting the peak value and trend of tension from tension parameters;

[0051] The trend of change was obtained through linear regression analysis. The slope was used for quantification, with a positive slope indicating an increasing trend and a negative slope indicating a decreasing trend.

[0052] Linear regression analysis was used to quantify the changing trend. Data points within a certain time window were collected from the movement trajectory and speed monitoring system. Data within the same time window was collected. The timestamps of all data points were aligned, and data cleaning and preprocessing were performed. A linear regression model was established, applying linear regression analysis to the data points of tension parameters and movement trajectory. The timestamps of the data points were used as independent variables (X-axis), and the tension parameter or movement speed was used as the dependent variable (Y-axis). The linear regression model was trained, and the model parameters (slope and intercept) were obtained. The slope represents the rate of change of tension or movement speed over time; a positive slope indicates an increasing trend, and a negative slope indicates a decreasing trend.

[0053] Determining whether the data meets the preset theft behavior criteria involves constructing a cable anti-theft monitoring model from the data obtained through feature extraction, comparing the model output result Y with a preset threshold, and determining that theft has occurred if Y is greater than or equal to the preset threshold, and that no theft has occurred if Y is less than the preset threshold.

[0054] The cable theft monitoring model is represented as follows:

[0055]

[0056] Where Y represents the overall score. v represents the average speed at which an object moves. max F represents the maximum velocity of the object recorded within the measurement time window, Δv represents the rate of change of the object's velocity, and F represents the maximum velocity of the object within the measurement time window. max β represents the maximum tensile force recorded within the measurement time window. F The slope represents the trend of tensile force change, a, b, c, d, and e represent weighting coefficients, and tanh(·) represents the hyperbolic tangent function. This represents the natural logarithmic transformation of the average velocity, e c·Δv The sigmoid function, with base e, is an exponential function. F) represents an S-shaped logical function, abs(β) F ) represents the absolute value function.

[0057] If theft is detected, a data pop-up window for theft information is triggered and a text message is sent to staff. An alarm sound is broadcast in the security area and the lights are controlled to flash.

[0058] If it is determined that no theft has occurred, identity verification and behavioral analysis are performed on the personnel within the security area.

[0059] The identity verification and behavior analysis process includes automatically triggering the behavior pattern analysis module when the system's algorithm determines that no theft has occurred. This module collects the movement trajectory and behavior data of the person in question within the monitoring area, analyzes the data using an abnormal behavior detection algorithm, and analyzes whether the individual's behavior conforms to historically marked abnormal behavior patterns. This invention uses the isolated forest algorithm for analysis, outputting a score based on the trained isolated forest model. If the score exceeds a preset threshold, it is judged as an abnormal behavior pattern.

[0060] If the behavioral pattern analysis results show potential abnormal behavior, a biometric identification strategy is triggered. The deployed biometric identification technology (this invention deploys facial recognition and voiceprint recognition) is used to attempt to confirm the individual's identity. Biometric data is collected and matched with known templates in the database to verify whether the individual is an authorized person or a known suspect.

[0061] If biometrics cannot confirm an individual's identity, an interactive verification strategy is initiated. An audio question is uttered to the individual via a speaker, and the response is received and analyzed via a microphone. The content of the response is analyzed to determine whether the individual's response is reasonable or conforms to known security protocols, thereby further confirming the individual's intent.

[0062] While conducting interactive verification, environmental data is comprehensively analyzed, and environmental sensor data (temperature, light conditions) is monitored and analyzed in real time. Attention is paid to environmental changes that may be related to individual behavior. If an environmental change matching abnormal behavior is detected (a change in light is detected during a period when no one should be active), this information is combined with the aforementioned analysis results to conduct a final safety assessment.

[0063] Integrate all collected and analyzed data (behavioral patterns, biometrics, interactive responses, and environmental awareness) to build an ensemble model. The model accepts this data and assigns weights to different data sources based on historical data. These weights are determined by the contribution of each data type to identifying theft in historical cases. Each data type is converted into quantifiable features. Abnormal scores for behavioral patterns, matching probabilities for biometrics, consistency scores for interactive responses, and anomalous indicators for environmental awareness are converted into standardized scores. All features are normalized to ensure they are on the same order of magnitude, facilitating ensemble analysis. A decision tree is used to integrate various data types. Based on training data, the model automatically learns how to comprehensively assess potential security threats from various features. A decision threshold is set; when the comprehensive analysis result exceeds this threshold, a theft is identified, and a security alert is triggered.

[0064] Example 2

[0065] Reference Figure 2 According to one embodiment of the present invention, a cable anti-theft monitoring system is provided, including: a target object identification module, a tension sensor and an alarm module;

[0066] The target object recognition module captures images of the anti-theft area at preset time intervals using a camera, obtains video information of the anti-theft area, extracts images from it to obtain image information of the anti-theft area, identifies objects in the image information to obtain displacement data, and determines their movement trajectory.

[0067] The tension sensor acquires the tension parameter data of the cable. Under the action of external force, the elastic element of the tension sensor undergoes elastic deformation, causing the resistance strain gauge attached to its surface to deform as well. The resistance value changes with the deformation. The measuring circuit converts this resistance change into a measurable electrical signal, thereby acquiring the tension parameter data of the cable, and comparing the movement trajectory of the target object with the tension parameter value of the cable.

[0068] The alarm module compares the tensile parameter data and movement data with the standards used to judge theft. If the comparison results meet the standards for theft, the theft is confirmed. The alarm module will send a data pop-up or SMS containing theft information to the client to notify the staff in a timely manner. At the same time, it will broadcast alarm prompts and control the flashing lights in the anti-theft area to deter cable thieves.

[0069] Example 3

[0070] One embodiment of the present invention differs from the previous two embodiments in that:

[0071] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0073] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0074] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0075] Example 4

[0076] As one embodiment of the present invention, a cable anti-theft monitoring method is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0077] The advantages of the cable anti-theft monitoring method provided by this invention compared with traditional methods are verified in terms of improving anti-theft efficiency, reducing false alarm rate, and economic benefits.

[0078] Two independent test areas were set up to simulate cable layouts in an urban environment. Each test area was configured with identical environmental conditions and cable configurations to ensure data consistency and comparability.

[0079] This invention incorporates a high-resolution night vision camera, an integrated tension sensor, an advanced data processing unit, and an automated alarm system.

[0080] The traditional method involves installing a standard-resolution camera and a basic tension sensor, using a simple threshold to trigger the alarm system.

[0081] Scenario Simulation: Theft: Simulates different types of theft attempts during the day, including cutting and pulling cables. Daily Activities: Simulates cable maintenance, cleaning, and contact incidents caused by large animals at night. Data Recording: High-speed cameras and data recording equipment are used for events in each scenario to ensure all key data is captured. Experimental results are shown in Table 1.

[0082] Table 1 Comparison of Experimental Results

[0083] Scenario System Total number of events Number of correct alarms Number of false alarms Number of undetected thefts Theft, day Invention 50 48 1 1 Theft, day Conventional system 50 40 5 5 Routine, night Invention 50 47 2 1 Routine, night Conventional system 50 35 10 5

[0084] The high accuracy in theft scenarios and low false alarm rate in everyday scenarios demonstrated by this invention's method, which integrates image analysis and tensile force monitoring technologies, effectively distinguishes between theft and daily activities. By reducing false alarms, the rapid response capability provided by this invention minimizes potential losses from theft, and its ability to quickly identify and respond to anomalies significantly outperforms traditional systems.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A cable anti-theft monitoring method, characterized in that, include: Use cameras to capture images and obtain video information of the protected area; Key images are extracted from video information, and the objects in the key images are analyzed to obtain displacement data and determine the movement trajectory and speed. The tensile force on the cable is measured, and the change in tensile force is detected by the deformation of the resistance strain gauge; The movement trajectory and speed of the target object are compared with the tension parameters of the cable in real time to determine whether the data meets the preset theft behavior standards.

2. The cable anti-theft monitoring method as described in claim 1, characterized in that: The acquisition of video information includes deploying night vision cameras to automatically capture two frames of video per second; and automatically adjusting the resolution and enhancing the contrast of the captured video frames. The analysis of objects in key images includes applying a scene change detection algorithm to automatically extract key frames from the video stream, running the Faster R-CNN algorithm in each key frame to identify and locate objects in the image; The DeepSORT tracking algorithm is applied to the objects identified in the keyframes. The position coordinate sequence of each object is extracted from the tracking data provided by the DeepSORT algorithm. By calculating the changes in continuous coordinates, the movement trajectory and speed of the object are output.

3. The cable anti-theft monitoring method as described in claim 2, characterized in that: The method of detecting tensile force changes by deformation of resistance strain gauges includes installing resistance strain gauges at key stress points of the cable, connecting the resistance strain gauges to a measuring circuit via wires, calibrating the installed strain gauges and measuring circuit, monitoring the cable tension in real time, recording the resistance change data from the strain gauges, and converting the resistance value change into a tensile force parameter.

4. The cable anti-theft monitoring method as described in claim 3, characterized in that: The real-time comparison includes feature extraction of movement trajectory and speed data and cable tension parameter data, time synchronization and format unification of movement trajectory, speed and tension parameter data, and extraction of average, maximum and rate of change of movement speed from movement trajectory and speed data; Extract the peak value and trend of tensile force from the tensile force parameters; The changing trend was obtained through linear regression analysis. The slope was used for quantification, with a positive slope indicating an increasing trend and a negative slope indicating a decreasing trend.

5. The cable anti-theft monitoring method as described in claim 4, characterized in that: The determination of whether the data meets the preset theft behavior standard includes constructing a cable anti-theft monitoring model through data obtained by feature extraction, comparing the model output result Y with a preset threshold, and judging that theft has occurred if Y is greater than or equal to the preset threshold, and judging that no theft has occurred if Y is less than the preset threshold.

6. The cable anti-theft monitoring method as described in claim 5, characterized in that: The cable anti-theft monitoring model is represented as follows: Where Y represents the overall score. v represents the average speed at which an object moves. max F represents the maximum velocity of the object recorded within the measurement time window, Δv represents the rate of change of the object's velocity, and F represents the maximum velocity of the object within the measurement time window. max β represents the maximum tensile force recorded within the measurement time window. F The slope represents the trend of tensile force change, a, b, c, d, and e represent weighting coefficients, and tanh(·) represents the hyperbolic tangent function. This represents the natural logarithmic transformation of the average velocity, e c·Δv The sigmoid function, with base e, is an exponential function. F ) represents an S-shaped logical function, abs(β) F ) represents the absolute value function.

7. The cable anti-theft monitoring method as described in claim 6, characterized in that: If theft is detected, a data pop-up window for theft information is triggered and a text message is sent to staff. An alarm sound is broadcast in the security area and the lights are controlled to flash. If it is determined that no theft has occurred, identity verification and behavioral analysis are performed on the personnel within the security area.

8. A system employing the cable anti-theft monitoring method as described in any one of claims 1 to 7, characterized in that, include: Target object recognition module, tension sensor, and alarm module; The target object recognition module captures image information of the anti-theft area through a camera at preset time intervals, obtains video information of the anti-theft area, extracts images from it to obtain image information of the anti-theft area, identifies objects in the image information to obtain displacement data, and identifies them to determine their movement trajectory. The tension sensor acquires the tension parameter data of the cable; the elastic element of the tension sensor undergoes elastic deformation under the action of external force, causing the resistance strain gauge attached to its surface to deform as well, and the resistance value changes with the deformation. The measuring circuit converts this resistance change into a measurable electrical signal, thereby acquiring the tension parameter data of the cable, and comparing the movement trajectory of the target object with the tension parameter value of the cable. The alarm module compares the tensile parameter data and movement data with the standards used to judge theft. If the comparison results meet the standards for theft, the theft is confirmed. The alarm module will send a data pop-up or SMS containing theft information to the client to notify the staff in a timely manner. At the same time, it will broadcast alarm prompts and control the flashing lights in the anti-theft area to deter cable thieves.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the cable anti-theft monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the cable anti-theft monitoring method according to any one of claims 1 to 7.

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