A regional security early warning system based on electronic fence

Abstract

The invention relates to the field of electronic fence early warning, and discloses a regional security early warning system based on electronic fences, comprising an electronic fence configuration module, which is used for setting a first electronic fence and a second electronic fence digital boundary, and dividing a plurality of safety areas; a plurality of unmanned aerial vehicle management modules, which are used for managing unmanned aerial vehicle data; a key generation module, which is used for randomly generating keys; a decryption module, which is used for obtaining corresponding instructions; a monitoring module, which is used for obtaining sampling data; a fence analysis module, which is used for analyzing keys and sampling data to analyze whether entry into a target area is allowed; and an early warning module, which is used for issuing an early warning; and since the corresponding decryption mode corresponding to each safety area is different, the corresponding instructions of the unmanned aerial vehicle correspond to its safety area, so when the unmanned aerial vehicle executes the corresponding instruction, it can be judged that the unmanned aerial vehicle corresponds to the safety area, and by calling the corresponding unmanned aerial vehicle management module, it can be judged whether the unmanned aerial vehicle is allowed to pass, thereby shortening the recognition time and improving the safety.

Description

Technical Field

[0001] The invention relates to the field of electronic fence early warning, and in particular to a regional security early warning system based on electronic fence. Background Art

[0002] With the development of science and technology, drones have developed rapidly. Drones can be equipped with high-definition cameras and a variety of sensors to perform tasks such as patrolling, filming, agricultural monitoring, and environmental monitoring. They can also monitor and shoot remote targets. In some work units that need to use a large number of drones to perform tasks, a large number of drones have to perform various tasks every day. Drones look similar and are difficult to identify with the naked eye. If an external drone enters, it is easy to cause information data leakage.

[0003] Electronic fences are an effective means of preventing external drones from intruding. Electronic fences are set up in target areas and can identify drones through radio frequency signals or by setting response passwords.

[0004] However, electronic fences use radio frequency signals or set response passwords to identify drones, which are easy to decipher and do not provide high security protection. In addition, drones are easily damaged when performing tasks, and some are difficult to detect by observing the appearance of the drone, making it impossible to repair the drone in time. Summary of the invention

[0005] The purpose of the present invention is to provide a regional security early warning system based on electronic fence to solve the following technical problems:

[0006] How to improve the security of electronic fences and detect abnormal drone status in a timely manner.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An electronic fence configuration module, used to set a first electronic fence and a second electronic fence digital boundary at the boundary of the target area, and divide the target area into multiple safety areas;

[0009] Multiple drone management modules for managing drone data in corresponding safety areas;

[0010] A key generation module, used for randomly generating a key; and sending the key to the drone entering the second electronic fence digital boundary through wireless communication;

[0011] A decryption module is provided on the drone and is used to decrypt the key and obtain the corresponding instruction; the decryption method of the decryption module of the drone in each of the security areas is different;

[0012] The monitoring module is used to obtain sampling data; the sampling data includes real-time image data, real-time positioning data, real-time thermal imaging picture data of the drone, and real-time sound data;

[0013] The fence analysis module is used to analyze the sampled data and the corresponding instructions to obtain the attitude standard of the corresponding drone; and obtain the drone state coefficient according to the attitude standard and the real-time thermal imaging image data and real-time sound data analysis; the drone state coefficient is:

[0014] By formula Calculate the drone status coefficient Q ;

[0015] in, is the judgment function, when hour, ;when hour, ; The highest temperature during the process of the drone completing the corresponding command; This is the operating temperature when the drone is working normally; The average area in the thermal imaging data that exceeds the operating temperature of the drone during the drone's completion of the corresponding command; The maximum decibel of sound emitted by the drone when completing the corresponding command; This is the decibel level when the drone is working normally; For posture standard; , are the first and second weight coefficients.

[0016] As a further solution of the present invention: the fence analysis module further obtains the trajectory matching degree and device model of the corresponding drone by analyzing the sampled data and the corresponding instructions, and determines whether the drone matches the designated safety area according to the trajectory matching degree and the device model; if it matches, the drone is allowed to pass, otherwise it is not allowed to pass;

[0017] As a further solution of the present invention: the early warning system also includes an early warning module, which is connected to the fence analysis module and is used to issue an early warning to the management terminal when the fence analysis module identifies a drone that is not allowed to pass.

[0018] As a further solution of the present invention: the corresponding instruction includes completing a specified starting action, a specified motion trajectory and a specified ending action.

[0019] As a further solution of the present invention: the fence analysis module obtains the device model of the drone by analyzing the real-time image data; the fence analysis module obtains the trajectory matching degree by analyzing the real-time positioning data, real-time image data, the specified start action, the specified motion trajectory and the specified end action; the fence analysis module obtains the posture standard degree by analyzing the specified start action and the specified end action.

[0020] As a further solution of the present invention: the working process of the fence analysis module is:

[0021] S10: Obtain a key through a key generation module; and decrypt the key in turn through various decryption methods to generate multiple corresponding instructions;

[0022] S20: Acquire real-time image data and real-time positioning data through the monitoring module;

[0023] S30: Analyze the real-time positioning data and real-time image data according to preset rules, and determine whether the drone is allowed to enter based on the analysis results;

[0024] S40: If the drone is not allowed to enter, send a signal to the early warning module;

[0025] S50: If the drone is allowed to enter, the drone state coefficient is obtained; and the state coefficient is sent to the corresponding drone management module.

[0026] As a further solution of the present invention: the preset rule is:

[0027] S100: The fence analysis module performs action recognition and device model recognition on real-time image data;

[0028] S200: When a designated start action of one of the corresponding instructions is identified, the real-time positioning data points are connected to obtain a real-time motion trajectory;

[0029] S300: Calculate the trajectory matching degree between the real-time motion trajectory and the specified motion trajectory; at the same time, send the identified device model to the corresponding drone management module for matching;

[0030] S400: When the designated end action is recognized, the real-time motion trajectory recognition is ended; at the same time, the trajectory matching degree between the real-time motion trajectory and the designated motion trajectory is compared with the threshold, and based on the comparison result and the drone data matching result between the drone and the drone management module, it is determined whether the drone can pass.

[0031] As a further embodiment of the present invention: The highest temperature during the drone's completion of a specified motion trajectory; The average area in the thermal imaging image data that exceeds the operating temperature of the drone during the drone's specified motion trajectory; The maximum decibel of sound emitted by the drone when completing a specified motion trajectory; The attitude standard for the drone to complete the specified start action and the specified end action.

[0032] Beneficial effects of the present invention:

[0033] (1) The present invention uses different decryption methods corresponding to each safety area, and the corresponding instructions obtained by the drone correspond to its safety area. Therefore, when the drone executes the corresponding instructions, it can be determined that the drone corresponds to the safety area. The fence analysis module determines whether the drone matches the specified safety area based on the trajectory matching degree and the device model; if it matches, it is allowed to pass, otherwise it is not allowed to pass; since the decryption methods corresponding to each safety area are different, the corresponding instructions obtained by the drone correspond to its safety area. Therefore, when the drone executes the corresponding instructions, it can be determined that the drone corresponds to the safety area. By calling the corresponding drone management module, it can be quickly determined whether the drone belongs to the safety area based on the device model. Even if the external drone imitates the behavior of the internal drone, it cannot match the drone data in the corresponding safety area, and an early warning is quickly issued through the management terminal; the fence analysis module obtains the drone state coefficient based on the attitude standard and the sampling data; and sends the drone state coefficient to the corresponding drone management module; so that the staff can promptly inspect and repair the drone based on the drone state information;

[0034] (2) The present invention obtains the drone status coefficient according to the attitude standard and sampling data through the fence analysis module; sends the drone status coefficient to the corresponding drone management module; and enables the staff to promptly inspect and repair the drone according to the drone status information. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 A schematic diagram of a system framework of an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1As shown, in one embodiment, a regional security warning system based on an electronic fence is provided, comprising:

[0039] An electronic fence configuration module, used to set a first electronic fence and a second electronic fence digital boundary at the boundary of the target area, and divide the target area into multiple safety areas;

[0040] Multiple drone management modules for managing drone data in corresponding safety areas;

[0041] A key generation module, used for randomly generating a key; and sending the key to the drone entering the second electronic fence digital boundary through wireless communication;

[0042] A decryption module is provided on the drone and is used to decrypt the key and obtain the corresponding instruction; the decryption method of the decryption module of the drone in each of the security areas is different;

[0043] A monitoring module, used to obtain sampling data of the drone between the first electronic fence digital boundary and the second electronic fence digital boundary;

[0044] The fence analysis module is used to analyze the sampled data and corresponding instructions to obtain the trajectory matching degree and attitude standard degree of the corresponding drone and the device model;

[0045] The fence analysis module determines whether the drone matches the designated safety area based on the trajectory matching degree and the device model; if it matches, it is allowed to pass, otherwise it is not allowed to pass;

[0046] The fence analysis module obtains the drone state coefficient according to the attitude standard and the sampling data; and sends the drone state coefficient to the corresponding drone management module;

[0047] The early warning module is connected to the fence analysis module and is used to issue an early warning to the management terminal when the fence analysis module identifies a drone that is not allowed to pass;

[0048] Through the above technical scheme, this embodiment sets a first electronic fence and a second electronic fence digital boundary at the boundary of the target area, and divides the target area into multiple security areas; manages the drone data of the corresponding security areas through multiple drone management modules; sets a separate drone management module for the security area, so that the number of drones managed by each drone management module is small, the operation is simple and convenient, and information cross-talk and leakage between multiple security areas are prevented; a key is randomly generated by a key generation module and the key is sent to the drone entering the second electronic fence digital boundary through wireless communication; the drone uses the decryption module to decrypt the key using its corresponding decryption method to obtain the corresponding instruction; the decryption methods of the decryption modules of the drones in each security area are different, so when the drones in each security area receive the same key, the corresponding instructions after decryption are also different, so it can be used to confuse external drones; the drone completes the corresponding instruction before entering the first electronic fence digital boundary, and the monitoring module obtains the drone at the first electronic fence digital boundary and the second electronic fence digital boundary. The fence analysis module analyzes the sampling data and the corresponding instructions to obtain the trajectory matching degree, posture standard degree and device model of the corresponding drone; the fence analysis module determines whether the drone matches the designated safety area according to the trajectory matching degree and the device model; if it matches, it is allowed to pass, otherwise it is not allowed to pass; since the decryption method corresponding to each safety area is different, the corresponding instructions obtained by the drone correspond to its safety area, so when the drone executes the corresponding instruction, it can be determined that the drone corresponds to the safety area, and by calling the corresponding drone management module, it can be quickly determined whether the drone belongs to the safety area according to the device model, even if the external drone imitates the behavior of the internal drone, it cannot match the drone data in the corresponding safety area, and quickly issues an early warning through the management terminal; the fence analysis module obtains the drone state coefficient according to the posture standard degree and the sampling data; sends the drone state coefficient to the corresponding drone management module; so that the staff can timely inspect and repair the drone according to the drone state information.

[0049] The drone data includes historical data and equipment models;

[0050] As an implementation mode of the present invention, the corresponding instruction includes completing a specified starting action, a specified motion trajectory, and a specified ending action;

[0051] Through the above technical solution, this embodiment decrypts the key through the decryption module to obtain the corresponding instructions; the corresponding instructions are to complete the specified start action, the specified motion trajectory and the specified end action; it is convenient for the fence analysis module to quickly identify the start and end during the flight of the drone, match the motion trajectory, and perform corresponding instruction recognition.

[0052] The sampling data includes real-time image data and real-time positioning data;

[0053] The monitoring unit obtains the real-time image data of the UAV through the camera and the real-time positioning data of the UAV through the lidar; and sends the real-time image data and real-time positioning data to the analysis unit.

[0054] As an embodiment of the present invention, the fence analysis module obtains the device model of the drone by analyzing real-time image data; the fence analysis module obtains the trajectory matching degree by analyzing the real-time positioning data, real-time image data, the specified start action, the specified motion trajectory and the specified end action; the fence analysis module obtains the posture standard degree by analyzing the specified start action and the specified end action.

[0055] As an implementation mode of the present invention, the working process of the fence analysis module is as follows:

[0056] S10: Obtain a key through a key generation module; and decrypt the key in turn through various decryption methods to generate multiple corresponding instructions;

[0057] S20: Acquire real-time image data and real-time positioning data through the monitoring module;

[0058] S30: Analyze the real-time positioning data and real-time image data according to preset rules, and determine whether the drone is allowed to enter based on the analysis results;

[0059] S40: If the drone is not allowed to enter, send a signal to the early warning module;

[0060] S50: If the drone is allowed to enter, the drone status coefficient is obtained; and the drone status information is sent to the corresponding drone management module;

[0061] Through the above technical solution, this embodiment obtains the key through the key generation module; and decrypts the key in turn through various decryption methods to generate multiple corresponding instructions; then obtains real-time image data and real-time positioning data through the monitoring module; analyzes the real-time positioning data and real-time image data according to preset rules, and judges whether the drone is allowed to enter according to the analysis results; if the drone is not allowed to enter, sends a signal to the early warning module; if the drone is allowed to enter, obtains the drone state coefficient; and sends the drone state information to the corresponding drone management module; judges the drone state in advance through the drone state coefficient, and sends the drone state information to the corresponding drone management module, so that when the drone enters the corresponding safety area, the management personnel can prepare in advance;

[0062] As an implementation mode of the present invention, the preset rule is:

[0063] S100: The fence analysis module performs action recognition and device model recognition on real-time image data;

[0064] S200: When a designated start action of one of the corresponding instructions is identified, the real-time positioning data points are connected to obtain a real-time motion trajectory;

[0065] S300: Calculate the trajectory matching degree between the real-time motion trajectory and the specified motion trajectory; at the same time, send the identified device model to the corresponding drone management module for matching;

[0066] S400: When the designated end action is recognized, the real-time motion trajectory recognition is ended; at the same time, the trajectory matching degree between the real-time motion trajectory and the designated motion trajectory is compared with the threshold, and based on the comparison result and the drone data matching result between the drone and the drone management module, it is determined whether the drone can pass.

[0067] Through the above technical solution, this embodiment uses the fence analysis module to perform action recognition and device model recognition on real-time image data; when the specified start action with one of the corresponding instructions is recognized, the real-time positioning data points are connected to obtain the real-time motion trajectory; the trajectory matching degree between the real-time motion trajectory and the specified motion trajectory is calculated; at the same time, the recognized device model is sent to the corresponding drone management module for matching; the two matches are performed simultaneously to reduce the matching time; when the specified end action is recognized, the real-time motion trajectory recognition is ended; at the same time, the trajectory matching degree between the real-time motion trajectory and the specified motion trajectory is compared with the threshold, and according to the comparison result and the drone data matching result between the drone and the drone management module, it is determined whether the drone can pass; if the real-time motion trajectory matches the specified motion trajectory higher than the threshold and the drone data of the drone and the drone management module are successfully matched, the drone is allowed to pass; if the real-time motion trajectory matches the specified motion trajectory lower than the threshold or the drone data of the drone and the drone management module are unsuccessful, the drone is not allowed to pass, and an early warning is issued through the early warning module to improve safety;

[0068] It should be noted that the calculation methods of the trajectory matching degree between the real-time motion trajectory and the specified motion trajectory and the posture standard degree of the action are all existing technologies and will not be described in detail here.

[0069] As an implementation mode of the present invention, the sampling data also includes real-time thermal imaging image data and real-time sound data of the drone; the drone state coefficient is:

[0070] By formula Calculate the drone status coefficient Q ;

[0071] in, is the judgment function, when hour, ;when hour, ; The highest temperature during the drone's completion of a specified motion trajectory; This is the operating temperature when the drone is working normally; The average area in the thermal imaging image data that exceeds the operating temperature of the drone during the drone's specified motion trajectory; The maximum decibel of sound emitted by the drone when completing a specified motion trajectory; This is the decibel level when the drone is working normally; The attitude standard for the drone to complete the specified start action and the specified end action; , are the first and second weight coefficients;

[0072] Through the above technical solution, this embodiment obtains real-time thermal imaging image data of the drone through a thermal imager; obtains real-time sound data through a decibel meter; and obtains real-time sound data through a formula Calculate the drone status coefficient Q ;when The larger the value, the higher the temperature of the drone, the greater the possibility of failure of the drone, and the larger the drone state coefficient Q; The larger the value, the louder the drone's sound is, the greater the possibility of the drone's failure, and the larger the drone's state coefficient Q is; when the attitude standard The smaller it is, the less standard the drone action is and the larger the drone state coefficient Q is. When the drone status information is sent to the drone status management module, the staff can promptly inspect and repair the drone according to the drone status information; the drone status information includes the actual specified start action, the specified end action picture and the standard action comparison picture of the drone; thermal imaging picture data and sound data;

[0073] Among them, the attitude standard of the drone completing the specified start action and the specified end action The first weight coefficient is obtained by comparing the actual start action and end action pictures of the drone with the standard action comparison pictures; and the second weight coefficient It is obtained based on experience and will not be described in detail here.

[0074] As an implementation manner of the present invention, the threshold value is:

[0075] By formula Calculating the threshold ;

[0076] in, is the preset threshold; is the adjustment coefficient; is the standard total flight time; is the actual total flight time; The average UAV status coefficient for the past preset time period; is the standard UAV state coefficient;

[0077] Through the above technical solution, this embodiment obtains the historical data of the drone after the drone data of the drone management module is successfully matched; the historical data includes the actual total flight time ; The number of flights N in the past preset time period and the corresponding drone state coefficient each time ; Substitute into the formula Calculating the threshold ; Adjust the threshold by analyzing the historical data of the drone ; The time difference between the standard total flight time and the actual total flight time; time difference The larger the value is, the shorter the UAV has been in use. The larger the value, the average drone status coefficient for the past preset time period. The larger the value, the worse the overall condition of the drone; the action may not be standard, so the threshold Reduce; avoid the situation where the trajectory matching degree is lower than the threshold due to drone status problems and entry into the target area is not allowed.

[0078] It should be noted that the preset threshold , adjustment coefficient , standard total flight time C and standard drone status coefficient It is set based on experience and will not be described in detail here.

[0079] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A regional security warning system based on electronic fence, characterized in that: include: An electronic fence configuration module, used to set a first electronic fence and a second electronic fence digital boundary at the boundary of the target area, and divide the target area into multiple safety areas; Multiple drone management modules for managing drone data in corresponding safety areas; A key generation module, used for randomly generating keys; and sending the key to the drone entering the digital boundary of the second electronic fence via wireless communication; A decryption module is provided on the drone and is used to decrypt the key and obtain the corresponding instruction; the decryption method of the decryption module of the drone in each of the security areas is different; The monitoring module is used to obtain sampling data; the sampling data includes real-time image data, real-time positioning data, real-time thermal imaging picture data of the drone, and real-time sound data; The fence analysis module is used to analyze the sampled data and the corresponding instructions to obtain the attitude standard of the corresponding UAV; and obtain the UAV state coefficient according to the attitude standard and the real-time thermal imaging image data and real-time sound data analysis; The corresponding instructions include completing a specified starting action, a specified motion trajectory, and a specified ending action; The UAV state coefficient is: By formula Calculate the drone status coefficient ; in, is the judgment function, when hour, ;when hour, ; The highest temperature during the process of the drone completing the corresponding command; This is the operating temperature when the drone is working normally; The average area in the thermal imaging data that exceeds the operating temperature of the drone during the drone's completion of the corresponding command; The maximum decibel of sound emitted by the drone when completing the corresponding command; This is the decibel level when the drone is working normally; For posture standard; , are the first and second weight coefficients; The working process of the fence analysis module is as follows: S10: Obtain a key through a key generation module; and decrypt the key in turn through various decryption methods to generate multiple corresponding instructions; S20: Acquire real-time image data and real-time positioning data through the monitoring module; S30: Analyze the real-time positioning data and real-time image data according to preset rules, and determine whether the drone is allowed to enter based on the analysis results; S40: If the drone is not allowed to enter, send a signal to the early warning module; S50: If the drone is allowed to enter, the drone state coefficient is obtained; and the state coefficient is sent to the corresponding drone management module; The preset rules are: S100: The fence analysis module performs action recognition and device model recognition on real-time image data; S200: When a designated start action of one of the corresponding instructions is identified, the real-time positioning data points are connected to obtain a real-time motion trajectory; S300: Calculate the trajectory matching degree between the real-time motion trajectory and the specified motion trajectory; at the same time, send the identified device model to the corresponding drone management module for matching; S400: When the designated end action is recognized, the real-time motion trajectory recognition is ended; at the same time, the trajectory matching degree between the real-time motion trajectory and the designated motion trajectory is compared with the threshold, and according to the comparison result and the drone data matching result between the drone and the drone management module, it is determined whether the drone can pass; By formula Calculating the threshold ; in, is the preset threshold; is the adjustment coefficient; is the standard total flight time; is the actual total flight time; The average UAV status coefficient for the past preset time period; is the standard drone status coefficient.

2. According to claim 1, a regional security warning system based on electronic fence is characterized in that: The fence analysis module also obtains the trajectory matching degree and device model of the corresponding drone by analyzing the sampled data and the corresponding instructions, and determines whether the drone matches the designated safety area according to the trajectory matching degree and the device model; If they match, the passage is allowed; otherwise, the passage is not allowed.

3. The regional security warning system based on electronic fence according to claim 2 is characterized in that: The early warning system also includes an early warning module, which is connected to the fence analysis module and is used to issue an early warning to the management terminal when the fence analysis module identifies a drone that is not allowed to pass.

4. The regional security warning system based on electronic fence according to claim 3 is characterized in that: The fence analysis module obtains the device model of the drone by analyzing the real-time image data; the fence analysis module obtains the trajectory matching degree by analyzing the real-time positioning data, real-time image data, the specified start action, the specified motion trajectory and the specified end action; the fence analysis module obtains the posture standard degree by analyzing the specified start action and the specified end action.

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