Method and system for realizing unmanned aerial vehicle control based on 5G law enforcement recorder

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a method and system for achieving unmanned aerial vehicle control based on a 5G law enforcement recorder, and the method comprises the steps that 1, the 5G law enforcement recorder is used for receiving and analyzing data broadcasted by an unmanned aerial vehicle, and the data comprises the position, the model code, the control position, the real-name registration and registration authentication state and other information of the unmanned aerial vehicle; step 2, the 5G law enforcement recorder obtains broadcast data of the unmanned aerial vehicle by means of integrated ADS-B and Remote ID technologies, and judges whether the unmanned aerial vehicle is in a black flight state according to the broadcast data; according to the scheme, by means of a 5G law enforcement recorder, the management problem of the unmanned aerial vehicle is solved, the system is small, exquisite and low in cost, integrates ADS-B and other technologies, can rapidly judge black flight, match sensitive area data, pre-warn and intervene to guarantee safety, can deal with communication interference, intelligently adjust parameters according to scenes, and further promotes the management of the unmanned aerial vehicle to be efficient depending on historical data optimization management and control.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicle (UAV) control, and particularly to a method and system for UAV control based on a 5G law enforcement recorder. Background Art

[0002] With the rapid development of technology, UAVs have been extremely widely used in civilian and commercial fields. The sharp increase in the number of UAVs has also brought a series of serious management problems. On the one hand, some UAV users have weak safety awareness and do not conduct real-name registration and certification as required, which greatly increases the difficulty of UAV flight supervision. On the other hand, the phenomenon of "illegal flight" persists. Many UAVs fly into sensitive areas such as airport clearance protection areas, military management areas, and densely populated areas without permission, seriously threatening public safety. According to relevant statistics, the number of safety accidents caused by "illegal flight" of UAVs has been on the rise in recent years, posing great potential risks to aviation safety and the safety of people's lives and property.

[0003] Currently, traditional UAV control methods rely on some professional equipment and have significant defects. Common UAV detection methods are not integrated into law enforcement recorders but require large ground stations and large antennas. Not only are the equipment costs high, but also the UAV has a large operating range and a large number of ground stations need to be configured, which further increases the investment cost. These devices are not only bulky but also extremely inconvenient to carry, with poor flexibility in actual law enforcement processes and difficult to meet the requirements of rapid response. In sharp contrast, 5G law enforcement recorders are small, portable, and cost-effective, and a single person can effectively control UAVs within a range of several hundred meters to one kilometer, with a relatively considerable coverage area. However, current UAV control methods are difficult to be integrated into small-sized devices such as 5G law enforcement recorders, resulting in the inability to achieve an economical and convenient UAV management mode; Existing ADS-B and RemoteID technologies can, to a certain extent, obtain some broadcast data of UAVs, but they have deficiencies in data parsing, integration, and collaborative application with law enforcement means. They cannot quickly and accurately determine whether a UAV is in an illegal flight state, nor can they timely and effectively control the trajectory and regulate UAVs that illegally invade sensitive areas. In the face of a complex communication environment, existing technologies also seem powerless in communication interference identification and response, and cannot efficiently ensure the communication stability of UAV flight supervision. Therefore, there is an urgent practical need to develop an efficient, accurate, and reliable UAV control method and system. Summary of the Invention

[0004] To solve the problems raised in the above background art, the present application provides a method and system for UAV control based on a 5G law enforcement recorder.

[0005] The present application provides a method for realizing drone control based on a 5G law enforcement recorder, adopting the following technical solution: A method for realizing drone control based on a 5G law enforcement recorder includes the following steps: Step 1: Use the 5G law enforcement recorder to receive and analyze the data broadcast by the drone. The data covers information such as the position, model code, control position, and real-name registration and certification status of the drone; Step 2: The 5G law enforcement recorder uses the integrated ADS-B and RemoteID technologies to obtain the broadcast data of the drone, and determines whether the drone is in an illegal flight state based on this; The judgment process is as follows: According to the received data, verify whether the flight area of the drone belongs to a controlled area and whether it has obtained a flight permit from the Civil Aviation Administration, or whether it is in a suitable flight area but is restricted by local management. If either of the above conditions is not met, it is determined as an illegal flight, and the positions of the drone operator and the aircraft are located, and further control measures are taken.

[0006] Preferably, the steps for precisely regulating the flight trajectory of the drone based on the collected real-time data to solve the problem of illegal intrusion into sensitive areas are as follows: Real-time collect the position information and motion parameters broadcast by the drone through ADS-B and RemoteID technologies; Match and analyze the collected data with the sensitive area division layer in the geographic information system; Based on the matching result, when it is found that the drone approaches or enters a sensitive area, trigger a warning signal; If the current distance of the drone from the sensitive area is D and the safety distance is set as S, when the formula D≤S is satisfied, the trajectory intervention mechanism is started, where D represents the shortest distance from the current position of the drone to the boundary of the sensitive area, and S represents the safety protection range of the sensitive area.

[0007] Preferably, the specific steps of the trajectory intervention mechanism started based on the warning signal are as follows: Analyze the velocity vector direction of the drone and predict the target flight position within the future time t; Generate a new flight route constraint boundary based on the prediction result to guide the drone to exit the sensitive area; Broadcast the re-planned safe path to the drone and wait for response confirmation; If the speed of the drone is V, the turning radius is R, and the target safe heading deviation angle is θ, then a deceleration instruction of subtracting θ is issued under the condition judgment V×sin(θ)≥R / T to adjust the flight path stability, where V represents the current flight speed, R represents the minimum allowable turning radius, θ represents the expected heading correction angle, and T represents the estimated time for correction to be completed.

[0008] Preferably, the trajectory correction strategy based on re-planning a safe path includes the following steps: Detect whether the drone follows the broadcast safe path and collect its real-time deviation data; In the case of significant deviation, re-evaluate the route stability based on the feedback correction logic and dynamically adjust the constraint points; Record the historical trajectory points of each correction behavior and upload them to the cloud management platform for storage; When the deviation value Δd exceeds the set threshold K and the continuous duration reaches P seconds, according to Judge whether to trigger the forced landing procedure, where Δd represents the cumulative flight trajectory deviation length, t represents the detection interval period, α is the weight factor, P represents the cumulative time of exceeding the specified trajectory deviation, and β is the preset tolerance ratio.

[0009] Preferably, the effect monitoring steps based on the forced landing procedure are as follows: Capture the height and attitude change curve data of the drone from the trajectory deviation to the recovery of the normal path; Analyze whether there is an abnormal landing risk based on the curve characteristics and intervene in potential dangerous situations in advance; Count the number of successful interventions and their corresponding environmental variables to form an improvement case library to support subsequent optimization; If there is a difference between the current air pressure P and the standard air pressure Po, use Evaluate the landing safety, where P represents the height pressure parameter sensed by the drone, Po is the ground atmospheric reference pressure, and Z respectively represent the pressure fluctuation tolerance coefficient and the actual height.

[0010] Preferably, the improvement rule steps for the effectiveness of the coercive measures based on historical statistical data are as follows: Recover the relevant record documents and data analysis reports generated by each implementation of the coercive measures; After splitting the data into a set of feature items, discover hidden association rules based on the pattern mining tool; Verify and apply the optimal control method combination to iteratively improve the overall efficiency; In the case where the success rate of a specific scheme is determined to be lower than the benchmark ratio Φ, let the success rate of the current scheme be , use Calculate the optimization weight , where is the optimization weight, where represents the success rate of the i-th measure, N represents the total number of statistics, λ is the expected performance coefficient, and Φ is the target efficiency baseline.

[0011] Preferably, the communication interference identification strategy based on spectrum analysis includes the following links: Collect the real-time spectrum occupancy information of the drone and the relevant background noise level information; Compare the frequency band distribution characteristics to quickly locate whether there are camouflage characteristics in the suspected communication source; Construct a suspected signal link model to verify whether the hypothesis holds and generate a detailed interference analysis log; If the bandwidth width W of the spectrum occupancy exceeds the normal range M and is accompanied by a power level fluctuation amplitude E≥G, use the formula E×L>C+M / B to define the degree of attack threat, where L represents the total duration of the monitoring window, and C and B are the compensation coefficient and the allowable error magnification factor inherent in the system design.

[0012] Preferably, the steps for taking targeted control of the determined threat signal are as follows: Set the priority blocking level order and deploy the frequency shielding operation plan in stages; Sample the current spectrum distribution map at fixed intervals to verify whether the suppression strategy is effective; If there are still some areas that are not completely blocked, automatically adapt and enhance the shielding parameters to continue to try to cover; When the suppression progress A of the interference source approximately reaches the complete elimination level, the hypothesis condition , that is, terminate the process and output the summary conclusion. Here, the symbols are defined as: A is the percentage of the current reduction level, B indicates the theoretical expected ideal zero state value, reflects the upper and lower limit values of the final acceptable deviation interval, and L is the total time period experienced during the evaluation.

[0013] Preferably, the steps for increasing the dynamic adjustment intensity in the process of dealing with the remaining residual signals are as follows: Establish a multi-level response scheme framework that adapts to different scenario requirements by combining the real-time status feedback data of the drone.

[0014] Strengthen the allocation tilt of protection resources during the peak period to ensure the clear and reliable communication maintenance ability of the main channel; Release redundant devices during the non-busy period for regular patrol and inspection to maintain the solid safety bottom line of the airspace; When weighing that the load factor X and the energy consumption budget Y are positively correlated, set the critical switching limit Y>μ f(X), where μ is the unit load gain cost factor, and f(X) is a function depicting the non-linear relationship between the load factor X and the relevant influencing factors. The non-linear function , where 、 、 are constants, , .

[0015] A drone control system implemented based on a 5G law enforcement recorder, including: Information collection and processing module: Through the 5G law enforcement recorder integration technology, it collects and analyzes the data of the UAV's location, identity, operation, and compliance certification in real time, and at the same time collects on-site videos to provide information support for subsequent processes; Flight status determination module: According to the collected data, it verifies the compliance of the UAV flight area according to established rules. If it does not meet the management requirements, it is determined as abnormal flight. During this period, the video records the whole process and the surrounding dynamics for backtracking; Positioning and control module: After determining the abnormal flight of the UAV, it uses the positioning function of the 5G law enforcement recorder combined with other technologies to lock the positions of the drone operator and the aircraft, and implements control according to preset strategies, including transmitting alarms and videos to the regulatory authorities, and using video analysis to assist in control decisions.

[0016] In summary, the present application includes at least one of the following beneficial technical effects: The UAV control solution based on the 5G law enforcement recorder in the embodiments of the present disclosure, with its innovative design, effectively solves many problems in current UAV management. The 5G law enforcement recorder is small, portable and low-cost, overcoming the drawbacks of traditional devices, greatly improving the flexibility of law enforcement, significantly expanding the regulatory coverage, and through the integration of ADS-B and RemoteID technologies, it can quickly and accurately analyze UAV broadcast data and promptly determine illegal flights. Among them, through flight altitude determination, a true altitude exceeding 120 in the suitable flight area also belongs to the situation of illegal flight. Moreover, the solution matches the real-time data of the UAV with the sensitive area layer. Once it is found that the UAV is approaching or entering a sensitive area, it quickly issues an alarm and starts trajectory intervention to effectively protect the safety of the sensitive area; After forced landing, by analyzing the altitude and attitude data of the UAV, it prevents abnormal landings in advance, evaluates the landing safety in combination with factors such as air pressure, comprehensively ensures the safe landing of the UAV, and can effectively combat illegal flight behaviors. In terms of coping with communication interference, it uses spectrum analysis to quickly locate suspicious sources, constructs a model to accurately define the threat level, reasonably sets the blocking level, shields in stages and automatically adjusts parameters to ensure communication stability. At the same time, it reasonably allocates resources according to different scenarios, intelligently switches working modes, reduces power consumption, and ensures the stable operation of the system; In addition, the solution mines association rules based on historical data, optimizes the combination of control methods, significantly improves the effectiveness of coercive measures, integrates the control methods with the 5G law enforcement recorder, constructs an economical and convenient management mode, reduces the management threshold, promotes the development of UAV management towards high efficiency and convenience, and effectively promotes the overall progress of this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the method for UAV control; Figure 2 It is a flowchart of the UAV control system module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Embodiments of the present application will be described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0019] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0020] The embodiments of the present application disclose a method and system for realizing drone control based on a 5G law enforcement recorder. Referring to the accompanying drawings, a method for realizing drone control based on a 5G law enforcement recorder will be described next: wherein claim 1 lays the basic framework for the entire drone control method; Step 1: The 5G law enforcement recorder, as the core device, utilizes its powerful data reception ability to collect various key data broadcast by drones. These data are like the "identity files" of drones, including location information (which can accurately locate the coordinates of the drone in the air), model coding (used to identify the type and specifications of the drone), control location (indicating the location of the drone operator), and real-name registration and certification status (an important basis for determining whether the drone is flying legally and compliant). By analyzing these data, law enforcement officers can initially understand the basic situation of the drone. Step 2: The ADS-B technology automatically broadcasts its own position, altitude, speed, etc. to the ground station and other aircraft through devices carried by the aircraft or drone itself. The RemoteID technology provides remote identification functions for drones, enabling regulators to identify the identity of drones at a distance. The 5G law enforcement recorder utilizes these two technologies to obtain more accurate and comprehensive drone broadcast data, and then determines whether the drone is in an illegal flight state based on these data. Illegal flight means flight without permission and poses a potential threat to public safety and airspace order. Step 3: After receiving the data, law enforcement officers verify the drone flight area according to clear rules. If the drone is in a controlled area, it must have obtained a flight permit from the Civil Aviation Administration to fly; if it is in a flyable area, it also needs to follow local management restrictions. Once these conditions are not met, it is determined as an illegal flight. Among them, through the determination of flight altitude, flying above 120 true altitude in the flyable area also belongs to the situation of illegal flight. After determining the illegal flight, the 5G law enforcement recorder uses the positioning function in combination with other technologies to quickly lock the positions of the drone operator and the aircraft, providing key support for subsequent control measures.

[0021] In the flight supervision system of drones, strict control of the flight altitude is one of the key elements for determining the legality of flight. According to relevant regulations, within the approved flight area, when the flight altitude of a drone exceeds 120 meters above mean sea level (amsl), it is considered an illegal flight situation. Above mean sea level (amsl), as a key indicator for measuring the actual altitude of a drone relative to the ground, is of great significance in flight safety management. Within the framework of setting the approved flight area, 120 meters amsl is a clear safety boundary. This altitude limit is not set arbitrarily but is determined by comprehensively considering many factors, such as the takeoff and landing routes of civil airliners, the height distribution of urban buildings, the effective coverage range of communication signals, and other air or ground facilities that may be affected by drone flights. When the system accurately monitors through the drone broadcast data collected by a 5G law enforcement recorder, combined with its high-precision altimetry equipment and advanced positioning technology, that the flight altitude of a drone within the approved flight area exceeds 120 meters amsl, it will quickly initiate the illegal flight determination process. Once it is determined that the behavior is an illegal flight, a series of rigorous follow-up handling measures will be immediately carried out.

[0022] Precise regulation of drone flight trajectories to prevent illegal intrusion into sensitive areas: Real-time data collection: Through ADS-B and RemoteID technologies, continuously collect the position information and motion parameters broadcast by drones in real time. This information is like a "real-time map" of the drone's flight, enabling supervisors to always grasp its flight dynamics. Data matching and analysis: Match the collected data with the sensitive area division layer in the Geographic Information System (GIS). The GIS is like a digital map database, and the sensitive area division layer clearly marks which areas need to be protected. Through this matching, the position relationship between the drone and the sensitive area can be quickly determined. Early warning signal triggering: When the matching result shows that the drone is approaching or entering a sensitive area, immediately trigger an early warning signal. This early warning signal is like sounding an "alarm" for supervisors, reminding them to pay attention to the movement of the drone. Trajectory intervention mechanism triggering: Set a safety distance S. When the shortest distance D from the current position of the drone to the boundary of the sensitive area satisfies D ≤ S, activate the trajectory intervention mechanism. This is to take timely measures to change the flight trajectory of the drone when it may pose a threat to the safety of the sensitive area.

[0023] Based on the trajectory intervention mechanism triggered by the early warning signal, it explains how to intervene in the drone trajectory after the early warning signal is triggered: Target Flight Position Prediction: Analyze the direction of the UAV's velocity vector, which contains both the magnitude and direction information of the UAV's velocity. By analyzing it and combining with the future time t, predict the target flight position of the UAV after time t. This is like anticipating the UAV's "next move" in advance.

[0024] Generation of New Flight Route Constraint Boundaries: Generate new flight route constraint boundaries based on the prediction results. This boundary is like re-planning a "safe passage" for the UAV to guide it out of the sensitive area and avoid threatening the sensitive area; Broadcast and Confirmation of Safe Path: Broadcast the re-planned safe path to the UAV and then wait for the UAV's response confirmation. This step ensures that the UAV can receive the new path information and feedback whether it accepts the path; Deceleration Instruction Condition Judgment: When the UAV's speed is V, the turning radius is R, and the target safety course deviation angle is θ, if the condition V×sin(θ)≥R / T is satisfied, a deceleration instruction is issued. This condition judgment is to ensure the stability of the UAV's flight path when adjusting the UAV's flight path and prevent flight instability caused by excessive speed, too small turning radius, etc.

[0025] Based on the trajectory correction strategy of the re-planned safe path, focus on the UAV's execution of the re-planned path and correction measures; Path Following Detection and Data Collection: Continuously detect whether the UAV follows the broadcast safe path and collect its real-time deviation data. These data can reflect the deviation between the UAV's actual flight path and the planned path; Route Stability Evaluation and Constraint Point Adjustment: When significant deviation occurs, re-evaluate the route stability based on the feedback correction logic. According to the evaluation results, dynamically adjust the constraint points. The constraint points are like "control points" on the path, and by adjusting them, guide the UAV back to the correct flight path; Recording and Uploading of Historical Trajectory Points: Record the historical trajectory points of each correction behavior and upload them to the cloud management platform for storage. These historical data are of great significance for subsequent analysis of the UAV's flight behavior and optimization of control strategies; Judgment for Triggering the Forced Landing Procedure: When the deviation value Δd (cumulative flight trajectory deviation length) exceeds the set threshold K and the continuous duration reaches P seconds, according to +αP≤βK to judge whether to trigger the forced landing procedure. This formula comprehensively considers the deviation length, detection interval period t, weight factor α, cumulative time P of exceeding the specified trajectory deviation, and preset tolerance ratio β. Through such comprehensive judgment, take forced landing measures when necessary to ensure safety.

[0026] Monitor the effect after the execution of the forced landing procedure and focus on the evaluation and optimization of the execution effect of the forced landing procedure; Capture of altitude and attitude change curve data: Capture the altitude and attitude change curve data of the drone during the process from trajectory deviation to restoring the normal path (assuming here that the forced landing procedure successfully returns the drone to a safe state). These curves can intuitively reflect the flight state changes of the drone throughout the process; Analysis and intervention of abnormal landing risks: Analyze whether there are abnormal landing risks based on the characteristics of the curves. For example, if the altitude drops too quickly, the attitude is unstable, etc., it may indicate abnormal landing risks. Once risks are detected, intervene in potential dangerous situations in advance to ensure the safe landing of the drone; Statistics of the number of successful interventions and environmental variables: Statistically record the number of successful interventions and their corresponding environmental variables, such as weather conditions, geographical environment, etc. These data form an improvement case database, providing practical case support for subsequent optimization of the forced landing procedure and other control measures; Landing safety assessment: When there is a difference between the current air pressure P and the standard air pressure Po, use |P - Po| ≤ k·Z² to evaluate the landing safety, where k is the pressure fluctuation tolerance coefficient, which is set according to the actual situation and is used to measure the acceptable range of air pressure differences; P is the altitude pressure parameter sensed by the drone, Po is the ground atmospheric reference pressure, and Z represents the actual altitude. Through this formula, evaluate the impact of air pressure factors on the landing safety of the drone.

[0027] Improvement rules for the effectiveness of coercive measures based on historical statistical data, and enhance the effectiveness of coercive measures through historical data; Recovery of relevant documents and reports: Recover the relevant record documents and data analysis reports generated each time a coercive measure (such as forced landing, etc.) is implemented. These documents and reports contain a large amount of information such as the specific situation of the implementation of the coercive measure, the state of the drone at that time, and environmental factors; Data splitting and mining of association rules: Split the data into a set of feature items. For example, use the drone model, flight area, weather, etc. as different feature items, and then discover hidden association rules based on pattern mining tools. For example, it may be found that a certain model of drone is more likely to have violations under specific weather and flight areas and requires specific coercive measures; Verification and application of the optimal control method combination: Verify and apply the optimal control method combination. By analyzing the mined association rules, find the optimal combination of coercive measures for different situations, and then apply these combinations to iteratively improve the overall efficiency and continuously optimize the drone control measures; ​​, calculate the optimized weight w, where Si represents the success rate of the i-th measure, λ is the expected performance coefficient, which reflects the expected degree of improvement in the success rate of the plan, and Φ is the target effectiveness baseline. By calculating the optimized weight, relevant decision parameters (such as the implementation conditions and intensities of mandatory measures) are adjusted until the new plan converges to a higher stable state, improving the effectiveness of mandatory measures.

[0028] A communication interference recognition strategy based on spectrum analysis, focusing on identifying interference situations in drone communication; Spectrum and noise information collection: Collect the real-time spectrum occupancy of drones and relevant background noise level information. The spectrum is like a "frequency map" of communication. By collecting this information, understand the frequency range used by drone communication and the noise situation in the surrounding environment; Location of suspicious communication sources: Compare the frequency band distribution characteristics to quickly locate whether there are camouflage characteristics in the suspicious communication sources. Some illegal drones may camouflage the communication frequency band to evade supervision. Through this comparison, anomalies can be detected in a timely manner; Construction and verification of the suspected signal link model: Construct a suspected signal link model to verify whether the hypothesis holds. If a suspicious communication source is found, build a model to simulate its communication link, verify whether it really has interference behavior, and generate a detailed interference analysis log to record the entire recognition process and results; Definition of the degree of attack threat: If the bandwidth W of spectrum occupancy exceeds the normal range M and is accompanied by a power level fluctuation amplitude E≥G, use the formula E×L>C+M / B to define the degree of attack threat, where L represents the total duration of the monitoring window, and C and B are the compensation coefficient and allowable error multiple inherent in the system design. This formula comprehensively considers factors such as spectrum occupancy bandwidth, power level fluctuation, and monitoring time to quantitatively evaluate the degree of attack threat of communication interference.

[0029] Take targeted regulation for the determined threat signals. After identifying the threat signals, take the following regulation measures; Setting the blocking level order and deploying the shielding plan: Set the priority blocking level order to determine which interference signals need to be processed first, and then deploy the frequency shielding operation plan in stages to gradually shield the threat signals; Verification of the effect of the suppression strategy: Sample the current spectrum distribution map at fixed intervals. By comparing the spectrum maps of different cycles, verify whether the suppression strategy has an effect. If it is found that the intensity of the interference signal decreases and the frequency band returns to normal, it means that the suppression strategy is effective; Automatic adaptation and coverage of shielding parameters: If there are some areas that are not completely blocked, automatically adapt and enhance the shielding parameters to continue to try to cover. According to the situation of the unblocked areas, adjust parameters such as the intensity and range of frequency shielding to ensure the comprehensive suppression of threat signals; Process termination condition judgment: When the interference source suppression progress A approximately reaches the complete elimination level (assuming the condition |(A - B)| / L < δε), the process is terminated and the summary conclusion is output. Here, A is the percentage of the current reduction level, B represents the theoretically expected ideal zero state value, L is the total time period experienced during the entire evaluation process, and δε reflects the upper and lower limit values of the final acceptable deviation range. Whether the interference source has been effectively suppressed is judged through this condition. When the condition is met, it is considered that the regulation is successful. During the process of processing the remaining residual signals, the dynamic adjustment intensity is increased.

[0030] Dynamic adjustment of the remaining residual signals after processing the interference signals; Establishment of a multi-level response scheme framework: A multi-level response scheme framework suitable for different scenario requirements is established by combining the real-time status feedback data of the UAV. Different levels of response strategies are formulated according to factors such as the flight status of the UAV and the surrounding environment; Strengthening of resource allocation during peak periods: During peak periods, such as during large-scale events or in the urban center area where UAVs are frequently used, the allocation of protective resources is tilted and more resources are used to ensure the clear and reliable communication maintenance ability of the main channels and guarantee the communication security of important areas; Equipment deployment during off-peak periods: During off-peak periods, redundant equipment is deployed to perform regular patrol and inspection tasks to maintain the bottom line of airspace security, make reasonable use of idle equipment, and improve resource utilization efficiency; Setting of the critical switching boundary: When weighing the positive correlation between the load factor X and the energy consumption budget Y, the critical switching boundary Y > μf(X) is set, where μ is the unit load gain cost factor, and f(X) is a function depicting the non-linear relationship between the load factor X and related influencing factors. The non-linear function , where 、 、 are constants, , , this function is determined by performing regression analysis on historical data and comprehensively considering factors such as the number of UAVs, signal strength, and equipment performance. b ≠ 1, X > 0. a determines the slope of the function and affects the change rate of the function value when the load factor X changes; b is the base of the logarithm, and different bases will cause the growth rate of the function to be different; c is the constant term used to adjust the intercept of the function on the y-axis. When the system parameters meet this condition, the working mode is switched immediately to reduce the risk of power consumption waste, realize the reasonable utilization of resources and the effective control of power consumption, and output a summary report.

[0031] A UAV control system based on a 5G law enforcement recorder describes the system composition for implementing the above UAV control method; Information collection and processing module: Through the integration technology of 5G law enforcement recorders, the drone location, identity, control and compliance certification data are collected and analyzed in real time, and on-site videos are collected at the same time. These videos can provide more intuitive on-site information and provide comprehensive information support for subsequent processes; Flight status determination module: Based on the collected data, the compliance of the drone flight area is checked according to the established rules. If it does not meet the management requirements, it will be determined as abnormal flight. During the entire determination process, the video records the entire process and surrounding dynamics for subsequent retrospective analysis to facilitate the identification of the causes and circumstances of abnormal flight; Positioning and control module: After determining that the drone is flying abnormally, the pilot and the aircraft are locked with the help of the 5G law enforcement recorder positioning function and other technologies, and then control is implemented according to the preset strategy, including sending alarms and videos to regulatory authorities, and using video analysis to assist in control decisions. Through various measures, effective control of abnormally flying drones can be achieved.

[0032] When the system determines that a drone is in an illegal flying state based on the ADS-B and RemoteID technologies integrated in the 5G law enforcement recorder and the received drone broadcast data, it will quickly and methodically carry out a series of follow-up measures to ensure airspace safety and public order.

[0033] The above-mentioned drone control system firstly obtains the aircraft code accurately with the RemoteID data. This code is like the "ID number" of the aircraft and is the key clue to trace its related information. With the effective connection with the Civil Aviation Administration database, the control system can successfully find out the real-name information bound to the aircraft. This process relies on advanced data interaction technology to achieve efficient collaboration between different management systems, making it impossible for the responsible persons hidden behind the illegal flight activities to escape. Then the control system further obtains the contact information of the real-name person through deep connection with the public security system. After confirming the accuracy of the contact information, the system will take immediate action and send a warning to the real-name person by SMS or phone, clearly requiring him to immediately terminate the illegal drone flight activities. This warning is not only a serious reminder of violations, but also an emergency measure to ensure safety, aiming to stop illegal flights at the first time and reduce potential risks. When necessary, relevant law enforcement agencies will arrest those responsible for illegal flights in accordance with laws and regulations. This measure reflects the seriousness and authority of the law, forms a strong deterrent to illegal flights, and encourages drone users to strictly abide by flight regulations and maintain airspace safety. Among them, the RemoteID technology also provides the remote controller location information. The system cleverly utilizes this key data and, combined with the high-precision map system, can directly plan the navigation route to guide law enforcement officers to quickly reach the location of the remote controller and implement on-site control. During this process, the map system will, in real time, plan the optimal path for law enforcement officers based on traffic conditions and terrain information to ensure that they can reach the scene at the fastest speed to promptly stop and handle illegal drone flights. After law enforcement officers arrive at the scene, they will take corresponding measures according to the actual situation, such as temporarily detaining the drone, giving on-site education to the violators, or imposing penalties according to law, etc., to eliminate the hidden danger of illegal drone flights from the source and maintain the safety and order of the airspace.

[0034] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations to the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for realizing drone control based on a 5G law enforcement recorder, characterized in that, The steps include: Step 1: Use a 5G law enforcement recorder to receive and parse the data broadcast by the drone. The data covers information such as the position, model code, control position, and real-name registration and certification status of the drone. Step 2: The 5G law enforcement recorder uses the integrated ADS-B and RemoteID technologies to obtain the broadcast data of the drone and determines whether the drone is in an illegal flight state based on this. The judgment process in Step 3 is as follows: According to the received data, verify whether the flight area of the drone belongs to a controlled area and whether it has obtained a flight permit from the Civil Aviation Administration, or whether it is in a flyable area but is restricted by local management; if any of the above conditions is not met, it is determined as an illegal flight, and the positions of the drone operator and the aircraft are located, and further control measures are then taken.

2. The method for realizing drone control based on a 5G law enforcement recorder according to claim 1, wherein, The method for regulating the flight trajectory of a drone based on the collected real-time data includes: Real-time collect the position information and motion parameters broadcast by the drone through ADS-B and RemoteID technologies. Match and analyze the collected data with the sensitive area division layer in the geographic information system. Based on the matching result, trigger a warning signal when it is found that the drone approaches or enters a sensitive area. If the current distance of the drone from the sensitive area is D and the safety distance is set as S, when the formula D≤S is satisfied, the trajectory intervention mechanism is activated, where D represents the shortest distance from the current position of the drone to the boundary of the sensitive area, and S represents the safety protection range of the sensitive area.

3. The method for realizing drone control based on a 5G law enforcement recorder according to claim 2, wherein The specific steps of the trajectory intervention mechanism activated based on the warning signal are as follows: Analyze the velocity vector direction of the drone and predict the target flight position within the future time t. Generate a new flight route constraint boundary based on the prediction result to guide the drone to exit the sensitive area. Broadcast the re-planned safe path to the drone and wait for a response confirmation. If the speed of the drone is V, the turning radius is R, and the target safe heading deviation angle is θ, then a deceleration instruction of reducing θ is issued under the condition judgment of V×sin(θ)≥R / T to adjust the flight path stability, where V represents the current flight speed, R represents the minimum allowable turning radius, θ represents the expected heading correction angle, and T represents the expected time for the correction to be completed.

4. The method for realizing drone control based on a 5G law enforcement recorder according to claim 3, wherein, The trajectory correction strategy based on re-planning the safe path includes the following steps: Detect whether the drone follows the broadcast safe path and collect its real-time deviation data. In the case of significant deviation, re-evaluate the route stability based on the feedback correction logic and dynamically adjust the constraint points. Record the historical trajectory points of each correction behavior and upload them to the cloud management platform for storage. When the deviation value Δd exceeds the set threshold K and the duration reaches P seconds, it is determined according to whether to trigger the forced landing procedure, where Δd represents the cumulative flight trajectory deviation length, t represents the detection interval period, α is the weighting factor, P represents the cumulative time of deviation beyond the specified trajectory, and β is the preset tolerance ratio.

5. The method for realizing drone control based on a 5G law enforcement recorder according to claim 4, wherein, The effect monitoring steps based on the forced landing procedure are as follows: Capture the data of the height and attitude change curves of the drone from the trajectory deviation to the restoration of the normal path. Analyze whether there is an abnormal landing risk based on the curve characteristics and intervene in potential dangerous situations in advance. Count the number of successful interventions and their corresponding environmental variables to form an improvement case library to support subsequent optimization. When there is a difference between the current air pressure P and the standard air pressure Po, use to evaluate the landing safety, where P represents the altitude pressure parameter sensed by the UAV, and Po is the ground atmospheric reference pressure. And and Z respectively represent the pressure fluctuation tolerance coefficient and the actual altitude.

6. The method for realizing drone control based on a 5G law enforcement recorder according to claim 5, wherein, The improvement rules for the effectiveness of the enforcement measures based on historical statistical data are as follows: Recover the relevant record documents and data analysis reports generated by each implementation of the enforcement measures. After splitting the data into a set of feature items, discover hidden association rules based on the pattern mining tool. Verify and apply the optimal combination of control methods to iteratively improve the overall efficiency; When it is determined that the success rate of a specific solution is lower than the baseline ratio Φ, let the success rate of the current solution be , use to calculate the optimization weight , where is the optimization weight, where represents the success rate of the i-th measure, N represents the total number of statistics, λ is the expected performance coefficient, and Φ is the target effectiveness baseline.

7. The method for realizing drone control based on a 5G law enforcement recorder according to claim 6, wherein The communication interference recognition strategy based on spectrum analysis includes the following steps: Collect the real-time spectrum occupancy of the UAV and the information of relevant background noise levels; Compare the frequency band distribution characteristics to quickly locate whether there are camouflage characteristics in the suspected communication source; Construct a suspected signal link model to verify whether the hypothesis holds and generate a detailed interference analysis log; If the bandwidth W of the spectrum occupancy exceeds the normal range M and is accompanied by a power level fluctuation amplitude E≥G, use the formula E×L>C+M / B to define the degree of attack threat, where L represents the total duration of the monitoring window, and C and B are the compensation coefficient and allowable error magnification factor inherent in the system design.

8. The method for realizing drone control based on a 5G law enforcement recorder according to claim 7, characterized in that, The following are the steps for targeted regulation of the determined threat signals: Set the priority blocking level order and deploy the frequency shielding operation plan in stages; Sample the current spectrum distribution map at fixed intervals to verify whether the suppression strategy is effective; If there are some areas that are not completely blocked, automatically adapt and enhance the shielding parameters and continue to try to cover; When the interference source suppression progress A approximately reaches the complete elimination level, assume the condition , that is, terminate the process and output the summary conclusion. Here, the defined symbols are: A is the percentage of the current reduction level, and B indicates the theoretically expected ideal zero state value, reflecting the upper and lower limit values of the final acceptance deviation interval, and L is the total time period experienced during the entire evaluation process.

9. The method for realizing drone control based on a 5G law enforcement recorder according to claim 8, characterized in that, During the process of dealing with the remaining residual signals, add the following steps for dynamic adjustment intensity: Establish a multi-level response scheme framework that adapts to different scenario requirements in combination with the real-time status feedback data of the UAV; During the peak period, strengthen the allocation tilt of protection resources to ensure the clear and reliable communication maintenance ability of the main channel; During the non-busy period, release redundant devices for routine patrol and inspection to maintain the solid safety bottom line of the airspace; When weighing that the load factor X and the energy consumption budget Y are positively correlated, set the critical switching threshold Y > μ f(X), where μ is the unit load gain cost factor and f(X) is a non-linear function of the load factor X. The non-linear function , where 、 、 are constants, , .

10. A drone control system implemented based on a 5G law enforcement recorder, characterized in that, The system is executed by the method for UAV control based on the 5G law enforcement recorder described in claim 9, and the system includes: Information acquisition and processing module: Through the 5G law enforcement recorder integration technology, collect the UAV position, identity, control and compliance certification data in real time and analyze them. At the same time, collect the on-site video to provide information support for the subsequent process; Flight status determination module: According to the collected data, verify the compliance of the UAV flight area according to the established rules. If it does not meet the management requirements, it is determined as abnormal flight. The video recording of the whole process and the surrounding dynamics during this period are used for retrospective analysis; Positioning and control module: After determining the abnormal flight of the UAV, use the positioning function of the 5G law enforcement recorder in combination with other technologies to lock the positions of the flyer and the aircraft, and implement control according to the preset strategy, including sending alarms and videos to the regulatory department, and using video analysis to assist in control decision-making.

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