Unmanned aerial vehicle sight distance limitation monitoring management method and device based on 5G network

Abstract

The invention discloses an unmanned aerial vehicle sight distance limitation monitoring management method and device based on a 5G network, and the method comprises the steps: obtaining the position information of a plurality of unmanned aerial vehicles in real time, and calculating the actual distance between the unmanned aerial vehicle and an unmanned aerial vehicle controller; environment data and a network coverage state of the unmanned aerial vehicle are collected in real time, the environment data and the network coverage state are transmitted to a preset calculation model to calculate a VLoS threshold value of the unmanned aerial vehicle, and the VLoS threshold value is updated according to a calculation result; the actual distance is compared with a current VloS threshold value, and when the actual distance does not exceed the VloS threshold value, the unmanned aerial vehicle is controlled to act through an unmanned aerial vehicle controller; and when the actual distance of the unmanned aerial vehicle exceeds the VLoS threshold value, the UTM is triggered to take over the control right, and path control is performed through the UTM, so that the actual distance between the unmanned aerial vehicle and the unmanned aerial vehicle controller is controlled within the VLoS threshold value. According to the invention, the VLoS threshold can be updated in real time according to the environment and the network, the current visual range of the direct unmanned aerial vehicle is adjusted, the operation mode is flexibly switched, and the flight safety is ensured.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control, and in particular to unmanned aerial vehicle sight range limitation monitoring and management. Background Art

[0002] With the widespread application of drones in agriculture, logistics, surveillance and other fields, the demand for their flight safety and airspace compliance is increasing, especially in visual line of sight (VLoS) operations and beyond visual line of sight (BVLoS) operations, strict supervision is particularly critical. The VLoS mode requires the operator to control the drone within visual range for emergency response, while the BVLoS mode allows the drone to fly within invisible distances, which requires high-stability remote communication support.

[0003] Most existing drone controls rely on WiFi or other short-range communication technologies, which are limited by distance and signal strength and cannot meet the requirements for stable control over long distances and in complex environments. Especially in beyond-line-of-sight (BVLoS) operations, unstable signals and network delays increase the safety risks of drone flight. In addition, the lack of efficient path conflict detection and management mechanisms during coordinated flight of multiple drones can easily lead to flight conflicts, limiting the flexibility and safety of drones in missions.

[0004] Therefore, there is an urgent need for a drone control method and device that can solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for monitoring and managing UAV line-of-sight limitations based on a 5G network, which can update the VLoS threshold in real time according to the environment and network, and adjust the threshold directly controlled by the UAV controller, so that the control mode can be flexibly switched according to actual needs, adapt to various flight environments, and ensure flight safety.

[0006] In order to achieve the above-mentioned purpose, the present invention provides a 5G network-based drone line-of-sight limitation monitoring and management method, comprising: an analysis network element of a 5G core network obtains the location information of several drones in real time through a 5G network, and calculates the actual distance between the drone and the drone controller; the analysis network element collects the environmental data and network coverage status of the drone in real time, transmits the environmental data and network coverage status to a preset calculation model to calculate the VLoS threshold of the drone, and updates the VLoS threshold according to the calculation result; the analysis network element compares the actual distance with the current VloS threshold, and when the actual distance does not exceed the VLoS threshold, directly controls the drone action through the drone controller; when the actual distance of the drone exceeds the VLoS threshold, triggers the UTM to take over control, and performs path control through the UTM to control the actual distance between the drone and the drone controller within the VloS threshold.

[0007] Preferably, the analysis network element also collects the airspace management information and flight altitude of the drone in real time, and transmits the environmental data, airspace management information, flight altitude and network coverage status to a preset calculation model to calculate the VLoS threshold of the drone. The worse the environmental data, the smaller the VLoS threshold; the greater the control restriction of the airspace management information, the smaller the VLoS threshold; the lower the flight altitude, the smaller the VLoS threshold; the worse the network coverage status, the smaller the VLoS threshold.

[0008] Preferably, the analysis network element also sends the calculated VLoS threshold to the drone controller, so that the drone controller displays the current VLoS threshold.

[0009] Preferably, the environmental data includes a combination of one or more of visibility, wind speed, and rainfall; the network coverage status includes a combination of one or more of signal strength distribution, network load status, and delay data; and the airspace management information includes a combination of one or more of flight area restriction information, no-fly zone information, and restricted flight zone information.

[0010] Preferably, the analysis network element also detects in real time whether the drone enters a no-fly zone, and if so, triggers the UTM to take over control, and performs path control through the UTM, so that the drone leaves the no-fly zone.

[0011] Preferably, the analysis network element also compares the location information of the UAV with a preset geographic boundary database to determine whether the UAV enters a beyond-visual-range permitted area. If so, the BVLoS threshold is switched to a BVLoS threshold to put the UAV into an over-limit mode. In the over-limit mode, the UAV controller communicates with the UAV through a 5G network, the BVLoS is greater than the VLoS threshold, and the geographic boundary database includes information on beyond-visual-range permitted areas.

[0012] More preferably, when switching to the out-of-limit mode, the analysis network element also temporarily stores the key data of the drone and synchronizes the key data to the drone controller; before switching to the out-of-limit mode, the analysis network element also performs signal coverage and delay detection. When the network signal strength and delay data meet the beyond-line-of-sight communication requirements, the BVLoS threshold is switched to the BVLoS threshold to allow the drone to enter the out-of-limit mode. If the detection fails, it is prohibited to maintain the current working mode.

[0013] Preferably, the analysis network element also monitors the flight mission requirements and network coverage status in real time, and switches the current communication mode of the UAV according to the flight mission requirements and network coverage status, wherein the communication modes include a direct communication mode (C2 communication mode) between the UAV and the UAV controller, an assisted communication mode through a 5G network (network-assisted C2 communication mode) and a UTM navigation communication mode (UTM navigation C2 communication mode).

[0014] Preferably, the preset calculation model is a random forest regression model.

[0015] Preferably, it also includes the steps of generating a compliance report and performing model optimization; the step of generating a compliance report includes: after the task is completed, the analysis network element transmits the flight data of the UAV to the NMS network element, and the NMS network element generates a compliance report based on the flight data and detects violation events; the step of performing model optimization includes: the analysis network element extracts the compliance data in the historical compliance report, uses the random forest model to analyze the violation characteristics, and obtains the Vlos threshold improvement plan based on the violation characteristics to update the calculation model of the Vlos threshold.

[0016] The present invention also provides a 5G network-based unmanned aerial vehicle line of sight limitation monitoring and management device, including a unmanned aerial vehicle controller, a base station, an AMF network element, an NWDAF network element, and a UTM. The NWDAF network element communicates with the unmanned aerial vehicle controller through the base station and the AMF network element. The NWDAF network element is an analysis network element of the 5G core network. The unmanned aerial vehicle controller, the base station, the AMF network element, the NWDAF network element, and the UTM respectively have a processor, a memory, and operating instructions stored in the memory. The operating instructions enable the processor to execute the 5G network-based unmanned aerial vehicle line of sight limitation monitoring and management method as described above.

[0017] The present invention can automatically adjust the VLoS threshold by real-time monitoring of the environment, the location of the UAV and the network status, so that the worse the environmental data is, the smaller the VLoS threshold is; the greater the current flight area restriction is, the smaller the VLoS threshold is; the lower the flight altitude is, the smaller the VLoS threshold is; the worse the network coverage status is, the smaller the VLoS threshold is, so as to adapt to various flight conditions and ensure flight safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the UAV visual range restriction monitoring and management method based on 5G network of the present invention.

[0019] Figure 2 It is a structural diagram of the drone line-of-sight limitation monitoring and management device based on 5G network of the present invention. DETAILED DESCRIPTION

[0020] In order to explain the technical content, structural features, achieved objectives and effects of the present invention in detail, the following is a detailed description in conjunction with the implementation methods and the accompanying drawings.

[0021] refer to Figure 1 and Figure 2 The present invention discloses a 5G network-based unmanned aerial vehicle sight range limitation monitoring and management device, including a unmanned aerial vehicle controller, a base station, an AMF network element, an NWDAF network element, and a UTM. The NWDAF network element communicates with the unmanned aerial vehicle controller through the base station and the AMF network element. The NWDAF network element is an analysis network element of the 5G core network. The 5G network-based unmanned aerial vehicle sight range limitation monitoring and management device is used to execute a 5G network-based unmanned aerial vehicle sight range limitation monitoring and management method.

[0022] refer to Figure 1 The 5G network-based drone sight-limited monitoring and management method includes steps S11 to S15.

[0023] S11, the NWDAF network element obtains the regional information of several drones in real time, and calculates the actual distance between the drones and the controller.

[0024] After the drone takes off, the NWDAF network element obtains the geographical information of the drone and the drone controller in real time through the base station, and uses differential positioning technology to calculate the actual distance between the drone and the drone controller.

[0025] S12, the NWDAF network element collects the environmental data and network coverage status of the drone in real time, transmits the environmental data and network coverage status to a preset calculation model to calculate the VLoS threshold of the drone, and updates the VLoS threshold according to the calculation result. When the drone is initially started, the VLoS threshold is set according to the calculation result.

[0026] In order to more comprehensively adjust the VLoS threshold, the NWDAF network element also collects the airspace management information and flight altitude of the drone, and transmits the environmental data, airspace management information, flight altitude and network coverage status to the preset calculation model to calculate the VLoS threshold of the drone, and updates the VLoS threshold based on the calculation result. Among them, the worse the environmental data, the smaller the VLoS threshold, the greater the current flight area restriction, the smaller the VLoS threshold, the lower the flight altitude, the smaller the VLoS threshold, and the worse the network coverage status, the smaller the VLoS threshold.

[0027] The environmental data, airspace management information, flight altitude and network coverage status are transmitted to the random forest regression model to calculate the VLoS threshold of the drone. Of course, the VLoS threshold can also be calculated in real time based on other models.

[0028] Among them, the environmental data includes a combination of one or more of visibility, wind speed, and rainfall; the network coverage status includes a combination of one or more of signal strength distribution, network load status, and delay data; the airspace management information includes a combination of one or more of flight area restriction information, no-fly zone information, and restricted flight zone information.

[0029] Mission types include patrolling, surveying, taking photos, or a combination of these;

[0030] The NWDAF network element further sends the calculated VLoS threshold to the UAV controller so that the UAV controller displays the current VLoS threshold.

[0031] S13, the NWDAF network element compares the actual distance with the currently set VloS threshold.

[0032] S14, when the actual distance of the UAV to the NWDAF network element does not exceed the VLoS threshold, the UAV controller directly controls the UAV action. At this time, if the UAV is in the UTM control mode, the UTM takes over the control authority, and switches the control mode to the normal mode, and the UAV controller directly controls the UAV action. At this time, the communication mode is the direct communication mode or the auxiliary communication mode, and the direct communication mode is prioritized.

[0033] S15, when the actual distance of the UAV exceeds the VLoS threshold, the NWDAF network element triggers the UTM to take over control and perform path control through the UTM. At this time, if the UAV is in normal mode, it switches to the UTM control mode, and if the UAV is in the UTM control mode, it remains unchanged.

[0034] In step S15, path control through the UTM specifically includes: the UTM evaluates the task types and urgency of several drones in real time, assigns priorities according to the task types and urgency, prioritizes obstacle-free flight paths for drones with high-priority tasks and controls the drones to eventually return to within the allowed VloS threshold range, forces drones with low-priority tasks to pause or return, and for drones with medium-priority tasks, optimizes the flight path while avoiding the flight path of drones with high-priority tasks and controls the drones to eventually return to within the allowed VloS threshold range.

[0035] Preferably, it also includes step S16, the step of generating a compliance report: after the mission is completed, the NWDAF network element transmits the flight data of the drone to the NMS network element, and the NMS network element generates a compliance report based on the flight data and detects violations.

[0036] Preferably, after step S16, step S17 is also included, which is a step of model optimization: the NWDAF network element extracts compliance data from historical compliance reports, uses a random forest model to analyze violation features, and obtains a Vlos threshold improvement plan based on the violation features to update the calculation model of the Vlos threshold.

[0037] Among them, the UAV visual range restriction monitoring and management method also includes: the NWDAF network element detects whether the UAV enters a no-fly zone, and if so, triggers the UTM to take over control and perform path control through the UTM.

[0038] Among them, the UAV visual range limitation monitoring and management method also includes: the NWDAF network element also compares the position of the UAV with a preset geographic boundary database to determine whether the UAV enters a preset beyond visual range permitted area. If so, the BVLoS threshold is switched to the BVLoS threshold to make the UAV enter the beyond-limit mode. In the beyond-limit mode, the UAV controller communicates with the UAV through the 5G network, the BVLoS is greater than the VLoS threshold, and the geographic boundary database includes beyond-visual-range permitted area information.

[0039] The UAV line-of-sight limitation monitoring and management method also includes: the NWDAF network element also monitors the network coverage, airspace management information, flight mission distance and the actual distance in real time, and switches the current communication mode of the UAV according to the network coverage, airspace management information, flight mission distance and the actual distance. The communication mode includes a direct communication mode between the UAV and the UAV controller, an auxiliary communication mode through the 5G network, and a navigation communication mode controlled by UTM.

[0040] Preferably, there is also a step of planning a path before takeoff: the NWDAF network element obtains the mission request sent by the UAV controller, plans multiple flight paths through a preset algorithm, selects several nodes in each of the flight paths, obtains the airspace management information, environmental data and network coverage status of the nodes, scores the airspace management information, environmental data and network coverage status, and performs weighted calculation on the airspace management information, environmental data and network coverage status based on the scoring results to obtain the total weight of each of the flight paths, and selects the optimal flight path as the preset planned path based on the total weight.

[0041] Specifically, the UAV controller sends a mission request to the NWDAF network element through the base station and AMF network element. The mission request includes detailed mission data: flight path requirements, start and end time, mission type (including one or more combinations of patrol, measurement and photography), expected flight altitude, and communication mode preference. After receiving the mission request, the NWDAF network element queries the environmental data, airspace management information, mission type and network coverage status of the area where the UAV is located, and plans the flight path based on the environmental data, airspace management information, mission type and network coverage status.

[0042] Specifically, the NWDAF network element dynamically calculates the optimal flight path area through the Dijkstra algorithm based on environmental data, airspace management information, mission type and network coverage status, selects several nodes in the optimal flight path area, and adjusts the weight of the optimal flight path by constructing a weighted graph and calculating the environmental data, airspace management information and network coverage status of each node, and then obtains the optimal flight path based on the weight calculation formula.

[0043] For example, the NWDAF network element uses the Dijkstra algorithm to dynamically calculate the optimal flight path: by constructing a weighted graph and adjusting node weights based on environmental data, airspace management information, and network coverage status, NWDAF calculates the path with the lowest weight to ensure flight safety. The weight formula for each path is:

[0044] The total weight of the flight path:

[0045]

[0046] Weights related to environmental data:

[0047] Weights related to airspace management information: W airspace , if the path passes through a restricted or no-fly zone, it is set to infinity; otherwise, it is defined as W airspace =k4

[0048] Weights related to network coverage status:

[0049] The wind speed in the path area is v (unit: m / s), the rainfall is r (unit: mm / h), the visibility is vis (unit: m), the 5G signal strength is s (unit: dBm), and the delay is d (unit: ms). The weighting coefficient is dynamically adjusted in different environments. The specific meaning and value are shown in the following table:

[0050]

[0051]

[0052] Preferably, the NWDAF network element monitors the network signal strength and delay data of the drone's location in real time, and adopts an early warning mechanism when the network signal strength and delay data reach the safety signal threshold set by the system. Specifically: the NWDAF network element also monitors the drone's current network signal strength and delay data in real time, and determines the numerical range of the network signal strength and delay data, and sends a primary warning to the drone controller when the network signal strength and delay data are in the first numerical range. When the network signal strength and delay data are in the second numerical range, it is recommended to lower the flight altitude and recommend the drone to switch to a better path. When the network signal strength and delay data are in the third numerical range, it is recommended that the drone return to a safe area and re-plan a path with stable signals; when the network signal strength and delay data are in the fourth numerical range, the automatic return mode is triggered and the current task is suspended. In the first numerical range, the second numerical range, the third numerical range and the fourth numerical range, the network signal strength gradually decreases and the delay data gradually increases.

[0053] refer to Figure 1 and Figure 2 , an example is given to describe the drone sight-limited monitoring and management method based on 5G network of the present invention:

[0054] 1. After the drone is started, it sends a mission request to the NWDAF network element through the 5G network. The mission request includes the flight path, mission time, mission type, expected flight altitude, communication mode preference, etc.

[0055] 2. The NWDAF network element starts the mission data analysis and path optimization calculation based on the mission request. The NWDAF network element plans the optimal flight path area through the Dijkstra algorithm, selects several nodes in the flight path area, and adjusts the weight of the optimal flight path by constructing a weighted graph and calculating the environmental data, airspace management information, mission type and network coverage status of each node, and then obtains the optimal flight path based on the weight calculation formula. The NWDAF network element sends the optimal flight path to the aircraft manager through the 5G network.

[0056] The NWDAF network element also collects the environmental data and network coverage status of the drone, transmits the environmental data and network coverage status to a preset calculation model to calculate the VLoS threshold of the drone, and sends the VLoS threshold as the initial VLoS threshold to the flight manager.

[0057] The NWDAF network element also sends the actual distance between the UAV and the UAV controller to the UAV controller.

[0058] 3. The flight manager sends corresponding control commands to the drone based on the optimal flight path for the drone to execute, and displays the actual distance and VLoS threshold between the current drone and the drone controller in real time.

[0059] At this time, the UAV is within the visual range of the UAV controller operator, and the UAV controller directly controls the operation of the UAV through wireless communication and is in direct communication mode.

[0060] 4. The NWDAF network element collects the environmental data and network coverage status of the drone in real time, and transmits the environmental data and network coverage status to the preset calculation model to calculate the VLoS threshold of the drone, and updates the VLoS threshold according to the calculation result. If the VLoS threshold increases or decreases, the NWDAF network element sends the updated VLoS threshold to the drone controller. For example: when encountering environmental changes or mission requirements are adjusted, the NWDAF network element inputs the collected real-time data into the above VLoS threshold calculation model, and the calculation model calculates the optimal VLoS threshold that meets the current conditions based on the real-time data. For example, in foggy days or low visibility conditions, NWDAF will lower the VLoS threshold to keep the drone at a closer flight distance to reduce the risk caused by insufficient line of sight; if the mission requires the drone to perform wide-area monitoring at a higher flight altitude, the system will appropriately increase the VLoS threshold to allow the drone to cover a larger area.

[0061] The NWDAF network element monitors in real time whether the drone enters the no-fly zone. If so, it triggers the UTM to take over control and performs path control through the UTM to make the drone leave the no-fly zone.

[0062] The NWDAF network element monitors the location information of the drone in real time, and also compares the location information of the drone with the preset geographic boundary database to determine whether the drone enters the beyond-visual-range permitted area. If so, the BVLoS threshold is switched to the BVLoS threshold to make the drone enter the over-limit mode. In the over-limit mode, the drone controller communicates with the drone through the 5G network. The beyond-visual-range permitted area generally refers to an area where drone operations can be carried out safely, generally an open area without interference. When switching between over-limit mode and normal mode (non-over-limit mode), the NWDAF network element also stores the key status data of the drone (location information, speed information and mission status) and synchronizes it to the drone controller to ensure the integrity of the data. At the same time, when switching modes, the NWDAF network element also establishes a backup communication channel to prevent data loss or extension during the switching process. After the switch is completed, the NWDAF network element synchronizes the new operating status to the drone controller and the ground control end to ensure the consistency of the mission status and control information before and after the switch. If after switching to the over-limit mode, it is found that the current retention signal strength or delay does not meet the requirements, the NWDAF network element will be forced to remain in the normal mode (i.e., the normal mode that controls the actual distance of the drone within the VLoS threshold) to ensure the reliability of the communication link and the safety of the mission.

[0063] Among them, when switching to the over-limit mode, if the drone enters a permitted area that allows BVLoS operations (beyond line of sight permitted area), the NWDAF network element will automatically switch the VLoS threshold to a BVLoS limit that supports longer distances based on the drone's current geographic location. After switching, the NWDAF network element first performs signal coverage and delay checks to ensure that the signal strength and delay data of the 5G network in this area can support beyond-line-of-sight communications. If network conditions are met, NWDAF adjusts the current VLoS threshold to an extended range suitable for BVLoS operations to support long-distance mission requirements.

[0064] The NWDAF network element monitors the network coverage and flight mission requirements in real time, and switches the current communication mode of the drone according to the network coverage and flight mission requirements. The communication modes include direct communication mode between the drone and the drone controller, auxiliary communication mode through the 5G network, and UTM navigation communication mode. Among them, when the NWDAF network element is in normal mode and the network coverage is good, the drone is controlled to be in direct communication mode. If the network coverage is not good, it can be switched to the auxiliary communication mode of the 5G network. In long-distance missions where the actual distance exceeds the VLoS threshold (in over-limit mode), if the 5G network signal is stable, the drone is controlled to be in auxiliary communication mode through the 5G network, and the drone controller communicates with the drone through the 5G network. In missions performed in sensitive airspaces with airspace management information or missions that require long-term autonomous flight, the drone is controlled to select the UTM navigation communication mode, that is, the UTM takes over the control to control the drone's actions. Flight mission requirements include the current working mode, the time required to complete the mission, and the priority of the mission.

[0065] The NWDAF network element monitors the network signal strength and delay data of the drone's location in real time, and takes an early warning mechanism when the network signal strength and delay data reach the safety signal threshold set by the system. The specific early warning mechanism is as follows:

[0066] (1) Primary warning: The signal strength is between -85dBm and -88dBm, or the delay is between 50ms and 70ms. The system sends a primary warning notification to the drone controller via the 5G network without adjusting the flight parameters.

[0067] (2) Secondary warning: The signal strength is between -88dBm and -92dBm, or the delay increases to between 70ms and 100ms. The system recommends that the drone lower its flight altitude to improve signal strength. NWDAF combines real-time data to recommend that the drone switch to a better path.

[0068] (3) Emergency warning: When the signal strength is lower than -92dBm or the delay exceeds 100ms, the system recommends that the drone return to a safe area. The NWDAF network element plans the nearest stable signal path in real time, triggers the automatic return mode when the signal is extremely poor, and suspends the current mission.

[0069] Among them, the NWDAF network element also performs corresponding priority processing and path adjustment in a multi-UAV collaborative environment: an independent VLoS monitoring channel is set up for each UAV, and the flight paths of multiple UAVs are jointly monitored and managed through real-time position monitoring and path conflict detection algorithms. The NWDAF module summarizes the location information, speed and mission route of each UAV, predicts potential path intersections through conflict detection algorithms and analyzes their impact. When two or more UAVs are detected to have a possible path conflict, the system prioritizes the UAVs with low-priority tasks to adjust the path to ensure the continuity and safety of high-priority tasks. If necessary, the system will send a path adjustment instruction to the controller or switch to BVLoS mode to ensure that the safe flight distance between UAVs is sufficient. In addition, the NWDAF network element also combines GIS data to identify static obstacles (such as buildings and mountains) and dynamic environmental factors (such as wind speed and climate change) in the airspace. When a UAV approaches an obstacle, the system triggers a dynamic obstacle avoidance mechanism, recalculates a safe path through the Dijkstra algorithm, and guides the UAV to bypass the obstacle.

[0070] 5. After the mission is completed, the NWDAF network element transmits the flight data of the drone to the NMS network element. The NMS network element generates a compliance report based on the flight data and detects violations. After the compliance report is completed, it is archived for review by the regulatory authorities and operators. The flight data will be cleaned during the transmission process to remove irrelevant data and outliers.

[0071] 6. The NWDAF network element extracts compliance data from historical compliance reports, analyzes violation features using a random forest model, and obtains Vlos threshold and path planning improvement solutions based on the violation features to update the Vlos threshold calculation model and path planning strategy.

[0072] In summary, the present invention has the following advantages: 1. Automatically adjust the VLoS threshold by real-time monitoring of the environment, drone location and network status to adapt to various flight conditions and ensure flight safety. 2. Intelligent switching of multi-mode C2 communications can be performed to cope with different flight environments and ensure the communication stability of drones in long distances and complex areas. 3. Path conflict detection and management of multi-drone collaboration: Using high-precision location data and four-dimensional space modeling, the system performs real-time conflict detection on multi-drone flight paths, and implements path adjustments for low-priority drones based on priority. 4. Mission initialization and optimal path planning: When initializing the mission, the system combines airspace management regulations, real-time weather information and 5G network coverage to generate the optimal flight path for the drone and set the initial VLoS threshold. 5. Based on historical flight data and compliance reports, the AI ​​engine extracts high-risk areas and violation patterns, and dynamically optimizes path planning and VLoS settings for future missions.

[0073] 7. Automatically trigger UTM takeover when the drone exceeds the VLoS limit or the path conflict is not resolved, giving priority to ensuring the flight safety of high-priority tasks. 8. Real-time location monitoring and flight data transmission of drones are achieved through the 5G network to ensure the control center's real-time status visualization and flight compliance of drones. 9. The system sets up a joint VLoS monitoring channel for multiple drones, dynamically adjusts individual VLoS limits based on multi-machine collaborative information, and ensures the compliance and safety of drones in intensive operating environments. 10. The system automatically collects and verifies flight data, generates detailed compliance reports and archives them to the cloud, providing reliable data for subsequent reviews and compliance management.

[0074] VLoS (Visual Line of Sight): Visual Line of Sight control means that the drone flies within the operator's line of sight to ensure visible control of the drone. If the drone exceeds this distance, beyond visual line of sight (BVLoS) communication support is required. BVLoS (Beyond Visual Line of Sight): Beyond visual line of sight means that the drone is beyond the operator's visible range and requires 5G network or drone traffic management (UTM) system to ensure remote communication and control. AMF (Access and Mobility Management Function): Access and mobility management function is part of the 5G core network and is responsible for drone access control and forwarding mission requests. NWDAF (Network Data Analytics Function): Network data analysis function, used to analyze the drone's mission data, environmental factors, and calculate the optimal flight path and dynamically adjust the VLoS threshold. Dijkstra algorithm: A graph algorithm for shortest path calculation. NWDAF uses this algorithm to optimize the drone's flight path to ensure that the path avoids bad weather, insufficient network coverage, or airspace restrictions. PCF (Policy Control Function): Policy control function, used to query airspace management regulations to ensure that the flight path of the drone meets airspace requirements. NMS (Network Management System): Network management system, used to monitor the signal coverage and load of the 5G network to ensure that the drone has a stable communication link during flight. C2 communication: refers to the control and communication link between the drone and the operator. Including C2 direct communication (within line of sight), network-assisted C2 communication (remote control supported by 5G network) and UTM navigation C2 communication (automatic flight beyond line of sight). UTM (Unmanned Traffic Management): UAV traffic management system, used to manage drone flight missions and conflict detection in airspace to ensure flight safety. Random forest regression model: a machine learning model. Differential positioning technology: used to accurately measure the position of the drone to ensure real-time monitoring of VLoS restrictions. GIS (Geographic Information System): Geographic Information System, used to identify obstacles (such as buildings, mountains) and dynamic environmental factors in the airspace, and provide data support for the dynamic obstacle avoidance of drones.

[0075] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the scope of the patent application of the present invention are still within the scope covered by the present invention.

Claims

1. A 5G network-based drone visual range restriction monitoring and management method, characterized by: include: The analysis network element of the 5G core network obtains the location information of several drones in real time through the 5G network, and calculates the actual distance between the drones and the drone controller; The analysis network element collects the environmental data and network coverage status of the drone in real time, transmits the environmental data and network coverage status to a preset calculation model to calculate the VLoS threshold of the drone, and updates the VLoS threshold according to the calculation result; The analysis network element compares the actual distance with the current VloS threshold. When the actual distance does not exceed the VLoS threshold, the drone action is directly controlled by the drone controller. When the actual distance of the drone exceeds the VLoS threshold, the UTM is triggered to take over control, and the path control is performed through the UTM to control the actual distance between the drone and the drone controller within the VloS threshold.

2. The method for monitoring and managing unmanned aerial vehicle sight-limited according to claim 1, characterized in that: The analysis network element also collects the airspace management information and flight altitude of the drone in real time, and transmits the environmental data, airspace management information, flight altitude and network coverage status to a preset calculation model to calculate the VLoS threshold of the drone. The worse the environmental data, the smaller the VLoS threshold; the greater the control restrictions of the airspace management information, the smaller the VLoS threshold; the lower the flight altitude, the smaller the VLoS threshold; and the worse the network coverage status, the smaller the VLoS threshold.

3. The method for monitoring and managing unmanned aerial vehicle sight-limited according to claim 1, characterized in that: The environmental data includes a combination of one or more of visibility, wind speed, and rainfall; the network coverage status includes a combination of one or more of signal strength distribution, network load status, and delay data; the airspace management information includes a combination of one or more of flight area restriction information, no-fly zone information, and restricted flight zone information.

4. The method for monitoring and managing unmanned aerial vehicle sight-limited according to claim 1, characterized in that: The analysis network element also detects in real time whether the drone enters a no-fly zone. If so, the UTM is triggered to take over control and the path is controlled by the UTM to make the drone leave the no-fly zone.

5. The method for monitoring and managing unmanned aerial vehicle sight-limited control according to claim 1, characterized in that: The analysis network element also compares the location information of the drone with a preset geographic boundary database to determine whether the drone enters a beyond-visual-range permitted area. If so, the BVLoS threshold is switched to a BVLoS threshold to put the drone into an over-limit mode. In the over-limit mode, the drone controller communicates with the drone through a 5G network, the BVLoS is greater than the VLoS threshold, and the geographic boundary database includes information on beyond-visual-range permitted areas.

6. The method for monitoring and managing unmanned aerial vehicle sight-limited according to claim 5, characterized in that: When switching to the beyond-limit mode, the analysis network element also temporarily stores the key data of the drone and synchronizes the key data to the drone controller; before switching to the beyond-limit mode, the analysis network element also performs signal coverage and delay detection. When the network signal strength and delay data meet the beyond-line-of-sight communication requirements, the BVLoS threshold is switched to the BVLoS threshold to enable the drone to enter the beyond-limit mode. If the detection fails, it is prohibited to maintain the current working mode.

7. The method for monitoring and managing unmanned aerial vehicle sight-limited according to claim 5, characterized in that: The analysis network element also monitors the flight mission requirements and network coverage status in real time, and switches the current communication mode of the UAV according to the flight mission requirements and network coverage status. The communication modes include a direct communication mode between the UAV and the UAV controller, an auxiliary communication mode through the 5G network, and a UTM navigation communication mode.

8. The method for monitoring and managing unmanned aerial vehicle sight-limited according to claim 1, characterized in that: The default calculation model is the random forest regression model.

9. The method for monitoring and managing unmanned aerial vehicle sight-limited according to claim 1, characterized in that: It also includes steps for generating compliance reports and performing model optimization; The step of generating a compliance report includes: the analysis network element transmits the flight data of the drone to the NMS network element after the task is completed, and the NMS network element generates a compliance report based on the flight data and detects violation events; The steps of model optimization include: the analysis network element extracts compliance data from historical compliance reports, uses a random forest model to analyze violation features, obtains a Vlos threshold improvement plan based on the violation features, and updates the calculation model of the Vlos threshold.

10. A 5G network-based drone visual range limit monitoring and management device, characterized in that: It includes a drone controller, a base station, an AMF network element, an NWDAF network element, and a UTM. The NWDAF network element communicates with the drone controller through the base station and the AMF network element. The NWDAF network element is an analysis network element of the 5G core network. The drone controller, the base station, the AMF network element, the NWDAF network element, and the UTM respectively have a processor, a memory, and operating instructions stored in the memory. The operating instructions can enable the processor to execute the drone line-of-sight limitation monitoring and management method based on the 5G network as described in any one of claims 1 to 9.

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