Unmanned aerial vehicle line-of-sight restriction monitoring management method and device based on 5g network

Abstract

This invention discloses a method and device for monitoring and managing line-of-sight (VLoS) limitations of unmanned aerial vehicles (UAVs) based on a 5G network. The method includes: acquiring the location information of several UAVs in real time and calculating the actual distance between the UAVs and their controller; collecting environmental data and network coverage status of the UAVs in real time, transmitting the environmental data and network coverage status to a preset calculation model to calculate the VLoS threshold for the UAVs, and updating the VLoS threshold based on the calculation results; comparing the actual distance with the current VLoS threshold, and controlling the UAV's actions through the UAV controller when the actual distance does not exceed the VLoS threshold; and triggering a UTM (Unified Device Manager) to take over control when the actual distance exceeds the VLoS threshold, using the UTM for path control to keep the actual distance between the UAVs and their controller within the VLoS threshold. This invention can update the VLoS threshold in real time based on the environment and network, adjust the current line-of-sight range of the UAVs, flexibly switch operating modes, and ensure flight safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle control, in particular to the visual range limit monitoring and management of unmanned aerial vehicles. BACKGROUND

[0002] With the wide application of unmanned aerial vehicles in the fields of agriculture, logistics, monitoring, etc., the demand for flight safety and airspace compliance is increasing, especially in visual range (VLoS) operation and beyond visual range (BVLoS) operation, strict supervision is particularly critical. VLoS mode requires the operator to control the unmanned aerial vehicle within the visual range, which is convenient for emergency response, while BVLoS mode allows the unmanned aerial vehicle to fly at an invisible distance, which requires high stability of remote communication support.

[0003] Existing unmanned aerial vehicle control mostly relies on WiFi or other short-distance communication technology, which is limited by distance and signal strength, and is difficult to meet the stable control demand in long-distance and complex environment, especially in beyond visual range (BVLoS) operation, due to unstable signal and network delay, which increases the safety hazard of unmanned aerial vehicle flight. In addition, when multiple unmanned aerial vehicles fly cooperatively, there is a lack of efficient path conflict detection and management mechanism, which easily leads to flight conflict, limiting the flexibility and safety of unmanned aerial vehicles in tasks.

[0004] Therefore, there is an urgent need for an unmanned aerial vehicle control method and device that can solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide a 5G network-based visual range limit monitoring and management method and device for unmanned aerial vehicles, which can update the VLos threshold value in real time according to the environment and network, adjust the threshold value directly controlled by the unmanned aerial vehicle controller, and make the control mode flexible switching according to the actual needs, adapt to various flight environments, and ensure flight safety.

[0006] In order to achieve the above purpose, the present application provides a 5G network-based visual range limit monitoring and management method for unmanned aerial vehicles, comprising: a 5G core network analysis network element obtains the position information of a plurality of unmanned aerial vehicles in real time through a 5G network, and calculates the actual distance between the unmanned aerial vehicles and the unmanned aerial vehicle controller; the analysis network element collects environmental data and network coverage state of the unmanned aerial vehicles in real time, and transmits the environmental data and network coverage state to a preset calculation model to calculate the VLos threshold value of the unmanned aerial vehicles, and updates the VLos threshold value according to the calculation result; the analysis network element compares the actual distance with the current VLos threshold value, and directly controls the unmanned aerial vehicle action through the unmanned aerial vehicle controller when the actual distance does not exceed the VLos threshold value; when the actual distance of the unmanned aerial vehicle exceeds the VLos threshold value, the UTM takes over the control right, and the path control is performed through the UTM to control the actual distance between the unmanned aerial vehicle and the unmanned aerial vehicle controller within the VLos threshold value.

[0007] Preferably, the analysis network element further collects airspace management information, flight height of the UAV in real time, and inputs the environment data, airspace management information, flight height and network coverage state into a preset calculation model to calculate the VLos threshold of the UAV, the worse the environment data, the smaller the VLos threshold, the greater the control limit of the airspace management information, the smaller the VLos threshold, the lower the flight height, the smaller the VLos threshold, and the worse the network coverage state, the smaller the VLos threshold.

[0008] Preferably, the analysis network element further sends the calculated VLos threshold to the UAV controller to enable the UAV controller to display the current VLos threshold.

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

[0010] Preferably, the analysis network element further detects in real time whether the UAV enters a no-fly zone, and if so, triggers the UTM to take over control, controls the path through the UTM, and enables the UAV to leave the no-fly zone.

[0011] Preferably, the analysis network element further compares the position information of the UAV with a preset geographical boundary database to determine whether the UAV enters a beyond visual line of sight (BVLoS) permitted area, and if so, switches the VLos threshold to a BVLos threshold to enable the UAV to enter a BVLoS mode, the UAV controller communicates with the UAV through a 5G network in the BVLoS mode, the BVLos threshold is greater than the VLos threshold, and the geographical boundary database includes BVLoS permitted area information.

[0012] More preferably, when switching to the BVLoS mode, the analysis network element further temporarily stores key data of the UAV and synchronizes the key data to the UAV controller; after switching to the BVLoS mode, the analysis network element further detects signal coverage and delay, switches the VLos threshold to the BVLos threshold when the network signal strength and delay data meet the BVLoS communication requirements to enable the UAV to enter the BVLoS mode, and forces the UAV to remain in the ordinary mode when the network signal strength and delay data do not meet the BVLoS communication requirements.

[0013] Preferably, the analysis network element also monitors the flight task demand and network coverage state in real time, and switches the current communication mode of the unmanned aerial vehicle according to the flight task demand and network coverage state, the communication mode including a direct communication mode between the unmanned aerial vehicle and the unmanned aerial vehicle controller (C2 communication mode), an auxiliary communication mode through the 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, the method further comprises the steps of generating a compliance report and performing model optimization; the step of generating a compliance report comprises: the analysis network element transmitting flight data of the unmanned aerial vehicle to a network management system NMS network element after the task is completed, and the network management system NMS network element generating a compliance report and detecting a violation event according to the flight data; the step of performing model optimization comprises: the analysis network element extracts compliance data in historical compliance reports, analyzes violation characteristics by using a random forest model, obtains a VLos threshold improvement scheme according to the violation characteristics, and updates the calculation model of the VLos threshold.

[0016] The application also provides a 5G network-based unmanned aerial vehicle line-of-sight restriction monitoring and management device, comprising an unmanned aerial vehicle controller, a base station, an AMF network element, an NWDAF network element, and a UTM, wherein 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 a 5G core network, and the unmanned aerial vehicle controller, the base station, the AMF network element, the NWDAF network element, and the UTM each have a processor, a memory, and operation instructions stored in the memory, and the operation instructions can make the processor execute the 5G network-based unmanned aerial vehicle line-of-sight restriction monitoring and management method.

[0017] The application can automatically adjust the VLos threshold by real-time monitoring of the environment, the unmanned aerial vehicle position, and the network status, so that the VLos threshold is smaller when the environment data is worse, the VLos threshold is smaller when the current flight area restriction is larger, the VLos threshold is smaller when the flight height is lower, and the VLos threshold is smaller when the network coverage status is worse, thereby adapting to various flight conditions and ensuring flight safety. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 FIG. 1 is a flowchart of the 5G network-based unmanned aerial vehicle line-of-sight restriction monitoring and management method of the application.

[0019] Figure 2 FIG. 2 is a structural diagram of the 5G network-based unmanned aerial vehicle line-of-sight restriction monitoring and management device of the application. DETAILED DESCRIPTION

[0020] To make the technical content, structural features, achieved purposes and effects of the present application clear, the following will be described in detail in combination with the embodiments and the accompanying drawings.

[0021] With reference to Figure 1 and Figure 2 The present application discloses a UAV VLoS restriction monitoring management device based on a 5G network, which comprises a UAV controller, a base station, an AMF network element, an NWDAF network element and a UTM. The NWDAF network element communicates with the UAV 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 UAV VLoS restriction monitoring management device based on the 5G network is used to execute a UAV VLoS restriction monitoring management method based on the 5G network.

[0022] With reference to Figure 1 The UAV VLoS restriction monitoring management method based on the 5G network comprises steps S11 to S15.

[0023] S11, the NWDAF network element acquires the regional information of a plurality of UAVs in real time and calculates the actual distance between the UAVs and the controller.

[0024] After the UAV takes off, the NWDAF network element acquires the regional information of the UAV and the UAV controller in real time through the base station and calculates the actual distance between the UAV and the UAV controller by using the differential positioning technology.

[0025] S12, the NWDAF network element collects the environmental data and the network coverage state where the UAV is located in real time, delivers the environmental data and the network coverage state to a preset calculation model to calculate the VLoS threshold of the UAV, and updates the VLoS threshold according to the calculation result. When the UAV 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 airspace management information and flight height where the UAV is located, delivers the environmental data, airspace management information, flight height and network coverage state to a preset calculation model to calculate the VLoS threshold of the UAV, and updates the VLoS threshold according to the calculation result. Among them, the worse the environmental data, the smaller the VLoS threshold, the greater the current flight area restriction, the lower the flight height, the smaller the VLoS threshold, and the worse the network coverage state, the smaller the VLoS threshold.

[0027] Among them, the environmental data, airspace management information, flight height and network coverage state are delivered to a random forest regression model to calculate the VLoS threshold of the UAV. Of course, other models can also be used to calculate the VLoS threshold in real time.

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

[0029] The task type includes one or more combinations of patrol, measurement, and photographing.

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

[0031] The NWDAF network element compares the actual distance with the current set VLoS threshold.

[0032] When the actual distance of the UAV does not exceed the VLoS threshold, the NWDAF network element controls the UAV directly through the UAV controller. If the UAV is in the UTM control mode, the UTM takes over the control right, and the control mode is switched to the normal mode, so that the UAV is directly controlled by the UAV controller. The communication mode at this time is the direct communication mode or the auxiliary communication mode, and the direct communication mode is preferred.

[0033] When the actual distance of the UAV exceeds the VLoS threshold, the NWDAF network element triggers the UTM to take over the control right and perform path control through the UTM. If the UAV is in the normal mode, the UTM control mode is switched, and if the UAV is in the UTM control mode, it remains unchanged.

[0034] In step S15, the path control through the UTM specifically includes: the UTM evaluates the task type and the urgency of a plurality of UAVs in real time, assigns priorities according to the task type and the urgency, preferentially assigns an unobstructed flight path to a UAV with a high-priority task and controls the UAV to finally return to within the allowed VLoS threshold range, forces a UAV with a low-priority task to suspend or return, and optimizes the flight path of a UAV with a medium-priority task while avoiding the flight path of a UAV with a high-priority task and controls the UAV to finally return to within the allowed VLoS threshold range.

[0035] Preferably, the method further includes the step S16 of generating a compliance report: after the task is completed, the NWDAF network element transmits flight data of the UAV to the NMS network element, and the NMS network element generates a compliance report and detects a violation event according to the flight data.

[0036] Preferably, after step S16, step S17 is further included, that is, a model optimization step: the NWDAF network element extracts compliance data in the historical compliance report, analyzes the violation features by using the random forest model, obtains the Vlos threshold improvement scheme according to the violation features, and updates the calculation model of the Vlos threshold.

[0037] The UAV visual range limit monitoring management method further includes: the NWDAF network element detects whether the UAV enters a no-fly zone, and if so, triggers the UTM to take control, and controls the path through the UTM.

[0038] The UAV visual range limit monitoring management method further includes: the NWDAF network element also compares the position of the UAV with a preset geographical boundary database to determine whether the UAV enters a preset beyond visual range permission area, and if so, switches the VLoS threshold to a BVLoS threshold, so that the UAV enters a beyond limit mode, the UAV controller communicates with the UAV through a 5G network in the beyond limit mode, the BVLoS is greater than the VLoS threshold, and the geographical boundary database includes beyond visual range permission area information.

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

[0040] Preferably, before taking off, there is also a step of planning a path: the NWDAF network element obtains a task request sent by the UAV controller, plans a plurality of flight paths by using a preset algorithm, selects a plurality of nodes in each flight path, obtains airspace management information, environmental data, and network coverage state of the nodes, scores the airspace management information, environmental data, and network coverage state, and performs weighted calculation on the airspace management information, environmental data, and network coverage state according to the scoring results to obtain a total weight of each flight path, and selects an optimal flight path as a preset planning path according to the total weight.

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

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

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

[0044] The total weight of the flight path is:

[0045]

[0046] The weight related to the environmental data is:

[0047] The weight related to the airspace management information is: If the path passes through a flight restricted area or a flight prohibited area, it is set to infinity; otherwise, it is defined as

[0048] The weight related to the network coverage state is:

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

[0050]

[0051] Preferably, the NWDAF network element monitors the network signal strength and latency data of the drone's location in real time, and initiates an early warning mechanism when the network signal strength and latency data reach the system-set safety signal threshold. Specifically: the NWDAF network element also monitors the drone's current network signal strength and latency data in real time, and determines the numerical range of the network signal strength and latency data. When the network signal strength and latency data are within a first numerical range, it sends a primary warning to the drone controller; when the network signal strength and latency data are within a second numerical range, it suggests reducing the flight altitude and recommends that the drone switch to a better path; when the network signal strength and latency data are within a third numerical range, it suggests that the drone return to a safe area and replan a path with a stable signal; when the network signal strength and latency data are within a fourth numerical range, it triggers an automatic return-to-home mode and suspends the current task. In the first, second, third, and fourth numerical ranges, the network signal strength gradually decreases, and the latency data gradually increases.

[0052] refer to Figure 1 and Figure 2 The following example illustrates the drone line-of-sight limitation monitoring and management method based on a 5G network according to the present invention:

[0053] 1. After the drone is started, it sends a mission request to the NWDAF network element through the 5G network. The mission request includes flight path, mission time, mission type, expected flight altitude, communication mode preference, etc.

[0054] 2. The NWDAF network element initiates task data analysis and path optimization calculations based on the task request. The NWDAF network element plans the optimal flight path area using Dijkstra's algorithm. Within this area, several nodes are selected, and a weighted graph is constructed. The weights of each node are adjusted based on its environmental data, airspace management information, task type, and network coverage status. The optimal flight path is then obtained according to the weight calculation formula. The NWDAF network element transmits the optimal flight path to the flight manager via the 5G network.

[0055] The NWDAF network element also collects environmental data and network coverage status of the UAV, and sends the environmental data and network coverage status to a preset calculation model to calculate the UAV's VLoS threshold. The VLoS threshold is then sent to the flight manager as the initial VLoS threshold.

[0056] The NWDAF network element also sends the actual distance between the drone and the drone controller to the drone controller.

[0057] 3. The flight manager sends corresponding control commands to the UAV according to the optimal flight path to make the UAV execute, and displays the actual distance between the UAV and the UAV controller and the VLoS threshold in real time.

[0058] At this time, the UAV is within the visual range of the UAV controller operator, and the UAV controller directly controls the UAV through wireless communication, and is in a direct communication mode.

[0059] 4. The NWDAF network element collects environmental data and network coverage state of the UAV in real time, and inputs the environmental data and network coverage state into a preset calculation model to calculate the VLoS threshold of the UAV, updates the VLoS threshold according to the calculation result, and if the VLoS threshold becomes larger or smaller, the NWDAF network element sends the updated VLoS threshold to the UAV controller. For example, when the environment changes or the task requirements are adjusted, the NWDAF network element inputs the collected real-time data into the calculation model of the VLoS threshold above, and the calculation model calculates the optimal VLoS threshold according to the real-time data. For example, in foggy or low-visibility conditions, the NWDAF reduces the VLoS threshold, so that the UAV maintains a shorter flight distance, thereby reducing the risk of insufficient line of sight; if the task requires the UAV to perform wide-area monitoring at a higher flight altitude, the system appropriately increases the VLoS threshold, so that the UAV covers a larger area.

[0060] The NWDAF network element monitors whether the UAV enters a no-fly zone in real time, and if so, triggers the UTM to take control, controls the path through the UTM, and makes the UAV leave the no-fly zone.

[0061] The NWDAF network element monitors the position information of the UAV in real time, and also compares the position information of the UAV with a preset geographical boundary database to determine whether the UAV enters a beyond visual line of sight (BVLoS) permission area. If so, the VLoS threshold is switched to a BVLoS threshold, so that the UAV enters an ultra-limit mode, and the UAV controller communicates with the UAV through a 5G network in the ultra-limit mode. The beyond visual line of sight permission area generally refers to an area in which the UAV operation is safe, and is generally an open and interference-free area. When switching between the ultra-limit mode and the ordinary mode (non-ultra-limit mode), the NWDAF network element also stores the key state data (position information, speed information, and task state) of the UAV and synchronizes the data to the UAV controller to ensure the integrity of the data. At the same time, the NWDAF network element also establishes a backup communication channel when switching modes to prevent data loss or delay during the switching process. After the switching is completed, the NWDAF network element synchronizes the new operation state to the UAV controller and the ground control terminal to ensure the consistency of the task state and control information before and after the switching. If the network signal strength or delay does not meet the requirements after switching to the ultra-limit mode, the NWDAF network element will be forced to remain in the ordinary mode (i.e., the actual distance of the UAV is within the VLoS threshold in the conventional mode), to ensure the reliability of the communication link and the safety of the task.

[0062] In the ultra-limit mode switching, if the UAV enters a permission area (beyond visual line of sight permission area) that allows BVLoS operation, the NWDAF network element will automatically switch the VLoS threshold to a BVLoS threshold that supports a longer distance based on the current geographical location of the UAV. After switching, the NWDAF network element is first checked for signal coverage and delay to ensure that the signal strength and delay data of the 5G network in the area can support beyond visual line of sight communication. If the network conditions are met, the NWDAF will adjust the current VLoS threshold to an extended range suitable for BVLoS operation to support long-distance task requirements.

[0063] The NWDAF network element monitors network coverage and flight task requirements in real time, and switches the current communication mode of the unmanned aerial vehicle according to the network coverage and flight task requirements, the communication mode including a direct communication mode between the unmanned aerial vehicle and the unmanned aerial vehicle controller, an auxiliary communication mode through a 5G network, and a UTM navigation communication mode. When the NWDAF network element is in a normal mode and the network coverage is good, the unmanned aerial vehicle is controlled to be in the direct communication mode. If the network coverage is poor, the unmanned aerial vehicle can be switched to the auxiliary communication mode through the 5G network. In a long-distance task that will actually exceed the VLoS threshold (in an out-of-limit mode), if the 5G network signal is stable, the unmanned aerial vehicle is controlled to be in the auxiliary communication mode through the 5G network, and the unmanned aerial vehicle controller communicates with the unmanned aerial vehicle through the 5G network. If the airspace management information is a task performed in a sensitive airspace or a task that needs to be autonomously flown for a long time, the unmanned aerial vehicle is controlled to select the UTM navigation communication mode, that is, the UTM takes over the control right to control the unmanned aerial vehicle. The flight task requirements include the current working mode, the task completion time, the task priority, and the like.

[0064] The NWDAF network element monitors network signal strength and delay data at the location of the unmanned aerial vehicle in real time, and adopts a warning mechanism when the network signal strength and delay data reach a safety signal threshold set by the system. The specific warning mechanism is as follows:

[0065] (1) Primary warning: the signal strength is between -85 dBm and -88 dBm, or the delay is between 50 ms and 70 ms. The system sends a primary warning notification to the unmanned aerial vehicle controller through the 5G network, without adjusting the flight parameters.

[0066] (2) Secondary warning: the signal strength is between -88 dBm and -92 dBm, or the delay is increased to between 70 ms and 100 ms. The system suggests that the unmanned aerial vehicle reduce the flight height to improve the signal strength. The NWDAF combines real-time data to recommend that the unmanned aerial vehicle switch to a more optimal path.

[0067] (3) Emergency warning: the signal strength is lower than -92 dBm, or the delay exceeds 100 ms. The system suggests that the unmanned aerial vehicle return to a safe area, the NWDAF network element plans a nearest signal stable path in real time, triggers an automatic return mode in a poor signal condition, and suspends the current task.

[0068] In the multi-UAV cooperative environment, the NWDAF network element also performs corresponding priority processing and path adjustment: 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 aggregates the position information, speed and task route of each UAV, predicts potential path intersection points through conflict detection algorithms and analyzes their impact. When detecting that two or more UAVs may have path conflicts, 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 sends path adjustment instructions to the controller or switches to the BVLoS mode to ensure sufficient safe flight distance between UAVs. In addition, the NWDAF network element also identifies static obstacles (such as buildings, mountains) and dynamic environmental factors (such as wind speed, climate change) in the airspace in combination with GIS data. When the UAV approaches an obstacle, the system triggers a dynamic obstacle avoidance mechanism to recalculate a safe path through the Dijkstra algorithm to guide the UAV to bypass the obstacle.

[0069] 5、After the completion of the task, the NWDAF network element transmits the flight data of the UAV to the NMS network element, and the NMS network element generates a compliance report and detects irregular events based on the flight data. After the completion of the compliance report, it is archived for the regulatory authorities and the operator to review. During the transmission process, the flight data will be cleaned to remove irrelevant data and outliers.

[0070] 6、The NWDAF network element extracts compliance data from historical compliance reports, analyzes irregular features using a random forest model, and obtains a Vlos threshold and an improved path planning scheme based on the irregular features to update the calculation model of the Vlos threshold and the strategy of path planning.

[0071] In summary, the present application has the following advantages: 1. Automatically adjust the VLoS threshold by real-time monitoring of the environment, UAV position and network status, adapt to various flight conditions and ensure flight safety. 2. Intelligent switching of multi-mode C2 communication to cope with different flight environments and ensure communication stability of UAVs in long-distance and complex areas. 3. Path conflict detection and management of multiple UAVs: using high-precision position data and four-dimensional space modeling, the system performs real-time conflict detection on the flight paths of multiple UAVs, and adjusts the paths of low-priority UAVs according to priority. 4. Task initialization and optimal path planning: the system generates optimal flight paths for UAVs and sets initial VLoS threshold during task initialization, combining airspace management regulations, real-time weather information and 5G network coverage. 5. Based on historical flight data and compliance reports, the AI engine extracts high-risk areas and violation patterns, dynamically optimizes path planning and VLoS settings for future tasks. 7. Automatically trigger UTM takeover when UAV exceeds VLoS limit or path conflict is not resolved, prioritizing flight safety for high-priority tasks. 8. Real-time position monitoring and flight data transmission of UAVs through 5G network, ensuring real-time visualization of UAV status and flight compliance by control center. 9. The system sets up a joint VLoS monitoring channel for multiple UAVs, dynamically adjusts the overall VLoS limit based on multi-machine cooperation information, and ensures compliance and safety of UAVs in dense operation environment. 10. The system automatically collects and verifies flight data, generates detailed compliance reports and archives them to the cloud, providing reliable data for subsequent review and compliance management.

[0072] VLoS (Visual Line of Sight): Visual Line of Sight control, refers to the UAV flying within the operator's line of sight to ensure visible control of the UAV. If the UAV exceeds this distance, Beyond Visual Line of Sight (BVLoS) communication support is required. BVLoS (Beyond Visual Line of Sight): Beyond Visual Line of Sight, refers to the UAV beyond the operator's visible range, requiring 5G network or Unmanned Traffic Management (UTM) system to ensure remote communication and control. AMF (Access and Mobility Management Function): Access and Mobility Management Function, is a part of the 5G core network, responsible for access control of the UAV and forwarding of task requests. NWDAF (Network Data Analytics Function): Network Data Analytics Function, used to analyze task data, environmental factors of the UAV, and calculate the optimal flight path and dynamically adjust the VLoS threshold. Dijkstra algorithm: a graph algorithm for shortest path calculation. The NWDAF uses this algorithm to optimize the UAV flight path to ensure that the path avoids areas with adverse weather, insufficient network coverage, or airspace restrictions. PCF (Policy Control Function): Policy Control Function, used to query airspace management regulations to ensure that the UAV flight path meets airspace requirements. NMS (Network Management System): Network Management System, used to monitor the signal coverage and load of the 5G network to ensure stable communication link for the UAV during flight. C2 communication: refers to the control and communication link between the UAV and the operator. Includes 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): Unmanned Traffic Management system, used to manage UAV flight tasks 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 UAV 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, providing data support for dynamic obstacle avoidance for the UAV.

[0073] The above disclosure is only the preferred embodiment of the present application, of course, cannot limit the scope of the present application, therefore, the equivalent changes made in the scope of the present application patent application, still belongs to the scope of the present application.

Claims

1. A method for monitoring and managing line-of-sight limitations of unmanned aerial vehicles (UAVs) based on a 5G network, characterized in that: The application relates to a method for controlling a UAV (Unmanned Aerial Vehicle) in a 5G network. The method comprises the following steps: a 5G core network analysis element obtains the position information of a plurality of UAVs in real time through the 5G network, and calculates the actual distance between the UAVs and a UAV controller; the analysis element collects environmental data, network coverage state, airspace management information and flight height of the UAVs in real time, and inputs the airspace management information, flight height, environmental data and network coverage state into a preset calculation model to calculate the VLoS threshold of the UAVs, and updates the VLoS threshold according to the calculation result; 2. The UAV line-of-sight limit monitoring management method of claim 1, wherein: the analysis element compares the actual distance with the current VLoS threshold, directly controls the UAVs through the UAV controller when the actual distance does not exceed the VLoS threshold, triggers the UTM to take over the control right when the actual distance of the UAVs exceeds the VLoS threshold, and controls the path through the UTM to control the actual distance between the UAVs and the UAV controller within the VLoS threshold; the analysis element also compares the position information of the UAVs with a preset geographical boundary database to determine whether the UAVs enter a beyond visual range permitted area, switches the VLoS threshold to a BVLoS threshold if the UAVs enter the beyond visual range permitted area, and makes the UAVs enter a beyond limit mode; in the beyond limit mode, the UAV controller communicates with the UAVs through the 5G network, the BVLoS threshold is greater than the VLoS threshold, and the geographical boundary database comprises beyond visual range permitted area information.

3. The UAV line-of-sight limit monitoring management method of claim 1, wherein: The worse the environmental data is, the smaller the VLoS threshold is; the greater the control and management limit of the airspace management information is, the smaller the VLoS threshold is; the lower the flight height is, the smaller the VLoS threshold is; and the worse the network coverage state is, the smaller the VLoS threshold is.

4. The UAV line-of-sight limit monitoring management method of claim 1, wherein: The environmental data comprises a combination of one or more of visibility, wind speed and rainfall; the network coverage state comprises a combination of one or more of signal strength distribution, network load state and delay data; and the airspace management information comprises a combination of one or more of flight area restriction information, no-fly zone information and restricted flight zone information.

5. The unmanned line-of-sight limit monitoring management method of claim 1, wherein: The analysis element also detects whether the UAVs enter a no-fly zone in real time, triggers the UTM to take over the control right if the UAVs enter the no-fly zone, and controls the path through the UTM to make the UAVs leave the no-fly zone. When the beyond limit mode is switched, the analysis element also temporarily stores the key data of the UAVs and synchronizes the key data to the UAV controller; after the beyond limit mode is switched, the analysis element also detects the signal coverage and delay, switches the VLoS threshold to the BVLoS threshold when the network signal strength and delay data meet the beyond visual range communication requirements, so that the UAVs enter the beyond limit mode, and forces the UAVs to remain in the ordinary mode when the network signal strength and delay data do not meet the beyond visual range communication requirements.

6. The unmanned line-of-sight limit monitoring management method of claim 1, wherein: The analysis network element also monitors flight task requirements and network coverage state in real time, and switches the current communication mode of the unmanned aerial vehicle according to the flight task requirements and the network coverage state, the communication mode including a direct communication mode between the unmanned aerial vehicle and the unmanned aerial vehicle controller, an auxiliary communication mode through a 5G network, and a UTM navigation communication mode.

7. The unmanned line-of-sight limit monitoring management method of claim 1, wherein: The preset calculation model is a random forest regression model.

8. The unmanned line-of-sight limit monitoring management method of claim 1, wherein: The steps of generating a compliance report and performing model optimization are also included. The step of generating a compliance report includes: transmitting flight data of the unmanned aerial vehicle to a network management system (NMS) network element by the analysis network element after the task is completed, and generating a compliance report and detecting a violation event according to the flight data by the network management system (NMS) network element. The step of performing model optimization includes: extracting compliance data in historical compliance reports by the analysis network element, analyzing violation characteristics by using a random forest model, obtaining a VLoS threshold improvement scheme according to the violation characteristics, and updating the calculation model of the VLoS threshold. 9.A 5G network-based UAV line-of-sight restriction monitoring management apparatus, characterized in that: The unmanned aerial vehicle controller, the base station, the AMF network element, the NWDAF network element, and the UTM are included, 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 a 5G core network, and the unmanned aerial vehicle controller, the base station, the AMF network element, the NWDAF network element, and the UTM each have a processor, a memory, and operation instructions stored in the memory, the operation instructions can make the processor execute the method for monitoring and managing a line-of-sight limit of an unmanned aerial vehicle based on a 5G network according to any one of claims 1-8.

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