A method and system for measuring the real operable flight range of a drone

Abstract

The application discloses a method and system for measuring a real operable flight range of a UAV, and belongs to the technical field of UAV flight control. In order to solve the problem of automatically generating a direction detection point and automatically scheduling a UAV to perform a detection task, airport data and UAV data of the UAV are acquired, a unified detection parameter set is generated, a theoretical circular coverage range is converted into an executable direction target point, a direction target point set or a segmented direction point set is generated, a unified task structure is constructed, a detection task set is generated, automatic scheduling is performed, an event sequence and a corresponding position record are generated, then the event sequence and the corresponding position record are converted into distances from a center point of the airport, a farthest effective boundary point is obtained, an abnormal point is corrected, a real operable flight range of the UAV is generated based on the corrected farthest effective boundary point, and a deployment evaluation report is generated. The application improves the reliability of a real range boundary.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) flight control technology, specifically relating to a method and system for determining the actual operable flight range of a UAV. Background Technology

[0002] In scenarios such as forestry patrols, nature reserve monitoring, boundary inspections, fire verification, emergency rescue, and routine inspections of drone airports, engineering practices often use drones or drone airports as the center, estimating their theoretical coverage capacity with a fixed radius. For example, an 8-kilometer radius around the airport is considered the drone's coverage area, and airport site selection, mission planning, and deployment acceptance are based on this. The theoretical radius only reflects the ideal coverage under conditions of equipment capability, communication links, and safe return-to-home. In real-world environments, factors such as mountain obstructions, forest terrain, base station distribution, antenna installation direction, airport deployment height, image transmission links, remote control links, network links, and return-to-home strategies all affect the actual operable range of the drone. Therefore, the actual operable range is usually not a regular circle, but rather closer to an irregular boundary with indentations.

[0003] Current practices rely heavily on manual test flights or experience-based judgment to determine the flyable range, which has three main shortcomings: First, the test flight direction and recording methods are not consistent, making it difficult to retest the results; second, if the mission is planned directly according to the theoretical circle, the flight path may enter areas with unstable communication or where reliable return is not possible; and third, the acceptance of airport site selection or deployment lacks quantitative basis, which can easily lead to situations where theoretical coverage is sufficient but actual coverage has significant gaps. Summary of the Invention

[0004] The problem this invention aims to solve is to realize the automatic generation of direction detection points and the automatic scheduling of UAVs to perform detection tasks, and proposes a method and system for determining the actual operable flight range of UAVs.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for determining the actual operable flight range of a drone includes the following steps:

[0007] S1. Acquire airport data and drone data, including airport center point, drone model nominal or configuration coverage radius, number of directions, safety threshold, return-to-home strategy, and generate a unified detection parameter set after boundary verification;

[0008] S2. Based on the unified detection parameter set obtained in step S1, the theoretical circular coverage area is converted into executable directional target points, generating a set of directional target points or a segmented directional point set.

[0009] S3. Based on the set of directional target points obtained in step S2, and combined with the safety threshold and return strategy obtained in step S1, a unified task structure is constructed to generate a set of exploration tasks; then, according to the risk priority in the direction order, execution commands are generated for the tasks in the set of exploration tasks according to the direction, and automatic scheduling and execution are performed.

[0010] S4. Monitor the automatic scheduling execution process of step S3, compare the status samples of the execution process with the safety threshold, and generate event sequences and corresponding location records;

[0011] S5. Convert the generated event sequence and corresponding location records from step S4 into distances from the airport center point to obtain the farthest effective boundary point;

[0012] S6. Correct the outlier of the farthest effective boundary point obtained in step S5, and generate the actual operable flight range of the UAV based on the corrected farthest effective boundary point.

[0013] S7. Compare and evaluate the actual operable flight range of the drone generated in step S6 with the theoretical flight range of the drone, and generate a deployment evaluation report.

[0014] Furthermore, the expression for generating the unified probe parameter set in step S1 is:

[0015] Q={C,R,N,E_level,T_safe,R_max,ρ_j,H_return,V_probe,B_safe,K_abn,τ}

[0016] Where Q is the unified detection parameter set, C is the airport center point, R is the theoretical coverage radius, N is the number of directional detectors, E_level is the efficiency level, T_safe is the safety threshold, R_max is the maximum allowable detection distance, ρ_j is the proportion of the detection radius of the j-th segment, H_returnwe is the return altitude, V_probe is the detection flight speed, B_safe is the safety buffer distance, K_abn is the anomaly judgment coefficient, and τ is the risk judgment threshold coefficient.

[0017] Furthermore, step S2, based on C, R, N, and ρ_j obtained in step S1, divides the 360° into N equal parts with C as the center, and calculates the direction angle theta_i of the i-th direction, the theoretical target point, and the theoretical local segment direction point. The calculation formula is as follows:

[0018] theta_i=2πi / N

[0019] P_i=project(C,R,theta_i)

[0020] P_{i,j}=project(C,ρ_j×R,theta_i)

[0021] Where project is the longitude and latitude calculation function; P_i is the theoretical target point in the i-th direction, and P_{i,j} is the local segment direction point in the j-th segment of the i-th direction;

[0022] Then, the set of directional target points P={P_i} or the set of segmented directional points P'={P_{i,j}} are obtained by summing them up.

[0023] Furthermore, the expression for constructing the unified task structure in step S3 is as follows: Task_{i,j}={taskId,C,P_{i,j},theta_i,ρ_j,R,R_max,H_return,V_probe,T_safe,T_timeout,onThresholdReturn}

[0024] Where Task_{i,j} is the task object of the j-th segment in the i-th direction, taskId is the task batch number, T_timeout is the task timeout time, and onThresholdReturn is the threshold that triggers the return action;

[0025] T_timeout=2×(ρ_j×R) / V_probe+T_margin

[0026] Where T_margin is the takeoff and landing, hovering and communication margin; ρ_j×R is the target detection range corresponding to the j-th segment;

[0027] Then we obtain the probe task set TaskSet={Task_{i,j}}.

[0028] Furthermore, the expression for generating the execution order `executeOrder` in step S3 is:

[0029] executeOrder=sort(TaskSet,bytheta_ithenρ_jascending)

[0030] Where sort is the sorting function, and bytheta_ithenρ_jascending sorts the data first by direction angle and then by segment ratio.

[0031] Then, during the sequential execution, the task status in each direction will flow along the execution state set. The execution state set state_i includes pending execution, in execution, completed, active return, lost connection recovery, and failed and awaiting retesting.

[0032] Furthermore, in step S4, status data is obtained through UAV telemetry reporting, airport or nest status interface, flight control event log, WebSocket / MQTT downlink messages and task scheduling receipts. The status sample collected in the i-th direction for the k-th time is denoted as M_{i,k}.

[0033] Then, the state sample is compared with the safety threshold T_safe, an event is generated, and the position corresponding to the generated event is recorded. The expression is:

[0034] E_{i,k}=classify(M_{i,k},T_safe)

[0035] Where E_{i,k} represents the event corresponding to the state sample collected in the i-th direction for the k-th time, and classify is the comparison and classification function; an event sequence Event_i={E_{i,k}} is established based on the generated events.

[0036] Furthermore, in step S5, the location records corresponding to the generated event sequence are converted into distances from the airport center point. Then, when a link quality in a certain direction falls below a safety threshold, flight control triggers a return-to-home, the system actively returns to home, or the UAV recovers after losing contact or maintains a stable connection after reaching the theoretical target point, candidate boundary distances are generated, expressed as:

[0037] D_i=min(D_threshold_i,D_return_i,D_lost_i,R_max_i)

[0038] B_i = project(C, D_i, theta_i)

[0039] Where D_threshold_i represents the center distance when the link threshold is triggered in the i-th direction, D_return_i represents the center distance when the return is triggered in the i-th direction, D_lost_i represents the last valid position before the loss of contact in the i-th direction or the center distance calibrated after communication is restored, R_max_i represents the maximum allowable detection distance in the i-th direction, D_i is the farthest valid boundary distance in the i-th direction, min is the minimum value function, and B_i is the boundary point coordinate in the i-th direction.

[0040] Furthermore, the anomaly point check in step S6 includes situations where the distance to the farthest effective boundary in a certain direction changes significantly compared to the adjacent directions, and the task record shows temporary obstruction, wind deviation, insufficient power, task interruption, positioning abnormality, or equipment status abnormality. If these conditions are met, the corresponding direction will be marked as an anomaly point. For anomalies, priority will be given to generating retest tasks. If retesting is not possible, conservative corrections will be made based on adjacent effective boundary points to obtain the corrected set of farthest effective boundary points.

[0041] Then, based on the corrected farthest effective boundary point, the points are sorted by direction angle theta_i from smallest to largest and connected to form a closed polygon. Then, closure checks, boundary smoothing, and topology repair are performed to obtain the actual operable flight range Ω_real of the UAV.

[0042] Furthermore, step S7 involves comparative evaluation, including calculating the effective coverage ratio, gap area, gap direction, and risk direction.

[0043] A system for determining the true operable flight range of a drone includes a processor, a memory, and a computer program stored in the memory and run on the processor. When the computer program runs, it implements the steps of the method for determining the true operable flight range of a drone.

[0044] The beneficial effects of this invention are:

[0045] This invention provides a method for determining the actual operable flight range of a UAV, capable of identifying the deviation between the theoretical 8km circular coverage area and the actual operable range; transforming the experience-based judgment-based flight range identification process into an automated, repeatable, and quantifiable measurement workflow; providing early warnings or triggering return-to-home when link quality deteriorates, reducing the risk of UAVs being lost, crashed, or unable to return after losing contact; providing a more realistic basis for UAV airport site selection, avoiding deployment in locations with severe communication obstruction or abnormal coverage patterns; generating quantitative acceptance results after deployment to determine whether the airport has achieved the expected coverage effect; directly using irregular actual ranges for subsequent mission planning, avoiding the deployment of flight paths in areas that are difficult to control; and offering an adjustable number of directions, balancing measurement efficiency and range accuracy. Anomalies can be addressed through retesting or conservative correction, thereby improving the reliability of the actual range boundary. Attached Figure Description

[0046] Figure 1 This is a flowchart of a method for determining the actual operable flight range of a drone according to the present invention;

[0047] Figure 2 This is a schematic diagram comparing the theoretical coverage and the actual operable scope of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0049] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0050] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 and attached Figure 2 Detailed explanation is as follows:

[0051] Example 1:

[0052] A method for determining the actual operable flight range of a drone includes the following steps:

[0053] S1. Acquire airport data and drone data, including airport center point, drone model nominal or configuration coverage radius, number of directions, safety threshold, return-to-home strategy, and generate a unified detection parameter set after boundary verification;

[0054] Furthermore, the expression for generating the unified probe parameter set in step S1 is:

[0055] Q={C,R,N,E_level,T_safe,R_max,ρ_j,H_return,V_probe,B_safe,K_abn,τ}

[0056] Where Q is the unified detection parameter set, C is the airport center point, R is the theoretical coverage radius, N is the number of directional detectors, E_level is the efficiency level, T_safe is the safety threshold, R_max is the maximum allowable detection distance, ρ_j is the proportion of the detection radius of the j-th segment, H_returnwe is the return altitude, V_probe is the detection flight speed, B_safe is the safety buffer distance, K_abn is the anomaly judgment coefficient, and τ is the risk judgment threshold coefficient.

[0057] Furthermore, the system prioritizes retrieving the airport's center point C=(φ,λ), aircraft nominal or configuration coverage radius R, default return altitude H_return, default detection speed V_probe, and link security threshold set T_safe from the airport archives. If the archives lack a center point, it can be obtained through map point selection, current takeoff point positioning, or current task context. If the number of directions N is missing, it is automatically mapped according to the accuracy level E_level, where N=12 for low-precision or high-efficiency mode, N=24 for balanced mode, and N=36 or N=48 for high-precision mode. Then, boundary checks are performed on the input parameters: R must be greater than 0 and must not exceed the minimum value among the aircraft safety capability, no-fly zone distance, and manual limit; R_max is the minimum value among R, aircraft safety distance, and manual limit; ρ_j defaults to {1.00} when not input, indicating direct detection based on the theoretical circular target point; when segmented approximation is enabled, it can be {0.60, 0.80, 0.95, 1.00}.

[0058] Furthermore, the formula corresponding to step S1 is:

[0059] Q=normalize(C,R,N,E_level,T_safe,R_max,ρ_j,H_return,V_probe,B_safe,K_abn,τ);

[0060] R_max=min(R,R_aircraft,R_no_fly,R_manual);

[0061] N = N_inputifprovidedelsemap(E_level,{High Efficiency:12,Balanced:24,High Precision:36 or 48});

[0062] S2. Based on the unified detection parameter set obtained in step S1, the theoretical circular coverage area is converted into executable directional target points, generating a set of directional target points or a segmented directional point set.

[0063] Furthermore, step S2, based on C, R, N, and ρ_j obtained in step S1, divides the 360° into N equal parts with C as the center, and calculates the direction angle theta_i of the i-th direction, the theoretical target point, and the theoretical local segment direction point. The calculation formula is as follows:

[0064] theta_i=2πi / N

[0065] P_i=project(C,R,theta_i)

[0066] P_{i,j}=project(C,ρ_j×R,theta_i)

[0067] Where project is the longitude and latitude calculation function; P_i is the theoretical target point in the i-th direction, and P_{i,j} is the local segment direction point in the j-th segment of the i-th direction;

[0068] Then, the set of directional target points P={P_i} or the set of segmented directional points P'={P_{i,j}} are obtained by summing them up.

[0069] Furthermore, `project(C,d,theta)` represents the calculation of the target's latitude and longitude based on the center point C, distance d, and azimuth angle theta. Preferably, this can be achieved using a local plane approximation or a geodetic forward calculation method.

[0070] lat=lat_C+(d×cos(theta) / R_e)×180 / π

[0071] lng=lng_C+(d×sin(theta) / (R_e×cos(lat_C)))×180 / π

[0072] Where R_e is the Earth's radius, and lat_C and lng_C are the latitude and longitude of the center point, respectively. For an airport coverage area of ​​8 kilometers, the above local approximation can meet the accuracy requirements for point generation; if the system already has a GIS geodetic calculation library, it can also be directly called. For scenarios that require progressively approximating the boundary, multiple segmented points are generated in the same direction using ρ_j: the value of ρ_j comes from the parameter package, and is {1.00} when not configured; when conservative detection is enabled, the preferred range is 0.50 to 1.00, with example values ​​of 0.60, 0.80, 0.95, and 1.00.

[0073] S3. Based on the set of directional target points obtained in step S2, and combined with the safety threshold and return strategy obtained in step S1, a unified task structure is constructed to generate a set of exploration tasks; then, according to the risk priority in the direction order, execution commands are generated for the tasks in the set of exploration tasks according to the direction, and automatic scheduling and execution are performed.

[0074] Furthermore, the expression for constructing the unified task structure in step S3 is as follows: Task_{i,j}={taskId,C,P_{i,j},theta_i,ρ_j,R,R_max,H_return,V_probe,T_safe,T_timeout,onThresholdReturn}

[0075] Where Task_{i,j} is the task object of the j-th segment in the i-th direction, taskId is the task batch number, T_timeout is the task timeout time, and onThresholdReturn is the threshold that triggers the return action;

[0076] T_timeout=2×(ρ_j×R) / V_probe+T_margin

[0077] Where T_margin is the takeoff and landing, hovering and communication margin; ρ_j×R is the target detection range corresponding to the j-th segment;

[0078] Then we obtain the probe task set TaskSet={Task_{i,j}}.

[0079] Furthermore, the expression for generating the execution order `executeOrder` in step S3 is:

[0080] executeOrder=sort(TaskSet,bytheta_ithenρ_jascending)

[0081] Where sort is the sorting function, and bytheta_ithenρ_jascending sorts the data first by direction angle and then by segment ratio.

[0082] Then, during the sequential execution, the task status in each direction will flow along the execution state set. The execution state set state_i includes pending execution, in execution, completed, active return, lost connection recovery, and failed and awaiting retesting.

[0083] Furthermore, the system receives a set of directional target points and converts each target point into a recognizable probe mission object. This conversion does not merely record the location; rather, it incorporates the directional target points, flight constraints, and safety policies into a unified mission structure.

[0084] Furthermore, the system receives TaskSets and distributes them sequentially according to direction, risk priority, or manually specified order. The system maintains an execution status (state_i) for each direction, including pending execution, in progress, completed, active return, recovery from disconnection, and failure pending retesting. When segmentation points are enabled, tasks within the same direction are executed in ascending order of ρ_j. The next segment task is only distributed when the current segment task is stably completed or the conditions for continued probing are met.

[0085] state_i∈{pending, running, finished, returned, lostRecovered, retryRequired};

[0086] S4. Monitor the automatic scheduling execution process of step S3, compare the status samples of the execution process with the safety threshold, and generate event sequences and corresponding location records;

[0087] Furthermore, in step S4, status data is obtained through UAV telemetry reporting, airport or nest status interface, flight control event log, WebSocket / MQTT downlink messages and task scheduling receipts. The status sample collected in the i-th direction for the k-th time is denoted as M_{i,k}.

[0088] M_{i,k}={time,position,distanceToC,rcQuality,videoQuality,networkState,rtkState,battery,returnFlag,lostFlag};

[0089] Then, the state sample is compared with the safety threshold T_safe, an event is generated, and the position corresponding to the generated event is recorded. The expression is:

[0090] E_{i,k}=classify(M_{i,k},T_safe)

[0091] Where E_{i,k} represents the event corresponding to the state sample collected in the i-th direction and k-th time, and classify is a comparison and classification function. Its function is to compare the state sample M_{i,k} collected in the i-th direction and k-th time with the safety threshold set T_safe and classify it into a specific event type; and to establish an event sequence Event_i={E_{i,k}} based on the generated events.

[0092] For example, an `rcLow` event is generated when the remote control quality is below a threshold, a `videoLow` event is generated when the image transmission quality is below a threshold, a `returnTriggered` event is generated when the flight controller reports a return-to-home flag, a `lost` event is generated when communication is interrupted, and a `recovered` event is generated when communication is restored. The output event sequence is `Event_i={E_{i,k}}`, the last valid position `LastValid_i`, the return-to-home trigger position `ReturnPos_i`, and the loss-of-connection recovery position `RecoverPos_i`.

[0093] S5. Convert the generated event sequence and corresponding location records from step S4 into distances from the airport center point to obtain the farthest effective boundary point;

[0094] Furthermore, in step S5, the location records corresponding to the generated event sequence are converted into distances from the airport center point. Then, when a link quality in a certain direction falls below a safety threshold, flight control triggers a return-to-home, the system actively returns to home, or the UAV recovers after losing contact or maintains a stable connection after reaching the theoretical target point, candidate boundary distances are generated, expressed as:

[0095] D_i=min(D_threshold_i,D_return_i,D_lost_i,R_max_i)

[0096] B_i = project(C, D_i, theta_i)

[0097] Where D_threshold_i represents the center distance when the link threshold is triggered in the i-th direction, D_return_i represents the center distance when the return is triggered in the i-th direction, D_lost_i represents the last valid position before the loss of contact in the i-th direction or the center distance calibrated after communication is restored, R_max_i represents the maximum allowable detection distance in the i-th direction, D_i is the farthest valid boundary distance in the i-th direction, min is the minimum value function, and B_i is the boundary point coordinate in the i-th direction, that is, the true boundary position obtained by projecting along the azimuth angle theta_i and distance D_i with the center point C as the reference.

[0098] Further:

[0099] D_threshold_i=distance(C,position(first(E_{i,k}∈{rcLow,videoLow,networkLow})));

[0100] D_return_i=distance(C,ReturnPos_i);

[0101] D_lost_i=distance(C,calibrate(LastValid_i,RecoverPos_i,flightLog_i));

[0102] Where distance(·) represents the geographical distance calculation; first(·) represents the location of the first triggering of the corresponding event; calibrate(·) represents the conservative calibration of the loss boundary based on the last valid position, the restored communication position, and the flight control log; D_threshold_i represents the center distance when the link threshold is triggered in the i-th direction; D_return_i represents the center distance when the return-to-home event is triggered; D_lost_i represents the center distance obtained from the last valid position before loss of contact or the calibration obtained after the restoration of communication; R_max_i represents the maximum allowable detection distance in this direction; and D_i is the farthest valid boundary distance in the i-th direction. B_i is obtained by projecting the center point C, the direction angle theta_i, and D_i, and serves as the boundary point for generating the true range in this direction. The purpose of using min(·) is to conservatively confirm the safety boundary: as long as any safety event indicates that the direction is close to the operable boundary, the boundary is no longer extrapolated outward. If a direction does not trigger the threshold, return-to-home event, or loss of contact event, and stably reaches the target point, then the direction can take D_i = R_max_i or D_i = R.

[0103] S6. Correct the outlier of the farthest effective boundary point obtained in step S5, and generate the actual operable flight range of the UAV based on the corrected farthest effective boundary point.

[0104] Furthermore, the anomaly point check in step S6 includes situations where the distance to the farthest effective boundary in a certain direction changes significantly compared to the adjacent directions, and the task record shows temporary obstruction, wind deviation, insufficient power, task interruption, positioning abnormality, or equipment status abnormality. If these conditions are met, the corresponding direction will be marked as an anomaly point. For anomalies, priority will be given to generating retest tasks. If retesting is not possible, conservative corrections will be made based on adjacent effective boundary points to obtain the corrected set of farthest effective boundary points.

[0105] Then, based on the corrected farthest effective boundary point, the points are sorted by direction angle theta_i from smallest to largest and connected to form a closed polygon. Then, closure checks, boundary smoothing, and topology repair are performed to obtain the actual operable flight range Ω_real of the UAV.

[0106] Furthermore, abnormal_i = |D_i - median(D_{i-1},D_i,D_{i+1})| > K_abn × R;

[0107] B_i_status=retryRequiredifabnormal_iandcause_i∈{taskInterrupted,lowBattery,gpsAbnormal,temporaryBlock}elsevalid

[0108] Where K_abn comes from the outlier determination coefficient of S1; D_{i-1} and D_{i+1} are the boundary distances of adjacent directions; cause_i comes from the execution state of S4 and the event sequence of S5. S7 does not regenerate the direction target point, but instead determines whether B_i obtained from S6 is reliable. For reliable points, the output is... For outliers, priority is given to generating retest tasks; if retesting is not possible, conservative corrections are made based on adjacent valid boundary points.

[0109]

[0110]

[0111] Then, the output is the corrected set of boundary points. .

[0112] Furthermore, the output set of corrected boundary points The points are sorted by direction angle theta_i in ascending order and then connected to form a closed polygon. If there is a direction to be retested, a conservative connection is first made based on adjacent valid points, and this direction is marked as to be retested in the report. Then, closure checks, boundary smoothing, and topology repair are performed to obtain the real operable flight range Ω_real.

[0113]

[0114] Ω_real=topologyFix(smooth(Ω_raw))

[0115] Ω_plan=buffer(Ω_real,-B_safe)

[0116] Here, `close(·)` indicates a closed boundary, `sortByTheta(·)` sorts by orientation angle, `smooth(·)` smooths local spikes or jagged boundaries, `topologyFix(·)` fixes self-intersections, duplicate points, and non-closed boundaries, and `Ω_plan` represents the recommended task planning range obtained by shrinking a safe buffer distance inward from the actual range to avoid subsequent tasks executing close to the actual boundary. This range is usually not circular but will form irregular boundaries due to changes in terrain, occlusion, communication conditions, and deployment environment.

[0117] S7. Compare and evaluate the actual operable flight range of the drone generated in step S6 with the theoretical flight range of the drone, and generate a deployment evaluation report.

[0118] Furthermore, step S7 involves comparative evaluation, including calculating the effective coverage ratio, gap area, gap direction, and risk direction.

[0119] Furthermore, based on Ω_real and Ω_plan, a theoretical circular range Ω_ref is generated according to the center point C and the theoretical radius R. The system overlays Ω_real and Ω_ref to calculate the effective coverage ratio, gap area, gap direction, and risk direction, and generates a deployment assessment report accordingly.

[0120] Ω_ref=circle(C,R)

[0121] coverageRatio=Area(Ω_real∩Ω_ref) / Area(Ω_ref)

[0122] gapArea = Area(Ω_ref - Ω_real)

[0123] riskDirections={theta_i|D_i <R×τ}

[0124] Here, Area(·) represents area calculation; Ω_ref comes from C and τ comes from the deployment acceptance strategy, with an example value of 0.8. When the actual boundary distance in a certain direction is less than R×τ, that direction is marked as a risk direction. The final output includes coverageRatio, gapArea, riskDirections, Ω_plan, and deployment recommendations. Ω_plan can be written into the boundary of subsequent task planning to avoid flight paths entering the actual operational range.

[0125] The following is an application example of this embodiment:

[0126] 1. Example of a theoretical coverage radius of 8 kilometers;

[0127] Taking a UAV airport in a forest area as an example, the system reads the airport coordinates as the center point C and receives the theoretical coverage radius R=8000m. The system generates 24 directional detection points with a default N=24, and the angle between adjacent directions is 15°. The target points in each direction are located on the theoretical circumference; for high-risk directions, the system can also generate segmented detection points of 60%, 80%, 95%, and 100%.

[0128] The dispatch system dispatches UAVs to perform reconnaissance missions in directional order. While the UAVs fly in the designated direction, the system continuously monitors the remote control link, image transmission link, network link, positioning status, battery level, and return-to-home status. When a link threshold is triggered at 6.2km in a certain direction and the UAVs actively return to home, the system records the corresponding candidate boundary point. When a UAV flies to 8km in another direction and maintains a stable connection, the theoretical circular target point is used as the boundary point for that direction.

[0129] After detecting all 24 directions, the system obtained 24 farthest valid boundary points. Connecting them in angular order reveals that the actual operable range is significantly concave in the direction obstructed by mountains, while it approaches the theoretical radius in the direction with open communication. For directions where occasional mission failures occurred, the system marked them as awaiting retesting; for obvious anomalies, conservative corrections were made, ultimately generating the actual operable flight range.

[0130] 2. Examples of deployment and acceptance testing;

[0131] After airport deployment is completed, the system executes the above-mentioned real coverage determination process and compares Ω_real and Ω_ref. If the coverageRatio is lower than the preset acceptance threshold, or if certain key patrol directions are identified as risk directions, the system will indicate that the current deployment location has insufficient coverage and suggest adjusting the antenna direction, adding relays, adjusting the airport location, or modifying the mission planning range.

[0132] 3. Examples of safety boundaries in task planning;

[0133] In the subsequent mission planning phase, the system no longer directly uses the theoretical circle Ω_ref as the flyable boundary, but instead adopts the actual range Ω_real or Ω_plan after shrinking inward for safety buffer. If the route generation module detects that waypoints or route segments exceed Ω_plan, it will prompt adjustments to the mission area or reduce the mission radius to reduce the risk of the drone being lost, crashed, or unable to return after losing contact.

[0134] Example 2:

[0135] A system for determining the true operable flight range of a drone includes a processor, a memory, and a computer program stored in the memory and run on the processor. When the computer program is run, it implements the steps of the method for determining the true operable flight range of a drone as described in Example 1.

[0136] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0137] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for determining the actual operable flight range of a drone, characterized in that, Includes the following steps: S1. Acquire airport data and drone data, including airport center point, drone model nominal or configuration coverage radius, number of directions, safety threshold, return-to-home strategy, and generate a unified detection parameter set after boundary verification; S2. Based on the unified detection parameter set obtained in step S1, the theoretical circular coverage area is converted into executable directional target points, generating a set of directional target points or a segmented directional point set. S3. Based on the set of directional target points obtained in step S2, and combined with the safety threshold and return strategy obtained in step S1, a unified task structure is constructed to generate a set of exploration tasks; then, according to the risk priority in the direction order, execution commands are generated for the tasks in the set of exploration tasks according to the direction, and automatic scheduling and execution are performed. S4. Monitor the automatic scheduling execution process of step S3, compare the status samples of the execution process with the safety threshold, and generate event sequences and corresponding location records; S5. Convert the generated event sequence and corresponding location records from step S4 into distances from the airport center point to obtain the farthest effective boundary point; S6. Correct the outlier of the farthest effective boundary point obtained in step S5, and generate the actual operable flight range of the UAV based on the corrected farthest effective boundary point. S7. Compare and evaluate the actual operable flight range of the drone generated in step S6 with the theoretical flight range of the drone, and generate a deployment evaluation report.

2. The method for determining the actual operable flight range of a UAV according to claim 1, characterized in that, The expression for generating the unified probe parameter set in step S1 is: Q={C,R,N,E_level,T_safe,R_max,ρ_j,H_return,V_probe,B_safe,K_abn,τ} Where Q is the unified detection parameter set, C is the airport center point, R is the theoretical coverage radius, N is the number of directional detections, E_level is the efficiency level, T_safe is the safety threshold, R_max is the maximum allowable detection distance, ρ_j is the proportion of the detection radius of the j-th segment, H_returnwe is the return altitude, V_probe is the detection flight speed, B_safe is the safety buffer distance, K_abn is the anomaly judgment coefficient, and τ is the risk judgment threshold coefficient.

3. A method for determining the actual operable flight range of a UAV according to claim 1 or 2, characterized in that, Step S2, based on C, R, N, and ρ_j obtained in step S1, divides the 360° circle into N equal parts with C as the center, and calculates the direction angle theta_i of the i-th direction, the theoretical target point, and the theoretical local segment direction point. The calculation formula is as follows: theta_i=2πi / N P_i=project(C,R,theta_i) P_{i,j}=project(C,ρ_j×R,theta_i) Where project is the longitude and latitude calculation function; P_i is the theoretical target point in the i-th direction, and P_{i,j} is the local segment direction point in the j-th segment of the i-th direction; Then, the set of directional target points P={P_i} or the set of segmented directional points P'={P_{i,j}} are obtained by summing them up.

4. The method for determining the actual operable flight range of a UAV according to claim 3, characterized in that, The expression for constructing the unified task structure in step S3 is: Task_{i,j}={taskId,C,P_{i,j},theta_i,ρ_j,R,R_max,H_return,V_probe,T_safe,T_timeout,onThresholdReturn} Among them, Ta sk_{i,j} is the task object of the j-th segment in the i-th direction, taskId is the task batch number, T_timeout is the task timeout time, and onThresholdReturn is the threshold that triggers the return action; T_timeout=2×(ρ_j×R) / V_probe+T_margin Where T_margin is the takeoff and landing, hovering and communication margin; ρ_j×R is the target detection range corresponding to the j-th segment; Then we obtain the probe task set TaskSet={Task_{i,j}}.

5. The method for determining the actual operable flight range of a UAV according to claim 4, characterized in that, The expression for generating the execution order `executeOrder` in step S3 is: executeOrder=sort(TaskSet,bytheta_ithenρ_jascending) Where sort is the sorting function, and bytheta_ithenρ_jascending sorts the data first by direction angle and then by segment ratio. Then, during the sequential execution, the task status in each direction will flow along the execution state set. The execution state set state_i includes pending execution, in execution, completed, active return, lost connection recovery, and failed and awaiting retesting.

6. The method for determining the actual operable flight range of a UAV according to claim 5, characterized in that, In step S4, status data is obtained through UAV telemetry reporting, airport or nest status interface, flight control event log, WebSocket / MQTT downlink messages and task scheduling receipts. The status sample collected in the i-th direction for the k-th time is denoted as M_{i,k}. Then, the state sample is compared with the safety threshold T_safe, an event is generated, and the position corresponding to the generated event is recorded. The expression is: E_{i,k}=classify(M_{i,k},T_safe) Where E_{i,k} represents the event corresponding to the state sample collected in the i-th direction for the k-th time, and classify is the comparison and classification function; an event sequence Event_i={E_{i,k}} is established based on the generated events.

7. The method for determining the actual operable flight range of a UAV according to claim 6, characterized in that, In step S5, the location records corresponding to the generated event sequence are converted into distances from the airport center point. Then, when a link quality in a certain direction falls below a safety threshold, flight control triggers a return-to-home, the system actively returns to home, or the UAV recovers after losing contact or maintains a stable connection after reaching the theoretical target point, candidate boundary distances are generated, expressed as: D_i=min(D_threshold_i,D_return_i,D_lost_i,R_max_i) B_i = project(C, D_i, theta_i) Where D_threshold_i represents the center distance when the link threshold is triggered in the i-th direction, D_return_i represents the center distance when the return is triggered in the i-th direction, D_lost_i represents the last valid position before the loss of contact in the i-th direction or the center distance calibrated after communication is restored, R_max_i represents the maximum allowable detection distance in the i-th direction, D_i is the farthest valid boundary distance in the i-th direction, min is the minimum value function, and B_i is the coordinate of the boundary point in the i-th direction.

8. The method for determining the actual operable flight range of a UAV according to claim 7, characterized in that, The anomaly check in step S6 includes situations where the distance to the farthest effective boundary in a certain direction changes significantly compared to the adjacent direction, and the task record shows temporary obstruction, wind deviation, insufficient power, task interruption, positioning abnormality, or equipment status abnormality. If these conditions are met, the corresponding direction will be marked as an anomaly. For anomalies, priority will be given to generating retest tasks. For points that cannot be retested, a conservative correction is made based on adjacent valid boundary points to obtain the corrected set of the farthest valid boundary points. Then, based on the corrected farthest effective boundary point, the points are sorted by direction angle theta_i from smallest to largest and connected to form a closed polygon. Then, closure checks, boundary smoothing, and topology repair are performed to obtain the actual operable flight range Ω_real of the UAV.

9. The method for determining the actual operable flight range of a UAV according to claim 8, characterized in that, The comparative evaluation in step S7 includes calculating the effective coverage ratio, gap area, gap direction, and risk direction.

10. A system for determining the actual operable flight range of a UAV, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed, implements the steps of a method for determining the true operable flight range of a UAV as described in any one of claims 1-9.

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