Fixed-wing unmanned aerial vehicle landing time prediction calculation method, system, device and medium

Abstract

Embodiments of the present disclosure provide a fixed-wing unmanned aerial vehicle landing time prediction calculation method, system, device and medium. The method comprises: obtaining recovery waypoint coordinates and model data of the unmanned aerial vehicle; dividing a landing flight section of the unmanned aerial vehicle according to the recovery waypoint coordinates, including an approach segment, an approach segment and an exit segment; according to the model data, respectively reverse calculating the time consumption of each landing flight section; accumulating the time consumption of each landing flight section to obtain the total landing time of the unmanned aerial vehicle. Embodiments of the present disclosure calculate the recovery time consumption of the unmanned aerial vehicle in real time according to the recovery waypoint and the performance simulation data set of the unmanned aerial vehicle, give the arrival time of each waypoint, and use the height of each waypoint to reverse the time consumption calculation, so that the user can master the recovery time consumption of the unmanned aerial vehicle in real time, provide prediction assistance for the landing time of the airport fixed-wing unmanned aerial vehicle, especially provide time reference for large-scale landing of the unmanned aerial vehicle airport, thereby helping the timeliness and economy of the unmanned aerial vehicle landing recovery process.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure belong to the technical field of fixed-wing unmanned aerial vehicle landing time prediction, and particularly relate to a fixed-wing unmanned aerial vehicle landing time prediction calculation method, system, device and medium. BACKGROUND

[0002] Unmanned aerial vehicle recovery technology is a key link in unmanned aerial vehicle task execution, and especially in scenarios such as logistics transportation, emergency rescue, military reconnaissance, accurate prediction of unmanned aerial vehicle landing time is of great significance to task scheduling efficiency, airspace resource allocation and safety guarantee. The traditional method usually performs linear estimation based on the real-time position of the unmanned aerial vehicle and the preset waypoint, but is affected by factors such as dynamic changes in flight performance, multi-stage coordinated adjustment and complex environmental interference, and the prediction method simply relying on the experience model or static path planning often has problems such as insufficient precision and poor adaptability.

[0003] At present, unmanned aerial vehicle landing time prediction mainly relies on two types of technologies: one is real-time position tracking based on GPS positioning and inertial navigation, combined with fixed waypoints for segmented time consumption accumulation; the other is to train a statistical model through historical flight data to predict the overall landing time consumption.

[0004] However, the existing technology still has many defects, for example: dynamic performance modeling is insufficient, the flight performance differences of different unmanned aerial vehicle models (such as turn radius, glide ratio, taxi speed, etc.) are not fully considered, resulting in limited model generalization ability; multi-stage collaborative calculation is missing, and the reverse calculation mechanism is not used to dynamically adjust the time consumption of each stage, making it difficult to deal with complex scenarios such as altitude layer switching, circling descent, etc., especially when there is a deviation between the actual altitude of the unmanned aerial vehicle and the theoretical calculated value, the error is significantly accumulated step by step; the data granularity is rough, the traditional fitting data set uses fixed altitude intervals (such as 500m or more), which cannot accurately match the fine descent requirement of the low-altitude approach stage, affecting the local precision of time prediction. SUMMARY

[0005] Embodiments of the present disclosure aim to at least solve one of the technical problems existing in the prior art, and provide a fixed-wing unmanned aerial vehicle landing time prediction calculation method, system, device and medium.

[0006] One aspect of the present disclosure provides a fixed-wing unmanned aerial vehicle landing time prediction calculation method, the method comprising:

[0007] obtaining recovery waypoint coordinates and model data of the unmanned aerial vehicle; wherein the recovery waypoint includes the current position of the unmanned aerial vehicle, the circling start and end point, the FAF circle cut-in point, the approach point and the landing point, and the model data includes flight performance fitting data set, landing taxi performance fitting data set and taxi-out performance fitting data set;

[0008] The recovery waypoint coordinates are used to divide a landing flight section of the UAV, including an approach segment, an approach segment, and an exit segment.

[0009] The time consumption of each landing flight section is inversely calculated according to the type data.

[0010] The total landing time of the UAV is obtained by accumulating the time consumption of each landing flight section.

[0011] Further, the approach segment includes a pre-approach segment, a hovering descent segment, a straight flight segment, and a turning descent segment.

[0012] The time consumption of each landing flight section is inversely calculated according to the type data, including:

[0013] The descent height and time consumption of the turning descent segment are calculated according to the flight performance fitting data set.

[0014] The descent height and time consumption of the straight flight segment are calculated according to the current position of the UAV and the descent height of the turning descent segment.

[0015] The time consumption of the pre-approach segment and the hovering descent segment is calculated according to the current position of the UAV and the descent height of the straight flight segment and the turning descent segment.

[0016] The time consumption of the pre-approach segment, the hovering descent segment, the straight flight segment and the turning descent segment is accumulated to obtain the time consumption of the approach segment.

[0017] Further, the time consumption of the turning descent segment is calculated according to the flight performance fitting data set, including:

[0018] The FAF circle arc length is calculated according to the recovery waypoint coordinates and the FAF circle center coordinates.

[0019] The time consumption of the turning descent segment is calculated according to the FAF circle arc length and the flight performance fitting data set.

[0020] The descent height of the turning descent segment is obtained according to the time consumption of the turning descent segment.

[0021] Further, the descent height and time consumption of the straight flight segment are calculated according to the current position of the UAV and the descent height of the turning descent segment, including:

[0022] The flight distance of the straight flight segment is calculated according to the current position of the UAV and the descent height of the turning descent segment.

[0023] The time consumption of the straight flight segment is calculated according to the flight distance of the straight flight segment and the flight performance fitting data set.

[0024] According to the time consumption of the straight flight segment, the descent height of the straight flight segment is obtained.

[0025] Further, the time consumption of the pre-approach segment and the circling descent segment is calculated according to the current position height of the UAV and the descent heights of the straight flight segment and the turning descent segment.

[0026] According to the current position of the UAV and the descent heights of the straight flight segment and the turning descent segment, the flight distance of the pre-approach segment is calculated.

[0027] According to the flight distance of the approach segment and the flight performance fitting data set, the time consumption of the pre-approach segment is calculated.

[0028] According to the time consumption of the pre-approach segment, the expected descent height of the pre-approach segment is obtained.

[0029] According to the current position of the UAV, the expected descent height of the pre-approach segment and the flight performance fitting data set, the number of circles required for the UAV to circle and descend at the minimum turning radius is calculated.

[0030] The number of circles is rounded off by using rounding, and the required whole number of circles of the UAV in the circling and descending segment is obtained.

[0031] According to the difference between the number of circles and the whole number of circles, the time consumption of the circling and descending segment is compensated.

[0032] According to the time consumption compensation of the circling and descending segment, the descent height of the circling and descending segment is obtained.

[0033] Further, the time consumption of each landing leg is inversely calculated according to the type data, respectively.

[0034] The distance to be flown from the approach point to the touchdown point of the UAV, the average time consumption from the landing roll to the exit of the landing runway is obtained.

[0035] According to the distance to be flown and the average approach speed of the UAV, the time consumption of the UAV from the approach point to the touchdown point is obtained.

[0036] The time consumption from the approach point to the touchdown point and the average time consumption from the landing roll to the exit of the landing runway are accumulated to obtain the time consumption of the approach segment.

[0037] Further, the time consumption of each landing leg is inversely calculated according to the type data, respectively.

[0038] The average time consumption from entering the contact channel to stopping in the taxiway of the UAV is obtained, and the time consumption of the exit segment is obtained.

[0039] Another aspect of the present disclosure provides a fixed-wing unmanned aerial vehicle landing time prediction calculation system, the system comprising:

[0040] an acquisition module configured to acquire a recovery waypoint coordinate of the unmanned aerial vehicle and model data, wherein the recovery waypoint comprises a current position of the unmanned aerial vehicle, a hovering start / stop point, a FAF circle entry point, an approach point and a touchdown point, and the model data comprises a flight performance fitting data set, a landing roll performance fitting data set and a taxi-out performance fitting data set;

[0041] a division module configured to divide a landing flight section of the unmanned aerial vehicle into an approach section, an approach section and a taxi-out section according to the recovery waypoint coordinate;

[0042] a calculation module configured to reversely calculate a time consumption of each of the landing flight sections according to the model data;

[0043] a cumulative module configured to cumulatively calculate the time consumption of each of the landing flight sections to obtain a total landing time of the unmanned aerial vehicle.

[0044] Still another aspect of the present disclosure provides an electronic device, characterized in that comprising:

[0045] at least one processor; and

[0046] a memory in communication with the at least one processor, configured to store one or more programs, which when executed by the at least one processor, can enable the at least one processor to implement the fixed-wing unmanned aerial vehicle landing time prediction calculation method described above.

[0047] Still another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the fixed-wing unmanned aerial vehicle landing time prediction calculation method described above.

[0048] The fixed-wing unmanned aerial vehicle landing time prediction calculation method, system, device and medium of the present disclosure can calculate the recovery time consumption of the unmanned aerial vehicle in real time according to the recovery waypoint and the performance simulation data set of the unmanned aerial vehicle, can give the calculated arrival time of each waypoint, and can use the height of each waypoint to reversely calculate the time consumption of the height reduction, so that the user can master the recovery time consumption of the unmanned aerial vehicle in real time, and the present disclosure provides a prediction aid for the landing time of the fixed-wing unmanned aerial vehicle of the airport, especially provides a time reference for the large-scale landing of the unmanned aerial vehicle of the airport, thereby helping to improve the timeliness and economy of the landing and recovery process of the unmanned aerial vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 a flowchart of a fixed-wing unmanned aerial vehicle landing time prediction calculation method of an embodiment of the present disclosure;

[0050] Figure 2A fixed-wing unmanned aerial vehicle landing process schematic diagram for another embodiment of the present disclosure;

[0051] Figure 3 A fixed-wing unmanned aerial vehicle landing time prediction calculation system structure schematic diagram for another embodiment of the present disclosure;

[0052] Figure 4 An electronic device structure schematic diagram for another embodiment of the present disclosure. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present disclosure.

[0054] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a full understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be used. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring the aspects of the present disclosure.

[0055] The flowcharts shown in the drawings are only exemplary descriptions, and do not necessarily include all contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can be further divided, and some operations / steps can be combined or partially combined, so the actual execution order may be changed according to the actual situation.

[0056] It should be understood that although the terms first, second, third, etc. can be used in the present disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be called the second component without departing from the teachings of the present disclosure concept. As used in the present disclosure, the term "and / or" includes all combinations of any one and one or more of the associated listed items.

[0057] Those skilled in the art can understand that the drawings are only schematic diagrams of exemplary embodiments, and the modules or flows in the drawings are not necessarily required to implement the present disclosure, and therefore cannot be used to limit the protection scope of the present disclosure.

[0058] As Figure 1As shown, one embodiment of the present disclosure provides a fixed-wing unmanned aerial vehicle landing time prediction calculation method, which comprises:

[0059] Step S1, obtaining the recovery waypoint coordinates and model data of the unmanned aerial vehicle.

[0060] Specifically, the embodiment of the present disclosure adopts an accurate approach method based on equiangular path (FAF circle landing), and FAF stands for Final Approach Fix. According to the known information such as airport information and FAF point position, the process time of the fixed-wing unmanned aerial vehicle from the current position through the FAF circle, the FAF point into the approach segment, the approach segment and the landing taxiing / out phase is planned.

[0061] Referring to the fixed-wing unmanned aerial vehicle landing process schematic diagram as shown in Figure 2 The recovery waypoint specifically includes the current position P1 of the unmanned aerial vehicle, the circling start-stop point P2, the FAF circle cut-in point P3, the approach point P4 and the touchdown point P5. The recovery waypoint coordinates at least include the longitude, latitude and height of the current position P1 of the unmanned aerial vehicle. The model data includes the flight performance fitting data set, the landing taxiing performance fitting data set and the out performance fitting data set of the corresponding model of the unmanned aerial vehicle, wherein the flight performance fitting data set can include the minimum turning radius of the unmanned aerial vehicle, the straight flight average ground speed and the straight flight average glide ratio of each height layer, and the circling average ground speed and the circling average glide ratio of each height layer.

[0062] Step S2, dividing the landing flight segment of the unmanned aerial vehicle according to the recovery waypoint coordinates, including the approach segment, the approach segment and the out segment.

[0063] Specifically, as shown in Figure 2 The landing time consumption calculation segment of the embodiment of the present disclosure is divided into three stages: the approach segment (P1 to P4), the approach segment (P4 to P5) and the out segment (after P5). The approach segment can be further divided into the pre-approach segment from the current position P1 of the unmanned aerial vehicle to the circling start-stop point P2, the circling descent segment of the unmanned aerial vehicle at the circling start-stop point P2 (turning circle O1 with minimum turning radius), the straight flight segment from the circling start-stop point P2 to the FAF circle cut-in point P3 (tangent line of turning circle O1 and FAF circle O2), and the turning descent segment from the FAF circle cut-in point P3 to the approach point P4.

[0064] Step S3, respectively calculating the time consumption of each landing flight segment in reverse according to the model data.

[0065] Specifically, first, the time consumption of the approach segment is calculated in reverse from the turning descent segment:

[0066] ①The coordinates of the FAF circle cut-in point P3, the coordinates of the approach point P4 and the coordinates of the center of the FAF circle O2 are calculated respectively to calculate the tangent line of the FAF circle O2 and the straight flight segment from the circling start-stop point P2 to the FAF circle cut-in point P3 (tangent line of turning circle O1 and FAF circle O2).x The angle of the arc (included angle) of the shaft, then calculate the difference between the two angles, using the formula to calculate the arc length of the circle l 3, the specific formula is the difference between the two angles multiplied by the radius of the FAF circle; ②According to the model data of the unmanned aerial vehicle, read the average ground speed and average glide rate of the unmanned aerial vehicle at the height H4 of the height layer at the approach point P4 (select and use according to the flight performance fitting data set of each type of unmanned aerial vehicle); ③Back-propagating the required time from the current height layer to the last height layer, for example, the height layer interval is 100m, then the required time is 100m divided by the average glide rate of the unmanned aerial vehicle at the height layer (m / s), denoted as t 41 , unit: s; then calculate the flight distance d1 in t 41 time, and judge the relationship between d1 and l 3; ④If d1≥ l 3, stop calculating, and the required time t 41 is updated to t4= l 3 / the average ground speed of the unmanned aerial vehicle at the height layer; if d1< l 3, repeat the calculation in step ③ above until the cumulative flight distance d1 is greater than or equal to the arc length l 3; ⑤According to the flight time t4, calculate the descent height of the turn and descent segment, and back-propagate the height H3 of the FAF circle entry point P3.

[0067] II. Then calculate the time consumption of the straight flight segment:

[0068] 1. Compare the FAF circle entry point P3 height H3 with the current height H1 of the unmanned aerial vehicle, if H1≤H3, then H3=H1, H2=H1, at this time, only through the turn and descent segment to meet the approach height requirement, the subsequent navigation segment is no longer calculated, and the flight distance l 2 of the straight flight segment is 0.

[0069] 2. Compare the FAF circle entry point P3 height H3 with the current height H1 of the unmanned aerial vehicle, if H1>H3, then: ①Keep the height H3 value of the FAF circle entry point P3 unchanged; ②Read the average ground speed and average glide rate of the unmanned aerial vehicle at the height H3 (which can be selected and used according to the flight performance fitting data set of each type of unmanned aerial vehicle); ③Calculate the flight distance l 2 of the straight flight segment, and back-propagate the required time from the height H3 to the last height layer, for example, the height layer interval is 1000m, then the required time is 1000m divided by the average glide rate of the unmanned aerial vehicle at the height layer (m / s), denoted as t 31 , unit: s; calculate the flight distance d2 of the unmanned aerial vehicle in time t 31 , and judge the relationship between d2 and l 2; ④If d2≥ l 2, stop calculating, and the time t31 Update t3 = t3 + t2 l 2 / the average ground speed of the UAV in the height layer; if d2 < 1000, continue to repeat the calculation in step ③ until the cumulative flight distance d2 is greater than or equal to 1000; ⑤ calculate the descent height of the straight flight segment according to the flight time t3, and inversely calculate the expected height H2 of the take-off and landing point P2 of the hovering segment. l 2, then continue to repeat the calculation in step ③ until the cumulative flight distance d2 is greater than or equal to 1000; ⑤ calculate the descent height of the straight flight segment according to the flight time t3, and inversely calculate the expected height H2 of the take-off and landing point P2 of the hovering segment. l 2; ⑤ calculate the descent height of the straight flight segment according to the flight time t3, and inversely calculate the expected height H2 of the take-off and landing point P2 of the hovering segment.

[0070] Three, then calculate the time consumption of the pre-approach segment and the hovering descent segment:

[0071] 1. Compare the hovering take-off and landing point P2 height H2 with the current UAV height H1, if H1≤H2, then H2=H1, at this time only through the straight flight segment and the turn descent segment can meet the approach height requirement, the flight distance of the hovering descent segment is 0, and the pre-approach segment is no longer calculated.

[0072] 2. Compare the hovering take-off and landing point P2 height H2 with the current UAV height H1, if H1>H2, then: ① keep the hovering take-off and landing point P2 height H2 unchanged; ② take the average ground speed and average glide rate of the UAV in the H2 height layer; ③ calculate the pre-approach segment flight distance l 1, inversely calculate the time required from the height H2 to the last height layer, take the height layer interval 1000m, then the required time t 11 =1000 / the average glide rate of the UAV in the height layer, unit: s; calculate the flight distance d1 within the time t 11 , and judge the relationship between d1 and l 1; ④ if d1≥ l 1, stop calculating, the time t 11 is updated to t1= l 1 / the average ground speed of the UAV in the height layer; if d1 < 1000, continue to repeat the calculation in step ③ until the cumulative flight distance d2 is greater than or equal to 1000; ⑤ calculate the descent height of the straight flight segment according to the flight time t3, and inversely calculate the expected height H2 of the take-off and landing point P2 of the hovering segment. l 1, then continue to repeat the calculation in step ③ until the cumulative flight distance d2 is greater than or equal to 1000; ⑤ calculate the descent height of the straight flight segment according to the flight time t3, and inversely calculate the expected height H2 of the take-off and landing point P2 of the hovering segment. l 1; ⑤ calculate the descent height of the straight flight segment according to the flight time t3, and inversely calculate the expected height H2 of the take-off and landing point P2 of the hovering segment. 11

[0073] 3. Compare the expected height H 11 of the current UAV position P1 with the current UAV height H1, if H1≤H 11 , then H 11 =H1, that is, the height remains unchanged, at this time only through the pre-approach segment, the straight flight segment and the turn descent segment can meet the approach height requirement, the flight distance of the hovering descent segment is 0, and the calculation is no longer performed.

[0074] 4. Compare the expected height H 11 ​Given the drone's current altitude H1, if H1 > H 11 Then: ① The expected height H 11 The value remains unchanged; ② Take H 11 The average ground speed and average glide rate of the drone within the altitude layer; ③ Backward calculation from the expected altitude H 11 The time required to reach the next higher altitude is denoted as t. 21 The calculation method is the same as step ③ in step 2 above, and will not be repeated here; based on time t 21 Calculate from the expected height H 11 The flight distance to the next higher altitude is denoted as d. 21 And calculate the number of turns required for the UAV to descend with the minimum turning radius r, denoted as a1, and the calculation formula is the flight distance d. 21 / Minimum turning circumference; ④Continue to reverse the calculation until the altitude reaches the current altitude H1 of the drone, and obtain the cumulative time t2_tem and cumulative flight distance. l 11 1. The cumulative number of turns a0; at this time, the cumulative number of turns a0 is not an integer, so it is rounded to the nearest integer and recorded as a; 5. If 0 < (a0 - a) ≤ 0.5, then time compensation needs to be added to the cumulative time t2_tem. The compensation time is the time required to fly (a0 - a) turns at the minimum turning radius r below the H2 altitude layer, denoted as t. 20 At this point, t2 = t2_tem + t 20 If 0 < (a - a0) < 0.5, then time compensation needs to be subtracted from the cumulative time t2_tem. The compensation time is the time required for the UAV to fly (a - a0) circles with the minimum turning radius r at altitude H2, denoted as t. 20 At this point, t2 = t2_tem - t 20 ⑥ Perform height compensation in the H2 height layer, with a compensation height value of t. 20 Multiply the average glide rate of the drone within the H2 altitude layer, and update the altitude H2 of the starting and ending points of the hovering.

[0075] At this point, the time taken for the pre-approach segment, the circling and descent segment, the straight flight segment, and the turning and descent segment, calculated from the above process, is accumulated to obtain the total time taken for the approach segment, T1 = t4 + t3 + t2 + t1, and the final approach time is also calculated.

[0076] IV. Estimating the time taken for the approach phase:

[0077] ① Calculate the waiting flight distance of the UAV from approach point P4 and touchdown point P5. l 5. Thus, the time t5 taken for the drone to travel from the approach point to the landing point can be calculated. l5 / average speed of the approach segment of the UAV; ②reading the landing roll performance fitting data set in the UAV model data to obtain the average time t6 of the landing roll to the roll-off of the landing runway; ③calculating the time consumption T2 of the approach segment = t5+ t6, so as to obtain the final landing time and the final approach time of the UAV.

[0078] V. calculating the time consumption of the roll-off segment:

[0079] ①reading the model data of the UAV to obtain the average time t7 from entering the connecting channel to stopping in the taxiway; ②obtaining the time consumption T3 of the roll-off segment = t7, so as to obtain the completion time of the roll-off.

[0080] Step S4, accumulating the time consumption of each landing flight segment to obtain the total landing time of the UAV.

[0081] Specifically, the time consumption T1 of the approach segment, the time consumption T2 of the approach segment and the time consumption T3 of the roll-off segment calculated in the previous step S3 are accumulated to obtain the total landing time T = T1+ T2+ T3 of the UAV.

[0082] The fixed-wing UAV landing time prediction and calculation method of the embodiment of the present disclosure can calculate the UAV recovery time consumption in real time according to the recovery waypoint and the UAV performance simulation data set, can give the calculated arrival time of each waypoint, and can use the height of each waypoint to inversely calculate the time consumption of the height reduction, so that the user can master the UAV recovery time consumption in real time, provides prediction assistance for the landing time of the airport fixed-wing UAV, especially provides a time reference for the large-scale landing of the UAV airport, thereby helping the timeliness and economy of the UAV landing and recovery process.

[0083] As shown in FIG. 1, Figure 3 Another embodiment of the present disclosure provides a fixed-wing UAV landing time prediction and calculation system, which comprises:

[0084] The acquisition module 310 is configured to acquire the recovery waypoint coordinates and the model data of the UAV; wherein the recovery waypoint comprises the current position of the UAV, the hovering start and stop point, the FAF circle cut-in point, the approach point and the landing point, and the model data comprises the flight performance fitting data set, the landing roll performance fitting data set and the roll-off performance fitting data set;

[0085] The division module 320 is configured to divide the landing flight segments of the UAV according to the recovery waypoint coordinates, including the approach segment, the approach segment and the roll-off segment;

[0086] The calculation module 330 is configured to inversely calculate the time consumption of each landing flight segment according to the model data;

[0087] The accumulation module 340 is configured to accumulate the time consumption of each landing flight segment to obtain the total landing time of the UAV.

[0088] Specifically, the fixed-wing unmanned aerial vehicle landing time prediction calculation system of the embodiment of the present disclosure is used to implement the fixed-wing unmanned aerial vehicle landing time prediction calculation method described in the above embodiment, and the specific implementation process has been described in detail in the above embodiment, which will not be repeated here.

[0089] The fixed-wing unmanned aerial vehicle landing time prediction calculation system of the embodiment of the present disclosure can calculate the time consumption of the unmanned aerial vehicle in real time according to the recovery waypoints and the unmanned aerial vehicle performance simulation data set, can give the calculated arrival time of each waypoint, and can calculate the time consumption of the height reduction by using the height of each waypoint in reverse, so that the user can master the time consumption of the unmanned aerial vehicle in real time, and the present disclosure provides a prediction aid for the landing time of the fixed-wing unmanned aerial vehicle of the airport, especially provides a time reference for the large-scale landing of the unmanned aerial vehicle of the airport, thereby helping the timeliness and economy of the landing and recovery process of the unmanned aerial vehicle.

[0090] As shown in Figure 4 Another embodiment of the present disclosure provides an electronic device, comprising:

[0091] at least one processor 401; and a memory 402 connected with the at least one processor 401, for storing one or more programs, when the one or more programs are executed by the at least one processor 401, the at least one processor 401 can implement the fixed-wing unmanned aerial vehicle landing time prediction calculation method described above.

[0092] The memory 402 and the processor 401 are connected in a bus mode, the bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 401 and memories 402 together. The bus can also connect various other circuits such as peripheral devices, voltage stabilizers and power management circuits, which are well known in the art, and therefore, they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, which provide a unit for communicating with various other devices on the transmission medium. The data processed by the processor 401 is transmitted on the wireless medium through the antenna, and further, the antenna also receives data and transmits the data to the processor 401.

[0093] The processor 401 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management and other control functions. And the memory 402 can be used to store the data used by the processor 401 in the execution operation.

[0094] Still another embodiment of the present disclosure provides a computer readable storage medium storing a computer program, which, when executed by a processor, implements the fixed-wing unmanned aerial vehicle landing time prediction calculation method described above.

[0095] The computer readable storage medium can be included in the system and the electronic device of the present disclosure, or can exist independently.

[0096] The computer readable storage medium can be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. Specifically, the computer readable storage medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, an optical fiber, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0097] The computer readable storage medium can also include a data signal carried in a baseband or as part of a carrier wave. Specifically, the computer readable program code can be carried in a data signal that is an electromagnetic signal, an optical signal, or any suitable combination thereof.

[0098] It can be understood that the above embodiments are only exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Various modifications and improvements can be made by those of ordinary skill in the art without departing from the spirit and essence of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A method for predicting and calculating the landing time of a fixed-wing unmanned aerial vehicle (UAV), characterized in that, The method includes: Obtain the coordinates of the recovery waypoints and model data of the UAV; wherein, the recovery waypoints include the current position of the UAV, the starting and ending points of the hovering, the FAF circle entry point, the approach point and the touchdown point, and the model data includes the flight performance fitting dataset, the landing taxiing performance fitting dataset and the departure performance fitting dataset; The landing segment of the UAV is divided according to the coordinates of the recovery waypoint, including the approach segment, the approach phase, and the exit segment; wherein, the approach segment includes the pre-approach phase, the circling and descent phase, the straight flight phase, and the turning and descent phase in sequence. Based on the model data, the time consumption of each landing segment is calculated in reverse: specifically, based on the flight performance fitting dataset, the altitude and time consumption of the turning and descent segment are calculated; based on the current position of the UAV and the altitude of the turning and descent segment, the altitude and time consumption of the straight flight segment are calculated; based on the current position of the UAV and the altitude of the straight flight segment and the turning and descent segment, the time consumption of the pre-approach segment and the circling and descent segment are calculated; the time consumption of the pre-approach segment, the circling and descent segment, the straight flight segment, and the turning and descent segment are accumulated to obtain the time consumption of the approach segment. The total landing time of the UAV is obtained by summing the time spent on each landing segment.

2. The method according to claim 1, characterized in that, The step of calculating the altitude and time of the turning and descent phase based on the flight performance fitting dataset includes: Calculate the arc length of the FAF circle based on the coordinates of the recovery waypoint and the center coordinates of the FAF circle; Based on the FAF arc length and the flight performance fitting dataset, the time taken for the turning and altitude reduction phase is estimated. The height reduction of the turning and height reduction section is obtained based on the time taken for the turning and height reduction section.

3. The method according to claim 1, characterized in that, The calculation of the altitude descent and time for the straight flight phase based on the drone's current position and altitude, and the altitude descent during the turning and descent phase, includes: Calculate the flight distance of the straight flight segment based on the current position of the drone and the altitude of the turning and descent segment; Based on the flight distance of the direct flight segment and the flight performance fitting dataset, the flight time of the direct flight segment is estimated. The altitude of descent for the direct flight segment is obtained based on the time taken for the direct flight segment.

4. The method according to claim 1, characterized in that, The step of calculating the time consumption of the pre-approach phase and the circling and descent phase based on the current position and altitude of the UAV and the descent altitude of the straight flight phase and the turning and descent phase includes: The flight distance of the pre-approach segment is calculated based on the current position of the UAV and the altitude of the straight flight segment and the altitude drop segment. Based on the flight distance of the approach segment and the flight performance fitting dataset, the time consumption of the pre-approach segment is estimated; Based on the time consumed by the pre-entry section, the expected elevation drop of the pre-entry section is obtained; Based on the current position of the UAV, the expected altitude of the pre-approach segment, and the flight performance fitting dataset, calculate the number of circles required for the UAV to descend with the minimum turning radius; The number of revolutions is rounded to the nearest integer to obtain the required number of revolutions for the UAV in the hovering and descent phase. The time consumption of the circling and descending section is compensated based on the difference between the number of laps and the total number of laps. The descent height of the spiraling descent section is obtained based on the time compensation of the spiraling descent section.

5. The method according to any one of claims 1 to 4, characterized in that, The step of reverse-calculating the time consumption of each landing segment based on the model data includes: The flight distance of the UAV from the approach point to the touchdown point and the average time taken from the landing runway to exiting the landing runway are obtained. Based on the expected flight distance and the average speed of the UAV during the approach phase, the time taken for the UAV to travel from the approach point to the touchdown point is obtained; The approach phase time is obtained by accumulating the time taken from the approach point to the touchdown point and the average time taken from the landing runway to exiting the landing runway.

6. The method according to any one of claims 1 to 4, characterized in that, The step of reverse-calculating the time consumption of each landing segment based on the model data includes: The average time taken for the drone to travel from entering the connecting roadway to stopping on the taxiway is obtained to determine the time taken for the exit segment.

7. A system for predicting and calculating the landing time of a fixed-wing unmanned aerial vehicle (UAV), characterized in that, The system includes: The acquisition module is used to acquire the coordinates of the recovery waypoints and model data of the UAV; wherein, the recovery waypoints include the current position of the UAV, the hovering start and end points, the FAF circle entry point, the approach point and the touchdown point, and the model data includes flight performance fitting datasets, landing taxiing performance fitting datasets and departure performance fitting datasets; The segmentation module is used to divide the landing segment of the UAV according to the coordinates of the recovery waypoint, including the approach segment, the approach phase, and the exit segment; wherein, the approach segment includes the pre-approach phase, the circling and descent phase, the straight flight phase, and the turning and descent phase in sequence; The calculation module is used to reverse-calculate the time consumption of each landing segment based on the model data. Specifically, it calculates the altitude and time consumption of the turning and descent segment based on the flight performance fitting dataset; calculates the altitude and time consumption of the straight flight segment based on the current position of the UAV and the altitude of the turning and descent segment; calculates the time consumption of the pre-approach segment and the circling and descent segment based on the current position of the UAV and the altitude of the straight flight segment and the turning and descent segment; and accumulates the time consumption of the pre-approach segment, the circling and descent segment, the straight flight segment, and the turning and descent segment to obtain the time consumption of the approach segment. The cumulative module is used to accumulate the time spent on each landing segment to obtain the total landing time of the UAV.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor is used to store one or more programs that, when executed by the at least one processor, enable the at least one processor to implement the fixed-wing UAV landing time prediction calculation method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for predicting and calculating the landing time of a fixed-wing UAV as described in any one of claims 1 to 6.

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