Android-based intelligent machine nest control method, device and medium

Abstract

The invention discloses an Android-based intelligent machine nest control method, equipment and medium, which relate to the technical field of electric power inspection, and solve the problem of large position deviation when a current unmanned aerial vehicle (UAV) lands. The method comprises the following steps: calculating a landing safety index of a machine nest based on machine nest environment data and flight data, and judging whether the machine nest has landing conditions according to the landing safety index; performing attitude correction on the UAV according to flight attitude data and flight environment data; after the attitude correction is completed, guiding the UAV to land according to the current coordinates of a laser receiving point in the UAV in a three-dimensional coordinate system; after receiving a landing signal, an Android control terminal sends a landing command to the UAV, and at the same time the machine nest opens a hatch to obtain a landing platform for the UAV, and the UAV executes the landing command and lands on the landing platform. The invention realizes accurate and safe landing of the UAV through an intelligent machine nest.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power inspection, and specifically relates to an Android-based intelligent machine nest control method, device and medium. Background Art

[0002] The machine nest is an automated facility that provides charging, storage, maintenance and management functions for drones. It is usually used for regular inspection, charging, scheduling and data exchange of drones. The machine nest generally includes a landing platform, a charging system, a data transmission system, and the automatic control and monitoring functions of the drone. It provides a safe docking place for drones, which can automatically return and complete necessary maintenance work after the drone performs its mission, ensuring that the drone can efficiently and continuously perform tasks such as power inspection, environmental monitoring, logistics distribution, etc.

[0003] The Chinese invention patent application with application number 202111407743.7 proposes a method for accurately landing a UAV on a machine nest platform, and the method includes the following steps: after receiving a landing command, the UAV locates the machine nest platform through RTK; the UAV identifies the main identification code and the auxiliary identification code through the fasterrcnn target, and calculates and returns the position information of the main identification code and the auxiliary identification code; the position of the UAV is adjusted according to the position information, so that the UAV lands on the machine nest platform and the UAV's tripod is landing in the groove; the UAV uses its own gravity to slide the tripod to the center of the groove to achieve precise landing.

[0004] However, the above-mentioned prior art only relies on fasterrcnn target recognition to identify the main identification code and the auxiliary identification code, and uses RTK to locate the UAV. The accuracy of the main identification code and the auxiliary identification code cannot be guaranteed in bad weather, and the attitude of the UAV will fluctuate, thereby affecting the stability and safety of the UAV landing. In addition, obstacles will interfere with RTK positioning, resulting in a large position deviation when the UAV lands.

[0005] To this end, the present invention proposes an Android-based smart machine nest control method, device and medium. Summary of the invention

[0006] The purpose of the present invention is to propose an Android-based smart machine nest control method, device and medium to solve the problems raised in the above background technology.

[0007] The technical problems to be solved by the present invention are:

[0008] How to control the safe and accurate landing of drones through the smart nest.

[0009] In the first aspect, in order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0010] Smart machine nest control method based on Android, smart machine nest control method:

[0011] Step S1, obtaining the environment data of the drone nest corresponding to the parking area and the flight data of the drone;

[0012] Step S2, calculating the landing safety index of the nest based on the nest environment data and the flight data, and determining whether the nest has the landing conditions according to the landing safety index;

[0013] Step S3, the Android control terminal generates a data collection instruction and sends it to the drone. After receiving the data collection instruction, the drone obtains its own aircraft attitude data and the flight environment data of the drone's current position;

[0014] Step S4, performing attitude correction on the UAV according to the flight attitude data and the flight environment data;

[0015] Step S5, after the posture correction is completed, the UAV is guided to land according to the current coordinates of the laser receiving point in the three-dimensional coordinate system;

[0016] Step S6, after receiving the landing signal, the Android control terminal sends a landing command to the drone, and at the same time, the machine nest opens the hatch to obtain the landing platform of the drone, and the drone executes the landing command and lands on the landing platform.

[0017] Furthermore, the environment data of the machine nest is the real-time wind speed and the real-time wind direction of the machine nest in the parking area corresponding to the machine nest;

[0018] The flight data includes the real-time flight speed, target direction and maximum speed of the drone.

[0019] Furthermore, the step S2 includes the following sub-steps:

[0020] Step S21, a three-dimensional coordinate system is established with the ground as a plane, the point on the ground where the three-dimensional center of the machine nest is mapped is recorded as the origin, and the coordinates of the laser emission point in the landing platform corresponding to the machine nest in the three-dimensional coordinate system are obtained;

[0021] Step S22, calculating the inclination angle of the landing platform through the coordinates of the laser emission point, and determining the landing condition of the machine nest according to the inclination angle;

[0022] Step S23, obtaining the real-time wind speed JC of the aircraft nest corresponding to the parking area of ​​the aircraft nest, and obtaining the maximum safe wind speed threshold JCm when the UAV is landing;

[0023] When the real-time wind speed of the aircraft nest is greater than or equal to the maximum safe wind speed threshold for the drone to land, the Android control terminal sends a waiting landing command to the drone;

[0024] When the real-time wind speed of the aircraft nest is less than the maximum safe wind speed threshold for the drone to land, the wind speed safety index is calculated using the formula FS=1-(JC / JCm).

[0025] Furthermore, the step S2 further includes the following sub-steps:

[0026] Step S24, if the real-time wind direction of the machine nest corresponding to the parking area is opposite to the target travel direction of the UAV, the real-time wind speed of the machine nest is compared with the maximum travel speed of the UAV. When the real-time wind speed of the machine nest is greater than or equal to the maximum travel speed of the UAV, it is determined that the UAV does not have the landing conditions, and the Android control terminal sends a waiting landing command to the UAV; when the real-time wind speed of the machine nest is less than the maximum travel speed of the UAV, it is determined that the UAV has the landing conditions, and the value of the wind direction safety index FX is X2;

[0027] If the real-time wind direction of the nest corresponding to the parking area of ​​the nest is the same as the target travel direction of the UAV, the real-time wind speed of the nest is compared with the maximum travel speed of the UAV. When the real-time wind speed of the nest is greater than the maximum travel speed of the UAV, the UAV is judged to be unqualified for landing, and the Android control terminal sends a waiting for landing command to the UAV; when the real-time wind speed of the nest is less than or equal to the maximum travel speed of the UAV, the UAV is judged to be qualified for landing, and the value of the wind direction safety index is X2 at this time; wherein, X2>X1>1;

[0028] Step S25, the landing safety index AQ of the drone is calculated by a formula, and the specific formula is as follows:

[0029] AQ=a1×FS+a2×FX, where a1 and a2 are weight coefficients of fixed values, and a1>a2;

[0030] Step S26, when the landing safety index of the parking area corresponding to the machine nest is greater than the landing safety index threshold, proceed to the next step;

[0031] When the landing safety index of the parking area corresponding to the machine nest is less than or equal to the landing safety index threshold, it is determined that the drone does not have the landing conditions, and the Android control terminal sends a waiting landing command to the drone.

[0032] Furthermore, the calculation process of the tilt angle is specifically as follows:

[0033] Step S221, obtaining the coordinates of the laser emission point in the landing platform corresponding to the machine nest;

[0034] Step S222, calculating the distance between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane;

[0035] Step S223, if the distances between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane are the same, proceed to the next step;

[0036] If the distances between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane are different, the difference between the distances between the laser emission point coordinates is calculated. When the difference exceeds the set threshold, it is determined that the drone does not have the conditions for landing. The Android control terminal sends a waiting for landing command to the drone. When the difference does not exceed the set threshold, it proceeds to the next step.

[0037] Furthermore, the flight attitude data includes the real-time pitch angle, real-time roll angle, and real-time yaw angle of the drone;

[0038] The flight environment data includes the real-time wind speed and real-time wind direction of the drone at the current location of the drone.

[0039] Furthermore, the step S4 includes the following sub-steps:

[0040] Step S41, obtaining the real-time wind direction and real-time wind speed of the drone at the current position of the drone;

[0041] Step S42: if the real-time wind speed of the drone is greater than the real-time flight speed, the real-time wind direction of the drone at the current position of the drone is obtained. When the real-time wind direction of the drone is in the same direction as the correction direction of the drone, the correction amount is subtracted from the attitude adjustment amount to obtain the actual adjustment amount of the drone. When the real-time wind direction of the drone is in a different direction from the correction direction of the drone, the correction amount is added to the attitude adjustment amount to obtain the actual adjustment amount of the drone.

[0042] The process of obtaining the correction amount is as follows:

[0043] Step S421, subtracting the real-time flight speed from the real-time wind speed of the drone to obtain a speed deviation;

[0044] Step S422, setting a correction amount for attitude correction of the drone according to the speed deviation;

[0045] When the speed deviation ∈[Y1, Y2), the correction amount of attitude correction is M1;

[0046] When the speed deviation ∈[Y2, Y3), the correction amount of attitude correction is M2;

[0047] When the speed deviation ∈[Y3, Y4], the correction amount of the posture correction is M3; where Y1<Y2<Y3<Y4, M3>M2>M1;

[0048] Step S43, if the real-time wind speed of the UAV is less than or equal to the real-time flight speed, the UAV does not need to apply a correction amount;

[0049] Step S44, calculating the actual pitch angle MBF, the actual roll angle MBG and the actual yaw angle MBP of the UAV for attitude correction by formulas, and taking the actual pitch angle, the actual roll angle and the actual yaw angle of the UAV as attitude adjustment parameters;

[0050] The actual pitch angle formula of the drone is: MBF=FY±ΔD1, ΔD1 is the correction value of the pitch angle, and FY is the real-time pitch angle;

[0051] The actual roll angle formula of the drone is: MBG=GZ±ΔD2, ΔD2 is the correction value of the roll angle, and GZ is the real-time roll angle;

[0052] The actual yaw angle formula of the drone is: MBP=PH±ΔD3, ΔD3 is the correction value of the yaw angle, and PH is the real-time yaw angle;

[0053] Step S45, correcting the attitude of the drone according to the attitude adjustment parameters.

[0054] Furthermore, the step S5 includes the following sub-steps:

[0055] Step S51, obtaining the current coordinates of the laser receiving point on the drone in the three-dimensional coordinate system, and the laser emission point of the landing platform corresponds one to one with the laser receiving point of the drone;

[0056] Step S52, the UAV after posture correction is completed performs translation operation;

[0057] Step S53, when the drone reaches a fixed distance from the landing platform, the laser emission point emits laser, and if the four groups of laser receiving points all receive the laser emitted by the laser emission point, a landing signal is generated and sent to the Android control terminal;

[0058] If any two groups of laser receiving points do not receive the laser emitted by the laser emitting point, the UAV will re-guide the landing operation.

[0059] In a second aspect, a computer device is provided, the computer device comprising:

[0060] A memory storing a computer program;

[0061] The processor is communicatively connected with the memory, and when the computer program is executed by the processor, the intelligent machine nest control method is implemented.

[0062] In a third aspect, a computer-readable storage medium stores a computer program, which implements an intelligent machine nest control method when executed by a processor.

[0063] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0064] 1. The present invention calculates the landing safety index of the nest based on the nest environment data and flight data, determines whether the nest has the landing conditions according to the landing safety index, and collects the aircraft attitude data of the UAV and the flight environment data of the current position of the UAV to realize the attitude correction of the UAV;

[0065] 2. After the attitude correction of the UAV is completed, the present invention performs a guided landing operation on the UAV according to the current coordinates of the laser receiving point in the UAV in the three-dimensional coordinate system. The aircraft nest opens the hatch to obtain the landing platform of the UAV, and the UAV executes the landing command to land on the landing platform, thereby achieving precise landing of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0067] Figure 1 is a scene graph of the present invention;

[0068] Figure 2 This is a schematic diagram of the structure of the landing platform in the middle machine nest;

[0069] Figure 3 is a flow chart of the method of the present invention;

[0070] Figure 4 is a schematic diagram of the real-time pitch angle in the present invention;

[0071] Figure 5 is a schematic diagram of the real-time roll angle in the present invention;

[0072] Figure 6 is a schematic diagram of the real-time yaw angle in the present invention;

[0073] Figure 7 It is a schematic diagram of the structure of the computer device in the present invention. DETAILED DESCRIPTION

[0074] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] Example 1: Please refer to Figure 1-Figure 6 As shown, the technical solution provided by the present invention is: an intelligent machine nest control method based on Android, the method is as follows:

[0076] Step S1, obtaining the environment data of the drone nest corresponding to the parking area and the flight data of the drone;

[0077] It should be specifically noted that the nest environment data is the real-time wind speed and real-time wind direction of the nest corresponding to the parking area of ​​the nest, and the flight data is the real-time flight speed, target travel direction and maximum travel speed of the drone;

[0078] Specifically, the parking area is a circle with the machine nest as the center and a fixed length as the radius, which serves as the collection range of the machine nest environment data.

[0079] Step S2, calculating the landing safety index of the nest based on the nest environment data and the flight data, and determining whether the nest has the landing conditions according to the landing safety index;

[0080] In this embodiment, step S2 includes the following sub-steps:

[0081] Step S21, a three-dimensional coordinate system is established with the ground as a plane, the point on the ground where the three-dimensional center of the machine nest is mapped is recorded as the origin, and the coordinates of the laser emission point in the landing platform corresponding to the machine nest in the three-dimensional coordinate system are obtained;

[0082] It should be specifically stated that there are four sets of laser emitting points in the landing platform corresponding to the machine nest, and four sets of laser receiving points on the lower end of the drone. The laser emitting points are used to emit lasers that can be received by the laser receiving points;

[0083] In practice, the east direction can be taken as the X-axis, the north direction as the Y-axis, the plane formed by the X-axis and the Y-axis as the horizontal plane (ground), and the direction perpendicular to the horizontal plane (ground) formed by the X-axis and the Y-axis as the Z-axis. Users or staff can establish the coordinate axis directions according to their own needs.

[0084] Step S22, calculating the inclination angle of the landing platform through the coordinates of the laser emission point, and determining the landing condition of the machine nest according to the inclination angle;

[0085] The calculation process of the tilt angle is specifically as follows:

[0086] Step S221, obtaining the coordinates of the laser emission point in the landing platform corresponding to the machine nest;

[0087] Step S222, calculating the distance between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane;

[0088] Step S223, if the distances between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane are the same, proceed to the next step;

[0089] If the distances between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane are different, the difference between the distances between the laser emission point coordinates is calculated. When the difference exceeds the set threshold, it is determined that the drone does not have the conditions for landing. The Android control terminal sends a waiting for landing command to the drone. When the difference does not exceed the set threshold, it proceeds to the next step.

[0090] Step S23, calculating the wind speed safety index FS for the drone to land on the machine nest;

[0091] Get the real-time wind speed JC of the drone nest in the parking area corresponding to the drone nest, and get the maximum safe wind speed threshold JCm when the drone lands;

[0092] When the real-time wind speed of the aircraft nest is greater than or equal to the maximum safe wind speed threshold for the drone to land, the wind speed safety index is zero, and the Android control terminal sends a waiting landing command to the drone;

[0093] When the real-time wind speed of the aircraft nest is less than the maximum safe wind speed threshold for the drone to land, the wind speed safety index is calculated using the calculation formula. The calculation formula is as follows:

[0094] FS=1-(JC / JCm);

[0095] Step S24, obtaining a wind direction safety index FX for the drone to land on the nest based on the nest environment data of the parking area corresponding to the nest and the flight data of the drone;

[0096] Specifically, if the real-time wind direction of the machine nest corresponding to the parking area is opposite to the target travel direction of the UAV, the real-time wind speed of the machine nest is compared with the maximum travel speed of the UAV. When the real-time wind speed of the machine nest is greater than or equal to the maximum travel speed of the UAV, it is determined that the UAV does not have the landing conditions, and the Android control terminal sends a waiting landing command to the UAV. At this time, the wind direction safety index is zero; when the real-time wind speed of the machine nest is less than the maximum travel speed of the UAV, it is determined that the UAV has the landing conditions, and the value of the wind direction safety index FX is X2;

[0097] If the real-time wind direction of the nest corresponding to the parking area of ​​the nest is the same as the target travel direction of the UAV, the real-time wind speed of the nest is compared with the maximum travel speed of the UAV. When the real-time wind speed of the nest is greater than the maximum travel speed of the UAV, the UAV is judged to be unqualified for landing, and the Android control terminal sends a waiting for landing command to the UAV. At this time, the wind direction safety index is zero; when the real-time wind speed of the nest is less than or equal to the maximum travel speed of the UAV, the UAV is judged to be qualified for landing, and the value of the wind direction safety index is X2; wherein, X2>X1>1;

[0098] Step S25, according to the wind speed safety index FS and the wind direction safety index FX, the landing safety index AQ of the drone is calculated by the formula, and the specific formula is as follows:

[0099] AQ=a1×FS+a2×FX, where a1 and a2 are weight coefficients of fixed values, and a1>a2;

[0100] Step S26, when the landing safety index of the parking area corresponding to the machine nest is greater than the landing safety index threshold, proceed to step S3;

[0101] When the landing safety index of the parking area corresponding to the machine nest is less than or equal to the landing safety index threshold, it is determined that the drone does not have the landing conditions, and the Android control terminal sends a waiting landing command to the drone.

[0102] Step S3, the Android control terminal generates a data collection instruction and sends it to the drone. After receiving the data collection instruction, the drone obtains its own aircraft attitude data and the flight environment data of the drone's current position;

[0103] Specifically, the flight attitude data includes the real-time pitch angle, real-time roll angle, and real-time yaw angle of the drone, and the flight environment data includes the real-time wind speed and real-time wind direction of the drone at the current location of the drone;

[0104] It needs to be explained that, with the target travel direction of the UAV as the positive direction, the real-time pitch angle of the UAV is the up and down tilt angle between the nose of the UAV and the horizontal plane, the real-time roll angle is the left and right tilt angle between the two sides of the UAV and the horizontal plane, and the real-time yaw angle is the angle between the current travel direction of the UAV and the target travel direction.

[0105] Step S4, performing attitude correction on the UAV according to the flight attitude data and the flight environment data;

[0106] In this embodiment, step S4 includes the following sub-steps:

[0107] Step S41, obtaining the real-time wind direction and real-time wind speed of the drone at the current position of the drone;

[0108] Step S42: if the real-time wind speed of the drone is greater than the real-time flight speed, the real-time wind direction of the drone at the current position of the drone is obtained. When the real-time wind direction of the drone is in the same direction as the correction direction of the drone, the correction amount is subtracted from the attitude adjustment amount to obtain the actual adjustment amount of the drone. When the real-time wind direction of the drone is in a different direction from the correction direction of the drone, the correction amount is added to the attitude adjustment amount to obtain the actual adjustment amount of the drone.

[0109] In this embodiment, the correction amount obtaining process is specifically as follows:

[0110] Step S421, subtracting the real-time flight speed from the real-time wind speed of the drone to obtain a speed deviation;

[0111] Step S422, setting a correction amount for attitude correction of the drone according to the speed deviation;

[0112] When the speed deviation ∈[Y1, Y2), the correction amount of attitude correction is M1;

[0113] When the speed deviation ∈[Y2, Y3), the correction amount of attitude correction is M2;

[0114] When the speed deviation ∈[Y3, Y4], the correction amount of the posture correction is M3; where Y1<Y2<Y3<Y4, M3>M2>M1;

[0115] Step S43, if the real-time wind speed of the UAV is less than or equal to the real-time flight speed, the UAV does not need to apply a correction amount;

[0116] For example, if the two sides of the drone are tilted with the east lower and the west higher, and the real-time wind direction of the drone is east wind, then the real-time wind direction of the drone is in the same direction as the correction direction of the drone. If the two sides of the drone are tilted with the east lower and the west higher, and the real-time wind direction of the drone is westerly, then the real-time wind direction of the drone is in different directions from the correction direction of the drone.

[0117] Step S44, calculating the actual pitch angle MBF, the actual roll angle MBG and the actual yaw angle MBP of the UAV for attitude correction by formulas, and taking the actual pitch angle, the actual roll angle and the actual yaw angle of the UAV as attitude adjustment parameters;

[0118] Specifically, the actual pitch angle formula of the drone is: MBF=FY±ΔD1, ΔD1 is the correction value of the pitch angle, and FY is the real-time pitch angle;

[0119] The actual roll angle formula of the drone is: MBG=GZ±ΔD2, ΔD2 is the correction value of the roll angle, and GZ is the real-time roll angle;

[0120] The actual yaw angle formula of the drone is: MBP=PH±ΔD3, ΔD3 is the correction value of the yaw angle, and PH is the real-time yaw angle;

[0121] Step S45, correcting the attitude of the drone according to the attitude adjustment parameters.

[0122] Step S5, after the posture correction is completed, the UAV is guided to land according to the current coordinates of the laser receiving point in the three-dimensional coordinate system;

[0123] Step S51, obtaining the current coordinates of the laser receiving point on the drone in the three-dimensional coordinate system, and the laser emission point of the landing platform corresponds one to one with the laser receiving point of the drone;

[0124] In this embodiment, the drone is a completely symmetrical object, that is, left-right symmetry and front-back symmetry, so the coordinates of any laser receiving point can correspond to the coordinates of any laser emitting point;

[0125] Step S52, the UAV after posture correction is completed performs translation operation;

[0126] Step S53, when the drone reaches a fixed distance from the landing platform, the laser emission point emits laser, and if the four groups of laser receiving points all receive the laser emitted by the laser emission point, a landing signal is generated and sent to the Android control terminal;

[0127] If any two groups of laser receiving points do not receive the laser emitted by the laser emitting point, the UAV will re-guide the landing operation.

[0128] Step S6, after receiving the landing signal, the Android control terminal sends a landing command to the drone, and at the same time, the machine nest opens the hatch to obtain the landing platform of the drone, and the drone executes the landing command and lands on the landing platform;

[0129] In practice, when the drone lands on the landing platform, the nest can close the hatch;

[0130] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0131] Embodiment 2: Figure 7The structural diagram of a computer device is illustrated, and the computer device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The processor may call the logic instructions in the memory to execute an intelligent machine nest control method based on Android, and the method includes: obtaining the machine nest environment data of the parking area corresponding to the machine nest and the flight data of the unmanned aerial vehicle; calculating the landing safety index of the machine nest based on the machine nest environment data and the flight data, and judging whether the machine nest has the landing conditions according to the landing safety index; the Android control terminal generates a data acquisition instruction and sends it to the unmanned aerial vehicle, and the unmanned aerial vehicle obtains its own aircraft attitude data and the flight environment data of the current position of the unmanned aerial vehicle after receiving the data acquisition instruction; the attitude of the unmanned aerial vehicle is corrected according to the flight attitude data and the flight environment data; after the attitude correction is completed, the unmanned aerial vehicle is guided to land according to the current coordinates of the laser receiving point in the unmanned aerial vehicle in the three-dimensional coordinate system; after receiving the landing signal, the Android control terminal sends a landing instruction to the unmanned aerial vehicle, and at the same time, the machine nest opens the hatch to obtain the landing platform of the unmanned aerial vehicle, and the unmanned aerial vehicle executes the landing instruction and lands on the landing platform.

[0132] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0133] Embodiment 3: The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the Android-based intelligent machine nest control method provided by the above methods, the method including: obtaining the machine nest environment data of the parking area corresponding to the machine nest and the flight data of the drone; calculating the landing safety index of the machine nest based on the machine nest environment data and the flight data, and determining whether the machine nest has the landing conditions according to the landing safety index; the Android control terminal generates a data acquisition instruction and sends it to the drone, and after receiving the data acquisition instruction, the drone obtains its own aircraft attitude data and the flight environment data of the current position of the drone; performs attitude correction on the drone according to the flight attitude data and the flight environment data; after the attitude correction is completed, guides the drone to land according to the current coordinates of the laser receiving point in the drone in the three-dimensional coordinate system; after receiving the landing signal, the Android control terminal sends a landing instruction to the drone, and at the same time the machine nest opens the hatch to obtain the landing platform of the drone, and the drone executes the landing instruction and lands on the landing platform.

[0134] Embodiment 4: The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the above-mentioned Android-based intelligent machine nest control method, the method comprising: obtaining machine nest environmental data of the parking area corresponding to the machine nest and flight data of the drone; calculating a landing safety index of the machine nest based on the machine nest environmental data and the flight data, and determining whether the machine nest has landing conditions according to the landing safety index; an Android control terminal generates a data acquisition instruction and sends it to the drone, and after receiving the data acquisition instruction, the drone obtains its own aircraft attitude data and flight environment data of the current position of the drone; performs attitude correction on the drone according to the flight attitude data and the flight environment data; after the attitude correction is completed, performs a guided landing operation on the drone according to the current coordinates of the laser receiving point in the drone in the three-dimensional coordinate system; after receiving the landing signal, the Android control terminal sends a landing instruction to the drone, and at the same time the machine nest opens the hatch to obtain the landing platform of the drone, and the drone executes the landing instruction and lands on the landing platform.

[0135] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0136] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. The method for controlling an intelligent machine nest based on Android is characterized in that: Intelligent machine nest control method: Step S1, obtaining the environment data of the drone nest corresponding to the parking area and the flight data of the drone; Step S2, calculating the landing safety index of the nest based on the nest environment data and the flight data, and determining whether the nest has the landing conditions according to the landing safety index; Step S3, the Android control terminal generates a data collection instruction and sends it to the drone. After receiving the data collection instruction, the drone obtains its own aircraft attitude data and the flight environment data of the drone's current position; Step S4, performing attitude correction on the UAV according to the flight attitude data and the flight environment data; Step S5, after the posture correction is completed, the UAV is guided to land according to the current coordinates of the laser receiving point in the three-dimensional coordinate system; Step S6, after receiving the landing signal, the Android control terminal sends a landing command to the drone, and at the same time, the machine nest opens the hatch to obtain the landing platform of the drone, and the drone executes the landing command and lands on the landing platform.

2. The method for controlling an Android-based smart machine nest according to claim 1, characterized in that: The environment data of the machine nest is the real-time wind speed and wind direction of the machine nest in the parking area corresponding to the machine nest; The flight data includes the real-time flight speed, target direction and maximum speed of the drone.

3. The method for controlling an Android-based smart machine nest according to claim 2, characterized in that: The step S2 includes the following sub-steps: Step S21, a three-dimensional coordinate system is established with the ground as a plane, the point on the ground where the three-dimensional center of the machine nest is mapped is recorded as the origin, and the coordinates of the laser emission point in the landing platform corresponding to the machine nest in the three-dimensional coordinate system are obtained; Step S22, calculating the inclination angle of the landing platform through the coordinates of the laser emission point, and determining the landing condition of the machine nest according to the inclination angle; Step S23, obtaining the real-time wind speed JC of the aircraft nest corresponding to the parking area of ​​the aircraft nest, and obtaining the maximum safe wind speed threshold JCm when the UAV is landing; When the real-time wind speed of the aircraft nest is greater than or equal to the maximum safe wind speed threshold for the drone to land, the Android control terminal sends a waiting landing command to the drone; When the real-time wind speed of the aircraft nest is less than the maximum safe wind speed threshold for the drone to land, the wind speed safety index is calculated using the formula FS=1-(JC / JCm).

4. The method for controlling an Android-based smart machine nest according to claim 3, characterized in that: The step S2 further comprises the following sub-steps: Step S24, if the real-time wind direction of the machine nest corresponding to the parking area is opposite to the target travel direction of the UAV, the real-time wind speed of the machine nest is compared with the maximum travel speed of the UAV. When the real-time wind speed of the machine nest is greater than or equal to the maximum travel speed of the UAV, it is determined that the UAV does not have the landing conditions, and the Android control terminal sends a waiting landing command to the UAV; when the real-time wind speed of the machine nest is less than the maximum travel speed of the UAV, it is determined that the UAV has the landing conditions, and the value of the wind direction safety index FX is X2; If the real-time wind direction of the nest corresponding to the parking area of ​​the nest is the same as the target travel direction of the UAV, the real-time wind speed of the nest is compared with the maximum travel speed of the UAV. When the real-time wind speed of the nest is greater than the maximum travel speed of the UAV, the UAV is judged to be unqualified for landing, and the Android control terminal sends a waiting for landing command to the UAV; when the real-time wind speed of the nest is less than or equal to the maximum travel speed of the UAV, the UAV is judged to be qualified for landing, and the value of the wind direction safety index is X2 at this time; wherein, X2>X1>1; Step S25, the landing safety index AQ of the drone is calculated by a formula, and the specific formula is as follows: AQ=a1×FS+a2×FX, where a1 and a2 are weight coefficients of fixed values, and a1>a2; Step S26, when the landing safety index of the parking area corresponding to the machine nest is greater than the landing safety index threshold, proceed to the next step; When the landing safety index of the parking area corresponding to the machine nest is less than or equal to the landing safety index threshold, it is determined that the drone does not have the landing conditions, and the Android control terminal sends a waiting landing command to the drone.

5. The method for controlling an Android-based smart machine nest according to claim 3, characterized in that: The calculation process of the tilt angle is as follows: Step S221, obtaining the coordinates of the laser emission point in the landing platform corresponding to the machine nest; Step S222, calculating the distance between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane; Step S223, if the distances between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane are the same, proceed to the next step; If the distances between the Z-axis coordinates of all laser emission point coordinates and the horizontal plane are different, the difference between the distances between the laser emission point coordinates is calculated. When the difference exceeds the set threshold, it is determined that the drone does not have the conditions for landing. The Android control terminal sends a waiting for landing command to the drone. When the difference does not exceed the set threshold, it proceeds to the next step.

6. The method for controlling an Android-based smart machine nest according to claim 4, characterized in that: The flight attitude data includes the real-time pitch angle, real-time roll angle and real-time yaw angle of the drone; The flight environment data includes the real-time wind speed and real-time wind direction of the drone at the current location of the drone.

7. The method for controlling an Android-based smart machine nest according to claim 6, characterized in that: The step S4 includes the following sub-steps: Step S41, obtaining the real-time wind direction and real-time wind speed of the drone at the current position of the drone; Step S42: if the real-time wind speed of the drone is greater than the real-time flight speed, the real-time wind direction of the drone at the current position of the drone is obtained. When the real-time wind direction of the drone is in the same direction as the correction direction of the drone, the correction amount is subtracted from the attitude adjustment amount to obtain the actual adjustment amount of the drone. When the real-time wind direction of the drone is in a different direction from the correction direction of the drone, the correction amount is added to the attitude adjustment amount to obtain the actual adjustment amount of the drone. The process of obtaining the correction amount is as follows: Step S421, subtracting the real-time flight speed from the real-time wind speed of the drone to obtain a speed deviation; Step S422, setting a correction amount for attitude correction of the drone according to the speed deviation; When the speed deviation ∈[Y1, Y2), the correction amount of attitude correction is M1; When the speed deviation ∈[Y2, Y3), the correction amount of attitude correction is M2; When the speed deviation ∈[Y3, Y4], the correction amount of the posture correction is M3; where Y1<Y2<Y3<Y4, M3>M2>M1; Step S43, if the real-time wind speed of the UAV is less than or equal to the real-time flight speed, the UAV does not need to apply a correction amount; Step S44, calculating the actual pitch angle MBF, the actual roll angle MBG and the actual yaw angle MBP of the UAV for attitude correction by formulas, and taking the actual pitch angle, the actual roll angle and the actual yaw angle of the UAV as attitude adjustment parameters; The actual pitch angle formula of the drone is: MBF=FY±ΔD1, ΔD1 is the correction value of the pitch angle, and FY is the real-time pitch angle; The actual roll angle formula of the drone is: MBG=GZ±ΔD2, ΔD2 is the correction value of the roll angle, and GZ is the real-time roll angle; The actual yaw angle formula of the drone is: MBP=PH±ΔD3, ΔD3 is the correction value of the yaw angle, and PH is the real-time yaw angle; Step S45, correcting the attitude of the drone according to the attitude adjustment parameters.

8. The method for controlling an Android-based smart machine nest according to claim 7, characterized in that: The step S5 includes the following sub-steps: Step S51, obtaining the current coordinates of the laser receiving point on the drone in the three-dimensional coordinate system, and the laser emission point of the landing platform corresponds one to one with the laser receiving point of the drone; Step S52, the UAV after posture correction is completed performs translation operation; Step S53, when the drone reaches a fixed distance from the landing platform, the laser emission point emits laser, and if the four groups of laser receiving points all receive the laser emitted by the laser emission point, a landing signal is generated and sent to the Android control terminal; If any two groups of laser receiving points do not receive the laser emitted by the laser emitting point, the UAV will re-guide the landing operation.

9. A computer device, characterized in that: The computer device comprises: A memory storing a computer program; A processor is communicatively connected to the memory, and when the computer program is executed by the processor, the method described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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