UAV emergency control method and system for multi-airport collaborative inspection

Abstract

The present invention provides a drone emergency control method and system for coordinated multi-airport inspections, belonging to the field of drone inspection technology. When a drone or airport loses network signal, an ad hoc network can be automatically selected based on network signal strength to enable real-time information transmission between the drone and the airport, ensuring timely and effective transmission of inspection data. After a drone loses connection, the system can control the drone and determine the optimal emergency landing airport based on the drone's battery level, flight altitude, and other real-time conditions, as well as airport conditions, enabling emergency recovery of the drone upon return to the airport, thus maximizing drone flight safety.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) inspection technology, and in particular to an emergency control method and system for unmanned aerial vehicle (UAV) for coordinated inspection of multiple airports. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] During autonomous inspections, drones use airborne communication modules to transmit real-time video data and location information over long distances, as well as remotely control the drone. Considering the flight safety of the drone during route execution, when the drone's network signal is weak or poor, the drone executes the preset action and returns to the take-off point. If there is a persistent lack of network signal, the drone's positioning may jump during flight, causing the drone's position to drift, ultimately leading to the drone crashing or being lost.

[0004] Under current circumstances, most drones can safely return to their take-off points and land at airports. However, there are also cases where drones fly long distances during their return, causing their positions to drift or colliding with other drones or obstacles, leading to the drones exploding. Since it is impossible to obtain the drone's real-time position information, it is difficult to determine the drone's final position. This increases the difficulty of emergency recovery control of drones and increases the workload for airport and drone maintenance.

[0005] After a drone loses contact, regardless of whether the current drone situation allows it to complete the entire inspection mission, it can only exit the preset route and return to the takeoff point and land at the airport according to the preset actions. However, the inspection mission is not fully completed, and the data required for the inspection mission cannot be obtained. If the interrupted inspection mission needs to be completed, the inspection mission needs to be reissued, and the drone will have to perform the inspection mission again. The collected data also needs to be recollected, resulting in repeated inspection operations, low overall inspection efficiency, and waste of drone resources. Moreover, if the drone's position drifts, it will be impossible to intervene and control the drone to land safely, which can easily cause the drone to fall, causing damage to the drone or secondary injuries. At the same time, the drone's position information cannot be obtained, making it more difficult to locate the drone. Summary of the Invention

[0006] In order to address the shortcomings of the existing technology, the present invention provides a drone emergency control method and system for collaborative inspections at multiple airports. When there is no network signal at the drone or the airport, the self-organizing network can be automatically selected according to the network signal strength to realize real-time information feedback between the drone and the airport, thereby ensuring timely and effective feedback of inspection data. After the drone is disconnected, the drone can be controlled and the optimal emergency landing airport can be determined based on the real-time conditions such as the drone's battery level and flight altitude and the airport conditions, so that the drone can be returned to the airport for emergency recovery, thereby maximizing the flight safety of the drone.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a drone emergency control method for collaborative inspection of multiple airports.

[0009] A method for emergency control of drones for coordinated inspections at multiple airports. Both drones and drone airports are equipped with corresponding self-organizing network modules, including the following process:

[0010] For any UAV, real-time detection of the set of self-organizing network modules that can achieve network connection;

[0011] If the network signal strength of the current network connection is lower than the set threshold, all ad hoc network module sets are traversed, and the ad hoc network module corresponding to the maximum network signal strength in the ad hoc network module set is selected for emergency ad hoc network connection, wherein the maximum network signal strength is greater than or equal to the set threshold.

[0012] As a further limitation of the first aspect of the present invention, the emergency control method further includes a multi-objective optimization process, including:

[0013] Multi-objective optimization of UAV emergency control is performed with the minimum UAV power consumption, the lowest flight altitude and the shortest flight distance as the multi-objective function. The multi-objective function is solved, and the obtained solution space is processed to obtain the UAV inspection control strategy.

[0014] As a further limitation of the first aspect of the present invention, solving the multi-objective function and processing the obtained solution space to obtain the inspection control strategy of the UAV includes:

[0015] According to all the constraints that affect the UAV's return to the airport, the evaluation condition matrix of the solution space is constructed;

[0016] According to the evaluation condition matrix of the solution space, a value is assigned to each solution in the solution space, and the number of all airports that meet the landing requirements is recorded as k , the scoring results of all airports are recorded as , the evaluation function is recorded as ,but ,j Indicates the j Constraints

[0017] ;

[0018] in, , represents the weighted evaluation value corresponding to a series of constraints such as drone battery power, flight distance, flight altitude, airport distance, network signal strength, etc., where the weight value of each constraint is determined by the actual situation;

[0019] According to the scoring results, the scoring values of all solutions in the solution space are traversed, and after the search traversal is completed, the solution with the highest scoring value is selected as the optimal solution;

[0020] If the optimal solution is the drone's takeoff airport, there is no need to intervene in the drone's flight control. The drone will return and land at the takeoff airport according to the preset trajectory and actions.

[0021] If the optimal solution is not the take-off airport, the system will automatically and remotely issue a command to recover the drone based on the latest determined landing airport, and control the drone to land at the latest determined landing airport to complete the emergency recovery of the drone.

[0022] As a further limitation of the first aspect of the present invention, all constraints that affect the drone's return to the airport include: network signal strength, distance from the airport, altitude from other drones, current drone battery power, whether there are drones in the pre-selected landing airport, and whether the airport models are the same.

[0023] In a second aspect, the present invention provides a drone emergency control system for collaborative inspections at multiple airports.

[0024] A drone emergency control system for multi-airport collaborative inspection, where drones and drone airports are equipped with corresponding self-organizing network modules, including:

[0025] The real-time signal detection unit is configured to: for any UAV, detect in real time the set of self-organizing network modules that can achieve network connection;

[0026] The emergency self-organizing network unit is configured as follows: if the network signal strength of the current network connection is lower than the set threshold, it traverses all self-organizing network module sets and selects the self-organizing network module corresponding to the maximum network signal strength in the self-organizing network module set for self-organizing network connection, wherein the maximum network signal strength is greater than or equal to the set threshold.

[0027] As a further limitation of the second aspect of the present invention, the emergency control method further includes a multi-objective optimization process, including:

[0028] Multi-objective optimization of UAV emergency control is performed with the minimum UAV power consumption, the lowest flight altitude and the shortest flight distance as the multi-objective function. The multi-objective function is solved, and the obtained solution space is processed to obtain the UAV inspection control strategy.

[0029] As a further limitation of the second aspect of the present invention, solving the multi-objective function and processing the obtained solution space to obtain the inspection control strategy of the UAV includes:

[0030] According to all the constraints that affect the UAV's return to the airport, the evaluation condition matrix of the solution space is constructed;

[0031] According to the evaluation condition matrix of the solution space, a value is assigned to each solution in the solution space, and the number of all airports that meet the landing requirements is recorded as k , the scoring results of all airports are recorded as , the evaluation function is recorded as ,but , j Indicates the j Constraints

[0032] According to the scoring results, the scoring values of all solutions in the solution space are traversed, and after the search traversal is completed, the solution with the highest scoring value is selected as the optimal solution;

[0033] If the optimal solution is the drone's takeoff airport, there is no need to intervene in the drone's flight control. The drone will return and land at the takeoff airport according to the preset trajectory and actions.

[0034] If the optimal solution is not the take-off airport, the system will automatically and remotely issue a command to recover the drone based on the latest determined landing airport, and control the drone to land at the latest determined landing airport to complete the emergency recovery of the drone.

[0035] In a third aspect, the present invention provides a computer device comprising: a processor and a computer-readable storage medium;

[0036] a processor adapted to execute a computer program;

[0037] A computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by the processor, implements the drone emergency control method for multi-airport collaborative inspection as described in the first aspect of the present invention.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the drone emergency control method for multi-airport collaborative inspection as described in the first aspect of the present invention.

[0039] In a fifth aspect, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the drone emergency control method for multi-airport collaborative inspection as described in the first aspect of the present invention.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The present invention innovatively proposes an emergency control method for drones for collaborative inspections at multiple airports. If the network signal strength of the current network connection is lower than the set threshold, all ad hoc network module sets are traversed, and the ad hoc network module corresponding to the maximum network signal strength in the ad hoc network module set is selected for emergency ad hoc network connection. The ad hoc network is automatically selected to realize real-time information feedback between the drone and the airport, ensuring that the inspection data is transmitted in a timely and effective manner.

[0042] 2. The present invention innovatively proposes a drone emergency control method for multi-airport collaborative inspection. After the drone is disconnected, the drone can be controlled and the optimal emergency landing airport can be determined based on the real-time conditions such as the drone's battery level and flight altitude and the airport conditions. The emergency recovery of the drone back to the airport is realized, thus maximizing the flight safety of the drone.

[0043] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0045] Figure 1 A schematic flow chart of the emergency control method for drones in multi-airport collaborative inspections provided in Example 1 of the present invention;

[0046] Figure 2 This is a schematic diagram of the principle of a drone emergency control system for multi-airport collaborative inspections provided by Example 2 of the present invention;

[0047] Figure 3 A schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0050] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0051] Example 1:

[0052] This implementation method proposes a drone emergency control method for multi-airport collaborative inspection. Each drone and each airport is equipped with a self-organizing network module. When each drone performs an inspection task, it can automatically form a network with the airport or drones within the inspection range, and realize the connection and change of self-organizing network nodes through the network signal strength constraint condition, and report the data information during the task execution in real time through the network to avoid the loss of connection between the drone and the airport control center; at the same time, when the drone flies abnormally, the network nodes are interconnected through the self-organizing network to ensure the normal network communication of the drone. According to the constraints such as the drone power, flight altitude, flight distance and the airport, the best landing airport is obtained, and the emergency landing at the nearest airport is realized to complete the safe recovery of the drone.

[0053] Specifically, the process includes the following:

[0054] S1: UAV self-organizing network.

[0055] S1.1: Determine the self-organizing network modules that need to be connected, and record the total number of each self-organizing network module as num , real-time detection of the self-organizing network module signal that can realize network connection, and record it as ,in ;

[0056] S1.2: Set the threshold of network signal to If the current network signal strength is lower than , then traverse all modules S and select The modules corresponding to the maximum value of are connected in an ad hoc network to realize the connection between the UAV and the control center, and the real-time feedback of the UAV and airport information and the issuance of control instructions from the control center are realized through the network;

[0057] S1.3: If after traversal, the maximum value is still less than , the existing connection is maintained and waits for connection. If there is still no network signal that meets the conditions after the set time, the drone will perform the preset action and return to the route.

[0058] S2: When a drone is recovered in an emergency, determining the best landing airport mainly involves the following steps:

[0059] S2.1: Determine the control objectives for the emergency recovery of the UAV, namely, the minimum power consumption, the lowest flight altitude, and the shortest flight distance. This involves constructing a multi-objective optimization function for the minimum power consumption, the lowest flight altitude, and the shortest flight distance.

[0060] S2.2: Analyze the factors that affect the drone's return to the airport as constraints, including network signal strength, distance from the airport, altitude from other drones, current drone battery level, whether there are drones at the pre-selected landing airport, and whether the airport model is the same; based on all factors that affect the drone's return to the airport, construct an evaluation condition matrix for the solution space , The form is as follows:

[0061] (1);

[0062] in, Indicates landing k The constraints corresponding to the landing airports.

[0063] in, It is expressed as follows:

[0064] (2);

[0065] in represents the jth constraint corresponding to the kth airport.

[0066] S2.3: Construct the solution space. According to the evaluation condition matrix of the solution space in S2.2 , design an evaluation function to measure the quality of the solution, and assign a value to each solution in the above solution space, record the number of all airports that meet the landing requirements as k, and record the scoring results of all airports as , the evaluation function is recorded as , we get:

[0067] (3);

[0068] (4);

[0069] in, , represents the weighted evaluation value corresponding to the above series of constraints, such as drone battery power, flight distance, flight altitude, airport distance, network signal strength, etc. The weighted value of each constraint is determined by the actual situation.

[0070] According to the scoring results calculated in formula (3), traverse the solution space The evaluation values of all solutions are calculated, and the solution with the highest evaluation value is selected as the optimal solution after the search traversal. If the optimal solution is the take-off airport of the UAV, there is no need to intervene in the flight control of the UAV. The UAV returns and lands at the take-off airport according to the preset trajectory and action. If the optimal solution is not the take-off airport, the command is automatically issued remotely based on the determined landing airport to recover the UAV, and the UAV is controlled to land at the determined airport to complete the emergency recovery of the UAV.

[0071] Example 2:

[0072] like Figure 2 As shown, this implementation provides a drone emergency control system for multi-airport collaborative inspection. Both drones and drone airports are equipped with corresponding self-organizing network modules, including:

[0073] The real-time signal detection unit is configured to: for any UAV, detect in real time the set of self-organizing network modules that can achieve network connection;

[0074] The emergency self-organizing network unit is configured as follows: if the network signal strength of the current network connection is lower than the set threshold, it traverses all self-organizing network module sets and selects the self-organizing network module corresponding to the maximum network signal strength in the self-organizing network module set for self-organizing network connection, wherein the maximum network signal strength is greater than or equal to the set threshold.

[0075] In this implementation, preferably, a multi-objective optimization unit is further included, and the multi-objective optimization unit is configured to:

[0076] Multi-objective optimization of UAV emergency control is performed with the minimum UAV power consumption, the lowest flight altitude and the shortest flight distance as the multi-objective function. The multi-objective function is solved, and the obtained solution space is processed to obtain the UAV inspection control strategy.

[0077] In this implementation, preferably, based on all the constraints that affect the drone's return to the airport, an evaluation condition matrix of the solution space is constructed. According to the evaluation condition matrix of the solution space, a numerical value is assigned to each solution in the solution space. The number of all airports that meet the landing requirements is recorded as k, and the scoring results of all airports are recorded as , the evaluation function is recorded as ,but , j Indicates the j Constraints

[0078] Based on the scoring results, the scoring values of all solutions in the solution space are traversed, and after the search and traversal, the solution with the highest scoring value is selected as the optimal solution; if the optimal solution is the drone's take-off airport, there is no need to intervene in the drone's flight control, and the drone will return and land at the take-off airport according to the preset trajectory and actions; if the optimal solution is not the take-off airport, the drone will be automatically and remotely commanded to recover the drone based on the latest determined landing airport, and the drone will be controlled to land at the latest determined landing airport to complete the emergency recovery of the drone.

[0079] The specific working process is described in Example 1 and will not be repeated here.

[0080] It is understandable that the above-mentioned units can be separately or completely combined into one or several other units to form a whole, or one (or more) of the units can be further divided into multiple functionally smaller units to form a whole, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the multi-airport collaborative inspection drone emergency control system can also include other units. In actual applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0081] According to another embodiment of the present application, the system described in this embodiment and the drone emergency control method for multi-airport collaborative inspection of the embodiment of the present application can be constructed by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0082] Example 3:

[0083] like Figure 3 As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0084] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0085] The processor 1001 (also called CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0086] The processor 1001 is configured to execute the following process:

[0087] For any UAV, real-time detection of the set of self-organizing network modules that can achieve network connection;

[0088] If the network signal strength of the current network connection is lower than the set threshold, all ad hoc network module sets are traversed, and the ad hoc network module corresponding to the maximum network signal strength in the ad hoc network module set is selected for emergency ad hoc network connection, wherein the maximum network signal strength is greater than or equal to the set threshold.

[0089] Multi-objective optimization of UAV emergency control is carried out with the goal of minimizing UAV power consumption, flight altitude, and flight distance. An evaluation condition matrix of the solution space is constructed based on all constraints affecting the UAV's return to the airport.

[0090] According to the evaluation condition matrix of the solution space, a numerical value is assigned to each solution in the solution space, the number of all airports that meet the landing requirements is recorded as k, and the scoring results of all airports are recorded as , the evaluation function is recorded as ,but , j Indicates the j Constraints

[0091] According to the scoring results, the scoring values of all solutions in the solution space are traversed, and after the search traversal is completed, the solution with the highest scoring value is selected as the optimal solution;

[0092] If the optimal solution is the drone's takeoff airport, there is no need to intervene in the drone's flight control. The drone will return and land at the takeoff airport according to the preset trajectory and actions.

[0093] If the optimal solution is not the take-off airport, the system will automatically and remotely issue a command to recover the drone based on the latest determined landing airport, and control the drone to land at the latest determined landing airport to complete the emergency recovery of the drone.

[0094] Example 4:

[0095] This implementation provides a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device within an electronic device that stores programs and data. It should be understood that the computer-readable storage medium herein may include both built-in storage media within the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides storage space that stores the processing system of the electronic device.

[0096] Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device; alternatively, it may be at least one computer-readable storage medium located remotely from the processor.

[0097] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process:

[0098] For any UAV, real-time detection of the set of self-organizing network modules that can achieve network connection;

[0099] If the network signal strength of the current network connection is lower than the set threshold, all ad hoc network module sets are traversed, and the ad hoc network module corresponding to the maximum network signal strength in the ad hoc network module set is selected for emergency ad hoc network connection, wherein the maximum network signal strength is greater than or equal to the set threshold.

[0100] Multi-objective optimization of UAV emergency control is carried out with the goal of minimizing UAV power consumption, flight altitude, and flight distance. An evaluation condition matrix of the solution space is constructed based on all constraints affecting the UAV's return to the airport.

[0101] According to the evaluation condition matrix of the solution space, a numerical value is assigned to each solution in the solution space, the number of all airports that meet the landing requirements is recorded as k, and the scoring results of all airports are recorded as , the evaluation function is recorded as ,but , j Indicates the j Constraints

[0102] According to the scoring results, the scoring values of all solutions in the solution space are traversed, and after the search traversal is completed, the solution with the highest scoring value is selected as the optimal solution;

[0103] If the optimal solution is the drone's takeoff airport, there is no need to intervene in the drone's flight control. The drone will return and land at the takeoff airport according to the preset trajectory and actions.

[0104] If the optimal solution is not the take-off airport, the system will automatically and remotely issue a command to recover the drone based on the latest determined landing airport, and control the drone to land at the latest determined landing airport to complete the emergency recovery of the drone.

[0105] Example 5:

[0106] This implementation provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following process:

[0107] For any UAV, real-time detection of the set of self-organizing network modules that can achieve network connection;

[0108] If the network signal strength of the current network connection is lower than the set threshold, all ad hoc network module sets are traversed, and the ad hoc network module corresponding to the maximum network signal strength in the ad hoc network module set is selected for emergency ad hoc network connection, wherein the maximum network signal strength is greater than or equal to the set threshold.

[0109] Multi-objective optimization of UAV emergency control is carried out with the goal of minimizing UAV power consumption, flight altitude, and flight distance. An evaluation condition matrix of the solution space is constructed based on all constraints affecting the UAV's return to the airport.

[0110] According to the evaluation condition matrix of the solution space, a numerical value is assigned to each solution in the solution space, the number of all airports that meet the landing requirements is recorded as k, and the scoring results of all airports are recorded as , the evaluation function is recorded as ,but , j Indicates the j Constraints

[0111] According to the scoring results, the scoring values of all solutions in the solution space are traversed, and after the search traversal is completed, the solution with the highest scoring value is selected as the optimal solution;

[0112] If the optimal solution is the drone's takeoff airport, there is no need to intervene in the drone's flight control. The drone will return and land at the takeoff airport according to the preset trajectory and actions.

[0113] If the optimal solution is not the take-off airport, the system will automatically and remotely issue a command to recover the drone based on the latest determined landing airport, and control the drone to land at the latest determined landing airport to complete the emergency recovery of the drone.

[0114] Those skilled in the art will appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data processing device such as a server or data center that integrates one or more available media. Available media can be magnetic media (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state drive (SSD)).

[0116] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A drone emergency control method for multi-airport collaborative inspection, characterized in that: Both drones and drone airports are equipped with corresponding self-organizing network modules, including the following processes: For any UAV, the set of self-organizing network modules that can achieve network connection is detected in real time, and the total number of self-organizing network modules is recorded as num, recorded as ,in ; If the network signal strength of the current network connection is lower than the set threshold , then traverse all the self-organizing network module sets S, and select the self-organizing network module corresponding to the maximum network signal strength in the self-organizing network module set for emergency self-organizing network connection, where the maximum network signal strength is greater than or equal to the set threshold; If after traversal, the maximum value is still less than , then maintain the existing connection and wait for connection. If there is still no network signal that meets the conditions after the set time, the drone will perform the preset action and return to the route; The multi-objective optimization process includes: Multi-objective optimization of UAV emergency control is performed with the multi-objective function of minimizing UAV power consumption, minimizing flight altitude, and minimizing flight distance. The multi-objective function is solved, and the obtained solution space is processed to obtain the inspection control strategy of the UAV. Specifically: Based on all the constraints that affect the drone's return to the airport, an evaluation condition matrix of the solution space is constructed. ; in, Indicates landing k The constraints corresponding to the landing airports, in, It is expressed as follows: , represents the jth constraint corresponding to the kth airport; All constraints that affect the drone's return to the airport, including: network signal strength, distance from the airport, distance and altitude to other drones, current drone battery level, presence of drones at the pre-selected landing airport, and whether the airports are of the same model; According to the evaluation condition matrix of the solution space, a numerical value is assigned to each solution in the solution space, the number of all airports that meet the landing requirements is recorded as k, and the scoring results of all airports are recorded as , the evaluation function is recorded as ,but , j Indicates the j Constraints, ,in, , represents the weighted evaluation value corresponding to the constraints of drone battery power, flight distance, flight altitude, airport distance, and network signal strength, where the weight value of each constraint is determined by the actual situation; According to the scoring results, the scoring values of all solutions in the solution space are traversed, and after the search traversal is completed, the solution with the highest scoring value is selected as the optimal solution; If the optimal solution is the drone's takeoff airport, there is no need to intervene in the drone's flight control. The drone will return and land at the takeoff airport according to the preset trajectory and actions. If the optimal solution is not the take-off airport, the system will automatically send a remote command to recover the drone based on the latest determined landing airport, and control the drone to land at the latest determined landing airport to complete the emergency recovery of the drone. When there is no network signal for the drone or the airport, the self-organizing network is automatically selected according to the network signal strength to realize the real-time information transmission between the drone and the airport, ensuring the timely and effective transmission of inspection data; after the drone is disconnected, the drone is controlled and the optimal emergency landing airport is determined according to the real-time status of the drone's battery power, flight altitude and airport conditions, so as to realize the emergency recovery of the drone back to the airport.

2. A multi-airport collaborative inspection UAV emergency control system, characterized by: Both drones and drone airports are equipped with corresponding self-organizing network modules, including: The real-time signal detection unit is configured to: for any UAV, detect in real time the set of self-organizing network modules that can achieve network connection; The emergency ad hoc network unit is configured to: if the network signal strength of the current network connection is lower than a set threshold, traverse all ad hoc network module sets and select the ad hoc network module corresponding to the maximum network signal strength in the ad hoc network module set for ad hoc network connection, wherein the maximum network signal strength is greater than or equal to the set threshold; If after traversal, the maximum value is still less than , then maintain the existing connection and wait for connection. If there is still no network signal that meets the conditions after the set time, the drone will perform the preset action and return to the route; The multi-objective optimization unit is configured to perform multi-objective optimization of the emergency control of the UAV with the multi-objective function of minimizing the power consumption of the UAV, minimizing the flight altitude, and minimizing the flight distance of the UAV, solve the multi-objective function, process the obtained solution space, and obtain the inspection control strategy of the UAV; Specifically: Based on all the constraints that affect the UAV's return to the airport, an evaluation condition matrix of the solution space is constructed; All constraints that affect the drone's return to the airport, including: network signal strength, distance from the airport, distance and altitude to other drones, current drone battery level, presence of drones at the pre-selected landing airport, and whether the airports are of the same model; According to the evaluation condition matrix of the solution space, a numerical value is assigned to each solution in the solution space, the number of all airports that meet the landing requirements is recorded as k, and the scoring results of all airports are recorded as , the evaluation function is recorded as ,but , j Indicates the j Constraints According to the scoring results, the scoring values of all solutions in the solution space are traversed, and after the search traversal is completed, the solution with the highest scoring value is selected as the optimal solution; If the optimal solution is the drone's takeoff airport, there is no need to intervene in the drone's flight control. The drone will return and land at the takeoff airport according to the preset trajectory and actions. If the optimal solution is not the take-off airport, the system will automatically and remotely issue a command to recover the drone based on the latest determined landing airport, and control the drone to land at the latest determined landing airport to complete the emergency recovery of the drone.

3. A computer device, characterized in that: include: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, the method for emergency control of drones for coordinated inspection of multiple airports as claimed in claim 1 is implemented.

4. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the drone emergency control method for multi-airport collaborative inspection as claimed in claim 1.

5. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the drone emergency control method for multi-airport collaborative inspection as claimed in claim 1.

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