State monitoring method and device for lifting unmanned aerial vehicle and storage medium

Abstract

The invention discloses a state monitoring method and device for a lifting unmanned aerial vehicle and a storage medium. The method comprises the steps that putting information of a put object and flight state information of the lifting unmanned aerial vehicle are acquired; performing unmanned aerial vehicle flight environment modeling according to the launching information, and generating an environment map and an environment matrix; performing path planning by combining the delivery information and the flight state information, and determining a plurality of flight paths; carrying out distribution risk assessment on the plurality of flight paths based on an AHP-fuzzy comprehensive evaluation algorithm to obtain an optimal flight path; the lifting unmanned aerial vehicle is controlled to lift the thrown object according to the optimal flight path, and position information and attitude information are collected; and the hoisting state of the hoisting unmanned aerial vehicle is monitored in real time according to the position information and the attitude information. According to the method, the optimal flight path is determined by evaluating the distribution risk of the flight path, the hoisting state is monitored in real time according to the collected information of hoisting of the unmanned aerial vehicle in the optimal flight path, the monitoring accuracy of hoisting of the unmanned aerial vehicle is effectively improved, and the hoisting efficiency and safety are guaranteed.

Description

Technical Field

[0001] The present application relates to the field of satellite transmission service technology, and in particular to a method, device and storage medium for monitoring the status of a hoisting drone. Background Art

[0002] With the continuous development of drone technology, especially large-load lifting drones, their application in logistics distribution, emergency rescue, post-disaster reconstruction and other fields is becoming more and more extensive. Remote control and data transmission through satellite transmission services enable lifting drones to efficiently and safely complete various tasks in complex environments. However, during the execution of their missions, lifting drones are affected by many factors, such as the flight environment, weather conditions, and the drone's own status. Moreover, since lifting missions often involve the handling of heavy objects and long-distance flights, drones face certain risks during the lifting process. Especially when flying in complex environments, real-time monitoring of the drone's flight status and lifting status is required to ensure the safety and efficiency of the lifting mission.

[0003] Traditional methods of monitoring the condition of lifting drones mainly rely on the pilot's visual judgment and manual control. This method is not only inefficient, but also prone to misjudgment and errors in complex environments, increasing the risk of lifting missions. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device and storage medium for monitoring the status of a lifting drone, aiming to solve the technical problems that traditional methods for monitoring the status of lifting drones have poor efficiency and accuracy, and increase the risk of lifting tasks.

[0005] To achieve the above objectives, the present application proposes a method for monitoring the status of a lifting drone, which includes: Obtain information on the placement of objects and the flight status of the lifting drone; Modeling the UAV flight environment based on the deployment information to generate an environment map and an environment matrix; Performing path planning based on the environment map, the environment matrix, the delivery information, and the flight status information to determine multiple flight paths; Performing a delivery risk assessment on the plurality of flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain the optimal flight path; Controlling the lifting drone to lift the droplet according to the optimal flight path and collecting position information and posture information of the lifting drone during the lifting process; The lifting status of the lifting drone is monitored in real time according to the position information and posture information.

[0006] In one embodiment, the step of modeling the UAV flight environment based on the delivery information to generate an environment map and an environment matrix includes: Determine the delivery location and delivery area according to the delivery information; Obtaining ground information through geographic information systems; Constructing a three-dimensional model of the UAV flight environment based on the ground information, the delivery location, and the delivery area; Dividing the flight environment into a plurality of grids according to the three-dimensional model, wherein each grid contains corresponding environmental feature information; Generate an environment map based on the environmental feature information of each grid; The environment map is rasterized with equal lengths in the X, Y, and Z directions to obtain an environment matrix, wherein the environment map is used to represent the layout and obstacles of the flight environment, and the environment matrix is ​​used to represent the traffic status and risk level of each grid in the flight environment.

[0007] In one embodiment, performing path planning based on the environment map, the environment matrix, the delivery information, and the flight status information to determine multiple flight paths includes: The environment map and the environment matrix are processed using a hybrid frog leaping algorithm to obtain multiple potential paths from a starting point to an end point; Setting a frog population, wherein each frog in the frog population represents a potential flight path; Calculate the path length and risk value of each frog based on the traffic status and risk level of the environmental matrix; The frog population is divided into a plurality of subgroups, and a local search is performed on each subgroup to obtain a local optimal solution, wherein during the local search process, information is exchanged between individual frogs in each subgroup to update their respective positions and paths; When the termination condition is met, the frogs of all subgroups are merged, and the local optimal solutions of each subgroup are globally exchanged to obtain the current optimal flight path; The environment map and the environment matrix are updated according to the current optimal flight path, and the step of processing the environment map and the environment matrix using a hybrid frog leaping algorithm to obtain multiple potential paths from the starting point to the end point is performed until multiple flight paths that meet the requirements are obtained.

[0008] In one embodiment, performing path planning based on the environment map, the environment matrix, the delivery information, and the flight status information to determine multiple flight paths further includes: Using a tabu search algorithm to search the environment map and the environment matrix to obtain multiple feasible flight paths from a starting point to an end point; Selecting any feasible flight path from the multiple feasible paths as an initial solution, and adding the initial solution to an empty taboo table to obtain a taboo table, wherein the taboo table is used to store the searched flight paths; Determine whether the taboo objects in the taboo table meet the amnesty conditions according to the set amnesty criteria; If satisfied, the taboo object is released from the taboo table and the taboo object is used as the current optimal solution; If not, selecting a path other than the taboo objects in the taboo table from the multiple feasible flight paths as a candidate solution; Calculating the fitness value of the candidate solution according to a preset evaluation function; Selecting the candidate solution with the best fitness value as the current solution, and replacing the taboo object that entered the taboo table earliest in the taboo table with the current solution; Determine whether the current solution satisfies a termination condition; If it is satisfied, then output the flight path that meets the requirements; If not, return to the step of searching the environment graph and the environment matrix using the tabu search algorithm to obtain multiple feasible flight paths from the starting point to the end point, and continue the iterative search until multiple flight paths that meet the requirements are obtained.

[0009] In one embodiment, performing path planning based on the environment map, the environment matrix, the delivery information, and the flight status information to determine multiple flight paths further includes: Using a variable domain search algorithm to search the environment map and the environment matrix to obtain multiple initial flight paths; Setting any one of the multiple initial flight paths as a current solution and defining a domain structure of the current solution; Randomly select a solution in the domain of the current solution as a candidate solution; Evaluate the path length and risk value of the candidate solution according to the traffic status and risk level of the environment matrix to obtain the evaluation result; If the evaluation result shows that the candidate solution is better than the current solution, the candidate solution is accepted as a new current solution, and the domain structure is updated; If the evaluation result shows that the candidate solution is not better than the current solution and satisfies the preset acceptance criteria, then the candidate solution is accepted as the new current solution or the current solution is kept unchanged according to the preset probability, and candidate solutions are re-selected within the domain of the current solution for evaluation until the stop condition of the domain search is met; Output a flight path that meets the requirements according to the stopping condition, or return to the step of using a variable domain search algorithm to search the environment map and the environment matrix to obtain multiple initial flight paths, and continue the iterative search until multiple flight paths that meet the requirements are obtained.

[0010] In one embodiment, the delivery risk assessment of the plurality of flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain the optimal flight path includes: Determining delivery risk assessment indicators, wherein the delivery risk assessment indicators include at least flight path length, flight altitude, obstacle density, and weather conditions; Constructing a weight matrix and a fuzzy evaluation matrix based on the distribution risk assessment indicators; Performing a synthetic operation on the weight matrix and the fuzzy evaluation matrix to obtain a comprehensive risk assessment model; Calculating a comprehensive risk assessment score for each flight path according to the comprehensive risk assessment model; The flight paths are prioritized from low to high according to the comprehensive risk assessment scores to obtain a flight path sequence, and the flight path with the highest priority in the flight path sequence is taken as the optimal flight path.

[0011] In one embodiment, constructing a weight matrix and a fuzzy evaluation matrix based on the delivery risk assessment index includes: Constructing a risk indicator judgment matrix based on the risk factors of each of the flight paths; Determining the relative importance score of each risk indicator according to the risk indicator judgment matrix; Determine the corresponding indicator weight based on the relative importance score of each risk indicator; Constructing a weight matrix according to the indicator weights; Constructing an evaluation factor set and a comment set, wherein the evaluation factor set includes risk factors of each of the flight paths, and the comment set includes multiple risk levels; Determining a fuzzy evaluation value of each flight path on each of the delivery risk assessment indicators based on the evaluation factor set and the comment set; A fuzzy evaluation matrix is ​​constructed according to the fuzzy evaluation values.

[0012] In one embodiment, after real-time monitoring of the lifting status of the lifting drone according to the position information and the posture information, the method further includes: Calculate the swing angle and swing amplitude of the lifting drone in real time according to the position information and attitude information; generating a load displacement signal according to the swing angle and the swing amplitude; Get the desired trajectory of the drone; Inputting the load displacement signal and the desired trajectory into an energy-coupled anti-sway controller to generate an anti-sway control instruction, wherein the energy-coupled anti-sway controller is determined by calculating the swing energy of the lifting drone and the position change of the load center of mass according to a swing dynamics model; The posture of the lifting drone is adjusted according to the anti-sway control instruction.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a state monitoring device for a hoisting drone, the state monitoring device for a hoisting drone comprising: The acquisition module is used to obtain the release information of the dropped objects and the flight status information of the lifting drone; A modeling module is used to model the UAV flight environment based on the deployment information and generate an environment map and an environment matrix; a planning module, configured to perform path planning based on the environment map, the environment matrix, the delivery information, and the flight status information, and determine multiple flight paths; An evaluation module, configured to perform a delivery risk evaluation on the plurality of flight paths based on an AHP-fuzzy comprehensive evaluation algorithm to obtain an optimal flight path; a control module, configured to control the lifting drone to lift the droplet according to the optimal flight path and to collect position information and posture information of the lifting drone during the lifting process; The monitoring module is used to monitor the lifting status of the lifting drone in real time based on the position information and posture information.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the state monitoring method of the lifting drone as described above are implemented.

[0015] The present application obtains the placement information of the object to be placed and the flight status information of the lifting drone; models the drone flight environment according to the placement information to generate an environment map and an environment matrix; performs path planning according to the environment map, the environment matrix, the placement information and the flight status information to determine multiple flight paths; performs distribution risk assessment on the multiple flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain the optimal flight path; controls the lifting drone to lift the object according to the optimal flight path and collects the position information and attitude information of the lifting drone during the lifting process; monitors the lifting status of the lifting drone in real time according to the position information and attitude information. In the above manner, by using the AHP-fuzzy comprehensive evaluation algorithm to perform distribution risk assessment on the planned multiple flight paths to obtain the optimal flight path, the lifting efficiency and accuracy can be effectively improved, and then the drone is controlled to lift according to the optimal flight path and the lifting status is monitored in real time according to the information during the lifting process, which effectively improves the monitoring accuracy of the lifting drone, while further improving the safety and reliability of the lifting drone and reducing the distribution risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flow chart of the first embodiment of the method for monitoring the status of a hoisting drone provided in this application; Figure 2 A flow chart illustrating a second embodiment of the method for monitoring the condition of a hoisting drone is provided in this application; Figure 3 This is a schematic diagram of the module structure of the status monitoring device for lifting drones in an embodiment of the present application.

[0019] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0021] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solutions of the embodiments of the present application are: obtaining the delivery information of the delivery object and the flight status information of the lifting drone; modeling the drone flight environment according to the delivery information to generate an environment map and an environment matrix; performing path planning according to the environment map, the environment matrix, the delivery information and the flight status information to determine multiple flight paths; performing distribution risk assessment on the multiple flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain the optimal flight path; controlling the lifting drone to lift the delivery object according to the optimal flight path and collecting the position information and attitude information of the lifting drone during the lifting process; and monitoring the lifting status of the lifting drone in real time according to the position information and attitude information.

[0023] Traditional methods of monitoring the condition of lifting drones mainly rely on the pilot's visual judgment and manual control. This method is not only inefficient, but also prone to misjudgment and errors in complex environments, increasing the risk of lifting missions.

[0024] This application provides a solution that uses the AHP-fuzzy comprehensive evaluation algorithm to evaluate the distribution risks of multiple planned flight paths to determine the optimal flight path, which can effectively improve the lifting efficiency and accuracy, and then control the drone to lift along the optimal flight path and monitor the lifting status in real time based on the information during the lifting process, effectively improving the monitoring accuracy of the lifting drone, while further improving the safety and reliability of the lifting drone and reducing the distribution risk.

[0025] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or a state monitoring device for a lifting drone capable of performing the above functions. Below, this embodiment and the following embodiments are described using the state monitoring device for a lifting drone as the execution subject.

[0026] Based on this, the embodiment of the present application provides a state monitoring method for a lifting drone, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the status monitoring method for hoisting drones in this application.

[0027] In this embodiment, the state monitoring method of the lifting drone includes steps S10 to S60: Step S10: Acquire the delivery information of the delivered object and the flight status information of the lifting drone.

[0028] It should be noted that a lifting drone is an unmanned aerial vehicle used to lift various objects. In this embodiment, the lifting drone is a heavy-load drone designed to lift heavy items. The lifting drone communicates and transmits data with a ground control center via satellite transmission services. Drop information may include, but is not limited to, key data such as the weight, volume, shape, and target drop location of the object. Flight status information includes real-time parameters such as the drone's current position, speed, altitude, flight direction, and battery charge. This information is crucial for subsequent flight path planning and lifting status monitoring.

[0029] It is understandable that the delivery information and flight status information can be collected through the drone's own sensors, GPS positioning system, camera and other equipment, or obtained through communication with a ground control station or other related equipment. This embodiment does not impose specific restrictions on this.

[0030] Step S20: Modeling the UAV flight environment based on the delivery information to generate an environment map and an environment matrix.

[0031] It should be noted that in this implementation, the drop information includes the drop location and drop area. The UAV flight environment modeling is based on these drop locations and drop areas, combined with geographic information system (GIS) data, meteorological data, obstacle information, and the UAV's own performance parameters, using 3D modeling technology to construct a virtual flight environment for the UAV to lift and drop objects.

[0032] It can be understood that the environment map is used to represent the layout and obstacles of the flight environment, and the environment matrix is ​​a mathematical model that includes various factors in the flight environment and is used to represent the traffic status and risk level of each grid in the flight environment.

[0033] In a feasible implementation, step S20 may include steps A11 to A16: Step A11: Determine the delivery location and delivery area according to the delivery information.

[0034] It should be noted that the delivery location and delivery area determined based on the delivery information include the geographical coordinates of the target delivery location and the boundaries of the target delivery area, as well as possible obstacles and restrictions within the area.

[0035] Step A12: Obtain ground information through a geographic information system.

[0036] It's important to note that ground information can be obtained through a geographic information system (GIS). This information includes ground elevation (digital elevation model, DEM) and surface obstacle data. Ground information is typically represented as a discrete grid of elevation data, where each grid value represents the elevation of that location. Using this ground information, a model of the ground surface for the drone's flight can be constructed.

[0037] Step A13: Construct a three-dimensional model of the UAV flight environment based on the ground information, the delivery location, and the delivery area.

[0038] It's important to note that the 3D model of the drone's flight environment is a virtual flight space constructed using 3D modeling software or algorithms, based on ground information captured by a geographic information system (GIS), combined with the drop location and area. This model accurately depicts the drone, the drop object, and various obstacles and constraints in the flight environment.

[0039] Step A14: Divide the flight environment into a plurality of grids according to the three-dimensional model, wherein each grid contains corresponding environmental feature information.

[0040] It should be noted that in order to analyze the layout and obstacle conditions of the flight environment, the entire flight environment can be divided into multiple grids. Assuming that the size of each grid is Δx×Δy×Δz, the grid division method can be defined as follows:

[0041] in, is the X, Y, and Z coordinates of a point in three-dimensional space. Floor() represents the rounding down operation, so that the index of each grid can be obtained.

[0042] It is understandable that the environmental feature information of each grid includes the obstacle type of the grid (such as buildings, trees, power lines, etc.) and the relative position relationship between the obstacle and the grid.

[0043] Step A15: Generate an environment map based on the environmental feature information of each grid.

[0044] It's important to note that the environment map is used to visualize the layout and obstacles of the flight environment. Different colors or symbols are used to represent different types of obstacles and the traffic status of the grid. For example, red can be used to indicate prohibited flight areas, green to indicate safe flight areas, and yellow to indicate areas requiring special attention. This allows for a visual display of the obstacle layout and traffic status of the flight environment.

[0045] Step A16: Rasterize the environment map in equal lengths in the X, Y, and Z directions to obtain an environment matrix, wherein the environment map is used to represent the layout and obstacles of the flight environment, and the environment matrix is ​​used to represent the traffic status and risk level of each grid in the flight environment.

[0046] It's important to note that the environmental matrix is ​​a mathematical model that incorporates various factors within the flight environment and represents the traffic status and risk level of each grid within it. For example, each grid's risk level can be quantitatively scored based on factors such as obstacle type, height, and density, and then represented in a matrix format. This information allows the drone to select the optimal flight path, avoiding high-risk grid areas and ensuring the safety and reliability of the lifting process.

[0047] It is understandable that after the environment map is generated, the environment map is rasterized to obtain the environment matrix. Each element M of the environment matrix ijk Represents the traffic status and risk level of the flight environment at the i-th grid, j-th grid, and k-th grid. Assume that the traffic status S of each grid is ijk for:

[0048] For each grid, the traffic status can be evaluated based on factors such as obstacles, terrain height, airflow, etc. Risk level R ijk The calculation formula is:

[0049] Among them, R ijk is the risk level, For each element of the environment matrix, Indicates whether the grid is occupied by obstacles, Indicates whether the ground elevation of the grid poses a risk to flight. Indicates the effect of wind speed or airflow on flight in this grid, weighted coefficient 、 、 According to the importance of different factors, w1+w2+w3=1 is satisfied.

[0050] It is worth noting that in order to perform the rasterization of the environment matrix, the grid size in each direction must be determined first. Assuming that the rasterization sizes in the X, Y, and Z directions are Δx, Δy, and Δz respectively, the dimensions of the environment matrix E after rasterization are:

[0051] in, is an element in the rasterized environment matrix, representing the state of a grid in the flight environment. i, j, and k are the grid indices corresponding to the X, Y, and Z directions. X represents the total length of the environment model in the X direction. Δx is the grid size, i.e., the length of each grid in the X direction. Y represents the total length of the environment model in the Y direction. Δy is the grid size, i.e., the length of each grid in the Y direction. Z represents the total length of the environment model in the Z direction. Δz is the grid size, i.e., the length of each grid in the Z direction.

[0052] Step S30: performing path planning according to the environment map, the environment matrix, the delivery information and the flight status information to determine multiple flight paths.

[0053] It should be noted that in this embodiment, path planning can utilize a variety of algorithms for flight path search and optimization, such as the hybrid leapfrog algorithm, tabu search algorithm, variable domain search algorithm, and genetic algorithm, though this embodiment does not impose specific limitations. These algorithms comprehensively consider the drone's performance parameters, the characteristics of the flight environment, and the specific requirements of the delivery mission. Through multiple iterations and calculations, they ultimately determine multiple feasible flight paths. These paths are clearly represented in the flight environment map and the environment matrix, and the drone can fly according to these paths to ensure the safety and accuracy of the lifting process.

[0054] It's understandable that the path planning process also requires consideration of the drone's flight status, including its current position, speed, and attitude. This information can be acquired in real time by the drone's sensors and used to adjust the flight path. For example, if a drone encounters an unexpected obstacle or changes in weather conditions, the flight path can be replanned based on the new environmental and flight status information to ensure the drone can safely navigate around the obstacle or adapt to the changing weather conditions.

[0055] It should be understood that when determining the flight path, it is also necessary to consider the drone's takeoff and landing points, as well as various flight constraints such as maximum altitude, minimum speed, and obstacle avoidance strategies. By comprehensively considering these factors, it is possible to ensure that the drone can complete the delivery mission safely and efficiently during the lifting process.

[0056] In a feasible embodiment, step S30 may include: using a hybrid frog leaping algorithm to process the environmental map and the environmental matrix to obtain multiple potential paths from the starting point to the end point; setting a frog population, wherein each frog in the frog population represents a potential flight path; calculating the path length and risk value of each frog based on the traffic status and risk level of the environmental matrix; dividing the frog population into multiple subgroups, and performing a local search on each subgroup to obtain a local optimal solution, wherein, during the local search process, information is exchanged between individual frogs in each subgroup to update their respective positions and paths; when a termination condition is met, merging the frogs of all subgroups, and performing a global information exchange on the local optimal solutions of each subgroup to obtain a current optimal flight path; updating the environmental map and the environmental matrix according to the current optimal flight path, and executing the step of using the hybrid frog leaping algorithm to process the environmental map and the environmental matrix to obtain multiple potential paths from the starting point to the end point, until multiple flight paths that meet the requirements are obtained.

[0057] It should be noted that in this implementation, a hybrid frog-leaping algorithm was used to search and optimize flight paths, resulting in multiple flight paths that met the requirements. This algorithm, based on swarm intelligence, simulates both the group and individual behaviors of frogs during foraging to search and optimize flight paths. In the algorithm, a frog population is divided into multiple subgroups, and individual frogs within each subgroup exchange information to update their respective positions and paths. Through multiple iterations and calculations, multiple feasible flight paths are ultimately obtained. These paths are clearly represented in the flight environment map and the environment matrix, and the drone can fly according to these paths to ensure the safety and accuracy of the lifting process.

[0058] Understandably, when using the hybrid leapfrog algorithm for path planning, it's also necessary to consider the drone's performance parameters, the characteristics of the flight environment, and the specific requirements of the delivery mission. These factors all influence the selection and optimization of the flight path. By comprehensively considering these factors, a flight path more tailored to the actual situation can be obtained, improving the efficiency and safety of the lifting mission.

[0059] It's worth noting that the hybrid frog leaping algorithm, used for flight path search and optimization, can generate multiple flight paths that meet the requirements. These paths are clearly represented in the flight environment map and the environment matrix, and the drone can fly according to these paths. Furthermore, by comprehensively considering the drone's performance parameters, the characteristics of the flight environment, and the specific requirements of the delivery mission, the efficiency and safety of the lifting mission can be further improved.

[0060] In the specific implementation, set the frog population F={F1,F2,…,F N}, where N is the number of frogs. Each frog represents a potential path, and the length and risk of the path can be calculated from the environment matrix. The path P of each frog is i It can be represented as a series of nodes P from the starting point S to the end point T i ={(x1,y1,z1),(x2,y2,z2),…,(x m ,y m ,z m )}, where m is the number of nodes in the path.

[0061] For each frog F i The path P i , whose path length L(P i ) and risk value R(P i ) is calculated as:

[0062]

[0063] in, is the Euclidean distance between adjacent nodes k+1 and k in the path, m is the number of nodes in the path, is the transit state of the environment matrix, is the risk level of the environmental matrix.

[0064] Divide the frog population into Q subgroups F q , where q∈[1,Q], and perform local search on each subgroup. The frog individuals in each subgroup update their positions through information exchange. Assume that the frog individual F i In subgroup F q The update formula in is:

[0065] in, F is the frog individual i The new path after the local search is updated, F is the frog individual i The current path, Frog individual F i The local optimal path in the current subgroup, is the global optimal path, that is, the optimal path in the entire population, is the step size factor of the local search, which controls how the individual approaches the local optimal path. is the factor of global information exchange, which controls how individuals approach the global optimal path.

[0066] For each subgroup F q , local optimal solution It is updated by comparing the optimal path of all frog individual path lengths and risks. When the local search ends, all local optimal solutions in the frog population are are merged and the global optimal path is updated through global information exchange The algorithm iterates until the termination condition is met. The termination condition can be that the maximum number of iterations Tmax is reached or the change in the path optimization value is less than a certain threshold. Finally, according to the current optimal path Update the environment map and environment matrix and perform the above steps until the required number or quality of flight paths is met.

[0067] In a feasible embodiment, step S30 may also include: using a taboo search algorithm to search the environment map and the environment matrix to obtain multiple feasible flight paths from the starting point to the end point; selecting any feasible flight path from the multiple feasible paths as an initial solution, and adding the initial solution to an empty taboo table to obtain a taboo table, wherein the taboo table is used to store the searched flight paths; judging whether the taboo objects in the taboo table meet the amnesty conditions according to the set amnesty criteria; if so, releasing the taboo objects from the taboo table and using the taboo objects as the current optimal solution; if not, adding the taboo objects in the taboo table to the current optimal solution. A path other than the taboo objects in the taboo table is selected from multiple feasible flight paths as a candidate solution; the fitness value of the candidate solution is calculated according to a preset evaluation function; the candidate solution with the best fitness value is selected as the current solution, and the taboo object that enters the taboo table earliest is replaced by the current solution; it is determined whether the current solution meets the termination condition; if so, a flight path that meets the requirement is output; if not, the step of searching the environment map and the environment matrix using the taboo search algorithm is returned to obtain multiple feasible flight paths from the starting point to the end point, and the iterative search is continued until multiple flight paths that meet the requirement are obtained.

[0068] It should be noted that in this embodiment, a tabu search algorithm is used to search and optimize flight paths, thereby obtaining multiple flight paths that meet the requirements. The tabu search algorithm is an intelligent optimization algorithm based on local neighborhood search. It uses a tabu table to record previously searched solutions to prevent the algorithm from falling into local optimal solutions. In the tabu search algorithm, a specific search strategy is first used based on the environment map and environment matrix to obtain multiple feasible flight paths. Then, one of these feasible paths is selected as the initial solution and added to the tabu table. Next, the algorithm selects the optimal solution from the set of candidate solutions based on the set amnesty criteria and preset evaluation function, and updates the tabu table. Through continuous iterative search, the tabu search algorithm can gradually approach the global optimal solution, ultimately obtaining multiple flight paths that meet the requirements. These paths not only take into account the performance parameters of the drone and the characteristics of the flight environment, but also fully consider the specific requirements of the delivery mission, thereby ensuring the safety and accuracy of the lifting process.

[0069] It's understandable that when using the taboo search algorithm for path planning, the design of the exemption criteria and evaluation function is crucial. The exemption criteria determine whether a taboo object in the taboo table can be exempted—that is, whether it can be reselected as the current optimal solution. This is determined by the fitness value of the taboo object, the number of iterations, and other algorithm parameters. By properly setting the exemption criteria, the algorithm can avoid premature convergence to a local optimal solution, thereby increasing the likelihood of finding the global optimal solution. The evaluation function, used to evaluate candidate solutions, is typically designed based on the drone's performance parameters, the characteristics of the flight environment, and the specific requirements of the delivery mission. By comprehensively considering these factors, a flight path more realistically tailored to the situation can be obtained, further improving the efficiency and safety of the lifting mission.

[0070] In its implementation, the tabu search algorithm's iterative process continuously approaches the global optimal solution. In each iteration, the algorithm searches for new candidate solutions based on the current solution and the tabu table, evaluating their merits using an evaluation function. Then, based on a special criteria, it decides whether to accept the new candidate as the current solution and updates the tabu table. This process repeats until a termination criterion is met. These criterion can typically be set as reaching the maximum number of iterations, the change in the path optimization value being less than a certain threshold, or finding multiple flight paths that meet the requirements. Ultimately, the tabu search algorithm outputs a set of flight paths that meet the requirements. These paths not only take into account the drone's performance parameters and the characteristics of the flight environment, but also fully consider the specific requirements of the delivery mission. The drone can then fly along these paths to ensure the safety and accuracy of the lifting process.

[0071] Multiple feasible paths P = {P1, P2, ..., Pn} can be obtained through the search algorithm, where each path P iIt is the path from the starting point S to the end point T. A path is selected from the set of feasible paths as the initial solution, denoted as P init , add the initial solution to the tabu table T. For the path P in the tabu table i Determine whether the amnesty conditions are met. The amnesty conditions are determined by a function P i Judgment: If the amnesty conditions are met, then path P i is released and used as the current optimal solution P best If the path in the taboo table does not meet the amnesty condition, then the path that is not in the taboo table is selected from the feasible path set as the candidate solution set C. For each candidate solution P j , calculate its fitness value f(P j ), the fitness function is:

[0072] in, Represents path P j The total cost, is the number or effect of obstacles in the path, 、 is the weighting factor.

[0073] Select the candidate solution P with the best fitness value curr , take it as the current solution, and take the current solution P curr Replace the taboo object that entered the taboo table the earliest (FIFO strategy), and determine whether the termination condition is met based on the preset termination condition (such as the maximum number of iterations or reaching the optimal solution). If it is met, output the current optimal solution P best If the termination condition is not met, the tabu search iteration continues.

[0074] In a feasible embodiment, step S30 may also include: using a variable domain search algorithm to search the environment map and the environment matrix to obtain multiple initial flight paths; setting any one of the multiple initial flight paths as the current solution and defining the domain structure of the current solution; randomly selecting a solution within the domain of the current solution as a candidate solution; evaluating the path length and risk value of the candidate solution according to the traffic status and risk level of the environment matrix to obtain an evaluation result; if the evaluation result is that the candidate solution is better than the current solution, accepting the candidate solution as the new current solution and updating the domain structure; if the evaluation result is that the candidate solution is not better than the current solution and meets the preset acceptance criteria, accepting the candidate solution as the new current solution or keeping the current solution unchanged according to a preset probability, and re-selecting a candidate solution within the domain of the current solution for evaluation until the stop condition of the domain search is met; outputting a flight path that meets the requirements according to the stop condition, or returning to the step of searching the environment map and the environment matrix using the variable domain search algorithm to obtain multiple initial flight paths, and continuing the iterative search until multiple flight paths that meet the requirements are obtained.

[0075] It should be noted that in this embodiment, a variable domain search algorithm is used to search and optimize flight paths, thereby obtaining multiple flight paths that meet the requirements. The variable domain search algorithm is an intelligent optimization algorithm that searches for more optimal solutions by continuously changing the solution domain structure. In the variable domain search algorithm, a specific search strategy is first used based on the environment map and environment matrix to obtain multiple initial flight paths. Then, one of these initial paths is selected as the current solution, and its domain structure is defined. The domain structure generally includes a set of possible solutions near the current solution, which are generated from the current solution through certain transformation rules.

[0076] In each iteration, the algorithm randomly selects a solution within the current solution's domain as a candidate solution and evaluates the candidate's path length and risk value based on the traffic conditions and risk level of the environment matrix. If the candidate solution is better than the current solution, it is accepted as the new current solution and the domain structure is updated. If the candidate solution is not better than the current solution but meets the preset acceptance criteria, it is either accepted as the new current solution based on a preset probability or the current solution is retained and a new candidate solution is selected within the current solution's domain for evaluation. This process is repeated until a stopping condition is met.

[0077] Stopping conditions can typically be set when the maximum number of iterations is reached, the change in the path optimization value is less than a certain threshold, or multiple flight paths that meet the requirements are found. Ultimately, the variable domain search algorithm outputs a set of flight paths that meet the requirements. These paths not only take into account the performance parameters of the UAV and the characteristics of the flight environment, but also fully consider the specific requirements of the delivery mission, thereby ensuring the safety and accuracy of the lifting process.

[0078] In the specific implementation, a path P is selected from multiple initial paths init As the current solution P curr . Define the domain structure N(P curr ), this field represents the difference between the current solution P curr Adjacent path solution space. From the current solution area N(P curr ) randomly selects a path P cand As a candidate solution, the candidate solution P cand , according to the traffic status and risk level in the environment matrix M, the total length L(P cand ) and risk value R(P cand ), L(P cand ) is the sum of the weights of all edges in the path, R(P cand ) is the sum of the risk levels r(e) of each edge in the path, taking into account the influence of traffic conditions and obstacles. According to the evaluation results, if the candidate solution P cand Better than the current solution P curr , then accept the candidate solution as the new current solution. Define the superiority criterion f(P cand ) to determine whether the candidate solution is better than the current solution. cand ) <f(P curr ) (i.e. the candidate solution is better), then accept the candidate solution as the new current solution. If the candidate solution is not better than the current solution, i.e. f(P cand )≥f(P curr ), but meets the preset acceptance criteria, then according to a certain probability p accept Accept the candidate solution. Set the stopping condition ξ, which may be based on the quality of the path found, the number of searches, the time, etc. When the stopping condition is met, output the current best flight path P best , if the stopping condition is not met, new candidate solutions will continue to be selected for evaluation within the domain of the current solution.

[0079] Step S40: performing a delivery risk assessment on the plurality of flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain an optimal flight path.

[0080] It should be noted that the AHP-fuzzy comprehensive evaluation algorithm is a comprehensive evaluation algorithm that combines the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods. AHP decomposes complex problems into multiple levels and factors, compares and determines the relative importance of each factor, constructs a judgment matrix, and then determines the weight of each factor. Fuzzy comprehensive evaluation methods utilize the principles of fuzzy mathematics to transform qualitative problems into quantitative assessments. Fuzzy membership functions are used to describe the degree of membership of each factor to the evaluation target, thereby conducting a comprehensive evaluation.

[0081] In its implementation, the AHP-fuzzy comprehensive evaluation algorithm decomposes the delivery risk of a flight path into multiple levels and factors, such as path length, number of obstacles, flight environment, and drone performance. Then, through expert scoring or questionnaires, judgment information on the impact of each factor on delivery risk is collected to construct a judgment matrix. Next, the AHP method is used to calculate the weight of each factor, reflecting its relative importance to delivery risk. In the fuzzy comprehensive evaluation stage, the fuzzy membership function for each factor is determined based on historical data and expert experience, and the actual value of each factor is converted into a fuzzy membership. Then, combining the weights and fuzzy membership of each factor, a fuzzy comprehensive evaluation model is used to calculate the comprehensive delivery risk evaluation value for each flight path. Finally, the flight paths are ranked according to their comprehensive evaluation values, and the path with the lowest comprehensive evaluation value is selected as the optimal flight path.

[0082] Step S50: Control the lifting drone to lift the drop object according to the optimal flight path and collect the position information and posture information of the lifting drone during the lifting process.

[0083] It should be noted that in the process of controlling the lifting drone to lift the dropped objects according to the optimal flight path, the integrated sensors and monitoring system capture the precise position of the drone, such as longitude, latitude and altitude, as well as the attitude information of the drone, including pitch angle, yaw angle and roll angle, in real time.

[0084] Step S60: monitoring the lifting status of the lifting drone in real time according to the position information and posture information.

[0085] It's important to note that the drone's position and attitude information can be used to assess its lifting stability, speed, and whether it deviates from the planned path in real time. If any anomalies are detected, such as unstable attitude or deviation from the optimal path, an alert mechanism is immediately triggered, notifying the operator to take timely action to ensure the safe and smooth progress of the lifting mission.

[0086] In a feasible implementation manner, step S60 may further include steps B11 to B15: Step B11: Calculate the swing angle and swing amplitude of the lifting drone in real time based on the position information and attitude information.

[0087] It's important to note that calculating the swing angle and amplitude can be used to further assess the stability and safety of a drone during a lift. Real-time monitoring of the swing angle and amplitude provides a more accurate understanding of the drone's dynamic performance during the lift, allowing for timely adjustments to prevent accidents caused by excessive drone movement.

[0088] Step B12: generating a load displacement signal according to the swing angle and the swing amplitude.

[0089] It's important to note that the load displacement signal reflects the load's displacement during the lifting process. By analyzing this signal, the operator can intuitively understand the load's trajectory and stability. If the load displacement signal shows an abnormality, such as excessive or unstable displacement, immediate adjustments can be made to prevent the load from falling or being damaged.

[0090] Step B13: Obtain the desired trajectory of the UAV.

[0091] It's important to note that the desired trajectory is pre-set based on the lifting mission requirements and the optimal flight path. It represents the ideal path the drone should follow during the lift. By obtaining the drone's desired trajectory, it can be compared with the actual flight trajectory to assess the drone's flight accuracy and degree of deviation. If the actual flight trajectory deviates significantly from the desired trajectory, an alarm is issued, alerting the operator to take corrective action, ensuring the drone accurately follows the planned path and successfully completes the lift mission.

[0092] Step B14: Input the load displacement signal and the desired trajectory into an energy-coupled anti-sway controller to generate an anti-sway control instruction, wherein the energy-coupled anti-sway controller is determined by calculating the swing energy of the lifting drone and the position change of the load center of mass based on the swing dynamics model.

[0093] It's important to note that the energy-coupled sway control analyzes the deviation between the load's displacement signal and the desired trajectory, calculating the drone's sway energy and the change in the load's center of mass. This calculation then generates precise sway control instructions. These instructions are designed to effectively reduce or eliminate sway by adjusting the drone's flight attitude and power output, enabling it to fly more smoothly and accurately along the desired trajectory.

[0094] Step B15: adjusting the posture of the lifting drone according to the anti-sway control instruction.

[0095] It should be noted that by executing the anti-swing control instruction, the flight attitude and power output of the lifting drone are adjusted to reduce or eliminate the swing.

[0096] It's understandable that when executing sway control commands, the lifting drone adjusts its flight attitude and power output according to the commands, such as adjusting propeller speed, changing flight direction or altitude, thereby effectively controlling and eliminating sway. This process ensures the stability and safety of the lifting drone during the lifting process, improving the efficiency and success rate of the lifting mission.

[0097] This embodiment obtains the delivery information of the object and the flight status information of the lifting drone; models the drone flight environment based on the delivery information to generate an environment map and an environment matrix; performs path planning based on the environment map, the environment matrix, the delivery information and the flight status information to determine multiple flight paths; performs distribution risk assessment on the multiple flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain the optimal flight path; controls the lifting drone to lift the object according to the optimal flight path and collects the position information and attitude information of the lifting drone during the lifting process; and monitors the lifting status of the lifting drone in real time based on the position information and attitude information. In the above manner, by using the AHP-fuzzy comprehensive evaluation algorithm to perform distribution risk assessment on the planned multiple flight paths to obtain the optimal flight path, the lifting efficiency and accuracy can be effectively improved, and then the drone is controlled to lift according to the optimal flight path and the lifting status is monitored in real time based on the information during the lifting process, which effectively improves the monitoring accuracy of the lifting drone, while further improving the safety and reliability of the lifting drone and reducing the delivery risk.

[0098] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 In the method for monitoring the status of the lifting drone, step S40 further includes steps S401 to S406: Step S401: Determine delivery risk assessment indicators, which include at least flight path length, flight altitude, obstacle density, and weather conditions.

[0099] It should be noted that the selection of delivery risk assessment indicators is based on a comprehensive consideration of the various risk factors that may be encountered during the flight of a lifting drone. The flight path length directly affects the drone's flight time and energy consumption. A shorter path means higher efficiency and lower costs. The flight altitude affects the drone's field of view and obstacle avoidance capabilities. An appropriate altitude can improve the drone's flight stability while ensuring safety. Obstacle density reflects the complexity of the flight path. A dense obstruction increases the difficulty and risk of the drone's flight. Weather conditions such as wind speed, direction, and temperature can affect the drone's flight performance. Severe weather conditions may cause the drone to become unstable or even unable to complete its mission. By comprehensively considering these delivery risk assessment indicators, the risk level of different flight paths can be more comprehensively and accurately assessed, providing strong support for selecting the optimal flight path for lifting drones.

[0100] Step S402: constructing a weight matrix and a fuzzy evaluation matrix based on the delivery risk assessment indicators.

[0101] It should be noted that the weight matrix is ​​used to represent the relative importance of each delivery risk assessment indicator in the evaluation process, while the fuzzy evaluation matrix is ​​used to describe the performance of each indicator on different flight paths. By constructing these two matrices, the delivery risk assessment indicators can be quantified.

[0102] It's understandable that when constructing a weight matrix, methods like expert scoring and the analytic hierarchy process can be used to assign weights to each indicator based on its importance and impact. When constructing a fuzzy evaluation matrix, however, it's necessary to score and fuzzify the performance of each indicator on different flight paths based on historical data and expert experience to create a fuzzy evaluation matrix.

[0103] In a feasible implementation, step S402 may include steps C11 to C17: Step C11: constructing a risk indicator judgment matrix based on the risk factors of each flight path.

[0104] It's important to note that the risk indicator judgment matrix is ​​used to quantitatively assess how each flight path performs under different risk factors. By constructing this matrix, the risk levels of different flight paths can be intuitively compared, providing a basis for selecting the optimal flight path. When constructing this matrix, the impact of various risk factors on the flight path, such as flight path length, altitude, obstacle density, and weather conditions, must be considered. Each risk factor is then weighted and quantitatively scored based on actual circumstances.

[0105] Step C12: Determine the relative importance score of each risk indicator according to the risk indicator judgment matrix.

[0106] It's important to note that the relative importance score reflects the weight of each risk indicator in the assessment process and is crucial for constructing the weight matrix. Proper weight assignment ensures the accuracy and reliability of the assessment results. When determining the relative importance scores of each risk indicator, methods such as the Analytic Hierarchy Process (AHP) can be used to compare each indicator pairwise and determine the weights for each indicator based on the comparison results. This process requires full consideration of the interplay and constraints between indicators to ensure a rational and scientific weight assignment.

[0107] Step C13: Determine the corresponding indicator weight according to the relative importance score of each risk indicator.

[0108] It should be noted that the corresponding indicator weight is determined according to the relative importance score of each risk indicator, that is, the specific weight value of each risk indicator in the weight matrix is ​​obtained by normalizing the relative importance score.

[0109] Step C14: Construct a weight matrix according to the indicator weights.

[0110] It should be noted that when constructing the weight matrix, the weight values ​​of each risk indicator need to be arranged and combined according to certain rules to form a two-dimensional matrix. This matrix will serve as the basis for the subsequent fuzzy comprehensive evaluation to calculate the comprehensive risk score of each flight path.

[0111] Step C15: constructing an evaluation factor set and a comment set, wherein the evaluation factor set includes the risk factors of each of the flight paths, and the comment set includes multiple risk levels.

[0112] It's important to note that the evaluation factor set is a collection of risk factors for each flight path, encompassing all factors that could potentially impact flight path risk. The comment set, on the other hand, categorizes the performance of each risk factor into levels such as low risk, medium risk, and high risk. By constructing both the evaluation factor set and the comment set, we can provide clear criteria and a basis for subsequent fuzzy comprehensive evaluation.

[0113] In the specific implementation, the risk factors of the flight path are used as the evaluation factor set, including path length, risk level, number of obstacles, etc. The risk level is divided into different comment sets, such as low risk, medium risk, high risk, etc.

[0114] Step C16: Determine the fuzzy evaluation value of each flight path on each of the delivery risk assessment indicators based on the evaluation factor set and the comment set.

[0115] It's important to note that the fuzzy evaluation value is calculated using fuzzy mathematics by mapping each flight path's performance on a set of evaluation factors to a set of comments. This process accounts for the complexity and uncertainty of each flight path under different risk factors. Through fuzzification, it can more accurately reflect the risk level of each flight path. When determining the fuzzy evaluation value, it is necessary to fully consider historical data, expert experience, and other factors to ensure the objectivity and accuracy of the evaluation results.

[0116] Step C17: Constructing a fuzzy evaluation matrix according to the fuzzy evaluation values.

[0117] It should be noted that the fuzzy evaluation matrix is ​​formed by arranging and combining the fuzzy evaluation values ​​of each flight path on the distribution risk assessment index according to certain rules to form a two-dimensional matrix.

[0118] It can be understood that through the fuzzy evaluation matrix, we can intuitively see the performance of each flight path on different distribution risk assessment indicators, thereby providing a more comprehensive and accurate basis for selecting the optimal flight path.

[0119] In practice, appropriate fuzzy mathematical methods, such as fuzzy cluster analysis and fuzzy comprehensive evaluation, can be used to calculate and analyze the fuzzy evaluation matrix, obtain the comprehensive risk score of each flight path, and sort them according to the score to select the optimal flight path. This process will effectively improve the delivery efficiency and safety of lifting drones and reduce delivery risks.

[0120] Step S403: performing a synthesis operation on the weight matrix and the fuzzy evaluation matrix to obtain a comprehensive risk assessment model.

[0121] It's important to note that the comprehensive risk assessment model is the result of a combined operation of the weight matrix and the fuzzy evaluation matrix. It comprehensively considers the importance of each delivery risk assessment indicator and the performance of each flight path on these indicators. This combined operation yields a numerical value reflecting the overall risk level of each flight path, known as the comprehensive risk score.

[0122] In practice, the weight matrix and fuzzy evaluation matrix are combined using a weighted average method. The weights of each indicator in the weight matrix are multiplied by the corresponding fuzzy evaluation values ​​in the fuzzy evaluation matrix, and the sum is calculated to obtain a comprehensive risk assessment score for each flight path. A lower comprehensive risk assessment score indicates a lower risk and a higher priority for the flight path.

[0123] Step S404: Calculate the comprehensive risk assessment score of each flight path according to the comprehensive risk assessment model.

[0124] It's important to note that the comprehensive risk assessment score quantifies the risk level of each flight path. It takes into account multiple factors, including flight path length, altitude, obstacle density, and weather conditions, as well as their relative importance in the assessment process. By calculating this comprehensive risk assessment score, the risk levels of different flight paths can be intuitively compared and ranked, providing strong support for selecting the optimal flight path for lifting drones.

[0125] In practice, a weighted average method can be used to calculate and analyze the comprehensive risk assessment model, obtaining a comprehensive risk assessment score for each flight path. These scores are then sorted to select the optimal flight path. This process will effectively improve the efficiency and safety of drone delivery, reduce delivery risks, and has significant practical application value.

[0126] Step S405: Prioritizing the flight paths from low to high according to the comprehensive risk assessment scores to obtain a flight path sequence, and selecting the flight path with the highest priority in the flight path sequence as the optimal flight path.

[0127] It's important to note that by ranking the comprehensive risk assessment scores, we can intuitively identify which flight paths have lower and higher risk levels. Selecting the highest-priority flight path as the optimal flight path ensures that the lifting drone chooses the safest and most efficient path when performing its mission, thereby improving delivery efficiency and safety.

[0128] This embodiment constructs a weight matrix and a fuzzy evaluation matrix through the AHP-fuzzy comprehensive evaluation algorithm, and performs comprehensive risk evaluation based on the comprehensive risk evaluation model obtained through the synthesis operation, thereby effectively improving the accuracy and efficiency of path selection.

[0129] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the condition monitoring method of the lifting drone of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0130] This application also provides a state monitoring device for hoisting drones, please refer to Figure 3 , the state monitoring device of the lifting drone includes: The acquisition module 10 is used to obtain the placement information of the dropped object and the flight status information of the lifting drone.

[0131] The modeling module 20 is used to model the UAV flight environment according to the delivery information and generate an environment map and an environment matrix.

[0132] The planning module 30 is used to perform path planning based on the environment map, the environment matrix, the delivery information and the flight status information to determine multiple flight paths.

[0133] The evaluation module 40 is used to perform a delivery risk evaluation on the plurality of flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain an optimal flight path.

[0134] The control module 50 is used to control the lifting drone to lift the drop object according to the optimal flight path and collect the position information and posture information of the lifting drone during the lifting process.

[0135] The monitoring module 60 is used to monitor the lifting status of the lifting drone in real time according to the position information and posture information.

[0136] The state monitoring device for a lifting drone provided in this application adopts the state monitoring method for a lifting drone in the above-mentioned embodiment, which can solve the technical problem that the traditional state monitoring method for a lifting drone has poor efficiency and accuracy, and increases the risk of lifting tasks. Compared with the prior art, the beneficial effects of the state monitoring device for a lifting drone provided in this application are the same as the beneficial effects of the state monitoring method for a lifting drone provided in the above-mentioned embodiment, and the other technical features of the state monitoring device for a lifting drone are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0137] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the condition monitoring method of the lifting drone in the above-mentioned embodiment.

[0138] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash memory), optical fiber, CD-ROM (CD-Read Only Memory), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0139] The computer-readable storage medium may be included in the condition monitoring device of the lifting drone; or it may exist independently without being assembled into the condition monitoring device of the lifting drone.

[0140] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the state monitoring device of the lifting drone, the state monitoring device of the lifting drone: obtains the delivery information of the delivery object and the flight status information of the lifting drone; models the drone flight environment according to the delivery information, and generates an environment map and an environment matrix; performs path planning according to the environment map, the environment matrix, the delivery information and the flight status information, and determines multiple flight paths; performs distribution risk assessment on the multiple flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain the optimal flight path; controls the lifting drone to lift the delivery object according to the optimal flight path and collects the position information and attitude information of the lifting drone during the lifting process; and monitors the lifting status of the lifting drone in real time according to the position information and attitude information.

[0141] The computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0142] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0143] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0144] The computer-readable storage medium provided in this application is a computer-readable storage medium storing computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for monitoring the condition of a lifting drone. This computer-readable storage medium can address the technical issues of conventional methods for monitoring the condition of lifting drones, which suffer from poor efficiency and accuracy and increase the risk of lifting operations. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the method for monitoring the condition of a lifting drone provided in the aforementioned embodiments and are not further elaborated here.

[0145] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for monitoring the status of a hoisting drone, characterized in that: The method comprises: Obtain information on the placement of objects and the flight status of the lifting drone; Modeling the UAV flight environment based on the deployment information to generate an environment map and an environment matrix; Performing path planning based on the environment map, the environment matrix, the delivery information, and the flight status information to determine multiple flight paths; Performing a delivery risk assessment on the plurality of flight paths based on the AHP-fuzzy comprehensive evaluation algorithm to obtain the optimal flight path; Controlling the lifting drone to lift the droplet according to the optimal flight path and collecting position information and posture information of the lifting drone during the lifting process; The lifting status of the lifting drone is monitored in real time according to the position information and posture information.

2. The method according to claim 1, wherein The step of modeling the UAV flight environment according to the delivery information and generating an environment map and an environment matrix includes: Determine the delivery location and delivery area according to the delivery information; Obtaining ground information through geographic information systems; Constructing a three-dimensional model of the UAV flight environment based on the ground information, the delivery location, and the delivery area; Dividing the flight environment into a plurality of grids according to the three-dimensional model, wherein each grid contains corresponding environmental feature information; Generate an environment map based on the environmental feature information of each grid; The environment map is rasterized with equal lengths in the X, Y, and Z directions to obtain an environment matrix, wherein the environment map is used to represent the layout and obstacles of the flight environment, and the environment matrix is ​​used to represent the traffic status and risk level of each grid in the flight environment.

3. The method according to claim 1, wherein The performing path planning according to the environment map, the environment matrix, the delivery information, and the flight status information to determine multiple flight paths includes: The environment map and the environment matrix are processed using a hybrid frog leaping algorithm to obtain multiple potential paths from a starting point to an end point; Setting a frog population, wherein each frog in the frog population represents a potential flight path; Calculate the path length and risk value of each frog based on the traffic status and risk level of the environmental matrix; The frog population is divided into a plurality of subgroups, and a local search is performed on each subgroup to obtain a local optimal solution, wherein during the local search process, information is exchanged between individual frogs in each subgroup to update their respective positions and paths; When the termination condition is met, the frogs of all subgroups are merged, and the local optimal solutions of each subgroup are globally exchanged to obtain the current optimal flight path; The environment map and the environment matrix are updated according to the current optimal flight path, and the step of processing the environment map and the environment matrix using a hybrid frog leaping algorithm to obtain multiple potential paths from the starting point to the end point is performed until multiple flight paths that meet the requirements are obtained.

4. The method according to claim 1, wherein The performing of path planning according to the environment map, the environment matrix, the delivery information and the flight status information to determine multiple flight paths further includes: Using a tabu search algorithm to search the environment map and the environment matrix to obtain multiple feasible flight paths from a starting point to an end point; Selecting any feasible flight path from the multiple feasible paths as an initial solution, and adding the initial solution to an empty taboo table to obtain a taboo table, wherein the taboo table is used to store the searched flight paths; Determine whether the taboo objects in the taboo table meet the amnesty conditions according to the set amnesty criteria; If satisfied, the taboo object is released from the taboo table and the taboo object is used as the current optimal solution; If not, selecting a path other than the taboo objects in the taboo table from the multiple feasible flight paths as a candidate solution; Calculating the fitness value of the candidate solution according to a preset evaluation function; Selecting the candidate solution with the best fitness value as the current solution, and replacing the taboo object that entered the taboo table earliest in the taboo table with the current solution; Determine whether the current solution satisfies a termination condition; If it is satisfied, then output the flight path that meets the requirements; If not, return to the step of searching the environment graph and the environment matrix using the tabu search algorithm to obtain multiple feasible flight paths from the starting point to the end point, and continue the iterative search until multiple flight paths that meet the requirements are obtained.

5. The method according to claim 1, wherein The performing of path planning according to the environment map, the environment matrix, the delivery information and the flight status information to determine multiple flight paths further includes: Using a variable domain search algorithm to search the environment map and the environment matrix to obtain multiple initial flight paths; Setting any one of the multiple initial flight paths as a current solution and defining a domain structure of the current solution; Randomly select a solution in the domain of the current solution as a candidate solution; Evaluate the path length and risk value of the candidate solution according to the traffic status and risk level of the environment matrix to obtain the evaluation result; If the evaluation result shows that the candidate solution is better than the current solution, the candidate solution is accepted as a new current solution, and the domain structure is updated; If the evaluation result shows that the candidate solution is not better than the current solution and satisfies the preset acceptance criteria, then the candidate solution is accepted as the new current solution or the current solution is kept unchanged according to the preset probability, and candidate solutions are re-selected within the domain of the current solution for evaluation until the domain search stop condition is met; Output a flight path that meets the requirements according to the stopping condition, or return to the step of using a variable domain search algorithm to search the environment map and the environment matrix to obtain multiple initial flight paths, and continue the iterative search until multiple flight paths that meet the requirements are obtained.

6. The method according to claim 1, wherein The delivery risk assessment of the plurality of flight paths is performed based on the AHP-fuzzy comprehensive evaluation algorithm to obtain the optimal flight path, including: Determining delivery risk assessment indicators, wherein the delivery risk assessment indicators include at least flight path length, flight altitude, obstacle density, and weather conditions; Constructing a weight matrix and a fuzzy evaluation matrix based on the distribution risk assessment indicators; Performing a synthetic operation on the weight matrix and the fuzzy evaluation matrix to obtain a comprehensive risk assessment model; Calculating a comprehensive risk assessment score for each flight path according to the comprehensive risk assessment model; The flight paths are prioritized from low to high according to the comprehensive risk assessment scores to obtain a flight path sequence, and the flight path with the highest priority in the flight path sequence is taken as the optimal flight path.

7. The method according to claim 6, wherein The construction of a weight matrix and a fuzzy evaluation matrix based on the distribution risk assessment index includes: Constructing a risk indicator judgment matrix based on the risk factors of each of the flight paths; Determining the relative importance score of each risk indicator according to the risk indicator judgment matrix; Determine the corresponding indicator weight based on the relative importance score of each risk indicator; Constructing a weight matrix according to the indicator weights; Constructing an evaluation factor set and a comment set, wherein the evaluation factor set includes risk factors of each of the flight paths, and the comment set includes multiple risk levels; Determining a fuzzy evaluation value of each flight path on each of the delivery risk assessment indicators based on the evaluation factor set and the comment set; A fuzzy evaluation matrix is ​​constructed according to the fuzzy evaluation values.

8. The method according to any one of claims 1 to 7, characterized in that After the real-time monitoring of the lifting status of the lifting drone according to the position information and the posture information, the method further includes: Calculate the swing angle and swing amplitude of the lifting drone in real time according to the position information and attitude information; generating a load displacement signal according to the swing angle and the swing amplitude; Get the desired trajectory of the drone; Inputting the load displacement signal and the desired trajectory into an energy-coupled anti-sway controller to generate an anti-sway control instruction, wherein the energy-coupled anti-sway controller is determined by calculating the swing energy of the lifting drone and the position change of the load center of mass according to a swing dynamics model; The posture of the lifting drone is adjusted according to the anti-sway control instruction.

9. A state monitoring device for a hoisting drone, characterized in that: The state monitoring device for the hoisting drone includes: The acquisition module is used to obtain the release information of the dropped objects and the flight status information of the lifting drone; A modeling module is used to model the UAV flight environment based on the deployment information and generate an environment map and an environment matrix; a planning module, configured to perform path planning based on the environment map, the environment matrix, the delivery information, and the flight status information, and determine multiple flight paths; An evaluation module, configured to perform a delivery risk evaluation on the plurality of flight paths based on an AHP-fuzzy comprehensive evaluation algorithm to obtain an optimal flight path; a control module, configured to control the lifting drone to lift the droplet according to the optimal flight path and to collect position information and posture information of the lifting drone during the lifting process; The monitoring module is used to monitor the lifting status of the lifting drone in real time based on the position information and posture information.

10. A storage medium, characterized in that: The storage medium stores a state monitoring program for a lifting drone, and when the state monitoring program for the lifting drone is executed by the processor, the state monitoring method for a lifting drone according to any one of claims 1 to 8 is implemented.

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