A low-altitude monitoring network deployment method and system based on a drone nest

Abstract

This invention discloses a method and system for deploying a low-altitude monitoring network based on UAV nests, comprising: selecting a set of pre-selected UAV nest locations that meet the deployment conditions; determining the monitoring area to be covered according to the monitoring task; performing variable-scale gridding processing on the monitoring area based on terrain and surface information, using grid cells as monitoring target objects, adjusting the grid cells to obtain the monitoring area; constructing a UAV nest deployment problem model; solving for the optimal nest deployment scheme using a heuristic search algorithm based on the UAV nest deployment problem model; and quickly determining and deploying new UAV nest locations based on the already deployed nest locations using the UAV nest deployment problem model when the deployment environment changes. This invention significantly improves monitoring coverage and response speed to emergencies by optimizing the UAV nest deployment strategy, thereby meeting the needs of low-altitude monitoring tasks.

Description

Technical Field

[0001] This invention relates to the field of low-altitude monitoring technology, and in particular to a method and system for deploying a low-altitude monitoring network based on UAV nests. Background Technology

[0002] Low-altitude monitoring tasks place extremely high demands on real-time performance and efficiency. Unmanned aerial vehicles (UAVs), with their high efficiency, flexibility, and ease of deployment, have become an important tool in low-altitude monitoring. However, in practical applications, the wide distribution of natural resources and the complexity of terrain limit the continuous monitoring capabilities of UAVs over large areas due to factors such as endurance, flight distance, and inspection range. UAV nests provide centralized take-off, landing, charging, maintenance, and storage functions, enabling multiple UAVs to continuously perform aerial monitoring, patrol, and mission operations within a single area. Due to limitations such as complex terrain, uneven building distribution, and no-fly zones, optimizing the deployment of UAV nests has become a key challenge. Existing technologies lack systematic deployment methods in complex terrain and limited resources, resulting in low monitoring efficiency and high operating costs. Therefore, how to optimize the deployment of UAV nests to achieve efficient coverage of the monitored area within limited time and resources to meet the needs of natural resource monitoring tasks is a critical problem that urgently needs to be solved in the current technological field. Summary of the Invention

[0003] Based on this, the present invention provides a method and system for deploying a low-altitude monitoring network based on UAV nests to solve the problem of how to optimize the deployment of UAV nests so that they can achieve efficient coverage of the monitoring area within limited time and resources.

[0004] In a first aspect, the present invention provides a method for deploying a low-altitude monitoring network based on unmanned aerial vehicle (UAV) nests, comprising:

[0005] Based on the real-scene 3D building model and no-fly zone information, pre-selected deployment sites for drone nests are obtained, and a set of pre-selected drone nest sites that meet the deployment conditions are selected according to the deployment restrictions; the deployment restrictions include flat roofs, high-rise buildings, surrounding environment and no-fly zones;

[0006] The monitoring area to be covered is determined based on the monitoring task. The monitoring area is then processed into a variable-scale grid based on the terrain and surface information of the digital elevation model and digital surface model. The grid cells are used as the objects of monitoring needs. The grid cells are adjusted according to no-fly information and the complexity of terrain and buildings to obtain the monitoring area.

[0007] Define a set of demand objects and a set of pre-selected locations for drone nests, set decision variables to indicate whether to deploy drone nests at the pre-selected locations, and set decision variables to indicate whether the demand objects are monitored by a specific drone nest, and construct a drone nest deployment problem model;

[0008] Based on the aforementioned UAV nest deployment problem model, the optimal nest deployment scheme is solved using a heuristic search algorithm.

[0009] Based on the existing set of pre-selected drone nest locations, it is determined whether there are any changes in the deployment environment. If so, new drone nest locations are quickly determined and deployed based on the already deployed nest locations using the drone nest deployment problem model.

[0010] The acquisition of the pre-selected deployment locations for the drone nests includes,

[0011] Based on real-scene 3D building data, flat-roofed buildings are identified, and buildings with a roof slope of less than 5° and a roof area of ​​more than 200 square meters are selected as pre-selected deployment points for drone nests.

[0012] Select high-rise buildings taller than 20 meters with power supply and network connectivity as pre-selected deployment sites for drone nests;

[0013] Assess the flight adaptability within a 50-meter radius of the candidate sites, and select candidate sites without tall obstacles and with environments suitable for drone take-off, landing and flight as pre-selected deployment sites for drone nests;

[0014] Candidate sites located within the no-fly zone or within 100 meters of the no-fly zone boundary will be eliminated.

[0015] The acquisition of the monitoring area includes,

[0016] After determining the scope of the monitoring area, the scope of the monitoring area is gridded. Each grid cell is defined as the monitoring target. The grid division takes into account the terrain features. Areas with high terrain or complex terrain are further divided to obtain the monitoring area.

[0017] The construction of the model for the drone nest deployment problem includes,

[0018] Define a set of demand objects I and a set of pre-selected drone nest locations J, where i∈I, j∈J, and set the coverage radius of the nests to r. j Based on the terrain and building features surrounding the drone nest deployment site, the monitoring range of each nest is pre-defined; the decision variable X is introduced. j and Y ij X j ∈{0,1},Y ij ∈{0,1},

[0019]

[0020] Where, r h ≤r j ≤r max r h R represents the standard minimum coverage radius. max Indicates the standard maximum coverage radius;

[0021] Based on decision variable X j and Y ij The drone nest deployment problem is modeled as an integer linear programming problem, with the objective of minimizing the number of nests deployed while achieving full coverage of the monitoring area. The objective function expression is:

[0022]

[0023] in, Indicates the number of nests to select. This is a penalty item;

[0024] The inclusion of a penalty term in the objective function causes the model to prioritize nests with smaller radii to ensure coverage quality, while allowing for a reduction in the number of nests by increasing the radius; if the nest coverage radius is set to exceed the standard minimum coverage radius r... h The penalty term is then calculated as follows:

[0025] P j =λ·(r j -r h )·X j ;

[0026] Where λ represents the penalty coefficient.

[0027] The solution to the optimal nesting deployment scheme includes,

[0028] A heuristic search algorithm is used to solve the UAV nest deployment model. The heuristic search algorithm continuously iterates and optimizes within the search space until a preset goal is reached, and then outputs the optimal solution of the nest deployment scheme.

[0029] The determination and deployment of new drone nest locations include,

[0030] If the deployment environment changes, the deployment plan needs to be adjusted quickly to restore monitoring capabilities. The UAV nest deployment problem model simulates and calculates the coverage of the nest when the deployment environment changes, and identifies the objects that cannot be effectively covered.

[0031] New pre-selected locations are introduced, and their contribution to restoring coverage is calculated. A heuristic search algorithm is used to find the optimal new nest deployment scheme and determine the deployment location of the new nests.

[0032] A second aspect of the present invention provides a low-altitude monitoring network deployment system based on UAV nests, comprising:

[0033] The module for filtering the pre-selected drone nest locations is used to obtain pre-selected deployment locations for drone nests based on real-world 3D building models and no-fly zone information, and to filter out a set of pre-selected drone nest locations that meet the deployment conditions according to deployment restrictions.

[0034] The monitoring area acquisition module is used to determine the monitoring area to be covered according to the monitoring task. Based on the terrain and surface information of the digital elevation model and digital surface model, the monitoring area is processed into a variable-scale grid. The grid cell is used as the monitoring requirement object. The grid cell is adjusted according to the no-fly information and the complexity of terrain and buildings to obtain the monitoring area.

[0035] The module for constructing the drone nest deployment problem model is used to define the set of demand objects and the set of pre-selected drone nest locations, set decision variables to indicate whether to deploy drone nests at the pre-selected locations, set decision variables to indicate whether the demand objects are monitored by a specific drone nest, and construct the drone nest deployment problem model.

[0036] The optimal nest deployment scheme solution module is used to solve the optimal nest deployment scheme based on the UAV nest deployment problem model through a heuristic search algorithm.

[0037] The new drone nest location deployment module is used to determine whether there are changes in the deployment environment based on the existing set of pre-selected drone nest locations. If so, it quickly determines and deploys new drone nest locations based on the already deployed nest locations using the drone nest deployment problem model.

[0038] A third aspect of the present invention provides an electronic device, comprising: a processor; and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the method described above.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method described above.

[0040] Beneficial Effects: This invention combines various data sources, including 3D real-world building models, digital elevation models, digital surface models, and no-fly zone information, to rationally select pre-selected deployment locations for UAV nests. It also enhances adaptability to complex terrain through multi-scale grid division. Based on integer linear programming, the invention optimizes the UAV nest deployment problem model, constructing a comprehensive and efficient UAV nest deployment scheme that ensures full coverage of the monitoring area while minimizing nest deployment costs. Combined with a heuristic search algorithm, the system can quickly solve for the optimal deployment scheme and rapidly generate additional nest deployment schemes in case of emergencies, ensuring the stability and flexibility of the monitoring network.

[0041] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0042] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of the invention. Wherein:

[0043] Figure 1 This is a flowchart of the method for deploying a low-altitude monitoring network for unmanned aerial vehicle (UAV) nests according to the present invention;

[0044] Figure 2a This is a schematic diagram of the pre-selected location deployment of UAV nests provided by the present invention;

[0045] Figure 2b This is a schematic diagram of the multi-scale grid division process of the monitoring area provided by the present invention;

[0046] Figure 3 This is a schematic diagram of the optimal nesting deployment scheme provided by the present invention;

[0047] Figure 4a This is a schematic diagram of the affected area after an existing machine nest is damaged by unforeseen factors, according to the present invention.

[0048] Figure 4b This is a schematic diagram illustrating the new UAV nest location deployment effect provided by the present invention;

[0049] Figure 5 This is a schematic diagram of a low-altitude monitoring network deployment system for unmanned aerial vehicle (UAV) nests provided by the present invention;

[0050] Figure 6 This is a block diagram of an electronic device provided according to the present invention. Detailed Implementation

[0051] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0052] In a first aspect, the present invention provides a method for deploying a low-altitude monitoring network based on unmanned aerial vehicle (UAV) nests, comprising:

[0053] The flowchart of the deployment method of the low-altitude monitoring network for UAV nests is as follows: Figure 1 As shown.

[0054] S1: Based on a realistic 3D building model and no-fly zone information, pre-selected deployment sites for drone nests are obtained, and a set of pre-selected drone nest sites that meet the deployment conditions is selected according to deployment restrictions. It should be noted that:

[0055] The deployment restrictions include flat roofs, tall buildings, surrounding environment, and no-fly zones;

[0056] The acquisition of the pre-selected deployment locations for the drone nests includes,

[0057] ① Flat-roofed building selection: Based on real-scene 3D building data, identify flat-roofed buildings and select buildings with a roof slope of less than 5° and a roof area of ​​more than 200 square meters as pre-selected deployment points for drone nests;

[0058] ② Screening of high-rise buildings: Select high-rise buildings that are more than 20 meters tall and have power supply and network connection as pre-selected deployment sites for drone nests;

[0059] ③ Surrounding environment screening: Evaluate the flight adaptability within a 50-meter radius of the candidate site, and select candidate sites without tall obstacles and with an environment suitable for drone take-off, landing and flight as pre-selected deployment sites for drone nests;

[0060] ④ Eliminate no-fly zones: Eliminate candidate sites located within no-fly zones or within 100 meters of the no-fly zone boundary.

[0061] Figure 2a A schematic diagram of the pre-selected locations for drone nesting sites.

[0062] S2: Determine the monitoring area to be covered based on the monitoring task. Utilize topographic and surface information from digital elevation models and digital land surface models to perform variable-scale gridding of the monitoring area. Use grid cells as the monitoring target, considering no-fly zones and the complexity of terrain and buildings to adjust the grid cells and obtain the monitoring area. Based on the requirements of natural resource monitoring tasks, this embodiment delineates the monitoring area and performs multi-scale gridding, dynamically adjusting the deployment density of UAV nests to improve adaptability to complex terrain and monitoring accuracy. It should be noted that:

[0063] The acquisition of the monitoring area includes,

[0064] After determining the scope of the monitoring area, the scope of the monitoring area is gridded. Each grid cell is defined as the monitoring target. The grid division takes into account the terrain features. Areas with high terrain or complex terrain are further divided to obtain the monitoring area. Figure 2b A schematic diagram of the multi-scale grid division process for the monitoring area.

[0065] This embodiment uses Yuhang District of Hangzhou City as the coverage area for natural resource monitoring. After determining the area scope, this embodiment uses a digital elevation model to perform variable-scale gridding processing on the study area. Specifically, for areas below 500 meters above sea level, a 1500m × 1500m grid is used; for mountainous areas above 500 meters above sea level, due to their complex terrain, a 750m × 750m grid is used to enhance the monitoring capability for complex terrain. This embodiment defines each grid cell as a monitoring target. Special consideration is given to flight restriction limits; no-fly zones are excluded from the grid to avoid including no-fly zones in the monitoring coverage area, thereby ensuring the flight safety of the UAV.

[0066] S3: Define the set of demand objects and the set of pre-selected locations for drone nests, set decision variables to indicate whether to deploy drone nests at the pre-selected locations, set decision variables to indicate whether the demand objects are monitored by a specific drone nest, and build a drone nest deployment problem model;

[0067] like Figure 3 The diagram shown is a schematic representation of the optimal nesting deployment scheme obtained by using the method of the present invention in an exemplary embodiment of this application.

[0068] The construction of the model for the drone nest deployment problem includes,

[0069] Define a set of demand objects I and a set of pre-selected drone nest locations J, where i∈I, j∈J, and set the coverage radius of the nests to r. j Based on the terrain and building features surrounding the drone nest deployment site, the monitoring range of each nest is pre-defined; the decision variable X is introduced. j and Yij X j ∈{0,1},Y ij ∈{0,1},

[0070]

[0071] Where, r h ≤r j ≤r max r h R represents the standard minimum coverage radius. max Indicates the standard maximum coverage radius;

[0072] Y ij This can be specifically represented as a reachability matrix between a demand object and a nest:

[0073]

[0074] Where m represents the number of demand objects, and n represents the number of pre-selected locations for drone nests;

[0075] Furthermore, the accessibility between the UAV nest and the grid target is determined through distance measurement and GIS geographic analysis. The accessibility analysis steps are as follows: (1) Determine if the distance is within the safe working distance range: Determine if the straight-line distance from the UAV nest to the grid target is within the safe working range. (2) Determine if there are any flight obstacles in between: Based on the flight altitude and path from the nest to the target, check for obstructions such as mountains and buildings through view analysis. If the route is blocked, the path is considered unreachable. If the distance between the UAV nest and the grid target is within the safe working range and there are no flight obstacles, then Y is given. ij A value of 1 indicates that it is within the reachable range; otherwise, it is 0.

[0076] Based on decision variable X j and Y ij The drone nest deployment problem is modeled as an integer linear programming problem, with the objective of minimizing the number of nests deployed while achieving full coverage of the monitoring area. The objective function expression is:

[0077]

[0078] in, Indicates the number of nests to select. This is a penalty factor used to balance the number of nests and their coverage effect;

[0079] If the coverage radius of the nest is set to exceed the standard minimum coverage radius r h The penalty term is then calculated as follows:

[0080] Pj =λ·(r j -r h )·X j ;

[0081] Where λ represents the penalty coefficient, λ∈[0,1], which is used to control the intensity of the penalty.

[0082] The inclusion of a penalty term in the objective function encourages the preference for nests with smaller radii to ensure coverage quality, while allowing for a reduction in the number of nests by increasing the radius.

[0083] Furthermore, the constraints of the drone nest deployment problem model are as follows:

[0084]

[0085] in, To ensure coverage constraints, all objects i must be covered by at least one drone nest deployed at a pre-selected point j, guaranteeing full coverage of the monitoring area; j +(1-Y ij )·M≥d ij The radius constraint indicates that when the nest deployment point j is selected for monitoring the object i (i.e., Y), ij =1) Coverage radius r j It must be ensured that the distance d from the demand object i to the nest j is greater than the distance d. ij M is a sufficiently large constant to ensure that the constraint does not affect the results when the nest is not selected; X j ∈{0,1} and Y ij ∈{0,1} is a parameter constraint for X j and Y ij The values ​​of P are constrained to ensure the integrity and operability of the model; 0≤P j ≤1 is a constraint on the penalty term to ensure that its value is between 0 and 1.

[0086] This model considers the actual needs of UAV nest deployment and models them appropriately. By introducing coverage constraints, the comprehensiveness of the monitoring task is ensured. The objective function is designed so that the model can optimize nest selection as much as possible while ensuring coverage requirements, reasonably balancing the number of nests and coverage effect, thereby achieving more efficient resource allocation.

[0087] S4: Based on the aforementioned UAV nest deployment problem model, the optimal nest deployment scheme is solved using a heuristic search algorithm;

[0088] The solution to the optimal nesting deployment scheme includes,

[0089] A heuristic search algorithm is used to solve the UAV nest deployment model. The heuristic search algorithm continuously iterates and optimizes within the search space until a preset goal is reached, and then outputs the optimal solution of the nest deployment scheme.

[0090] The heuristic search algorithm first prioritizes locations that can cover more target objects as initial locations. Secondly, through iterative heuristic search, it continuously makes local adjustments, such as adding or removing locations, to optimize coverage and deployment quantity. After each adjustment, the objective function value is recalculated, and a neighborhood search is used to find a better solution near the current solution, continuously selecting the scheme with the optimal objective function value for further optimization. The algorithm stops when it reaches the preset goal (all constraints are satisfied, and the objective function value no longer improves after multiple iterations), outputs the current optimal solution, and determines the best nest deployment scheme.

[0091] A heuristic search algorithm is used to find the optimal nesting deployment scheme. The specific process is as follows.

[0092] (1) Initial solution setting

[0093] In the complex search space, the pre-selected points that can cover more demanding objects are selected as the initial solution, and the initial radius of the UAV nest is uniformly set as the standard minimum coverage radius r. h As the initial solution (x) (0) ,r (0) );

[0094] (2) Iterative update

[0095] In each iteration, the current solution is updated, and the current optimal nest position x is selected. (k) and coverage radius r (k) The optimization process can be represented as:

[0096]

[0097] Where, x (k) f(x) represents the optimal solution selected in the k-th iteration. j ,r j ) represents the objective function, (x) j ,r j () represents the nest location and corresponding coverage radius that minimize the objective function while satisfying all constraints;

[0098] If the current configuration satisfies all constraints, continue optimization; otherwise, adjust the drone nest location or coverage radius to improve coverage; use the neighborhood search method to continue exploring possible better solutions in the vicinity of the current solution to ensure local optimization and gradually approach the global optimum.

[0099] (3) Evaluate the current solution

[0100] After each local adjustment iteration, evaluate the objective function value of the current solution:

[0101] Z k =f(x) (k) ,r (k) );

[0102] Among them, Z k Let f(x) represent the objective function value in the k-th iteration. (k) ,r (k) ) represents the current solution x (k) and r (k) The corresponding objective function;

[0103] (4) Termination conditions

[0104] The iterative process of the heuristic search algorithm will continue until the following preset termination condition is met: constraint conditions All conditions are met, and the objective function value no longer improves after multiple iterations:

[0105] |Z (k+1) -Z (k) |<∈;

[0106] Among them, |Z (k+1) -Z (k) | represents the difference between the (k+1)th and kth iteration objective function values, where ∈ is a small threshold representing the allowable error; when |Z (k+1) -Z (k) When the value of | is less than ∈, it indicates that the objective function value has stabilized and the heuristic search algorithm can terminate. The heuristic search algorithm stops after reaching the optimization objective and outputs the final determined optimal nest deployment scheme. Through the above steps (1)-(4), the heuristic search algorithm can efficiently search for and find the optimal or near-optimal solution that satisfies the objective and constraints.

[0107] S5: Based on the existing set of pre-selected drone nest locations, determine whether there are any changes in the deployment environment. If so, quickly determine and deploy new drone nest locations based on the already deployed nest locations using the drone nest deployment problem model.

[0108] The determination and deployment of new drone nest locations include,

[0109] If the deployment environment changes, the deployment plan needs to be adjusted quickly to restore monitoring capabilities. The UAV nest deployment problem model simulates and calculates the coverage of the nest when the deployment environment changes, and identifies the objects that cannot be effectively covered.

[0110] New pre-selected locations are introduced, and their contribution to restoring coverage is calculated. A heuristic search algorithm is used to find the optimal new nest deployment scheme and determine the deployment location of the new nests.

[0111] Figure 4a This refers to the area affected by unforeseen damage to existing aircraft housings. Figure 4b This diagram illustrates the effect of deploying the new drone nest location.

[0112] This embodiment first simulates the coverage of the nest due to changes in the deployment environment, and calculates the objects that cannot be covered due to the change in deployment environment, such as... Figure 4a As shown in the diagram. Based on existing nests, new pre-selected locations are introduced for analysis, calculating the potential contribution of each new location to coverage restoration. Combined with the locations of existing undamaged nests, candidate locations for new nests that can maximize coverage restoration are quickly selected, such as... Figure 4b As shown in the diagram. In this process, this embodiment does not require a global solution for the overall solution; instead, it employs a local optimization strategy, prioritizing points that can restore coverage to critical areas most quickly. Through rapid iteration, the coverage effect of each newly added point is locally adjusted, and efficient solutions are deployed first based on the needs of the affected areas. This optimization method can provide a rapid recovery solution for damaged situations in a short time.

[0113] This embodiment uses Yuhang District of Hangzhou City as the experimental area, with a total monitoring range covering 942 square kilometers. The raw data used in the experiment include a 3D urban building model, vector data of construction land and cultivated land areas, vector data of UAV no-fly zones, and digital elevation model (DEM) data. To verify the superiority of the method proposed in this invention, the algorithm proposed in this invention is compared with the traditional manual deployment method based on empirical rules. The evaluation indicators of the deployment results include the required number of UAV nests, regional coverage rate, average effective coverage area per UAV nest, boundary blind spots, and coverage redundancy rate. Specific results are shown in Table 1.

[0114] Table 1 Comparison of the effects of manual deployment and algorithmic deployment methods

[0115]

[0116] Comparative experimental results show that the deployment method proposed in this invention significantly outperforms traditional manual deployment methods in several key indicators. Specifically, this method reduces the number of drone nests while increasing regional coverage to 100%, increasing the effective coverage area by 17.8%, and completely eliminating monitoring blind spots, thus greatly improving the comprehensiveness and accuracy of monitoring. The coverage redundancy rate is reduced from 24% to 13%, significantly optimizing resource utilization efficiency. The experimental results demonstrate the advantages of the algorithmic deployment method in terms of economy, monitoring performance, and resource allocation, providing strong support for the scientific deployment of UAV low-altitude monitoring networks.

[0117] In a second aspect, this invention provides a low-altitude monitoring network deployment system based on UAV nests (such as...). Figure 5 (As shown), including:

[0118] The unmanned aerial vehicle (UAV) nest pre-selection site set filtering module 1 is used to obtain the pre-selection deployment sites of UAV nests based on the real-scene 3D building model and no-fly zone information, and filter out the set of pre-selection sites of UAV nests that meet the deployment conditions according to the deployment restrictions.

[0119] The monitoring area acquisition module 2 is used to determine the monitoring area range that needs to be covered according to the monitoring task. Based on the terrain and surface information of the digital elevation model and digital surface model, the monitoring area range is processed into a variable-scale grid. The grid unit is used as the monitoring requirement object. The grid unit is adjusted according to the no-fly information and the complexity of terrain and buildings to obtain the monitoring area.

[0120] The UAV nest deployment problem model construction module 3 is used to define the set of demand objects and the set of pre-selected UAV nest locations, set decision variables to indicate whether to deploy nests at the pre-selected locations, and set decision variables to indicate whether the demand objects are monitored by a specific nest, thus constructing a UAV nest deployment problem model.

[0121] The optimal nest deployment scheme solution module 4 is used to solve the optimal nest deployment scheme based on the UAV nest deployment problem model using a heuristic search algorithm.

[0122] The new UAV nest location deployment module 5 is used to determine whether there is a change in the deployment environment based on the existing set of pre-selected UAV nest locations. If so, it quickly determines and deploys new UAV nest locations based on the already deployed nest locations using the UAV nest deployment problem model.

[0123] A third aspect of the present invention provides an electronic device, comprising: a processor; and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the method described above.

[0124] like Figure 6 As shown, the electronic device 100 is the core processing unit of this system, undertaking the key tasks of data processing and algorithm execution. This device mainly consists of a processor 101 and a memory 103, which are connected via a bus 102 to achieve efficient information transmission and data processing. The structure of this electronic device 100 does not constitute a limitation on the embodiments of this application.

[0125] The processor 101 can be a CPU, DSP, ASIC, FPGA, or other programmable logic device, specifically designed to execute the logic framework and algorithms related to UAV nest deployment. Its functions include key tasks such as real-time data analysis, monitoring area division, and solving for the optimal deployment scheme, ensuring the efficient operation of the UAV nest low-altitude monitoring network in complex environments.

[0126] Bus 102 is used to transfer information between processor 101 and memory 103. It can be a PCI bus or an EISA bus, supporting multiple data transfer modes, including address, data, and control signals. This design ensures real-time data processing and feedback, enabling the system to quickly respond to changes in monitoring tasks and optimize UAV nest deployment strategies. For ease of illustration, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0127] The memory 103 may include various types of storage devices such as ROM and RAM, or any other medium capable of storing program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. The memory 103 is used to store static information and dynamic data required for the deployment of the UAV nest low-altitude monitoring network. It can store algorithms, model parameters, flight paths, and deployment strategies related to the monitoring mission, ensuring that the system can efficiently call and process this information during execution.

[0128] In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method described above.

[0129] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0130] The units may or may not be physically separate. The components shown as units can be one or more physical units, meaning they can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0132] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, essentially, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for deploying a low-altitude monitoring network based on UAV nests, characterized in that, include: Based on the real-scene 3D building model and no-fly zone information, pre-selected deployment sites for drone nests are obtained, and a set of pre-selected drone nest sites that meet the deployment conditions are selected according to the deployment restrictions; the deployment restrictions include flat roofs, high-rise buildings, surrounding environment and no-fly zones; The monitoring area to be covered is determined based on the monitoring task. The monitoring area is then processed into a variable-scale grid based on the terrain and surface information of the digital elevation model and digital surface model. The grid cells are used as the objects of monitoring needs. The grid cells are adjusted according to no-fly zone information, terrain and building complexity to obtain the monitoring area. Define a set of demand objects and a set of pre-selected drone nest locations. Set a first decision variable to indicate whether to deploy drone nests at the pre-selected locations, and set a second decision variable to indicate whether the demand objects are monitored by a specific drone nest. Construct a drone nest deployment problem model and optimize the drone nest deployment problem model based on the integer linear programming method to minimize the number of drone nests deployed under the premise of full coverage of the monitoring area. The model takes the minimum sum of the number of drone nests to be selected and the penalty term for exceeding the standard minimum coverage radius as the optimization objective, and uses coverage constraints and radius constraints as constraints. Based on the aforementioned UAV nest deployment problem model, the optimal nest deployment scheme is solved using a heuristic search algorithm. Based on the existing set of pre-selected drone nest locations, it is determined whether there are any changes in the deployment environment. If so, new drone nest locations are quickly determined and deployed based on the already deployed nest locations using the drone nest deployment problem model.

2. The method for deploying a low-altitude monitoring network based on UAV nests according to claim 1, characterized in that: The acquisition of the pre-selected deployment locations for the drone nests includes, Based on real-scene 3D building data, flat-roofed buildings are identified, and buildings with a roof slope of less than 5° and a roof area of ​​more than 200 square meters are selected as pre-selected deployment points for drone nests. Select high-rise buildings taller than 20 meters with power supply and network connectivity as pre-selected deployment sites for drone nests; Assess the flight adaptability within a 50-meter radius of the candidate sites, and select candidate sites without tall obstacles and with environments suitable for drone take-off, landing and flight as pre-selected deployment sites for drone nests; Candidate sites located within the no-fly zone or within 100 meters of the no-fly zone boundary will be eliminated.

3. A method for deploying a low-altitude monitoring network based on UAV nests according to claim 1 or 2, characterized in that: The acquisition of the monitoring area includes, After determining the scope of the monitoring area, the scope of the monitoring area is gridded. Each grid cell is defined as the monitoring target. The grid division takes into account the terrain features. Areas with high terrain or complex terrain are further divided to obtain the monitoring area.

4. The method for deploying a low-altitude monitoring network based on UAV nests according to claim 3, characterized in that: The construction of the model for the drone nest deployment problem includes, Define the collection of requirement objects and the set of pre-selected locations for drone nests ,in , Set the coverage radius of the nest to be Based on the terrain and building features surrounding the drone deployment sites, the monitoring range of each drone nest is pre-defined; decision variables are introduced. and , , ； ； in, , Indicates the standard minimum coverage radius. Indicates the standard maximum coverage radius; Based on decision variables and The drone nest deployment problem is modeled as an integer linear programming problem, with the objective of minimizing the number of nests deployed while achieving full coverage of the monitoring area. The objective function expression is: ； in, Indicates the number of nests to select. This is a penalty item; If the coverage radius of the cell is set to exceed the standard minimum coverage radius The penalty term is then calculated as follows: ； in, This represents the penalty coefficient.

5. A method for deploying a low-altitude monitoring network based on UAV nests according to claim 4, characterized in that: The solution to the optimal nesting deployment scheme includes, A heuristic search algorithm is used to solve the UAV nest deployment model. The heuristic search algorithm continuously iterates and optimizes within the search space until a preset goal is reached, and then outputs the optimal solution of the nest deployment scheme.

6. The method for deploying a low-altitude monitoring network based on UAV nests according to claim 5, characterized in that: The determination and deployment of new drone nest locations include, If the deployment environment changes, the deployment plan needs to be adjusted quickly to restore monitoring capabilities. The UAV nest deployment problem model simulates and calculates the coverage of the nest when the deployment environment changes, and identifies the objects that cannot be effectively covered. New pre-selected locations are introduced, and their contribution to restoring coverage is calculated. A heuristic search algorithm is used to find the optimal new nest deployment scheme and determine the deployment location of the new nests.

7. A system employing the low-altitude monitoring network deployment method based on UAV nests as described in any one of claims 1-6, characterized in that, include: The module for filtering the pre-selected drone nest locations is used to obtain pre-selected deployment locations for drone nests based on real-world 3D building models and no-fly zone information, and to filter out a set of pre-selected drone nest locations that meet the deployment conditions according to deployment restrictions. The monitoring area acquisition module is used to determine the monitoring area to be covered according to the monitoring task. Based on the terrain and surface information of the digital elevation model and digital surface model, the monitoring area is processed into a variable-scale grid. The grid cell is used as the monitoring requirement object. The grid cell is adjusted according to the no-fly information, terrain and building complexity to obtain the monitoring area. The UAV nest deployment problem model construction module is used to define the set of demand objects and the set of pre-selected UAV nest locations. A first decision variable is set to indicate whether to deploy nests at the pre-selected locations, and a second decision variable is set to indicate whether the demand objects are monitored by a specific nest. The UAV nest deployment problem model is constructed, and the UAV nest deployment problem model is optimized based on the integer linear programming method to minimize the number of nests deployed under the premise of full coverage of the monitoring area. The model uses the minimum sum of the number of nests to be selected and the penalty term for exceeding the standard minimum coverage radius as the optimization objective, and coverage constraints and radius constraints as constraints. The optimal nest deployment scheme solution module is used to solve the optimal nest deployment scheme based on the UAV nest deployment problem model through a heuristic search algorithm. The new drone nest location deployment module is used to determine whether there are changes in the deployment environment based on the existing set of pre-selected drone nest locations. If so, it quickly determines and deploys new drone nest locations based on the already deployed nest locations using the drone nest deployment problem model.

8. An electronic device, comprising: processor; And a memory for storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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