Unmanned aerial vehicle path planning method and device, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of unmanned aerial vehicle routing inspection, and discloses an unmanned aerial vehicle path planning method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a routing inspection region of an unmanned aerial vehicle, dividing the routing inspection region into a plurality of routing inspection sub-regions, inputting the area maximum noise of the plurality of inspection sub-areas to a preset maximum flight speed calculation formula and a preset minimum flight height calculation formula, calculating the maximum flight speed and the minimum flight height of the unmanned aerial vehicle in each inspection sub-area, and determining the maximum flight speed and the minimum flight height of the unmanned aerial vehicle in each inspection sub-area based on the maximum flight speed and the minimum flight height in combination with a preset unmanned aerial vehicle path planning algorithm. Calculating an inspection path of the unmanned aerial vehicle in the inspection area; through the maximum flight speed and the minimum flight height calculated based on the area maximum noise of each inspection sub-area, and in combination with the preset unmanned aerial vehicle path planning algorithm, the inspection path of the unmanned aerial vehicle in the inspection area is calculated, so that the path planning efficiency of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present application relates to the technical field of drone patrol, and more specifically, to a drone path planning method, device, electronic device, and storage medium. Background Art

[0002] With the rapid development of drone technology, drones are increasingly widely used in the field of patrol. Drone patrol has the advantages of high efficiency, low cost, and strong accessibility to high-risk areas, so it is widely used in equipment patrol work in industries such as power, petroleum, and chemical industry. However, the problem of noise pollution generated by drones during patrol is gradually emerging. Especially in noise-sensitive areas such as cities, residential areas, and schools, the noise of drones may have an adverse impact on people's lives and work.

[0003] In existing drone patrol path planning algorithms, the main focus is on how to maximize the patrol efficiency and coverage of drones, while less consideration is given to the problem of noise pollution. Traditional drone patrol path planning algorithms often adopt fixed patrol speeds and altitudes, and cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times. This results in the noise of drones in some noise-sensitive areas may cause unnecessary interference to people's lives, and even lead to complaints and disputes.

[0004] Therefore, in order to solve the technical problem that traditional drone patrol path planning algorithms cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times, there is an urgent need for a drone path planning method, device, electronic device, and storage medium. Summary of the Invention

[0005] The purpose of the present application is to provide a drone path planning method, device, electronic device, and storage medium. By calculating the maximum flight speed and minimum flight altitude based on the maximum noise in each patrol sub-region, and combining with a preset drone path planning algorithm, the patrol path of the drone in the patrol area is calculated, solving the problem that traditional drone patrol path planning algorithms cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times. By dynamically calculating the optimal flight parameters for each region, the noise impact of the drone on sensitive areas is specifically reduced, and the path planning efficiency of the drone is improved.

[0006] In a first aspect, the present application provides a drone path planning method, including: Obtain the patrol area of the drone; Divide the patrol area into multiple patrol sub-regions; Input the maximum noise of each of the multiple inspection sub - regions into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and the minimum flight height of the drone in each of the inspection sub - regions; Based on the maximum flight speed and the minimum flight height, and in combination with a preset drone path planning algorithm, calculate the inspection path of the drone in the inspection region.

[0007] The drone path planning method provided by this application can realize the planning of the drone path. By calculating the maximum flight speed and the minimum flight height based on the maximum noise of each inspection sub - region, and in combination with a preset drone path planning algorithm, the inspection path of the drone in the inspection region is calculated, solving the problem that the traditional drone inspection path planning algorithm cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times. By dynamically calculating the optimal flight parameters for each region, the noise impact of the drone on sensitive regions is specifically reduced, improving the path planning efficiency of the drone.

[0008] Optionally, inputting the maximum noise of each of the multiple inspection sub - regions into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculating the maximum flight speed and the minimum flight height of the drone in each of the inspection sub - regions includes: Obtain the inspection time corresponding to each of the inspection sub - regions; According to the inspection sub - region and the inspection time, and through a preset noise level division criterion, determine the maximum noise of the drone in each of the inspection sub - regions during the corresponding inspection time; Input the maximum noise of the inspection sub - region into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and the minimum flight height of the drone in each of the inspection sub - regions.

[0009] The drone path planning method provided by this application can realize the planning of the drone path. By using a preset noise level division criterion for dual - dimension evaluation, it can generate differentiated maximum noise thresholds for different regions (such as residential areas and industrial areas) and different time periods (such as weekdays and holidays). Inputting the maximum noise threshold into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, the maximum flight speed and the minimum flight height are calculated, enabling the flight parameters of the drone to be automatically adjusted according to changes in time and space. This spatio - temporal coupled noise evaluation method breaks through the limitations of the traditional fixed - threshold method and realizes the adaptive balance between noise control and inspection task requirements.

[0010] Optionally, according to the inspection sub - regions and the inspection time, by using a preset noise level division criterion, the maximum noise in the regions when the drone inspects each of the inspection sub - regions during the corresponding inspection time is determined, including: Read a noise level division table pre - constructed based on a preset noise level division criterion; According to the inspection sub - regions and the inspection time, determine the maximum noise in the regions when the drone inspects each of the inspection sub - regions during the corresponding inspection time from the noise level division table.

[0011] Optionally, the preset drone path planning algorithm involves an objective function constructed based on a trajectory smoothing constraint function, a collision constraint function, a dynamic feasibility constraint function, and a noise impact reduction constraint function.

[0012] Optionally, the preset drone path planning algorithm is constructed based on the following steps: Construct the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function; Construct the noise impact reduction constraint function with the noise impact as a constraint term; Use the noise impact reduction constraint function to optimize the preliminary objective function to obtain the preset drone path planning algorithm.

[0013] The drone path planning method provided by this application can realize the planning of the drone path. For the specific problem of noise pollution, a noise impact reduction constraint function is separately constructed and introduced as an optimization term into the preliminary objective function constructed based on the smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function. It not only retains the core functions of the traditional path planning algorithm but also makes targeted improvements for the special requirements of noise - sensitive areas. It can dynamically adjust path parameters (such as flight speed and altitude) without destroying the original path planning logic, thereby minimizing noise pollution while meeting comprehensive constraint conditions.

[0014] Optionally, constructing the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function includes: Construct the trajectory smoothing constraint function based on a preset flight trajectory smoothness condition; Construct the collision constraint function based on a preset collision avoidance condition; Construct the dynamic feasibility constraint function based on the drone flight ability limit condition; Set corresponding coefficients for the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function.

[0015] Optionally, based on the maximum flight speed and the minimum flight altitude, and in combination with a preset UAV path planning algorithm, the inspection path of the UAV in the inspection area is calculated, including: Based on the maximum flight speed and the minimum flight altitude, and in combination with a preset UAV path planning algorithm, the actual flight speed and actual flight altitude of the UAV in each inspection sub-area are calculated; According to the actual flight speed and the actual flight altitude, the inspection path of the UAV in the inspection area is constructed.

[0016] In a second aspect, the present application provides a UAV path planning device, including: An acquisition module, configured to acquire the inspection area of the UAV; A division module, configured to divide the inspection area into a plurality of inspection sub-areas; A first calculation module, configured to input the maximum noise in each of the plurality of inspection sub-areas into a preset maximum flight speed calculation formula and a minimum flight altitude calculation formula, and calculate the maximum flight speed and the minimum flight altitude of the UAV in each inspection sub-area; A second calculation module, configured to calculate the inspection path of the UAV in the inspection area based on the maximum flight speed and the minimum flight altitude, and in combination with a preset UAV path planning algorithm.

[0017] The UAV path planning device calculates the inspection path of the UAV in the inspection area by combining the maximum flight speed and the minimum flight altitude calculated based on the maximum noise in each inspection sub-area with a preset UAV path planning algorithm, solves the problem that the traditional UAV inspection path planning algorithm cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times, and reduces the noise impact of the UAV on sensitive areas by dynamically calculating the optimal flight parameters for each region, thereby improving the path planning efficiency of the UAV.

[0018] In a third aspect, the present application provides an electronic device, including a processor and a memory, where the memory stores a computer program executable by the processor, and when the processor executes the computer program, the steps in the UAV path planning method described above are run.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the UAV path planning method described above are run.

[0020] Beneficial effects: The drone path planning method, device, electronic device, and storage medium provided in this application calculate the maximum flight speed and minimum flight altitude based on the maximum noise in each inspection sub-region, and combine with a preset drone path planning algorithm to calculate the inspection path of the drone in the inspection area. This solves the problem that traditional drone inspection path planning algorithms cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times. By dynamically calculating the optimal flight parameters for each region, it specifically reduces the noise impact of the drone on sensitive areas and improves the path planning efficiency of the drone. Brief Description of the Drawings

[0021] Figure 1 It is a flowchart of the drone path planning method provided by an embodiment of this application.

[0022] Figure 2 It is a schematic structural diagram of the drone path planning device provided by an embodiment of this application.

[0023] Figure 3 It is a schematic structural diagram of the electronic device provided by an embodiment of this application.

[0024] Label Description: 1. Acquisition Module; 2. Division Module; 3. First Calculation Module; 4. Second Calculation Module; 301. Processor; 302. Memory; 303. Communication Bus. Detailed Embodiments

[0025] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Usually, the components of the embodiments of this application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of this application that is required to be protected, but only represents the selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0026] It should be noted that: Similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0027] Please refer to Figure 1 , Figure 1A method for unmanned aerial vehicle (UAV) path planning in some embodiments of the present application is used to plan the path of the UAV, including the steps: Step S101: Obtain the inspection area of the UAV. Step S102: Divide the inspection area to obtain multiple inspection sub-areas. Step S103: Input the maximum noise of each inspection sub-area into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and minimum flight height of the UAV in each inspection sub-area. Step S104: Based on the maximum flight speed and minimum flight height, combined with a preset UAV path planning algorithm, calculate the inspection path of the UAV in the inspection area.

[0028] This UAV path planning method calculates the inspection path of the UAV in the inspection area by combining the maximum flight speed and minimum flight height calculated based on the maximum noise of each inspection sub-area with a preset UAV path planning algorithm, solving the problem that the traditional UAV inspection path planning algorithm cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times. By dynamically calculating the optimal flight parameters for each area, it specifically reduces the noise impact of the UAV on sensitive areas and improves the path planning efficiency of the UAV.

[0029] Specifically, in step S101, the inspection area of the UAV is obtained, and the inspection area refers to the area that has been pre-divided and requires automated UAV inspection.

[0030] Specifically, in step S102, according to geographical regions, the inspection area is divided to obtain multiple inspection sub-areas, such as dividing the inspection area into inspection sub-areas such as schools and residential areas.

[0031] Specifically, in step S103, inputting the maximum noise of each inspection sub-area into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculating the maximum flight speed and minimum flight height of the UAV in each inspection sub-area includes: Obtain the inspection time corresponding to the inspection sub-area. According to the inspection sub-area and inspection time, through a preset noise level division criterion, determine the maximum noise of the area when the UAV inspects each inspection sub-area during the corresponding inspection time. Input the maximum noise of each inspection sub-area into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and minimum flight height of the UAV in each inspection sub-area.

[0032] In step S103, by obtaining the inspection time, a coupling relationship between the time dimension and the regional attributes is established. According to the combination of the regional type and time of the inspection sub-region, through the preset noise level division criterion, the maximum noise of the inspection sub-region during the corresponding inspection time is obtained. The maximum noise of the region is input into the preset maximum flight speed calculation formula and the minimum flight height calculation formula to calculate the maximum flight speed and the minimum flight height of the UAV in each inspection sub-region, thereby realizing the dynamic matching of flight parameters with changes in time and space and avoiding the noise exceeding the standard or the loss of inspection efficiency caused by fixed parameters. Among them, a positive correlation between noise and speed is set in the maximum flight speed calculation formula, and a negative correlation between noise and height is set in the minimum flight height calculation formula. When the maximum noise of the region decreases, a lower maximum flight speed and a higher minimum flight height are automatically generated.

[0033] Among them, the preset maximum flight speed calculation formula is specifically: ; Among them, is the maximum flight speed, and N is the maximum noise of the region.

[0034] The preset minimum flight height calculation formula is specifically: ; Among them, is the minimum flight height.

[0035] Specifically, in step S103, according to the inspection sub-region and the inspection time, through the preset noise level division criterion, the maximum noise of the region when the UAV inspects each inspection sub-region during the corresponding inspection time is determined, including: Read the noise level division table pre-constructed based on the preset noise level division criterion; According to the inspection sub-region and the inspection time, determine the maximum noise of the region when the UAV inspects each inspection sub-region during the corresponding inspection time from the noise level division table.

[0036] In step S103, by establishing a standardized area-time two-dimensional noise limit query mechanism, complex dynamic environmental factors are transformed into structured data query operations. Based on the preset noise level classification criteria, a pre-constructed classification table establishes a corresponding relationship between different geographical attribute areas and operation time periods. Among them, the preset noise level classification criteria include that the maximum noise set during the day is greater than the maximum noise set during the night period, and the noise in areas such as schools and residential areas is less than the noise in areas such as commercial areas and industrial areas. For example, a lower noise threshold is set for residential areas during the night period, and a higher threshold is set for industrial areas during the day. When the spatial coordinates and execution time of a specific inspection task are obtained, by matching the area category and time period fields in the classification table, the corresponding maximum allowable noise value is directly extracted, and the area maximum noise when the drone inspects each inspection sub-area during the corresponding inspection time is obtained.

[0037] Among them, the noise level classification table is specifically as shown in the following table:

[0038] As can be seen from the above table, different areas have different maximum noise levels at different times. By extracting the maximum noise levels corresponding to the inspection time and each inspection sub-area, the area maximum noise corresponding to the drone is obtained.

[0039] Specifically, the preset UAV path planning algorithm involves an objective function constructed based on a trajectory smoothing constraint function, a collision constraint function, a dynamic feasibility constraint function, and a noise impact reduction constraint function; in step S104, the preset UAV path planning algorithm is constructed based on the following steps: Construct a trajectory smoothing constraint function, a collision constraint function, and a dynamic feasibility constraint function to construct a preliminary objective function; Taking the noise impact as a constraint term, construct a noise impact reduction constraint function; Use the noise impact reduction constraint function to optimize the preliminary objective function to obtain the preset UAV path planning algorithm.

[0040] In step S104, it is necessary to pre-construct a UAV path planning algorithm. First, a preliminary objective function is formed by constructing a trajectory smoothing constraint function, a collision constraint function, and a dynamic feasibility constraint function. This step ensures that the path planning meets the basic functional requirements, such as flight stability, safety, and UAV physical performance limitations. Subsequently, for the specific problem of noise pollution, a noise reduction impact constraint function is separately constructed and introduced as an optimization term into the preliminary objective function to optimize the preliminary objective function and obtain a preset UAV path planning algorithm. This phased optimization method not only retains the core functions of traditional path planning algorithms but also makes targeted improvements for the special requirements of noise-sensitive areas. By taking the noise impact constraint as an independent optimization term, it is possible to dynamically adjust path parameters (such as flight speed and altitude) without disrupting the original path planning logic, thereby minimizing noise pollution while meeting comprehensive constraint conditions.

[0041] Among them, the preset UAV path planning algorithm is specifically as follows: ; Among them, is the objective function; is the minimized objective function; is the trajectory smoothing constraint function, is the coefficient of the trajectory smoothing constraint function; is the collision constraint function, is the coefficient of the collision constraint function; is the dynamic feasibility constraint function, is the coefficient of the dynamic feasibility constraint function; is the noise reduction impact constraint function, is the coefficient of the noise reduction impact constraint function. Among them, the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function are existing technologies and will not be elaborated here. Among them, the coefficients of the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function can be set according to actual needs.

[0042] Among them, the noise reduction impact constraint function is specifically as follows: ; Among them, v is the actual flight speed of the UAV during inspection, and h is the actual flight height of the UAV during inspection. Among them, is dimensionless data. Therefore, , , and only take their numerical values to achieve dimensionless.

[0043] Set a constraint function to reduce the impact of noise , so that the UAV path planning algorithm can plan an inspection path that meets the maximum noise level allowed in each area and each time period, reduce the noise impact on urban residents during UAV inspection, and improve the living comfort of residents.

[0044] Specifically, when pre-constructing the UAV path planning algorithm, construct a trajectory smoothing constraint function, a collision constraint function, and a dynamic feasibility constraint function to construct a preliminary objective function, including: Based on the preset flight trajectory smoothness condition, construct a trajectory smoothing constraint function; Based on the preset collision avoidance condition, construct a collision constraint function; Based on the UAV flight ability limit condition, construct a dynamic feasibility constraint function; Set corresponding coefficients for the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function.

[0045] When pre-constructing the UAV path planning algorithm, by constructing different types of constraint functions step by step and assigning corresponding weight coefficients, a preliminary objective function is formed, which solves the problem of single focus on efficiency and neglect of multi-constraint collaboration in traditional path planning. First, the trajectory smoothing constraint function constructed based on the flight trajectory smoothness condition can ensure the continuity and smoothness of the UAV path, avoiding control difficulties or additional energy consumption caused by sudden trajectory changes. Secondly, the collision constraint function generated by the preset collision avoidance condition directly targets the physical limitations of obstacles in the inspection environment, forcing the planned path to meet the safety obstacle avoidance requirements. Furthermore, the dynamic feasibility constraint function constructed based on the UAV flight ability limit condition integrates the actual flight performance of the UAV (such as maximum acceleration, turning angular velocity) into the planning process, avoiding generating paths beyond the hardware capabilities. Finally, by setting specific coefficients for each constraint function, the priority of different constraint conditions can be dynamically adjusted. For example, increasing the weight of the collision constraint in complex obstacle areas, or strengthening the impact of trajectory smoothness in energy-saving scenarios, thus achieving the fusion and balance of multi-dimensional constraints at the objective function level.

[0046] Among them, the trajectory smoothing constraint function constructed based on the preset flight trajectory smoothness condition, the collision constraint function constructed based on the preset collision avoidance condition, and the dynamic feasibility constraint function constructed based on the UAV flight ability limit condition are prior arts, and will not be elaborated here.

[0047] Specifically, in step S104, based on the maximum flight speed and the minimum flight height, combined with the preset UAV path planning algorithm, calculate the inspection path of the UAV in the inspection area, including: Based on the maximum flight speed and the minimum flight altitude, combined with a preset UAV path planning algorithm, calculate the actual flight speed and the actual flight altitude of the UAV in each inspection sub-region; According to the actual flight speed and the actual flight altitude, construct the inspection path of the UAV in the inspection area.

[0048] In step S104, take the maximum flight speed and the minimum flight altitude of the UAV in each inspection sub-region as the constraint conditions of the constraint function for reducing noise impact and input them into the preset UAV path planning algorithm. Use existing algorithms such as the gradient descent method or the genetic algorithm to calculate the actual flight speed and the actual flight altitude of the UAV in each inspection sub-region. Among them, existing algorithms such as the gradient descent method or the genetic algorithm are prior arts and will not be elaborated here.

[0049] Based on the actual flight speed and the actual flight altitude of each inspection sub-region, use existing path planning algorithms, such as existing algorithms like the A* search algorithm, the Diikstra algorithm, and the ant colony algorithm, to generate a three-dimensional trajectory that takes into account noise compliance and inspection feasibility, and obtain the inspection path of the UAV in the inspection area. Among them, existing algorithms such as the A* search algorithm, the Diikstra algorithm, and the ant colony algorithm are prior arts and will not be elaborated here.

[0050] As can be seen from the above, this UAV path planning method obtains the inspection area of the UAV, divides the inspection area into multiple inspection sub-regions, inputs the maximum noise of the multiple inspection sub-regions into the preset maximum flight speed calculation formula and the minimum flight altitude calculation formula, calculates the maximum flight speed and the minimum flight altitude of the UAV in each inspection sub-region, and based on the maximum flight speed and the minimum flight altitude, combined with the preset UAV path planning algorithm, calculates the inspection path of the UAV in the inspection area; thereby, through the maximum flight speed and the minimum flight altitude calculated based on the maximum noise of each inspection sub-region, combined with the preset UAV path planning algorithm, calculates the inspection path of the UAV in the inspection area, solves the problem that the traditional UAV inspection path planning algorithm cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times, and by dynamically calculating the optimal flight parameters of each region, specifically reduces the noise impact of the UAV on sensitive regions, and improves the path planning efficiency of the UAV.

[0051] Reference Figure 2 , this application provides a UAV path planning device for planning the UAV path, including: An acquisition module 1 for acquiring the inspection area of the UAV; A division module 2 for dividing the inspection area into multiple inspection sub-regions; The first calculation module 3 is configured to input the maximum noise of multiple inspection sub - regions into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and the minimum flight height of the UAV in each inspection sub - region. The second calculation module 4 is configured to calculate the inspection path of the UAV in the inspection region based on the maximum flight speed and the minimum flight height, in combination with a preset UAV path planning algorithm.

[0052] This UAV path planning device calculates the inspection path of the UAV in the inspection region by combining the maximum flight speed and the minimum flight height calculated based on the maximum noise of each inspection sub - region with a preset UAV path planning algorithm, solving the problem that traditional UAV inspection path planning algorithms cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times. By dynamically calculating the optimal flight parameters for each region, it specifically reduces the noise impact of the UAV on sensitive regions and improves the path planning efficiency of the UAV.

[0053] Specifically, when the acquisition module 1 is executed, it acquires the inspection region of the UAV. The inspection region refers to a pre - divided region that requires automated UAV inspection.

[0054] Specifically, when the division module 2 is executed, it divides the inspection region into multiple inspection sub - regions according to geographical regions. For example, the inspection region is divided into inspection sub - regions such as schools and residential areas.

[0055] Specifically, when the first calculation module 3 inputs the maximum noise of multiple inspection sub - regions into a preset maximum flight speed calculation formula and a minimum flight height calculation formula to calculate the maximum flight speed and the minimum flight height of the UAV in each inspection sub - region, it executes: Acquire the inspection time corresponding to the inspection sub - region; According to the inspection sub - region and the inspection time, through a preset noise level division criterion, determine the maximum noise of the region when the UAV inspects each inspection sub - region during the corresponding inspection time; Input the maximum noise of each inspection sub - region into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and the minimum flight height of the UAV in each inspection sub - region.

[0056] When the first calculation module 3 is executing, by obtaining the inspection time, it establishes the coupling relationship between the time dimension and the regional attributes. According to the combination of the regional type and time of the inspection sub-region, through the preset noise level division criterion, it obtains the maximum noise of the inspection sub-region during the corresponding inspection time, and inputs the maximum noise of the region into the preset maximum flight speed calculation formula and the minimum flight height calculation formula, and calculates the maximum flight speed and the minimum flight height of the UAV in each inspection sub-region, thereby realizing the dynamic matching of the flight parameters with the changes in time and space, and avoiding the noise exceeding the standard or the loss of inspection efficiency caused by fixed parameters. Among them, a positive correlation between noise and speed is set in the maximum flight speed calculation formula, and a negative correlation between noise and height is set in the minimum flight height calculation formula. When the maximum noise of the region decreases, automatically generate a lower maximum flight speed and a higher minimum flight height.

[0057] Among them, the preset maximum flight speed calculation formula is specifically: ; Among them, is the maximum flight speed, and N is the maximum noise of the region.

[0058] The preset minimum flight height calculation formula is specifically: ; Among them, is the minimum flight height.

[0059] Specifically, when the first calculation module 3 determines the maximum noise of the region when the UAV inspects each inspection sub-region during the corresponding inspection time according to the inspection sub-region and the inspection time through the preset noise level division criterion, it executes: Read the noise level division table pre-constructed based on the preset noise level division criterion; According to the inspection sub-region and the inspection time, determine the maximum noise of the region when the UAV inspects each inspection sub-region during the corresponding inspection time from the noise level division table.

[0060] When the first computing module 3 is executed, by establishing a standardized area-time two-dimensional noise limit query mechanism, complex dynamic environmental factors are converted into structured data query operations. Based on the preset noise level classification criteria, a pre-constructed classification table establishes a corresponding relationship between different geographical attribute areas and operation time periods. Among them, the preset noise level classification criteria include that the maximum noise set during the day is greater than the maximum noise set during the night period, and the noise in areas such as schools and residential areas is less than the noise in areas such as commercial areas and industrial areas. For example, a lower noise threshold is set for residential areas during the night period, and a higher threshold is set for industrial areas during the day. When the spatial coordinates and execution time of a specific inspection task are obtained, by matching the area category and time period fields in the classification table, the corresponding maximum allowable noise value is directly extracted, and the area maximum noise when the drone conducts inspections on each inspection sub-area during the corresponding inspection time is obtained.

[0061] Among them, the noise level classification table is specifically as shown in the following table:

[0062] As can be seen from the above table, different areas have different maximum noise levels at different times. By extracting the maximum noise levels corresponding to the inspection time and each inspection sub-area, the area maximum noise corresponding to the drone is obtained.

[0063] Specifically, the preset drone path planning algorithm involves an objective function constructed based on a trajectory smoothing constraint function, a collision constraint function, a dynamic feasibility constraint function, and a noise impact reduction constraint function; when the second computing module 4 is executed, the preset drone path planning algorithm is constructed based on the following steps: Construct a trajectory smoothing constraint function, a collision constraint function, and a dynamic feasibility constraint function to construct a preliminary objective function; Taking the noise impact as a constraint term, construct a noise impact reduction constraint function; Use the noise impact reduction constraint function to optimize the preliminary objective function to obtain the preset drone path planning algorithm.

[0064] When the second computing module 4 is executing, it is necessary to pre-construct an unmanned aerial vehicle (UAV) path planning algorithm. First, a preliminary objective function is formed by constructing a trajectory smoothing constraint function, a collision constraint function, and a dynamic feasibility constraint function. This step ensures that the path planning meets the basic functional requirements, such as flight stability, safety, and UAV physical performance limitations. Subsequently, for the specific problem of noise pollution, a noise reduction impact constraint function is separately constructed and introduced as an optimization term into the preliminary objective function to optimize the preliminary objective function and obtain a preset UAV path planning algorithm. This phased optimization method not only retains the core functions of traditional path planning algorithms but also makes targeted improvements for the special requirements of noise-sensitive areas. By taking the noise impact constraint as an independent optimization term, the path parameters (such as flight speed and altitude) can be dynamically adjusted without disrupting the original path planning logic, thereby minimizing noise pollution while meeting the comprehensive constraint conditions.

[0065] Among them, the preset UAV path planning algorithm is specifically as follows: ; Among them, is the objective function; is the minimized objective function; is the trajectory smoothing constraint function, is the coefficient of the trajectory smoothing constraint function; is the collision constraint function, is the coefficient of the collision constraint function; is the dynamic feasibility constraint function, is the coefficient of the dynamic feasibility constraint function; is the noise reduction impact constraint function, is the coefficient of the noise reduction impact constraint function. Among them, the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function are existing technologies and will not be elaborated here. Among them, the coefficients of the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function can be set according to actual needs.

[0066] Among them, the noise reduction impact constraint function is specifically as follows: ; Among them, v is the actual flight speed of the UAV during inspection, and h is the actual flight altitude of the UAV during inspection. Among them, is dimensionless data. Therefore, , , and only take their numerical values to achieve dimensionless.

[0067] Set a constraint function to reduce the noise impact , so that the UAV path planning algorithm can plan an inspection path that meets the maximum noise level allowed in each area and each time period, reduce the noise impact on urban residents during UAV inspection, and improve the living comfort of residents.

[0068] Specifically, when pre-constructing the UAV path planning algorithm, construct a trajectory smoothing constraint function, a collision constraint function, and a dynamic feasibility constraint function to construct a preliminary objective function, including: Based on the preset flight trajectory smoothness condition, construct a trajectory smoothing constraint function; Based on the preset collision avoidance condition, construct a collision constraint function; Based on the UAV flight ability limit condition, construct a dynamic feasibility constraint function; Set corresponding coefficients for the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function.

[0069] When pre-constructing the UAV path planning algorithm, by constructing different types of constraint functions step by step and assigning corresponding weight coefficients, a preliminary objective function is formed, which solves the problem of single focus on efficiency and neglect of multi-constraint collaboration in traditional path planning. First, the trajectory smoothing constraint function constructed based on the flight trajectory smoothness condition can ensure the continuity and smoothness of the UAV path, avoiding control difficulties or additional energy consumption caused by sudden trajectory changes. Secondly, the collision constraint function generated by the preset collision avoidance condition directly targets the physical limitations of obstacles in the inspection environment, forcing the planned path to meet the safety obstacle avoidance requirements. Furthermore, the dynamic feasibility constraint function constructed based on the UAV flight ability limit condition integrates the actual flight performance of the UAV (such as maximum acceleration, turning angular velocity) into the planning process, avoiding generating paths beyond the hardware capabilities. Finally, by setting specific coefficients for each constraint function, the priority of different constraint conditions can be dynamically adjusted. For example, increasing the weight of the collision constraint in complex obstacle areas, or strengthening the influence of trajectory smoothness in energy-saving scenarios, so as to complete the integration and balance of multi-dimensional constraints at the objective function level.

[0070] Among them, the trajectory smoothing constraint function constructed based on the preset flight trajectory smoothness condition, the collision constraint function constructed based on the preset collision avoidance condition, and the dynamic feasibility constraint function constructed based on the UAV flight ability limit condition are prior arts, and details thereof are not described herein.

[0071] Specifically, when the second calculation module 4 calculates the inspection path of the UAV in the inspection area based on the maximum flight speed and the minimum flight height, in combination with the preset UAV path planning algorithm, it executes: Based on the maximum flight speed and the minimum flight altitude, combined with a preset UAV path planning algorithm, calculate the actual flight speed and actual flight altitude of the UAV in each inspection sub-area; According to the actual flight speed and actual flight altitude, construct the inspection path of the UAV in the inspection area.

[0072] When the second calculation module 4 executes, take the maximum flight speed and the minimum flight altitude of the UAV in each inspection sub-area as the constraint conditions of the constraint function for reducing noise impact and input them into the preset UAV path planning algorithm. Use existing algorithms such as the gradient descent method or the genetic algorithm to calculate the actual flight speed and actual flight altitude of the UAV in each inspection sub-area. Among them, existing algorithms such as the gradient descent method or the genetic algorithm are prior arts and will not be elaborated here.

[0073] Based on the actual flight speed and actual flight altitude of each inspection sub-area, use existing path planning algorithms, such as existing algorithms like the A* search algorithm, the Dijkstra algorithm, and the ant colony algorithm, to generate a three-dimensional trajectory that takes into account noise compliance and inspection feasibility, and obtain the inspection path of the UAV in the inspection area. Among them, existing algorithms such as the A* search algorithm, the Dijkstra algorithm, and the ant colony algorithm are prior arts and will not be elaborated here.

[0074] As can be seen from the above, this UAV path planning device obtains the inspection area of the UAV, divides the inspection area into multiple inspection sub-areas, inputs the maximum noise in the multiple inspection sub-areas into the preset maximum flight speed calculation formula and the minimum flight altitude calculation formula, calculates the maximum flight speed and the minimum flight altitude of the UAV in each inspection sub-area, and based on the maximum flight speed and the minimum flight altitude, combined with the preset UAV path planning algorithm, calculates the inspection path of the UAV in the inspection area; thus, through the maximum flight speed and the minimum flight altitude calculated based on the maximum noise in each inspection sub-area, combined with the preset UAV path planning algorithm, calculates the inspection path of the UAV in the inspection area, solves the problem that the traditional UAV inspection path planning algorithm cannot be flexibly adjusted according to the differences in noise sensitivity in different regions and at different times, and by dynamically calculating the optimal flight parameters for each region, specifically reduces the noise impact of the UAV on sensitive areas and improves the path planning efficiency of the UAV.

[0075] Please refer to Figure 3 , Figure 3A schematic structural diagram of an electronic device provided by an embodiment of the present application. The present application provides an electronic device, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not marked). The memory 302 stores a computer program executable by the processor 301. When the electronic device runs, the processor 301 executes the computer program to execute the unmanned aerial vehicle path planning method in any optional implementation manner of the above embodiment to achieve the following functions: obtaining the inspection area of the unmanned aerial vehicle, dividing the inspection area to obtain a plurality of inspection sub-areas, inputting the maximum noise of each inspection sub-area into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, calculating the maximum flight speed and the minimum flight height of the unmanned aerial vehicle in each inspection sub-area, and based on the maximum flight speed and the minimum flight height, combining a preset unmanned aerial vehicle path planning algorithm, calculating the inspection path of the unmanned aerial vehicle in the inspection area.

[0076] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it executes the unmanned aerial vehicle path planning method in any optional implementation manner of the above embodiment to achieve the following functions: obtaining the inspection area of the unmanned aerial vehicle, dividing the inspection area to obtain a plurality of inspection sub-areas, inputting the maximum noise of each inspection sub-area into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, calculating the maximum flight speed and the minimum flight height of the unmanned aerial vehicle in each inspection sub-area, and based on the maximum flight speed and the minimum flight height, combining a preset unmanned aerial vehicle path planning algorithm, calculating the inspection path of the unmanned aerial vehicle in the inspection area. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0077] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0078] In addition, the units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] Furthermore, in each embodiment of this application, the various functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0080] In this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0081] The above are only the embodiments of this application and are not used to limit the protection scope of this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

Claims

1. A method for UAV path planning, which is used to plan the path of a UAV, and is characterized in that, Including the steps: Obtain the inspection area of the drone; Divide the inspection area to obtain multiple inspection sub-areas; Input the maximum noise of each of the multiple inspection sub-areas into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and the minimum flight height of the drone in each of the inspection sub-areas; Based on the maximum flight speed and the minimum flight height, and in combination with a preset drone path planning algorithm, calculate the inspection path of the drone in the inspection area.

2. The method for unmanned aerial vehicle path planning according to claim 1, wherein Input the maximum noise of each of the multiple inspection sub-areas into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and the minimum flight height of the drone in each of the inspection sub-areas, including: Obtain the inspection time corresponding to each of the inspection sub-areas; According to the inspection sub-area and the inspection time, and through a preset noise level division criterion, determine the maximum noise of the area when the drone inspects each of the inspection sub-areas during the corresponding inspection time; Input the maximum noise of the area of the inspection sub-area into a preset maximum flight speed calculation formula and a minimum flight height calculation formula, and calculate the maximum flight speed and the minimum flight height of the drone in each of the inspection sub-areas.

3. The drone path planning method according to claim 2, wherein, According to the inspection sub-area and the inspection time, and through a preset noise level division criterion, determine the maximum noise of the area when the drone inspects each of the inspection sub-areas during the corresponding inspection time, including: Read a pre-constructed noise level division table based on a preset noise level division criterion; According to the inspection sub-area and the inspection time, determine from the noise level division table the maximum noise of the area when the drone inspects each of the inspection sub-areas during the corresponding inspection time.

4. The drone path planning method according to claim 1, wherein The preset drone path planning algorithm involves an objective function constructed based on a trajectory smoothing constraint function, a collision constraint function, a dynamic feasibility constraint function, and a noise impact reduction constraint function.

5. The drone path planning method according to claim 4, characterized in that, The preset drone path planning algorithm is constructed based on the following steps: Construct the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function; Construct the noise impact reduction constraint function with the noise impact as a constraint term; Use the noise impact reduction constraint function to optimize the preliminary objective function to obtain the preset drone path planning algorithm.

6. The method for path planning of an unmanned aerial vehicle according to claim 5, wherein Construct the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function, including: Based on a preset flight trajectory smoothness condition, construct the trajectory smoothing constraint function; Based on a preset collision avoidance condition, construct the collision constraint function; Based on the drone flight ability limit condition, construct the dynamic feasibility constraint function; Set corresponding coefficients for the trajectory smoothing constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function.

7. The method for path planning of an unmanned aerial vehicle according to claim 1, wherein Based on the maximum flight speed and the minimum flight altitude, and in combination with a preset UAV path planning algorithm, calculate the inspection path of the UAV in the inspection area, including: Based on the maximum flight speed and the minimum flight altitude, and in combination with a preset UAV path planning algorithm, calculate the actual flight speed and actual flight altitude of the UAV in each inspection sub - area; According to the actual flight speed and the actual flight altitude, construct the inspection path of the UAV in the inspection area.

8. An unmanned aerial vehicle path planning device for planning the path of an unmanned aerial vehicle, characterized in that, Including: An acquisition module, configured to acquire the inspection area of the UAV; A division module, configured to divide the inspection area into multiple inspection sub - areas; A first calculation module, configured to input the maximum noise of multiple inspection sub - areas into a preset maximum flight speed calculation formula and a minimum flight altitude calculation formula, and calculate the maximum flight speed and minimum flight altitude of the UAV in each inspection sub - area; A second calculation module, configured to calculate the inspection path of the UAV in the inspection area based on the maximum flight speed and the minimum flight altitude, and in combination with a preset UAV path planning algorithm.

9. An electronic device, characterized in that, Including a processor and a memory, the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the UAV path planning method according to any one of claims 1 - 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it runs the steps in the UAV path planning method according to any one of claims 1 - 7.

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