UAV path planning method, device, electronic device and storage medium

Abstract

The present application belongs to the technical field of drone inspection, and discloses a drone path planning method, device, electronic device and storage medium. The method includes: obtaining an inspection area of ​​a drone, dividing the inspection area into multiple inspection sub-areas, inputting the regional maximum noise of the multiple inspection sub-areas into a preset maximum flight speed calculation formula and a minimum flight altitude calculation formula, calculating the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area, and calculating the inspection path of the drone in the inspection area based on the maximum flight speed and the minimum flight altitude in combination with a preset drone path planning algorithm; calculating the inspection path of the drone in the inspection area by calculating the maximum flight speed and the minimum flight altitude based on the regional maximum noise of each inspection sub-area in combination with a preset drone path planning algorithm, thereby improving the path planning efficiency of the drone.

Description

Technical Field

[0001] The present application relates to the technical field of drone inspections, 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 being used for inspections. Drone inspections, with their advantages of high efficiency, low cost, and strong accessibility to high-risk areas, are widely used for equipment inspections in industries such as the power industry, petroleum, and chemical industry. However, the noise pollution generated by drones during these inspections has also become increasingly prominent, especially in noise-sensitive areas such as cities, residential areas, and schools, where drone noise can adversely impact people's lives and work.

[0003] Existing drone inspection path planning algorithms primarily focus on maximizing inspection efficiency and coverage, with less consideration given to noise pollution. Traditional drone inspection path planning algorithms often use fixed inspection speeds and altitudes, failing to flexibly adjust to varying noise sensitivities in different areas and at different times. As a result, drone noise can cause unnecessary disruption to people's lives in noise-sensitive areas, potentially leading to complaints and disputes.

[0004] Therefore, in order to solve the technical problem that traditional drone inspection path planning algorithms cannot flexibly adjust according to the differences in noise sensitivity in different areas and at different times, a drone path planning method, device, electronic device and storage medium are urgently needed. Summary of the Invention

[0005] The purpose of this 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 regional noise of each inspection sub-area, combined with a preset drone path planning algorithm, the inspection path of the drone in the inspection area is calculated, thereby solving the problem that traditional drone inspection path planning algorithms cannot flexibly adjust according to the differences in noise sensitivity in different areas and at different times. By dynamically calculating the optimal flight parameters of each area, the noise impact of the drone on sensitive areas is targetedly reduced, thereby improving the drone's path planning efficiency.

[0006] In a first aspect, the present application provides a method for planning a path for a drone, comprising:

[0007] Get the inspection area of ​​the drone;

[0008] Dividing the inspection area into a plurality of inspection sub-areas;

[0009] Inputting the maximum regional noise of the plurality of inspection sub-areas into a preset maximum flight speed calculation formula and a minimum flight altitude calculation formula, and calculating the maximum flight speed and minimum flight altitude of the UAV in each inspection sub-area;

[0010] Based on the maximum flight speed and the minimum flight altitude, combined with a preset UAV path planning algorithm, the inspection path of the UAV in the inspection area is calculated.

[0011] The drone path planning method provided in this application can realize drone path planning. By calculating the maximum flight speed and minimum flight altitude based on the maximum regional noise of each inspection sub-area, combined with a preset drone path planning algorithm, the inspection path of the drone in the inspection area is calculated, which solves the problem that the traditional drone inspection path planning algorithm cannot flexibly adjust according to the differences in noise sensitivity in different areas and at different times. By dynamically calculating the optimal flight parameters of each area, the noise impact of the drone on sensitive areas is targetedly reduced, thereby improving the drone's path planning efficiency.

[0012] Optionally, the maximum regional noise of the plurality of inspection sub-areas is input into a preset maximum flight speed calculation formula and a minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the UAV in each inspection sub-area, including:

[0013] Obtaining the inspection time corresponding to each inspection sub-area;

[0014] According to the inspection sub-areas and the inspection time, the maximum regional noise when the drone inspects each of the inspection sub-areas within the corresponding inspection time is determined by using a preset noise level classification criterion;

[0015] The maximum noise in the inspection sub-area is input into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the UAV in each inspection sub-area.

[0016] The drone path planning method provided in this application can plan the drone path, perform dual-dimensional evaluation using preset noise level classification criteria, and generate differentiated regional maximum noise thresholds for different areas (such as residential areas and industrial areas) and different time periods (such as weekdays and holidays). The maximum noise threshold is input into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude, so that the drone flight parameters can be automatically adjusted with time and space changes. This time-space coupled noise assessment method breaks through the limitations of the traditional fixed threshold method and achieves an adaptive balance between noise control and inspection task requirements.

[0017] Optionally, according to the inspection sub-areas and the inspection time, and using a preset noise level classification criterion, determining the maximum regional noise when the drone inspects each of the inspection sub-areas within the corresponding inspection time includes:

[0018] Reading a noise level classification table pre-constructed based on preset noise level classification criteria;

[0019] According to the inspection sub-areas and the inspection time, the maximum regional noise when the drone inspects each of the inspection sub-areas within the corresponding inspection time is determined from the noise level classification table.

[0020] Optionally, the preset UAV path planning algorithm involves an objective function constructed based on a trajectory smoothness constraint function, a collision constraint function, a dynamic feasibility constraint function and a noise impact reduction constraint function.

[0021] Optionally, the preset UAV path planning algorithm is constructed based on the following steps:

[0022] Constructing the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function;

[0023] Taking the noise impact as a constraint term, constructing the noise impact reduction constraint function;

[0024] The noise impact reduction constraint function is used to optimize the preliminary objective function to obtain a preset UAV path planning algorithm.

[0025] The UAV path planning method provided in this application can realize the planning of UAV paths. For the specific problem of noise pollution, a noise impact reduction constraint function is separately constructed, and it is introduced as an optimization item into the preliminary objective function constructed based on the smoothness constraint function, collision constraint function and dynamic feasibility constraint function. It not only retains the core functions of the traditional path planning algorithm, but also makes targeted improvements to the special needs of noise-sensitive areas. It can dynamically adjust the path parameters (such as flight speed and altitude) without destroying the original path planning logic, thereby minimizing noise pollution while meeting the comprehensive constraints.

[0026] Optionally, a trajectory smoothness constraint function, a collision constraint function, and a dynamic feasibility constraint function are constructed to construct a preliminary objective function, including:

[0027] Based on the preset flight trajectory stability conditions, a trajectory smoothness constraint function is constructed;

[0028] Based on the preset collision avoidance conditions, a collision constraint function is constructed;

[0029] Based on the UAV flight capability constraints, a dynamic feasibility constraint function is constructed;

[0030] Corresponding coefficients are set for the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function.

[0031] Optionally, based on the maximum flight speed and the minimum flight altitude, in combination with a preset UAV path planning algorithm, calculating an inspection path of the UAV in the inspection area includes:

[0032] Based on the maximum flight speed and the minimum flight altitude, combined 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;

[0033] An inspection path of the UAV in the inspection area is constructed according to the actual flight speed and the actual flight altitude.

[0034] In a second aspect, the present application provides a drone path planning device, comprising:

[0035] The acquisition module is used to obtain the inspection area of ​​the drone;

[0036] A division module, configured to divide the inspection area into a plurality of inspection sub-areas;

[0037] A first calculation module is configured to input the maximum regional noise 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 minimum flight altitude of the UAV in each of the inspection sub-areas;

[0038] The second calculation module is used to calculate the inspection path of the UAV in the inspection area based on the maximum flight speed and the minimum flight altitude in combination with a preset UAV path planning algorithm.

[0039] This drone path planning device calculates the drone's inspection path in the inspection area by combining the maximum flight speed and minimum flight altitude calculated based on the maximum regional noise in each inspection sub-area with a preset drone path planning algorithm. This solves the problem that traditional drone inspection path planning algorithms cannot flexibly adjust to differences in noise sensitivity in different areas and at different times. By dynamically calculating the optimal flight parameters for each area, the noise impact of the drone on sensitive areas is targetedly reduced, thereby improving the drone's path planning efficiency.

[0040] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the drone path planning method described above.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, runs the steps in the drone path planning method described above.

[0042] Beneficial effects: The drone path planning method, device, electronic device and storage medium provided in this application calculate the inspection path of the drone in the inspection area by calculating the maximum flight speed and minimum flight altitude based on the maximum regional noise of each inspection sub-area, combined with a preset drone path planning algorithm. This solves the problem that traditional drone inspection path planning algorithms cannot flexibly adjust according to differences in noise sensitivity in different areas and at different times. By dynamically calculating the optimal flight parameters of each area, the noise impact of the drone on sensitive areas is targetedly reduced, thereby improving the drone's path planning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of the drone path planning method provided in an embodiment of the present application.

[0044] Figure 2 A schematic diagram of the structure of the drone path planning device provided in an embodiment of the present application.

[0045] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0046] Explanation of reference numerals: 1. Acquisition module; 2. Division module; 3. First calculation module; 4. Second calculation module; 301. Processor; 302. Memory; 303. Communication bus. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally 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 the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0048] 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 or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0049] Please refer to Figure 1 , Figure 1 A method for planning a drone path in some embodiments of the present application is provided, which is used to plan a drone path, including the following steps:

[0050] Step S101, obtaining the inspection area of ​​the drone;

[0051] Step S102: Divide the inspection area into multiple inspection sub-areas;

[0052] Step S103: Input the maximum regional noise of the multiple inspection sub-areas into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of each UAV in the inspection sub-area;

[0053] In step S104, based on the maximum flight speed and the minimum flight altitude, combined with a preset UAV path planning algorithm, the inspection path of the UAV in the inspection area is calculated.

[0054] This drone path planning method calculates the drone's inspection path in the inspection area by combining the maximum flight speed and minimum flight altitude calculated based on the maximum regional noise in each inspection sub-area with a preset drone path planning algorithm. This solves the problem that traditional drone inspection path planning algorithms cannot flexibly adjust to differences in noise sensitivity in different areas and at different times. By dynamically calculating the optimal flight parameters for each area, the noise impact of the drone on sensitive areas is targeted reduced, thereby improving the drone's path planning efficiency.

[0055] Specifically, in step S101, the inspection area of ​​the drone is obtained, and the inspection area refers to a pre-divided area that needs to be inspected automatically by the drone.

[0056] Specifically, in step S102, the inspection area is divided into a plurality of inspection sub-areas according to geographical areas, such as the inspection area is divided into inspection sub-areas such as schools and residential areas.

[0057] Specifically, in step S103, the maximum regional noise of multiple inspection sub-areas is input into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area, including:

[0058] Get the inspection time corresponding to the inspection sub-area;

[0059] According to the inspection sub-area and inspection time, the preset noise level classification criteria are used to determine the maximum regional noise when the drone inspects each inspection sub-area within the corresponding inspection time;

[0060] The maximum noise level in each inspection sub-area is input into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area.

[0061] In step S103, by obtaining the inspection time, a coupling relationship between the time dimension and the regional attributes is established. Based on the regional type and time combination of the inspection sub-area, the maximum regional noise of the inspection sub-area during the corresponding inspection time is obtained through the preset noise level classification criteria. The regional maximum noise is input into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area. This achieves dynamic matching of flight parameters over time and space, avoiding noise exceeding the standard or loss of inspection efficiency caused by fixed parameters. Among them, the maximum flight speed calculation formula is set with a positive correlation between noise and speed, and the minimum flight altitude calculation formula is set with a negative correlation between noise and altitude. When the maximum regional noise decreases, a lower maximum flight speed and a higher minimum flight altitude are automatically generated.

[0062] The preset maximum flight speed calculation formula is as follows:

[0063] ;

[0064] in, is the maximum flight speed, and N is the maximum noise in the area.

[0065] The preset minimum flight altitude calculation formula is as follows:

[0066] ;

[0067] in, The minimum flight altitude.

[0068] Specifically, in step S103, according to the inspection sub-area and inspection time, the maximum noise level of the area when the drone inspects each inspection sub-area within the corresponding inspection time is determined by using the preset noise level classification criteria, including:

[0069] Reading a noise level classification table pre-constructed based on preset noise level classification criteria;

[0070] According to the inspection sub-area and inspection time, the maximum regional noise when the drone inspects each inspection sub-area within the corresponding inspection time is determined from the noise level classification table.

[0071] In step S103, by establishing a standardized regional-time dual-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 correspondence between different geographical attribute areas and working time periods, wherein 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, 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, and a higher threshold is set for industrial areas during the day. After obtaining the spatial coordinates and execution time of a specific inspection task, the corresponding maximum allowable noise value is directly extracted by matching the area category and time period fields in the classification table, and the maximum regional noise when the drone inspects each inspection sub-area within the corresponding inspection time is obtained.

[0072] The noise level classification table is shown in the following table:

[0073]

[0074] As can be seen from the table above, 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, we can obtain the maximum noise of the area corresponding to the drone.

[0075] 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:

[0076] Construct trajectory smoothness constraint function, collision constraint function and dynamic feasibility constraint function to construct a preliminary objective function;

[0077] Taking the noise impact as the constraint term, a noise impact reduction constraint function is constructed;

[0078] The preliminary objective function is optimized by using the noise impact reduction constraint function to obtain the preset UAV path planning algorithm.

[0079] In step S104, the UAV path planning algorithm must be pre-built. First, a preliminary objective function is formed by constructing trajectory smoothness constraints, collision constraints, and dynamic feasibility constraints. This step ensures that the path planning meets basic functional requirements, such as flight stability, safety, and the physical limitations of the UAV. Subsequently, to address the specific issue of noise pollution, a separate noise impact reduction constraint function is constructed and introduced as an optimization term into the preliminary objective function to optimize the preliminary objective function and obtain the preset UAV path planning algorithm. This phased optimization approach retains the core functionality of traditional path planning algorithms while also providing targeted improvements to address the unique needs of noise-sensitive areas. By treating the noise impact constraint as a separate optimization term, path parameters (such as flight speed and altitude) can be dynamically adjusted without disrupting the original path planning logic, thereby minimizing noise pollution while satisfying the comprehensive constraints.

[0080] Among them, the preset drone path planning algorithm is specifically as follows:

[0081] ;

[0082] in, is the objective function; is the objective function to be minimized; is the trajectory smoothness constraint function, is the coefficient of the trajectory smoothness 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; In order to reduce the impact of noise on the constraint function, To reduce the influence of noise on the constraint function coefficients, the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function are existing technologies and will not be described in detail here. The coefficients of the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function can be set according to actual needs.

[0083] Among them, the noise reduction constraint function

[0084] Specifically:

[0085] ;

[0086] Among them, v is the actual flight speed of the UAV during the inspection, and h is the actual flight altitude of the UAV during the inspection. is dimensionless data, so 、 、 and Only take its value to achieve dimensionless.

[0087] Set the noise reduction constraint function , which enables the drone path planning algorithm to plan an inspection route that meets the maximum noise level allowed in each area and time period, reducing the noise impact on urban residents during drone inspections and improving residents' living comfort.

[0088] Specifically, when pre-building the UAV path planning algorithm, the trajectory smoothness constraint function, collision constraint function, and dynamic feasibility constraint function are constructed to construct a preliminary objective function, including:

[0089] Based on the preset flight trajectory stability conditions, a trajectory smoothness constraint function is constructed;

[0090] Based on the preset collision avoidance conditions, a collision constraint function is constructed;

[0091] Based on the UAV flight capability constraints, a dynamic feasibility constraint function is constructed;

[0092] Corresponding coefficients are set for the trajectory smoothness constraint function, collision constraint function, and dynamic feasibility constraint function to construct a preliminary objective function.

[0093] When pre-building the UAV path planning algorithm, different types of constraint functions are constructed step by step and assigned corresponding weights to form a preliminary objective function. This solves the problem of traditional path planning, which focuses solely on efficiency while ignoring the coordination of multiple constraints. First, a trajectory smoothness constraint function, constructed based on flight trajectory smoothness conditions, ensures the continuity and smoothness of the UAV's path, avoiding control difficulties or excessive energy consumption caused by sudden trajectory changes. Second, a collision constraint function, generated through pre-set collision avoidance conditions, directly addresses the physical limitations of obstacles in the inspection environment, forcing the planned path to meet safe obstacle avoidance requirements. Third, a dynamic feasibility constraint function, constructed based on the UAV's flight capability constraints, incorporates the UAV's actual flight performance (such as maximum acceleration and steering angular velocity) into the planning process, avoiding the generation of paths that exceed the hardware capabilities. Finally, by assigning specific coefficients to each constraint function, the priority of different constraints can be dynamically adjusted. For example, the weight of collision constraints can be increased in areas with complex obstacles, or the influence of trajectory smoothness can be emphasized in scenarios requiring energy conservation. This achieves the integration and balance of multi-dimensional constraints at the objective function level.

[0094] Among them, the trajectory smoothness 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 capability limitation condition are existing technologies and will not be described in detail here.

[0095] Specifically, in step S104, based on the maximum flight speed and the minimum flight altitude, combined with a preset UAV path planning algorithm, the inspection path of the UAV in the inspection area is calculated, including:

[0096] Based on the maximum flight speed and minimum flight altitude, combined with the preset UAV path planning algorithm, the actual flight speed and actual flight altitude of the UAV in each inspection sub-area are calculated;

[0097] According to the actual flight speed and actual flight altitude, the inspection path of the UAV in the inspection area is constructed.

[0098] In step S104, the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area are input into a preset drone path planning algorithm as constraints for the noise impact reduction constraint function. Using existing algorithms such as gradient descent or genetic algorithms, the actual flight speed and altitude of the drone in each inspection sub-area are calculated. These existing algorithms, such as gradient descent and genetic algorithms, are currently available and will not be described in detail here.

[0099] Based on the actual flight speed and altitude of each inspection sub-area, existing path planning algorithms, such as the A* search algorithm, Diikstra's algorithm, and the ant colony algorithm, are used to generate a three-dimensional trajectory that balances noise compliance and inspection feasibility, resulting in the drone's inspection path within the inspection area. These algorithms, such as the A* search algorithm, Diikstra's algorithm, and the ant colony algorithm, are currently available and will not be described in detail here.

[0100] As can be seen from the above, the UAV path planning method obtains the inspection area of ​​the UAV, divides the inspection area into multiple inspection sub-areas, inputs the regional maximum noise of 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, by calculating the maximum flight speed and the minimum flight altitude based on the regional maximum noise of each inspection sub-area, combined with the preset UAV path planning algorithm, the inspection path of the UAV in the inspection area is calculated, which solves the problem that the traditional UAV inspection path planning algorithm cannot be flexibly adjusted according to the differences in noise sensitivity in different areas and at different times, and by dynamically calculating the optimal flight parameters of each area, the noise impact of the UAV on sensitive areas is targetedly reduced, thereby improving the path planning efficiency of the UAV.

[0101] refer to Figure 2 The present application provides a UAV path planning device for planning a UAV path, comprising:

[0102] Acquisition module 1 is used to obtain the inspection area of ​​the drone;

[0103] Division module 2, used to divide the inspection area into multiple inspection sub-areas;

[0104] The first calculation module 3 is used to input the maximum regional noise of multiple inspection sub-areas into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of each drone in the inspection sub-area;

[0105] The second calculation module 4 is used to calculate the inspection path of the UAV in the inspection area based on the maximum flight speed and the minimum flight altitude in combination with a preset UAV path planning algorithm.

[0106] This drone path planning device calculates the drone's inspection path in the inspection area by combining the maximum flight speed and minimum flight altitude calculated based on the maximum regional noise in each inspection sub-area with a preset drone path planning algorithm. This solves the problem that traditional drone inspection path planning algorithms cannot flexibly adjust to differences in noise sensitivity in different areas and at different times. By dynamically calculating the optimal flight parameters for each area, the noise impact of the drone on sensitive areas is targetedly reduced, thereby improving the drone's path planning efficiency.

[0107] Specifically, when the acquisition module 1 is executed, it obtains the inspection area of ​​the drone, and the inspection area refers to a pre-divided area that needs to be automatically inspected by the drone.

[0108] Specifically, when the division module 2 is executed, it divides the inspection area into multiple inspection sub-areas according to geographical areas, such as dividing the inspection area into inspection sub-areas such as schools and residential areas.

[0109] Specifically, the first calculation module 3 inputs the maximum noise of multiple inspection sub-areas into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula, and calculates the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area, and executes:

[0110] Get the inspection time corresponding to the inspection sub-area;

[0111] According to the inspection sub-area and inspection time, the preset noise level classification criteria are used to determine the maximum regional noise when the drone inspects each inspection sub-area within the corresponding inspection time;

[0112] The maximum noise level in each inspection sub-area is input into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area.

[0113] When the first calculation module 3 is executed, it obtains the inspection time, establishes a coupling relationship between the time dimension and the regional attribute, and obtains the maximum regional noise of the inspection sub-region during the corresponding inspection time based on the regional type and time combination of the inspection sub-region and the preset noise level classification criteria. The maximum regional noise is input into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the drone in each inspection sub-region. This achieves dynamic matching of flight parameters with time and space changes, avoiding noise exceeding the standard or loss of inspection efficiency caused by fixed parameters. Among them, the maximum flight speed calculation formula is set with a positive correlation between noise and speed, and the minimum flight altitude calculation formula is set with a negative correlation between noise and altitude. When the maximum regional noise decreases, a lower maximum flight speed and a higher minimum flight altitude are automatically generated.

[0114] The preset maximum flight speed calculation formula is as follows:

[0115] ;

[0116] in, is the maximum flight speed, and N is the maximum noise in the area.

[0117] The preset minimum flight altitude calculation formula is as follows:

[0118] ;

[0119] in, The minimum flight altitude.

[0120] Specifically, the first calculation module 3 determines the maximum noise level of each inspection sub-area when the drone inspects each inspection sub-area within the corresponding inspection time according to the inspection sub-area and the inspection time using the preset noise level classification criteria, and executes:

[0121] Reading a noise level classification table pre-constructed based on preset noise level classification criteria;

[0122] According to the inspection sub-area and inspection time, the maximum regional noise when the drone inspects each inspection sub-area within the corresponding inspection time is determined from the noise level classification table.

[0123] When the first calculation module 3 is executed, it converts complex dynamic environmental factors into structured data query operations by establishing a standardized regional-time dual-dimensional noise limit query mechanism. Based on the preset noise level classification criteria, a pre-constructed classification table establishes a correspondence between different geographical attribute areas and operating 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, 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, residential areas set a lower noise threshold during the night, and industrial areas set a higher threshold during the day. After obtaining the spatial coordinates and execution time of a specific inspection task, the corresponding maximum allowable noise value is directly extracted by matching the regional category and time period fields in the classification table, and the maximum regional noise when the drone inspects each inspection sub-area within the corresponding inspection time is obtained.

[0124] The noise level classification table is shown in the following table:

[0125]

[0126] As can be seen from the table above, 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, we can obtain the maximum noise of the area corresponding to the drone.

[0127] 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; when the second calculation module 4 is executed, the preset UAV path planning algorithm is constructed based on the following steps:

[0128] Construct trajectory smoothness constraint function, collision constraint function and dynamic feasibility constraint function to construct a preliminary objective function;

[0129] Taking the noise impact as the constraint term, a noise impact reduction constraint function is constructed;

[0130] The preliminary objective function is optimized by using the noise impact reduction constraint function to obtain the preset UAV path planning algorithm.

[0131] During execution, the second computation module 4 pre-constructs the UAV path planning algorithm. First, a preliminary objective function is formed by constructing trajectory smoothness constraints, collision constraints, and dynamic feasibility constraints. This step ensures that the path planning meets basic functional requirements, such as flight stability, safety, and the physical limitations of the UAV. Subsequently, a separate noise impact reduction constraint function is constructed to address the specific issue of noise pollution. This function is then incorporated into the preliminary objective function as an optimization term to optimize the preliminary objective function and obtain the preset UAV path planning algorithm. This phased optimization approach retains the core functionality of traditional path planning algorithms while also providing targeted improvements to address the unique needs of noise-sensitive areas. By treating the noise impact constraint as a separate optimization term, path parameters (such as flight speed and altitude) can be dynamically adjusted without disrupting the original path planning logic, thereby minimizing noise pollution while satisfying the comprehensive constraints.

[0132] Among them, the preset drone path planning algorithm is specifically as follows:

[0133] ;

[0134] in, is the objective function; is the objective function to be minimized; is the trajectory smoothness constraint function, is the coefficient of the trajectory smoothness 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; In order to reduce the impact of noise on the constraint function, To reduce the influence of noise on the constraint function coefficients, the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function are existing technologies and will not be described in detail here. The coefficients of the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function can be set according to actual needs.

[0135] Among them, the noise reduction constraint function Specifically:

[0136] ;

[0137] Among them, v is the actual flight speed of the UAV during the inspection, and h is the actual flight altitude of the UAV during the inspection. is dimensionless data, so 、 、 and Only take its value to achieve dimensionless.

[0138] Set the noise reduction constraint function , which enables the drone path planning algorithm to plan an inspection route that meets the maximum noise level allowed in each area and time period, reducing the noise impact on urban residents during drone inspections and improving residents' living comfort.

[0139] Specifically, when pre-building the UAV path planning algorithm, the trajectory smoothness constraint function, collision constraint function, and dynamic feasibility constraint function are constructed to construct a preliminary objective function, including:

[0140] Based on the preset flight trajectory stability conditions, a trajectory smoothness constraint function is constructed;

[0141] Based on the preset collision avoidance conditions, a collision constraint function is constructed;

[0142] Based on the UAV flight capability constraints, a dynamic feasibility constraint function is constructed;

[0143] Corresponding coefficients are set for the trajectory smoothness constraint function, collision constraint function, and dynamic feasibility constraint function to construct a preliminary objective function.

[0144] When pre-building the UAV path planning algorithm, different types of constraint functions are constructed step by step and assigned corresponding weights to form a preliminary objective function. This solves the problem of traditional path planning, which focuses solely on efficiency while ignoring the coordination of multiple constraints. First, a trajectory smoothness constraint function, constructed based on flight trajectory smoothness conditions, ensures the continuity and smoothness of the UAV's path, avoiding control difficulties or excessive energy consumption caused by sudden trajectory changes. Second, a collision constraint function, generated through pre-set collision avoidance conditions, directly addresses the physical limitations of obstacles in the inspection environment, forcing the planned path to meet safe obstacle avoidance requirements. Third, a dynamic feasibility constraint function, constructed based on the UAV's flight capability constraints, incorporates the UAV's actual flight performance (such as maximum acceleration and steering angular velocity) into the planning process, avoiding the generation of paths that exceed the hardware capabilities. Finally, by assigning specific coefficients to each constraint function, the priority of different constraints can be dynamically adjusted. For example, the weight of collision constraints can be increased in areas with complex obstacles, or the influence of trajectory smoothness can be emphasized in scenarios requiring energy conservation. This achieves the integration and balance of multi-dimensional constraints at the objective function level.

[0145] Among them, the trajectory smoothness 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 capability limitation condition are existing technologies and will not be described in detail here.

[0146] Specifically, when the second calculation module 4 calculates the inspection path of the drone in the inspection area based on the maximum flight speed and the minimum flight altitude in combination with the preset drone path planning algorithm, it executes:

[0147] Based on the maximum flight speed and minimum flight altitude, combined with the preset UAV path planning algorithm, the actual flight speed and actual flight altitude of the UAV in each inspection sub-area are calculated;

[0148] According to the actual flight speed and actual flight altitude, the inspection path of the UAV in the inspection area is constructed.

[0149] During execution, the second calculation module 4 inputs the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area as constraints for the noise impact reduction constraint function into a preset drone path planning algorithm. Using existing algorithms such as gradient descent or genetic algorithms, the module calculates the actual flight speed and altitude of the drone in each inspection sub-area. These existing algorithms, such as gradient descent and genetic algorithms, are currently available and will not be described in detail here.

[0150] Based on the actual flight speed and altitude of each inspection sub-area, existing path planning algorithms, such as the A* search algorithm, Diikstra's algorithm, and the ant colony algorithm, are used to generate a three-dimensional trajectory that balances noise compliance and inspection feasibility, resulting in the drone's inspection path within the inspection area. These algorithms, such as the A* search algorithm, Diikstra's algorithm, and the ant colony algorithm, are currently available and will not be described in detail here.

[0151] As can be seen from the above, the UAV path planning device obtains the inspection area of ​​the UAV, divides the inspection area into multiple inspection sub-areas, inputs the regional maximum noise of 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, by calculating the maximum flight speed and the minimum flight altitude based on the regional maximum noise of each inspection sub-area, combined with the preset UAV path planning algorithm, the inspection path of the UAV in the inspection area is calculated, which solves the problem that the traditional UAV inspection path planning algorithm cannot be flexibly adjusted according to the differences in noise sensitivity in different areas and at different times, and by dynamically calculating the optimal flight parameters of each area, the noise impact of the UAV on sensitive areas is targetedly reduced, thereby improving the path planning efficiency of the UAV.

[0152] Please refer to Figure 3 , Figure 3 This is a structural schematic diagram of an electronic device provided in 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 shown). The memory 302 stores a computer program executable by the processor 301. When the electronic device is running, the processor 301 executes the computer program to execute the drone path planning method in any optional implementation of the above embodiment to achieve the following functions: obtaining an inspection area of ​​the drone, dividing the inspection area into multiple inspection sub-areas, inputting the maximum regional noise of the multiple inspection sub-areas into a preset maximum flight speed calculation formula and a minimum flight altitude calculation formula, calculating the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area, and calculating the inspection path of the drone in the inspection area based on the maximum flight speed and the minimum flight altitude, combined with a preset drone path planning algorithm.

[0153] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the drone path planning method in any optional implementation of the above embodiment is executed to achieve the following functions: obtaining the inspection area of ​​the drone, dividing the inspection area into multiple inspection sub-areas, inputting the maximum regional noise of the multiple inspection sub-areas into a preset maximum flight speed calculation formula and a minimum flight altitude calculation formula, calculating the maximum flight speed and minimum flight altitude of the drone in each inspection sub-area, and based on the maximum flight speed and the minimum flight altitude, combined with a preset drone path planning algorithm, calculating the inspection path of the drone in the inspection area. The storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage device, flash memory, magnetic disk or optical disk.

[0154] 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 schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0155] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the purpose of the solution of this embodiment.

[0156] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0157] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0158] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A UAV path planning method for planning a UAV path, characterized in that: Including steps: Get the inspection area of ​​the drone; Dividing the inspection area into a plurality of inspection sub-areas; Inputting the maximum regional noise of the plurality of inspection sub-areas into a preset maximum flight speed calculation formula and a minimum flight altitude calculation formula, and calculating the maximum flight speed and minimum flight altitude of the UAV in each inspection sub-area; Based on the maximum flight speed and the minimum flight altitude, combined with a preset UAV path planning algorithm, an inspection path of the UAV in the inspection area is calculated; The preset UAV path planning algorithm involves an objective function constructed based on a trajectory smoothness constraint function, a collision constraint function, a dynamic feasibility constraint function, and a noise impact reduction constraint function; The preset UAV path planning algorithm is constructed based on the following steps: Constructing the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function; Taking the noise impact as a constraint term, constructing the noise impact reduction constraint function; Utilizing the noise impact reduction constraint function, the preliminary objective function is optimized to obtain a preset UAV path planning algorithm; Construct trajectory smoothness constraint function, collision constraint function and dynamic feasibility constraint function to construct a preliminary objective function, including: Based on the preset flight trajectory stability conditions, a trajectory smoothness constraint function is constructed; Based on the preset collision avoidance conditions, a collision constraint function is constructed; Based on the UAV flight capability constraints, a dynamic feasibility constraint function is constructed; Corresponding coefficients are set for the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function.

2. The UAV path planning method according to claim 1, characterized in that: Inputting the maximum noise of the plurality of inspection sub-areas into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the UAV in each inspection sub-area includes: Obtaining the inspection time corresponding to each inspection sub-area; According to the inspection sub-areas and the inspection time, the maximum regional noise when the drone inspects each of the inspection sub-areas within the corresponding inspection time is determined by using a preset noise level classification criterion; The maximum noise in the inspection sub-area is input into the preset maximum flight speed calculation formula and minimum flight altitude calculation formula to calculate the maximum flight speed and minimum flight altitude of the UAV in each inspection sub-area.

3. The UAV path planning method according to claim 2, characterized in that: According to the inspection sub-areas and the inspection time, the maximum regional noise when the drone inspects each of the inspection sub-areas within the corresponding inspection time is determined by using a preset noise level classification criterion, including: Reading a noise level classification table pre-constructed based on preset noise level classification criteria; According to the inspection sub-areas and the inspection time, the maximum regional noise when the drone inspects each of the inspection sub-areas within the corresponding inspection time is determined from the noise level classification table.

4. The UAV path planning method according to claim 1, characterized in that: Based on the maximum flight speed and the minimum flight altitude, combined 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, combined 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; An inspection path of the UAV in the inspection area is constructed according to the actual flight speed and the actual flight altitude.

5. A UAV path planning device for planning a UAV path, characterized in that: include: The acquisition module is used to obtain the inspection area of ​​the drone; A division module, configured to divide the inspection area into a plurality of inspection sub-areas; A first calculation module is configured to input the maximum regional noise 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 minimum flight altitude of the UAV in each of the inspection sub-areas; A second calculation module is configured to calculate an inspection path of the UAV in the inspection area based on the maximum flight speed and the minimum flight altitude in combination with a preset UAV path planning algorithm; The preset UAV path planning algorithm involves an objective function constructed based on a trajectory smoothness constraint function, a collision constraint function, a dynamic feasibility constraint function, and a noise impact reduction constraint function; The preset UAV path planning algorithm is constructed based on the following steps: Constructing the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function; Taking the noise impact as a constraint term, constructing the noise impact reduction constraint function; Utilizing the noise impact reduction constraint function, the preliminary objective function is optimized to obtain a preset UAV path planning algorithm; Construct trajectory smoothness constraint function, collision constraint function and dynamic feasibility constraint function to construct a preliminary objective function, including: Based on the preset flight trajectory stability conditions, a trajectory smoothness constraint function is constructed; Based on the preset collision avoidance conditions, a collision constraint function is constructed; Based on the UAV flight capability constraints, a dynamic feasibility constraint function is constructed; Corresponding coefficients are set for the trajectory smoothness constraint function, the collision constraint function, and the dynamic feasibility constraint function to construct a preliminary objective function.

6. An electronic device, characterized in that: It includes 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 drone path planning method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the drone path planning method according to any one of claims 1 to 4 are executed.

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