An unmanned aerial vehicle path planning method based on ecological sensitivity and related equipment

Abstract

The present application provides a kind of unmanned aerial vehicle path planning method and related equipment based on ecological sensitivity, obtain the multiple predicted paths between the current node and the next node of unmanned aerial vehicle;Unmanned aerial vehicle corresponding total power consumption and ecological sensitivity operator are calculated in advance when unmanned aerial vehicle flies along the predicted path;Based on the total power consumption and ecological sensitivity operator corresponding to unmanned aerial vehicle at each time point when unmanned aerial vehicle flies along the predicted path, the energy cost value corresponding to the predicted path is determined.In the case where the total power consumption is the same, the greater the ecological sensitivity operator, the lower the energy cost value, the predicted path with the minimum energy cost value is selected as the main path between the current node and the next node of unmanned aerial vehicle, so as to induce the path to deviate from sensitive area.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicles (UAVs), and more specifically, to a UAV path planning method and related equipment based on ecological sensitivity. Background Technology

[0002] With the rapid development of low-altitude airspace, the global low-altitude economy has entered a phase of explosive growth. Unmanned aerial vehicles (UAVs) are no longer just simple photography or transportation tools, but have evolved into aerial mobile laboratories integrating advanced sensing capabilities. In the field of ecological meteorology, UAVs have become core monitoring equipment due to their high mobility, cost-effectiveness, and ability to penetrate into areas traditionally blinded by ground stations (such as high mountains and deep valleys, forest canopies, and wetland core areas).

[0003] However, ecological monitoring tasks place almost stringent demands on data accuracy. To achieve a quantitative description of local micro-meteorology phenomena, it is necessary to capture minute fluctuations in temperature, humidity, air pressure, wind shear, and atmospheric composition at scales of hundreds of meters or even tens of meters. This trend towards higher precision directly drives the strategic transformation of UAV path planning technology from spatial coverage to feature-based response. Existing path planning technologies, whether in logistics and general surveying, are primarily based on geometric spatial search and have not established a deep connection with the physical properties of ecosystems and meteorological dynamics. This has become the primary technical barrier restricting the operationalization of precision meteorology. Summary of the Invention

[0004] The purpose of this invention is to provide an ecologically sensitive unmanned aerial vehicle (UAV) path planning method and related equipment to improve the above-mentioned problems.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0006] In a first aspect, embodiments of the present invention provide a drone path planning method based on ecological sensitivity, the method comprising:

[0007] Obtain multiple predicted paths for the drone from the current node to the next node;

[0008] The total power consumption and ecological sensitivity operator of the UAV at each time point during the UAV's flight along the predicted path are calculated in advance;

[0009] Based on the total power consumption and ecological sensitivity operator of the UAV at each time point during the UAV's flight along the predicted path, the energy cost corresponding to the predicted path is determined.

[0010] The predicted path with the lowest energy cost is taken as the main path for the drone from the current node to the next node.

[0011] Secondly, embodiments of the present invention provide a drone path planning device based on ecological sensitivity, the device comprising:

[0012] The first processing unit is used to obtain multiple predicted paths for the UAV from the current node to the next node;

[0013] The second processing unit is used to pre-calculate the total power consumption and ecological sensitivity operator of the UAV at each time point when the UAV flies along the predicted path;

[0014] The second processing unit is also used to determine the energy cost corresponding to the predicted path based on the total power consumption and ecological sensitivity operator of the UAV at each time point when the UAV flies along the predicted path;

[0015] The second processing unit is also used to select the predicted path with the minimum energy cost as the main path for the UAV from the current node to the next node.

[0016] Thirdly, embodiments of the present invention provide a storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0017] Fourthly, embodiments of the present invention provide an electronic device, the electronic device comprising: a processor and a memory, the memory being used to store one or more programs; when the one or more programs are executed by the processor, the above-described method is implemented.

[0018] Compared to existing technologies, the present invention provides a method and related equipment for UAV path planning based on ecological sensitivity. This method acquires multiple predicted paths for the UAV from the current node to the next node; pre-calculates the total power consumption and ecological sensitivity operator of the UAV at each time point while flying along the predicted paths; and determines the energy cost of the predicted paths based on the total power consumption and ecological sensitivity operator at each time point. With the same total power consumption, a larger ecological sensitivity operator results in a lower energy cost. The predicted path with the lowest energy cost is selected as the main path for the UAV from the current node to the next node, thereby inducing the path to shift towards sensitive areas.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0022] Figure 2 This is a flowchart illustrating the UAV path planning method based on ecological sensitivity provided in an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of a drone path planning device based on ecological sensitivity provided in an embodiment of the present invention.

[0024] In the diagram: 10-Processor; 11-Memory; 12-Bus; 13-Communication interface; 701-First processing unit; 702-Second processing unit. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0029] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0030] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0031] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0032] This invention provides an electronic device, which can be a central control device for a drone, or a mobile phone, computer, or server device that is communicatively connected to the drone's central control device. Please refer to... Figure 1 This is a schematic diagram of the structure of an electronic device. The electronic device includes a processor 10, a memory 11, and a bus 12. The processor 10 and the memory 11 are connected via the bus 12. The processor 10 is used to execute executable modules, such as computer programs, stored in the memory 11.

[0033] Processor 10 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the ecological sensitivity-based UAV path planning method can be completed through integrated logic circuits in the hardware or software instructions within processor 10. Processor 10 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0034] The memory 11 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage.

[0035] Bus 12 can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. Figure 1 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus 12 or one type of bus 12.

[0036] The memory 11 is used to store programs, such as programs corresponding to an eco-sensitive UAV path planning device. The eco-sensitive UAV path planning device includes at least one software functional module that can be stored in the memory 11 as software or firmware, or embedded in the operating system (OS) of the electronic device. Upon receiving an execution instruction, the processor 10 executes the program to implement the eco-sensitive UAV path planning method.

[0037] The electronic device provided in this embodiment of the invention may further include a communication interface 13. The communication interface 13 is connected to the processor 10 via a bus.

[0038] It should be understood that, Figure 1The structure shown is only a partial schematic diagram of the electronic device; the electronic device may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0039] The UAV path planning method based on ecological sensitivity provided in this invention can be applied to, but is not limited to, [various applications]. Figure 1 For the specific process of the electronic devices shown, please refer to [link / reference]. Figure 2 The ecological sensitivity-based drone path planning methods include S10, S20, S30 and S40, which are described in detail below.

[0040] S10: Obtain multiple predicted paths for the drone from the current node to the next node.

[0041] S20 pre-calculates the total power consumption and ecological sensitivity operator (ESO) of the UAV at each time point as it flies along the predicted path.

[0042] The interval between adjacent time points is: , The value can be, but is not limited to, 1ms, 10ms, and 1s.

[0043] S30 determines the energy cost corresponding to the predicted path based on the total power consumption of the UAV at each time point during its flight along the predicted path and the ecological sensitivity operator.

[0044] Among these, with the same total power consumption, the larger the ecological sensitivity operator, the lower the energy cost. For example, by dividing the power term by the sensitivity operator... This makes in In high-sensitivity areas, the energy cost per unit of information decreases, causing the induced path to shift towards the sensitive area.

[0045] S40 uses the predicted path with the lowest energy cost as the main path for the drone from the current node to the next node.

[0046] In the UAV path planning method based on ecological sensitivity provided in this embodiment of the invention, the energy cost of the predicted path is determined based on the total power consumption of the UAV at each time point during its flight along the predicted path and the ecological sensitivity operator. With the same total power consumption, a larger ecological sensitivity operator results in a lower energy cost. The predicted path with the lowest energy cost is then used as the main path for the UAV from the current node to the next node, thereby inducing the path to shift towards the sensitive area.

[0047] Optionally, the formula for calculating the energy cost value corresponding to the predicted path is:

[0048]

[0049]

[0050] in, This represents the energy cost corresponding to the predicted path. This represents the coordinates of the UAV at time t as it flies along the predicted path. Let t represent the t-th time point when the UAV flies along the predicted path, and T represent the total time when the UAV flies along the predicted path. This indicates that the UAV is flying along the predicted path at time t (coordinate point). Total power consumption at (location) This represents the coordinates of the UAV at time t as it flies along the predicted path. Ecological sensitivity operator at the location, For positive integers, This represents the coordinates of the UAV at time t as it flies along the predicted path. To avoid collision risk, This represents the coordinates of the UAV at time t as it flies along the predicted path. The distance exceeds the geographical fence of low-altitude airspace. and This serves as a penalty factor to ensure that the path meets the stringent requirements of obstacle avoidance and low-altitude airspace geofencing.

[0051] Based on the previous text, the content in S20 can be broken down into the pre-calculation of the ecological sensitivity operator of the UAV at each time point when the UAV flies along the predicted path and the pre-calculation of the total power consumption of the UAV at each time point when the UAV flies along the predicted path. It should be noted that the UAV is not analyzing according to the predicted path at this time; this process is the pre-calculation process.

[0052] Please refer to the following text for the pre-calculation of the ecological sensitivity operators for the UAV at each time point when the UAV flies along the predicted path, including: S211 and S212, which are explained in detail below.

[0053] S211 pre-calculates the normalized vegetation index, surface radiation temperature, and meteorological Shannon entropy at each time point during the UAV's flight along the predicted path.

[0054] In one optional implementation, after the UAV obtains the surface radiation temperature at each coordinate point at the current node, it normalizes the obtained surface radiation temperature to obtain the surface radiation temperature corresponding to each time point when the UAV flies along the predicted path.

[0055] S212. Based on the normalized vegetation index, surface radiation temperature, and meteorological Shannon entropy at each time point during the UAV's flight along the predicted path, determine the ecological sensitivity operator corresponding to the UAV at each time point during the UAV's flight along the predicted path.

[0056] Optionally, the formula for the ecological sensitivity operator is:

[0057]

[0058] in, This indicates that the coordinates of the UAV at time t are as follows: , This represents the coordinates of the UAV at time t as it flies along the predicted path. Ecological sensitivity operator at the location, Represents vegetation gradient weights (identifying the "ecological transition zone" where forests, farmland, and wetlands meet, where heat balance changes drastically, using the first derivative). The weight of the Laplace term for surface temperature is indicated (the Laplace operator is used to identify the center and diffusion edge of local heat sources / heat sinks to guide vertical profile scanning). Indicates atmospheric turbulence weight (using Shannon entropy to calculate local atmospheric turbulence and identify areas of severe fluctuation in meteorological parameters). For the sign of differentiation, Represents coordinate points Normalized Difference Vegetation Index (NDVI) at the location Represents coordinate points The surface radiation temperature at that location. Represents coordinate points Shannon entropy at the location.

[0059] First-order derivative of the NDVI field using the Sobel or Prewitt operator ( The physical significance of this item lies in identifying ecological ecotones. At the edges of forests and cultivated land, wetlands and arid land, the horizontal gradients of heat balance and water vapor flux are the largest, making them high-incidence areas for microclimate changes.

[0060] Find the second derivative of the surface temperature field ( This term is used to identify the heat source and heat sink center and its diffusion edge. When the second derivative has an extreme value, it indicates the starting point of the local thermal circulation (such as a thermal plume), guiding the UAV to capture the rising branch path of the local thermal circulation.

[0061] Standard deviations of air humidity and air pressure measured by airborne meteorological probes The Shannon entropy H is used to quantify fluctuations in humidity and air pressure. When When the frequency increases, it indicates that the system has entered a turbulent mixing region, and the sampling frequency needs to be increased (from 1Hz to 10-20Hz).

[0062] Optionally, The coordinates obtained by the drone at the current node Normalized Difference Vegetation Index (NDVI) at the location The coordinates obtained by the drone at the current node The surface radiation temperature at that location (can be obtained using an airborne infrared sensor). The coordinates obtained by the drone at the current node The meteorological Shannon entropy at the location can be used to ignore the error between the current time and the t-th time point when the predicted path is taken.

[0063]

[0064] in, This indicates the coordinates obtained by the drone at the current node. Shannon entropy at the location of the weather, This represents the coordinates of the i-th feature at time t when the UAV flies along the predicted path. The probability distribution within the sampling window (which can be obtained based on observations from ground-based meteorological stations) has the first characteristic as air humidity and the second characteristic as the standard deviation of air pressure, where 1 ≤ i ≤ 2.

[0065] The formula for the Normalized Difference Vegetation Index (NDVI) is:

[0066]

[0067] in, Indicates the spectral reflectance in the near-infrared band. The spectral reflectance in the red band can be obtained from either an airborne multispectral camera or satellite imagery data.

[0068] In one alternative implementation, the drone is equipped with a multispectral visual sensor (MSI), a high dynamic meteorological probe array (MET-Probe), and an active laser methane / carbon dioxide scanner.

[0069] Multispectral Vision Sensor (MSI): Equipped with a high frame rate, narrow-band pushbroom camera, specifically designed for precision agriculture and ecological monitoring. This module connects to the edge computing unit via the MIPI-CSI 2.0 interface, supporting real-time hardware-level NDVI resolution.

[0070] The High Dynamic Weather Probe (MET-Probe) integrates an ultrasonic 3D anemometer (sampling frequency ≥100Hz), a fast-response platinum resistance thermometer, and a ceramic capacitive humidity sensor. Its probe head, through aerodynamic optimization, is mounted in the laminar flow zone in front of the UAV's nose to eliminate propeller wash interference.

[0071] Active laser methane / carbon dioxide scanner: Employing tunable diode laser absorption spectroscopy (TDLAS) technology, it obtains the concentration of greenhouse gases in the atmospheric column above the Earth's surface by vertically downward detection, and its data stream is injected into the path engine in real time via a high-speed serial protocol.

[0072] Optionally, based on the foregoing, this embodiment of the invention also provides an optional implementation method, please refer to the following. The UAV path planning method based on ecological sensitivity further includes: S50, as detailed below.

[0073] S50, when the ecological sensitivity operator obtained at the current time exceeds the adaptive threshold (τ), dynamically adjusts the vegetation gradient weight, the Laplace term weight of the land surface temperature, and the atmospheric turbulence weight according to the recursive least squares method.

[0074] The adaptive threshold is the average value of the ecological sensitivity operators obtained from historical time points, which include multiple consecutive time points prior to the current time. By adjusting the weight coefficients, computational idling is prevented on homogeneous terrains (such as vast deserts or uniform grasslands).

[0075] Optionally, the total power consumption of the UAV at each time point during the flight of the UAV along the predicted path is calculated in advance, including S221 and S222, which are described in detail below.

[0076] S221, obtain the parasitic power, induced power and payload power consumption of the UAV at each time point when the UAV flies along the predicted path in advance.

[0077] S222, based on the parasitic power, induced power and payload power consumption of the UAV at each time point when the UAV flies along the predicted path, determine the total power consumption of the UAV at each time point when the UAV flies along the predicted path.

[0078] Optionally, the formula for the total power consumption of the drone is:

[0079]

[0080] in, This indicates that the UAV is flying along the predicted path at time t (coordinate point). Total power consumption at (location) Indicates air density, Indicates the drag coefficient. This indicates the windward surface area of ​​the drone. This represents the relative velocity of the UAV at time t relative to the wind field where the UAV is located, as the UAV flies along the predicted path. The table shows the mass (kg) of the UAV at time point t as it flies along the predicted path. Represents gravitational acceleration. Pi The efficiency factor representing the drone. Indicates the wingspan of the drone. This represents the power consumption of the k-th sensor. This represents the on / off state of the k-th sensor at time t. This indicates the total number of sensors carried by the drone. Indicates parasitic power. Indicates induced power, This indicates the power consumption of the load.

[0081] Quality Items For drones carrying liquid sampling devices or deployable probes, mass It is a function that varies with the path, so it can be considered a constraint variable in path planning. Load switch It is a Boolean decision variable, controlled by the ESO operator. When entering the low-sensitivity region, i.e. When it is less than the low sensitivity threshold, (Hibernation mode) significantly improves energy efficiency.

[0082] Optionally, the relative speed can be determined based on the drone's ground speed and wind field data where the drone is located. .

[0083] Please see Figure 3 , Figure 3 An ecologically sensitive drone path planning device is provided as an embodiment of the present invention. Optionally, the ecologically sensitive drone path planning device is applied to the electronic device described above.

[0084] The drone path planning device based on ecological sensitivity includes: a first processing unit 701 and a second processing unit 702.

[0085] The first processing unit 701 is used to obtain multiple predicted paths for the UAV from the current node to the next node;

[0086] The second processing unit 702 is used to pre-calculate the total power consumption and ecological sensitivity operator of the UAV at each time point when the UAV flies along the predicted path.

[0087] The second processing unit 702 is also used to determine the energy cost corresponding to the predicted path based on the total power consumption and ecological sensitivity operator of the UAV at each time point when the UAV flies along the predicted path.

[0088] The second processing unit 702 is also used to select the predicted path with the minimum energy cost as the main path for the UAV from the current node to the next node.

[0089] It should be noted that the UAV path planning device based on ecological sensitivity provided in this embodiment can execute the method flow shown in the above-described method flow embodiment to achieve the corresponding technical effects. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above-described embodiments.

[0090] This invention also provides a storage medium storing computer instructions and programs. When read and executed, these instructions and programs perform the eco-sensitivity-based UAV path planning method described above. The storage medium may include memory, flash memory, registers, or a combination thereof.

[0091] The following provides an electronic device, which can be a central control device for a drone, or a mobile device, computer device, or server device that communicates with the drone's central control device. This electronic device, for example... Figure 1 As shown, the above-described UAV path planning method based on ecological sensitivity can be implemented. Specifically, the electronic device includes: a processor 10, a memory 11, and a bus 12. The processor 10 may be a CPU. The memory 11 is used to store one or more programs, which, when executed by the processor 10, execute the UAV path planning method based on ecological sensitivity described in the above embodiment.

[0092] In summary, the present invention provides a method and related equipment for UAV path planning based on ecological sensitivity. This method acquires multiple predicted paths for the UAV from the current node to the next node; pre-calculates the total power consumption and ecological sensitivity operator of the UAV at each time point while flying along the predicted paths; and determines the energy cost of the predicted paths based on the total power consumption and ecological sensitivity operator at each time point. With the same total power consumption, a larger ecological sensitivity operator results in a lower energy cost. The predicted path with the lowest energy cost is selected as the main path for the UAV from the current node to the next node, thereby inducing the path to shift towards sensitive areas.

[0093] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0094] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A drone path planning method based on ecological sensitivity, characterized in that, The method includes: Obtain multiple predicted paths for the drone from the current node to the next node; The total power consumption and ecological sensitivity operator of the UAV at each time point when the UAV flies along the predicted path are calculated in advance; Based on the total power consumption and ecological sensitivity operator of the UAV at each time point when the UAV flies along the predicted path, the energy cost corresponding to the predicted path is determined. The predicted path with the lowest energy cost is used as the main path for the drone from the current node to the next node. The ecological sensitivity operator corresponding to the UAV at each time point when the UAV flies along the predicted path is pre-calculated, including: pre-calculating the normalized vegetation index, surface radiation temperature and meteorological Shannon entropy corresponding to each time point when the UAV flies along the predicted path; and determining the ecological sensitivity operator corresponding to the UAV at each time point when the UAV flies along the predicted path based on the normalized vegetation index, surface radiation temperature and meteorological Shannon entropy corresponding to each time point when the UAV flies along the predicted path. The formula for the ecological sensitivity operator is: in, This indicates that the coordinates of the UAV at time t are as follows: , Represents vegetation gradient weights. Indicates the weight of the Laplace term for surface temperature; Indicates the weight of atmospheric turbulence. To determine the sign of the derivative, Represents coordinate points Normalized Difference Vegetation Index (NDVI) at the location Represents coordinate points The surface radiation temperature at that location. Represents coordinate points Shannon entropy at the location.

2. The UAV path planning method based on ecological sensitivity as described in claim 1, characterized in that, The formula for calculating the energy cost value corresponding to the predicted path is: in, This represents the energy cost corresponding to the predicted path. This represents the coordinates of the UAV at time t as it flies along the predicted path. Let t represent the t-th time point when the UAV flies along the predicted path, and T represent the total time when the UAV flies along the predicted path. This represents the total power consumption of the drone at time t while it flies along the predicted path. This represents the coordinates of the UAV at time t as it flies along the predicted path. Ecological sensitivity operator at the location, For positive integers, This represents the coordinates of the UAV at time t as it flies along the predicted path. To avoid collision risk, This represents the coordinates of the UAV at time t as it flies along the predicted path. The distance exceeds the geographical fence of low-altitude airspace. and This is a penalty factor.

3. The UAV path planning method based on ecological sensitivity as described in claim 1, characterized in that, The method further includes: When the ecological sensitivity operator obtained at the current time exceeds the adaptive threshold, the vegetation gradient weight, the Laplace term weight of the land surface temperature, and the atmospheric turbulence weight are dynamically adjusted according to the recursive least squares method. The adaptive threshold is the average value of the ecological sensitivity operators obtained at historical time points, which include multiple consecutive time points before the current time.

4. The UAV path planning method based on ecological sensitivity as described in claim 1, characterized in that, The total power consumption of the drone at each time point during its flight along the predicted path is pre-calculated, including: Obtain the parasitic power, induced power, and payload power consumption of the UAV at each time point when the UAV flies along the predicted path, calculated in advance; Based on the parasitic power, induced power, and payload power consumption of the UAV at each time point during its flight along the predicted path, the total power consumption of the UAV at each time point during its flight along the predicted path is determined.

5. The UAV path planning method based on ecological sensitivity as described in claim 4, characterized in that, The formula for the total power consumption of the drone is: in, This represents the total power consumption of the drone at time t while it flies along the predicted path. Indicates air density, Indicates the drag coefficient. This indicates the windward surface area of ​​the drone. This represents the relative velocity of the drone at time t relative to the wind field where the drone is located, as the drone flies along the predicted path. The table shows the drone's mass at time t as it flies along the predicted path. Represents gravitational acceleration. Pi The efficiency factor representing the drone. Indicates the wingspan of the drone. This represents the power consumption of the k-th sensor. This represents the on / off state of the k-th sensor at time t. This indicates the total number of sensors carried by the drone. Indicates parasitic power. Indicates induced power, This indicates the power consumption of the load.

6. A drone path planning device based on ecological sensitivity, characterized in that, The device includes: The first processing unit is used to obtain multiple predicted paths for the UAV from the current node to the next node; The second processing unit is used to pre-calculate the total power consumption and ecological sensitivity operator of the UAV at each time point when the UAV flies along the predicted path; The second processing unit is also used to determine the energy cost corresponding to the predicted path based on the total power consumption of the UAV and the ecological sensitivity operator at each time point when the UAV flies along the predicted path; The second processing unit is also used to select the predicted path with the lowest energy cost as the main path for the UAV from the current node to the next node; The ecological sensitivity operator corresponding to the UAV at each time point when the UAV flies along the predicted path is pre-calculated, including: pre-calculating the normalized vegetation index, surface radiation temperature and meteorological Shannon entropy corresponding to each time point when the UAV flies along the predicted path; and determining the ecological sensitivity operator corresponding to the UAV at each time point when the UAV flies along the predicted path based on the normalized vegetation index, surface radiation temperature and meteorological Shannon entropy corresponding to each time point when the UAV flies along the predicted path. The formula for the ecological sensitivity operator is: in, This indicates that the coordinates of the UAV at time t are as follows: , Represents vegetation gradient weights. Indicates the weight of the Laplace term for surface temperature; Indicates the weight of atmospheric turbulence. To determine the sign of the derivative, Represents coordinate points Normalized Difference Vegetation Index (NDVI) at the location Represents coordinate points The surface radiation temperature at that location. Represents coordinate points Shannon entropy at the location.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-5.

8. An electronic device, characterized in that, include: Processor and memory, the memory being used to store one or more programs; When the one or more programs are executed by the processor, the method as described in any one of claims 1-5 is implemented.

Images