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

Abstract

The application belongs to the technical field of unmanned aerial vehicle path planning, and discloses an unmanned aerial vehicle path planning method and device, an electronic device and a storage medium, the method comprising: acquiring electromagnetic induction information of an unmanned aerial vehicle in a preset range, inputting the electromagnetic induction information into a preset multi-dimensional electromagnetic radiation cost model, calculating an electromagnetic radiation cost of the unmanned aerial vehicle in the preset range, based on the electromagnetic radiation cost, using a preset cost update rule to plan a flight path for the unmanned aerial vehicle, and obtaining an optimal flight path of the unmanned aerial vehicle in the preset range; through the above method, the flight safety of the unmanned aerial vehicle in a complex electromagnetic environment is improved.

Description

Technical Field

[0001] This application relates to the technical field of unmanned aerial vehicle (UAV) path planning, and more specifically, to a UAV path planning method, apparatus, electronic device, and storage medium. Background Technology

[0002] Currently, UAV path planning technology is widely used in many fields, but traditional methods mainly focus on basic factors such as geometric obstacle avoidance, path length, and energy consumption, neglecting the profound impact of complex electromagnetic environments on flight safety. In scenarios such as substations, high-voltage lines, and urban electromagnetically dense areas, strong electromagnetic radiation can cause UAV communication interruptions, navigation drift, and even equipment damage.

[0003] Existing technologies, such as satellite-based path planning methods (like GPS or BeiDou RTK), are prone to signal attenuation and multipath effects in strong electromagnetic fields, causing positioning accuracy to drop from centimeter-level to meter-level or even fail. Although some studies have attempted to introduce simple electromagnetic threshold judgments (such as fixed field strength thresholds), these methods can only cope with static environments and cannot adapt to dynamic fluctuations in electromagnetic fields (such as changes in electromagnetic field strength caused by equipment start-up and shutdown, and weather changes), and they do not quantify the cumulative effects and frequency domain characteristics of electromagnetic radiation. For example, harmonic interference and pulsed magnetic fields near high-voltage transmission lines may cause frequency-band-specific interference to UAV sensors, and traditional path planning algorithms, lacking multi-dimensional electromagnetic modeling capabilities, struggle to generate interference-resistant paths.

[0004] Furthermore, the coupling between existing electromagnetic sensing and path planning is insufficient. Most solutions employ isolated electromagnetic maps (such as single-band field strength maps) without comprehensively considering multi-dimensional factors such as electromagnetic radiation intensity, frequency weight, exposure time, and equipment sensitivity. This can lead to situations where the planned path, while avoiding local strong electromagnetic points, results in equipment performance degradation due to prolonged exposure to moderate electromagnetic fields. In addition, path planning algorithms (such as A* or Dijkstra's algorithm) typically prioritize the shortest path, treating electromagnetic constraints merely as simple penalties without deeply integrating them into the core weight allocation mechanism of the cost function. This design struggles to balance conflicts between electromagnetic safety and other objectives (such as path length and energy consumption). For example, drones might take excessively long detours to avoid electromagnetic risks, increasing the probability of collisions or mission timeouts.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide a method, device, electronic device, and storage medium for UAV path planning. By using a preset cost update rule, and based on the electromagnetic radiation cost of the UAV within a preset range calculated by inputting electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model, the method plans the UAV's flight path to obtain the optimal flight path within the preset range. This addresses the problems of existing UAV path planning methods, such as neglecting the influence of complex electromagnetic environments, insufficient coupling between electromagnetic sensing and path planning, and difficulty in balancing electromagnetic safety with conflicts with other objectives, thus making it difficult to accurately generate UAV paths resistant to electromagnetic interference. The planned path not only avoids local strong electromagnetic points but also effectively avoids equipment performance degradation that may be caused by long-term exposure to moderate electromagnetic fields, thereby improving the flight safety of UAVs in complex electromagnetic environments.

[0007] Firstly, this application provides a method for unmanned aerial vehicle (UAV) path planning, including:

[0008] Acquire electromagnetic induction information of the drone within a preset range;

[0009] The electromagnetic induction information is input into a preset multidimensional electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range.

[0010] Based on the electromagnetic radiation cost, the flight path of the UAV is planned using a preset cost update rule to obtain the optimal flight path of the UAV within the preset range.

[0011] The UAV path planning method provided in this application can plan the path of a UAV. By using a preset cost update rule, the method calculates the electromagnetic radiation cost of the UAV within a preset range by inputting electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model, and then plans the flight path of the UAV to obtain the optimal flight path of the UAV within the preset range. This solves the problems of existing UAV path planning methods, such as ignoring the influence of complex electromagnetic environment, insufficient coupling between electromagnetic sensing and path planning, and difficulty in balancing electromagnetic safety and conflicts with other targets, which makes it difficult to accurately generate UAV paths resistant to electromagnetic interference. The planned path can not only avoid local strong electromagnetic points, but also effectively avoid the equipment performance degradation that may be caused by long-term exposure to moderate electromagnetic fields, thereby improving the flight safety of UAVs in complex electromagnetic environments.

[0012] Optionally, the preset multidimensional electromagnetic radiation cost model includes a preset field strength cost calculation model, a preset frequency band hazard weight calculation model, a preset exposure time cost calculation model, a preset equipment sensitivity calculation model, and a preset electromagnetic radiation cost model.

[0013] Optionally, the electromagnetic induction information is input into a preset multidimensional electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range, including:

[0014] The electromagnetic induction information is input into the preset field strength cost calculation model, the preset frequency band hazard weight calculation model, the preset exposure time cost calculation model, and the preset equipment sensitivity calculation model, respectively, to calculate the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity of the UAV within the preset range;

[0015] The field strength cost, the frequency band hazard weight, the exposure time cost, and the equipment sensitivity are input into a preset electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range.

[0016] The UAV path planning method provided in this application can plan the path of UAVs. By comprehensively evaluating the impact of electromagnetic radiation on UAVs from multiple dimensions, the electromagnetic risk assessment is more comprehensive and detailed, avoiding the limitations of single electromagnetic parameter assessment, thereby planning a safer flight path and improving the accuracy and operability of electromagnetic risk assessment.

[0017] Optionally, based on the electromagnetic radiation cost, a preset cost update rule is used to plan the flight path of the UAV to obtain the optimal flight path of the UAV within the preset range, including:

[0018] Based on the electromagnetic radiation cost and combined with the preset cost update rule, the flight cost between each adjacent flight node of the UAV within the preset range is calculated.

[0019] Based on the flight cost, multiple flight paths of the UAV within the preset range are calculated;

[0020] The minimum value is extracted from the total path cost corresponding to each of the flight paths, and the flight path corresponding to the minimum value is determined as the optimal flight path of the UAV within the preset range.

[0021] The UAV path planning method provided in this application can plan the path of UAVs. By using Dijkstra's algorithm to efficiently search and determine the optimal flight path, it ensures that the globally optimal path is found while considering the electromagnetic radiation cost, avoiding local optimal solutions and improving the efficiency and reliability of path planning.

[0022] Optionally, extracting the minimum value from the total path cost corresponding to each of the flight paths, and determining the flight path corresponding to the minimum value as the optimal flight path of the UAV within the preset range, includes:

[0023] Based on the flight nodes in each flight path, the total flight cost, total flight distance, and total flight energy consumption of each flight path are calculated.

[0024] The total path cost corresponding to each flight path is calculated based on the total flight cost, the total flight distance, and the total flight energy consumption.

[0025] The minimum value is extracted from the total cost of the path, and the flight path corresponding to the minimum value is determined as the optimal flight path of the UAV within the preset range.

[0026] Optionally, based on the electromagnetic radiation cost, and using a preset cost update rule, after planning the flight path of the UAV to obtain the optimal flight path of the UAV within the preset range, the method further includes:

[0027] During the flight of the UAV based on the optimal flight path, the optimal flight path is adjusted based on the predicted change in the UAV's electromagnetic field strength and the change in electromagnetic radiation cost.

[0028] Optionally, the optimal flight path is adjusted based on the predicted change in the electromagnetic field strength and the change in electromagnetic radiation cost of the UAV, including:

[0029] The electromagnetic field strength and electromagnetic radiation cost of the UAV are acquired in real time.

[0030] The electromagnetic field strength is input into a preset electromagnetic field strength change prediction model to calculate the predicted change value of the electromagnetic field strength of the UAV.

[0031] Determine whether the predicted change value of the electromagnetic field intensity is greater than the preset electromagnetic field tolerance threshold of the UAV, and determine whether the difference between two adjacent electromagnetic radiation costs is greater than the preset electromagnetic radiation tolerance threshold of the UAV.

[0032] When the predicted change in electromagnetic field intensity is greater than the preset electromagnetic field tolerance threshold for the UAV, or the difference between two adjacent electromagnetic radiation costs is greater than the preset electromagnetic radiation tolerance threshold for the UAV, it is determined that the optimal flight path of the UAV has a safety hazard. Starting from the current flight node, based on the real-time acquired electromagnetic radiation cost and the preset cost update rule, the flight path of the UAV is replanned to adjust the optimal flight path of the UAV.

[0033] Secondly, this application provides a drone path planning device, comprising:

[0034] The acquisition module is used to acquire electromagnetic induction information of the UAV within a preset range;

[0035] The calculation module is used to input the electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model and calculate the electromagnetic radiation cost of the UAV within the preset range.

[0036] The planning module is used to plan the flight path of the UAV based on the electromagnetic radiation cost and using a preset cost update rule, so as to obtain the optimal flight path of the UAV within the preset range.

[0037] This UAV path planning device, through a preset cost update rule, calculates the UAV's electromagnetic radiation cost within a preset range by inputting electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model. It then plans the UAV's flight path to obtain the optimal flight path within the preset range. This solves the problems of existing UAV path planning methods, such as ignoring the influence of complex electromagnetic environments, insufficient coupling between electromagnetic sensing and path planning, and difficulty in balancing electromagnetic safety with conflicts with other targets, thus making it difficult to accurately generate UAV paths resistant to electromagnetic interference. The planned path not only avoids local strong electromagnetic points but also effectively avoids the equipment performance degradation that may be caused by long-term exposure to moderate electromagnetic fields, thereby improving the flight safety of UAVs in complex electromagnetic environments.

[0038] Thirdly, this application provides an electronic device including a processor and a memory, the memory storing a computer program executable by the processor, wherein when the processor executes the computer program, it performs the steps in the UAV path planning method described above.

[0039] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps in the UAV path planning method described above.

[0040] Beneficial effects: The UAV path planning method, device, electronic equipment, and storage medium provided in this application, through a preset cost update rule, calculate the electromagnetic radiation cost of the UAV within a preset range by inputting electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model, and perform flight path planning for the UAV to obtain the optimal flight path of the UAV within the preset range. This solves the problems of existing UAV path planning methods, such as ignoring the influence of complex electromagnetic environments, insufficient coupling between electromagnetic sensing and path planning, and difficulty in balancing electromagnetic safety and conflicts with other targets, which makes it difficult to accurately generate UAV paths resistant to electromagnetic interference. The planned path can not only avoid local strong electromagnetic points, but also effectively avoid the equipment performance degradation that may be caused by long-term exposure to moderate electromagnetic fields, thereby improving the flight safety of UAVs in complex electromagnetic environments. Attached Figure Description

[0041] Figure 1 A flowchart of the UAV path planning method provided in the embodiments of this application.

[0042] Figure 2 This is a schematic diagram of the structure of the UAV path planning device provided in the embodiments of this application.

[0043] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0044] Labeling Explanation: 1. Acquisition Module; 2. Calculation Module; 3. Planning Module; 301. Processor; 302. Memory; 303. Communication Bus. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0046] 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 application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0047] Please refer to Figure 1 , Figure 1 This application provides a method for planning the path of a drone in some embodiments, which includes the following steps:

[0048] Step S101: Obtain electromagnetic induction information of the UAV within a preset range;

[0049] Step S102: Input the electromagnetic induction information into the preset multidimensional electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range.

[0050] Step S103: Based on the electromagnetic radiation cost, the flight path of the UAV is planned using a preset cost update rule to obtain the optimal flight path of the UAV within a preset range.

[0051] This UAV path planning method, through a preset cost update rule, calculates the UAV's electromagnetic radiation cost within a preset range by inputting electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model. It then plans the UAV's flight path to obtain the optimal flight path within the preset range. This addresses the problems of existing UAV path planning methods, such as neglecting the influence of complex electromagnetic environments, insufficient coupling between electromagnetic sensing and path planning, and difficulty in balancing electromagnetic safety with conflicts with other objectives, thus hindering the accurate generation of UAV paths resistant to electromagnetic interference. The planned path not only avoids local strong electromagnetic points but also effectively mitigates equipment performance degradation that may result from long-term exposure to moderate electromagnetic fields, thereby improving the flight safety of UAVs in complex electromagnetic environments.

[0052] Specifically, in step S101, electromagnetic induction information of the UAV within a preset range is acquired. This electromagnetic induction information refers to data such as the electromagnetic field strength and frequency of the surrounding environment detected by the UAV's onboard sensors during flight. This data forms the basis for assessing the impact of the electromagnetic environment on the UAV. The preset range can be set based on the data acquisition range of the sensors.

[0053] Specifically, in step S102, the preset multidimensional electromagnetic radiation cost model includes a preset field strength cost calculation model, a preset frequency band hazard weight calculation model, a preset exposure time cost calculation model, a preset equipment sensitivity calculation model, and a preset electromagnetic radiation cost model.

[0054] The pre-defined field strength cost calculation model is used to assess the potential impact of the electromagnetic field strength in the UAV's environment on flight safety and equipment operation. The pre-defined field strength cost calculation model is as follows:

[0055] ;

[0056] in, The price paid for field strength; It is a natural constant; This is the slope coefficient, used to control the steepness of the function, and is generally set to 0.5; Let be the electromagnetic field strength at position i (i.e., flight node i); The electromagnetic field tolerance threshold for drones can be obtained from the instruction manual or experiments. When the electromagnetic field strength... Much greater than the electromagnetic field tolerance threshold of drones At that time, the cost of field strength The closer it is to 1, the higher the risk of the current electromagnetic field strength.

[0057] A pre-defined frequency band hazard weighting calculation model is used to identify and assess the degree of hazard posed by different electromagnetic frequency bands to UAVs and their payloads. The pre-defined frequency band hazard weighting calculation model is as follows:

[0058] ;

[0059] in, Frequency band hazard weighting; The hazard level of frequency band n can be determined experimentally. Let i be the frequency of the electromagnetic wave at position i. Center of sensitive frequency bands (such as GPS band 1.5GHz, WiFi band 2.4GHz); is the bandwidth coefficient of frequency band n; n represents the frequency band of the electromagnetic wave corresponding to position point i; N is the sum of the frequency bands of electromagnetic waves; It is a natural exponential function.

[0060] A pre-defined exposure time cost calculation model is used to measure the cumulative risk caused by the duration of drone exposure in a specific electromagnetic environment. The pre-defined exposure time cost calculation model is as follows:

[0061] ;

[0062] in, This comes at the cost of exposure time; The maximum time penalty coefficient can be obtained through experimentation; is the attenuation constant, which is generally set to 0.1; t is the dwell time of the UAV in the electromagnetic field.

[0063] A pre-defined device sensitivity calculation model is used to assess the sensitivity of different devices carried by the drone to electromagnetic radiation. The pre-defined device sensitivity calculation model is as follows:

[0064] ;

[0065] in, For equipment sensitivity; Let m be the sensitivity weight of component m in the drone; Let M be the interference effect function of component m under the corresponding electromagnetic field strength and frequency and electromagnetic wave frequency (which can be obtained by fitting based on historical fault data); m is a component in the UAV, such as the flight control system, image transmission system and other components; M is the total number of components in the UAV.

[0066] A pre-defined electromagnetic radiation cost model serves as the core integrating model. It combines the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity calculated from the various sub-models to ultimately calculate the overall electromagnetic radiation cost of the UAV within a pre-defined range. Its purpose is to provide a comprehensive and quantitative electromagnetic radiation risk indicator. The pre-defined electromagnetic radiation cost model is as follows:

[0067] ;

[0068] in, This comes at the cost of electromagnetic radiation.

[0069] Specifically, in step S102, the electromagnetic induction information is input into a preset multidimensional electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range, including:

[0070] Electromagnetic induction information is input into preset field strength cost calculation models, preset frequency band hazard weight calculation models, preset exposure time cost calculation models, and preset equipment sensitivity calculation models, respectively, to calculate the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity of the UAV within a preset range;

[0071] By inputting the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity into the preset electromagnetic radiation cost model, the electromagnetic radiation cost of the UAV within the preset range is calculated.

[0072] In step S102, by first decomposing the electromagnetic induction information and inputting it into multiple specialized calculation models—namely, the field strength cost calculation model, the frequency band hazard weight calculation model, the exposure time cost calculation model, and the equipment sensitivity calculation model—a refined assessment of the electromagnetic environment can be performed from multiple dimensions, yielding key intermediate parameters such as field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity. These intermediate parameters comprehensively reflect the physical characteristics of the electromagnetic environment, the potential hazards to the UAV system, the UAV's exposure time, and the UAV's own anti-interference capabilities. Inputting these parameters into a preset electromagnetic radiation cost model allows for the comprehensive consideration of all relevant factors, thereby calculating a more accurate and comprehensive electromagnetic radiation cost, ensuring that the assessment of electromagnetic radiation cost is based on multi-dimensional and fine-grained information.

[0073] Specifically, in step S103, based on the electromagnetic radiation cost, a preset cost update rule is used to plan the flight path of the UAV to obtain the optimal flight path of the UAV within a preset range, including:

[0074] Based on the electromagnetic radiation cost and combined with the preset cost update rules, the flight cost between each adjacent flight node of the UAV within the preset range is calculated.

[0075] Based on the flight cost, multiple flight paths of the UAV within a preset range are calculated;

[0076] The minimum value is extracted from the total cost of each flight path, and the flight path corresponding to the minimum value is determined as the optimal flight path of the UAV within the preset range.

[0077] In step S103, the flight cost between adjacent flight nodes of the UAV within a preset range is accurately calculated based on the real-time or preset electromagnetic radiation cost and a preset cost update rule. The preset cost update rule updates the flight cost of each flight node using a preset cost update model. Specifically, the preset cost update model is as follows:

[0078] ;

[0079] in, Let $j$ be the flight cost of the flying node $j$. Let $\frac{ ... Let i be the flight cost of the flight node i; Let i be the flight node; For flight node j; Let be the Euclidean distance between flight node j and flight node i; This is the electromagnetic cost weighting factor, typically set to 0.3; The electromagnetic radiation cost for flight node i; The electromagnetic radiation cost of flight node j.

[0080] By using the aforementioned pre-defined cost update model, the quantified electromagnetic radiation risk (electromagnetic radiation cost) can be directly incorporated into the path cost, ensuring that the planned path can effectively avoid high electromagnetic radiation areas.

[0081] In step S103, existing path planning algorithms, such as Dijkstra's algorithm or A* algorithm, are used to explore and generate multiple potential flight paths from the starting point to the destination based on the calculated flight cost.

[0082] Specifically, in step S103, the minimum value is extracted from the total path cost corresponding to each flight path, and the flight path corresponding to the minimum value is determined as the optimal flight path of the UAV within a preset range, including:

[0083] Based on the flight nodes in each flight path, the total flight cost, total flight distance, and total flight energy consumption of each flight path are calculated.

[0084] The total cost of each flight path is calculated based on the total flight cost, total flight distance, and total flight energy consumption.

[0085] The minimum value is extracted from the total path cost, and the flight path corresponding to the minimum value is determined as the optimal flight path for the UAV within the preset range.

[0086] In step S104, by analyzing every possible flight path the UAV might traverse within a preset range, the individual flight nodes constituting these paths are identified. For each flight node, its electromagnetic cumulative risk, physical distance contribution, and energy consumption in the electromagnetic environment are calculated, i.e., total flight cost, total flight distance, and total flight energy consumption. This step is the foundation of multi-dimensional path evaluation. It not only focuses on the "total flight cost" caused by electromagnetic radiation but also introduces two key indicators: "total flight distance (the sum of distances between all flight nodes in the flight path)" and "total flight energy consumption (total flight energy consumption = total flight distance / preset flight speed * energy consumption corresponding to the preset flight speed, or calculated using the UAV's inherent energy consumption model (the energy consumption model can be obtained from the instruction manual or experiments)." By acquiring flight node information, the performance of each path in terms of electromagnetic safety, physical distance, and energy consumption can be accurately quantified. Among them, "total flight cost" reflects the cumulative risk of the path in the electromagnetic environment and is a core consideration for electromagnetic safety; "total flight distance" is directly related to the time efficiency of mission completion and potential collision risks; and "total flight energy consumption" directly affects the UAV's endurance and operating costs. Comprehensive calculation of these three indicators provides comprehensive data support for subsequent calculations of total path cost, avoiding the one-sidedness that may result from evaluating a single indicator.

[0087] By comprehensively considering the three dimensions of total flight cost, total flight distance, and total flight energy consumption calculated previously, a unified evaluation value that fully reflects the advantages and disadvantages of different flight paths is formed. This balances the relationship between electromagnetic safety, flight efficiency, and energy consumption, and calculates the total path cost for each flight path. The total path cost can be calculated using a weighted summation method. The specific formula for calculating the total path cost is as follows:

[0088] ;

[0089] in, Let P be the total path cost of the flight path. Let P be the total flight cost for flight path P; Let P be the total flight distance along flight path P; Let P be the total energy consumption for flight path P; The weight of the total flight cost; The weight of the total flight distance; This is the weighting factor for the total energy consumption of flight. , , It can be dynamically adjusted through Pareto optimization. The greater the total path cost, the higher the risk induced by electromagnetic radiation (i.e., the higher the environmental risk). The larger the value, the greater the value.

[0090] After all candidate flight paths have undergone multi-dimensional comprehensive evaluation and their total costs have been obtained, the path with the lowest total cost is selected as the final decision result, i.e., the optimal flight path, by comparing these total costs. The path with the lowest total cost achieves the best balance among all considerations and is the safest, most economical, and most efficient flight plan for the UAV within the preset range.

[0091] Specifically, based on the electromagnetic radiation cost, and using a preset cost update rule, the UAV is planned for flight path. After obtaining the optimal flight path of the UAV within a preset range, the process also includes:

[0092] During the flight of the UAV based on the optimal flight path, the optimal flight path is adjusted based on the predicted changes in the UAV's electromagnetic field strength and the changes in electromagnetic radiation cost.

[0093] After the UAV completes its initial path planning and begins flight, its flight path is not static. By continuously monitoring and evaluating the predicted changes in electromagnetic field strength and electromagnetic radiation costs, potential electromagnetic risks can be detected in a timely manner, triggering a path adjustment mechanism.

[0094] Specifically, based on the predicted changes in the electromagnetic field intensity and the changes in electromagnetic radiation cost of the UAV, the optimal flight path is adjusted, including:

[0095] Real-time acquisition of the electromagnetic field strength and electromagnetic radiation cost of drones;

[0096] The electromagnetic field strength is input into the preset electromagnetic field strength change prediction model to calculate the predicted change value of the electromagnetic field strength of the UAV.

[0097] It determines whether the predicted change in electromagnetic field intensity is greater than the preset electromagnetic field tolerance threshold for the drone, and whether the difference between two adjacent electromagnetic radiation costs is greater than the preset electromagnetic radiation tolerance threshold for the drone.

[0098] When the predicted change in electromagnetic field intensity exceeds the preset electromagnetic field tolerance threshold for the UAV, or when the difference between two adjacent electromagnetic radiation costs exceeds the preset electromagnetic radiation tolerance threshold for the UAV, it is determined that there is a safety hazard in the optimal flight path of the UAV. Starting from the current flight node, the flight path of the UAV is replanned based on the real-time electromagnetic radiation cost and the preset cost update rule to adjust the optimal flight path of the UAV.

[0099] During the flight of the UAV, electromagnetic environment data of the current location is collected periodically through airborne sensors or communication modules, and the real-time electromagnetic radiation cost is calculated based on the multidimensional electromagnetic radiation cost model mentioned above.

[0100] Machine learning models (e.g., neural network models trained on historical data or time series prediction models) are used to predict the changing trends of electromagnetic field intensity that the drone may encounter over a future period, calculating the predicted changes in electromagnetic field intensity for the drone. For example, the predicted changes in electromagnetic field intensity can be predicted using the ARIMA time series model, which is specifically as follows:

[0101] ;

[0102] in, for Predicted change in electromagnetic field intensity at any given time; Let be the electromagnetic field intensity at time t; Let be the electromagnetic field intensity at time t-1; The first autoregressive coefficient, The second autoregressive coefficient, the first autoregressive coefficient Second autoregression coefficient This can be determined through historical data; This is the electromagnetic field intensity prediction function term, which can be obtained experimentally.

[0103] The system determines whether the predicted change in electromagnetic field intensity exceeds a preset electromagnetic field tolerance threshold for the drone, and whether the difference between two consecutive electromagnetic radiation cost measurements exceeds a preset electromagnetic radiation tolerance threshold for the drone. The electromagnetic field tolerance threshold measures the drone's safe upper limit in terms of electromagnetic field intensity, while the electromagnetic radiation tolerance threshold assesses whether the dynamic change in electromagnetic radiation cost exceeds an acceptable range. Both the electromagnetic field tolerance threshold and the electromagnetic radiation tolerance threshold can be set according to actual needs.

[0104] When the predicted change in electromagnetic field strength (predicted change value of electromagnetic field strength) or the fluctuation of actual electromagnetic radiation cost (difference between two adjacent electromagnetic radiation costs) exceeds the preset safety threshold (i.e., the preset drone electromagnetic field tolerance threshold or the preset drone electromagnetic radiation tolerance threshold), it is determined that there is a safety hazard in the drone's optimal flight path. The path adjustment is immediately triggered, taking the drone's current position as the new starting point, and combining the latest electromagnetic environment data to re-execute the path planning algorithm to generate a new, safer flight path. This ensures that the drone can continuously adapt to the constantly changing electromagnetic environment during flight, avoid entering high-risk areas, and significantly improve the safety and reliability of drone flight.

[0105] As shown above, this UAV path planning method acquires the electromagnetic induction information of the UAV within a preset range, inputs the electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model, calculates the electromagnetic radiation cost of the UAV within the preset range, and plans the flight path of the UAV based on the electromagnetic radiation cost using a preset cost update rule to obtain the optimal flight path of the UAV within the preset range. Thus, by using the preset cost update rule and based on the electromagnetic radiation cost of the UAV within the preset range calculated by inputting the electromagnetic induction information into the preset multidimensional electromagnetic radiation cost model, the method plans the flight path of the UAV within the preset range to obtain the optimal flight path of the UAV within the preset range. This solves the problems of existing UAV path planning methods, such as ignoring the influence of complex electromagnetic environments, insufficient coupling between electromagnetic sensing and path planning, and difficulty in balancing electromagnetic safety with conflicts with other targets, making it difficult to accurately generate UAV paths resistant to electromagnetic interference. The planned path not only avoids local strong electromagnetic points but also effectively avoids equipment performance degradation that may be caused by long-term exposure to moderate electromagnetic fields, improving the flight safety of UAVs in complex electromagnetic environments.

[0106] refer to Figure 2 This application provides a drone path planning device for planning the path of a drone, comprising:

[0107] Acquisition module 1 is used to acquire electromagnetic induction information of the UAV within a preset range;

[0108] Calculation module 2 is used to input electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within a preset range.

[0109] Planning module 3 is used to plan the flight path of the UAV based on the electromagnetic radiation cost and using a preset cost update rule to obtain the optimal flight path of the UAV within a preset range.

[0110] This UAV path planning device, through a preset cost update rule, calculates the UAV's electromagnetic radiation cost within a preset range by inputting electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model. It then plans the UAV's flight path to obtain the optimal flight path within the preset range. This solves the problems of existing UAV path planning methods, such as ignoring the influence of complex electromagnetic environments, insufficient coupling between electromagnetic sensing and path planning, and difficulty in balancing electromagnetic safety with conflicts with other targets, thus making it difficult to accurately generate UAV paths resistant to electromagnetic interference. The planned path not only avoids local strong electromagnetic points but also effectively avoids the equipment performance degradation that may be caused by long-term exposure to moderate electromagnetic fields, thereby improving the flight safety of UAVs in complex electromagnetic environments.

[0111] Specifically, when module 1 is executed, it acquires electromagnetic induction information of the UAV within a preset range. This electromagnetic induction information refers to data such as the electromagnetic field strength and frequency of the surrounding environment detected by the UAV's onboard sensors during flight. This data forms the basis for assessing the impact of the electromagnetic environment on the UAV. The preset range can be set based on the data acquisition range of the sensors.

[0112] Specifically, when the calculation module 2 is executed, the preset multidimensional electromagnetic radiation cost model includes a preset field strength cost calculation model, a preset frequency band hazard weight calculation model, a preset exposure time cost calculation model, a preset equipment sensitivity calculation model, and a preset electromagnetic radiation cost model.

[0113] The pre-defined field strength cost calculation model is used to assess the potential impact of the electromagnetic field strength in the UAV's environment on flight safety and equipment operation. The pre-defined field strength cost calculation model is as follows:

[0114] ;

[0115] in, The price paid for field strength; It is a natural constant; This is the slope coefficient, used to control the steepness of the function, and is generally set to 0.5; Let be the electromagnetic field strength at position i (i.e., flight node i); The electromagnetic field tolerance threshold for drones can be obtained from the instruction manual or experiments. When the electromagnetic field strength... Much greater than the electromagnetic field tolerance threshold of drones At that time, the cost of field strength The closer it is to 1, the higher the risk of the current electromagnetic field strength.

[0116] A pre-defined frequency band hazard weighting calculation model is used to identify and assess the degree of hazard posed by different electromagnetic frequency bands to UAVs and their payloads. The pre-defined frequency band hazard weighting calculation model is as follows:

[0117] ;

[0118] in, Frequency band hazard weighting; The hazard level of frequency band n can be determined experimentally. Let i be the frequency of the electromagnetic wave at position i. Center of sensitive frequency bands (such as GPS band 1.5GHz, WiFi band 2.4GHz); is the bandwidth coefficient of frequency band n; n represents the frequency band of the electromagnetic wave corresponding to position point i; N is the sum of the frequency bands of electromagnetic waves; It is a natural exponential function.

[0119] A pre-defined exposure time cost calculation model is used to measure the cumulative risk caused by the duration of drone exposure in a specific electromagnetic environment. The pre-defined exposure time cost calculation model is as follows:

[0120] ;

[0121] in, This comes at the cost of exposure time; The maximum time penalty coefficient can be obtained through experimentation; is the attenuation constant, which is generally set to 0.1; t is the dwell time of the UAV in the electromagnetic field.

[0122] A pre-defined device sensitivity calculation model is used to assess the sensitivity of different devices carried by the drone to electromagnetic radiation. The pre-defined device sensitivity calculation model is as follows:

[0123] ;

[0124] in, For equipment sensitivity; Let m be the sensitivity weight of component m in the drone; Let M be the interference effect function of component m under the corresponding electromagnetic field strength and frequency and electromagnetic wave frequency (which can be obtained by fitting based on historical fault data); m is a component in the UAV, such as the flight control system, image transmission system and other components; M is the total number of components in the UAV.

[0125] A pre-defined electromagnetic radiation cost model serves as the core integrating model. It combines the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity calculated from the various sub-models to ultimately calculate the overall electromagnetic radiation cost of the UAV within a pre-defined range. Its purpose is to provide a comprehensive and quantitative electromagnetic radiation risk indicator. The pre-defined electromagnetic radiation cost model is as follows:

[0126] ;

[0127] in, This comes at the cost of electromagnetic radiation.

[0128] Specifically, when the calculation module 2 inputs the electromagnetic induction information into the preset multidimensional electromagnetic radiation cost model and calculates the electromagnetic radiation cost of the UAV within the preset range, it executes:

[0129] Electromagnetic induction information is input into preset field strength cost calculation models, preset frequency band hazard weight calculation models, preset exposure time cost calculation models, and preset equipment sensitivity calculation models, respectively, to calculate the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity of the UAV within a preset range;

[0130] By inputting the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity into the preset electromagnetic radiation cost model, the electromagnetic radiation cost of the UAV within the preset range is calculated.

[0131] During execution, calculation module 2 decomposes electromagnetic induction information and inputs it into multiple specialized calculation models: a field strength cost calculation model, a frequency band hazard weight calculation model, an exposure time cost calculation model, and a device sensitivity calculation model. This allows for a refined assessment of the electromagnetic environment from multiple dimensions, yielding key intermediate parameters such as field strength cost, frequency band hazard weight, exposure time cost, and device sensitivity. These intermediate parameters comprehensively reflect the physical characteristics of the electromagnetic environment, the potential hazards to the UAV system, the UAV's exposure time, and its own anti-interference capabilities. Inputting these parameters into a preset electromagnetic radiation cost model comprehensively considers all relevant factors, resulting in a more accurate and comprehensive electromagnetic radiation cost calculation. This ensures that the assessment of electromagnetic radiation cost is based on multi-dimensional and fine-grained information.

[0132] Specifically, when planning module 3 plans the flight path of the UAV based on the electromagnetic radiation cost and using a preset cost update rule to obtain the optimal flight path of the UAV within a preset range, it executes:

[0133] Based on the electromagnetic radiation cost and combined with the preset cost update rules, the flight cost between each adjacent flight node of the UAV within the preset range is calculated.

[0134] Based on the flight cost, multiple flight paths of the UAV within a preset range are calculated;

[0135] The minimum value is extracted from the total cost of each flight path, and the flight path corresponding to the minimum value is determined as the optimal flight path of the UAV within the preset range.

[0136] During execution, planning module 3 accurately calculates the flight cost between adjacent flight nodes of the UAV within a preset range, based on real-time or preset electromagnetic radiation costs and preset cost update rules. The preset cost update rules update the flight cost of each flight node using a preset cost update model. Specifically, the preset cost update model is as follows:

[0137] ;

[0138] in, Let $j$ be the flight cost of the flying node $j$. Let $\frac{ ... Let i be the flight cost of the flight node i; Let i be the flight node; For flight node j; Let be the Euclidean distance between flight node j and flight node i; This is the electromagnetic cost weighting factor, typically set to 0.3; The electromagnetic radiation cost for flight node i; The electromagnetic radiation cost of flight node j.

[0139] By using the aforementioned pre-defined cost update model, the quantified electromagnetic radiation risk (electromagnetic radiation cost) can be directly incorporated into the path cost, ensuring that the planned path can effectively avoid high electromagnetic radiation areas.

[0140] When the planning module 3 is executed, it uses existing path planning algorithms, such as Dijkstra's algorithm or A* algorithm, to explore and generate multiple potential flight paths from the starting point to the destination based on the calculated flight cost.

[0141] Specifically, when planning module 3 extracts the minimum value from the total cost of each flight path to determine the flight path corresponding to the minimum value as the optimal flight path for the UAV within a preset range, it executes:

[0142] Based on the flight nodes in each flight path, the total flight cost, total flight distance, and total flight energy consumption of each flight path are calculated.

[0143] The total cost of each flight path is calculated based on the total flight cost, total flight distance, and total flight energy consumption.

[0144] The minimum value is extracted from the total path cost, and the flight path corresponding to the minimum value is determined as the optimal flight path for the UAV within the preset range.

[0145] During execution, Planning Module 3 analyzes every possible flight path the UAV might traverse within a preset range, identifies the individual flight nodes constituting these paths, and calculates the electromagnetic cumulative risk, physical distance contribution, and energy consumption of each flight node in the electromagnetic environment—that is, the total flight cost, total flight distance, and total flight energy consumption. This step forms the basis of multi-dimensional path evaluation. It not only focuses on the "total flight cost" caused by electromagnetic radiation but also introduces two key indicators: "total flight distance (the sum of distances between all flight nodes in the flight path)" and "total flight energy consumption (total flight energy consumption = total flight distance / preset flight speed * energy consumption corresponding to the preset flight speed, or calculated using the UAV's inherent energy consumption model (which can be obtained from the manual or experiments))." By acquiring flight node information, the performance of each path in terms of electromagnetic safety, physical distance, and energy consumption can be accurately quantified. Among them, "total flight cost" reflects the cumulative risk of the path in the electromagnetic environment and is a core consideration for electromagnetic safety; "total flight distance" is directly related to the time efficiency of mission completion and potential collision risks; and "total flight energy consumption" directly affects the UAV's endurance and operating costs. Comprehensive calculation of these three indicators provides comprehensive data support for subsequent calculations of total path cost, avoiding the one-sidedness that may result from evaluating a single indicator.

[0146] By comprehensively considering the three dimensions of total flight cost, total flight distance, and total flight energy consumption calculated previously, a unified evaluation value that fully reflects the advantages and disadvantages of different flight paths is formed. This balances the relationship between electromagnetic safety, flight efficiency, and energy consumption, and calculates the total path cost for each flight path. The total path cost can be calculated using a weighted summation method. The specific formula for calculating the total path cost is as follows:

[0147] ;

[0148] in, Let P be the total path cost of the flight path. Let P be the total flight cost for flight path P; Let P be the total flight distance along flight path P; Let P be the total energy consumption for flight path P; The weight of the total flight cost; The weight of the total flight distance; This is the weighting factor for the total energy consumption of flight. , , It can be dynamically adjusted through Pareto optimization. The greater the total path cost, the higher the risk induced by electromagnetic radiation (i.e., the higher the environmental risk). The larger the value, the greater the value.

[0149] After all candidate flight paths have undergone multi-dimensional comprehensive evaluation and their total costs have been obtained, the path with the lowest total cost is selected as the final decision result, i.e., the optimal flight path, by comparing these total costs. The path with the lowest total cost achieves the best balance among all considerations and is the safest, most economical, and most efficient flight plan for the UAV within the preset range.

[0150] Specifically, the drone path planning device also includes:

[0151] The adjustment module is used to adjust the optimal flight path based on the predicted changes in the drone's electromagnetic field strength and electromagnetic radiation cost during the drone's flight.

[0152] After the UAV completes its initial path planning and begins flight, its flight path is not static. The adjustment module can detect potential electromagnetic risks in a timely manner and trigger the path adjustment mechanism by continuously monitoring and evaluating the predicted changes in electromagnetic field strength and electromagnetic radiation costs.

[0153] Specifically, when adjusting the optimal flight path based on the predicted changes in the UAV's electromagnetic field strength and electromagnetic radiation cost, the adjustment module performs the following:

[0154] Real-time acquisition of the electromagnetic field strength and electromagnetic radiation cost of drones;

[0155] The electromagnetic field strength is input into the preset electromagnetic field strength change prediction model to calculate the predicted change value of the electromagnetic field strength of the UAV.

[0156] It determines whether the predicted change in electromagnetic field intensity is greater than the preset electromagnetic field tolerance threshold for the drone, and whether the difference between two adjacent electromagnetic radiation costs is greater than the preset electromagnetic radiation tolerance threshold for the drone.

[0157] When the predicted change in electromagnetic field intensity exceeds the preset electromagnetic field tolerance threshold for the UAV, or when the difference between two adjacent electromagnetic radiation costs exceeds the preset electromagnetic radiation tolerance threshold for the UAV, it is determined that there is a safety hazard in the optimal flight path of the UAV. Starting from the current flight node, the flight path of the UAV is replanned based on the real-time electromagnetic radiation cost and the preset cost update rule to adjust the optimal flight path of the UAV.

[0158] When the adjustment module is executed, during the flight of the UAV, it periodically collects electromagnetic environment data of the current location through airborne sensors or communication modules, and calculates the real-time electromagnetic radiation cost based on the multidimensional electromagnetic radiation cost model mentioned above.

[0159] Machine learning models (e.g., neural network models trained on historical data or time series prediction models) are used to predict the changing trends of electromagnetic field intensity that the drone may encounter over a future period, calculating the predicted changes in electromagnetic field intensity for the drone. For example, the predicted changes in electromagnetic field intensity can be predicted using the ARIMA time series model, which is specifically as follows:

[0160] ;

[0161] in, for Predicted change in electromagnetic field intensity at any given time; Let be the electromagnetic field intensity at time t; Let be the electromagnetic field intensity at time t-1; The first autoregressive coefficient, The second autoregressive coefficient, the first autoregressive coefficient Second autoregression coefficient This can be determined through historical data; This is the electromagnetic field intensity prediction function term, which can be obtained experimentally.

[0162] The system determines whether the predicted change in electromagnetic field intensity exceeds a preset electromagnetic field tolerance threshold for the drone, and whether the difference between two consecutive electromagnetic radiation cost measurements exceeds a preset electromagnetic radiation tolerance threshold for the drone. The electromagnetic field tolerance threshold measures the drone's safe upper limit in terms of electromagnetic field intensity, while the electromagnetic radiation tolerance threshold assesses whether the dynamic change in electromagnetic radiation cost exceeds an acceptable range. Both the electromagnetic field tolerance threshold and the electromagnetic radiation tolerance threshold can be set according to actual needs.

[0163] When the predicted change in electromagnetic field strength (predicted change value of electromagnetic field strength) or the fluctuation of actual electromagnetic radiation cost (difference between two adjacent electromagnetic radiation costs) exceeds the preset safety threshold (i.e., the preset drone electromagnetic field tolerance threshold or the preset drone electromagnetic radiation tolerance threshold), it is determined that there is a safety hazard in the drone's optimal flight path. The path adjustment is immediately triggered, taking the drone's current position as the new starting point, and combining the latest electromagnetic environment data to re-execute the path planning algorithm to generate a new, safer flight path. This ensures that the drone can continuously adapt to the constantly changing electromagnetic environment during flight, avoid entering high-risk areas, and significantly improve the safety and reliability of drone flight.

[0164] As can be seen from the above, this UAV path planning device acquires the electromagnetic induction information of the UAV within a preset range, inputs the electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model, calculates the electromagnetic radiation cost of the UAV within the preset range, and plans the flight path of the UAV based on the electromagnetic radiation cost using a preset cost update rule to obtain the optimal flight path of the UAV within the preset range. Thus, by using the preset cost update rule, based on the electromagnetic radiation cost of the UAV within the preset range calculated by inputting the electromagnetic induction information into the preset multidimensional electromagnetic radiation cost model, the device plans the flight path of the UAV within the preset range to obtain the optimal flight path of the UAV within the preset range. This solves the problems of existing UAV path planning methods, such as ignoring the influence of complex electromagnetic environments, insufficient coupling between electromagnetic sensing and path planning, and difficulty in balancing electromagnetic safety and conflicts with other targets, making it difficult to accurately generate UAV paths resistant to electromagnetic interference. The planned path can not only avoid local strong electromagnetic points, but also effectively avoid the equipment performance degradation that may be caused by long-term exposure to moderate electromagnetic fields, thus improving the flight safety of UAVs in complex electromagnetic environments.

[0165] Please refer to Figure 3 , Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device includes 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 mechanism (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 UAV path planning method in any optional implementation of the above embodiments, so as to achieve the following functions: obtaining electromagnetic induction information of the UAV within a preset range, inputting the electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model, calculating the electromagnetic radiation cost of the UAV within the preset range, and based on the electromagnetic radiation cost, using a preset cost update rule to plan the flight path of the UAV to obtain the optimal flight path of the UAV within the preset range.

[0166] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it executes the UAV path planning method in any optional implementation of the above embodiments to achieve the following functions: obtaining electromagnetic induction information of the UAV within a preset range, inputting the electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model, calculating the electromagnetic radiation cost of the UAV within the preset range, and based on the electromagnetic radiation cost, using a preset cost update rule to plan the flight path of the UAV to obtain the optimal flight path of the UAV within the preset range. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0167] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0168] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0169] Furthermore, the functional modules in the various embodiments of this 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.

[0170] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0171] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for planning the path of an unmanned aerial vehicle (UAV), characterized in that, Including the following steps: Acquire electromagnetic induction information of the drone within a preset range; The electromagnetic induction information is input into a preset multidimensional electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range. Based on the electromagnetic radiation cost, the flight path of the UAV is planned using a preset cost update rule to obtain the optimal flight path of the UAV within the preset range. The preset multidimensional electromagnetic radiation cost model includes a preset field strength cost calculation model, a preset frequency band hazard weight calculation model, a preset exposure time cost calculation model, a preset equipment sensitivity calculation model, and a preset electromagnetic radiation cost model. The electromagnetic induction information is input into a preset multidimensional electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range, including: The electromagnetic induction information is input into the preset field strength cost calculation model, the preset frequency band hazard weight calculation model, the preset exposure time cost calculation model, and the preset equipment sensitivity calculation model, respectively, to calculate the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity of the UAV within the preset range; The field strength cost, the frequency band hazard weight, the exposure time cost, and the equipment sensitivity are input into a preset electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range. The preset field strength cost calculation model is as follows: ； in, The price paid for field strength; It is a natural constant; The slope coefficient; Let be the electromagnetic field strength at position i; The electromagnetic field tolerance threshold for unmanned aerial vehicles (UAVs); The preset frequency band hazard weight calculation model is as follows: ； in, Frequency band hazard weighting; The frequency band hazard level for frequency band n; Let i be the frequency of the electromagnetic wave at position i. Center of sensitive frequency band; is the bandwidth coefficient of frequency band n; n represents the frequency band of the electromagnetic wave corresponding to position point i; N is the sum of the frequency bands of electromagnetic waves; It is a natural exponential function; The preset exposure time cost calculation model is as follows: ； in, This comes at the cost of exposure time; This represents the maximum time penalty coefficient. t is the decay constant; t is the dwell time of the UAV in the electromagnetic field; The preset device sensitivity calculation model is specifically as follows: ； in, For equipment sensitivity; Let m be the sensitivity weight of component m in the drone; Let m be the interference effect function of component m under the corresponding electromagnetic field strength and frequency and electromagnetic wave frequency; m is the component in the UAV; M is the total number of components in the UAV; The preset electromagnetic radiation cost model is specifically as follows: ； in, This comes at the cost of electromagnetic radiation.

2. The UAV path planning method according to claim 1, characterized in that, Based on the electromagnetic radiation cost, and using a preset cost update rule, flight path planning is performed on the UAV to obtain the optimal flight path of the UAV within the preset range, including: Based on the electromagnetic radiation cost and combined with the preset cost update rule, the flight cost between each adjacent flight node of the UAV within the preset range is calculated. Based on the flight cost, multiple flight paths of the UAV within the preset range are calculated; The minimum value is extracted from the total path cost corresponding to each of the flight paths, and the flight path corresponding to the minimum value is determined as the optimal flight path of the UAV within the preset range.

3. The UAV path planning method according to claim 2, characterized in that, Extracting the minimum value from the total path cost corresponding to each of the aforementioned flight paths, and determining the flight path corresponding to the minimum value as the optimal flight path for the UAV within the preset range, includes: Based on the flight nodes in each flight path, the total flight cost, total flight distance, and total flight energy consumption of each flight path are calculated. The total path cost corresponding to each flight path is calculated based on the total flight cost, the total flight distance, and the total flight energy consumption. The minimum value is extracted from the total cost of the path, and the flight path corresponding to the minimum value is determined as the optimal flight path of the UAV within the preset range.

4. The UAV path planning method according to claim 3, characterized in that, Based on the electromagnetic radiation cost, and using a preset cost update rule, the UAV is used to plan its flight path. After obtaining the optimal flight path of the UAV within the preset range, the process further includes: During the flight of the UAV based on the optimal flight path, the optimal flight path is adjusted based on the predicted change in the UAV's electromagnetic field strength and the change in electromagnetic radiation cost.

5. The UAV path planning method according to claim 4, characterized in that, Based on the predicted changes in the electromagnetic field intensity and the changes in electromagnetic radiation cost of the UAV, the optimal flight path is adjusted, including: The electromagnetic field strength and electromagnetic radiation cost of the UAV are acquired in real time. The electromagnetic field strength is input into a preset electromagnetic field strength change prediction model to calculate the predicted change value of the electromagnetic field strength of the UAV. Determine whether the predicted change value of the electromagnetic field intensity is greater than the preset electromagnetic field tolerance threshold of the UAV, and determine whether the difference between two adjacent electromagnetic radiation costs is greater than the preset electromagnetic radiation tolerance threshold of the UAV. When the predicted change in electromagnetic field intensity is greater than the preset electromagnetic field tolerance threshold for the UAV, or the difference between two adjacent electromagnetic radiation costs is greater than the preset electromagnetic radiation tolerance threshold for the UAV, it is determined that the optimal flight path of the UAV has a safety hazard. Starting from the current flight node, based on the real-time acquired electromagnetic radiation cost and the preset cost update rule, the flight path of the UAV is replanned to adjust the optimal flight path of the UAV.

6. A drone path planning device for planning the path of a drone, characterized in that, include: The acquisition module is used to acquire electromagnetic induction information of the UAV within a preset range; The calculation module is used to input the electromagnetic induction information into a preset multidimensional electromagnetic radiation cost model and calculate the electromagnetic radiation cost of the UAV within the preset range. The planning module is used to plan the flight path of the UAV based on the electromagnetic radiation cost and using a preset cost update rule, so as to obtain the optimal flight path of the UAV within the preset range. The preset multidimensional electromagnetic radiation cost model includes a preset field strength cost calculation model, a preset frequency band hazard weight calculation model, a preset exposure time cost calculation model, a preset equipment sensitivity calculation model, and a preset electromagnetic radiation cost model. The electromagnetic induction information is input into a preset multidimensional electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range, including: The electromagnetic induction information is input into the preset field strength cost calculation model, the preset frequency band hazard weight calculation model, the preset exposure time cost calculation model, and the preset equipment sensitivity calculation model, respectively, to calculate the field strength cost, frequency band hazard weight, exposure time cost, and equipment sensitivity of the UAV within the preset range; The field strength cost, the frequency band hazard weight, the exposure time cost, and the equipment sensitivity are input into a preset electromagnetic radiation cost model to calculate the electromagnetic radiation cost of the UAV within the preset range. The preset field strength cost calculation model is as follows: ； in, The price paid for field strength; It is a natural constant; The slope coefficient; Let be the electromagnetic field strength at position i; The electromagnetic field tolerance threshold for unmanned aerial vehicles (UAVs); The preset frequency band hazard weight calculation model is as follows: ； in, Frequency band hazard weighting; The frequency band hazard level for frequency band n; Let i be the frequency of the electromagnetic wave at position i. Center of sensitive frequency band; is the bandwidth coefficient of frequency band n; n represents the frequency band of the electromagnetic wave corresponding to position point i; N is the sum of the frequency bands of electromagnetic waves; It is a natural exponential function; The preset exposure time cost calculation model is as follows: ； in, This comes at the cost of exposure time; This represents the maximum time penalty coefficient. t is the decay constant; t is the dwell time of the UAV in the electromagnetic field; The preset device sensitivity calculation model is specifically as follows: ； in, For equipment sensitivity; Let m be the sensitivity weight of component m in the drone; Let m be the interference effect function of component m under the corresponding electromagnetic field strength and frequency and electromagnetic wave frequency; m is the component in the UAV; M is the total number of components in the UAV; The preset electromagnetic radiation cost model is specifically as follows: ； in, This comes at the cost of electromagnetic radiation.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program executable by the processor, which, when executing the computer program, performs the steps in the UAV path planning method as described in any one of claims 1-5.

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

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