Unmanned aerial vehicle multi-altitude layer route planning method and system oriented to low-altitude risk distribution

Abstract

The present application relates to the technical field of unmanned aerial vehicle route planning, in particular to a low-altitude risk distribution oriented unmanned aerial vehicle multi-height layer route planning method and system. Obtain the original risk influence range of the target unmanned aerial vehicle in multiple height layers; obtain the real-time scheduling scheme of the unmanned aerial vehicle in the target area, evaluate the airspace risk distribution information of the target area in combination with the real-time characteristics of the task; based on high-precision geographic information data, obtain the ground function partition information of the target area, and correspondingly evaluate the ground risk distribution information on the predetermined plane route of the target unmanned aerial vehicle; based on the airspace risk distribution information, the ground risk distribution information and the original risk influence range, combined with the path planning algorithm based on the greedy strategy, joint search is carried out on multiple height layers to obtain the multi-height layer space route of the target unmanned aerial vehicle. The present application can realize precise risk control and efficient operation optimization of the multi-height layer route of the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight path planning technology, and in particular to a method and system for planning multi-altitude flight paths of UAVs guided by low-altitude risk distribution. Background Technology

[0002] Current low-altitude UAV flight path planning methods are mostly based on single-altitude planar path planning, relying heavily on static obstacle maps. Some methods incorporate basic UAV performance, but fail to systematically integrate multi-dimensional risk factors. Existing technologies suffer from one-sided risk assessment, lack of altitude-level planning, and singular optimization objectives, which can easily lead to airspace conflicts or misjudgments of ground risks, increase energy consumption and operating time, and result in poor adaptability to complex low-altitude environments. Summary of the Invention

[0003] This invention addresses the problems of one-sided risk assessment, lack of altitude layer planning, and single optimization objective in existing technologies by providing a low-altitude risk distribution-oriented UAV multi-altitude layer flight path planning method and system.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0005] In a first aspect, the present invention provides a low-altitude risk distribution-oriented multi-altitude flight path planning method for unmanned aerial vehicles (UAVs), comprising: obtaining the original risk impact range of the target UAV at multiple altitude layers based on the intrinsic static characteristics and real-time characteristics of the mission; obtaining a real-time UAV scheduling scheme within the target area, and evaluating the obtained airspace risk distribution information of the target area in conjunction with the real-time characteristics of the mission; obtaining ground functional zoning information of the target area based on high-precision geographic information data, and correspondingly evaluating the ground risk distribution information on the predetermined planar flight path of the target UAV; and performing a joint search at multiple altitude layers based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, combined with a path planning algorithm based on a greedy strategy, to obtain the multi-altitude spatial flight path of the target UAV.

[0006] Optionally, based on the intrinsic static characteristics and real-time mission characteristics of the target UAV, the original risk impact range of the target UAV at multiple altitude layers is obtained, including: obtaining the flight performance parameters of the target UAV and extracting the corresponding intrinsic static characteristics, wherein the intrinsic static characteristics include the most unfavorable cruise speed and aerodynamic stability parameters; obtaining the predetermined planar flight path and mission load parameters of the target UAV and outputting them as the real-time mission characteristics; constructing a risk impact range model of the target UAV based on the intrinsic static characteristics and the real-time mission characteristics, combined with a prior physical property model, wherein the risk impact range model uses the elevation parameters of the altitude layers as unique variables; inputting the elevation parameters of multiple altitude layers into the risk impact range model to obtain the original risk impact range of multiple altitude layers.

[0007] The process includes: acquiring real-time UAV scheduling schemes within the target area; evaluating and acquiring airspace risk distribution information of the target area based on the real-time characteristics of the task; acquiring real-time UAV scheduling schemes at various altitudes within the target area, wherein the real-time UAV scheduling schemes include UAV intrinsic characteristics and UAV payload characteristics; extracting the most unfavorable original risk impact range from the original risk impact range, and using a sliding window method to divide the target area into temporal partitions along the predetermined plane flight path of the target UAV, thereby acquiring multiple path temporal partitions, wherein the predetermined plane flight path is determined based on the real-time characteristics of the task; traversing multiple path temporal partitions and determining analysis time series labels based on the predetermined plane flight path; traversing multiple path temporal partitions, using multiple airspace altitudes as analysis planes respectively, and performing airspace risk assessment based on the real-time UAV scheduling schemes and the analysis time series labels to acquire airspace risk distribution information; wherein the airspace risk distribution information includes multiple partition risk distribution clusters corresponding to the multiple path temporal partitions, and each partition risk distribution cluster includes risk distribution maps at multiple airspace altitudes.

[0008] The process involves traversing multiple path time-series partitions, using multiple airspace altitudes as analysis planes, and combining the UAV real-time scheduling scheme with the analysis time-series labels to perform airspace risk assessment and obtain airspace risk distribution information. This includes: randomly selecting an airspace altitude from the multiple path time-series partitions as a first analysis plane, and extracting a first UAV intrinsic feature set and a first UAV payload feature set accordingly; matching and obtaining a first benchmark risk index based on the first UAV payload features; evaluating and obtaining a first risk diffusion coefficient based on the first UAV intrinsic features and a preset evaluation function; weighting the first benchmark risk index and the first risk diffusion coefficient based on the mapping relationship between the UAV intrinsic features and the UAV payload features to obtain a first plane risk scatter plot; smoothly fitting the first plane risk scatter plot to obtain a first altitude risk distribution map; traversing multiple airspace altitudes of the path time-series partitions to obtain multiple risk distribution maps, outputting them as the partition risk distribution clusters; and traversing multiple path time-series partitions to obtain multiple partition risk distribution clusters, outputting them as airspace risk distribution information.

[0009] The process involves acquiring ground functional zoning information for the target area based on high-precision geographic information data, and evaluating the ground risk distribution information along the predetermined flight path of the target UAV. This includes: determining ground risk boundaries by combining the predetermined flight path with the most unfavorable original risk impact range; extracting ground functional zoning information and zoning traffic heat information for the target area using the ground risk boundaries as constraints; determining benchmark weights based on the ground functional zoning information, and calculating the regional risk index for each functional zoning using the normalized zoning traffic heat information as an adjustment coefficient; and generating the ground risk distribution information by performing spatial interpolation calculations based on the regional risk index.

[0010] Optionally, based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, a joint search is performed at multiple altitude layers using a path planning algorithm based on a greedy strategy to obtain the multi-altitude spatial flight path of the target UAV. This includes: constructing a greedy objective function, wherein the greedy objective function includes: a ground direct risk factor determined based on the ground risk distribution information and the original risk impact range corresponding to the altitude layer; an airspace conflict risk factor determined based on the airspace risk distribution information and the original risk impact range corresponding to the altitude layer; minimizing the greedy objective function as the optimization objective, and using the predetermined plane flight path as a constraint, a joint search is performed at multiple altitude layers based on a greedy strategy to obtain the multi-altitude spatial flight path.

[0011] Optionally, based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, a joint search is performed at multiple altitude levels using a path planning algorithm based on a greedy strategy to obtain the multi-altitude spatial flight path of the target UAV. Prior to this, the method further includes: acquiring historical low-altitude wind field data of the target airspace; determining the most unfavorable wind condition parameters based on the historical low-altitude wind field data, and calculating a wind field correction coefficient for shape correction of the original risk impact range, wherein the wind field correction coefficient is anisotropic; and updating the original risk impact range axially using the wind field correction coefficient.

[0012] Secondly, the present invention provides a low-altitude risk distribution-oriented multi-altitude flight path planning system for unmanned aerial vehicles (UAVs), comprising:

[0013] The original risk impact range acquisition module is used to acquire the original risk impact range of the target UAV at multiple altitude layers based on the intrinsic static characteristics and real-time characteristics of the mission.

[0014] The airspace risk distribution information evaluation module is used to obtain real-time scheduling schemes for UAVs within the target area and evaluate the airspace risk distribution information of the target area in conjunction with the real-time characteristics of the task.

[0015] The ground risk distribution information evaluation module is used to obtain ground functional zoning information of the target area based on high-precision geographic information data, and to evaluate the ground risk distribution information on the predetermined plane flight path of the target UAV accordingly.

[0016] The multi-altitude spatial route acquisition module is used to perform joint search at multiple altitude levels based on the airspace risk distribution information, the ground risk distribution information and the original risk impact range, combined with a path planning algorithm based on a greedy strategy, to obtain the multi-altitude spatial route of the target UAV.

[0017] By implementing this invention, it is possible to obtain the original risk impact range of the target UAV at multiple altitude layers based on the intrinsic static characteristics and real-time characteristics of the mission, providing a "UAV risk benchmark" for subsequent risk assessment and flight path planning, avoiding misjudgment of risks due to ignoring UAV performance differences, and ensuring the pertinence and accuracy of risk analysis.

[0018] By implementing this invention, it is possible to obtain a real-time scheduling scheme for UAVs within a target area, evaluate the airspace risk distribution information of the target area in conjunction with the real-time characteristics of the task, dynamically capture the real-time operating status of other UAVs in the airspace, clarify the airspace conflict risks at different altitudes and time periods, avoid the overlap of flight path planning with other UAVs in time and space, and improve the safety of airspace operation.

[0019] By implementing this invention, it is possible to obtain ground functional zoning information of the target area based on high-precision geographic information data, and evaluate the ground risk distribution information on the predetermined plane flight path of the target UAV accordingly, accurately identify high-risk ground areas, avoid the risk range of UAVs covering sensitive ground areas, reduce the risk of damage to ground personnel and property, and improve the ground safety of the flight path.

[0020] By implementing this invention, it is possible to obtain multi-altitude spatial routes of the target UAV by jointly searching at multiple altitude levels based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, combined with a path planning algorithm based on a greedy strategy. While ensuring low risk, the greedy strategy reduces the frequency of altitude changes, lowers UAV energy consumption, and balances safety and operational efficiency, thus achieving risk-controllable and energy-efficient route optimization.

[0021] In summary, by implementing this invention, precise risk management and efficient operation optimization of UAV multi-altitude flight paths can be achieved, avoiding airspace conflicts and ground safety hazards, reducing energy consumption and altitude changes, and significantly improving the safety, relevance, and economy of low-altitude UAV flight path planning. Attached Figure Description

[0022] Figure 1 A flowchart illustrating a low-altitude risk distribution-oriented UAV multi-altitude flight path planning method provided by the present invention;

[0023] Figure 2 This is a schematic diagram of the structure of a low-altitude risk distribution-oriented UAV multi-altitude flight path planning system provided by the present invention.

[0024] In the attached diagram, the components represented by each number are as follows:

[0025] The module includes: Module 11 for obtaining the scope of original risk impact, Module 12 for evaluating airspace risk distribution information, Module 13 for evaluating ground risk distribution information, and Module 14 for obtaining multi-altitude spatial routes. Detailed Implementation

[0026] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0029] Example 1, as Figure 1 As shown, this invention provides a method and system for planning multi-altitude flight paths of unmanned aerial vehicles (UAVs) guided by low-altitude risk distribution, including:

[0030] S100: Based on the intrinsic static characteristics and real-time characteristics of the target UAV, obtain the original risk impact range of the target UAV at multiple altitude levels;

[0031] S200: Obtain the real-time scheduling scheme for UAVs within the target area, and evaluate the airspace risk distribution information of the target area in conjunction with the real-time characteristics of the task.

[0032] S300: Based on high-precision geographic information data, obtain ground functional zoning information of the target area, and evaluate the ground risk distribution information on the predetermined plane flight path of the target UAV accordingly;

[0033] S400: Based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, a joint search is performed at multiple altitude levels using a path planning algorithm based on a greedy strategy to obtain the multi-altitude spatial flight path of the target UAV.

[0034] In step S100 of this application embodiment, based on the intrinsic static characteristics and real-time characteristics of the target UAV, the original risk impact range of the target UAV at multiple altitude layers is obtained, including:

[0035] The flight performance parameters of the target UAV are obtained, and the intrinsic static features are extracted accordingly, wherein the intrinsic static features include the most unfavorable cruise speed and aerodynamic stability parameters.

[0036] Obtain the target UAV's predetermined planar flight path and mission payload parameters, and output them as the real-time characteristics of the mission;

[0037] Based on the intrinsic static features and the real-time features of the mission, a risk impact range model for the target UAV is constructed by combining the prior physical property model, wherein the risk impact range model uses the elevation parameter of the altitude layer as the only variable.

[0038] Input the elevation parameters of multiple height layers into the risk impact range model to obtain the original risk impact range of multiple height layers.

[0039] In this embodiment of the application, the purpose of step S100 is to accurately determine the original risk impact range that the target UAV may cause due to events such as crashes or failures at different altitude levels, so as to provide the UAV's own risk benchmark for subsequent airspace risk, ground risk assessment and multi-altitude flight path planning, avoid risk misjudgment caused by ignoring the differences in UAV performance and mission, and ensure the pertinence and accuracy of subsequent risk analysis.

[0040] The first step is to obtain the flight performance parameters of the target UAV and extract the corresponding intrinsic static features.

[0041] Specifically, it is necessary to first obtain the flight performance parameters of the target UAV, and then extract the two core intrinsic static features from them: the most unfavorable cruise speed and the aerodynamic stability parameters.

[0042] The definition of the most unfavorable cruise speed is "the maximum speed that meets the mission endurance requirements." At this speed, if the UAV fails, the initial kinetic energy is greater and the risk impact range is wider.

[0043] The aerodynamic stability parameter can be measured using the aerodynamic derivative. When the UAV can automatically return to its original equilibrium state after being disturbed, it is defined as statically stable, and the aerodynamic derivative satisfies the static stability criterion, so the aerodynamic derivative is quantified as "1". If the aerodynamic derivative does not satisfy the static stability criterion, it is assigned a number greater than 1 based on the degree to which the aerodynamic derivative deviates from the stability criterion, serving as the aerodynamic stability parameter. A larger aerodynamic stability parameter indicates worse stability, greater trajectory deviation after the UAV loses control, and a wider range of risk impact.

[0044] The second step involves acquiring the target UAV's predetermined planar flight path and mission payload parameters, outputting them as the real-time mission characteristics. The predetermined planar flight path refers to the planar position of the flight path, and the mission payload parameters include payload weight and type, which affect the UAV's inertia and impact force upon impact. These two types of data are then integrated into the real-time mission characteristics.

[0045] The third step is to construct a risk impact range model for the target UAV based on the intrinsic static features and the real-time features of the mission, combined with the prior material property model. The risk impact range model uses the elevation parameter of the altitude layer as the only variable.

[0046] The prior physical property model mainly includes two core sub-models: the free fall model and the aerodynamic model.

[0047] The free fall model is based on the principle of gravity and air resistance balance in classical mechanics. It assumes that the UAV will enter a state of unpowered fall after failure. By inputting parameters such as UAV weight, external dimensions (parameters affecting aerodynamic stability), and initial fall speed (i.e. the most unfavorable cruise speed), it calculates the time, vertical acceleration, and basic range of the UAV's trajectory before finally touching the ground from different altitude layers. It is the basic framework model of the risk impact range.

[0048] The aerodynamic model further refines the impact of ambient airflow on the drone's descent trajectory by introducing aerodynamic derivatives to quantify the drone's aerodynamic characteristics. For example, when the drone's aerodynamic derivative is greater than 1 (statically unstable state), the aerodynamic model calculates the lateral offset caused by airflow disturbances during descent, correcting the "perfectly circular base range" output by the free-fall model to a more realistic "elliptical or irregular range." In special airflow environments such as high-rise building "wind tunnels," the aerodynamic model can also adjust the air resistance coefficient by incorporating historical wind field data, making the risk range prediction more accurate.

[0049] The prior physical property model uses only the elevation parameter of the height layer as the sole variable, ensuring that the risk range of different height layers can be calculated separately in the future, thus meeting the planning requirements of multiple height layers.

[0050] The fourth step involves inputting the elevation parameters of multiple altitude layers into the risk impact range model to obtain the original risk impact range for each altitude layer. This means inputting the elevation parameters of multiple altitude layers, such as 100 meters, 150 meters, and 200 meters, one by one into the constructed risk impact range model. The model then calculates the original risk impact range for each altitude layer, which represents the spatial boundary that might be affected by a drone failure at that altitude.

[0051] In step S200 of this application embodiment, the real-time scheduling scheme for UAVs within the target area is obtained, and the airspace risk distribution information of the target area is evaluated in conjunction with the real-time characteristics of the task, including:

[0052] Obtain real-time scheduling schemes for UAVs at various altitude levels within the target area, wherein the real-time scheduling schemes for UAVs include UAV intrinsic characteristics and UAV payload characteristics;

[0053] Extract the most unfavorable original risk impact range from the original risk impact range, and combine it with the sliding window method to divide the target area into time-series partitions along the predetermined plane route of the target UAV to obtain multiple path time-series partitions. The predetermined plane route is determined based on the real-time characteristics of the task.

[0054] Traverse multiple path time-series partitions and determine the analysis time-series labels in conjunction with the predetermined plane route;

[0055] By traversing multiple path time-series partitions and using multiple airspace heights as analysis planes, airspace risk assessment is performed in conjunction with the UAV real-time scheduling scheme and the analysis time-series labels to obtain the airspace risk distribution information.

[0056] The airspace risk distribution information includes multiple partition risk distribution clusters corresponding to multiple path time-series partitions, and each partition risk distribution cluster includes multiple airspace height risk distribution maps.

[0057] In this embodiment of the application, the purpose of step S200 is to dynamically capture the airspace conflict risks at different altitudes and time periods within the target area, generate accurate airspace risk distribution information, provide a dynamic airspace risk map for subsequent multi-altitude flight path planning, avoid overlapping risks between the target UAV flight path and other UAVs in the spatiotemporal and altitude dimensions, and ensure airspace operation safety.

[0058] First, it is necessary to obtain real-time scheduling plans for drones at various altitudes within the target area. This involves collecting real-time scheduling plans for other drones at all altitudes within the target area, including the drone's intrinsic characteristics and payload characteristics. The drone's intrinsic characteristics, namely the most unfavorable cruise speed and aerodynamic stability parameters of other drones, determine its own risk range; the drone's payload characteristics, namely the payload weight and type, affect its risk level.

[0059] Next, it is necessary to extract the most unfavorable original risk impact range from the original risk impact range, and combine it with the sliding window method to divide the target area into temporal partitions along the predetermined plane route of the target UAV to obtain multiple path temporal partitions, wherein the predetermined plane route is determined based on the real-time characteristics of the task.

[0060] This involves extracting the most unfavorable original risk impact range with the largest coverage area and highest risk level from the multi-altitude layer original risk impact range obtained from S100, such as the risk range of a high-payload, low-stability UAV at a certain altitude. Then, using the target UAV's predetermined planar flight path as a benchmark, a sliding window method is employed to divide the entire flight path and surrounding airspace along the flight direction into multiple continuous "path-time partitions," such as one partition corresponding to one minute of flight distance. This decomposes large-scale, long-term airspace risks into small-scale, short-term partition risks, reducing the computational complexity of subsequent risk assessments while ensuring that the risk analysis accurately matches the flight timeline of the target UAV.

[0061] Then, it is necessary to traverse multiple path time-series partitions and determine the analysis time-series labels in conjunction with the predetermined plane flight path. That is, traverse all path time-series partitions, and calculate the time when the target UAV enters each partition by combining the flight speed of the target UAV on the predetermined plane flight path and the distance length of each partition. For example, partition 1 corresponds to the flight time of minute 0-1, partition 2 corresponds to minute 1-2, and each partition is labeled with a unique analysis time-series label, such as "T0-T1" or "T1-T2".

[0062] Furthermore, it is necessary to traverse multiple path time-series partitions, using multiple airspace heights as analysis planes, and combine the UAV real-time scheduling scheme with the analysis time-series labels to conduct airspace risk assessment and obtain the airspace risk distribution information.

[0063] In step S200 of this embodiment, multiple path time-series partitions are traversed, and multiple airspace altitudes are used as analysis planes. Airspace risk assessment is performed by combining the UAV real-time scheduling scheme with the analysis time-series labels to obtain airspace risk distribution information, including:

[0064] In the multiple path time-series partitions, a spatial altitude is randomly selected as the first analysis plane, and the first UAV intrinsic feature set and the first UAV payload feature set are extracted accordingly.

[0065] Based on the characteristics of the first UAV payload, a first benchmark risk index is obtained;

[0066] Based on the intrinsic characteristics of the first UAV and combined with the preset evaluation function, the first risk diffusion coefficient is evaluated and obtained.

[0067] Based on the mapping relationship between the intrinsic characteristics of UAVs and the characteristics of UAV payloads, the first plane risk scatter plot is obtained by weighting the first benchmark risk index and the first risk diffusion coefficient accordingly.

[0068] Smoothly fit the first plane risk scatter plot to obtain the first altitude risk distribution map;

[0069] Traverse multiple spatial domain heights of the path time-series partitions to obtain multiple risk distribution maps, and output them as the risk distribution clusters of the partitions;

[0070] Traverse multiple path time-series partitions to obtain multiple partition risk distribution clusters, and output the spatial risk distribution information.

[0071] In this embodiment of the application, the purpose of the above-mentioned subdivision step S200 is to perform refined airspace risk calculation for different altitude layers of each path time-series partition, transform the abstract UAV intrinsic characteristics and UAV payload characteristics into a visualized and quantifiable risk distribution map, and finally form airspace risk distribution information covering "time-series partition - altitude layer", providing an accurate airspace risk heat map for subsequent route planning, and ensuring that airspace conflict risks at different altitudes and at different times can be accurately avoided.

[0072] The first step is to randomly select an airspace altitude from the multiple path time-series partitions as the first analysis plane, and extract the first UAV intrinsic feature set and the first UAV payload feature set accordingly.

[0073] In other words, among the multiple path time series partitions that have been divided, an airspace altitude is randomly selected as the "first analysis plane", such as 120 meters altitude of a certain path time series partition. From the UAV real-time scheduling scheme of the UAV in the path time series partition, the "first UAV intrinsic feature set" of all other UAVs at this altitude is extracted, that is, the most unfavorable cruise speed and aerodynamic stability parameters of each UAV; and the "first UAV payload feature set", that is, the payload weight and type of each UAV.

[0074] The second step is to obtain a first baseline risk index based on the payload characteristics of the first UAV. Specifically, based on the extracted payload characteristics of the first UAV, such as payload weight, a corresponding "first baseline risk index" can be matched for each other UAV at that altitude by referring to a preset "payload-risk index mapping table". For example, in the payload-risk index mapping table: payload < 5kg corresponds to baseline risk index 1, 5kg ≤ payload < 10kg corresponds to risk index 2, and payload ≥ 10kg corresponds to risk index 3.

[0075] The third step is to evaluate and obtain the first risk diffusion coefficient based on the intrinsic characteristics of the first UAV and in conjunction with the preset evaluation function.

[0076] Specifically, it is necessary to use aerodynamic stability parameters, namely aerodynamic derivatives greater than or equal to 1 as defined in the previous steps, and substitute them into a preset evaluation function to calculate the "first risk diffusion coefficient" for each other UAV. For example, the risk diffusion coefficient is calculated as follows: Risk diffusion coefficient = 0.5 × aerodynamic derivative + 0.5 × (most unfavorable cruise speed / actual cruise speed).

[0077] It is easy to see from the above formula that the larger the aerodynamic derivative (the more unstable the drone), the higher the most unfavorable cruise speed, the wider the trajectory deviation after failure, the greater the risk diffusion coefficient, and the greater the correction range of the risk impact.

[0078] The fourth step is to obtain a first planar risk scatter plot by weighting the first benchmark risk index and the first risk diffusion coefficient based on the mapping relationship between the intrinsic characteristics of the UAV and the UAV payload characteristics.

[0079] Specifically, based on the preset mapping relationship between the intrinsic characteristics and payload characteristics of the UAV, the first benchmark risk index and the first risk diffusion coefficient of each other UAV need to be calculated by weighting. For example, based on historical experience, the weight of the benchmark risk index can be set to 0.6 and the weight of the risk diffusion coefficient can be set to 0.4.

[0080] The final risk value is calculated as: benchmark risk index × 0.6 + risk diffusion coefficient × 0.4. The calculated final risk value is then mapped to the real-time position (latitude and longitude) of the drone on the first analysis plane to generate a "first plane risk scatter plot". Each point in the plot represents the position of a drone and its corresponding risk value.

[0081] The fifth step is to smoothly fit the first planar risk scatter plot to obtain the first altitude risk distribution map.

[0082] Specifically, interpolation algorithms such as Kriging interpolation can be used to smoothly fit the discrete risk points in the first plane risk scatter plot, transforming isolated risk points into a "first-level risk distribution map" covering the entire first analysis plane. For example, red areas represent high-risk areas with a final risk value ≥ 3, yellow areas represent medium-risk areas with a final risk value ≤ 3, and green areas represent low-risk areas with a final risk value < 2.

[0083] The sixth step involves traversing multiple spatial heights of the path time-series partitions to obtain multiple risk distribution maps, which are then output as the partition risk distribution clusters. This process also involves traversing multiple path time-series partitions to obtain multiple partition risk distribution clusters, which are then output as the spatial risk distribution information.

[0084] That is, for all spatial heights of the current path time series partition, such as 100 meters, 110 meters, 120 meters...200 meters, repeat the above five steps to generate a risk distribution map for each height, and integrate these distribution maps into a "partition risk distribution cluster" for that partition.

[0085] Then, the above operations are performed on all path time-series partitions one by one to obtain the partition risk distribution cluster of each partition. Finally, the risk distribution clusters of all partitions are integrated to output "airspace risk distribution information" covering "full time-series partitions - full altitude layers".

[0086] In step S300 of this embodiment, based on high-precision geographic information data, ground functional zoning information of the target area is obtained, and ground risk distribution information on the predetermined plane flight path of the target UAV is evaluated accordingly, including:

[0087] By combining the predetermined planar flight path with the range of impact of the most unfavorable original risk, the ground risk boundary is determined;

[0088] Using ground risk boundaries as constraints, extract ground functional zoning information and zoning traffic heat information for the target area;

[0089] Based on the ground functional zoning information, a baseline weight is determined, and the regional risk index of each functional zoning is calculated using the normalized zoning traffic heat information as an adjustment coefficient.

[0090] Spatial interpolation is performed based on the regional risk index to generate the ground risk distribution information.

[0091] In this embodiment of the application, the purpose of step S300 is to accurately identify the risk distribution of the ground area below and around the predetermined flight path of the target UAV, identify high-risk ground areas such as densely populated areas and important facility areas, provide a ground risk map for subsequent multi-altitude flight path planning, avoid the risk range of the UAV failure covering sensitive ground areas, and reduce the risk of damage to ground personnel and property.

[0092] First, the ground risk boundary needs to be determined by combining the predetermined plane route with the range of the most unfavorable original risk impact.

[0093] That is, by combining the "target UAV's planned plane path" and the "most unfavorable original risk impact range" obtained in S100, the planned plane path is used as the central reference, and the maximum radius of the most unfavorable original risk impact range is used as the extension distance to delineate the "ground risk boundary" that needs to be assessed in key areas on the ground, that is, the outline of the ground area that may be affected after the UAV fails.

[0094] Then, ground functional zoning information and zoning traffic heat information of the target area need to be extracted, using ground risk boundaries as constraints.

[0095] Specifically, the ground risk boundary determined by the aforementioned steps is a spatial constraint, and two types of key information are extracted from high-precision geographic information data such as urban GIS maps.

[0096] First, there is information on ground functional zoning, which clarifies the classification of ground areas within the boundaries, such as residential areas, schools, hospitals, industrial parks, open green spaces, etc. Different functional zones have different risk sensitivities.

[0097] Second, there is the information on traffic intensity in different functional zones, which involves obtaining dynamic activity intensity data for each zone, such as population density in residential areas, pedestrian traffic during school hours, and vehicle traffic in industrial parks. The higher the traffic intensity in a zone, the more severe the risk and consequences.

[0098] Furthermore, it is necessary to determine the baseline weights based on the aforementioned ground functional zoning information, and to calculate the regional risk index for each functional zoning using the normalized zoning traffic heat information as the adjustment coefficient.

[0099] First, the "benchmark weight" needs to be determined based on the ground functional zoning information. Specifically, the weight value needs to be preset according to the risk sensitivity of different zones. For example, the benchmark weight is set to 0.8 for densely populated and vulnerable areas such as schools and hospitals, 0.5 for residential areas, and 0.1 for open green spaces.

[0100] Then, the traffic heat information of the partition is "normalized" to convert the original traffic data, such as 1000 people / hour and 500 vehicles / hour, into a standardized coefficient between 0 and 1, which is used as an "adjustment coefficient".

[0101] Next, the specific risk index for each functional zone is calculated using the formula "Regional Risk Index = Baseline Weight × Adjustment Coefficient". For example, for a densely populated area of ​​a city, the adjustment coefficient during peak hours is 1, and the regional risk index is 0.8 × 1 = 0.8; the adjustment coefficient during off-peak hours is 0.2, and the regional risk index is 0.8 × 0.2 = 0.16.

[0102] Finally, spatial interpolation calculations are needed based on the regional risk index to generate the ground risk distribution information.

[0103] This involves using a spatial interpolation algorithm to transform the "regional risk index" calculated in the previous steps into continuous risk values ​​covering the entire ground risk boundary, thereby generating "ground risk distribution information," typically presented in the form of a heat map, where red represents high-risk areas, yellow represents medium-risk areas, and green represents low-risk areas. This provides a visual representation of the spatial gradient distribution of ground risks.

[0104] The information, along with the original risk impact range, is combined with a path planning algorithm based on a greedy strategy to perform a joint search at multiple altitude levels to obtain the multi-altitude spatial flight path of the target UAV. Prior to this, it also includes:

[0105] Acquire historical low-altitude wind field data for the target airspace;

[0106] The most unfavorable wind condition parameters are determined based on the historical wind field data of the low-altitude environment, and wind field correction coefficients are calculated accordingly to correct the shape of the original risk impact range, wherein the wind field correction coefficients are anisotropic.

[0107] Based on the wind field correction coefficient, the original risk impact range is updated with axial correction.

[0108] In step S400 of this embodiment, based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, a joint search is performed at multiple altitude levels using a path planning algorithm based on a greedy strategy to obtain the multi-altitude spatial flight path of the target UAV, including:

[0109] Construct a greedy objective function, wherein the greedy objective function includes:

[0110] The ground direct risk factor is determined based on the ground risk distribution information and the original risk impact range corresponding to the height layer;

[0111] Airspace conflict risk factor determined based on the airspace risk distribution information and the original risk impact range corresponding to the altitude layer;

[0112] With minimizing the greedy objective function as the optimization objective and the predetermined planar route as the constraint, a joint search is performed at multiple altitude levels based on a greedy strategy to obtain the multi-altitude spatial route.

[0113] In step S400 of this embodiment, based on the airspace risk distribution information and the ground risk distribution...

[0114] In this embodiment of the application, the purpose of step S400 is to first correct the original risk impact range of the UAV to fit the actual wind conditions, then construct a greedy objective function that takes into account both ground and airspace risks, jointly search for the optimal route at multiple altitudes, and finally achieve a balance between risk minimization and operational efficiency, so as to ensure that the UAV flies safely and with low energy consumption in complex low-altitude environments.

[0115] To achieve the above objectives, the first step is to obtain historical wind field data of the low-altitude environment of the target airspace. This involves collecting historical wind field data accumulated over a long period of time in the target airspace, including parameters such as wind speed, wind direction, and wind frequency at different altitude levels. For example, data such as the annual average wind speed distribution at altitudes of 100–300 meters, the location of seasonal "wind gaps," and airflow intensity can be collected.

[0116] The second step is to determine the most unfavorable wind condition parameters based on the historical wind field data of the low-altitude environment, and to calculate the corresponding wind field correction coefficient for shape correction of the original risk impact range.

[0117] The most unfavorable wind condition parameter refers to the wind condition characteristic parameter selected from historical wind field data of the low-altitude environment of the target airspace that has the greatest "expansion" and "most significant trajectory deviation" in terms of the risk impact range after UAV failure. For example, the most unfavorable wind condition parameter can be the maximum instantaneous wind speed in the historical records of a certain altitude layer, such as 20 m / s.

[0118] Specifically, it is necessary to select the "most unfavorable wind condition parameter" that has the greatest impact on the risk range of the drone from historical wind field data; and calculate the "wind field correction coefficient" based on this most unfavorable wind condition parameter. Since different wind directions have different effects on the offset direction, the wind field correction coefficient needs to be differentiated by direction and has anisotropy. During the calculation, the correction base coefficient is first determined based on the most unfavorable wind speed.

[0119] For example, the wind speed data from historical wind field data at a certain altitude in the target airspace can be divided into multiple intervals, with the maximum instantaneous wind speed in the historical records at that altitude as the upper limit of the interval, such as 0-5 m / s, 5-10 m / s, 10-15 m / s, 15-20 m / s, etc. A corresponding "baseline correction factor" can be preset for each interval: 1.0 for 0-5 m / s, 1.2 for 5-10 m / s, 1.5 for 10-15 m / s, and 1.8 for 15-20 m / s.

[0120] Then, the direction is distinguished. For example, when the wind direction is the direction the drone is traveling, the wind field correction coefficient is 1.5 times the correction base coefficient. When the wind direction is at a 45° angle to the direction the drone is traveling, the wind field correction coefficient is 1.3 times the correction base coefficient. When the wind direction is opposite to the direction the drone is traveling, the wind field correction coefficient is 0.9 times the correction base coefficient. Following this logic, the wind field correction coefficients for different directions are finally obtained, which are used to correct the original circular risk range into an ellipse or irregular shape that fits the actual wind conditions.

[0121] The third step involves updating the original risk impact range axially by incorporating the wind field correction coefficient. This involves substituting the wind field correction coefficient into the original risk impact range model obtained in S100 and adjusting the spatial coordinates in the model axially, such as multiplying the risk radius by 1.5 along the wind direction axis, to obtain the corrected original risk impact range.

[0122] The fourth step is to construct the greedy objective function.

[0123] First, it is necessary to determine the direct ground risk factor based on the ground risk distribution information and the original risk impact range corresponding to the altitude layer.

[0124] Obtaining the ground direct risk factor involves overlaying the corrected original risk impact range with ground risk distribution information to calculate the percentage of the original risk impact range that covers the high-risk area. The higher the percentage, the larger the factor value. For example, covering 50% of the high-risk area corresponds to a ground direct risk factor of 0.8, and covering 10% of the high-risk area corresponds to a ground direct risk factor of 0.2.

[0125] Then, an airspace conflict risk factor needs to be determined based on the airspace risk distribution information and the original risk impact range corresponding to the altitude layer.

[0126] Obtaining the airspace conflict risk factor allows matching the corrected original risk impact range with airspace risk distribution information, i.e., the zonal risk distribution clusters, and calculating the overlap probability between the original risk impact range and the original risk impact range of other UAVs. The higher the probability, the larger the airspace conflict risk factor value. For example, an overlap probability of 80% corresponds to an airspace conflict risk factor of 0.9, 0% corresponds to an airspace conflict risk factor of 0.1, and so on.

[0127] Furthermore, it is necessary to optimize by minimizing the greedy objective function and constrain the predetermined planar route, and to perform a joint search at multiple altitude levels based on a greedy strategy to obtain the multi-altitude spatial route.

[0128] Specifically, the greedy objective function formula can be simplified to: Cost = α × Ground Direct Risk Factor + β × Airspace Conflict Risk Factor + γ × Climb Energy Consumption. Climb energy consumption is positively correlated with the actual energy consumption of the UAV reaching a certain altitude, used to balance the UAV's overall energy consumption. This climb energy consumption is a mapped value, of the same order of magnitude as the two risk factors, and dimensionless. For example, for a cargo UAV, a climb from 100m to 120m maps to a climb energy consumption of 0.15, a climb from 120m to 150m maps to a climb energy consumption of 0.25, and so on.

[0129] α, β, and γ are weighting coefficients, and their sum is 1. The specific values ​​can be set according to the task priority. For example, α is 0.6 for large drones and 0.4 for small drones. The value of γ can be appropriately increased for cargo drones and decreased for patrol drones.

[0130] Then, based on the "predetermined horizontal flight path" in S100, it is necessary to ensure that the searched flight path does not deviate from the preset path in the horizontal direction. Then, the entire predetermined horizontal flight path is divided into multiple segments according to the flight sequence, such as every 5 kilometers. For each segment, all selectable altitude layers are traversed, such as traversing multiple altitude layers between 100 and 200 meters, with each layer spaced 10 meters apart. The "total cost value" of selecting each altitude layer is calculated, and the altitude with the minimum total cost value of the segment is selected as the "local optimal altitude".

[0131] Next, the local optimal altitudes of each road segment are connected to form a complete multi-altitude layer flight path; if the difference in optimal altitude between adjacent road segments exceeds a preset threshold, such as 30 meters, the intermediate altitude is finely adjusted, such as gradually transitioning from 120 meters to 150 meters, to reduce the number of altitude changes.

[0132] Finally, the route coordinates obtained from the joint search, including longitude, latitude, and altitude, are organized into structured data and output as the final flight path of the target UAV, i.e., a multi-altitude spatial route, which can be directly used for flight control.

[0133] By implementing the low-altitude risk distribution-oriented UAV multi-altitude flight path planning method provided in the embodiments of this application, at least the following can be achieved:

[0134] 1. Precise risk management and efficient operation of UAV multi-altitude flight paths.

[0135] 2. Accurately determine the original risk range of drones at multiple altitudes to avoid misjudgment of risks.

[0136] 3. Provides multi-altitude joint search for optimal flight routes, balancing risk and energy consumption, and improving the flight safety and operational efficiency of UAVs.

[0137] Example 2, as Figure 2 As shown, based on the same inventive concept as the low-altitude risk distribution-oriented UAV multi-altitude flight path planning method provided in Embodiment 1, this embodiment of the invention also provides a low-altitude risk distribution-oriented UAV multi-altitude flight path planning system, including:

[0138] The original risk impact range acquisition module 11 is used to acquire the original risk impact range of the target UAV at multiple altitude layers based on the intrinsic static characteristics and real-time characteristics of the mission.

[0139] The airspace risk distribution information evaluation module 12 is used to obtain the real-time scheduling scheme of UAVs in the target area and evaluate the airspace risk distribution information of the target area in combination with the real-time characteristics of the task.

[0140] The ground risk distribution information evaluation module 13 is used to obtain ground functional zoning information of the target area based on high-precision geographic information data, and to evaluate the ground risk distribution information on the predetermined plane flight path of the target UAV accordingly.

[0141] The multi-altitude spatial route acquisition module 14 is used to perform joint search at multiple altitude levels based on the airspace risk distribution information, the ground risk distribution information and the original risk impact range, combined with a path planning algorithm based on a greedy strategy, to obtain the multi-altitude spatial route of the target UAV.

[0142] Furthermore, the original risk impact scope acquisition module 11 includes the following execution steps:

[0143] The flight performance parameters of the target UAV are obtained, and the intrinsic static features are extracted accordingly, wherein the intrinsic static features include the most unfavorable cruise speed and aerodynamic stability parameters.

[0144] Obtain the target UAV's predetermined planar flight path and mission payload parameters, and output them as the real-time characteristics of the mission;

[0145] Based on the intrinsic static features and the real-time features of the mission, a risk impact range model for the target UAV is constructed by combining the prior physical property model, wherein the risk impact range model uses the elevation parameter of the altitude layer as the only variable.

[0146] Input the elevation parameters of multiple height layers into the risk impact range model to obtain the original risk impact range of multiple height layers.

[0147] Furthermore, the airspace risk distribution information evaluation module 12 includes the following execution steps:

[0148] Obtain real-time scheduling schemes for UAVs at various altitude levels within the target area, wherein the real-time scheduling schemes for UAVs include UAV intrinsic characteristics and UAV payload characteristics;

[0149] Extract the most unfavorable original risk impact range from the original risk impact range, and combine it with the sliding window method to divide the target area into time-series partitions along the predetermined plane route of the target UAV to obtain multiple path time-series partitions. The predetermined plane route is determined based on the real-time characteristics of the task.

[0150] Traverse multiple path time-series partitions and determine the analysis time-series labels in conjunction with the predetermined plane route;

[0151] By traversing multiple path time-series partitions and using multiple airspace heights as analysis planes, airspace risk assessment is performed in conjunction with the UAV real-time scheduling scheme and the analysis time-series labels to obtain the airspace risk distribution information.

[0152] The airspace risk distribution information includes multiple partition risk distribution clusters corresponding to multiple path time-series partitions, and each partition risk distribution cluster includes multiple airspace height risk distribution maps.

[0153] The method involves acquiring real-time scheduling schemes for UAVs at various altitudes within the target area, wherein the real-time scheduling schemes for UAVs include UAV intrinsic characteristics and UAV payload characteristics.

[0154] Extract the most unfavorable original risk impact range from the original risk impact range, and combine it with the sliding window method to divide the target area into time-series partitions along the predetermined plane route of the target UAV to obtain multiple path time-series partitions. The predetermined plane route is determined based on the real-time characteristics of the task.

[0155] Traverse multiple path time-series partitions and determine the analysis time-series labels in conjunction with the predetermined plane route;

[0156] By traversing multiple path time-series partitions and using multiple airspace heights as analysis planes, airspace risk assessment is performed in conjunction with the UAV real-time scheduling scheme and the analysis time-series labels to obtain the airspace risk distribution information.

[0157] The airspace risk distribution information includes multiple partition risk distribution clusters corresponding to multiple path time-series partitions, and each partition risk distribution cluster includes multiple airspace height risk distribution maps.

[0158] Furthermore, the ground risk distribution information assessment module 13 includes the following execution steps:

[0159] By combining the predetermined planar flight path with the range of impact of the most unfavorable original risk, the ground risk boundary is determined;

[0160] Using ground risk boundaries as constraints, extract ground functional zoning information and zoning traffic heat information for the target area;

[0161] Based on the ground functional zoning information, a baseline weight is determined, and the regional risk index of each functional zoning is calculated using the normalized zoning traffic heat information as an adjustment coefficient.

[0162] Spatial interpolation is performed based on the regional risk index to generate the ground risk distribution information.

[0163] Furthermore, the multi-altitude spatial route acquisition module 14 includes the following execution steps:

[0164] Acquire historical low-altitude wind field data for the target airspace;

[0165] The most unfavorable wind condition parameters are determined based on the historical wind field data of the low-altitude environment, and wind field correction coefficients are calculated accordingly to correct the shape of the original risk impact range, wherein the wind field correction coefficients are anisotropic.

[0166] Based on the wind field correction coefficient, the original risk impact range is updated with axial correction.

[0167] Construct a greedy objective function, wherein the greedy objective function includes:

[0168] The ground direct risk factor is determined based on the ground risk distribution information and the original risk impact range corresponding to the height layer;

[0169] Airspace conflict risk factor determined based on the airspace risk distribution information and the original risk impact range corresponding to the altitude layer;

[0170] With minimizing the greedy objective function as the optimization objective and the predetermined planar route as the constraint, a joint search is performed at multiple altitude levels based on a greedy strategy to obtain the multi-altitude spatial route.

[0171] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0172] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0174] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0175] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0176] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0177] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A low-altitude risk distribution-oriented multi-altitude flight path planning method for unmanned aerial vehicles (UAVs), characterized in that, include: Based on the intrinsic static characteristics and real-time characteristics of the target UAV, the original risk impact range of the target UAV at multiple altitude levels is obtained. Obtain a real-time drone scheduling plan within the target area, and evaluate the airspace risk distribution information of the target area based on the real-time characteristics of the task. Based on high-precision geographic information data, the ground functional zoning information of the target area is obtained, and the ground risk distribution information on the predetermined plane flight path of the target UAV is evaluated accordingly. Based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, a joint search is performed at multiple altitude levels using a path planning algorithm based on a greedy strategy to obtain the multi-altitude spatial flight path of the target UAV. Among them, based on the intrinsic static characteristics and real-time characteristics of the target UAV, the original risk impact range of the target UAV at multiple altitude layers is obtained, including: The flight performance parameters of the target UAV are obtained, and the intrinsic static features are extracted accordingly, wherein the intrinsic static features include the most unfavorable cruise speed and aerodynamic stability parameters. Obtain the target UAV's predetermined planar flight path and mission payload parameters, and output them as the real-time characteristics of the mission; Based on the intrinsic static features and the real-time features of the mission, a risk impact range model for the target UAV is constructed by combining the prior physical property model, wherein the risk impact range model uses the elevation parameter of the altitude layer as the only variable. Input the elevation parameters of multiple height layers into the risk impact range model to obtain the original risk impact range of the multiple height layers; This includes acquiring a real-time drone scheduling plan within the target area, and, in conjunction with the real-time characteristics of the task, evaluating the airspace risk distribution information of the target area, including: The real-time scheduling schemes of other UAVs at various altitude levels within the target area are obtained. The real-time scheduling schemes of the UAVs include the intrinsic characteristics of the UAVs and the payload characteristics of the UAVs. The intrinsic characteristics of the UAVs include the most unfavorable cruise speed and aerodynamic stability parameters of other UAVs. Extract the most unfavorable original risk impact range from the original risk impact range, and combine it with the sliding window method to divide the target area into time-series partitions along the predetermined plane route of the target UAV to obtain multiple path time-series partitions. The predetermined plane route is determined based on the real-time characteristics of the task. Traverse multiple path time-series partitions and determine the analysis time-series labels in conjunction with the predetermined plane route; By traversing multiple path time-series partitions and using multiple airspace heights as analysis planes, airspace risk assessment is performed in conjunction with the UAV real-time scheduling scheme and the analysis time-series labels to obtain the airspace risk distribution information. The airspace risk distribution information includes multiple partition risk distribution clusters corresponding to multiple path time-series partitions, and each partition risk distribution cluster includes multiple airspace height risk distribution maps. This includes acquiring ground functional zoning information for the target area based on high-precision geographic information data, and evaluating the ground risk distribution information along the predetermined flight path of the target UAV, including: By combining the predetermined planar flight path with the range of impact of the most unfavorable original risk, the ground risk boundary is determined; Using ground risk boundaries as constraints, extract ground functional zoning information and zoning traffic heat information for the target area; Based on the ground functional zoning information, a baseline weight is determined, and the regional risk index of each functional zoning is calculated using the normalized zoning traffic heat information as an adjustment coefficient. Spatial interpolation is performed based on the regional risk index to generate the ground risk distribution information.

2. The low-altitude risk distribution-oriented UAV multi-altitude flight path planning method as described in claim 1, characterized in that, By traversing multiple path time-series partitions and using multiple airspace altitudes as analysis planes, airspace risk assessment is performed in conjunction with the UAV real-time scheduling scheme and the analysis time-series labels to obtain the airspace risk distribution information, including: In the multiple path time-series partitions, a spatial altitude is randomly selected as the first analysis plane, and the first UAV intrinsic feature set and the first UAV payload feature set are extracted accordingly. Based on the characteristics of the first UAV payload, a first benchmark risk index is obtained; Based on the intrinsic characteristics of the first UAV and combined with the preset evaluation function, the first risk diffusion coefficient is evaluated and obtained. Based on the mapping relationship between the intrinsic characteristics of UAVs and the characteristics of UAV payloads, the first plane risk scatter plot is obtained by weighting the first benchmark risk index and the first risk diffusion coefficient accordingly. Smoothly fit the first plane risk scatter plot to obtain the first altitude risk distribution map; Traverse multiple spatial domain heights of the path time-series partitions to obtain multiple risk distribution maps, and output them as the risk distribution clusters of the partitions; Traverse multiple path time-series partitions to obtain multiple partition risk distribution clusters, and output the spatial risk distribution information.

3. The low-altitude risk distribution-oriented UAV multi-altitude flight path planning method as described in claim 1, characterized in that, Based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, a joint search is performed at multiple altitude levels using a path planning algorithm based on a greedy strategy to obtain the multi-altitude spatial flight path of the target UAV, including: Construct a greedy objective function, wherein the greedy objective function includes: The ground direct risk factor is determined based on the ground risk distribution information and the original risk impact range corresponding to the height layer; Airspace conflict risk factor determined based on the airspace risk distribution information and the original risk impact range corresponding to the altitude layer; With minimizing the greedy objective function as the optimization objective and the predetermined planar route as the constraint, a joint search is performed at multiple altitude levels based on a greedy strategy to obtain the multi-altitude spatial route.

4. The low-altitude risk distribution-oriented UAV multi-altitude flight path planning method as described in claim 1, characterized in that, Based on the airspace risk distribution information, the ground risk distribution information, and the original risk impact range, a joint search is performed at multiple altitude levels using a path planning algorithm based on a greedy strategy to obtain the multi-altitude spatial flight path of the target UAV. Prior to this, the following steps are also included: Acquire historical low-altitude wind field data for the target airspace; The most unfavorable wind condition parameters are determined based on the historical wind field data of the low-altitude environment, and wind field correction coefficients are calculated accordingly to correct the shape of the original risk impact range, wherein the wind field correction coefficients are anisotropic. Based on the wind field correction coefficient, the original risk impact range is updated with axial correction.

5. A low-altitude risk distribution-oriented UAV multi-altitude flight path planning system, characterized in that, A method for implementing a low-altitude risk distribution-oriented UAV multi-altitude flight path planning method as described in any one of claims 1-4 includes: The original risk impact range acquisition module is used to acquire the original risk impact range of the target UAV at multiple altitude layers based on the intrinsic static characteristics and real-time characteristics of the mission. The airspace risk distribution information evaluation module is used to obtain real-time scheduling schemes for UAVs within the target area and evaluate the airspace risk distribution information of the target area in conjunction with the real-time characteristics of the task. The ground risk distribution information evaluation module is used to obtain ground functional zoning information of the target area based on high-precision geographic information data, and to evaluate the ground risk distribution information on the predetermined plane flight path of the target UAV accordingly. The multi-altitude spatial route acquisition module is used to obtain the multi-altitude spatial routes of the target UAV by jointly searching at multiple altitude levels based on the airspace risk distribution information, the ground risk distribution information and the original risk impact range, combined with a path planning algorithm based on a greedy strategy.

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