Low-altitude air route turbulence evaluation and dynamic optimization method, system and device based on wind measurement laser radar and storage medium

Abstract

The application discloses a low-altitude air route turbulence evaluation and dynamic optimization method, system, device and storage medium based on a wind measurement laser radar, which comprises: east-west wind speed components, north-south wind speed components and vertical airflow components to form a complete low-altitude three-dimensional wind field data set; based on the low-altitude three-dimensional wind field data set, a high turbulence area with potential flight risk or an air route node unsuitable for flight is identified; and for the high turbulence area with potential flight risk or the air route node unsuitable for flight, an optimal or suboptimal air route is screened out as a real-time flight path. The application can simultaneously depict the horizontal wind field structure and the vertical airflow variation characteristics in space; can effectively capture the small-scale wind speed fluctuation and turbulence structure caused by the terrain undulation, building group disturbance or thermal inhomogeneity in the low-altitude boundary layer, and can improve the low-altitude wind field expression to the 'instant state' and'structure state', and significantly enhance the perception ability to the sudden turbulence.

Description

Technical Field

[0001] This invention belongs to the field of UAV route planning technology, and particularly relates to a method, system, equipment and storage medium for low-altitude route turbulence assessment and dynamic optimization based on wind-measuring lidar. Background Technology

[0002] With the increasing development of low-altitude airspace resources, the application of drones in logistics delivery, agricultural surveying, and emergency response is growing rapidly. This requires airway management systems to have strong adaptability to complex atmospheric environments. Current low-altitude airway planning strategies mainly focus on static trajectory optimization and regional obstacle avoidance.

[0003] Atmospheric turbulence is a core hazard in low-altitude flight, which can cause aircraft instability, path deviation, and even structural damage. Studies show that low-altitude turbulence often originates from uneven airflow caused by surface thermal effects, building obstruction, or terrain fluctuations, and is particularly pronounced in urban or mountainous environments.

[0004] The relevant technologies have significant limitations in low-altitude route planning, mainly in their insufficient handling of dynamic atmospheric factors and difficulty in capturing local turbulence details, which affects flight efficiency and safety. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device, and storage medium for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar, in order to solve the problem of trajectory deviation and mission failure caused by drastic changes in low-altitude wind speed and direction.

[0006] The embodiments of this application are implemented as follows: a low-altitude flight path turbulence assessment and dynamic optimization method based on wind-measuring lidar, including: S01. Obtain the original observation dataset, which includes the first horizontal wind speed data, the second horizontal wind speed data, the horizontal wind direction and the vertical airflow velocity. S02. Spatial interpolation is performed on the first horizontal wind speed data and vertical airflow velocity to obtain the vertical airflow component, and at the same time, a three-dimensional wind field data grid covering the target low-altitude airspace is constructed. S03. Perform orthogonal decomposition on the second horizontal wind speed data and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid to obtain the east-west wind speed component and the north-south wind speed component. S04, the east-west wind speed component, the north-south wind speed component, and the vertical airflow component form a complete low-altitude three-dimensional wind field dataset. S05. Based on the low-altitude three-dimensional wind field dataset, identify high-turbulence areas or unsuitable flight path nodes with potential flight risks, and select the optimal or suboptimal flight path as the real-time flight path.

[0007] Optionally, in some embodiments of this application, a scanning wind-measuring lidar is used in PPI scanning mode to periodically scan the target area to obtain radial wind speed observation data at different heights and azimuths. The radial wind speed observation data is then used to obtain first horizontal wind speed data at different heights through a wind field inversion algorithm; and / or The inverse distance weighted interpolation method is used to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity to obtain the vertical airflow component; and / or Based on a low-altitude three-dimensional wind field dataset, and using turbulent kinetic energy and turbulent intensity as composite turbulence indicators, the turbulence risk of each route node and segment along the route is quantitatively assessed, identifying high-turbulence areas with potential flight risks or route nodes unsuitable for flight.

[0008] Optionally, in some embodiments of this application, the vertical wind-measuring lidar simultaneously performs vertical detection to acquire wind profile data consistent with the PPI scanning time. The wind profile data includes second horizontal wind speed, horizontal wind direction, and vertical airflow velocity at different altitude levels; and / or Within a sliding time window, statistical processing is performed on the low-altitude three-dimensional wind field dataset to calculate the turbulent kinetic energy (TKE) and turbulent intensity (TI) at each spatial location. Based on preset thresholds, a composite turbulence index (CTI) is constructed to quantitatively assess the turbulence risk at various route nodes and segments along the flight path; and / or Based on a low-altitude three-dimensional wind field dataset, and using turbulent kinetic energy and turbulent intensity as composite turbulence indices, the turbulence risk of each route node and segment along the route is quantitatively assessed. Combined with the aircraft's wind resistance capability, flight altitude, and operating conditions, the safety level of each route node is determined, thereby identifying high-turbulence areas with potential flight risks or route nodes unsuitable for flight.

[0009] Optionally, in some embodiments of this application, the method for obtaining the east-west wind speed component and the north-south wind speed component includes: For any grid point P(x, y, z) to be interpolated in the three-dimensional wind field data grid space, its wind field physical quantity is denoted as F(P). For the i-th vertical wind-measuring lidar observation point Pi(xi, yi, zi), its wind field physical quantity is denoted as F(Pi). The inverse distance weighted interpolation method is used to calculate the following: ; In the formula, p is the distance decay exponent, and p>0; N is the number of valid observation points participating in the interpolation calculation; Weight Defined as: ; Wherein, the spatial distance d(P, P) between the interpolation point and the observation point i), defined as the three-dimensional Euclidean distance: ; In the formula, For the grid points to be interpolated, , and These are the three-dimensional coordinates of the grid points to be interpolated. For the first One vertical wind-measuring lidar observation point, , and The first Three-dimensional coordinates of a vertical wind-measuring lidar observation point; Based on this, the second horizontal wind speed and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid are orthogonally decomposed to obtain the corresponding u and v wind speed components: ; In the formula, u represents the east-west wind speed component, v represents the north-south wind speed component, V is the second horizontal wind speed, and Φ is the horizontal wind direction; and / or ; This represents the fluctuation value of the east-west wind speed component. This represents the fluctuation value of the north-south wind speed component. This represents the vertical airflow component fluctuation value; ; In the formula, These are the average values ​​of the wind speed components u, v, and w within the sliding window time range, respectively. , , The first The instantaneous wind speed at any given moment includes the east-west wind speed component, the north-south wind speed component, and the vertical airflow component. ; In the formula, N is the number of valid samples within the sliding window time range. , , The first The instantaneous wind speed at any given moment includes the east-west wind speed component, the north-south wind speed component, and the vertical airflow component. , This represents the average horizontal wind speed within the current sliding window time range; ; CTI=0 represents a safe condition; CTI=1 represents moderate turbulence; CTI=2 represents severe turbulence; TKE th TIth All are first thresholds used to determine moderate turbulence; TI sev This is the second threshold used to determine severe turbulence.

[0010] Optionally, in some embodiments of this application, for high turbulence areas or route nodes that are identified as having potential flight risks or are unsuitable for flight, one or more candidate routes with low turbulence risks are generated based on the low-altitude three-dimensional wind field dataset obtained by inversion at the current time, and the optimal or second-best route is selected from them as the real-time flight path.

[0011] Optionally, in some embodiments of this application, when a waypoint is determined to be a severely turbulent node, a spatial search area with a horizontal search radius R is constructed with that waypoint as the spatial center. h and vertical search radius R v Limited cylindrical 3D search area , ; In the formula, The coordinates of the route nodes, Coordinates of candidate route nodes; By determining the turbulent kinetic energy and turbulence intensity of the candidate route nodes located within the cylindrical three-dimensional search area, alternative route nodes that meet the turbulence safety threshold requirements are selected.

[0012] Optionally, in some embodiments of this application, if multiple alternative route nodes that meet the safety conditions exist, the optimal alternative route node J is selected based on spatial distance and wind speed conditions: ; In the formula, d is the spatial distance between the candidate node and the original route node; V is the wind speed of the candidate node; α is the distance weighting coefficient; β is the wind speed weighting coefficient; Select the candidate node with the smallest J as the alternative route node, and smooth the route; and / or When multiple adjacent route nodes are identified as severely turbulent nodes within a continuous observation period, the current route is determined to have a continuous turbulence risk, and the route is regenerated based on the low-altitude three-dimensional wind field dataset obtained from the current inversion. Route smoothing is performed on the regenerated route or the dynamically generated alternative route. In subsequent observation periods, when the turbulence risk in the spatial region corresponding to the route is reduced to below the preset safety threshold, the route optimization state is automatically deactivated, and the original route is restored or the route adjustment level is reduced.

[0013] Accordingly, embodiments of this application also provide a low-altitude flight path turbulence assessment and dynamic optimization system based on wind-measuring lidar, including: The raw observation dataset module is used to obtain the raw observation dataset, which includes first horizontal wind speed data, second horizontal wind speed data, horizontal wind direction and vertical airflow velocity; The interpolation module is used to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity to obtain the vertical airflow component, and at the same time construct a three-dimensional wind field data grid covering the target low-altitude airspace. The wind speed component module is used to orthogonally decompose the second horizontal wind speed data and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid to obtain the east-west wind speed component and the north-south wind speed component. The low-altitude three-dimensional wind field dataset module consists of east-west wind speed components, north-south wind speed components, and vertical airflow components, forming a complete low-altitude three-dimensional wind field dataset. The real-time flight path selection module, based on a low-altitude three-dimensional wind field dataset, identifies high-turbulence areas with potential flight risks or unsuitable route nodes, and selects the optimal or suboptimal route as the real-time flight path.

[0014] Accordingly, embodiments of this application also provide a computer device, including a storage device and a processor, wherein the storage device stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0015] Accordingly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This application constructs a low-altitude three-dimensional wind field dataset based on data from scanning and vertical wind-measuring lidars. Compared to traditional methods that rely on numerical models or empirical statistics, this dataset can simultaneously characterize the horizontal wind field structure and vertical airflow variation features in space. By using inverse distance weighted interpolation to orthogonally decompose the second horizontal wind speed and direction and processing them with a time sliding window, the system can effectively capture small-scale wind speed fluctuations and turbulent structures within the low-altitude boundary layer caused by terrain undulations, building disturbances, or thermal inhomogeneities. This improves the low-altitude wind field representation from an "average state" to an "instantaneous state" and a "structural state," significantly enhancing the ability to perceive sudden turbulence and improving flight efficiency and safety.

[0017] By statistically processing the low-altitude three-dimensional wind field dataset within a sliding time window, turbulent kinetic energy and turbulence intensity are obtained through inversion. Furthermore, a composite turbulence index is constructed, transforming the originally difficult-to-quantify turbulence risk into a comparable and classifiable numerical index. By discretizing the route into multiple route nodes and extracting the corresponding turbulence parameters point by point, the transformation of route safety status from qualitative judgment to quantitative assessment is realized, making the identification of route turbulence risk more objective, stable, and repeatable.

[0018] When a route node is detected to be in a state of severe turbulence, this application can quickly search for alternative route nodes that meet the turbulence safety threshold in the local three-dimensional space based on the low-altitude three-dimensional wind field dataset obtained at the current moment, and dynamically adjust the route. This optimization process takes reducing turbulence risk as its core objective, while taking into account route continuity and flight feasibility, thereby effectively avoiding the aircraft from entering the strong turbulence region and reducing safety hazards such as attitude disturbances, track deviations and increased structural loads caused by wind shear and severe airflow fluctuations.

[0019] Based on minute-level updated wind lidar measurement data, a three-dimensional low-altitude wind field is constructed, and turbulence parameters are continuously updated within a sliding time window, enabling the system to respond promptly to rapid evolution of the low-altitude wind field and sudden turbulence changes. Compared to traditional route planning methods that rely on weather forecasts or low-frequency updated data, this application significantly shortens the time delay between the occurrence of weather changes and the effective implementation of route adjustments, enhancing the dynamic adaptability of low-altitude routes under complex weather conditions.

[0020] By introducing a turbulence risk identification mechanism under continuous observation periods, this application can distinguish between short-term local disturbances and persistent high-risk turbulence events, avoiding frequent and unnecessary route adjustments due to instantaneous noise or local anomalies. Furthermore, once the turbulence risk is reduced to below the safety threshold, the system can automatically deactivate the route optimization state and restore the original route or lower the adjustment level, thereby ensuring the smoothness and controllability of the route adjustment process and improving overall operational stability.

[0021] Employing a modular design, relevant parameters (such as turbulence determination threshold, spatial search radius, and time window length) can be flexibly configured according to the wind resistance capabilities and mission requirements of different types of aircraft, making it suitable for various low-altitude operation scenarios such as UAV logistics, emergency rescue, and urban patrol. Furthermore, this method is highly adaptable to the number of radar deployments and flight path configurations, facilitating rapid deployment and widespread application in different regions.

[0022] By implementing local route optimization under the premise of ensuring flight safety, rather than completely detouring or reconstructing routes on a large scale, this application can effectively control the increase in range and energy consumption, avoid the decline in operational efficiency caused by excessive avoidance, and thus achieve a better balance between safety and economy, which has good practical application value. Attached Figure Description

[0023] Figure 1 This is a flowchart of the low-altitude airway turbulence assessment and dynamic optimization method based on wind-measuring lidar of the present invention; Figure 2 This is a schematic diagram of the network observation of the wind-measuring lidar of the present invention; Figure 3 This is a schematic diagram of the results of low-altitude flight path turbulence assessment and dynamic optimization in this invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0025] The technical solution of this application is as follows: Please see Figure 1 This application provides a method for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar, including: S01. Obtain the original observation dataset, which includes the first horizontal wind speed data, the second horizontal wind speed data, the horizontal wind direction and the vertical airflow velocity. S02. Spatial interpolation is performed on the first horizontal wind speed data and vertical airflow velocity to obtain the vertical airflow component, and at the same time, a three-dimensional wind field data grid covering the target low-altitude airspace is constructed. S03. Perform orthogonal decomposition on the second horizontal wind speed data and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid to obtain the east-west wind speed component and the north-south wind speed component. S04, the east-west wind speed component, the north-south wind speed component, and the vertical airflow component form a complete low-altitude three-dimensional wind field dataset. S05. Based on the low-altitude three-dimensional wind field dataset, identify high-turbulence areas or unsuitable flight path nodes with potential flight risks, and select the optimal or suboptimal flight path as the real-time flight path.

[0026] In S01: In some embodiments, a scanning wind-measuring lidar is used to periodically scan the target area in PPI scanning mode to obtain radial wind speed observation data at different height levels and in different azimuth directions. The radial wind speed observation data is then used to obtain the first horizontal wind speed data at different height levels through a wind field inversion algorithm.

[0027] For example, wind field inversion algorithms such as VAD.

[0028] For example, the target area includes low-altitude airways and / or the airspace surrounding low-altitude airways.

[0029] Furthermore, the vertical wind-measuring lidar simultaneously conducts vertical detection to acquire wind profile data consistent with the PPI scanning time. The wind profile data includes the second horizontal wind speed, horizontal wind direction, and vertical airflow velocity at different height levels.

[0030] It is understandable that vertical wind-measuring lidar can simultaneously conduct vertical detection to acquire wind profile data that is consistent with the PPI scanning time, thereby forming a raw observation dataset that is consistent in time and complementary in space.

[0031] In S02: In some embodiments, the inverse distance weighted interpolation method is used to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity to obtain the vertical airflow component.

[0032] It is understandable that by using the inverse distance weighted interpolation method to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity, the physical quantities of the wind field at different heights and spatial locations can be reconstructed. This ensures that the first horizontal wind speed data and the vertical airflow velocity are consistent in terms of spatial resolution and data dimension, thereby constructing a three-dimensional wind field data grid that covers the target low-altitude airspace and realizing the continuous expression of the low-altitude wind field in three-dimensional space.

[0033] In S03: In some embodiments, the method for obtaining the east-west wind speed component and the north-south wind speed component includes: For any grid point P(x, y, z) to be interpolated in the three-dimensional wind field data grid space, its wind field physical quantity is denoted as F(P). For the i-th vertical wind-measuring lidar observation point Pi(xi, yi, zi), its wind field physical quantity is denoted as F(Pi). The inverse distance weighted interpolation method is used to calculate the following: ; In the formula, p is the distance decay exponent, and p>0; N is the number of valid observation points participating in the interpolation calculation; Weight Defined as: ; Wherein, the spatial distance d(P, P) between the interpolation point and the observation point i ), defined as the three-dimensional Euclidean distance: ; In the formula, For the grid points to be interpolated, , and These are the three-dimensional coordinates of the grid points to be interpolated. For the first One vertical wind-measuring lidar observation point, , and The first Three-dimensional coordinates of a vertical wind-measuring lidar observation point; Based on this, the second horizontal wind speed and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid are orthogonally decomposed to obtain the corresponding u and v wind speed components: ; In the formula, u represents the east-west wind speed component, v represents the north-south wind speed component, V is the second horizontal wind speed, and Φ is the horizontal wind direction.

[0034] It can be understood that p is the distance decay index, which is used to control the influence weight of the observation point on the interpolation result.

[0035] In S04: It can be understood that the east-west wind speed component u, the north-south wind speed component v, and the vertical airflow component w form a complete low-altitude three-dimensional wind field dataset (u, v, w).

[0036] It is understandable that the low-altitude three-dimensional wind field dataset is obtained through inversion, including interpolation inversion and VAD inversion.

[0037] In S05: In some embodiments, based on a low-altitude three-dimensional wind field dataset, and using turbulent kinetic energy and turbulent intensity as composite turbulence indices, the turbulence risk of each route node and segment along the route is quantitatively assessed, identifying high-turbulence areas with potential flight risks or route nodes unsuitable for flight.

[0038] Furthermore, statistical processing is performed on the low-altitude three-dimensional wind field dataset within a sliding time window to calculate the turbulent kinetic energy (TKE) and turbulent intensity (TI) at each spatial location. Based on a preset threshold, a composite turbulence index (CTI) is constructed to quantitatively assess the turbulence risk at each route node and segment along the route.

[0039] Furthermore, ; This represents the fluctuation value of the east-west wind speed component. This represents the fluctuation value of the north-south wind speed component. This represents the vertical airflow component fluctuation value; ; In the formula, These are the average values ​​of the wind speed components u, v, and w within the sliding window time range, respectively. , , The first The instantaneous wind speed at any given moment includes the east-west wind speed component, the north-south wind speed component, and the vertical airflow component. ; In the formula, N is the number of valid samples within the sliding window time range. , , The first The instantaneous wind speed at any given moment includes the east-west wind speed component, the north-south wind speed component, and the vertical airflow component. , This represents the average horizontal wind speed within the current sliding window time range; ; CTI=0 represents a safe condition; CTI=1 represents moderate turbulence; CTI=2 represents severe turbulence; TKE th TI th All are first thresholds used to determine moderate turbulence; TI sev This is the second threshold used to determine severe turbulence.

[0040] In some embodiments, based on a low-altitude three-dimensional wind field dataset, and using turbulent kinetic energy and turbulent intensity as composite turbulence indices, the turbulence risk of each route node and segment along the route is quantitatively assessed. Combined with the aircraft's wind resistance capability, flight altitude, and operating conditions, the safety level of each route node is determined, thereby identifying high-turbulence areas with potential flight risks or route nodes unsuitable for flight.

[0041] In some embodiments, for high turbulence areas or route nodes that are identified as having potential flight risks or are unsuitable for flight, one or more candidate routes with low turbulence risks are generated based on the low-altitude three-dimensional wind field dataset obtained by inversion at the current time, and the optimal or second-best route is selected from them as the real-time flight path.

[0042] Furthermore, when a route node is identified as a severely turbulent node, a spatial search area with a horizontal search radius R is constructed, centered on that route node. h and vertical search radius R v Limited cylindrical 3D search area , ; In the formula, The coordinates of the route nodes, Coordinates of candidate route nodes; By determining the turbulent kinetic energy and turbulence intensity of the candidate route nodes located within the cylindrical three-dimensional search area, alternative route nodes that meet the turbulence safety threshold requirements are selected.

[0043] Furthermore, if multiple alternative route nodes that meet the safety conditions exist, the optimal alternative route node J is selected based on spatial distance and wind speed conditions. ; In the formula, d is the spatial distance between the candidate node and the original route node; V is the wind speed of the candidate node; α is the distance weighting coefficient; β is the wind speed weighting coefficient; The candidate node with the smallest J is selected as the alternative route node, and the route is smoothed.

[0044] It is understandable that when there are multiple alternative route nodes that meet the safety conditions, the alternative route node with the smallest distance from the original route node in three-dimensional space and the lowest wind speed is selected as the route optimization result.

[0045] Furthermore, when multiple adjacent route nodes are identified as severely turbulent nodes within a continuous observation period, it is determined that the current route has a continuous turbulence risk, and the route is regenerated based on the low-altitude three-dimensional wind field dataset obtained from the current inversion. Route smoothing is performed on the regenerated route or the dynamically generated alternative route. In subsequent observation periods, when the turbulence risk in the spatial region corresponding to the route is reduced to below the preset safety threshold, the route optimization state is automatically deactivated, the original route is restored, or the route adjustment level is reduced.

[0046] It is understandable that route smoothing is performed on regenerated routes or dynamically generated alternative routes to eliminate discontinuities or abrupt spatial transitions in the routes, ensuring the continuity of the routes in three-dimensional space and the feasibility of flight.

[0047] For example, to prevent the route from maintaining a high-level alarm or over-adjustment state for an extended period after the turbulence risk has disappeared, a turbulence alarm cancellation and route status recovery mechanism is further established to achieve dynamic closed-loop management of the route's operational status. Specifically, during continuous operation, the composite turbulence index (CTI) corresponding to each route node is monitored in a time series within a continuous observation period. The number of continuous observation periods is set to M. When a route node meets the following condition within M consecutive observation periods: ; That is, when the system determines that the turbulence risk in the space region corresponding to the route node has been significantly reduced or disappeared if the system determines that the turbulence risk ... Once the above conditions are met, an alarm cancellation operation will be automatically performed, including canceling the severe turbulence alarm flag for the route node or segment; or restoring the operating status of the route node from dynamic optimization status to normal monitoring status; or reducing or canceling the route adjustment priority for the node and its adjacent segments, one or more of the following:

[0048] When a route reconstruction is triggered due to the risk of continuous severe turbulence, the original route is not restored immediately after the alarm is cleared. Instead, the original route and the current route are comprehensively evaluated based on the low-altitude three-dimensional wind field obtained at the current moment. Under the premise of meeting the flight safety threshold and route smoothing constraints, the system can choose to gradually restore the original route or maintain the optimized route as the new reference route.

[0049] By introducing the aforementioned turbulence alarm cancellation and route status recovery mechanism, the route adjustment process has a complete closed-loop logic of "trigger-adjustment-cancellation-recovery," avoiding frequent route switching or long-term unnecessary high-risk control states caused by short-term turbulence fluctuations, thereby further improving the stability, continuity, and engineering practicality of low-altitude route operations.

[0050] Secondly, embodiments of this application provide a low-altitude flight path turbulence assessment and dynamic optimization system based on wind-measuring lidar, comprising: The raw observation dataset module is used to obtain the raw observation dataset, which includes first horizontal wind speed data, second horizontal wind speed data, horizontal wind direction and vertical airflow velocity; The interpolation module is used to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity to obtain the vertical airflow component, and at the same time construct a three-dimensional wind field data grid covering the target low-altitude airspace. The wind speed component module is used to orthogonally decompose the second horizontal wind speed data and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid to obtain the east-west wind speed component and the north-south wind speed component. The low-altitude three-dimensional wind field dataset module consists of east-west wind speed components, north-south wind speed components, and vertical airflow components, forming a complete low-altitude three-dimensional wind field dataset. The real-time flight path selection module, based on a low-altitude three-dimensional wind field dataset, identifies high-turbulence areas with potential flight risks or unsuitable route nodes, and selects the optimal or suboptimal route as the real-time flight path.

[0051] In the original observation dataset module In some embodiments, a scanning wind-measuring lidar is used to periodically scan the target area in PPI scanning mode to obtain radial wind speed observation data at different height levels and in different azimuth directions. The radial wind speed observation data is then used to obtain the first horizontal wind speed data at different height levels through a wind field inversion algorithm.

[0052] For example, wind field inversion algorithms such as VAD.

[0053] For example, the target area includes low-altitude airways and / or the airspace surrounding low-altitude airways.

[0054] Furthermore, the vertical wind-measuring lidar simultaneously conducts vertical detection to acquire wind profile data consistent with the PPI scanning time. The wind profile data includes the second horizontal wind speed, horizontal wind direction, and vertical airflow velocity at different height levels.

[0055] It is understandable that vertical wind-measuring lidar can simultaneously conduct vertical detection to acquire wind profile data that is consistent with the PPI scanning time, thereby forming a raw observation dataset that is consistent in time and complementary in space.

[0056] In the interpolation module In some embodiments, the inverse distance weighted interpolation method is used to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity to obtain the vertical airflow component.

[0057] It is understandable that by using the inverse distance weighted interpolation method to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity, the physical quantities of the wind field at different heights and spatial locations can be reconstructed. This ensures that the first horizontal wind speed data and the vertical airflow velocity are consistent in terms of spatial resolution and data dimension, thereby constructing a three-dimensional wind field data grid that covers the target low-altitude airspace and realizing the continuous expression of the low-altitude wind field in three-dimensional space.

[0058] In the wind speed component module In some embodiments, the method for obtaining the east-west wind speed component and the north-south wind speed component includes: For any grid point P(x, y, z) to be interpolated in the three-dimensional wind field data grid space, its wind field physical quantity is denoted as F(P). For the i-th vertical wind-measuring lidar observation point Pi(xi, yi, zi), its wind field physical quantity is denoted as F(Pi). The inverse distance weighted interpolation method is used to calculate the following: ; In the formula, p is the distance decay exponent, and p>0; N is the number of valid observation points participating in the interpolation calculation; Weight Defined as: ; Wherein, the spatial distance d(P, P) between the interpolation point and the observation point i ), defined as the three-dimensional Euclidean distance: ; In the formula, For the grid points to be interpolated, , and These are the three-dimensional coordinates of the grid points to be interpolated. For the first One vertical wind-measuring lidar observation point, , and The first Three-dimensional coordinates of a vertical wind-measuring lidar observation point; Based on this, the second horizontal wind speed and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid are orthogonally decomposed to obtain the corresponding u and v wind speed components: ; In the formula, u represents the east-west wind speed component, v represents the north-south wind speed component, V is the second horizontal wind speed, and Φ is the horizontal wind direction.

[0059] It can be understood that p is the distance decay index, which is used to control the influence weight of the observation point on the interpolation result.

[0060] In the low-altitude three-dimensional wind field dataset module It can be understood that the east-west wind speed component u, the north-south wind speed component v, and the vertical airflow component w form a complete low-altitude three-dimensional wind field dataset (u, v, w).

[0061] It is understandable that the low-altitude three-dimensional wind field dataset is obtained through inversion, including interpolation inversion and VAD inversion.

[0062] In the real-time flight path filtering module In some embodiments, based on a low-altitude three-dimensional wind field dataset, and using turbulent kinetic energy and turbulent intensity as composite turbulence indices, the turbulence risk of each route node and segment along the route is quantitatively assessed, identifying high-turbulence areas with potential flight risks or route nodes unsuitable for flight.

[0063] Furthermore, statistical processing is performed on the low-altitude three-dimensional wind field dataset within a sliding time window to calculate the turbulent kinetic energy (TKE) and turbulent intensity (TI) at each spatial location. Based on a preset threshold, a composite turbulence index (CTI) is constructed to quantitatively assess the turbulence risk at each route node and segment along the route.

[0064] Furthermore, ; This represents the fluctuation value of the east-west wind speed component. This represents the fluctuation value of the north-south wind speed component. This represents the vertical airflow component fluctuation value; ; In the formula, These are the average values ​​of the wind speed components u, v, and w within the sliding window time range, respectively. , , The first The instantaneous wind speed at any given moment includes the east-west wind speed component, the north-south wind speed component, and the vertical airflow component. ; In the formula, N is the number of valid samples within the sliding window time range. , , The first The instantaneous wind speed at any given moment includes the east-west wind speed component, the north-south wind speed component, and the vertical airflow component. , This represents the average horizontal wind speed within the current sliding window time range; ; CTI=0 represents a safe condition; CTI=1 represents moderate turbulence; CTI=2 represents severe turbulence; TKE th TI th All are first thresholds used to determine moderate turbulence; TI sev This is the second threshold used to determine severe turbulence.

[0065] In some embodiments, based on a low-altitude three-dimensional wind field dataset, and using turbulent kinetic energy and turbulent intensity as composite turbulence indices, the turbulence risk of each route node and segment along the route is quantitatively assessed. Combined with the aircraft's wind resistance capability, flight altitude, and operating conditions, the safety level of each route node is determined, thereby identifying high-turbulence areas with potential flight risks or route nodes unsuitable for flight.

[0066] In some embodiments, for high turbulence areas or route nodes that are identified as having potential flight risks or are unsuitable for flight, one or more candidate routes with low turbulence risks are generated based on the low-altitude three-dimensional wind field dataset obtained by inversion at the current time, and the optimal or second-best route is selected from them as the real-time flight path.

[0067] Furthermore, when a route node is identified as a severely turbulent node, a spatial search area with a horizontal search radius R is constructed, centered on that route node. h and vertical search radius R vLimited cylindrical 3D search area , ; In the formula, The coordinates of the route nodes, Coordinates of candidate route nodes; By determining the turbulent kinetic energy and turbulence intensity of the candidate route nodes located within the cylindrical three-dimensional search area, alternative route nodes that meet the turbulence safety threshold requirements are selected.

[0068] Furthermore, if multiple alternative route nodes that meet the safety conditions exist, the optimal alternative route node J is selected based on spatial distance and wind speed conditions. ; In the formula, d is the spatial distance between the candidate node and the original route node; V is the wind speed of the candidate node; α is the distance weighting coefficient; β is the wind speed weighting coefficient; The candidate node with the smallest J is selected as the alternative route node, and the route is smoothed.

[0069] It is understandable that when there are multiple alternative route nodes that meet the safety conditions, the alternative route node with the smallest distance from the original route node in three-dimensional space and the lowest wind speed is selected as the route optimization result.

[0070] Furthermore, when multiple adjacent route nodes are identified as severely turbulent nodes within a continuous observation period, it is determined that the current route has a continuous turbulence risk, and the route is regenerated based on the low-altitude three-dimensional wind field dataset obtained from the current inversion. Route smoothing is performed on the regenerated route or the dynamically generated alternative route. In subsequent observation periods, when the turbulence risk in the spatial region corresponding to the route is reduced to below the preset safety threshold, the route optimization state is automatically deactivated, the original route is restored, or the route adjustment level is reduced.

[0071] It is understandable that route smoothing is performed on regenerated routes or dynamically generated alternative routes to eliminate discontinuities or abrupt spatial transitions in the routes, ensuring the continuity of the routes in three-dimensional space and the feasibility of flight.

[0072] For example, to prevent the route from maintaining a high-level alarm or over-adjustment state for an extended period after the turbulence risk has disappeared, a turbulence alarm cancellation and route status recovery mechanism is further established to achieve dynamic closed-loop management of the route's operational status. Specifically, during continuous operation, the composite turbulence index (CTI) corresponding to each route node is monitored in a time series within a continuous observation period. The number of continuous observation periods is set to M. When a route node meets the following condition within M consecutive observation periods: ; That is, when the system determines that the turbulence risk in the space region corresponding to the route node has been significantly reduced or disappeared if the system determines that the turbulence risk ... Once the above conditions are met, an alarm cancellation operation will be automatically performed, including canceling the severe turbulence alarm flag for the route node or segment; or restoring the operating status of the route node from dynamic optimization status to normal monitoring status; or reducing or canceling the route adjustment priority for the node and its adjacent segments, one or more of the following:

[0073] When a route reconstruction is triggered due to the risk of continuous severe turbulence, the original route is not restored immediately after the alarm is cleared. Instead, the original route and the current route are comprehensively evaluated based on the low-altitude three-dimensional wind field obtained at the current moment. Under the premise of meeting the flight safety threshold and route smoothing constraints, the system can choose to gradually restore the original route or maintain the optimized route as the new reference route.

[0074] By introducing the aforementioned turbulence alarm cancellation and route status recovery mechanism, the route adjustment process has a complete closed-loop logic of "trigger-adjustment-cancellation-recovery," avoiding frequent route switching or long-term unnecessary high-risk control states caused by short-term turbulence fluctuations, thereby further improving the stability, continuity, and engineering practicality of low-altitude route operations.

[0075] Thirdly, this application provides a computer device including a storage device and a processor. The storage device stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the low-altitude flight path turbulence assessment and dynamic optimization method based on wind-measuring lidar as described above.

[0076] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0077] The memory includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D-interface display memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device. Of course, the memory may include both internal storage units and external storage devices of the computer device. In this embodiment, the memory is often used to store the operating system and various application software installed on the computer device, such as the program code of the low-altitude flight path turbulence assessment and dynamic optimization method based on wind-measuring lidar. In addition, the memory can also be used to temporarily store various types of data that have been output or will be output.

[0078] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is typically used to control the overall operation of the computer device. In this embodiment, the processor is used to run program code stored in the memory or process data, for example, to run the program code for the low-altitude flight path turbulence assessment and dynamic optimization method based on wind-measuring lidar.

[0079] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the above-described method for low-altitude airway turbulence assessment and dynamic optimization based on wind-measuring lidar.

[0080] The computer-readable storage medium stores an interface display program, which can be executed by at least one processor to enable the at least one processor to perform the steps of the low-altitude airway turbulence assessment and dynamic optimization method based on wind-measuring lidar as described above.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the low-altitude flight path turbulence assessment and dynamic optimization method based on wind-measuring lidar described in the embodiments of this application.

[0082] The invention will be further described below with reference to application examples.

[0083] Example This embodiment uses urban low-altitude eVTOL flight routes as an application scenario to illustrate the specific structure, operation process, and technical effects of the low-altitude route turbulence assessment and dynamic optimization system based on wind-measuring lidar described in this application.

[0084] In this embodiment, the system includes the following functional modules: Step 1: Obtain measured wind field data using a wind-measuring lidar system deployed around or in key areas of low-altitude air routes. The scanning-type wind-measuring lidar employs a PPI scanning mode, periodically scanning the target area under multiple preset elevation angles to obtain radial wind speed observation data at different altitudes and azimuths. Simultaneously, the vertical-type wind-measuring lidar conducts vertical detection, acquiring wind profile data consistent with the PPI scanning time. This wind profile data includes horizontal wind speed, wind direction, and vertical airflow velocity information at different altitudes, thus forming a temporally consistent and spatially complementary raw observation dataset.

[0085] Step Two: Based on the measured data from multiple wind-measuring lidars acquired within the same observation period, the horizontal wind field information obtained by the scanning lidar and the vertical airflow information obtained by the vertical lidar are processed in a unified manner. Inverse range weighted interpolation is used to spatially interpolate the discrete observation point data, achieving interpolation reconstruction of wind field physical quantities at different altitudes and spatial locations. This ensures that the horizontal wind speed data and vertical airflow data maintain consistency in spatial resolution and data dimensionality, thereby constructing a three-dimensional wind field data grid covering the target's low-altitude airspace and realizing a continuous representation of the low-altitude wind field in three-dimensional space.

[0086] Step 3: Based on the constructed low-altitude three-dimensional wind field data grid, orthogonal decomposition is performed on the horizontal wind speed and direction data at each altitude grid point to obtain the corresponding u and v wind speed components, where the u component represents the east-west wind speed component and the v component represents the north-south wind speed component. Subsequently, the u and v wind speed components are combined with the interpolated vertical airflow component w. Further, based on mature three-dimensional wind field data (u, v, w), and according to the established turbulence theory model, the turbulent kinetic energy and turbulence intensity in the target airspace are inverted and calculated to form a quantitative dataset characterizing low-altitude turbulence features, providing a basic input for subsequent route safety assessment and planning.

[0087] Step 4: Based on the inverted low-altitude three-dimensional wind field and its corresponding turbulence parameters, conduct spatial multi-scale turbulence structure analysis of the target low-altitude airway and its adjacent airspace. Using turbulent kinetic energy and turbulence intensity as Composite Turbulence Index (CTI), quantitatively assess the turbulence risk of each airway node and segment along the route. Combined with the aircraft's wind resistance capability, flight altitude, and operating conditions, determine the safety level of each airway node, thereby identifying high-turbulence areas with potential flight risks or airway nodes unsuitable for flight.

[0088] Step 5: For the high-turbulence-risk route nodes or segments identified in Step 4, a real-time dynamic route optimization mechanism is initiated based on the low-altitude 3D wind field dataset retrieved at the current moment. Under the premise of meeting flight safety constraints, the spatial location, altitude distribution, or segment connectivity of the original routes are adjusted to generate one or more candidate route schemes with low turbulence risk. The optimal or second-best route is then selected as the real-time flight path, achieving adaptive optimization and safety assurance of low-altitude routes in complex turbulent environments.

[0089] In its actual operation, the system follows the procedure below: First, deploy scanning wind-measuring lidar and vertical wind-measuring lidar in the low-altitude flight path area of ​​the target city, such as... Figure 2 As shown, the scanning wind-measuring lidar uses a method of alternating multi-elevation angle PPI scanning and vertical radial beam scanning to periodically observe the wind field of the covered airway and its surrounding airspace; the vertical wind-measuring lidar simultaneously acquires wind speed, wind direction and vertical airflow data at different altitudes to form time-consistent measured wind field observation data.

[0090] Subsequently, based on multi-source radar data within the same observation period, the system uses inverse range weighted interpolation to spatially reconstruct the discrete observation point data, generating a three-dimensional regular grid wind field covering the low-altitude airspace of the target. For any grid point P(x, y, z) to be interpolated in space, its wind field physical quantity is denoted as F(P). For the i-th wind-measuring lidar observation point Pi(xi, yi, zi), its wind field physical quantity is denoted as F(Pi). The inverse range weighted interpolation method is then used to calculate the following: ; In the formula, p is the distance decay exponent, used to control the influence weight of the observation points on the interpolation result, and p > 0; N is the number of valid observation points participating in the interpolation calculation. The weights are... Defined as: ; The spatial distance d(P, Pi) between the point to be interpolated and the observation point is defined as the three-dimensional Euclidean distance: ; Based on this, the horizontal wind speed at each grid point is orthogonally decomposed to obtain the corresponding u and v wind speed components: ; Where u represents the east-west wind speed component, v represents the north-south wind speed component, and combined with the vertical airflow component w, a complete low-altitude three-dimensional wind field dataset is constructed.

[0091] Furthermore, the system performs statistical processing on the three-dimensional wind field data within a sliding time window, calculates the turbulent kinetic energy and turbulence intensity at each spatial location, and constructs a composite turbulence index (CTI) based on a preset threshold to quantitatively assess the turbulence risk at each route node along the low-altitude airway.

[0092] ; in These represent the fluctuating wind speed components in each direction of the three-dimensional space, i.e., the deviation between the instantaneous wind speed and the average wind speed within the sliding window range:

[0093] in These are the average values ​​of the wind speed components u, v, and w within the sliding window time range, respectively. ; In the formula, N is the number of valid samples within the sliding window time range; This represents the instantaneous wind speed component.

[0094] ; ; Wherein, CTI=0 represents a safe state; CTI=1 represents moderate turbulence; CTI=2 represents severe turbulence; TKEth and TIth are both first thresholds used to determine moderate turbulence; and TIsev is the second threshold used to determine severe turbulence.

[0095] When the system determines that a certain route node is in a state of severe turbulence (CTI=2), a cylindrical three-dimensional search area is constructed with that route node as the spatial center, defined by the horizontal search radius (Rh) and the vertical search radius (Rv). Within this region, a candidate set of route nodes that meet the turbulence safety threshold requirements is selected, and the optimal alternative route node J is selected based on spatial distance and wind speed conditions. ; Where d is the spatial distance between the candidate node and the original route node; V is the wind speed at the corresponding location; α and β are weighting coefficients. The candidate node with the smallest J is selected as the replacement route node, and the route is smoothed.

[0096] If multiple adjacent route nodes are all determined to be in a state of severe turbulence within a continuous observation period, the system triggers a comprehensive route reconstruction mechanism. Based on the currently retrieved 3D wind field, the route is regenerated and then subjected to 3D smoothing to ensure route continuity and flight feasibility. Specific route turbulence assessment and dynamic optimization results are provided below. Figure 3 . Figure 3This diagram illustrates the actual operational results of the low-altitude flight path turbulence assessment and dynamic optimization described in this application. The three-dimensional spatial coordinate system clearly defines the horizontal geographical location and the altitude distribution from 60m to 150m. Multiple layers of semi-transparent isosurfaces are used to finely characterize the hazardous turbulence areas that meet the criteria for the Composite Turbulence Index (CTI). In this illustrated scenario, the system, based on a graded threshold joint judgment mechanism of TKE > 2.0 m² / s² and TI > 0.1, accurately identifies the original flight path (black dashed line) encountering severe turbulence risk when passing through a high-kinetic-energy turbulence zone. These affected flight path nodes are marked with red dots in the diagram, thus triggering the flight path dynamic optimization mechanism based on a cylindrical three-dimensional spatial search area. Data invalid points marked with gray "x" symbols represent spatial locations where effective wind field physical quantities cannot be extracted due to lidar detection being blocked or exceeding the effective range. The system automatically identifies and removes such discrete observation points during the inversion process to ensure that the assessment conclusions are based on real and reliable measured data. By automatically searching and filtering alternative waypoints (represented by green dots) that meet safety thresholds within a local airspace, the system generates a recommended safe flight path (represented by a thick green dashed line) that successfully avoids the core area of ​​strong turbulence. It then utilizes smoothing technology to create an adjusted path that smoothly transitions from the original trajectory to the safe path (represented by a thin yellow dashed line). The real-time time window (20251114 00:09:00 - 00:55:29) and statistical information such as "Effective Surface Count: 10" displayed above the figure, combined with the final decision of "Recommendation: Flight is permitted," fully validates that this system can achieve quantitative assessment and adaptive safety assurance of low-altitude flight path risks in complex atmospheric environments using minute-level updated wind lidar measurement data.

[0097] In this embodiment, to prevent the route from maintaining a high-level alarm or over-adjustment state for an extended period after the turbulence risk has disappeared, the system further incorporates a turbulence alarm cancellation and route status recovery mechanism to achieve dynamic closed-loop management of the route's operational status. Specifically, during continuous system operation, the Composite Turbulence Index (CTI) for each route node is monitored over a continuous observation period. The number of continuous observation periods is set to M. When a route node meets the following condition within M consecutive observation periods: ; When a route node is determined to be in a safe state throughout a continuous observation period, the system determines that the turbulence risk in the corresponding spatial area has been significantly reduced or eliminated.

[0098] Once the above conditions are met, the system will automatically perform alarm cancellation operations, including but not limited to: (1) canceling the severe turbulence alarm flag for the route node or route segment; (2) restoring the operating status of the route node from "dynamic optimization status" to "normal monitoring status"; and (3) reducing or canceling the route adjustment priority for the node and its adjacent routes.

[0099] When a route reconstruction is triggered due to continuous severe turbulence risks, the system does not immediately restore the original route after the alarm is cleared. Instead, it performs a comprehensive evaluation of the original route and the current route based on the low-altitude three-dimensional wind field retrieved at the current moment. Provided that flight safety thresholds and route smoothing constraints are met, the system can choose to gradually restore the original route or maintain the optimized route as the new baseline route.

[0100] By introducing the aforementioned turbulence alarm cancellation and route status recovery mechanism, the route adjustment process has a complete closed-loop logic of "trigger-adjustment-cancellation-recovery," avoiding frequent route switching or long-term unnecessary high-risk control states caused by short-term turbulence fluctuations, thereby further improving the stability, continuity, and engineering practicality of low-altitude route operations.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar, characterized in that, include: Obtain the raw observation dataset, which includes first horizontal wind speed data, second horizontal wind speed data, horizontal wind direction and vertical airflow velocity; Spatial interpolation is performed on the first horizontal wind speed data and the vertical airflow velocity to obtain the vertical airflow component, and at the same time a three-dimensional wind field data grid covering the target low-altitude airspace is constructed. Orthogonally decompose the second horizontal wind speed data and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid to obtain the east-west wind speed component and the north-south wind speed component. The east-west wind speed components, the north-south wind speed components, and the vertical airflow components form a complete three-dimensional low-altitude wind field dataset. Based on a low-altitude three-dimensional wind field dataset, high-turbulence areas with potential flight risks or unsuitable route nodes are identified, and the optimal or suboptimal routes are selected as real-time flight paths.

2. The method for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar according to claim 1, characterized in that, Using a scanning wind-measuring lidar in PPI scanning mode, the target area is periodically scanned to obtain radial wind speed observation data at different heights and azimuths. This radial wind speed data is then used in a wind field inversion algorithm to obtain the first horizontal wind speed data at different heights; and / or The inverse distance weighted interpolation method is used to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity to obtain the vertical airflow component; and / or Based on a low-altitude three-dimensional wind field dataset, and using turbulent kinetic energy and turbulent intensity as composite turbulence indicators, the turbulence risk of each route node and segment along the route is quantitatively assessed, identifying high-turbulence areas with potential flight risks or route nodes unsuitable for flight.

3. The method for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar according to claim 2, characterized in that, A vertical wind-measuring lidar simultaneously performs vertical detection, acquiring wind profile data consistent with the PPI scanning time. This wind profile data includes second-level horizontal wind speed, horizontal wind direction, and vertical airflow velocity at different altitude levels; and / or Within a sliding time window, the low-altitude three-dimensional wind field dataset is statistically processed to calculate the turbulent kinetic energy (TKE) and turbulent intensity (TI) at each spatial location. Based on a preset threshold, a composite turbulence index (CTI) is constructed to quantitatively assess the turbulence risk at each route node and segment along the route. and / or Based on a low-altitude three-dimensional wind field dataset, and using turbulent kinetic energy and turbulent intensity as composite turbulence indices, the turbulence risk of each route node and segment along the route is quantitatively assessed. Combined with the aircraft's wind resistance capability, flight altitude, and operating conditions, the safety level of each route node is determined, thereby identifying high-turbulence areas with potential flight risks or route nodes unsuitable for flight.

4. The method for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar according to claim 1, characterized in that, Methods for obtaining the east-west and north-south wind speed components include: For any grid point P(x, y, z) to be interpolated in the three-dimensional wind field data grid space, its wind field physical quantity is denoted as F(P). For the i-th vertical wind-measuring lidar observation point Pi(xi, yi, zi), its wind field physical quantity is denoted as F(Pi). The inverse distance weighted interpolation method is used to calculate the following: ； In the formula, p is the distance decay exponent, and p>0; N is the number of valid observation points participating in the interpolation calculation; Weight Defined as: ； Wherein, the spatial distance d(P, P) between the interpolation point and the observation point i ), defined as the three-dimensional Euclidean distance: ； In the formula, For the grid points to be interpolated, , and These are the three-dimensional coordinates of the grid points to be interpolated. For the first One vertical wind-measuring lidar observation point, , and The first Three-dimensional coordinates of a vertical wind-measuring lidar observation point; Based on this, the second horizontal wind speed and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid are orthogonally decomposed to obtain the corresponding u and v wind speed components: ； In the formula, u represents the east-west wind speed component, v represents the north-south wind speed component, V is the second horizontal wind speed, and Φ is the horizontal wind direction; and / or ； This represents the fluctuation value of the east-west wind speed component. This represents the fluctuation value of the north-south wind speed component. This represents the vertical airflow component fluctuation value; ； In the formula, These are the average values ​​of the wind speed components u, v, and w within the sliding window time range, respectively. , , The first The instantaneous wind speed at any given moment includes the east-west wind speed component, the north-south wind speed component, and the vertical airflow component. ； In the formula, N is the number of valid samples within the sliding window time range. , , The first The instantaneous wind speed at any given moment includes the east-west wind speed component, the north-south wind speed component, and the vertical airflow component. , This represents the average horizontal wind speed within the current sliding window time range; ； CTI=0 represents a safe condition; CTI=1 represents moderate turbulence; CTI=2 represents severe turbulence; TKE th TI th All are first thresholds used to determine moderate turbulence; TI sev This is the second threshold used to determine severe turbulence.

5. The method for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar according to claim 1, characterized in that, For high-turbulence areas or unsuitable flight path nodes identified as having potential flight risks, one or more candidate flight paths with low turbulence risk are generated based on the low-altitude three-dimensional wind field dataset obtained from the inversion at the current time, and the optimal or second-best flight path is selected from them as the real-time flight path.

6. The method for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar according to claim 5, characterized in that, When a route node is identified as a severely turbulent node, a spatial search area with a horizontal search radius R is constructed using that route node as the spatial center. h and vertical search radius R v Limited cylindrical 3D search area , ； In the formula, The coordinates of the route nodes, Coordinates of candidate route nodes; By determining the turbulent kinetic energy and turbulence intensity of the candidate route nodes located within the cylindrical three-dimensional search area, alternative route nodes that meet the turbulence safety threshold requirements are selected.

7. The method for low-altitude flight path turbulence assessment and dynamic optimization based on wind-measuring lidar according to claim 6, characterized in that, If multiple alternative route nodes that meet the safety conditions exist, the optimal alternative route node J is selected based on spatial distance and wind speed conditions. ； In the formula, d is the spatial distance between the candidate node and the original route node; V is the wind speed of the candidate node; α is the distance weighting coefficient; β is the wind speed weighting coefficient; The candidate node with the smallest J is selected as the alternative route node, and the route is smoothed. and / or When multiple adjacent route nodes are identified as severely turbulent nodes within a continuous observation period, it is determined that the current route has a continuous turbulence risk, and the route is regenerated based on the low-altitude three-dimensional wind field dataset obtained from the current inversion. For newly generated routes or dynamically generated alternative routes, route smoothing is performed. In subsequent observation periods, when the turbulence risk in the space region corresponding to the route decreases to below the preset safety threshold, the route optimization state is automatically deactivated, the original route is restored, or the route adjustment level is reduced.

8. A low-altitude flight path turbulence assessment and dynamic optimization system based on wind-measuring lidar, characterized in that, include: The raw observation dataset module is used to obtain the raw observation dataset, which includes first horizontal wind speed data, second horizontal wind speed data, horizontal wind direction and vertical airflow velocity; The interpolation module is used to spatially interpolate the first horizontal wind speed data and the vertical airflow velocity to obtain the vertical airflow component, and at the same time construct a three-dimensional wind field data grid covering the target low-altitude airspace. The wind speed component module is used to orthogonally decompose the second horizontal wind speed data and horizontal wind direction at each height layer grid point of the three-dimensional wind field data grid to obtain the east-west wind speed component and the north-south wind speed component. The low-altitude three-dimensional wind field dataset module consists of east-west wind speed components, north-south wind speed components, and vertical airflow components, forming a complete low-altitude three-dimensional wind field dataset. The real-time flight path selection module, based on a low-altitude three-dimensional wind field dataset, identifies high-turbulence areas with potential flight risks or unsuitable route nodes, and selects the optimal or second-best route as the real-time flight path.

9. A computer device, characterized in that, It includes a storage device and a processor, the storage device storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The device stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1-7.

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