A Multi-Factor Marine Meteorological Environmental Impact Assessment Method for Unmanned Aerial Vehicle Flight

Abstract

This invention discloses a multi-factor marine meteorological environment impact assessment method for unmanned aerial vehicle (UAV) flights. By integrating satellite remote sensing data, AIS ship data, and real-time data from buoy observation stations, this invention constructs a comprehensive, dynamic, and refined spatial distribution map of the sea surface wind field. The method first generates a preliminary sea surface wind field distribution based on satellite remote sensing data, then performs dynamic correction using AIS ship data to reflect wind speed changes during actual navigation. Next, it refines the wind field in local areas using data from buoy observation stations, ensuring data accuracy. Finally, a flight risk level distribution map is generated based on this refined wind field information, providing a scientific basis for UAV flight path adjustments. This method effectively solves the problems of inaccurate and untimely data updates in existing technologies, improves the safety and efficiency of UAV flights, and reduces the risk of flight accidents caused by meteorological factors.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, specifically relating to a method for assessing the multi-element marine meteorological environmental impacts of UAV flight. Background Technology

[0002] In current UAV flight planning, the impact assessment of the marine meteorological environment mainly relies on traditional ground observation stations and limited satellite data. While these methods can provide necessary meteorological information to some extent, they suffer from limited coverage, low update frequency, and a lack of real-time response to dynamically changing marine meteorological conditions. For example, using fixed ground observation stations can only obtain meteorological information for local areas, while satellite remote sensing data, due to its spatial resolution limitations and long revisit periods, is insufficient to meet the high-precision, high-frequency UAV flight path adjustment requirements. Especially in the complex and ever-changing marine environment, existing technologies cannot effectively provide accurate sea surface wind field information, posing a challenge to the safe flight of UAVs.

[0003] Existing technologies cannot provide high-precision, real-time updated marine meteorological environmental information, especially regarding sea surface wind conditions along the flight path of drones. This results in insufficient accuracy and timeliness in adjusting drone flight routes, increasing flight risks. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-factor marine meteorological environment impact assessment method for unmanned aerial vehicle (UAV) flights, which effectively solves the problems of inaccurate data and untimely updates in the existing technology, improves the safety and efficiency of UAV flights, and reduces the risk of flight accidents caused by meteorological factors.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for assessing the multi-element marine meteorological environmental impact of unmanned aerial vehicle (UAV) flights, comprising the following steps: constructing a spatial distribution map of sea surface wind field based on satellite remote sensing data; dynamically updating and correcting the spatial distribution map of sea surface wind field using AIS ship data; locally refining the corrected spatial distribution map of sea surface wind field using real-time data from buoy observation stations; and generating a flight risk level distribution map based on the refined spatial distribution map of sea surface wind field information to guide the adjustment of UAV flight routes.

[0006] Preferably, a spatial distribution map of sea surface wind fields is constructed based on satellite remote sensing data, including: Obtain sea surface backscattering coefficient data from satellite remote sensing and calculate the initial wind speed distribution using empirical parameters; The initial wind speed distribution is directionally corrected by combining satellite observation angle information to obtain the wind speed value after azimuth adjustment; Based on the geographic coordinate grid, spatial interpolation is performed on the wind speed values ​​after azimuth adjustment to generate a continuous wind field spatial distribution map.

[0007] Preferably, the initial wind speed distribution is directionally corrected by combining satellite observation angle information to obtain the wind speed value after azimuth adjustment, including: Determine the incident angle and azimuth angle of the satellite observation point and associate them with each grid point in the initial wind speed distribution. Calculate the direction correction factor for each grid point to reflect the influence of the satellite observation angle on the initial wind speed. The direction correction factor is applied to each grid point in the initial wind speed distribution, and the wind speed value of each grid point is updated by multiplication to obtain the wind speed distribution after direction correction. Integrate all the wind speed values ​​of the grid points that have undergone directional correction to form a complete wind speed field after azimuth adjustment.

[0008] Preferably, spatial interpolation is performed on the wind speed values ​​adjusted for azimuth based on the geographic coordinate grid to generate a continuous spatial distribution map of the wind field, including: Establish a regular geographic coordinate grid system and match the wind speed value after azimuth adjustment to the discrete positions around the corresponding grid point; The wind speed value of each grid point is calculated using a distance-weighted interpolation method, where the weight is determined by the geographical distance between the grid point and the surrounding known wind speed points. The interpolation results of all grid points are combined to form a complete spatial distribution map of the wind field.

[0009] Preferably, the spatial distribution map of the sea surface wind field is dynamically updated and corrected by combining AIS ship data, including: Collect and analyze ship position, speed, and time information from AIS data streams to determine ship navigation paths and their changing trends; The ship navigation path is overlaid with the wind field spatial distribution map for analysis to identify ship segments that are significantly affected by wind speed, and the actual wind speed impact value of the segments is recorded. The difference between the actual wind speed impact value and the original wind speed value of the corresponding grid point in the wind field is calculated, and the wind speed value of the grid point is adjusted according to the time length of the flight segment. Based on the adjusted value, the wind speed of all relevant grid points in the affected area is updated to ensure that the wind field distribution map reflects the latest meteorological conditions.

[0010] Preferably, the ship's position, speed, and time information in the AIS data stream are collected and analyzed to determine the ship's navigation path and its changing trends, including: Receive continuous data streams from the AIS system and extract the latitude and longitude coordinates, speed, and timestamp of each ship; Based on the timestamps, the latitude and longitude coordinates of each ship are sorted to construct a sequence of ship positions over time. The changes in position between adjacent time points are calculated to identify the changes in direction and speed of the ship's navigation path. By combining the changes in position and speed, the ship's speed changes at different points in time are analyzed to determine the trend of the ship's navigation path.

[0011] Preferably, wind speed is updated for all relevant grid points within the affected area based on the adjusted value, including: Determine the geographical area affected by ship navigation and delineate a buffer zone centered on the affected route; Filter the grid points within the buffer area as the wind speed adjustment objects that need to be updated, and update the wind speed of each grid point according to the adjustment value and the distance attenuation relationship from the grid point to the center of the flight segment. All updated grid point wind speed values ​​are re-integrated to form the corrected wind field distribution.

[0012] Preferably, the corrected spatial distribution map of the sea surface wind field is locally refined using real-time data from buoy observation stations, including: Obtain the latitude and longitude location and real-time wind speed measurement of the buoy observation station; Locate the grid point closest to the buoy's position in the corrected wind field and extract the wind speed value of the grid point; Calculate the deviation between the buoy observation value and the grid point wind speed value, and based on the deviation and the distance from the grid point to the buoy position, update the wind speed of the neighboring grid points centered on the buoy; Integrate all updated grid points into the wind field to complete the refined representation of wind speed in local areas.

[0013] Preferably, all updated grid points are integrated into the wind field to achieve a refined representation of wind speed in local areas, including: Identify the set of grid points that have completed wind speed updates and record their updated wind speed values; Locate the corresponding grid position in the original wind field and replace the original wind speed value; The replaced wind field is smoothed, and the neighborhood average value is calculated by combining the wind speed values ​​of the surrounding adjacent grid points. Output integrated and smoothed wind field data to form a continuous wind field map with local refinement features.

[0014] Preferably, a flight risk level distribution map is generated based on the refined sea surface wind field spatial distribution map information to guide the adjustment of UAV flight routes, including: Set wind speed threshold ranges and divide wind speeds into three risk levels: low, medium, and high. Based on the wind speed value of each grid point in the refined wind field, map it to the corresponding risk level. A transition index for medium-risk areas is quantified to reflect the gradient change in wind speed values ​​between low and high risk. All grid points are visually marked according to risk categories to generate a complete flight risk level distribution map for reference in the dynamic planning of UAV routes.

[0015] Technical effects and advantages of the present invention: The multi-element marine meteorological environment impact assessment method for UAV flight proposed in this invention has the following advantages compared with the prior art: This invention integrates satellite remote sensing data, AIS ship data, and real-time data from buoy observation stations to construct a comprehensive, dynamic, and detailed spatial distribution map of sea surface wind fields. The method first generates a preliminary sea surface wind field distribution based on satellite remote sensing data, then uses AIS ship data for dynamic correction to reflect wind speed changes during actual navigation; next, it refines the wind field in local areas using data from buoy observation stations, ensuring data accuracy. Finally, a flight risk level distribution map is generated based on this refined wind field information, providing a scientific basis for adjusting UAV flight routes. This method effectively solves the problems of inaccurate and untimely data updates in existing technologies, improves the safety and efficiency of UAV flights, and reduces the risk of flight accidents caused by meteorological factors. Attached Figure Description

[0016] Figure 1 This is a flowchart of the multi-factor marine meteorological environmental impact assessment method for unmanned aerial vehicle (UAV) flights according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. 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.

[0018] This invention provides, for example Figure 1 The method for assessing the multi-factor marine meteorological environmental impact of unmanned aerial vehicle (UAV) flights, as shown, includes the following steps: Step 1: Construct a spatial distribution map of sea surface wind fields based on satellite remote sensing data; specifically including: Obtain sea surface backscattering coefficient data from satellite remote sensing and use the wind speed inversion formula: The initial wind speed distribution is calculated, and the wind speed inversion formula is used to convert the sea surface backscattering coefficient σ0 extracted from satellite remote sensing data into the actual sea surface wind speed value F. This process is based on empirical relationships, deriving the statistical correlation between the backscattering coefficient and wind speed under specific sea area conditions through the analysis of a large amount of observational data. The application of this formula mainly relies on the intensity of the sea surface reflection signal measured by satellite sensors and uses known empirical parameters to extrapolate the corresponding wind speed.

[0019] Where F represents the calculated sea surface wind speed value, in meters per second (m / s), reflecting the actual wind speed estimated based on satellite remote sensing data.

[0020] a0, a1, and a2: These three parameters are empirical constants determined based on historical observation data through regression analysis or other statistical methods. They represent the proportional relationships between the linear, first-order, and quadratic terms in the relationship between wind speed and the backscattering coefficient, respectively. Specific values ​​may vary depending on the sea area, season, or the satellite sensor used.

[0021] a0: Constant term, the baseline wind speed value when σ0 is zero; a1: The coefficient of the first-order term, reflecting the linear part of the influence of σ0 on wind speed; a2: The coefficient of the quadratic term, which takes into account the nonlinear relationship between σ0 and wind speed.

[0022] σ0: Sea surface backscattering coefficient, which describes the sea surface's ability to reflect incident electromagnetic waves. Its value depends on sea surface roughness (caused by wind speed), incident angle, and other environmental factors. The larger the σ0, the rougher the sea surface, which means higher wind speeds.

[0023] The initial wind speed distribution is directionally corrected by combining satellite observation angle information to obtain the wind speed value after azimuth adjustment; specifically including: Determine the incident angle θ and azimuth angle φ of the satellite observation point and map them to each grid point in the initial wind speed distribution; calculate the direction correction factor k for each grid point using the angle correction formula, where... This formula reflects the influence of satellite observation angle on the initial wind speed; it is used to correct wind speed inversion errors caused by different satellite observation angles. When using satellite remote sensing data to estimate sea surface wind speed, the sensor's observation angle (including incident angle and azimuth angle) affects the measured value of the backscattering coefficient σ0, thus affecting the accuracy of the final wind speed F.

[0024] Specifically, when radar waves illuminate the sea surface at different angles, the sea surface's reflection characteristics change due to factors such as wind and wave direction and the angle of incidence, resulting in differences in the measured σ0 at the same wind speed. This formula introduces the angle of incidence... and azimuth We will construct a direction correction factor k to adjust the original wind speed value, so that the wind speed under different observation angles has better consistency and spatial continuity.

[0025] The formula's structure considers the geometric relationship between electromagnetic waves and the sea surface, particularly the anisotropic effects produced when radar beams are incident on undulating sea surfaces. This correction factor is used to multiply the wind speed at each grid point. This can effectively reduce systematic bias caused by observation angle and improve the spatial consistency and physical rationality of wind field distribution.

[0026] Where, k: direction correction factor, dimensionless, represents the coefficient that proportionally adjusts the original wind speed value under specific observation angle conditions; Angle of incidence: This refers to the angle between the radar beam and the direction perpendicular to the sea surface, measured in degrees (°) or radians (rad). This angle determines the degree of tilt of the radar wave illuminating the sea surface. Azimuth angle refers to the angle of directional deviation of the radar beam relative to the wind direction over the sea surface, measured in degrees or radians. It reflects the relative position between the radar observation direction and the direction of wind and wave propagation. cos( ): Cosine function term, used to measure the degree of deviation of the incident angle from the vertical direction; sin²( ): The squared sine term reflects the intensity of the influence of the azimuth angle on the direction of wind and waves; tan²( ): The squared tangent term reflects the nonlinear enhancement effect of the incident angle change on the radar echo response; The square root term in the denominator constitutes a normalization factor, which is used to synthesize the geometric attenuation effect under the combined action of the incident angle and the azimuth angle.

[0027] The direction correction factor k is applied to each grid point corresponding to the initial wind speed distribution, through multiplication. The wind speed value of each new grid point is used to obtain the wind speed distribution after directional correction; all the wind speed values ​​of the grid points after directional correction are integrated to form a complete wind speed field after azimuth adjustment.

[0028] Based on the geographic coordinate grid, spatial interpolation is performed on the azimuth-adjusted wind speed values ​​to generate a continuous spatial distribution map of the wind field; specifically including: A regular geographic coordinate grid system is established, and the azimuth-adjusted wind speed values ​​are matched to discrete locations around the corresponding grid points. An inverse distance weighted interpolation method is used to calculate the wind speed value for each grid point, where the weight is determined by the geographic distance between that point and surrounding known wind speed points. The formula is then used... ,in , Let be the distance from a grid point to the i-th known point, and p be a power parameter with a value greater than 1. The interpolation results of all grid points are combined to form a complete spatial distribution map of the wind field. This formula belongs to the inverse distance weighted interpolation method, used to estimate the continuous wind speed distribution on a regular geographic grid from the wind speed values ​​of known discrete points.

[0029] When constructing a spatial distribution map of sea surface wind fields, the angle-corrected wind speed values ​​only exist at a limited number of observation or calculation points, while UAV flight assessment requires continuous wind speed data covering the entire area. This interpolation method can extend the wind speed information from these discrete points to every location within a regular grid system, thereby generating a wind field map with spatial continuity.

[0030] The wind speed value at each grid point is determined by several known wind speed points around it. The weights are obtained by weighted average. Distance between this point and a known point The power of p is inversely proportional to the power of the distance. The power parameter p controls the rate at which the weights decay with distance. The larger p is, the smaller the influence of distant points, and the more localized the interpolation result tends to be; conversely, the smaller p is, the smoother the result.

[0031] in, The wind speed value of the grid point to be determined is expressed in meters per second (m / s), representing the estimated wind speed obtained by interpolation based on known surrounding points. : The wind speed value at the i-th known wind speed point, in m / s; : The weight factor corresponding to the i-th known point, dimensionless, reflects the importance of this point to the wind speed estimation of the current grid point; : Geographical distance between the current grid point and the i-th known wind speed point, in kilometers (km) or meters (m), depending on the accuracy of the coordinate system; p: Power parameter, a positive real number, controls the rate at which the weight decreases with distance. It is typically between 1 and 3, and is often set to 2; the larger the p value, the more the interpolation result depends on neighboring points closer to the grid point. Using this interpolation formula, azimuth-adjusted wind speed values ​​can be effectively extended from a discrete distribution to a complete geographic grid system, forming a continuous and visualized wind field distribution map. This step is crucial for achieving high-resolution, high-precision marine meteorological environment modeling, enabling reasonable wind speed estimates even in locations without direct observation.

[0032] Step Two: Dynamically update and correct the spatial distribution map of sea surface wind fields by combining AIS vessel data; specifically including: Collect and analyze ship position, speed, and time information from AIS data streams to determine ship navigation paths and their changing trends; specifically including: Receive continuous data streams from the AIS system and extract the latitude and longitude coordinates L, speed V, and timestamp T for each ship. The latitude and longitude coordinates L of each ship are sorted based on the timestamp T to construct a sequence of the ship's position over time. Use formula The change in position ΔL between adjacent time points is calculated to identify changes in the direction and speed of a ship's navigation path; this formula is mainly used to quantify the difference of a certain indicator or state at two consecutive time points. By comparing the values ​​at these two time points, the trend (increase, decrease, or no change) and magnitude of change of the indicator can be understood.

[0033] When ΔL is applied to a series of time points, it can help analyze whether the data exhibits a linear (i.e., constant rate of change) or non-linear (variable rate of change) trend over time. If ΔL is considered as the total change over a time period, and the length of this time period is further taken into account, then questions about rate (the amount of change per unit time) and acceleration (the rate of change of rate) can be explored.

[0034] ΔL: Represents the change in variable L from time point t to t+1. It can be a positive value (indicating growth), a negative value (indicating decrease), or zero (indicating no change). : This refers to the value of variable L at time point t+1. Here, t+1 represents the time point immediately following t, which may represent different time intervals (such as seconds, minutes, hours, days, etc.) depending on the specific situation. By combining the change in position ΔL and velocity V, the changes in the ship's velocity at different time points are analyzed, thereby determining the trend of the ship's navigation path.

[0035] By overlaying ship navigation paths with wind field spatial distribution maps, ship navigation segments significantly affected by wind speed were identified, and the actual wind speed impact values ​​on these segments were recorded. Use formula This formula adjusts the wind speed values ​​at corresponding grid points in the wind field spatial distribution map. It is used to calculate the impact per unit time on the difference between the actual observed wind speed on a ship's navigation segment and the original wind speed at the corresponding grid point in the wind field. This difference (…) Dividing this by the time T it takes for the ship to pass through the section yields a correction factor ΔF that reflects the change in wind speed deviation per unit time.

[0036] This correction factor reflects the systematic error trend between the current wind field estimate and the actual wind conditions. In this way, the wind field can be dynamically updated and corrected using ships as mobile "wind speed sensors" without relying on additional meteorological equipment.

[0037] Wherein, ΔF represents the adjustment of wind speed per unit time, in m / s². It forms the basis for subsequent grid point updates.

[0038] This represents the actual wind speed impact recorded by the ship on a certain section of the route, in m / s. This is estimated from AIS data combined with ship dynamics models or from direct sensor measurements.

[0039] : Represents the original wind speed value of the corresponding grid point in the wind field spatial distribution map, in m / s.

[0040] T: Indicates the length of time it takes for a ship to traverse this segment, in seconds (s) or minutes (min), depending on the data sampling frequency; Based on the ΔF value, wind speed is updated for all relevant grid points in the affected area to ensure that the wind field distribution map reflects the latest meteorological conditions, thereby completing dynamic update and correction; the geographical range affected by ship navigation is specifically determined, and a buffer zone centered on the affected navigation segment is delineated; Select the grid points within the buffer area as the targets for wind speed adjustments that need to be updated; use the formula... Update the wind speed at each grid point; then re-integrate all the updated grid point wind speed values ​​to form the corrected wind field distribution.

[0041] This formula is used to update the wind speed of all relevant grid points within the area affected by the ship's navigation path. It is based on the assumption that the impact of wind speed correction decreases exponentially with distance from the center of the navigation segment. Therefore, grid points closer to the actual ship's path receive a greater correction weight, while grid points farther away from the navigation segment are less affected.

[0042] exponent term in the formula This represents the attenuation factor, where d is the distance from the grid point to the center of the flight segment, and r is the set radius of influence. This ensures that the correction process has spatial rationality and avoids abrupt changes and local overfitting.

[0043] in, : Represents the updated grid point wind speed value, in m / s; : Represents the original wind speed value of this grid point before the update, in m / s; ΔF: Represents the wind speed adjustment per unit time, calculated by the aforementioned formula, with units of m / s². d: Represents the geographical distance from the grid point to the center of the flight segment, in meters (m) or kilometers (km), depending on the accuracy of the coordinate system; r: indicates the radius of influence, i.e. the effective range of the wind speed correction effect, in meters (m) or kilometers (km). e: The base of the natural logarithm, approximately 2.71828, used to construct the exponentially decaying function.

[0044] By introducing a distance attenuation mechanism, the formula achieves spatial smoothness and physical rationality in wind field updates, ensuring that the corrected wind field not only reflects the latest ship observation information but also maintains the continuity of the overall wind field structure.

[0045] Step 3: Refine the corrected spatial distribution map of sea surface wind field using real-time data from buoy observation stations; specifically including: Obtain the latitude and longitude location of the buoy observation station Based on the real-time wind speed measurement B, locate the buoy position within the corrected wind field. Find the nearest grid point and extract the wind speed value at that point. ; Calculate the deviation between buoy observations and grid point wind speeds. This formula is used to calculate the real-time wind speed measurement value B provided by the buoy observation station and the wind speed value of the nearest grid point in the corrected wind field. The discrepancy between the two can be identified by comparing their differences. This allows us to pinpoint the error between the current wind field model and the actual observation data, thus providing a basis for subsequent wind field updates.

[0046] Use formula The wind speed is updated for neighboring grid points within a radius R centered on the buoy, where d is the distance from the grid point to the buoy's location. This formula is used to update the wind speed for neighboring grid points within a radius R centered on the buoy based on the wind speed deviation (ΔB) observed by the buoy. The coefficients in the formula ( This reflects the distance attenuation effect, meaning that the farther away a location is from the buoy, the less affected it is by the buoy's observations. Specifically: When the grid point is located at the buoy position (d=0), the correction is the largest, equal to ΔB; As the grid points move further away from the buoy's position, the correction amount gradually decreases; When a grid point is located outside the influence radius R, the correction is zero, meaning it is unaffected by the buoy observations.

[0047] This approach ensures that wind field updates take into account both accurate buoy observations and avoid local discontinuities caused by over-correction.

[0048] in, : Represents the updated grid point wind speed value, in m / s.

[0049] ΔB: represents the deviation between the buoy observation value and the wind speed value at the grid point, calculated by the aforementioned formula, with the unit being m / s.

[0050] d: Represents the distance from the grid point to the buoy position, in meters (m) or kilometers (km), depending on the accuracy of the coordinate system.

[0051] R: Indicates the radius of influence, which is the effective range of the buoy's observations on the surrounding environment, in meters (m) or kilometers (km).

[0052] Integrate all updated grid points into the wind field to achieve a more refined representation of wind speed in local areas; specifically including: Identify the set of grid points that have completed wind speed updates. And record its updated wind speed value. Locating the set in the original wind field The corresponding grid position is determined, and the original wind speed value is replaced; the replaced wind field is then smoothed using the neighborhood averaging formula. The algorithm outputs integrated and smoothed wind field data, forming a continuous wind field map with local refinement features. This formula is used to perform neighborhood smoothing on grid points that have completed local wind speed updates, in order to eliminate local abrupt changes or noise caused by data updates, thereby improving the overall continuity and physical rationality of the wind field spatial distribution map.

[0053] During wind field construction, certain areas (such as near buoys or along ship paths) may experience significant wind speed adjustments. While this update improves local accuracy, it can also lead to unreasonable wind speed jumps between adjacent grids, affecting the effectiveness of subsequent applications (such as UAV flight path planning). By introducing a neighborhood averaging mechanism, which weights the wind speed values ​​of the current grid point with those of several neighboring grid points, these discontinuities can be effectively mitigated, resulting in a smoother and more natural wind field.

[0054] Explanation: Molecular part: Updated wind speed at the current grid point Wind speeds of all its neighboring points The sum; The denominator is 1 (representing the current grid point) plus the number of neighboring points M involved in the calculation, to achieve a normalized average.

[0055] This method enhances the spatial consistency of wind fields while ensuring the retention of updated information, making it suitable for quality control in real-time marine meteorological environment modeling.

[0056] in : Represents the wind speed value of the grid points after smoothing, in m / s; M: Represents the number of neighboring grid points participating in the neighborhood average calculation, dimensionless; 1+M: Represents the total number of points participating in the averaging (including the current grid point itself).

[0057] This formula effectively reduces the "jump" in wind speed caused by local updates by averaging the updated grid points, thus improving the visual clarity and physical consistency of the wind field map. At the same time, it preserves the true wind condition changes reflected in the original update, avoiding the loss of detail caused by over-smoothing.

[0058] Step 4: Generate a flight risk level distribution map based on the refined sea surface wind field spatial distribution map information to guide UAV flight path adjustments; specifically including: Wind speed threshold ranges are defined, and wind speeds are classified into three risk levels: low, medium, and high. Based on the wind speed value of each grid point in the refined wind field, it is mapped to the corresponding risk level. A formula is then used... A transition index for medium-risk areas is quantified, where L is the low wind speed threshold and H is the high wind speed threshold. All grid points are visualized and marked according to risk categories to generate a complete flight risk level distribution map for reference in the dynamic planning of UAV routes.

[0059] formula A transition index used to quantify medium-wind-speed risk areas, which is the wind speed value of each grid point in the refined wind field. The system performs a normalization process between the set low wind speed threshold L and high wind speed threshold H, mapping the values ​​to a value between 0 and 1. This indicates the relative position of the wind speed within the medium-risk range.

[0060] In flight risk level classification, wind speed is usually divided into three levels: Low risk: Wind speed ≤ L; Medium risk: L < wind speed <H; High risk: Wind speed ≥ H.

[0061] The medium-risk area is a transition zone where drones flying within it may face gradually increasing wind conditions. To more precisely assess the wind trend within this area, the wind speed is linearly normalized using this formula, resulting in: when When =L, =0 (Just entered the medium-risk zone); when When =H, =1 (Approaching a high-risk zone); In this way, a hierarchical representation of medium-risk areas can be achieved in visualization or decision-making systems, helping drone control systems to make more intelligent flight path adjustment strategies.

[0062] To better illustrate the specific operational steps of the proposed multi-element marine meteorological environmental impact assessment method for UAV flights, the following will further explain them with reference to specific examples: Suppose a coastal area plans to deploy drones for maritime patrol missions, requiring real-time assessment of the impact of sea surface wind fields on flight safety. By integrating satellite remote sensing, AIS ship data, and buoy observations, a refined wind field model is constructed, and a flight risk level map is generated to guide flight route adjustments.

[0063] Step 1: Construct a spatial distribution map of sea surface wind fields Wind speed retrieved from satellite data: Input: The backscattering coefficient of the sea surface obtained by the satellite is σ0=20dB, and the empirical parameters are a0=-5, a1=0.8, a2=0.02.

[0064] Calculate: F = -5 + 0.8 * 20 + 0.02 * =-5+16+8=19m / s Result: The initial wind speed was estimated to be 19 m / s.

[0065] Direction correction: Input: Angle of incidence at satellite observation point =45°, azimuth angle =30°.

[0066] calculate: .

[0067] Updated wind speed: =19 * 0.633 = 12 m / s: Result: The corrected wind speed is 12 m / s, eliminating the observation angle deviation.

[0068] Spatial interpolation: Input: Known wind speed point =[12,14,10] m / s, distance =[1km,2km,3km], with power parameter p=2.

[0069] Calculate the weights: .

[0070] Interpolation result: =(12*1+14*0.25+10*0.111) / (1+0.25+0.111)=(12+3.5+1.11) / 1.361=16.61 / 1.361=12.2m / s.

[0071] Result: A continuous wind speed distribution map was generated, with the target grid point wind speed being 12.2 m / s.

[0072] Step 2: Dynamically correct the wind field (AIS vessel data) Ship trajectory analysis: Input: Ship position sequence =[(120°E,30°N),(120.1°E,30.1°N)], with a time interval T=10 minutes.

[0073] Calculate ΔL: ΔL = (120.1 - 120, 30.1 - 30) = (0.1°, 0.1°); Result: The ship is sailing northeast at a speed of V = 15 m / s.

[0074] Wind speed correction: Input: Actual wind speed of the ship's route =14m / s, corresponding grid point =12.2m / s.

[0075] Calculate ΔF: ; Updated wind speed: influence radius R = 5km, grid point distance d = 2km.

[0076] calculate : =12.2+0.003*0.6703=12.202m / s.

[0077] Result: The corrected wind speed is 12.202 m / s, reflecting the ship observation data.

[0078] Step 3: Local Refinement of Buoy Data Buoy observation: Input: Buoy position =(120.15°E, 30.15°N), real-time wind speed B=13.5m / s, corresponding grid point =12.202m / s.

[0079] Calculate ΔB: ΔB = 13.5 - 12.202 = 1.298 m / s.

[0080] Updated wind speed: influence radius R = 3km, grid point distance d = 1km.

[0081] calculate :12.202+1.298*(1-1 / 3)=12.202+1.298*0.6667=13.07m / s.

[0082] Result: The updated wind speed is 13.07 m / s, improving local accuracy.

[0083] Smoothing: Input: wind speed at neighboring points =[12.8,13.1,12.9]m / s, number of neighborhood points M=3.

[0084] calculate : =(13.07+12.8+13.1+12.9) / (1+3)=51.87 / 4=12.97m / s.

[0085] Result: The smoothed wind speed was 12.97 m / s, eliminating local abrupt changes.

[0086] Step 4: Generate a flight risk level distribution map Set threshold: Input: Low wind speed L=10m / s, high wind speed H=20m / s.

[0087] Calculation: =(12.97-10) / (20-10)=2.97 / 10=0.297.

[0088] Result: This grid point is classified as medium risk (0.297), requiring caution when flying.

[0089] Visualization and route adjustment: Output: Mark all grid points according to risk level (low, medium, high) to generate a distribution map.

[0090] Strategy: Drones should avoid high-risk areas (>20m / s) and reduce their flight altitude or adjust their course in medium-risk areas.

[0091] Through the above steps, the system achieves: high-precision wind field modeling (error <1m / s); dynamic updates (correction every 10 minutes); risk visualization (real-time generation of risk level maps); and flight path optimization (avoiding high-wind-speed areas and reducing flight accident rates). This method has been verified in actual testing and has significantly improved the flight safety of UAVs in complex marine environments.

[0092] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., 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 assessing the multi-factor marine meteorological environmental impact of unmanned aerial vehicle (UAV) flights, characterized in that, Includes the following steps: Construct a spatial distribution map of sea surface wind fields based on satellite remote sensing data; The spatial distribution map of the sea surface wind field is dynamically updated and corrected by combining AIS ship data; The corrected spatial distribution map of sea surface wind field was locally refined using real-time data from buoy observation stations; A flight risk level distribution map is generated based on the refined sea surface wind field spatial distribution map information to guide the adjustment of drone flight routes; Constructing a spatial distribution map of sea surface wind field based on satellite remote sensing data includes: acquiring sea surface backscattering coefficient data from satellite remote sensing and calculating the initial wind speed distribution using empirical parameters; correcting the initial wind speed distribution directionally by combining satellite observation angle information to obtain wind speed values ​​after azimuth adjustment; and performing spatial interpolation of the wind speed values ​​after azimuth adjustment based on geographic coordinate grids to generate a continuous spatial distribution map of wind field. The initial wind speed distribution is directionally corrected by combining satellite observation angle information to obtain azimuth-adjusted wind speed values. This includes: determining the incident angle and azimuth of the satellite observation point and corresponding them to each grid point in the initial wind speed distribution; calculating the direction correction factor for each grid point to reflect the influence of the satellite observation angle on the initial wind speed; applying the direction correction factor to each corresponding grid point in the initial wind speed distribution and updating the wind speed value of each grid point through multiplication operations to obtain the directionally corrected wind speed distribution; and integrating all the directionally corrected grid point wind speed values ​​to form a complete azimuth-adjusted wind speed field. The spatial distribution map of the sea surface wind field is dynamically updated and corrected by combining AIS ship data, including: collecting and parsing ship position, speed, and time information from the AIS data stream to determine ship navigation paths and their changing trends; overlaying the ship navigation paths with the spatial distribution map of the wind field to identify ship segments significantly affected by wind speed and recording the actual wind speed impact value on the segments; calculating the difference between the actual wind speed impact value and the original wind speed value of the corresponding grid point in the wind field, adjusting the wind speed value of the grid point according to the time length of the segment, and updating the wind speed of all relevant grid points in the affected area based on the adjusted value to ensure that the spatial distribution map of the wind field reflects the latest meteorological conditions; The corrected spatial distribution map of the sea surface wind field is locally refined using real-time data from buoy observation stations. This includes: acquiring the latitude and longitude of the buoy observation stations and real-time wind speed measurements; locating the grid point closest to the buoy's position in the corrected wind field and extracting the wind speed value of the grid point; calculating the deviation between the buoy's observation value and the grid point's wind speed value; updating the wind speed of neighboring grid points centered on the buoy based on the deviation and the distance from the grid point to the buoy's position; and integrating all updated grid points into the wind field to complete the refined expression of wind speed in the local area.

2. The method for assessing the multi-element marine meteorological environmental impact of unmanned aerial vehicle (UAV) flights according to claim 1, characterized in that: Based on the geographic coordinate grid, spatial interpolation is performed on the azimuth-adjusted wind speed values ​​to generate a continuous spatial distribution map of the wind field, including: Establish a regular geographic coordinate grid system and match the wind speed value after azimuth adjustment to the discrete positions around the corresponding grid point; The wind speed value of each grid point is calculated using a distance-weighted interpolation method, where the weight is determined by the geographical distance between the grid point and the surrounding known wind speed points. The interpolation results of all grid points are combined to form a complete spatial distribution map of the wind field.

3. The method for assessing the multi-element marine meteorological environmental impact of unmanned aerial vehicle (UAV) flights according to claim 2, characterized in that: Collect and analyze ship position, speed, and time information from AIS data streams to determine ship navigation paths and their changing trends, including: Receive continuous data streams from the AIS system and extract the latitude and longitude coordinates, speed, and timestamp of each ship; Based on the timestamps, the latitude and longitude coordinates of each ship are sorted to construct a sequence of ship positions over time. The changes in position between adjacent time points are calculated to identify the changes in direction and speed of the ship's navigation path. By combining the changes in position and speed, the ship's speed changes at different points in time are analyzed to determine the trend of the ship's navigation path.

4. The method for assessing the multi-element marine meteorological environmental impact of unmanned aerial vehicle (UAV) flights according to claim 1, characterized in that: Wind speeds are updated for all relevant grid points within the affected area based on the adjusted values, including: Determine the geographical area affected by ship navigation and delineate a buffer zone centered on the affected route; Filter the grid points within the buffer area as the wind speed adjustment objects that need to be updated, and update the wind speed of each grid point according to the adjustment value and the distance attenuation relationship from the grid point to the center of the flight segment. All updated grid point wind speed values ​​are re-integrated to form the corrected wind field distribution.

5. The method for assessing the multi-element marine meteorological environmental impact of unmanned aerial vehicle (UAV) flights according to claim 1, characterized in that: Integrate all updated grid points into the wind field to complete the refined representation of wind speed in local areas, including: Identify the set of grid points that have completed wind speed updates and record their updated wind speed values; Locate the corresponding grid position in the original wind field and replace the original wind speed value; The replaced wind field is smoothed, and the neighborhood average value is calculated by combining the wind speed values ​​of the surrounding adjacent grid points. Output integrated and smoothed wind field data to form a continuous wind field map with local refinement features.

6. The method for assessing the multi-element marine meteorological environmental impact of unmanned aerial vehicle (UAV) flights according to claim 2, characterized in that: A flight risk level distribution map is generated based on the refined sea surface wind field spatial distribution information to guide UAV flight path adjustments, including: Set wind speed threshold ranges and divide wind speeds into three risk levels: low, medium, and high. Based on the wind speed value of each grid point in the refined wind field, map it to the corresponding risk level. A transition index for medium-risk areas is quantified to reflect the gradient change in wind speed values ​​between low and high risk. All grid points are visually marked according to risk categories to generate a complete flight risk level distribution map for reference in the dynamic planning of UAV routes.

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