A wind field feature analysis system and method for aviation operation decision

Abstract

This invention relates to the field of wind field characteristic analysis and discloses a wind field characteristic analysis system and method for aviation operation decision-making. The system includes asynchronous interception of raw wind field fragments; decomposing transient wind direction changes, measurement micro-seismic characteristics, and time-stamp differences from different sampling sources into several multi-source wind field micro-segments; cross-source correlation tracking of potential overlap areas, measurement voids, and response delays among the multi-source wind field micro-segments; continuous splicing of local transitional pulsations, crosswind rift morphology, and wind speed gradient changes in the wind field micro-segments to extract fine-grained wind field evolution lines revealing the nascent potential of disturbances; joint segmented mapping of these fine-grained wind field evolution lines with the spatial geometry of a specific approach corridor, the temperature stratification structure of the terminal area, and near-surface drainage patterns; and generating wind field risk evolution indicators for aviation operation decision-making through local wind field disturbance profiles. This invention has the advantage of accurately identifying small-scale wind field changes.

Description

Technical Field

[0001] This invention relates to the field of wind field characteristic analysis, specifically to a wind field characteristic analysis system and method for aviation operation decision-making. Background Technology

[0002] In the operation of airports with complex terrain, flight area management departments need to make rapid assessments of local disturbances in the terminal approach phase based on multi-source wind field observation data. However, in typical plateau basin airports, transient crosswind deflection zones, occurring only on a scale of tens of meters, frequently appear in the descending channel approximately 500 to 900 meters ahead of runway 02. The occurrence of these deflection zones is influenced by surface temperature differences, canyon drainage, and apron thermal recirculation; they are short-lived and unpredictable in location, and are a major cause of track deviation and autothrottle response delays when aircraft enter the terminal glide path. While existing monitoring systems are equipped with lidar, anemometer towers, ADS-B ground stations, and low-altitude meteorological sensors, the data output by these devices exhibit significant differences in spatial granularity, time intervals, reference coordinates, and sampling sequence. Furthermore, wind information from the upper-altitude phase, terminal zone, and runway end is stored in multiple formats with inconsistent timestamps and a lack of associated indexes. Due to a lack of capability to uniformly organize, register across sources, and stitch together time series of these heterogeneous data, operations departments are unable to promptly identify the generation and movement trends of the aforementioned small-scale crosswind deflection bands from large-scale wind field records. This data processing deficiency directly leads to the following specific problems: even if a disturbance has already appeared in a monitoring source, its characteristics cannot be quickly extracted and reconstructed in the system, and the flight control center cannot receive sufficient advance wind field warnings, thus affecting the calculation of landing clearance windows, the assessment of go-around probability, and the selection of approach strategies by the crew. Therefore, it is essential to design a wind field characteristic analysis system and method that accurately identifies small-scale wind field changes for aviation operational decision-making. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a wind field characteristic analysis system and method for aviation operation decision-making, which has the advantage of accurately identifying small-scale wind field changes and solves the problems mentioned in the background technology.

[0004] To achieve the aforementioned objective of accurately identifying small-scale wind field changes, this invention provides the following technical solution: a wind field characteristic analysis method for aviation operation decision-making, comprising the following steps: Asynchronous interception of the original wind field fragments separates transient wind direction changes, measurement micro-seismic characteristics and time stamp differences from different sampling sources into several multi-source wind field micro-fragments. Cross-source correlation tracking is performed on potential overlapping areas, measurement voids and response delays among micro-segments of multi-source wind fields. Historical disturbance propagation paths, trajectory bending distribution and triggering conditions of sensing blind spots are introduced to obtain a multi-scale fusion index set characterizing local wind field instability. Based on the multi-scale fusion index set, the local transition pulsation, crosswind rift morphology and wind speed gradient changes in the wind field micro-segments are continuously spliced ​​together. After verifying the consistency of sudden turning points, measurement error signals and time misalignment keys in the spliced ​​sequence, fine-grained wind field evolution lines revealing the nascent potential of disturbance are extracted. The fine-grained wind field evolution lines are jointly segmented and mapped with the spatial geometry of a specific approach corridor, the temperature stratification structure of the terminal area, and the near-surface diversion pattern. During the mapping process, local compression segments, energy scattering segments, and short-period enhancement segments in the wind field lines are identified, and a local wind field disturbance profile with disturbance step attributes is constructed. By extracting disturbance nodes within a specific operating window through local wind field disturbance profiles and combining them with the refresh rate under real-time data stream updates, wind field risk evolution prompts are generated for aviation operation decision-making.

[0005] Preferably, the process of separating transient wind direction changes, measurement microseismic characteristics, and time stamp differences from different sampling sources into several multi-source wind field micro-segments is as follows: Based on the differences in sampling frequencies of weather radar, laser anemometer, and airborne wind field detection unit, the timestamps of the original wind field fragments are first calibrated across devices. An asynchronous interception strategy was used to initially screen continuous wind field records, and the wind direction change slope, fine seismic energy density and sampling interval offset of each sampling source were segmented by multiple indicators. Based on segmentation and combined with spatial neighborhood constraints, wind field segments with similar flow direction disturbance characteristics and close temporal markers are aggregated into independently managed multi-source wind field micro-segments.

[0006] Preferably, the process of cross-source correlation tracking of potential overlap areas, measurement voids, and response delays among multi-source wind field micro-segments is as follows: Based on the positional encoding and temporal sequence of wind field micro-fragments in three-dimensional space, a neighborhood matching matrix for cross-source fragments is constructed. By combining the reverse jump in wind speed at the segment boundary, the length of data holes, and the magnitude of response hysteresis, the correlation strength between segments is calculated, and overlapping areas and missing measurement areas in multi-source segments are identified. Dynamically track the delay and response links between segments to form a cross-source disturbance propagation sketch.

[0007] Preferably, the process of obtaining a multi-scale fusion index set characterizing local wind field instability is as follows: By structurally matching the cross-source disturbance propagation sketch with the disturbance propagation path in the historical wind field disturbance database, disturbance extension patterns in the same flow direction segment are identified. By analyzing the bend distribution density, abrupt changes in bend angle, and the corresponding sensing blind zone coverage in the wind field trajectory, the triggering area of ​​abnormal disturbances in the local flow field can be determined. By integrating bending features, blind spot information, and the time weight of propagation paths, a multi-scale fusion index set with scale-level attributes is formed.

[0008] Preferably, the process of continuously splicing together local transition pulsations, crosswind rift morphology, and wind speed gradient changes in micro-segments of the wind field is as follows: The local disturbance key points annotated by the multi-scale fusion index are traced back to the micro-fragment sequence to extract the local transition pulsation peak, crosswind rift morphology and wind speed gradient change direction. Based on feature similarity and local flow continuity, adjacent micro-segments are sequentially spliced ​​in a multi-dimensional feature space to construct a continuous perturbation splicing chain; Potential structural breakpoints are identified in the splicing chain, and the fragment boundaries near the breakpoints are recalibrated to generate a splicing sequence for consistency verification.

[0009] Preferably, the process of extracting fine-grained wind field evolution lines that reveal the potential for disturbance in the nascent ecosystem is as follows: Consistency checks are performed on sudden turning points in the spliced ​​sequence. By calculating the consistency index between the angle change rate of adjacent segments and the flow direction change in micro segments of multi-source wind fields, pseudo-turning points that do not match the characteristics of local disturbances are screened out. The measurement error signal is synchronously compared with the equipment performance indicators, noise frequency and sampling interval information to identify segments with abnormal amplitude or inconsistent with adjacent time periods, and these segments are marked as error-driven nodes. For time-series misaligned keys in the sequence, the continuity of cross-source segment timestamps and spatial locations is compared to determine whether there are abnormal nodes that deviate from the normal perturbation rhythm. After completing the consistency verification of sudden turning points, measurement error signals, and timing misalignment keys, the reliable segments that have been verified and eliminated are subjected to curvature fitting, and fine-grained wind field evolution lines reflecting the trend of disturbance emergence are extracted.

[0010] Preferably, the process of jointly segmenting and mapping fine-grained wind field evolution lines with the spatial geometry of a specific approach corridor, the temperature stratification structure of the terminal region, and the near-surface diversion pattern is as follows: Based on the spatial geometry of the approach corridor, the track envelope, and the typical glide path of the aircraft, fine-grained wind field evolution lines are projected onto the corresponding spatial profile. Based on the temperature stratification structure of the terminal area, the inversion height, and the influence of surface friction, the wind field lines are segmented and corrected under meteorological constraints. Based on the flow deflection pattern of the near-surface diversion mode, the segmented wind field lines are mapped a second time to form a segmented wind field structure set with regional characteristics.

[0011] Preferably, the process of constructing a local wind field disturbance profile with disturbance step properties is as follows: Based on the velocity contraction ratio, wind shear intensity, and local turbulent kinetic energy changes of the segmented wind field structure, the location and extent of the compression segment, scattering segment, and enhancement segment are identified. By combining the hierarchical relationship of the perturbation structure, the various segments are sorted according to the direction of energy change to form a local perturbation hierarchy chain with gradient information; Based on the local disturbance hierarchy chain, the profile is reconstructed to generate a local wind field disturbance profile that characterizes the step properties of wind field disturbance.

[0012] Preferably, the process of generating wind field risk evolution indicators for aviation operation decision-making is as follows: By extracting sensitive segments from the local wind field disturbance profile, we can identify the disturbance nodes that require special attention within a specific runtime window. By accessing real-time observation data streams, the energy changes, spatial expansion speed, and wind deflection rate of disturbed nodes can be continuously monitored. By combining the node change trend with the refresh rate under the real-time data stream update, wind farm risk evolution prompts are generated.

[0013] A wind field characteristic analysis system for aviation operation decision-making includes: Segment Compilation Module: Divides transient wind direction changes and minor seismic measurements from different sampling sources into multi-source wind field micro-segments for unified management; Cross-source aggregation module: It analyzes the overlapping and discontinuity relationships between micro-segments of multi-source wind fields and injects historical disturbance clues to form a fusion index set that reflects local instability; Sequence splicing module: Based on the fusion index set, local pulses and crosswind structures are continuously linked together, and evolution lines with disturbance initiation direction are extracted through key time point verification; Profile construction module: Matches and segments the evolution lines with the terminal area space and thermodynamic conditions to construct a local wind field interference profile; Risk alert module: It extracts key disturbance nodes within the runtime window using local wind field disturbance profiles and outputs wind field risk evolution alerts for decision-making in combination with real-time update rhythm.

[0014] Compared with existing technologies, this invention provides a wind field characteristic analysis system and method for aviation operation decision-making, which has the following beneficial effects: This invention achieves high-precision characterization of the initiation, evolution, and spatial distribution of local wind field disturbances through micro-fragmentation of multi-source wind field data, cross-source correlation tracking, multi-scale fusion index generation, and fine-grained evolution line extraction. It can effectively reveal key risk factors in aviation operations such as short-term small-scale wind speed abrupt changes, crosswind breakage, and local turbulent kinetic energy enhancement. By jointly mapping the evolution lines with approach corridor spatial geometry, terminal area temperature stratification, and near-surface drainage patterns, and constructing a local disturbance profile with disturbance step attributes, it can quickly identify high-risk disturbance nodes within a specific operating window. Combined with real-time observation data stream dynamic updates, it can realize real-time generation of wind field risk evolution prompts. This not only improves the perception accuracy and response speed of sudden local wind field changes in aviation operation decision-making, but also provides reliable and quantifiable decision support for flight scheduling, approach path optimization, and flight safety early warning through deep fusion and dynamic monitoring of multi-scale and multi-source data, significantly enhancing the safety, efficiency, and risk management capabilities of aviation operations. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method of the present invention; Figure 2 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Please refer to Figure 1 As shown in the figure, a wind field characteristic analysis method for aviation operation decision-making in an embodiment of the present invention includes the following steps: S1: Asynchronously intercept the original wind field fragments and separate the transient wind direction changes, measurement micro-seismic characteristics and time stamp differences in different sampling sources into several multi-source wind field micro-segments.

[0018] The process in S1 of separating transient wind direction changes, measurement microseismic characteristics, and time stamp differences from different sampling sources into several multi-source wind field micro-segments is as follows: Based on the differences in sampling frequencies of weather radar, laser anemometer, and airborne wind field detection unit, the timestamps of the original wind field fragments are first calibrated across devices. The timestamps of the original wind field fragments are then calibrated across devices, and the recording time of each sampling source is standardized. The differences in the acquisition intervals of different devices are transformed into a unified reference time benchmark. The data from low-frequency sampling devices are synchronized and matched with the data from high-frequency sampling devices to ensure that the wind field state corresponding to the same moment can be accurately compared and analyzed. An asynchronous interception strategy is used to initially screen continuous wind field records. The wind direction change slope, micro-vibration energy density, and sampling interval offset of each sampling source are segmented by multiple indicators. The raw data of each sampling source is scanned by a sliding window to calculate the instantaneous slope of wind direction change over time and the temporal change value of micro-vibration energy. Abnormal change points are identified according to preset thresholds, including sections with rapid wind direction deflection or sudden energy increase. These abnormal points are used as segmentation boundaries. Combined with the non-uniformity of the sampling interval, the continuous records are appropriately resampled or segmented to ensure that each micro-segment has relatively balanced characteristics in time and space. Based on segmentation and combined with spatial neighborhood constraints, wind field segments with similar flow direction disturbance characteristics and close temporal markers are aggregated into independently managed multi-source wind field micro-segments. By constructing a three-dimensional spatial neighborhood model, distance and similarity calculations are performed on the spatial location, wind direction vector, and energy characteristics of each segment. For segments with adjacent spatial locations and similar characteristics, a merging operation is performed to form a micro-segment unit, and parameters such as start and end time, location range, wind speed gradient, and disturbance intensity of each micro-segment are recorded. For segments with partial overlap or small gaps, the boundary data is smoothed using a neighborhood weighted average method to eliminate possible breaks or discontinuities.

[0019] S2: Cross-source correlation tracking is performed on potential overlapping areas, measurement voids and response delays among multi-source wind field micro-segments. Historical disturbance propagation paths, trajectory bending distribution and sensing blind zone triggering conditions are introduced to obtain a multi-scale fusion index set characterizing local wind field instability.

[0020] The process of cross-source correlation tracking of potential overlap areas, measurement voids, and response hysteresis among multi-source wind field micro-segments in S2 is as follows: Based on the location encoding and time sequence of wind field micro-segments in three-dimensional space, a neighborhood matching matrix for cross-source segments is constructed. The spatial coordinates (including latitude, longitude, and altitude information) of each micro-segment are standardized and encoded, and segments from different sampling sources are mapped to a unified three-dimensional grid. Based on the timestamp sequence of each segment, possible corresponding segment pairs within consecutive time periods are identified as potential matching candidates. The spatial distance, time interval, and wind direction similarity index between segments are calculated through a neighborhood search algorithm. These parameters are combined to form a neighborhood matching matrix, providing a basic data structure for cross-source association. By combining the reverse wind speed jump at the segment boundary, the length of data holes, and the magnitude of response hysteresis, the correlation strength between segments is calculated, and overlapping and missing areas in multi-source segments are identified. For each pair of potentially matched micro-segments, the rate of change of their boundary wind speed and the consistency of their direction are calculated. Segments with reverse wind direction jumps are subject to weight penalties to reduce spurious correlations. The length of holes in the time series is statistically analyzed to determine the possible locations of missing segments, which are then marked as data holes. For segments that may have response hysteresis, the hysteresis magnitude is calculated based on the time offset and the similarity of the disturbance waveform, and it is included in the correlation strength evaluation. A multi-index evaluation that integrates spatial, temporal, and wind field characteristics is used to generate a weighted correlation strength between each pair of segments. Dynamic tracking of delay and response links between segments is performed to form a cross-source disturbance propagation sketch. Using the neighborhood matching matrix and correlation strength results as initial inputs, each micro-segment is gradually extended along the time series to identify its possible response links and delay propagation paths. For time offsets or spatial misalignments in cross-source segments, the continuity and stability of the tracking links are maintained by dynamically adjusting weights and neighborhood matching strategies. Overlapping areas, missing nodes, and abnormal delay points during the tracking process are recorded in the disturbance propagation sketch. The energy transfer direction and amplitude relationship between wind field micro-segments is represented in graph structure form, revealing the propagation law of disturbances under multi-source sampling.

[0021] The process of obtaining the multi-scale fusion index set characterizing local wind field instability in S2 is as follows: By structurally matching the cross-source disturbance propagation sketch with disturbance propagation paths in the historical wind field disturbance database, disturbance extension patterns in segments with the same flow direction are identified. The micro-segment links in the cross-source disturbance propagation sketch are topologically encoded, and the spatial location, wind speed vector, and timestamp of each node are used as node attributes. Typical disturbance paths in the historical disturbance database are encoded in the same way. A graph matching algorithm is used to structurally compare the real-time transmission links with historical paths to identify segments with similar spatial distribution, consistent flow direction, and temporal continuity. Through this structural matching, potential extension patterns and common historical disturbance patterns in the real-time wind field can be revealed. By analyzing the bend distribution density, abrupt changes in bend angle, and corresponding blind zone coverage in the wind field trajectory, the triggering areas of abnormal disturbances in the local flow field can be identified. The curvature of the wind field evolution lines in the transfer sketch is calculated, and the bend points and bend angles of each segment are extracted. The distribution density and abrupt change amplitude of the bend points are further statistically analyzed to quantify the spatial areas where local disturbances may be concentrated or amplified. The blind zone of the aircraft or wind measurement equipment is superimposed with the location of the bend points for analysis to identify disturbance triggering areas that may not be directly measured under incomplete observation or data gaps. Through this comprehensive analysis, the key areas where potential abnormal disturbances occur in the local flow field can be located. By integrating bend characteristics, blind zone information, and the temporal weights of propagation paths, a multi-scale fusion index set with scale-level attributes is formed. The temporal dynamic characteristics of bend point distribution, blind zone coverage, and extension paths are normalized according to preset weights, and a multi-layer index structure is constructed. Each layer corresponds to different spatial and temporal scales. The intensity index, expansion probability, and possible triggering area of ​​local disturbances are recorded in each index node. Through a hierarchical classification method, both significant local disturbances and micro-disturbances are included in the index system, achieving multi-scale coverage from micro-segments to large-area flow fields. The generated multi-scale fusion index set can clearly characterize the instability features of local wind fields.

[0022] S3: Based on the multi-scale fusion index set, the local transition pulsations, crosswind rift morphology and wind speed gradient changes in the micro-segments of the wind field are continuously spliced ​​together. After verifying the consistency of sudden turning points, measurement error signals and time misalignment keys in the spliced ​​sequence, fine-grained wind field evolution lines that reveal the nascent potential of disturbances are extracted.

[0023] The process of continuously stitching together local transition pulsations, crosswind rift morphology, and wind speed gradient changes in wind field micro-segments in S3 is as follows: The key points of local disturbances annotated in the multi-scale fusion index are traced back to the micro-fragment sequence to extract the peak value of local transition pulses, the morphological contour of the crosswind rift zone, and the direction of wind speed gradient change. Based on the spatial location and timestamp of each key point in the index, the corresponding segment is located in the micro-fragment sequence. The corresponding segment is subjected to detailed analysis of wind speed, wind direction, and micro-disturbance energy to identify the peak point of instantaneous transition pulses. At the same time, the spatial contour morphology of the crosswind rift zone is extracted, including bandwidth, length, and direction changes. The local wind speed gradient and its direction of change within the segment are calculated. Through this process, each key disturbance point is accurately mapped to the original micro-fragment sequence. Based on feature similarity and local flow continuity, adjacent micro-segments are sequentially spliced ​​in a multi-dimensional feature space to construct a continuous perturbation splicing chain. For each pair of adjacent micro-segments, the similarity index of multi-dimensional features such as wind speed gradient direction, wind direction change rate, and pulsation intensity is calculated. Under the condition of satisfying the feature similarity threshold and local flow continuity, the segments are sequentially connected to form a continuous perturbation chain. For segments with slight temporal misalignment or spatial deviation, smoothing is performed by interpolation or weighted averaging to ensure the continuity and stability of the splicing chain in time and space. This splicing method in a multi-dimensional feature space can effectively reflect the continuous evolution trend of local perturbations at the micro-segment level. Potential structural breakpoints are identified in the splicing chain, and the segment boundaries near the breakpoints are recalibrated to generate a splicing sequence for consistency verification. By analyzing abrupt changes in wind speed gradients, sharp deflections in flow direction, and discontinuities in pulsation amplitude among segments in the splicing chain, possible structural breakpoints are identified. For the identified breakpoints, boundary fine-tuning is performed based on the characteristic distribution of neighboring segments, including timestamp adjustment, spatial smoothing, and eigenvalue weighted correction. After boundary calibration, the generated continuous splicing sequence can minimize the impact of human error and data anomalies on disturbance evolution analysis.

[0024] The process of extracting fine-grained wind field evolution lines that reveal the potential for disturbed nascent ecosystems from S3 is as follows: Consistency checks are performed on sudden turning points in the spliced ​​sequence. By calculating the consistency index between the angle change rate of adjacent segments and the flow direction change in multi-source wind field micro-segments, pseudo-turning points that do not match the characteristics of local disturbances are screened out. The angle change of each turning point in the spliced ​​sequence is quantitatively calculated to obtain the instantaneous deflection amplitude of local wind direction. The consistency of this angle change with the flow direction change pattern of the corresponding micro-segment is compared, and the consistency index is calculated to quantify whether the turning point conforms to the evolution law of local disturbances. Turning points with a consistency index below a set threshold are judged as pseudo-turning points and removed to ensure that the extracted wind field evolution lines reflect the real characteristics of disturbance initiation, rather than the illusions caused by sampling or noise. The measurement error signal is synchronously compared with the equipment performance indicators, noise frequency and sampling interval information to identify segments with abnormal amplitude or inconsistent with adjacent time periods and mark them as error driving nodes; the wind speed, wind direction and energy characteristics of each micro segment are statistically analyzed and compared with the equipment calibration parameters and historical noise distribution to analyze the consistency of data in the sampling interval and adjacent time periods, identify segments with abnormal jumps or sudden changes in amplitude that exceed the normal range, and mark these segments with abnormal amplitude or discontinuous time sequence as error driving nodes; For time-series misaligned keys in the sequence, the continuity of cross-source segment timestamps and spatial locations is compared to determine whether there are abnormal nodes that deviate from the normal perturbation rhythm. The timestamps of each cross-source segment are dynamically compared with their spatial locations to analyze whether they maintain continuity and regularity with neighboring segments. For nodes with obvious deviations, their potential impact on the perturbation evolution chain is assessed, and the deviating nodes are marked as abnormal time-series nodes. This process ensures the temporal continuity of fine-grained wind field evolution lines and the integrity of spatial evolution logic. After completing the consistency verification of sudden turning points, measurement error signals, and timing misalignment keys, the reliable segments that have been verified and rejected are subjected to curvature fitting, and fine-grained wind field evolution lines reflecting the trend of disturbance initiation are extracted. All verified micro-segments are connected in temporal and spatial order, and the wind speed, wind direction, and disturbance intensity of the continuous segments are smoothed by the curvature fitting algorithm to generate continuous curves representing the disturbance evolution. Curvature fitting not only preserves local transition pulsations and crosswind characteristics, but also smooths small fluctuations caused by noise. The extracted fine-grained wind field evolution lines accurately depict the trend of disturbance initiation and evolution.

[0025] S4: Jointly segment and map fine-grained wind field evolution lines with the spatial geometry of a specific approach corridor, the temperature stratification structure of the terminal area, and the near-surface diversion pattern. During the mapping process, identify local compression segments, energy scattering segments, and short-period enhancement segments in the wind field lines, and construct a local wind field disturbance profile with perturbation step attributes.

[0026] In S4, the process of jointly segmenting and mapping fine-grained wind field evolution lines with the spatial geometry of a specific approach corridor, the temperature stratification structure of the terminal region, and the near-surface diversion pattern is as follows: Based on the spatial geometry of the approach corridor, the track envelope, and the typical glide path of the aircraft, fine-grained wind field evolution lines are projected onto the corresponding spatial profiles. The three-dimensional geometric information of the approach corridor, including the longitudinal track, the lateral envelope, and the glide path at different flight altitudes, is obtained. Each sampling point of the fine-grained wind field evolution lines is mapped to the corresponding profile position according to its spatial coordinates, forming a wind field spatial representation that matches the actual geometry of the approach corridor. During the projection process, the coverage area of ​​the typical aircraft track is considered to ensure that each line has a corresponding mapping at different flight altitudes and track ranges. By combining the temperature stratification structure, inversion height, and surface friction effects in the terminal area, the wind field lines are segmented and corrected under meteorological constraints. The temperature profile in the terminal area is analyzed to determine the height and thickness of the temperature stratification and inversion layers. Based on the influence of temperature gradient changes and surface friction on wind speed and direction, the projected wind field lines are segmented according to stratification characteristics. For each segment, the wind speed intensity, direction deflection, and gradient changes of the wind field lines are adjusted to reflect the constraint effect of meteorological conditions on local wind field disturbances. Through this segmented correction, it is ensured that the wind field evolution lines can truly reflect the local flow field characteristics under complex meteorological conditions in the terminal area. Based on the flow direction deflection pattern of the near-surface diversion model, the segmented wind field lines are mapped a second time to form a segmented wind field structure set with regional characteristics. Typical flow direction patterns of near-surface wind direction are obtained, including the guiding effect of topography, runway layout and surface friction on local wind fields. The wind field lines segmented by meteorological constraints are then finely adjusted spatially to make their flow direction consistent with the deflection pattern of the near-surface diversion model. At the same time, wind speed gradient and local disturbance intensity are considered. The resulting segmented wind field structure set not only retains fine-grained evolution characteristics, but also reflects regional diversion effects, providing a basis for local wind field analysis under spatial, meteorological and diversion conditions for aviation operation decisions.

[0027] The process of constructing a local wind field disturbance profile with perturbation step properties in S4 is as follows: Based on the velocity contraction ratio, wind shear intensity, and local turbulent kinetic energy changes of the segmented wind field structure, the location and range of the compression segment, scattering segment, and enhancement segment are identified. The velocity contraction ratio of each segmented wind field is calculated, which is the relative ratio of local wind speed to spatial location, to determine the compression or acceleration state of the local wind field. The spatial distribution of wind shear intensity and turbulent kinetic energy is analyzed. Combined with velocity contraction information, the areas of energy concentration or dispersion in the local wind field are determined. Through the comprehensive evaluation of these indicators, segments with significant compression effects are marked as compression segments, segments with obvious energy dispersion are marked as scattering segments, and segments with obvious short-period energy enhancement are marked as enhancement segments, thereby completing the identification and spatial location of disturbance segments. Based on the hierarchical relationship of the perturbation structure, the various segments are sorted according to the direction of energy change to form a local perturbation hierarchy chain with gradient information. According to the compression segment, scattering segment and enhancement segment identified in the previous step, their spatial and temporal superposition relationship is analyzed, and each segment is sorted according to the increasing or decreasing order of local energy change to form a gradient chain from low energy to high energy or from high energy to low energy. This hierarchy chain not only reflects the spatial layout of the perturbation segments, but also reflects the hierarchical relationship of energy evolution. Based on the local disturbance hierarchy chain, a profile reconstruction is performed to generate a local wind field disturbance profile that characterizes the step properties of wind field disturbance. The sorted disturbance segments are mapped one by one along the spatial profile. The profile height and width are adjusted according to the energy intensity, range and gradient characteristics of each segment. The continuity and boundary transition between different disturbance segments are considered to ensure that the reconstructed profile has no obvious breaks in space and can truly reflect the energy step distribution of the local wind field. The resulting local wind field disturbance profile not only shows the spatial location of the compression, scattering and enhancement segments, but also presents the step properties of disturbance energy as height and distance change.

[0028] S5: By extracting disturbance nodes within a specific operating window through local wind field disturbance profiles and combining them with the refresh rate under real-time data stream updates, wind field risk evolution prompts are generated for aviation operation decision-making.

[0029] The process of generating wind field risk evolution indicators for aviation operation decision-making in S5 is as follows: By analyzing sensitive segments in the local wind field disturbance profile, we extract disturbance nodes that require key attention within a specific operating window. We analyze the spatial distribution and energy gradient characteristics of the compression, scattering, and enhancement segments in the local wind field disturbance profile to determine which segments may have a direct impact on flight path safety. Based on the local wind speed change rate, turbulent kinetic energy intensity, and segment duration of the disturbance segments, we extract the locations of key nodes and mark them as disturbance nodes that require key monitoring. The selection of these disturbance nodes considers both the spatial local impact and the temporal operating window. By accessing real-time observation data streams, the energy changes, spatial expansion velocity, and wind direction deflection rate of disturbance nodes are continuously monitored. Data from real-time meteorological radar, airborne wind field detectors, and ground-based wind measurement units are correlated with identified disturbance nodes. Through node coordinate mapping, the instantaneous changes in wind speed, wind direction, and turbulent kinetic energy are tracked. The spatial expansion velocity and directional deflection rate of disturbance nodes are calculated to determine whether the disturbance range exceeds expectations or shows an abnormal evolution trend. During continuous monitoring, data fusion and filtering strategies are introduced to remove noise and abnormal sampling points, ensuring the stability and reliability of monitoring results. By combining node change trends with the refresh rate of real-time data stream updates, wind field risk evolution prompts are generated. Based on the characteristics of disturbance nodes monitored in real time, energy change rate, spatial expansion rate, and wind direction deflection amplitude are calculated. The evolution trend of disturbance nodes within a specific operating window is analyzed. The node change trends are synchronized with the real-time data stream update frequency to assess the risk level and potential impact time window of each node. The analysis results are integrated to generate structured wind field risk evolution prompts, including disturbance location, intensity, evolution direction, and warning level. This provides actionable real-time risk references for aviation operation decision-making, enabling dynamic and safe route operation assistance.

[0030] Example 2: As Figure 2 As shown, a wind field characteristic analysis system for aviation operation decision-making includes: Segment Compilation Module: Divides transient wind direction changes and minor seismic measurements from different sampling sources into multi-source wind field micro-segments for unified management; Cross-source aggregation module: It analyzes the overlapping and discontinuity relationships between micro-segments of multi-source wind fields and injects historical disturbance clues to form a fusion index set that reflects local instability; Sequence splicing module: Based on the fusion index set, local pulses and crosswind structures are continuously linked together, and evolution lines with disturbance initiation direction are extracted through key time point verification; Profile construction module: Matches and segments the evolution lines with the terminal area space and thermodynamic conditions to construct a local wind field interference profile; Risk alert module: It extracts key disturbance nodes within the runtime window using local wind field disturbance profiles and outputs wind field risk evolution alerts for decision-making in combination with real-time update rhythm.

[0031] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0032] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A wind field characteristic analysis method for aviation operation decision-making, characterized in that, Includes the following steps: Asynchronous interception of the original wind field fragments separates transient wind direction changes, measurement micro-seismic characteristics and time stamp differences from different sampling sources into several multi-source wind field micro-fragments. Cross-source correlation tracking is performed on potential overlapping areas, measurement voids and response delays among micro-segments of multi-source wind fields. Historical disturbance propagation paths, trajectory bending distribution and triggering conditions of sensing blind spots are introduced to obtain a multi-scale fusion index set characterizing local wind field instability. Based on the multi-scale fusion index set, the local transition pulsation, crosswind rift morphology and wind speed gradient changes in the wind field micro-segments are continuously spliced ​​together. After verifying the consistency of sudden turning points, measurement error signals and time misalignment keys in the spliced ​​sequence, fine-grained wind field evolution lines revealing the nascent potential of disturbance are extracted. The fine-grained wind field evolution lines are jointly segmented and mapped with the spatial geometry of a specific approach corridor, the temperature stratification structure of the terminal area, and the near-surface diversion pattern. During the mapping process, local compression segments, energy scattering segments, and short-period enhancement segments in the wind field lines are identified, and a local wind field disturbance profile with disturbance step attributes is constructed. By extracting disturbance nodes within a specific operating window through local wind field disturbance profiles and combining them with the refresh rate under real-time data stream updates, wind field risk evolution prompts are generated for aviation operation decision-making.

2. The wind field characteristic analysis method for aviation operation decision-making according to claim 1, characterized in that, The process of separating transient wind direction changes, measurement microseismic characteristics, and time-stamp differences from different sampling sources into several multi-source wind field micro-segments is as follows: Based on the differences in sampling frequencies of weather radar, laser anemometer, and airborne wind field detection unit, the timestamps of the original wind field fragments are first calibrated across devices. An asynchronous interception strategy was used to initially screen continuous wind field records, and the wind direction change slope, fine seismic energy density and sampling interval offset of each sampling source were segmented by multiple indicators. Based on segmentation and combined with spatial neighborhood constraints, wind field segments with similar flow direction disturbance characteristics and close temporal markers are aggregated into independently managed multi-source wind field micro-segments.

3. The wind field characteristic analysis method for aviation operation decision-making according to claim 2, characterized in that, The process of cross-source correlation tracking of potential overlap areas, measurement voids, and response hysteresis among multi-source wind field micro-segments is as follows: Based on the positional encoding and temporal sequence of wind field micro-fragments in three-dimensional space, a neighborhood matching matrix for cross-source fragments is constructed. By combining the reverse jump in wind speed at the segment boundary, the length of data holes, and the magnitude of response hysteresis, the correlation strength between segments is calculated, and overlapping areas and missing measurement areas in multi-source segments are identified. Dynamically track the delay and response links between segments to form a cross-source disturbance propagation sketch.

4. The wind field characteristic analysis method for aviation operation decision-making according to claim 3, characterized in that, The process of obtaining a multi-scale fusion index set characterizing local wind field instability is as follows: By structurally matching the cross-source disturbance propagation sketch with the disturbance propagation path in the historical wind field disturbance database, disturbance extension patterns in the same flow direction segment are identified. By analyzing the bend distribution density, abrupt changes in bend angle, and the corresponding sensing blind zone coverage in the wind field trajectory, the triggering area of ​​abnormal disturbances in the local flow field can be determined. By integrating bending features, blind spot information, and the time weight of propagation paths, a multi-scale fusion index set with scale-level attributes is formed.

5. The wind field characteristic analysis method for aviation operation decision-making according to claim 4, characterized in that, The process of continuously splicing together local transition pulsations, crosswind rift morphology, and wind speed gradient changes in micro-segments of the wind field is as follows: The local disturbance key points annotated by the multi-scale fusion index are traced back to the micro-fragment sequence to extract the local transition pulsation peak, crosswind rift morphology and wind speed gradient change direction. Based on feature similarity and local flow continuity, adjacent micro-segments are sequentially spliced ​​in a multi-dimensional feature space to construct a continuous perturbation splicing chain; Potential structural breakpoints are identified in the splicing chain, and the fragment boundaries near the breakpoints are recalibrated to generate a splicing sequence for consistency verification.

6. The wind field characteristic analysis method for aviation operation decision-making according to claim 5, characterized in that, The process of extracting fine-grained wind field evolution lines that reveal the potential for disturbance in the nascent ecosystem is as follows: Consistency checks are performed on sudden turning points in the spliced ​​sequence. By calculating the consistency index between the angle change rate of adjacent segments and the flow direction change in micro segments of multi-source wind fields, pseudo-turning points that do not match the characteristics of local disturbances are screened out. The measurement error signal is synchronously compared with the equipment performance indicators, noise frequency and sampling interval information to identify segments with abnormal amplitude or inconsistent with adjacent time periods, and these segments are marked as error-driven nodes. For time-series misaligned keys in the sequence, the continuity of cross-source segment timestamps and spatial locations is compared to determine whether there are abnormal nodes that deviate from the normal perturbation rhythm. After completing the consistency verification of sudden turning points, measurement error signals, and timing misalignment keys, the reliable segments that have been verified and eliminated are subjected to curvature fitting, and fine-grained wind field evolution lines reflecting the trend of disturbance emergence are extracted.

7. The wind field characteristic analysis method for aviation operation decision-making according to claim 6, characterized in that, The process of jointly segmenting and mapping fine-grained wind field evolution lines with the spatial geometry of a specific approach corridor, the temperature stratification structure of the terminal area, and the near-surface diversion pattern is as follows: Based on the spatial geometry of the approach corridor, the track envelope, and the typical glide path of the aircraft, fine-grained wind field evolution lines are projected onto the corresponding spatial profile. Based on the temperature stratification structure of the terminal area, the inversion height, and the influence of surface friction, the wind field lines are segmented and corrected under meteorological constraints. Based on the flow deflection pattern of the near-surface diversion mode, the segmented wind field lines are mapped a second time to form a segmented wind field structure set with regional characteristics.

8. The wind field characteristic analysis method for aviation operation decision-making according to claim 7, characterized in that, The process of constructing a local wind field disturbance profile with perturbation step properties is as follows: Based on the velocity contraction ratio, wind shear intensity, and local turbulent kinetic energy changes of the segmented wind field structure, the location and extent of the compression segment, scattering segment, and enhancement segment are identified. By combining the hierarchical relationship of the perturbation structure, the various segments are sorted according to the direction of energy change to form a local perturbation hierarchy chain with gradient information; Based on the local disturbance hierarchy chain, the profile is reconstructed to generate a local wind field disturbance profile that characterizes the step properties of wind field disturbance.

9. A wind field characteristic analysis method for aviation operation decision-making according to claim 8, characterized in that, The process of generating wind field risk evolution indicators for aviation operation decision-making is as follows: By extracting sensitive segments from the local wind field disturbance profile, we can identify the disturbance nodes that require special attention within a specific runtime window. By accessing real-time observation data streams, the energy changes, spatial expansion speed, and wind deflection rate of disturbed nodes can be continuously monitored. By combining the node change trend with the refresh rate under the real-time data stream update, wind farm risk evolution prompts are generated.

10. A wind field characteristic analysis system for aviation operation decision-making, applied to the method described in any one of claims 1-9, characterized in that, include: Segment Compilation Module: Divides transient wind direction changes and minor seismic measurements from different sampling sources into multi-source wind field micro-segments for unified management; Cross-source aggregation module: It analyzes the overlapping and discontinuity relationships between micro-segments of multi-source wind fields and injects historical disturbance clues to form a fusion index set that reflects local instability; Sequence splicing module: Based on the fusion index set, local pulses and crosswind structures are continuously linked together, and evolution lines with disturbance initiation direction are extracted through key time point verification; Profile construction module: Matches and segments the evolution lines with the terminal area space and thermodynamic conditions to construct a local wind field interference profile; Risk alert module: It extracts key disturbance nodes within the runtime window using local wind field disturbance profiles and outputs wind field risk evolution alerts for decision-making in combination with real-time update rhythm.

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