Wind shear scene reproduction method and system based on multi-source data fusion

Abstract

The application provides a wind shear scene reproduction method and system based on multi-source data fusion, and relates to the technical field of meteorological data processing. First, multi-source wind shear dynamic data of different mobile collection carriers in a wind shear influence area is acquired; cross-carrier data track anchoring is performed based on dynamic evolution characteristics to form a data track set; scene symbiotic elements are mined and dynamically adapted and mapped with track data to generate an element track mapping body; through a multi-source data progressive fusion mode, dynamic correlation information of the element track mapping body is superimposed to form a wind shear evolution fusion representation; a scene embodiment restoration link is constructed based on the wind shear evolution fusion representation; a complete evolution process of the wind shear from occurrence to dissipation is dynamically presented through the scene embodiment restoration link, and a wind shear reproduction scene is output. The application improves the accuracy and comprehensiveness of wind shear scene reproduction.

Description

Technical Field

[0001] This invention relates to the field of meteorological data processing technology, and more specifically, to a method and system for reproducing wind shear scenarios based on multi-source data fusion. Background Technology

[0002] In meteorological research and related fields such as aviation and energy, wind shear is a highly hazardous and complex weather phenomenon. Accurately reproducing wind shear scenarios is crucial for a deeper understanding of its formation mechanisms, predicting its development trends, and formulating effective countermeasures.

[0003] Currently, wind shear scenario reproduction mainly relies on analysis of data from a single data source or a simple combination of data. Data acquired by different acquisition devices often exist independently, lacking an effective integration mechanism. For example, data collected by ground weather stations and airborne sensors are difficult to correlate due to differences in acquisition carriers, acquisition locations, and times, making it challenging to form a comprehensive and dynamic description of wind shear.

[0004] Meanwhile, existing methods fail to fully consider the dynamic evolution characteristics of wind shear and the symbiotic elements within the scene when processing wind shear data. Wind shear is not static but constantly changes over time and space, and its formation and development are interrelated with multiple factors. However, traditional methods struggle to uncover these complex relationships and cannot accurately represent the complete process of wind shear from its occurrence to its dissipation. This results in insufficient accuracy and comprehensiveness in reproducing wind shear scenarios, making it difficult to meet the needs of accurate understanding and effective response to wind shear in practical applications. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for reproducing wind shear scenarios based on multi-source data fusion, the method comprising:

[0006] Acquire multi-source wind shear dynamic data, which is a set of raw dynamic observation data recorded in real time by different mobile acquisition carriers in the wind shear influence area;

[0007] Based on the dynamic evolution characteristics of wind shear, cross-carrier data trajectory anchoring is performed on the multi-source wind shear dynamic data to form a wind shear data trajectory set, which is a sequence of related trajectories that reflects the evolution of data with wind shear.

[0008] Discover the symbiotic elements of the wind shear scene, and dynamically adapt and map the trajectory data in the wind shear data trajectory set with the symbiotic elements of the wind shear scene to generate an element trajectory mapping body;

[0009] By using a multi-source data progressive fusion method, the element trajectory mapping volume is dynamically correlated and superimposed to form a wind shear evolution fusion representation;

[0010] Based on the wind shear evolution fusion representation, a scene visualization restoration link is constructed. The complete evolution process of wind shear from occurrence to dissipation is dynamically presented through the scene visualization restoration link, and the wind shear reproduction scene is output.

[0011] Furthermore, embodiments of the present invention also provide a wind shear scene reproduction system based on multi-source data fusion, comprising:

[0012] A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described wind shear scenario reproduction method based on multi-source data fusion by executing the machine-executable instructions.

[0013] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-described wind shear scene reproduction method based on multi-source data fusion.

[0014] Based on the above, multi-source dynamic wind shear data recorded in real time by different mobile acquisition carriers within the wind shear-affected area is acquired. Based on the dynamic evolution characteristics of wind shear, cross-carrier data trajectory anchoring is performed on the multi-source data to form a wind shear data trajectory set. This set reflects the evolution of data with wind shear. Co-existing elements of the wind shear scenario are mined and dynamically adapted and mapped with the trajectory data to generate element trajectory mapping volumes. Furthermore, considering various influencing factors in the formation and development of wind shear, a progressive fusion method of multi-source data is used to dynamically superimpose the associated information on the element trajectory mapping volumes to form a wind shear evolution fusion representation. This achieves effective integration and deep fusion of multi-source data, accurately reflecting the dynamic changes in wind shear. Finally, based on the evolution fusion representation, a concrete scene reconstruction link is constructed, dynamically presenting the complete evolution process of wind shear from occurrence to dissipation, outputting accurate wind shear reproduction scenarios, greatly improving the understanding and response capabilities to wind shear. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the execution flow of the wind shear scene reproduction method based on multi-source data fusion provided in the embodiments of the present invention.

[0016] Figure 2 This is a schematic diagram of exemplary hardware and software components of the wind shear scene reproduction system based on multi-source data fusion provided in this embodiment of the invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a wind shear scene reproduction method based on multi-source data fusion provided in an embodiment of the present invention. The following is a detailed description of this wind shear scene reproduction method based on multi-source data fusion.

[0018] Step S110: Acquire multi-source wind shear dynamic data, which is a set of original dynamic observation data recorded in real time by different mobile acquisition carriers in the wind shear influence area.

[0019] In this embodiment, the wind shear influence area is defined as a specific airspace where different types of wind shear phenomena may occur. Various mobile data acquisition vehicles include meteorological observation drones, air traffic control radars, onboard meteorological sensors, and ground-based meteorological observation stations. Upon entering the wind shear influence area, these mobile data acquisition vehicles record raw dynamic observation data in real time according to a preset sampling frequency. Specifically, the meteorological observation drone can collect data on wind speed, wind direction, air pressure, temperature, and humidity at different altitudes within the airspace; the air traffic control radar can monitor the horizontal distribution of airflow speed and direction within the area; the onboard meteorological sensors record real-time wind shear intensity and airflow disturbance frequency encountered during flight; and the ground-based meteorological observation station mainly collects relevant near-surface meteorological parameters. The data recorded by all these different vehicles together constitute multi-source wind shear dynamic data. During the data collection process, for data involving privacy sensitivity, such as the specific location information of flights, data anonymization technology will be used to anonymize the sensitive identification information. For example, the direct association between flight number and specific location will be removed, and only the relative location and observation data within the wind shear influence area will be retained to prevent privacy leakage.

[0020] Step S120: Based on the dynamic evolution characteristics of wind shear, cross-carrier data trajectory anchoring is performed on the multi-source wind shear dynamic data to form a wind shear data trajectory set, which is a sequence of related trajectories that reflects the evolution of data with wind shear.

[0021] The dynamic evolution of wind shear is characterized by an unstable airflow state at the time of its formation, an expansion in range and intensity during the diffusion process, and a gradual stabilization of the airflow during the dissipation phase. Based on these characteristics, cross-carrier data trajectory anchoring of multi-source wind shear dynamic data aims to correlate data collected from different carriers at different times and spaces according to the evolution process of wind shear, forming a data trajectory set that can reflect the complete evolution of wind shear.

[0022] Step S121: Distinguish the attributes of the mobile acquisition carriers of multi-source wind shear dynamic data, and extract the evolution time features and spatial displacement features from the dynamic data corresponding to each mobile acquisition carrier. The evolution time features are the time sequence information of the data records, and the spatial displacement features are the continuous change information of the data acquisition location.

[0023] First, the acquired multi-source wind shear dynamic data is differentiated by carrier attribute. For example, the data storage format, identification information, or data source description can be used to identify which data comes from meteorological observation drones and which comes from air traffic control radar. Then, for the dynamic data corresponding to each mobile acquisition carrier, evolution time features and spatial displacement features are extracted. For evolution time features, the timestamp information in the data records is parsed and arranged chronologically to form a time series, such as the time sequence of continuous observation data from a meteorological observation drone from time T1 to time T2 within a certain time period. Spatial displacement features are extracted by connecting the coordinates of the acquisition location recorded in the data, such as latitude, longitude, and altitude, in chronological order to obtain the spatial movement trajectory of the mobile acquisition carrier, i.e., the continuous change of the data acquisition location, such as the spatial displacement features corresponding to the trajectory of the drone flying from point A to point B and then to point C.

[0024] Step S122: Based on the dynamic evolution law of wind shear from generation to diffusion and then to dissipation, the trajectory anchoring benchmark conditions between data from different mobile acquisition carriers are extracted. The trajectory anchoring benchmark conditions are the corresponding relationships of wind shear evolution stages that can be associated with data from different carriers.

[0025] The dynamic evolution of wind shear from generation to diffusion and dissipation exhibits phased characteristics. In the generation phase, irregular airflow begins to occur within the wind shear region, with drastic changes in wind speed and direction. In the diffusion phase, the area affected by wind shear gradually expands, and its intensity may initially increase before decreasing. In the dissipation phase, airflow tends to stabilize, and the wind shear characteristics gradually disappear. Based on these patterns, trajectory anchoring benchmarks are extracted. For example, using the starting time point of the wind shear generation phase and the airflow disturbance intensity in a specific area as benchmarks, when different mobile data acquisition vehicles collect data with airflow disturbance intensity reaching a certain threshold before and after this time point within this area, this data can be correlated with relevant data from the wind shear generation phase. Similarly, for the diffusion and dissipation phases, corresponding time ranges, spatial ranges, and characteristic parameter thresholds are also set as trajectory anchoring benchmarks to determine the correspondence between data from different carriers in the wind shear evolution phases.

[0026] Step S123: Based on the trajectory anchoring reference conditions, extract dynamic data segments corresponding to specific evolution stages of wind shear from the dynamic data of each mobile acquisition carrier. The dynamic data segments are continuous data records that can reflect the wind shear characteristics of the evolution stage.

[0027] Based on the extracted trajectory anchoring benchmark conditions, dynamic data from each mobile data acquisition vehicle is filtered and extracted. For example, for the wind shear generation stage, based on its corresponding time range and airflow disturbance intensity threshold, continuous data records within that time range and where the airflow disturbance intensity reaches the threshold are extracted from the dynamic data of the meteorological observation drone, forming dynamic data segments for the generation stage. Similarly, for the diffusion and dissipation stages, corresponding dynamic data segments are extracted from the dynamic data of different mobile data acquisition vehicles according to their respective benchmark conditions. These dynamic data segments can collectively reflect the characteristics of wind shear at specific evolution stages. For example, data segments from the generation stage can reflect the initial characteristics of airflow from instability to the formation of wind shear, while data segments from the diffusion stage can reflect the process of wind shear range expansion and intensity changes.

[0028] Step S124: Extract key evolution node information from each dynamic data segment. The key evolution node information is the recorded content corresponding to the time point and spatial point where the wind shear characteristics change significantly.

[0029] Each dynamic data segment is a continuous record reflecting a specific stage of wind shear evolution. Within these records, there are time and spatial points where wind shear characteristics undergo significant changes—these are critical evolution nodes. For example, in a dynamic data segment during the generation phase, there might be a point in time where wind speed suddenly increases to a new level, or a point in space where wind direction changes significantly. These time and spatial points are the critical evolution nodes. By analyzing the data in the dynamic data segments, such as calculating the rate of change of wind speed and direction, when the rate of change exceeds a set threshold, the corresponding time and spatial points are identified as critical evolution nodes. The record content corresponding to these nodes is extracted, including the specific time, spatial location, and data such as wind speed, wind direction, and air pressure at that location, as the critical evolution node information.

[0030] Step S125: Using key evolution node information as anchor points, establish the correlation trajectory lines between dynamic data segments corresponding to different mobile acquisition carriers. The correlation trajectory lines are logical paths connecting key nodes of data segments from different carriers.

[0031] By comparing key evolution node information from dynamic data segments collected by different mobile acquisition carriers, key nodes that are close in time and reflect similar wind shear characteristics are identified. For example, if a meteorological drone records a significant increase in wind speed at time T3 and location P1, and an air traffic control radar detects a similar wind speed change at a similar time T4 and location P2, and the relative positions of P1 and P2 within the wind shear influence area conform to the trend of wind shear diffusion, then these two key nodes can serve as anchor points. Based on these anchor points, logical paths connecting key nodes in data segments from different carriers are established, i.e., correlation trajectory lines. This trajectory line reflects the correlation between data from different carriers in describing the same wind shear evolution process, allowing data from different sources to corroborate and complement each other.

[0032] For example, step S125 may include:

[0033] Step S1251: Extract features from the key evolution node information in each dynamic data segment to obtain the wind shear feature parameters corresponding to each key node. The wind shear feature parameters are specific data contents that can identify the wind shear state of the key node.

[0034] For each key evolution node in a dynamic data segment, feature extraction is performed. Taking a key node m in the dynamic data segment generated by a meteorological observation UAV as an example, the information of node m includes a specific timestamp T_m, spatial coordinates (X_m, Y_m, Z_m), and recorded wind speed V_m, wind direction angle θ_m, and air pressure P_m. Feature parameters that identify the wind shear state of this node are extracted from the above information, including the wind speed change rate ΔV / Δt (obtained by calculating the difference between the wind speed at this node and the previous moment, divided by the time interval), the wind direction change amplitude Δθ, and the air pressure gradient value ∇P (calculated by combining the air pressure values ​​of neighboring spatial points). For key nodes in the dynamic data segment of air traffic control radar, their feature parameters may include the radial velocity V_r and velocity spectral width W_s at that node. Through feature extraction, each key node is assigned a set of wind shear feature parameters to characterize the wind shear state of that node.

[0035] Step S1252: Compare the key evolution node information of the dynamic data segments corresponding to different mobile acquisition carriers in pairs to identify key nodes with the same or similar wind shear characteristic parameters. The similar wind shear characteristic parameters are parameters used to reflect the same wind shear evolution state.

[0036] A key node feature parameter comparison matrix is ​​established, and all key nodes in the dynamic data segments of different mobile acquisition carriers are compared pairwise. For key node A of the meteorological observation UAV (feature parameter set {V_a, ΔV / Δt_a, θ_a, P_a, ∇P_a}) and key node B of the air traffic control radar (feature parameter set {V_r_b, W_s_b}), the similarity of their feature parameters is calculated. Here, subscripts a and b represent the parameters corresponding to node A and node B, respectively; V_a is the wind speed value of node A; ΔV / Δt_a is the wind speed change rate of node A; θ_a is the wind direction angle of node A; P_a is the air pressure value of node A; ∇P_a is the air pressure gradient value of node A; V_r_b is the radial velocity of node B; and W_s_b is the velocity spectral width of node B. Since the parameter types recorded by different carriers may be different, the parameters need to be mapped to a unified wind shear state description space. For example, the wind speed change rate ΔV / Δt of the UAV is compared with the radial velocity change rate ΔV_r / Δt of the radar (i.e., the rate of change of the radial velocity detected by the radar over time), and the wind direction change Δθ of the UAV is correlated with the radar velocity spectral width W_s (reflecting turbulence intensity and related to wind direction change). A similarity threshold is set. When the comprehensive similarity between key node A and key node B exceeds the threshold, they are determined to have the same or similar wind shear characteristic parameters, that is, they are considered to reflect the same wind shear evolution state. The similarity can be calculated using weighted Euclidean distance, and the weights are determined according to the importance of the parameters in representing the wind shear state. For example, the weight of the rate of change of wind speed is set as w_v, and the weight of the change of wind direction is set as w_θ. The comprehensive similarity S = w_v × (1 - |ΔV / Δt_a - ΔV_r_b| / ΔV_max) + w_θ × (1 - |Δθ_a - f(W_s_b)| / Δθ_max), where f(W_s_b) is the function that converts the velocity spectrum width W_s_b into the estimated value of the change of wind direction, ΔV_max is the maximum possible range of the rate of change of wind speed (used for normalization), and Δθ_max is the maximum possible range of the change of wind direction (used for normalization).

[0037] Step S1253: Establish node association identifiers for each group of key nodes with the same or similar wind shear characteristic parameters. The node association identifier is a unique identifier that marks the correspondence between key nodes of different carrier data segments.

[0038] For each group of key nodes with the same or similar wind shear characteristic parameters identified in step S1252, a node association identifier is established. This node association identifier is generated using an encoding method and includes information on the wind shear evolution stage, the node group number, and a preliminary indication of the association strength. For example, the identifier for the first group of associated nodes in the generation stage can be encoded as "GEN_GRP_001", and the identifier for the second group of associated nodes in the diffusion stage can be encoded as "DIF_GRP_002". A field is added to the data storage for each key node to record its associated node identifier. Simultaneously, the data structure corresponding to the association identifier records the source carrier, timestamp, spatial coordinates, and characteristic parameter values ​​of all key nodes included in the group, forming a node association group record.

[0039] Step S1254: Based on the node association identifier, construct an initial association line connecting key nodes of dynamic data segments from different mobile acquisition carriers. The initial association line is a basic logical path that only connects the corresponding key nodes.

[0040] Based on the node association identifiers established in step S1253, initial association lines are constructed to connect key nodes of dynamic data fragments from different mobile acquisition carriers. For each node association group, one or more initial association lines are generated to connect the key nodes within the group. For example, for the node association group "GEN_GRP_001", which includes key node A of a meteorological observation UAV, key node B of an air traffic control radar, and key node C of an onboard meteorological sensor, three initial association lines are constructed: line AB, line AC, and line BC. The above initial association lines only indicate the correspondence between nodes, but do not include the connection strength or details of the specific logical path. The initial association lines are stored in the form of a logical data structure, including the starting node identifier, the ending node identifier, and the connection type (such as bidirectional or unidirectional).

[0041] Step S1255: Extract the dynamic data content around the key nodes connected by the initial correlation line, and analyze the consistency of the changing trend of the dynamic data content. The consistency of the changing trend is the matching performance of the direction and magnitude of the data content changing over time.

[0042] For each initial correlation line, the dynamic data around the two key nodes it connects is extracted. Taking key nodes A (from a meteorological observation UAV) and B (from an air traffic control radar) connected by the initial correlation line as an example, the UAV dynamic data sequences within a time window Δt_window before and after node A are extracted. These data sequences contain parameters such as wind speed, wind direction, and air pressure from time point T_A-Δt_window to T_A+Δt_window. Similarly, the radar dynamic data sequences within the same time window Δt_window before and after node B are extracted, containing parameters such as radial velocity and velocity spectral width at the corresponding time points. The two data sequences are then analyzed to calculate the consistency of their changing trends.

[0043] The quantification of the consistency of change trends is achieved as follows: First, the two data sequences are time-aligned and interpolated to ensure they have the same sampling time points. Then, the consistency index of parameter change direction at each time point is calculated. For the wind speed parameter, the direction consistency index is defined as C_v(t) = sign(ΔV_uav(t)) × sign(ΔV_radar(t)), where ΔV_uav(t) is the rate of change of the UAV wind speed at time t, ΔV_radar(t) is the rate of change of the radar radial velocity at time t, and sign is the sign function (taking values ​​of +1, 0, and -1). When C_v(t) = +1, it indicates that the two change directions are the same; when C_v(t) = -1, it indicates that the change directions are opposite; when C_v(t) = 0, it indicates that at least one change rate is zero. To establish the correspondence between wind direction and velocity spectrum width, a mapping function g(W_s(t)) is established (which converts the velocity spectrum width W_s(t) into an estimate of wind direction change). Then, the consistency index of wind direction change C_θ(t) = sign(Δθ_uav(t)) × sign(Δg(W_s(t)) / Δt) is calculated.

[0044] Further calculations of the consistency of change magnitudes are performed by comparing the normalized change magnitudes of the two data sequences within the same time window. The magnitude consistency index is defined as A_v = 1 - |ΔV_uav_norm - ΔV_radar_norm|, where ΔV_uav_norm and ΔV_radar_norm are the normalized values ​​of the UAV wind speed change magnitude and radar radial velocity change magnitude, respectively. The comprehensive trend consistency index C_total is obtained by weighted summation of the direction consistency index and the magnitude consistency index, C_total = α × (ΣC_v(t) + ΣC_θ(t)) / (2N) + β × A_v, where N is the number of sampling points within the time window, α and β are weighting coefficients, and α + β = 1.

[0045] Step S1256: Based on the consistency of the change trend, adjust the connection strength of the initial correlation line so that the correlation lines between key nodes with consistent change trends form a tight connection state. The connection strength is a numerical indicator that quantifies the degree of correlation between key nodes of different carrier data segments.

[0046] Based on the comprehensive trend consistency index C_total calculated in step S1255, the connection strength of the initial correlation line is adjusted. The connection strength is represented by the value S_link, ranging from [0, 1], where 0 represents no correlation and 1 represents complete correlation. The initial value of the connection strength of the initial correlation line is set to S_init, for example, S_init = 0.5. S_link is updated according to C_total: S_link_new = S_init + γ × (C_total - C_thresh), where γ is the adjustment coefficient and C_thresh is the consistency threshold. When C_total is greater than C_thresh, S_link_new increases; when C_total is less than C_thresh, S_link_new decreases. Simultaneously, an upper limit S_max (e.g., 0.95) and a lower limit S_min (e.g., 0.1) for the connection strength are set to ensure that the adjusted S_link is within a reasonable range.

[0047] The specific calculation of connection strength also needs to consider the cumulative effect of multiple time windows. For the same initial correlation line, the consistency of the trend can be calculated separately on multiple time windows (such as the time window before the node, the time window after the node, and the time window containing the node), to obtain the comprehensive trend consistency index C_total_1 for the time window before the node, the comprehensive trend consistency index C_total_2 for the time window after the node, and the comprehensive trend consistency index C_total_3 for the time window containing the node. Then, the weighted average C_total_avg=(w1×C_total_1+w2×C_total_2+w3×C_total_3) / (w1+w2+w3). The connection strength is adjusted based on C_total_avg: S_link = Sigmoid(k × (C_total_avg - C_mid)), where the Sigmoid function maps the output to the (0, 1) interval, k is a parameter controlling the steepness of the Sigmoid function (the larger k is, the steeper the function changes near C_mid), and C_mid is the midpoint value of the Sigmoid function (when C_total_avg equals C_mid, S_link is 0.5). Through this adjustment, the connection lines between key nodes with highly consistent trends achieve a connection strength close to the upper limit S_max, forming a tight connection; while the connection strength of connection lines with inconsistent trends is reduced, even approaching S_min.

[0048] Step S1257: Integrate all the adjusted correlation lines with the corresponding key nodes and dynamic data segments to form correlation trajectory lines that can accurately correlate data segments from different carriers. The correlation trajectory lines are used to reflect the synchronous recording relationship of different carrier data on the same wind shear evolution process.

[0049] All the correlation lines whose connection strength was adjusted in step S1256 are integrated, and each correlation line now has a quantified connection strength value S_link. For multiple key nodes within the same node correlation group, a network structure of correlation trajectory lines is constructed based on the connection strength of their correlation lines. For example, if the connection strength between node A and node B is S_link_AB=0.92, the connection strength between node A and node C is S_link_AC=0.45, and the connection strength between node B and node C is S_link_BC=0.88, it indicates that there is a strong correlation between node A and node B, and between node B and node C, while the correlation between node A and node C is relatively weak. Based on this, a correlation trajectory line structure with node B as the central hub is formed: A—(S_link_AB)→B—(S_link_BC)→C, representing the propagation path of wind shear evolution from node A through node B to node C.

[0050] During the integration process, associated trajectory lines are bound to corresponding dynamic data segments. Each associated trajectory line is linked to the dynamic data segment to which the key node it connects belongs, and records information such as the source carrier, time range, and spatial range of the data segment. Simultaneously, a sequence of connection strength values ​​is stored in the associated trajectory line data structure for determining the fusion weights of data from different carriers during subsequent data fusion. Through this integration, associated trajectory lines that accurately link data segments from different carriers are formed. These trajectory lines reflect the synchronous recording relationship of different carrier data on the same wind shear evolution process, and the tightness of this relationship.

[0051] Step S126: Integrate all associated trajectory lines with the corresponding dynamic data segments to form a preliminary trajectory set covering the entire evolution stage of wind shear.

[0052] All established correlation trajectories are integrated with the various dynamic data segments they connect to. Each correlation trajectory corresponds to a logical link in the wind shear evolution process, while the dynamic data segments provide the specific data support. During integration, dynamic data segments and correlation trajectories belonging to the same stage are combined according to the sequence of wind shear evolution stages to form a preliminary trajectory set. This preliminary trajectory set can roughly reflect the entire evolution stage of wind shear from generation to diffusion to dissipation, but there may still be some cases where the correlation between data segments is not strong enough or is missing.

[0053] Step S127: Based on the dynamic evolution continuity of wind shear, supplement the missing evolution node associations in the preliminary trajectory set, so that each dynamic data segment can form a continuous trajectory network through the associated trajectory lines, and finally form a wind shear data trajectory set.

[0054] The dynamic evolution of wind shear is a continuous process, therefore its data trajectory should also be continuous. Examining the initial trajectory set reveals that some dynamic data segments lack necessary connecting trajectory lines, causing breaks in the wind shear evolution process. In such cases, supplementation based on the continuity of wind shear's dynamic evolution is necessary. For example, between two adjacent dynamic data segments in the diffusion phase, based on characteristics such as the speed and direction of wind shear diffusion, potential intermediate evolution nodes can be inferred, and these two data segments can be connected by connecting trajectory lines. After this supplementation and refinement, the dynamic data segments form a continuous and interconnected trajectory network through connecting trajectory lines, ultimately forming a complete set of wind shear data trajectories.

[0055] Step S130: Mine the symbiotic elements of the wind shear scene, dynamically adapt and map the trajectory data in the wind shear data trajectory set with the symbiotic elements of the wind shear scene, and generate the element trajectory mapping body.

[0056] Symbiotic elements in wind shear scenarios refer to various meteorological and environmental elements that accompany and influence the occurrence, development, and dissipation of wind shear. After mining these elements, the trajectory data in the wind shear data trajectory set are dynamically adapted and mapped to them, thereby establishing the correspondence between data and elements, clarifying which data reflects which element changes, and thus generating an element trajectory mapping body.

[0057] Step S131: By analyzing the interaction process between wind shear and the surrounding environment, we can discover the symbiotic elements of the wind shear scene that change synchronously with and influence each other in the evolution of wind shear. The symbiotic elements of the wind shear scene include airflow velocity change elements, air pressure distribution elements, temperature gradient elements, humidity distribution elements, and air density change elements.

[0058] The occurrence and development of wind shear are closely related to various meteorological elements in the surrounding environment. By analyzing the interaction between wind shear and the surrounding environment—for example, the formation of wind shear leads to changes in local airflow velocity, which in turn affects the distribution of air pressure, and changes in air pressure may in turn cause changes in temperature and humidity gradients—we can uncover symbiotic elements that change synchronously with and influence the evolution of wind shear. Airflow velocity changes directly reflect the intensity and motion of wind shear; air pressure distribution reflects the spatial differences in air pressure distribution within the wind shear region; temperature gradients represent the rate of temperature change between different regions and have a significant impact on airflow motion; humidity distribution reflects the distribution of water vapor content in the air and is related to phenomena such as cloud and fog formation; and air density changes are related to air pressure and temperature, affecting the buoyancy and motion characteristics of airflow.

[0059] Step S132: Perform dynamic feature decomposition on each wind shear scenario symbiotic element to obtain the change dimension of each wind shear scenario symbiotic element in different stages of wind shear evolution. The change dimension is used to reflect the specific aspects of the changes of the wind shear scenario symbiotic element as the wind shear evolves.

[0060] The characteristics of each coexisting element in a wind shear scenario differ at different stages of wind shear evolution. Dynamically decomposing the airflow velocity variation element, in the generation stage, its changing dimensions may include the initial fluctuation amplitude of velocity magnitude and the frequency of velocity direction changes; in the diffusion stage, it may include the spatial expansion range of velocity changes and the rate of change of the velocity gradient; in the dissipation stage, it may include the rate at which the velocity gradually stabilizes and the degree of attenuation of velocity fluctuation amplitude. Similarly, for the pressure distribution element, in the generation stage, there may be changes in the formation rate and initial intensity of the pressure gradient; in the diffusion stage, it may involve the expansion rate of the pressure distribution range and the movement direction of high-pressure and low-pressure areas; in the dissipation stage, it may include the rate of decrease of the pressure gradient and the degree to which the pressure distribution becomes more uniform. Through this decomposition, the specific changes of each coexisting element at different evolution stages are clarified.

[0061] Step S133: Based on the change dimension of each symbiotic element in the wind shear scenario, establish a dynamic description specification for the elements. The dynamic description specification is a unified standard for expressing the change characteristics of each symbiotic element at different evolution stages.

[0062] To ensure a unified description and comparison of the changing characteristics of different coexisting elements at different evolutionary stages, dynamic description standards for each element are established based on its change dimension. For example, for the fluctuation range of airflow velocity, the standard specifies that it is described as "at a certain evolutionary stage, the airflow velocity fluctuates within a certain numerical range, with a fluctuation frequency within a certain interval"; for the pressure gradient formation rate of pressure distribution, the standard describes it as "at a certain evolutionary stage, the pressure gradient increases from its initial value to a certain value at a certain rate." These standards include unified provisions for the naming of change dimensions, data recording formats, and feature description vocabulary, ensuring that data collected from different carriers and the changing characteristics of different elements can be described and processed according to a unified standard.

[0063] Step S134: Analyze the trajectory data in the wind shear data trajectory set, and extract the dynamic data items in the trajectory data that can correspond to the change dimension of the symbiotic element. The dynamic data items are the specific data content in the trajectory data that records the change of the symbiotic element.

[0064] The trajectory data in the wind shear data set contains a wealth of information that needs to be parsed to extract dynamic data items corresponding to the changing dimensions of symbiotic elements. For example, for the dimension of change in airflow velocity, the dynamic data items are the data recording airflow velocity values ​​at different time points recorded in the trajectory data. During the parsing process, it is necessary to identify the relevant fields and records in the trajectory data according to the definitions of each changing dimension in the dynamic description specification of the elements, ensuring that the extracted dynamic data items accurately reflect the changing characteristics of the symbiotic elements.

[0065] Step S135: According to the dynamic description specification of the elements, the extracted dynamic data items are adapted in stages with the corresponding wind shear scene co-existing elements and change dimensions to establish the correspondence between data and elements at different evolution stages.

[0066] The extracted dynamic data items are matched with the corresponding symbiotic elements and their change dimensions in the wind shear scenario, according to the element dynamic description specification. This matching is performed in stages. For example, a dynamic data item records the fluctuation of airflow velocity values ​​during a certain period of time in the wind shear generation stage. According to the element dynamic description specification, its corresponding symbiotic element is determined to be the airflow velocity change element, and the change dimension is the magnitude of velocity fluctuation during the generation stage. Through this staged adaptation, it is clear which symbiotic element, which evolution stage, and which change dimension each dynamic data item belongs to, thereby establishing the correspondence between data and elements at different evolution stages.

[0067] Step S1351: Classify all extracted dynamic data items according to the wind shear evolution stage to form dynamic data groups corresponding to each evolution stage. Each dynamic data group contains dynamic data items recorded by all mobile acquisition carriers in that evolution stage.

[0068] The evolution of wind shear includes generation, diffusion, and dissipation. All extracted dynamic data items are categorized based on their recorded time information and the corresponding evolution stage markers in the wind shear data trajectory set. For example, dynamic data items within the time range of the wind shear generation stage are grouped into a generation stage dynamic data group; similarly, dynamic data groups are formed for the diffusion and dissipation stages. Each dynamic data group compiles all relevant dynamic data items recorded by all mobile acquisition carriers within that evolution stage, facilitating subsequent data and element adaptation for different stages.

[0069] Step S1352: According to the dynamic description specification of the elements, determine the target data features corresponding to each wind shear scenario symbiotic element in each evolution stage. The target data features are the ideal data representation of the change dimension of the wind shear scenario symbiotic element in the evolution stage.

[0070] The dynamic description specification for elements clearly describes the changing dimensions of each coexisting element at different evolution stages, and the target data features are determined based on these descriptions. For example, for the changing dimension of the temperature gradient element during the wind shear diffusion stage, "the spatial expansion range of the temperature gradient," its target data feature might be defined as "during the diffusion stage, the spatial distribution range of the temperature gradient gradually expands over time, and the expansion rate varies in different directions." The target data features are determined based on theoretical analysis and past experience, and can ideally reflect the changing dimensional characteristics of the element at a specific evolution stage, serving as a standard for judging whether dynamic data items match it.

[0071] Step S1353: Compare the data items in the dynamic data group of each evolution stage with the target data features of each symbiotic element in that evolution stage, and initially screen out candidate data items that match the target data features.

[0072] For each dynamic data set in the evolution stage, each data item is traversed, and its content is compared with the target data features of each symbiotic element in that evolution stage. For example, in the dynamic data set of the diffusion stage, a data item records the expansion of the temperature gradient in different directions. This is compared with the target data feature of the temperature gradient element in the diffusion stage: "the spatial distribution range gradually expands over time, and the expansion rate varies in different directions." If the recorded content of the data item matches or is similar to the description of the target data feature, it is initially screened as a candidate data item, as it is considered to be potentially related to the change dimension of the symbiotic element.

[0073] Step S1354: Extract the core data content from the candidate data items, perform detailed matching with the change dimensions of the corresponding co-existing elements, and confirm that the core data content can accurately describe the characteristics of the change dimension.

[0074] Candidate data items may contain some secondary or irrelevant information, requiring the extraction of core data content. For example, a candidate data item might be about air pressure distribution elements, and its core data content could be air pressure values ​​at different time points and spatial locations, as well as the calculated results of air pressure gradients. A detailed matching process should be performed between this core data content and the corresponding co-existing element's change dimension, such as the "air pressure gradient formation rate" change dimension during the generation phase of the air pressure distribution element. This involves checking whether the core data content contains data records that reflect the change process and rate of change of the air pressure gradient from its initial value to a certain value, confirming that this data content accurately describes the characteristics of this change dimension.

[0075] Step S1355: Based on the matching results, assign corresponding candidate data items to the change dimension of each wind shear scenario co-occurring element in each evolution stage to form a preliminary correspondence.

[0076] Based on the detailed matching results, for each dimension of change of each symbiotic element in each evolution stage, the candidate data item with the highest matching degree is selected and assigned. A dimension of change may correspond to multiple candidate data items from different mobile acquisition carriers, and these data items describe the characteristics of that dimension of change from different perspectives. Organizing the above assignment relationships forms a preliminary correspondence, clarifying which candidate data items describe which dimension of change of which symbiotic element in which evolution stage.

[0077] Step S1356: Bind the data items in the preliminary correspondence to the evolution nodes in the wind shear data trajectory, so that the data items correspond to the specific nodes of the wind shear evolution.

[0078] Evolution nodes in wind shear data trajectories mark the time and spatial points where significant changes in wind shear characteristics occur, binding data items in the initial correspondence to these evolution nodes. For example, a data item describing a sudden increase in airflow velocity during the generation phase is bound to the evolution node where the airflow velocity begins to change significantly during the generation phase. In this way, data items are linked to specific nodes in the wind shear evolution, enabling the data to accurately reflect the characteristic changes of wind shear at specific nodes and enhancing the correlation between the data and the wind shear evolution process.

[0079] Step S1357: Integrate the correspondence of all evolution stages to form a data and element stage correspondence covering the entire evolution process of wind shear, all symbiotic elements and change dimensions.

[0080] The initial correspondences for each evolution stage are summarized and integrated to ensure coverage of the entire evolution process of wind shear, including its generation, diffusion, and dissipation, as well as all symbiotic elements in the wind shear scenario and their various dimensions of change. During integration, any duplicate or omitted data item assignments are checked, and the accuracy and rationality of the binding between data items and evolution nodes are verified. After integration, a complete staged correspondence between data and elements is formed, demonstrating which data items describe the different dimensions of change of each symbiotic element at each stage of wind shear evolution, and the correspondence between these data items and specific evolution nodes.

[0081] Step S136: Classify and integrate the dynamic data items corresponding to each wind shear scenario symbiotic element in each evolution stage to form a staged data group for the symbiotic element. The staged data group includes all dynamic data items of the wind shear scenario symbiotic element in each evolution stage of wind shear.

[0082] Based on the phased correspondence between data and elements, the dynamic data items corresponding to each symbiotic element in the wind shear scenario at different evolution stages are categorized and integrated. For example, for the temperature gradient element, all its dynamic data items in the generation stage are integrated to form a temperature gradient data group for the generation stage; similarly, temperature gradient data groups for the diffusion and dissipation stages are integrated. Each phased data group contains all relevant dynamic data items of the symbiotic element at a specific evolution stage. These data items come from different mobile acquisition carriers and reflect the changing characteristics of the element at that stage from multiple perspectives.

[0083] Step S137: Bind the staged data groups of all symbiotic elements to the corresponding wind shear data trajectories to form an element trajectory mapping body that includes the dynamic correspondence between elements, data, and evolution stages.

[0084] Each stage of the symbiotic element's data set is bound to the corresponding wind shear data trajectory in the wind shear data trajectory set. For example, the data set of the temperature gradient element generation stage is bound to the data trajectory of the wind shear generation stage, and the data set of the diffusion stage is bound to the data trajectory of the diffusion stage. In this way, a dynamic correspondence is established between the element, the data, and the evolution stage, forming an element trajectory mapping. Through this mapping, we can see what data supports each symbiotic element at each stage of wind shear evolution, and how this data reflects the changing characteristics of the element, laying the foundation for subsequent wind shear evolution fusion characterization.

[0085] Step S140: By using a multi-source data progressive fusion method, the element trajectory mapping volume is dynamically correlated with information to form a wind shear evolution fusion representation.

[0086] Multi-source data progressive fusion refers to the process of gradually superimposing dynamic data items from different mobile acquisition carriers according to the evolution stages of wind shear, so that the fused information can more comprehensively and accurately reflect the evolution process of wind shear. Performing the above processing on the element trajectory mapping volume aims to integrate data information from different carriers and elements at different stages to form a fused representation that comprehensively reflects the evolution characteristics of wind shear.

[0087] Step S141: Analyze the element trajectory mapping volume, separate the dynamic data items corresponding to different mobile acquisition carriers, and define the symbiotic elements, change dimensions and evolution stages of the wind shear scene to which each dynamic data item belongs.

[0088] A detailed analysis of the element trajectory mapping volume is performed to separate the dynamic data items recorded by different mobile acquisition carriers. For example, the mapping volume distinguishes which data items come from meteorological observation drones and which come from onboard meteorological sensors. Simultaneously, the co-existing elements of the wind shear scene to which each dynamic data item belongs are identified, such as whether it belongs to airflow velocity change elements or temperature gradient elements; its corresponding change dimension is determined, such as whether the airflow velocity fluctuates in magnitude or changes in direction; and the wind shear evolution stage of the data item is identified—whether it is the generation stage, diffusion stage, or dissipation stage. This analysis and definition prepares the ground for subsequent information overlay.

[0089] Step S142: Analyze the information complementarity dimension of dynamic data items corresponding to the same symbiotic element under different mobile acquisition carriers in the same evolution stage, and determine the information supplementation direction of each dynamic data item when describing the element characteristics of this evolution stage.

[0090] At the same evolutionary stage, dynamic data items corresponding to the same symbiotic element from different mobile data acquisition carriers may have different information focuses due to differences in factors such as acquisition location and equipment accuracy, potentially leading to information complementarity. For example, during the wind shear diffusion stage, airflow velocity data collected by meteorological drones may focus on velocity distribution at different altitudes, while data collected by ground-based meteorological observation stations may focus more on near-surface velocity changes. Analyzing the information complementarity dimensions of these data items—that is, determining which data items can supplement spatial coverage, which can supplement temporal resolution, and which can supplement parameter accuracy, etc.—clarifies the information supplementation direction of each dynamic data item in describing the characteristics of the element at this evolutionary stage; for example, some data items mainly supplement spatial distribution information, while others mainly supplement details of temporal changes.

[0091] Step S143: Based on the information supplementation direction, construct multi-source data progressive fusion rules. The progressive fusion rules are logical guidelines that guide the gradual integration of data items from different carriers according to the evolution stage.

[0092] Based on the determined information supplementation direction, a multi-source data progressive fusion rule is constructed. This rule specifies the order and method for information superimposing on different carrier data items of the same symbiotic element at different evolution stages. For example, the rule might stipulate that in the generation stage, data items reflecting the initial changes of the element are superimposed first, followed by supplementary spatial details; in the diffusion stage, data items with broader coverage are used as a foundation, followed by data items that improve local accuracy. The rule also includes a method for determining the information weight of data items, assigning different weights based on their supplementary information value and reliability. Data items with higher weights play a leading role in the fusion process, while those with lower weights serve as supplementary information. Furthermore, the rule clarifies conflict resolution methods during information superposition, specifying how to coordinate information from different data items based on factors such as data reliability and collection time when contradictions exist.

[0093] Step S144: According to the progressive fusion rule, information is superimposed on the dynamic data items of different carriers corresponding to all symbiotic elements in the first evolution stage of wind shear to form a comprehensive dynamic description of each symbiotic element in the first evolution stage.

[0094] The first stage of wind shear evolution is usually the generation stage. According to the progressive fusion rule, information is first superimposed on the dynamic data items of different carriers corresponding to all symbiotic elements in this stage.

[0095] Step S1441: Group all dynamic data items in the first evolution stage of wind shear according to the symbiotic elements of the wind shear scenario to form a multi-carrier data subset corresponding to each symbiotic element of the wind shear scenario. Each multi-carrier data subset contains the dynamic data items recorded by all mobile acquisition carriers of the symbiotic element of the wind shear scenario in the first stage.

[0096] The dynamic data items in the first evolution phase are categorized and grouped according to the coexisting elements of the wind shear scenario to which they belong. For example, all data items belonging to the airflow velocity change element are grouped into the airflow velocity multi-carrier data subset, and data items belonging to the air pressure distribution element are grouped into the air pressure distribution multi-carrier data subset, and so on. Each multi-carrier data subset gathers all dynamic data items of that coexisting element recorded by different mobile acquisition carriers in the first evolution phase, which facilitates centralized processing of multi-source data of the same element.

[0097] Step S1442: According to the progressive fusion rule, determine the information weight of different mobile acquisition carrier data items in each multi-carrier data subset. The information weight is a superimposed priority determined based on the accuracy and completeness of the data item's description of the feature characteristics.

[0098] Based on the method for determining information weights in the progressive fusion rules, information weights are assigned to data items from different mobile acquisition carriers within each multi-carrier data subset. For example, in a multi-carrier data subset focusing on airflow velocity changes, if the data collected by a meteorological drone has higher velocity measurement accuracy and higher temporal resolution, thus more accurately and completely describing the characteristics of airflow velocity changes during the generation phase, it is assigned a higher information weight. Conversely, data from a ground-based meteorological observation station, due to its fixed acquisition location and potential spatial limitations, is assigned a lower information weight. The level of information weight determines the priority of data items in the information superposition process; data items with higher weights will be considered and superimposed first.

[0099] Step S1443: Based on information weight, extract core information from each data item in the multi-carrier data subset, retain the core content in each data item that plays a key role in describing the feature characteristics, and remove irrelevant data content.

[0100] Based on the determined information weights, core information is extracted from each data item in the multi-carrier data subset. For data items with high information weights, key content reflecting the characteristics of the elements is extracted in detail, such as the specific numerical change sequence of airflow velocity and the time of change of velocity direction. For data items with low information weights, data content that can supplement the core information is extracted, such as supplementary velocity measurements at certain specific time points. At the same time, content irrelevant to the description of the element characteristics in the data items is removed, such as the internal coding of the data acquisition equipment and irrelevant environmental interference records, in order to reduce data redundancy and improve the efficiency and accuracy of information overlay.

[0101] Step S1444: Summarize all core information of the same multi-carrier data subset to form a core information set of the symbiotic element in the first stage. The core information set contains a comprehensive feature description of the symbiotic element of the wind shear scenario in the first stage.

[0102] The data items after core information extraction are summarized to form a core information set for each symbiotic element in the first evolution stage. For example, the core information set for the airflow velocity change element may include the velocity value change sequence at different altitudes, the time points and frequencies of velocity direction changes, and the amplitude range of velocity fluctuations. The above information comes from different mobile acquisition carriers. By summarizing, a comprehensive characteristic description of the symbiotic element in the first stage is formed, covering information reflecting the element's changes from different angles and levels.

[0103] Step S1445: Analyze the differences in information expression in the core information set, unify the information with different expression methods but consistent core content, and form a standardized information expression form.

[0104] Data items from different mobile data acquisition devices may be expressed in different ways when recording information, even if the core content is the same. For example, some data items describe airflow velocity direction using angles, while others use directional terms. Analyzing these differences in expression within the core information set, and following the dynamic description standards for elements, information with different expression methods but consistent core content is standardized. Velocity direction is uniformly converted into angle representations or directional terms, forming a standardized information expression format. This eliminates misunderstandings caused by different expression methods and ensures smooth information overlay.

[0105] Step S1446: According to the change dimension of the symbiotic element, classify and arrange the standardized core information so that the information can clearly present the dynamic characteristics of the element according to the change dimension.

[0106] Each symbiotic element has its specific dimensions of change. The standardized core information is categorized and arranged according to these dimensions. For example, for the airflow velocity change element, the core information is classified into dimensions such as "velocity magnitude fluctuation amplitude," "frequency of velocity direction change," and "velocity gradient change rate." Through this classification and arrangement, the information can be presented in an organized manner according to the dimensions of change, demonstrating the dynamic characteristics of the element in different aspects, which facilitates the subsequent formation of a comprehensive dynamic description.

[0107] Step S1447: Transform the standardized core information after classification and arrangement into a coherent descriptive text to form a comprehensive dynamic description that reflects the dynamic characteristics of the symbiotic element in the first evolution stage.

[0108] The standardized core information, after being categorized and arranged, is transformed into descriptive text using coherent natural language or a specific structured descriptive language. For example, a comprehensive dynamic description of airflow velocity variation during the generation stage could be: "During the wind shear generation stage, the airflow velocity fluctuates within a certain numerical range, with a fluctuation frequency within a certain interval; the velocity direction changes at a certain frequency, mainly changing within a certain azimuth range; the velocity gradient increases from its initial value to a certain value at a certain rate." This comprehensive dynamic description can fully reflect the dynamic characteristics of this symbiotic element in the first evolution stage and is a preliminary result of the overlay of multi-source data information.

[0109] Step S145: Using the comprehensive dynamic description of the first evolution stage as a basis, superimpose different carrier dynamic data items of the next evolution stage to form a cross-stage element dynamic evolution description. The cross-stage element dynamic evolution description is used to reflect the change process of the element from the first stage to the next stage.

[0110] Based on the comprehensive dynamic description of the first evolutionary stage, the next evolutionary stage, such as the diffusion stage, is processed. Following a similar approach to the first stage, dynamic data items from different carriers of the same symbiotic element in the diffusion stage are overlaid to obtain a comprehensive dynamic description of the diffusion stage. Then, the comprehensive dynamic descriptions of the generation stage and the diffusion stage are correlated to form a cross-stage dynamic evolution description of the element. For example, a cross-stage description of airflow velocity changes can reflect the process from the initial velocity fluctuations in the generation stage to the expansion of the velocity spatial distribution range and changes in the velocity gradient in the diffusion stage, demonstrating the continuous changes of the element between different stages.

[0111] Step S146: Sequentially complete the progressive superposition of dynamic data items for all evolution stages to form a complete dynamic evolution description of the symbiotic elements of each wind shear scenario. The dynamic evolution description covers the continuous change characteristics of the elements throughout the entire evolution of wind shear.

[0112] Following the method described above, dynamic data items are progressively superimposed on all evolution stages of wind shear. After completing the cross-stage description between the previous and current stages, the dynamic data items of the next stage are superimposed on the comprehensive dynamic description of the current stage to form a new cross-stage description. This process is repeated until the final evolution stage, the dissipation stage, is completed. Ultimately, a dynamic evolution description is formed for each coexisting element of the wind shear scenario. This dynamic evolution description covers the continuous change characteristics of the elements throughout the entire evolution process from the generation stage to the diffusion stage and then to the dissipation stage, including the specific performance of the elements in each stage and the transition between stages.

[0113] Step S147: Integrate the complete dynamic evolution descriptions of all symbiotic elements according to the evolution logic of wind shear to form a wind shear evolution fusion representation that reflects the dynamic changes and interactions of each element during the wind shear evolution process.

[0114] The evolution of wind shear is the result of the interaction and joint changes of various symbiotic elements. A complete dynamic evolution description of all symbiotic elements is integrated according to the evolutionary logic of wind shear, namely, the temporal sequence and the causal relationships between elements. For example, changes in airflow velocity lead to changes in air pressure distribution, and changes in air pressure distribution affect changes in temperature gradient, etc. Based on these interactions, the dynamic evolution descriptions of each element are linked together. The integrated wind shear evolution fusion characterization not only reflects the dynamic change process of each element but also reflects the mutual influence and action mechanisms between elements, comprehensively demonstrating the complete evolutionary characteristics of wind shear from its occurrence to its dissipation.

[0115] Step S150: Construct a scene representation link based on the wind shear evolution fusion representation, dynamically present the complete evolution process of wind shear from occurrence to dissipation through the scene representation link, and output the wind shear reproduction scene.

[0116] The scene visualization and reconstruction link refers to the path or process of transforming the abstract information in the wind shear evolution fusion representation into concrete and intuitive scene elements, and dynamically presenting them according to the wind shear evolution process. Constructing the above link based on the wind shear evolution fusion representation aims to transform the dynamic changes and interactions of various wind shear elements reflected in the fusion representation into a visualized scene, thereby dynamically presenting the complete evolution process of wind shear.

[0117] Step S151: Analyze the wind shear evolution fusion characterization and extract the complete dynamic evolution description of all symbiotic elements contained therein and the interaction information between elements.

[0118] A deep analysis of the wind shear evolution fusion characterization is conducted to extract a complete dynamic evolution description of all coexisting elements in the wind shear scene, such as the entire process of airflow velocity change from generation to dissipation, and the complete change process of temperature gradient. Simultaneously, information on the interactions between elements is extracted, such as how airflow velocity changes affect air pressure distribution, and how air pressure distribution changes in turn affect airflow velocity. This information forms the basis for constructing a concrete reconstruction of the scene, determining the dynamic behavior and interrelationships of each element within the scene.

[0119] Step S152: Based on the natural evolution sequence of wind shear, divide the scene representation into progressive stages, where each progressive stage corresponds to a scene presentation stage of the wind shear evolution.

[0120] The natural evolution of wind shear is generation, diffusion, and dissipation. Following this sequence, the scene representation is divided into progressive stages. Each progressive stage corresponds to a stage of wind shear evolution and is a specific link in the scene presentation. For example, the generation stage of scene representation corresponds to the generation stage of wind shear, specifically presenting the scene characteristics in the early stages of wind shear occurrence; the diffusion stage corresponds to the diffusion stage of wind shear, presenting the scene as the wind shear range expands and its intensity changes; and the dissipation stage corresponds to the dissipation stage of wind shear, presenting the scene as the wind shear gradually weakens until it disappears. Through this division, the scene presentation can synchronously correspond to the natural evolution process of wind shear.

[0121] Step S153: Allocate the complete dynamic evolution description and inter-element interaction information in the wind shear evolution fusion representation according to the progressive stages, and determine the dynamic feature information corresponding to each reduction stage.

[0122] The extracted complete dynamic evolution description and inter-element interaction information are allocated according to the predefined progressive stages of scene representation. For example, the description of airflow velocity changes, the initial change in air pressure distribution, and their interaction information during the wind shear generation stage are allocated to the generation stage of scene representation; the descriptions of element changes and interaction information during the diffusion stage are allocated to the diffusion stage, and so on. Through this allocation, the dynamic characteristic information corresponding to each reconstruction stage is determined, clarifying which element changes need to be presented in that stage and how the elements interact with each other.

[0123] Step S154: Convert the dynamic feature information corresponding to each restoration stage into scene visualization data. The scene visualization data is a record of specific features that can be directly converted into visual scene elements, including details of element changes, spatial distribution status, and temporal evolution rhythm.

[0124] The dynamic features of each reconstruction stage are transformed into scene visualization data that can be directly used to generate visual scene elements. For example, the dynamic features of airflow velocity changes are converted into velocity vector data at different locations, including velocity magnitude and direction. This data can be directly used to draw visual elements such as arrows representing airflow movement. The dynamic features of air pressure distribution are converted into isobar distribution data for drawing air pressure distribution maps. Scene visualization data includes specific details of element changes, such as the numerical range of velocity changes and the magnitude of air pressure gradients; the spatial distribution of elements, such as the velocity distribution in different regions and the locations of high and low pressure areas; and the temporal evolution rhythm, such as the rate of element change and the duration of different change processes.

[0125] Step S155: According to the order of the progressive stages, the scene visualization data of each stage are continuously connected to form a scene restoration data stream. The scene restoration data stream is a sequence of visualization data of each stage arranged in the order of wind shear evolution.

[0126] Following the sequential order of the scene visualization reconstruction stages—generation, diffusion, and dissipation—the scene visualization data for each stage are continuously connected. During this connection process, it is ensured that the ending data of the previous stage smoothly transitions to the beginning data of the next stage, maintaining data continuity and consistency. For example, the airflow velocity distribution data at the end of the generation stage should be used as the initial velocity distribution data at the beginning of the diffusion stage, and the velocity change data for the diffusion stage should be connected based on this. Through this connection, a scene reconstruction data stream arranged in the order of wind shear evolution is formed, containing all scene visualization data from the occurrence to the dissipation of wind shear.

[0127] Step S1551: Extract the core feature records from the scene visualization data of each progressive stage. The core feature records are key data contents used to reflect the main scene features of wind shear in that progressive stage.

[0128] Within the scenario visualization data of each progressive stage, there exist key data points that embody the main characteristics of wind shear at that stage—these are the core feature records. For example, in the generation stage, core feature records might include data such as the location, intensity, and main impact range of the initial airflow disturbance; in the diffusion stage, they might include key node data on the direction, velocity, and intensity changes of wind shear diffusion. Extracting these core feature records, which are the essence of the scenario visualization data, determines the main characteristics and performance of the wind shear scenario at that stage.

[0129] Step S1552: Analyze the correlation of changes in core feature records in adjacent progressive stages, and determine the transitional change pattern of core features from the previous stage to the next stage. The transitional change pattern is the natural change logic of core features evolving with wind shear.

[0130] There is an inherent correlation between the core feature records of adjacent progressive stages, reflecting the natural logic of wind shear evolution. Analyzing the differences and connections between the core feature records of the preceding and subsequent stages helps determine the transitional change patterns. For example, if the core feature of the generation stage is a certain value of airflow disturbance intensity at a specific location, and the core feature of the diffusion stage is that this disturbance intensity diffuses in a certain direction at a certain rate, then the transitional change pattern is that the airflow disturbance intensity develops from its initial value in the generation stage towards the diffusion direction at that rate. This pattern represents the natural change logic of core features evolving with wind shear and is crucial for achieving continuous connection of scene data.

[0131] Step S1553: Based on the transition change pattern, generate dynamic transition data between adjacent stages. The dynamic transition data is intermediate change description data connecting the core features of the previous stage and the core features of the next stage, which is used to reflect the gradual change process of the features.

[0132] Based on the established transition patterns, dynamic transition data is generated between adjacent stages. For example, based on the diffusion rate and direction of airflow disturbance intensity, a series of intermediate values ​​are generated between the disturbance intensity at the end of the generation stage and the disturbance intensity at the beginning of the diffusion stage. These values ​​are arranged in chronological order to form dynamic transition data. Dynamic transition data describes the gradual change of core features from one stage to the next, making the transition of scene data smoother and more natural, and avoiding abrupt jumps between stages.

[0133] Step S1554: Connect the scene visualization data and dynamic transition data of the previous stage with the scene visualization data of the next stage to form a stage connection unit. The stage connection unit is used to reflect the continuous scene changes between two adjacent stages.

[0134] The complete scene visualization data from the previous stage, the generated dynamic transition data, and the complete scene visualization data from the next stage are sequentially linked together. For example, the scene visualization data from the generation stage, the dynamic transition data from the generation stage to the diffusion stage, and the scene visualization data from the diffusion stage are connected in sequence to form a stage transition unit. This stage transition unit fully reflects the continuous scene change process from one stage to the next adjacent stage, including the stable scene of the previous stage, the intermediate transition scene, and the stable scene of the next stage.

[0135] Step S1555: Complete the connection processing of all adjacent progressive stages in sequence to generate multiple stage connection units, each stage connection unit corresponding to two consecutive restoration stages.

[0136] Following the above method, all adjacent progressive stages are sequentially connected. First, the generation and diffusion stages are processed to generate their stage connection units; then, the diffusion and dissipation stages are processed to generate corresponding stage connection units. This results in multiple stage connection units, each corresponding to two consecutive restoration stages, such as generation-diffusion connection units, diffusion-dissipation connection units, etc. These units are the basic components constituting the scene restoration data stream.

[0137] Step S1556: Extract the data flow logic in each stage connection unit so that the data transmission within the stage connection unit presents the natural evolution rhythm of wind shear.

[0138] Each stage transition unit contains data from the previous stage, transitional data, and data from the next stage. Data transfer within the unit should follow the natural evolution rhythm of wind shear. This data flow logic is extracted, such as ensuring that the time intervals for data transfer match the actual time proportion of wind shear evolution, and that the rate of data change conforms to the natural speed of wind shear characteristic changes. By clearly defining the data flow logic, the transfer of data within the stage transition units can realistically reflect the natural evolution rhythm of wind shear, enhancing the realism and credibility of the scene.

[0139] Step S1557: Integrate all stage connection units in the order of progressive stages to form a scene restoration data stream that can continuously present the scene characteristics of the entire wind shear evolution process. The data in the scene restoration data stream can be dynamically transferred in sequence to realize the continuous presentation of the scene.

[0140] All generated stage connection units are integrated according to the sequential order of the progressive stages, i.e., generation-diffusion connection units first, followed by diffusion-dissipation connection units. The integrated data stream contains all stage connection units of wind shear from generation to dissipation, and the data can flow dynamically in sequence. Data from the next stage of the previous connection unit flows to the previous stage of the next connection unit, realizing a continuous presentation of the scene and fully demonstrating the scene characteristics of the entire evolution process of wind shear.

[0141] Step S156: Based on the scene restoration data stream, construct a scene visualization restoration link that can carry data transmission and realize dynamic presentation. The scene visualization restoration link is a presentation path that connects each progressive stage and realizes dynamic data flow.

[0142] Based on the scene reconstruction data stream, a scene visualization reconstruction link is constructed. This link includes a data receiving module, a data processing module, a scene generation module, and a scene presentation module. The data receiving module receives the scene reconstruction data stream; the data processing module parses and transforms the data; the scene generation module generates corresponding visual scene elements based on the processed data; and the scene presentation module dynamically displays the generated scene according to the data flow sequence. The modules exchange data through specific interfaces and protocols, forming a presentation path that connects the progressive stages and enables dynamic data flow—the scene visualization reconstruction link.

[0143] Step S157: The scene visualization data of each stage is transmitted sequentially through the scene visualization reconstruction link, the wind shear scene characteristics of each progressive stage are dynamically presented, the complete evolution process of wind shear from occurrence to dissipation is fully restored, and the wind shear reproduction scene is output.

[0144] The scene visualization and reconstruction link is initiated, allowing the scene reconstruction data stream to be transmitted sequentially within the link. During this transmission, the scene generation module in the link generates corresponding visual scene elements based on the scene visualization data at each stage, such as arrows representing airflow movement, isobars representing air pressure distribution, and color distribution of temperature gradients. The scene presentation module dynamically displays these scene elements in the order of the progressive stages, presenting the wind shear scene characteristics of each progressive stage (the unstable airflow disturbance scene in the generation stage, the expansion and intensity change scene in the diffusion stage, and the gradually stabilizing airflow scene in the dissipation stage). Through the above dynamic presentation, the complete evolution process of wind shear from occurrence to dissipation is fully reconstructed, ultimately outputting a visually recognizable wind shear reconstruction scene.

[0145] For example, step S1571: Start the scene visualization restoration link and load the scene visualization data of the first progressive stage in the scene restoration data stream.

[0146] When it is necessary to reproduce a wind shear scene, the scene visualization reconstruction link is initiated first. After the link is initiated, the data receiving module begins to receive the scene reconstruction data stream and loads the scene visualization data of the first progressive stage, namely the generation stage. The above data includes visualization data of elements such as airflow speed, air pressure, and temperature in the generation stage, which is the basis for presenting the scene in the generation stage.

[0147] Step S1572: Transform the scene visualization data of the first stage into dynamic scene elements. The dynamic scene elements are visual representations that can present the wind shear characteristics of the stage, including visual elements corresponding to airflow movement patterns, visual elements corresponding to air pressure distribution, and visual elements corresponding to temperature gradient distribution.

[0148] The scene generation module processes the loaded initial stage scene visualization data, transforming it into dynamic scene elements. For airflow patterns, arrows of different lengths and directions are generated based on velocity vector data, with arrow colors differentiated according to velocity magnitude, forming visual elements of airflow movement. For air pressure distribution, isobars of different values ​​are drawn based on isobar data, with different colors representing high and low pressure, forming visual elements of air pressure distribution. For temperature gradient distribution, the spatial distribution of temperature is presented using color gradients based on temperature gradient data, forming visual elements of temperature gradient distribution. These dynamic scene elements can intuitively present the characteristics of wind shear during the generation stage.

[0149] Step S1573: Present the dynamic scene elements of the first stage through the scene visualization link, and fully demonstrate the scene characteristics of wind shear in the first stage.

[0150] The scene rendering module integrates and renders the generated initial dynamic scene elements, presenting them through a display device. During rendering, it ensures that all dynamic scene elements are displayed correctly, and that their positional relationships and motion states conform to the actual conditions of the wind shear generation stage. For example, airflow movement arrows should accurately point to the direction of airflow movement, and isobars should correctly reflect the air pressure distribution. Through this rendering, the scene characteristics of wind shear in the generation stage are fully displayed, such as the initial location and intensity of airflow disturbances and the initial air pressure distribution.

[0151] Step S1574: Load the dynamic transition data between the first stage and the next stage in the scene restoration data stream, and transform the dynamic transition data into transition scene elements to present the gradual change of core features from the first stage to the next stage.

[0152] After the first stage of the scene is presented, the data receiving module loads the dynamic transition data between the first stage (generation stage) and the next stage (diffusion stage) in the scene reconstruction data stream. The scene generation module transforms this dynamic transition data into transition scene elements, such as the gradual change in the direction and length of airflow arrows, and the slow change in the shape and value of isobars. Through these transition scene elements, the gradual process of the core features from the generation stage to the diffusion stage is presented, allowing the scene to smoothly transition from the generation stage to the diffusion stage.

[0153] Step S1575: Load the scene visualization data for the next stage. Based on the gradual change trend of the transition scene elements, transform the data of the next stage into corresponding dynamic scene elements to achieve a natural connection between the scene features of the two stages.

[0154] After the transitional scene elements are presented, the data receiving module loads the scene visualization data for the next stage (diffusion stage). Based on the gradual change in the transitional scene elements, the scene generation module transforms the data from the diffusion stage into corresponding dynamic scene elements. For example, the airflow arrows continue to change direction and length based on the transitional elements to conform to the airflow characteristics of the diffusion stage; isobars further expand or deform based on the shape of the transitional elements to reflect the air pressure distribution of the diffusion stage. In this way, the dynamic scene elements of the diffusion stage and the dynamic scene elements of the generation stage are naturally connected through the transitional scene elements, avoiding abrupt scene transitions.

[0155] Step S1576: Repeat the above process of loading and presenting transitional data and the next stage of data, and present all the dynamic scene elements and transitional scene elements of the progressive stages in sequence.

[0156] Following the process of loading and presenting transitional data and data for the next stage, subsequent progressive stages are processed sequentially. After the diffusion stage scene is presented, the dynamic transitional data between the diffusion and dissipation stages is loaded and transformed into transitional scene elements for presentation; then, the scene visualization data for the dissipation stage is loaded and transformed into dynamic scene elements for presentation. This process is repeated until all dynamic scene elements and transitional scene elements for all progressive stages have been presented sequentially, covering the entire evolution process of wind shear.

[0157] Step S1577: During the scene presentation at each stage, integrate all dynamic scene elements of that stage to fully demonstrate the spatial distribution characteristics, temporal evolution, and interaction effects of wind shear at that stage.

[0158] During the scene presentation at each progressive stage, the scene presentation module integrates all dynamic scene elements for that stage. For example, in the diffusion stage, visual elements of airflow movement, air pressure distribution, and temperature gradient distribution are overlaid to form a complete scene. Through integration, the spatial distribution characteristics of wind shear at that stage are comprehensively displayed, such as the spatial differences and relationships between different elements; the temporal evolution, such as the changes in elements over time; and the interaction effects of elements, such as how airflow movement affects air pressure and temperature distribution, and how changes in air pressure and temperature distribution react to airflow movement.

[0159] Step S1578: Connect all dynamic scene elements and transition scene elements of all stages in sequence to form a continuous dynamic scene sequence. This scene sequence is used to reflect the entire process of wind shear from the initial characteristics of the occurrence stage, the development characteristics of the diffusion stage to the attenuation characteristics of the dissipation stage.

[0160] All dynamic scene elements and transitional scene elements presented sequentially are chained together in chronological order. The dynamic scene elements of the generation phase are followed by the generation-diffusion transitional scene elements, then the dynamic scene elements of the diffusion phase, followed by the diffusion-dissipation transitional scene elements, and finally the dynamic scene elements of the dissipation phase. This forms a continuous dynamic scene sequence that fully reflects the entire process of wind shear, from the initial airflow disturbances in the generation phase, to the expansion and intensity changes in the diffusion phase, and finally to the gradual stabilization of the airflow in the dissipation phase.

[0161] Step S1579: Transform the continuous dynamic scene sequence into an intuitive and visible wind shear reproduction scene through the scene visualization reconstruction link. This wind shear reproduction scene is used to dynamically present all the key features and change details in the wind shear evolution process.

[0162] The scene rendering module performs final rendering and optimization of the continuous dynamic scene sequence, presenting it as an intuitive and visible wind shear reproduction scene through the display device. Users can observe the entire process of wind shear from its occurrence to its dissipation through this scene, including key features such as airflow movement, air pressure changes, and temperature distribution at each stage, as well as the details of how these features change over time.

[0163] Based on the same inventive concept, please refer to Figure 2 This document illustrates a schematic block diagram of a wind shear scene reproduction system based on multi-source data fusion, provided in an embodiment of this application, for executing the aforementioned wind shear scene reproduction method based on multi-source data fusion. The wind shear scene reproduction system based on multi-source data fusion may include a communication unit, a processor, and a machine-readable storage medium.

[0164] The communication unit is the interface for data interaction between the system and the outside world. It is responsible for receiving multi-source dynamic wind shear data from various mobile data acquisition carriers (such as meteorological detection drones, air traffic control radars, onboard meteorological sensors, and ground meteorological observation stations), and transmitting the processed wind shear reproduction scene data to external display devices or application systems for presentation or further application.

[0165] The processor is the control center of the system, responsible for executing machine-executable instructions stored in a machine-readable storage medium to realize the wind shear scene reproduction method based on multi-source data fusion described in this invention. The processor performs a series of processes on the received multi-source dynamic wind shear data, including cross-carrier data trajectory anchoring, scene symbiotic element mining and dynamic adaptation mapping, multi-source data progressive fusion, and scene visualization reconstruction, by running relevant software programs and / or modules. Ultimately, it generates a wind shear evolution fusion representation and constructs a scene visualization reconstruction link to output a dynamic wind shear reproduction scene. The processor can be a single processing core or a combination of multiple processing cores; for example, it can integrate an application processor and a modem processor to handle internal system computation and external communication tasks respectively.

[0166] Machine-readable storage media are used to store machine-executable instructions, related software programs, modules, intermediate data, and final results executed by the processor. These machine-executable instructions specifically include instruction sets for implementing wind shear scenario reproduction methods based on multi-source data fusion. Machine-readable storage media can be separated from the processor and accessed by the processor via a bus interface; alternatively, it can be integrated into the processor and interact with external systems via a communication unit.

[0167] Based on the same inventive concept Figure 2 The system shown executes instructions stored in the storage medium through a processor, thus fully realizing... Figure 1 The method shown ultimately outputs a wind shear reconstruction scenario through the communication unit, providing accurate and comprehensive dynamic evolution information of wind shear for applications such as meteorological research, aviation training, and disaster early warning.

[0168] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for reproducing wind shear scenarios based on multi-source data fusion, characterized in that, The method includes: Acquire multi-source wind shear dynamic data, which is a set of raw dynamic observation data recorded in real time by different mobile acquisition carriers in the wind shear influence area; Based on the dynamic evolution characteristics of wind shear, cross-carrier data trajectory anchoring is performed on the multi-source wind shear dynamic data to form a wind shear data trajectory set, which is a sequence of related trajectories that reflects the evolution of data with wind shear. Discover the symbiotic elements of the wind shear scene, and dynamically adapt and map the trajectory data in the wind shear data trajectory set with the symbiotic elements of the wind shear scene to generate an element trajectory mapping body; By using a multi-source data progressive fusion method, the element trajectory mapping volume is dynamically correlated and superimposed to form a wind shear evolution fusion representation; Based on the wind shear evolution fusion representation, a scene visualization restoration link is constructed. The complete evolution process of wind shear from occurrence to dissipation is dynamically presented through the scene visualization restoration link, and the wind shear reproduction scene is output.

2. The wind shear scene reproduction method based on multi-source data fusion according to claim 1, characterized in that, The dynamic evolution characteristics based on wind shear are used to anchor the multi-source wind shear dynamic data across carriers, forming a wind shear data trajectory set, including: The attributes of the mobile acquisition carriers for multi-source wind shear dynamic data are distinguished, and the evolution time features and spatial displacement features of the dynamic data corresponding to each mobile acquisition carrier are extracted. The evolution time features are the time sequence information of the data records, and the spatial displacement features are the continuous change information of the data acquisition location. Based on the dynamic evolution law of wind shear from generation to diffusion and then to dissipation, the trajectory anchoring benchmark conditions between data from different mobile acquisition carriers are extracted. The trajectory anchoring benchmark conditions are the corresponding relationship between the wind shear evolution stages of data from different carriers. Based on the trajectory anchoring reference conditions, dynamic data segments corresponding to specific evolution stages of wind shear are extracted from the dynamic data of each mobile acquisition carrier. These dynamic data segments are continuous data records that can reflect the wind shear characteristics of that evolution stage. Extract key evolution node information from each dynamic data segment. The key evolution node information is the recorded content corresponding to the time and spatial points where significant changes occur in wind shear characteristics. Using key evolution node information as anchor points, establish correlation trajectory lines between dynamic data segments corresponding to different mobile acquisition carriers. The correlation trajectory lines are logical paths connecting key nodes of data segments from different carriers. All associated trajectory lines are integrated with their corresponding dynamic data segments to form a preliminary trajectory set covering the entire evolution stage of wind shear; Based on the dynamic evolution continuity of wind shear, missing evolution node associations in the initial trajectory set are supplemented, enabling each dynamic data segment to form a continuous trajectory network through associated trajectory lines, ultimately forming a wind shear data trajectory set.

3. The wind shear scene reproduction method based on multi-source data fusion according to claim 1, characterized in that, The process of mining coexisting elements in the wind shear scene involves dynamically adapting and mapping the trajectory data in the wind shear data trajectory set with the coexisting elements of the wind shear scene to generate an element trajectory mapping body, including: By analyzing the interaction between wind shear and the surrounding environment, we can identify the symbiotic elements of wind shear scenarios that change synchronously with and influence each other in the evolution of wind shear. These symbiotic elements include airflow velocity variation elements, air pressure distribution elements, temperature gradient elements, humidity distribution elements, and air density variation elements. Dynamic feature decomposition is performed on each symbiotic element of the wind shear scenario to obtain the change dimension of each symbiotic element of the wind shear scenario at different stages of wind shear evolution. The change dimension is used to reflect the specific aspects of the changes of the symbiotic element of the wind shear scenario as the wind shear evolves. Based on the changing dimensions of symbiotic elements in each wind shear scenario, a dynamic description specification for elements is established. This dynamic description specification is a unified standard for expressing the changing characteristics of each symbiotic element at different evolution stages. The trajectory data in the wind shear data trajectory set is analyzed, and dynamic data items that can correspond to the change dimension of symbiotic elements are extracted from the trajectory data. The dynamic data items are the specific data content in the trajectory data that records the change of symbiotic elements. According to the aforementioned dynamic description specification for elements, the extracted dynamic data items are adapted in stages to the corresponding co-existing elements and change dimensions of the wind shear scenario, and the correspondence between data and elements at different evolution stages is established. The dynamic data items corresponding to each symbiotic element of the wind shear scenario at each evolution stage are classified and integrated to form a staged data group for the symbiotic element. The staged data group contains all the dynamic data items of the symbiotic element of the wind shear scenario at each evolution stage of wind shear. All symbiotic elements are bound to their phased data sets with corresponding wind shear data trajectories to form an element trajectory mapping body that includes the dynamic correspondence between elements, data, and evolution stages.

4. The wind shear scene reproduction method based on multi-source data fusion according to claim 1, characterized in that, The step of dynamically superimposing related information on the element trajectory mapping volume through a multi-source data progressive fusion method to form a wind shear evolution fusion representation includes: The element trajectory mapping volume is analyzed to separate the dynamic data items corresponding to different mobile acquisition carriers, and the symbiotic elements, change dimensions and evolution stages of the wind shear scene to which each dynamic data item belongs are defined. Analyze the information complementarity dimension of dynamic data items corresponding to the same symbiotic element on different mobile acquisition carriers under the same evolution stage, and determine the information supplementation direction of each dynamic data item when describing the feature of the element in this evolution stage; Based on the direction of information supplementation, a progressive fusion rule for multi-source data is constructed. The progressive fusion rule is a logical guideline that guides the gradual integration of data items from different carriers according to the evolution stage. According to the progressive fusion rule, information is superimposed on the dynamic data items of different carriers corresponding to all symbiotic elements in the first evolution stage of wind shear to form a comprehensive dynamic description of each symbiotic element in the first evolution stage. Based on the comprehensive dynamic description of the first evolution stage, dynamic data items of different carriers in the next evolution stage are superimposed to form a cross-stage dynamic evolution description of elements. The cross-stage dynamic evolution description of elements is used to reflect the change process of elements from the first stage to the next stage. The dynamic data items of all evolution stages are progressively superimposed to form a complete dynamic evolution description of the symbiotic elements of each wind shear scenario. The dynamic evolution description covers the continuous change characteristics of the elements in the entire evolution process of wind shear. The complete dynamic evolution descriptions of all symbiotic elements are linked and integrated according to the evolution logic of wind shear to form a wind shear evolution fusion representation that reflects the dynamic changes and interactions of each element during the wind shear evolution process.

5. The wind shear scene reproduction method based on multi-source data fusion according to claim 1, characterized in that, The scene visualization reconstruction link constructed based on the wind shear evolution fusion representation dynamically presents the complete evolution process of wind shear from occurrence to dissipation through the scene visualization reconstruction link, and outputs the wind shear reconstruction scene, including: The wind shear evolution fusion characterization was analyzed to extract the complete dynamic evolution description of all symbiotic elements and the interaction information between elements contained therein; Based on the natural evolution sequence of wind shear, the scene is divided into progressive stages for concrete reconstruction. Each progressive stage is a scene presentation segment that corresponds one-to-one with the wind shear evolution stage. The complete dynamic evolution description and element interaction information in the wind shear evolution fusion representation are allocated according to progressive stages, and the dynamic feature information corresponding to each reduction stage is determined. The dynamic feature information corresponding to each restoration stage is transformed into scene visualization data. The scene visualization data is a record of specific features that can be directly transformed into visual scene elements, including details of element changes, spatial distribution status, and temporal evolution rhythm. According to the chronological order of the progressive stages, the scene visualization data of each stage are continuously connected to form a scene reconstruction data stream. The scene reconstruction data stream is a sequence of visualization data of each stage arranged in the order of wind shear evolution. Based on the scene reconstruction data stream, a scene representation reconstruction link is constructed that can carry data transmission and realize dynamic presentation. The scene representation reconstruction link is a presentation path that connects each progressive stage and realizes dynamic data flow. The scene visualization data of each stage is transmitted sequentially through the scene visualization reconstruction link, dynamically presenting the wind shear scene characteristics of each progressive stage, completely reconstructing the evolution process of wind shear from occurrence to dissipation, and outputting the wind shear reproduction scene.

6. The wind shear scene reproduction method based on multi-source data fusion according to claim 2, characterized in that, The step of establishing the correlation trajectory lines between dynamic data segments corresponding to different mobile acquisition carriers, using key evolution node information as anchor points, includes: Feature extraction is performed on the key evolution node information in each dynamic data segment to obtain the wind shear feature parameters corresponding to each key node. The wind shear feature parameters are specific data contents that can identify the wind shear state of the key node. The key evolution node information of dynamic data segments corresponding to different mobile acquisition carriers is compared pairwise to identify key nodes with the same or similar wind shear characteristic parameters. The similar wind shear characteristic parameters are parameters used to reflect the same wind shear evolution state. Establish node association identifiers for each group of key nodes with the same or similar wind shear characteristic parameters. The node association identifiers are exclusive markers that mark the correspondence between key nodes in different carrier data segments. Based on node association identifiers, an initial association line is constructed to connect key nodes of dynamic data fragments from different mobile acquisition carriers. The initial association line is a basic logical path that only connects the corresponding key nodes. Extract the dynamic data content around the key nodes connected by the initial correlation line, and analyze the consistency of the changing trend of the dynamic data content. The consistency of the changing trend is the matching performance of the direction and magnitude of the data content changing over time. Based on the consistency of the changing trend, adjust the connection strength of the initial correlation lines so that the correlation lines between key nodes with consistent changing trends form a tight connection. All adjusted correlation lines are integrated with the corresponding key nodes and dynamic data segments to form correlation trajectory lines that can accurately link data segments from different carriers. These correlation trajectory lines are used to reflect the synchronous recording relationship of different carrier data on the same wind shear evolution process.

7. The wind shear scene reproduction method based on multi-source data fusion according to claim 3, characterized in that, The step involves adapting the extracted dynamic data items to the corresponding wind shear scene co-existing elements and change dimensions in stages, according to the dynamic description specification of the elements, to establish the correspondence between data and elements at different evolution stages, including: All extracted dynamic data items are classified according to the wind shear evolution stage to form dynamic data groups corresponding to each evolution stage. Each dynamic data group contains dynamic data items recorded by all mobile acquisition carriers in that evolution stage. In accordance with the aforementioned dynamic description specification for elements, the target data features corresponding to each symbiotic element of the wind shear scenario at each evolution stage are determined. The target data features are the ideal data representation of the change dimension of the symbiotic element of the wind shear scenario at this evolution stage. The data items in the dynamic data group of each evolution stage are compared with the target data features of each symbiotic element in that evolution stage to initially screen out candidate data items that match the target data features. Extract the core data content from the candidate data items and perform detailed matching with the change dimensions of the corresponding co-existing elements to confirm that the core data content can accurately describe the characteristics of the change dimension. Based on the matching results, candidate data items are assigned to the change dimensions of coexisting elements in each wind shear scenario at each evolution stage to form a preliminary correspondence. Bind the data items in the preliminary correspondence to the evolution nodes in the wind shear data trajectory, so that the data items correspond to the specific nodes of the wind shear evolution; The correspondences of all evolution stages are integrated to form a data and element stage correspondence that covers the entire evolution process of wind shear, all symbiotic elements and change dimensions.

8. The wind shear scene reproduction method based on multi-source data fusion according to claim 4, characterized in that, The process involves overlaying information from different carrier dynamic data items corresponding to all symbiotic elements in the first evolution stage of wind shear according to the progressive fusion rule, forming a comprehensive dynamic description of each symbiotic element in this first evolution stage, including: All dynamic data items in the first stage of wind shear evolution are grouped according to the symbiotic elements of the wind shear scenario to form a multi-carrier data subset corresponding to each symbiotic element of the wind shear scenario. Each multi-carrier data subset contains the dynamic data items recorded by all mobile acquisition carriers of the symbiotic element of the wind shear scenario in the first stage. According to the progressive fusion rule, the information weight of different mobile acquisition carrier data items in each multi-carrier data subset is determined. The information weight is a superimposed priority determined based on the accuracy and completeness of the data item's description of the feature characteristics. Based on information weight, core information is extracted from each data item in each multi-carrier data subset. The core content that plays a key role in describing the feature characteristics of each data item is retained, while irrelevant data content is removed. All core information of the same multi-carrier data subset is summarized to form the core information set of the symbiotic element in the first stage. The core information set contains the comprehensive feature description of the symbiotic element of the wind shear scenario in the first stage. Analyze the differences in information expression within the core information set, unify information with different expression methods but consistent core content, and form a standardized information expression format; According to the changing dimensions of this symbiotic element, the standardized core information is classified and arranged so that the information can clearly present the dynamic characteristics of the element according to the changing dimensions. The standardized core information after classification and arrangement is transformed into a coherent descriptive text, forming a comprehensive dynamic description that reflects the dynamic characteristics of the symbiotic element in the first evolutionary stage.

9. The wind shear scene reproduction method based on multi-source data fusion according to claim 5, characterized in that, The process of sequentially connecting the scene visualization data of each stage according to the progressive order to form a scene reconstruction data stream includes: Extract the core feature records from the scenario visualization data of each progressive stage. The core feature records are key data contents used to reflect the main scenario features of wind shear in that progressive stage. Analyze the correlation of changes in core feature records in adjacent progressive stages to determine the transitional change pattern of core features from the previous stage to the next stage. The transitional change pattern is the natural change logic of core features evolving with wind shear. Based on the transition change pattern, dynamic transition data between adjacent stages is generated. The dynamic transition data is intermediate change description data that connects the core features of the previous stage with the core features of the next stage, and is used to reflect the gradual change process of the features. The scene visualization data and dynamic transition data of the previous stage and the scene visualization data of the next stage are linked together to form a stage connection unit, which is used to reflect the continuous scene changes between two adjacent stages. The connection processing of all adjacent progressive stages is completed in sequence, generating multiple stage connection units, each stage connection unit corresponding to two consecutive restoration stages; Extract the data flow logic in each stage connection unit, so that the data transmission within the stage connection unit presents the natural evolution rhythm of wind shear; All stage connection units are integrated in the order of progressive stages to form a scene reconstruction data stream that can continuously present the scene characteristics of the entire wind shear evolution process. The data in the scene reconstruction data stream can be dynamically transferred in sequence to realize the continuous presentation of the scene.

10. A wind shear scene reproduction system based on multi-source data fusion, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the wind shear scene reproduction method based on multi-source data fusion as described in any one of claims 1 to 9 by executing the machine-executable instructions.

Images