Airport bird flock timely early warning prevention and control method and system based on radar monitoring

Abstract

The invention relates to the technical field of bird strike prevention and control, in particular to an airport bird flock timely early warning prevention and control method and system based on radar monitoring, which can scan a monitoring area in real time by using a radar to obtain real-time dynamic data of bird flock activities, integrate the real-time dynamic data into a monitoring data set and send the monitoring data set to a cloud server. Generating spatial mapping data capable of reflecting spatial density distribution of bird flocks in the monitoring area and gathering hot spots, and defining a risk area; if it is judged that the bird flock enters the risk area according to the real-time dynamic data, dynamic adjustment parameters are generated, and a scanning strategy of the radar monitoring system is optimized and adjusted; and generating a real-time updated monitoring report according to the real-time dynamic data, further generating a prevention and control instruction data set, and generating a final response scheme. According to the technical scheme, the target area is monitored based on the radar, the data is accurate, the coverage range is large, the radar scanning strategy is adjusted after the bird flock enters the risk area, the data monitoring precision is improved, and the generated monitoring report is high in precision and timeliness.

Description

Technical Field

[0001] The present invention relates to the technical field of bird strike prevention and control, and in particular to a method and system for timely early warning and prevention of bird flocks at airports based on radar monitoring. Background Art

[0002] Current methods for monitoring and controlling bird flock activity around airports have significant shortcomings. Many solutions rely too heavily on manual observation or a single data source, making it difficult to fully capture the dynamic changes in bird flock activity, especially when integrating multi-dimensional information in complex environments. Furthermore, existing methods lack the accuracy and timeliness of early warnings when predicting peak periods and key areas of bird activity, making it difficult to meet airports' needs for rapid response. Furthermore, when it comes to using technology to monitor the real-time dynamics of bird flock activity, traditional monitoring methods struggle to cover large areas, and data updates are slow, making it easy to miss key activity windows. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method and system for timely early warning and prevention of bird flocks at airports based on radar monitoring, so as to solve the problems in the existing technology of monitoring bird flock activities, such as slow data update, insufficient data accuracy and timeliness, and difficulty in covering large areas.

[0004] According to a first aspect of an embodiment of the present invention, a method for timely warning and control of bird flocks at airports based on radar monitoring is provided, comprising: Using a pre-deployed radar monitoring system, the monitoring area is scanned in real time to obtain real-time dynamic data on bird flock activity. Data on the location, number, and movement trajectory of the bird flocks are obtained from the scanned data and integrated into a monitoring data set. Generate spatial mapping data reflecting the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area based on the monitoring data set, define risk areas based on the spatial mapping data; and generate dynamic adjustment parameters if it is determined based on the real-time dynamic data that a bird flock has entered the risk area; Optimizing and adjusting the scanning strategy of the radar monitoring system according to the dynamic adjustment parameters; generating a real-time updated monitoring report based on the real-time dynamic data; Based on the real-time updated monitoring reports, a prevention and control instruction dataset containing structured information for guiding airport operation adjustments is generated, and the final response plan is generated based on the prevention and control instruction dataset.

[0005] Preferably, when generating spatial mapping data capable of reflecting the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area based on the monitoring data set, the method further includes: Performing time series segmentation on the data in the monitoring data set to generate bird flock activity segment data containing different time periods; Based on the bird flock activity segment data, feature extraction is performed on the preset key indicators in each time period to obtain the activity characteristics of the bird flock in each time period; When generating the spatial mapping data, hotspot areas of different levels in different time periods are generated according to the activity characteristics.

[0006] Preferably, the method further comprises: Cluster analysis of bird flock activity segment data; Obtain environmental data of the monitoring area; The cluster analysis results and environmental data were analyzed for environmental factors to obtain the activity characteristics of the bird flocks and the distribution of activity patterns of environmental factors; Establishing a bird flock activity prediction model under specific environmental conditions based on the distribution of activity patterns; It is determined whether the bird flock has entered a risk area based on the bird flock activity prediction model and the real-time dynamic data.

[0007] Preferably, the method further comprises: Obtain the flock behavior records within a preset historical time span, and extract the time elements, spatial elements, and activity characteristic elements from each flock behavior record; All extracted elements are integrated with the current distribution of bird flock activity patterns to construct a three-dimensional feature matrix of time, space and behavior; generating a feature data set reflecting the activity trend of the bird flock according to the three-dimensional feature matrix; Determining the peak activity period of the bird flock based on the characteristic data set; A time period adjustment parameter of the radar monitoring system is generated according to the peak activity period of the bird flock.

[0008] Preferably, the method further comprises: Set thresholds for multiple key indicators of bird flock activity trends based on user instructions; If any key indicator of the bird flock activity trend in the feature dataset exceeds the set threshold, the current feature dataset and the historical feature dataset are input into the machine learning model for secondary peak prediction, generating a peak prediction result with a time window and corresponding probability value; Marking high-risk periods based on the peak prediction results; Based on the marked high-risk periods, high-risk period adjustment parameters of the radar monitoring system are generated.

[0009] Preferably, the method further comprises: Determine the time window based on peak bird activity periods or high-risk periods; Data corresponding to the time window is filtered out from real-time dynamic data and historical monitoring data sets to extract spatial features. The extracted spatial features include the distribution of bird flock activity centers, density gradient changes, and trajectory concentration. Based on the spatial characteristics, a spatial density distribution map of bird flock activities and a hotspot library are established within the monitoring area through a spatial aggregation algorithm, and risk areas are defined based on the spatial density distribution map and the hotspot library. An airport infrastructure layer is obtained, and the risk area is located on the airport infrastructure layer.

[0010] Preferably, the method further includes: obtaining the peak flight period of the airport, and generating a flight peak adjustment parameter of the radar monitoring system according to the peak flight period of the airport.

[0011] Preferably, the method further comprises: The generated control and prevention instruction dataset includes information on bird flock type, projected path, risk level, recommended response timing, and intervention priority; The control and prevention instruction data set is sent to the airport flight dispatch system and ground operation system.

[0012] Preferably, when generating the spatial density distribution map and the hotspot library corresponding to the monitoring area, the following steps are also included: According to the uniform spatial resolution, the monitoring area is divided into regular grid cells to obtain a geographic grid model; Mapping the data in the monitoring data set to the grid corresponding to the geographic grid model, counting the number and density changes of bird flocks in each grid at unit time intervals, and establishing a spatiotemporal dynamic data set based on the statistical data; Based on the spatiotemporal dynamic dataset, the data on the grid is smoothed to generate a risk heat map.

[0013] According to a second aspect of an embodiment of the present invention, there is provided an airport bird flock timely warning and control system based on radar monitoring, comprising: The radar monitoring module is used to use a pre-deployed radar monitoring system to scan the monitoring area in real time to obtain real-time dynamic data on bird flock activities. The location, number, and movement trajectory of the bird flocks are obtained from the scanned data and integrated into a monitoring data set. A spatial mapping and comparison module is configured to generate spatial mapping data reflecting the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area based on the monitoring data set, define risk areas based on the spatial mapping data, and generate dynamic adjustment parameters if the bird flock is determined to have entered the risk area based on the real-time dynamic data; A scanning strategy optimization module is used to optimize and adjust the scanning strategy of the radar monitoring system according to the dynamic adjustment parameters; and generate a real-time updated monitoring report based on real-time dynamic data; The prevention and control instruction generation module is used to generate a prevention and control instruction dataset containing structured information for guiding airport operation adjustments based on real-time updated monitoring reports, and to generate a final response plan based on the prevention and control instruction dataset.

[0014] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects: It is understandable that the technical solution shown in the present invention can use radar to scan the monitoring area in real time, obtain real-time dynamic data of bird flock activities, integrate it into a monitoring data set, and then generate spatial mapping data that can reflect the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area, and define risk areas; if the real-time dynamic data determines that the bird flock has entered the risk area, dynamic adjustment parameters are generated to optimize the scanning strategy of the radar monitoring system; based on the real-time dynamic data, a real-time updated monitoring report is generated, and then a prevention and control instruction data set is generated to generate the final response plan. The technical solution shown in the present invention is based on radar monitoring of the target area, with accurate data and wide coverage. At the same time, the radar scanning strategy is adjusted after the bird flock enters the risk area, improving the data monitoring accuracy, so that the generated monitoring report is more accurate and timely. Through the generated prevention and control instruction data set, accurate early warning and prevention of bird flock activities can be achieved, effectively reducing the risk of bird strikes and ensuring aviation safety.

[0015] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] Figure 1 This is a schematic diagram showing the steps of a method for timely warning and prevention of bird flocks at airports based on radar monitoring according to an exemplary embodiment; Figure 2 The present invention is a schematic block diagram of an airport bird flock timely early warning and control system based on radar monitoring according to an exemplary embodiment. DETAILED DESCRIPTION

[0018] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0019] In one embodiment, Figure 1 This is a schematic diagram of a method for timely warning and controlling bird flocks at airports based on radar monitoring according to an exemplary embodiment. Figure 1 , providing a timely early warning and prevention method for bird flocks at airports based on radar monitoring, including: Step S11: Use the pre-deployed radar monitoring system to scan the monitoring area in real time to obtain real-time dynamic data of bird flock activities; obtain the location, number and movement trajectory of the bird flock from the scanned data and integrate them into a monitoring data set.

[0020] The deployed radar monitoring system has a large coverage area and can conduct all-weather scanning of the area around the airport, obtaining real-time dynamic data on bird activities, and then generating a monitoring data set containing location, number and movement trajectory, obtaining preliminary information on the distribution of bird flocks.

[0021] In practical applications, an X-band or C-band high-frequency phased array radar, configured with a multi-antenna array, achieves 360-degree coverage within a radius of approximately 10 kilometers around the airport. The radar unit uses the Doppler effect to measure the flight speed of the flock and, based on echo intensity, determines the target volume and density, thereby establishing a preliminary three-dimensional model of bird flock activity. Data acquisition is set at twice per second to ensure sufficient temporal resolution and coverage accuracy. The acquired echo data is filtered, target extracted, and tracked using a digital signal processor (DSP), generating a standardized monitoring data set containing individual bird coordinates, flight direction, velocity vector, and flock density parameters.

[0022] Step S12: Generate spatial mapping data that can reflect the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area based on the monitoring data set, and define the risk area based on the spatial mapping data; if the real-time dynamic data determines that the bird flock has entered the risk area, generate dynamic adjustment parameters.

[0023] In practice, a gridding algorithm is used to divide the monitoring area into fixed grid cells (e.g., 50×50 meters). Density-weighted interpolation is then performed on the monitoring data to generate a spatial density distribution map in the form of a heat map. Simultaneously, clustering based on an improved K-means algorithm is used to identify high-frequency areas of historical bird flock activity, creating a hotspot database. These spatial density distribution map and hotspot database serve as spatial mapping data. Real-time data is compared with this spatial mapping data. If a flock enters a defined risk area, the system triggers a dynamic adjustment process. Adjustment parameters include the radar scanning angular subdivision level, pulse repetition frequency (PRF), and the resolution threshold of radar signal processing.

[0024] Step S13: Optimize and adjust the scanning strategy of the radar monitoring system according to the dynamic adjustment parameters; and generate a real-time updated monitoring report according to the real-time dynamic data.

[0025] In practical applications, these dynamically adjusted parameters are used, along with an embedded fuzzy control algorithm, to comprehensively assess factors such as target activity frequency, density, and movement trends, determining radar power allocation, scanning priority, and data transmission bandwidth configuration. Based on this, the system generates real-time monitoring reports that include not only general flock information but also behavioral trend analysis and risk assessment indicators, providing a technical basis for downstream dynamic decision-making.

[0026] Step S14: Generate a prevention and control instruction data set containing structured information for guiding airport operation adjustments based on the real-time updated monitoring report, and generate a final response plan based on the prevention and control instruction data set.

[0027] In practice, step S14 integrates a rule-based inference mechanism to integrate and analyze real-time monitoring reports with the airport's operational plan (including take-off and landing slots and route distribution), generating a prioritized prevention and control instruction dataset. This dataset, output in a structured JSON format, includes specific operational instructions such as recommended flight time adjustments, activation zones for prevention and control equipment, and recommended interference device power levels. This dataset can be automatically connected to the airport's operational control system via an API, achieving a closed-loop response.

[0028] It is understandable that the technical solution shown in the present invention can use radar to scan the monitoring area in real time, obtain real-time dynamic data of bird flock activities, integrate it into a monitoring data set, and then generate spatial mapping data that can reflect the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area, and define risk areas; if the real-time dynamic data determines that the bird flock has entered the risk area, dynamic adjustment parameters are generated to optimize the scanning strategy of the radar monitoring system; based on the real-time dynamic data, a real-time updated monitoring report is generated, and then a prevention and control instruction data set is generated to generate the final response plan. The technical solution shown in the present invention is based on radar monitoring of the target area, with accurate data and wide coverage. At the same time, the radar scanning strategy is adjusted after the bird flock enters the risk area, improving the data monitoring accuracy, so that the generated monitoring report is more accurate and timely. Through the generated prevention and control instruction data set, accurate early warning and prevention of bird flock activities can be achieved, effectively reducing the risk of bird strikes and ensuring aviation safety.

[0029] Preferably, when generating the monitoring data set, the process further includes: using data cleaning techniques to remove noise and outliers in the monitoring data set. It is understood that using mean filtering, median filtering, and Z-score-based statistical methods to clean abnormal data points and environmental noise in the initial monitoring data set improves the accuracy of the data in subsequent processing.

[0030] In a preferred embodiment, when generating spatial mapping data reflecting the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area based on the monitoring data set, step S12 further includes: The data in the monitoring data set are segmented into time series to generate bird flock activity segment data containing different time periods; based on the bird flock activity segment data, feature extraction is performed on preset key indicators in each time period to obtain the activity characteristics of the bird flock in each time period; when generating spatial mapping data, hot spots of different levels in different time periods are generated according to the activity characteristics.

[0031] This example partitions the cleaned data based on time tags and employs a sliding window mechanism to segment the bird flock activity data into time series. For example, every 10 minutes is analyzed as a segment, thereby forming a continuous set of bird flock activity time segments. The system then extracts features based on key indicators such as changes in flock size, movement speed, and spatial density within each time segment, calculates corresponding activity feature parameters, and uses cluster analysis to identify bird flock behavior patterns in different time segments. These activity features are annotated as hotspots of varying levels within the spatial mapping data image and serve as an important basis for risk identification.

[0032] In this preferred embodiment, the method further includes: Perform cluster analysis on bird flock activity segment data; obtain environmental data of the monitoring area; perform environmental factor analysis on the cluster analysis results and environmental data to obtain the activity characteristics of the bird flock and the activity pattern distribution of environmental factors; establish a bird flock activity prediction model under specific environmental conditions based on the activity pattern distribution; and determine whether the bird flock has entered a risk area based on the bird flock activity prediction model and the real-time dynamic data.

[0033] This embodiment further introduces a clustering algorithm and environmental factor fusion analysis mechanism based on time series segmentation and feature extraction. First, the cleaned bird flock activity segment data is subjected to a K-means or DBSCAN cluster analysis based on time periods. This analysis forms cluster centers based on the number of birds, speed, density, and activity area, and divides typical activity patterns. The system then accesses the real-time and historical meteorological databases surrounding the airport to extract environmental data that matches the corresponding time period, including temperature, wind speed, wind direction, humidity, and other environmental data. Through correlation analysis (such as the Pearson correlation coefficient and Spearman rank correlation) or multivariate regression modeling, the mapping relationship between bird flock activity characteristics and environmental factor variables is identified. The distribution of bird flock activity characteristics and environmental factors is obtained, and a predictive model for bird flock aggregation or migration under specific environmental conditions is established.

[0034] This example combines cluster analysis results with environmental factor analysis to generate a set of multidimensional activity patterns. These patterns reveal common bird movement locations, intensities, and behavioral patterns within specific temperature or wind speed ranges. These patterns are then incorporated into forecasts for high-risk periods and areas, assisting the system in making early warning decisions under emerging weather conditions.

[0035] In this preferred embodiment, the method further includes: Obtain historical bird flock behavior records within a preset time span, extract time elements, space elements, and activity characteristic elements from each bird flock behavior record; fuse all extracted elements with the current bird flock activity pattern distribution to construct a three-dimensional time-space-behavior feature matrix; generate a feature data set reflecting the bird flock activity trend based on the three-dimensional feature matrix; derive the peak activity period of the bird flock based on the feature data set; and generate the time period adjustment parameters of the radar monitoring system based on the peak activity period of the bird flock.

[0036] In practice, this implementation method can further construct a comprehensive trend prediction model based on the identification of environmental factors and bird flock activity patterns. First, the system collects historical bird flock behavior records spanning at least 12 months. Data sources include radar monitoring systems, manual observation reports, and third-party databases. For each record, elements such as time tags (hour, week, month), spatial location (latitude and longitude, distance from the runway, etc.), and activity characteristics (number, flight altitude, speed) are extracted and integrated with the current distribution of bird flock activity patterns. The system uses a multidimensional tensor structure to construct a three-dimensional feature matrix of time, space, and behavior, and performs dimensionality reduction processing through principal component analysis (PCA) or t-SNE methods to extract representative feature factors.

[0037] The data in the three-dimensional feature matrix is ​​trained on a predictive model (such as an LSTM (Long Short-Term Memory) network or a random forest regression model) to generate trend modeling. This model identifies high-frequency activity patterns during specific time periods (e.g., early morning or evening), seasonality (e.g., spring migration), and spatial locations (e.g., the humid area east of the airport). Based on this data, the system generates a feature dataset reflecting bird flock activity trends. This feature dataset is used to identify potential peak activity periods. Based on these peak activity periods, the radar monitoring system's time adjustment parameters are generated to trigger appropriate monitoring frequency and prevention and control response strategies.

[0038] In this preferred embodiment, the method further includes: Thresholds for multiple key indicators of bird flock activity trends are set according to user instructions; if any key indicator of bird flock activity trends in a feature data set exceeds the set threshold, the current feature data set and the historical feature data set are input together into a machine learning model for secondary peak prediction, generating a peak prediction result with a time window and a corresponding probability value; high-risk periods are marked based on the peak prediction results; and based on the marked high-risk periods, high-risk period adjustment parameters for the radar monitoring system are generated.

[0039] In specific practice, this embodiment further introduces a machine learning prediction mechanism based on the construction of a feature data set to achieve accurate identification of the peak activity period of bird flocks.

[0040] The system first sets thresholds for several key activity trend indicators, such as a daily population growth rate exceeding 15%, an expansion of the activity range beyond a 300-meter radius, or a risk index exceeding 70% for a corresponding historical time period. When any or more of these thresholds are reached, the system automatically invokes a trained machine learning model for further peak prediction. This model, which can utilize algorithms such as support vector regression (SVR), gradient boosted decision trees (GBDT), or long short-term memory networks (LSTM), learns temporal distribution patterns based on historical data showing patterns of concentrated bird activity.

[0041] The prediction model inputs current and historical feature datasets, including time, location, activity intensity, and weather parameters. It outputs a set of predictions with time windows (e.g., 06:30–08:00) and corresponding probability values ​​(e.g., 87.3%). These predictions are used to mark high-risk time periods. The system then dynamically adjusts radar scanning priorities, pre-activation times for control equipment, and monitoring frequency configurations to ensure optimal allocation of control resources during critical periods.

[0042] In this way, the system implements a data-driven proactive risk identification strategy, making the early warning mechanism more time-sensitive and predictive accurate, and improving the airport's operational safety management capabilities during peak bird flock periods.

[0043] In this preferred embodiment, the method further includes: A time window is derived based on the peak activity period or high-risk period of bird flocks; data corresponding to the time window is filtered out from real-time dynamic data and historical monitoring data sets, and spatial features are extracted. The extracted spatial features include the distribution of bird flock activity centers, density gradient changes, and trajectory concentration; based on the spatial features, a spatial aggregation algorithm is used to establish a spatial density distribution map and an aggregation hotspot library of bird flock activities within the monitoring area, and risk areas are defined based on the spatial density distribution map and the aggregation hotspot library; an airport infrastructure layer is obtained, and the risk area is located on the airport infrastructure layer.

[0044] In practice, after completing the prediction for peak hours, this embodiment further introduces a spatial feature analysis mechanism to accurately locate bird flocking areas. The system first uses time windows derived from peak bird activity periods or high-risk periods to filter real-time monitoring data and historical comparative data within the corresponding time period, extracting representative spatial features, including the distribution of bird flock activity centers, density gradient changes, and trajectory concentration. Using spatial aggregation algorithms (such as kernel density estimation, Voronoi diagram analysis, or hotspot identification algorithms), a heat map of bird flock activity is created and areas are marked in the airport perimeter. The resulting spatial mapping is then refined into multiple risk-level areas, each with specific geographic coordinate boundaries, historical bird flock density statistics, and behavioral trend curves. The system integrates airport infrastructure layers (such as runways, taxiways, and terminal locations) to accurately locate high-risk areas on the airport operations map. This positioning information can be used to guide radar beam scheduling, optimize the deployment strategy of prevention and control equipment, and dynamically adjust flight takeoff and landing paths.

[0045] In a preferred embodiment, the method further includes: obtaining the peak flight period of the airport, and generating a flight peak adjustment parameter of the radar monitoring system according to the peak flight period of the airport.

[0046] This embodiment adopts a time period priority weight mechanism to adjust the radar scanning frequency according to the peak flight hours of the airport and limit the allocation of scanning resources to high-risk periods.

[0047] In this implementation, the scanning strategy optimization module incorporates a time-based priority weighting mechanism to dynamically allocate radar resources and schedule scanning tasks. The system first obtains the airport's daily flight schedule and divides the day into multiple time periods, such as early morning (06:00–08:00), midday (11:00–13:00), and evening (17:00–19:00). Weights are then assigned to each time period based on flight density, takeoff and landing frequency, and historical bird strike risk data. For example, a high-density flight period might be assigned a weight of 0.9, while a low-density period might only be assigned a weight of 0.3.

[0048] The system associates the radar scanning frequency, resolution, and scanning angle range with this weight factor in its scanning strategy, forming a dynamic parameter scheduling rule. Specifically, during high-weighted periods, the system automatically increases the radar scanning frequency (for example, from once per minute to once every 20 seconds), reduces the scanning angle range to improve spatial accuracy, and increases the update frequency of the target tracking algorithm. Simultaneously, during off-peak hours, radar resource utilization is appropriately reduced to maintain overall system energy consumption and load balance.

[0049] Furthermore, to centralize resource allocation, the system incorporates a scanning resource scheduling table that controls the spatial and temporal distribution of radar scanning beams and prioritizes multiple radar missions to high-risk areas and overlapping high-priority time periods. This strategy enables the system to cover critical areas and key time points despite limited resources, ensuring monitoring effectiveness and emergency response capabilities.

[0050] It should be noted that the method further includes: The generated control and prevention instruction dataset includes information on bird flock type, projected path, risk level, recommended response timing, and intervention priority; The control and prevention instruction data set is sent to the airport flight dispatch system and ground operation system.

[0051] In this embodiment, the system not only outputs structured control and prevention command data but also connects to the airport's operational control system via a pre-defined communication interface, enabling automatic information transmission and system-level coordinated responses. This interface adheres to a unified data communication protocol (such as a RESTful API or Sockets communication standard) and supports data encapsulation in JSON or XML formats, ensuring seamless parsing and reception of command content across different systems.

[0052] During specific operations, the control and prevention instruction generation module encapsulates control and prevention instructions, including high-risk time periods, regional coordinates, and recommended response measures (such as adjusting takeoff order, delaying flight plans, and activating bird repellent devices), into a structured data package and sends it through an interface to the airport's flight scheduling system and ground operations system. Based on the instruction information, the receiving system triggers corresponding logical judgments and operational processes, such as automatically adjusting flight schedules, notifying the control tower to change flight paths, and issuing warnings to ground personnel.

[0053] The control and prevention instruction dataset includes information on bird flock type, expected path, risk level, recommended response timing, and intervention priority to support the formulation of multi-level response strategies.

[0054] In this implementation, when generating a control and prevention instruction dataset, multi-dimensional information is integrated to construct structured response instructions, supporting a hierarchical response mechanism and strategy optimization. First, the system uses radar echo characteristics (such as target size, flight speed, and reflection intensity) combined with a historical database to identify and label bird flock types, such as migratory birds, waterfowl, and predatory species.

[0055] After identifying the flock's species, the system predicts its future path based on its trajectory and meteorological data. It also determines, based on the airport's operational area, whether there is any overlap with flight activity areas. The system assigns a risk level to each individual or group, taking into account factors such as flock size, species, trajectory, and flight density. Using a risk scoring model (such as fuzzy logic or Bayesian networks), the system assesses its potential impact on operational safety and presents it as a numerical grade (e.g., high, medium, or low).

[0056] The system also dynamically recommends response times (e.g., "activate within 5 minutes," "pre-process 30 minutes before takeoff") based on radar update cycles, flock speed, and response device activation times. It also prioritizes intervention measures (e.g., acoustic bird repellent, laser intervention, flight adjustments) and comprehensively evaluates them based on cost, timeliness, and applicability. Ultimately, the resulting control and prevention instruction dataset includes comprehensive response strategy recommendations, enabling the airport operations center to implement a tiered response strategy based on varying risk levels.

[0057] It should be noted that when generating the spatial density distribution map and hotspot database corresponding to the monitoring area, the following are also included: According to the uniform spatial resolution, the monitoring area is divided into regular grid units to obtain a geographic grid model; the data in the monitoring dataset is mapped to the grid corresponding to the geographic grid model, and the changes in the number and density of bird flocks in each grid are counted according to unit time intervals. A spatiotemporal dynamic dataset is established based on the statistical data; based on the spatiotemporal dynamic dataset, the data on the grid is smoothed to generate a risk heat map.

[0058] In this embodiment, the system first divides the monitoring area around the airport into regular grid cells at a uniform spatial resolution (e.g., 50 meters x 50 meters), forming a geographic grid model. The system then maps the initial monitoring data collected by the radar onto the corresponding grid cells and counts the number and density of bird flocks within each grid cell at intervals (e.g., every 10 minutes) to create a spatiotemporal dynamic dataset.

[0059] Based on this, the system smooths the raster data using a kernel density estimation algorithm or Gaussian spatial weighting method to generate a risk heat map. This heat map uses a color gradient visualization scheme to depict the intensity and changing trends of bird flock activity, typically using blue to red to represent a gradual transition from low to high density. A timeline dynamically illustrates changes in bird flock distribution over different time periods, creating a visual time series layer. This heat map can be overlaid on an airport Geographic Information System (GIS) map to assist operators in visually identifying high-risk areas and their evolving paths.

[0060] In another embodiment, see Figure 2 , providing an airport bird flock timely warning and control system based on radar monitoring, including: The radar monitoring module is used to use a pre-deployed radar monitoring system to scan the monitoring area in real time to obtain real-time dynamic data on bird flock activities. The location, number, and movement trajectory of the bird flocks are obtained from the scanned data and integrated into a monitoring data set. A spatial mapping and comparison module is configured to generate spatial mapping data reflecting the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area based on the monitoring data set, define risk areas based on the spatial mapping data, and generate dynamic adjustment parameters if the bird flock is determined to have entered the risk area based on the real-time dynamic data; A scanning strategy optimization module is used to optimize and adjust the scanning strategy of the radar monitoring system according to the dynamic adjustment parameters; and generate a real-time updated monitoring report based on real-time dynamic data; The prevention and control instruction generation module is used to generate a prevention and control instruction dataset containing structured information for guiding airport operation adjustments based on real-time updated monitoring reports, and to generate a final response plan based on the prevention and control instruction dataset.

[0061] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0062] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0063] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0064] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0065] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0066] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0067] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0068] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0069] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for timely warning and prevention of bird flocks at airports based on radar monitoring, characterized in that: include: Use the pre-deployed radar monitoring system to scan the monitoring area in real time to obtain real-time dynamic data of bird flock activities; The location, number and movement trajectory of the bird flocks are obtained from the scanned data and integrated into a monitoring data set; generating, based on the monitoring data set, spatial mapping data reflecting the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area, and defining risk areas based on the spatial mapping data; If the real-time dynamic data indicates that the flock of birds has entered a risk area, dynamic adjustment parameters are generated; Optimizing and adjusting the scanning strategy of the radar monitoring system according to the dynamic adjustment parameters; Generate real-time updated monitoring reports based on real-time dynamic data; Based on the real-time updated monitoring reports, a prevention and control instruction dataset containing structured information for guiding airport operation adjustments is generated, and the final response plan is generated based on the prevention and control instruction dataset.

2. The method according to claim 1, characterized in that When generating spatial mapping data that can reflect the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area based on the monitoring data set, it also includes: Performing time series segmentation on the data in the monitoring data set to generate bird flock activity segment data containing different time periods; Based on the bird flock activity segment data, feature extraction is performed on the preset key indicators in each time period to obtain the activity characteristics of the bird flock in each time period; When generating the spatial mapping data, hotspot areas of different levels in different time periods are generated according to the activity characteristics.

3. The method according to claim 2, characterized in that Also includes: Cluster analysis of bird flock activity segment data; Obtain environmental data of the monitoring area; The cluster analysis results and environmental data were analyzed for environmental factors to obtain the activity characteristics of the bird flocks and the distribution of activity patterns of environmental factors; Establishing a bird flock activity prediction model under specific environmental conditions based on the distribution of activity patterns; It is determined whether the bird flock has entered a risk area based on the bird flock activity prediction model and the real-time dynamic data.

4. The method according to claim 3, characterized in that Also includes: Obtain the flock behavior records within a preset historical time span, and extract the time elements, spatial elements, and activity characteristic elements from each flock behavior record; All extracted elements are integrated with the current distribution of bird flock activity patterns to construct a three-dimensional feature matrix of time, space and behavior; generating a feature data set reflecting the activity trend of the bird flock according to the three-dimensional feature matrix; Determining the peak activity period of the bird flock based on the characteristic data set; A time period adjustment parameter of the radar monitoring system is generated according to the peak activity period of the bird flock.

5. The method according to claim 4, characterized in that Also includes: Set thresholds for multiple key indicators of bird flock activity trends based on user instructions; If any key indicator of the bird flock activity trend in the feature dataset exceeds the set threshold, the current feature dataset and the historical feature dataset are input into the machine learning model for secondary peak prediction, generating a peak prediction result with a time window and corresponding probability value; Marking high-risk periods based on the peak prediction results; Based on the marked high-risk periods, high-risk period adjustment parameters of the radar monitoring system are generated.

6. The method according to claim 5, characterized in that Also includes: Determine the time window based on peak bird activity periods or high-risk periods; Data corresponding to the time window is filtered out from real-time dynamic data and historical monitoring data sets to extract spatial features. The extracted spatial features include the distribution of bird flock activity centers, density gradient changes, and trajectory concentration. Based on the spatial characteristics, a spatial density distribution map of bird flock activities and a hotspot library are established within the monitoring area through a spatial aggregation algorithm, and risk areas are defined based on the spatial density distribution map and the hotspot library. An airport infrastructure layer is obtained, and the risk area is located on the airport infrastructure layer.

7. The method according to claim 1, characterized in that Also includes: Obtain the peak flight hours of the airport and generate the flight peak adjustment parameters of the radar monitoring system based on the peak flight hours of the airport.

8. The method according to claim 1, characterized in that Also includes: The generated control and prevention instruction dataset includes information on bird flock type, projected path, risk level, recommended response timing, and intervention priority; The control and prevention instruction data set is sent to the airport flight dispatch system and ground operation system.

9. The method according to claim 1, characterized in that When generating the spatial density distribution map and hotspot database corresponding to the monitoring area, the following are also included: According to the uniform spatial resolution, the monitoring area is divided into regular grid cells to obtain a geographic grid model; Mapping the data in the monitoring data set to the grid corresponding to the geographic grid model, counting the number and density changes of bird flocks in each grid at unit time intervals, and establishing a spatiotemporal dynamic data set based on the statistical data; Based on the spatiotemporal dynamic dataset, the data on the grid is smoothed to generate a risk heat map.

10. A timely early warning and control system for bird flocks at airports based on radar monitoring, characterized in that: include: The radar monitoring module is used to scan the monitoring area in real time using a pre-deployed radar monitoring system to obtain real-time dynamic data of bird flock activities; The location, number and movement trajectory of the bird flocks are obtained from the scanned data and integrated into a monitoring data set; A spatial mapping and comparison module is used to generate spatial mapping data that can reflect the spatial density distribution and aggregation hotspots of bird flocks in the monitoring area based on the monitoring data set, and define risk areas based on the spatial mapping data; If the real-time dynamic data indicates that the flock of birds has entered a risk area, dynamic adjustment parameters are generated; A scanning strategy optimization module, configured to optimize and adjust the scanning strategy of the radar monitoring system according to the dynamic adjustment parameters; Generate real-time updated monitoring reports based on real-time dynamic data; The prevention and control instruction generation module is used to generate a prevention and control instruction dataset containing structured information for guiding airport operation adjustments based on real-time updated monitoring reports, and to generate a final response plan based on the prevention and control instruction dataset.

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