Air traffic control airspace risk warning methods, systems, electronic devices and storage media
By integrating multi-source data and using risk assessment models, dynamic perception and precise early warning of the airspace environment have been achieved, solving the problems of poor information correlation and delayed response in airspace monitoring, and improving the real-time performance and security of airspace management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA CONSTR FIRST DIV GROUP CONSTR & DEV
- Filing Date
- 2025-09-18
- Publication Date
- 2026-05-26
AI Technical Summary
The existing air traffic management system lacks a spatiotemporal alignment mechanism for multi-source data fusion in airspace monitoring, resulting in poor cross-modal information correlation, making it impossible to accurately assess complex risk scenarios. Furthermore, traditional risk warning methods are difficult to adapt to the dynamically changing airspace environment, and suffer from problems such as response lag and high false alarm rate.
Employing multi-source heterogeneous airspace data fusion technology, the data is corrected and format converted through spatiotemporal alignment, outlier detection, and interpolation compensation methods. A comprehensive risk assessment is conducted using a risk assessment model, and corresponding early warning strategies are triggered based on the assessment results, including voice prompts, control instruction suggestions, and emergency procedure guidance. Real-time data processing and optimization are performed using a distributed architecture of cloud and edge computing nodes.
It significantly improves the real-time performance and accuracy of airspace risk perception, supports dynamic assessment of complex scenarios such as flight conflicts, weather threats, and traffic overload, reduces the need for manual intervention, provides intuitive visualization guidance and decision support, and improves airspace resource utilization efficiency and aircraft operation safety.
Smart Images
Figure CN121075178B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to risk warning technology, and in particular to a method, system, electronic device and storage medium for air traffic control risk warning. Background Technology
[0002] Current air traffic management systems mainly rely on single data sources such as radar and ADS-B (Automatic Dependent Surveillance-Broadcast) for airspace monitoring, which lacks comprehensive perception capabilities for complex weather conditions, aircraft dynamics, and emergencies.
[0003] Traditional risk warning methods typically rely on static threshold judgments, which are ill-suited to dynamically changing airspace environments, resulting in issues such as response lag and high false alarm rates. Furthermore, existing systems lack effective spatiotemporal alignment mechanisms when fusing multi-source data, leading to poor cross-modal information correlation and an inability to accurately assess complex risk scenarios. With the rapid growth of air traffic, there is an urgent need for a solution capable of integrating multi-dimensional data in real time, dynamically sensing airspace risks, and achieving intelligent early warning. Summary of the Invention
[0004] This disclosure provides an air traffic control airspace risk early warning method, system, electronic device, and storage medium to solve the above-mentioned problems.
[0005] A first aspect of this disclosure provides a method for early warning of air traffic control airspace risks, including:
[0006] Acquire multi-source heterogeneous airspace data, wherein the multi-source heterogeneous airspace data includes radar monitoring data, signal data from automatic dependent surveillance broadcast, meteorological sensor data, flight plan data, and airport operation status data;
[0007] The multi-source heterogeneous spatial data is spatiotemporally aligned to obtain a fused data stream, and the fused data stream is corrected, denoised, and converted in format using outlier detection and interpolation compensation methods;
[0008] The fused data stream after correction, denoising, and format conversion is processed using a risk assessment model to obtain the comprehensive risk level of the current airspace, and a corresponding early warning strategy is triggered based on the comprehensive risk level, wherein the comprehensive risk level is one of high-risk state, medium-risk state, and low-risk state;
[0009] The warning is issued based on the aforementioned warning strategy, wherein the warning methods include generating voice prompts, control instruction suggestions, and emergency procedure guidance, and displaying the risk distribution through a visual interface;
[0010] The risk assessment model is optimized based on early warning feedback data and manual correction records. The early warning feedback data includes the controller's response to the early warning, the actual changes in airspace status after the early warning is triggered, the early warning response delay, and system false alarm and missed alarm data.
[0011] In some embodiments of this disclosure, the radar monitoring data includes the aircraft's position, altitude, and velocity vector;
[0012] The signal data of the automatic dependent surveillance broadcast includes flight identification code, heading angle and emergency status code;
[0013] The meteorological sensor data includes wind speed, visibility, cumulonimbus cloud distribution, and turbulence intensity;
[0014] The flight plan data includes waypoints, estimated arrival time, and remaining fuel.
[0015] The airport operational status data includes runway occupancy, parking stand status, and ground support resource availability.
[0016] In some embodiments of this disclosure, the method of correcting, denoising, and converting the fused data stream using outlier detection and interpolation compensation includes:
[0017] The consistency of the radar monitoring data and the signal data of the broadcast automatic correlation surveillance in the fused data stream is checked, and abnormal data points with spatial position deviations exceeding the threshold are removed.
[0018] The meteorological sensor data in the fused data stream is smoothed by using the Kalman filter method to smooth short-term fluctuations, and missing values caused by communication interruptions are filled in by data from adjacent nodes.
[0019] Logical verification is performed on time conflict items in the flight plan data of the fused data stream, and unreachable waypoints are corrected by associating them with airport operation status data;
[0020] The abnormal data points with spatial location deviations exceeding the threshold, the missing values caused by communication interruptions, and the fused data after correcting unreachable waypoints are converted into the input format corresponding to the risk assessment model.
[0021] In some embodiments of this disclosure, the process of using a risk assessment model to process the fused data stream after correction, denoising, and format conversion to obtain the comprehensive risk level of the current airspace includes:
[0022] The flow density calculation sub-model in the risk assessment model is used to statistically analyze the number and speed distribution of aircraft within a preset airspace grid.
[0023] The conflict probability prediction sub-model in the risk assessment model is used to calculate potential conflict points within a preset time period based on aircraft trajectory prediction results.
[0024] The feasibility of aircraft detour routes can be predicted by combining the meteorological threat assessment sub-model in the risk assessment model with the cumulonimbus cloud movement model and the turbulence influence range.
[0025] Based on the number and speed distribution of aircraft within the preset airspace grid, potential conflict points within the preset future time period, and the feasibility of the aircraft detour routes, the overall risk level of the current airspace is determined.
[0026] In some embodiments of this disclosure, the early warning based on the early warning strategy includes:
[0027] Generate flashing prompts on the controller's terminal and a list of anticipated conflict points under low-risk conditions;
[0028] Generate heading adjustment suggestions, altitude layer allocation plans, and voice alarms under medium-risk conditions;
[0029] In a high-risk situation, the emergency communication link is activated to automatically send avoidance instructions to the affected aircraft and initiate airspace traffic control contingency plans.
[0030] In some embodiments of this disclosure, optimizing the early warning strategy based on early warning feedback data and manual correction records includes:
[0031] Record controller operations and actual risk evolution results after the warning is triggered;
[0032] Based on the false alarm and false negative cases in the feedback data, adjust the threshold parameters and weight allocation of the risk assessment model;
[0033] Save the historical versions of the risk assessment model and verify the performance improvement of the risk assessment model after parameter adjustment.
[0034] A second aspect of this disclosure provides an air traffic control airspace risk early warning system, comprising:
[0035] The data acquisition module is used to acquire multi-source heterogeneous airspace data, which includes radar monitoring data, automatic dependent surveillance broadcast signal data, meteorological sensor data, flight plan data, and airport operation status data.
[0036] The data processing module is used to perform spatiotemporal alignment on the multi-source heterogeneous spatial data to obtain a fused data stream, and to correct, denoise and convert the fused data stream through outlier detection and interpolation compensation methods.
[0037] The model processing module is used to process the fused data stream after correction, denoising and format conversion using a risk assessment model to obtain the comprehensive risk level of the current airspace, and trigger the corresponding early warning strategy based on the comprehensive risk level, wherein the comprehensive risk level is one of high risk state, medium risk state and low risk state;
[0038] The early warning module is used to issue early warnings based on the early warning strategy. The early warning methods include generating voice prompts, control instruction suggestions, and emergency procedure guidance, and displaying the risk distribution through a visual interface.
[0039] The model optimization module is used to optimize the risk assessment model based on early warning feedback data and manual correction records. The early warning feedback data includes the controller's response to the early warning, the actual changes in airspace status after the early warning is triggered, the early warning response delay, and system false alarm and missed alarm data.
[0040] In some embodiments of this disclosure, the radar monitoring data includes the aircraft's position, altitude, and velocity vector;
[0041] The signal data of the automatic dependent surveillance broadcast includes flight identification code, heading angle and emergency status code;
[0042] The meteorological sensor data includes wind speed, visibility, cumulonimbus cloud distribution, and turbulence intensity;
[0043] The flight plan data includes waypoints, estimated arrival time, and remaining fuel.
[0044] The airport operational status data includes runway occupancy, parking stand status, and ground support resource availability.
[0045] In some embodiments of this disclosure, the data processing module is used to perform consistency verification on the radar monitoring data and the signal data of the automatic dependent surveillance broadcast in the fused data stream, and to remove abnormal data points with spatial position deviations exceeding a threshold; the data processing module is also used to smooth short-term fluctuations in the meteorological sensor data in the fused data stream using a Kalman filter method, and to supplement missing values caused by communication interruptions by using adjacent node data; the data processing module is also used to perform logical verification on time conflict items in the flight plan data in the fused data stream, and to correct unreachable waypoints by associating them with airport operation status data; the data processing module is also used to convert the fused data after removing abnormal data points with spatial position deviations exceeding the threshold, supplementing missing values caused by communication interruptions, and correcting unreachable waypoints into the input format corresponding to the risk assessment model.
[0046] In some embodiments of this disclosure, the model processing module is used to statistically analyze the number and speed distribution of aircraft within a preset airspace grid using the flow density calculation sub-model in the risk assessment model; the model processing module is also used to calculate potential conflict points within a preset future time period based on aircraft trajectory prediction results using the conflict probability prediction sub-model in the risk assessment model; the model processing module is also used to predict the feasibility of aircraft detour paths using the meteorological threat assessment sub-model in the risk assessment model combined with cumulonimbus cloud movement models and turbulence influence range; the model processing module is also used to determine the comprehensive risk level of the current airspace based on the number and speed distribution of aircraft within the preset airspace grid, the potential conflict points within the preset future time period, and the feasibility of aircraft detour paths.
[0047] In some embodiments of this disclosure, the early warning module is used to generate flashing prompts and a list of expected conflict points on the controller's terminal under low-risk conditions; the early warning module is also used to generate heading adjustment suggestions, altitude layer allocation schemes, and voice alarms under medium-risk conditions; the early warning module is also used to activate the emergency communication link under high-risk conditions, automatically send avoidance instructions to affected aircraft, and initiate airspace traffic control contingency plans.
[0048] In some embodiments of this disclosure, the model optimization module is used to record the controller's operation records and the actual risk evolution results after the warning is triggered; the model optimization module is also used to adjust the threshold parameters and weight allocation of the risk assessment model based on false alarms and missed alarms in the feedback data; the model optimization module is also used to save the historical model version of the risk assessment model and verify the performance improvement of the risk assessment model after parameter adjustment.
[0049] In some embodiments of this disclosure, the system adopts a distributed architecture including a cloud central node and edge computing nodes;
[0050] The cloud-based central node is used for global risk assessment and cross-regional collaborative early warning.
[0051] The edge computing nodes are deployed in each air traffic control branch to perform local real-time data processing and preliminary risk assessment.
[0052] The central node and edge nodes synchronize data through an encrypted private network, and a consistency protocol is used to ensure the credibility of the risk assessment results.
[0053] A third aspect of this disclosure provides an electronic device, comprising:
[0054] Memory, used to store computer program products;
[0055] A processor is configured to execute a computer program product stored in the memory, and when the computer program product is executed, to implement the method described in the first aspect above.
[0056] A fourth aspect of this disclosure provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the method described in the first aspect above.
[0057] A fifth aspect of this disclosure provides a computer program product including computer program instructions that, when executed by a processor, cause the processor to perform the method described in the first aspect.
[0058] The air traffic control airspace risk early warning method, system, electronic device, and storage medium disclosed in this embodiment significantly improve the real-time performance and accuracy of airspace risk perception through multimodal data fusion technology, supporting dynamic assessment of complex scenarios such as flight conflicts, weather threats, and traffic overload. The system employs an adaptive risk assessment model, which can automatically optimize early warning strategies based on environmental changes, reducing the need for manual intervention. Through a multi-level early warning output mechanism, it provides controllers with intuitive visual guidance and decision support, effectively shortening emergency response time. Simultaneously, its modular design is compatible with existing air traffic control facilities, supporting flexible expansion to multi-node deployment, significantly improving airspace resource utilization efficiency and aircraft operational safety.
[0059] The technical solutions of this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0060] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0061] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein:
[0062] Figure 1 This is a flowchart illustrating the air traffic control airspace risk early warning method in some embodiments of this disclosure;
[0063] Figure 2 This is a structural block diagram of the air traffic control airspace risk early warning system in some embodiments of this disclosure;
[0064] Figure 3 This is a structural block diagram of an electronic device in some embodiments of this disclosure. Detailed Implementation
[0065] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.
[0066] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.
[0067] It should also be understood that in the embodiments disclosed herein, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more.
[0068] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.
[0069] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.
[0070] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.
[0071] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.
[0072] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.
[0073] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.
[0074] The embodiments disclosed herein can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.
[0075] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.
[0076] Figure 1 This is a flowchart illustrating the air traffic control airspace risk warning method in some embodiments of this disclosure. For example... Figure 1 As shown, the air traffic control airspace risk early warning method includes the following steps:
[0077] S1: Acquire multi-source heterogeneous spatial data.
[0078] Among them, the multi-source heterogeneous airspace data includes radar monitoring data, Automatic Dependent Surveillance-Broadcast (ADS-B) signal data, meteorological sensor data, flight plan data, and airport operation status data, forming the original multimodal data pool.
[0079] Radar monitoring data includes aircraft position, altitude, and velocity vectors. ADS-B signal data includes flight identification codes, heading angles, and emergency status codes. Meteorological sensor data includes wind speed, visibility, cumulonimbus cloud distribution, and turbulence intensity. Flight plan data includes waypoints, estimated arrival times, and remaining fuel. Airport operational status data includes runway occupancy, parking stand status, and ground support resource availability. Radar monitoring data provides real-time aircraft motion vectors, ADS-B supplements flight identification and emergency status, meteorological sensors cover key parameters such as wind speed and cumulonimbus clouds, and flight plans are correlated with airport data to determine route feasibility, forming a multi-dimensional panoramic view of airspace status and providing complete input for risk assessment.
[0080] S2: Spatiotemporal alignment of multi-source heterogeneous spatial data is performed to obtain a fused data stream, and outlier detection and interpolation compensation methods are used to correct, denoise and convert the fused data stream.
[0081] Multi-source data is spatiotemporally aligned using a timestamp and spatial location association algorithm to generate a unified format fused data stream. Outlier detection and interpolation compensation algorithms correct for missing data or noise. The fused data is then converted into a pre-defined input format for a risk assessment model. Different modalities (such as radar tracks and meteorological cloud images) are mapped to a unified spatiotemporal coordinate system based on timestamps and geographic coordinates, eliminating time differences and spatial misalignments between sensors. Anomaly detection algorithms remove noisy data caused by signal jumps and equipment malfunctions, and interpolation algorithms fill in short-term missing values. The cleaned data is then converted into a structured format for subsequent model processing.
[0082] In some embodiments of this disclosure, step S2 may include the following steps:
[0083] S2-1: Perform consistency verification between radar monitoring data and ADS-B signal data, and remove abnormal data points whose spatial position deviation exceeds the threshold.
[0084] S2-2: The Kalman filter algorithm is used to smooth short-term fluctuations in meteorological sensor data, and missing values caused by communication interruptions are filled in by data from adjacent nodes.
[0085] S2-3: Logical verification of time conflict items in flight plan data. The system obtains different route information and the location where each route intersects with other routes. It verifies the time when the aircraft of the two routes pass through the intersection to avoid the situation where two aircraft arrive at the location at the same time. It also correlates with airport operation status data to correct unreachable waypoints. The system obtains airport and airspace control information in real time and corrects the routes to bypass temporarily closed airports or temporarily controlled airspace.
[0086] S2-4: Consistency checks are used to eliminate positional discrepancies between radar and ADS-B data, Kalman filtering smooths weather fluctuations, and logical checks correct flight plan conflicts, ensuring the spatiotemporal consistency and reliability of input data and avoiding noise data from interfering with risk assessment results.
[0087] S3: The risk assessment model is used to process the fused data stream after correction, denoising and format conversion to obtain the comprehensive risk level of the current airspace, and the corresponding early warning strategy is triggered based on the comprehensive risk level.
[0088] The overall risk level is one of three: high risk, medium risk, or low risk.
[0089] Feature extraction is performed on the fused data stream using a sliding time window, and pattern matching results are combined with those from a historical risk database to evaluate the overall risk level of the current airspace. For example, the current risk level is 0 when weather conditions have no impact on aircraft flight; 1 when weather conditions have a slight impact on aircraft flight; 2 when weather conditions have a significant impact on aircraft flight; and 3 when weather conditions have a severe impact on aircraft flight.
[0090] The same method can be used to evaluate the risk level of other data. When assessing the overall risk level, the risk level of each data point is obtained. When the risk level is 3, the overall risk level is determined to be high-risk; when the risk level is 2, the overall risk level is determined to be medium-risk; and when the risk level is 1, the overall risk level is determined to be low-risk.
[0091] The system extracts features such as aircraft density, flight path intersection probability, and weather threat coverage in the current airspace using a sliding time window (e.g., a 5-minute window). These features are then combined with risk patterns from similar scenarios in historical databases (e.g., flight detour conflicts caused by thunderstorms) to generate a weighted comprehensive risk level. The risk level generator categorizes the index into low, medium, and high levels based on preset thresholds and triggers corresponding early warning strategies.
[0092] In some embodiments of this disclosure, step S3 may include the following steps:
[0093] S3-1: Statistically analyze the number and speed distribution of aircraft within a preset airspace grid.
[0094] S3-2: Based on the aircraft trajectory prediction results, the system calculates potential conflict points within the next 5 to 15 minutes. The system acquires flight path data within the airspace, predicts the aircraft's flight path based on the real-time flight path of the aircraft and the positions of other flight paths, and then calculates potential conflict points that may occur in the future.
[0095] S3-3: Combining cumulonimbus cloud movement models with turbulence influence range to predict the feasibility of aircraft detour paths. Based on the area covered by the cumulonimbus cloud, its trajectory, the turbulence influence range, and the amount of fuel on board the aircraft, it is determined whether the aircraft can bypass the cumulonimbus cloud.
[0096] The overall risk level is obtained by weighting and summing traffic density, conflict probability coefficient, and meteorological threat level. Traffic density calculation quantifies airspace load, conflict probability prediction identifies potential collisions in advance based on flight path inference, and meteorological threat assessment combines cloud movement models to predict detour paths. The weighted summation mechanism integrates the influence of multiple factors, making the risk index closer to actual operational needs.
[0097] S4: Issue warnings based on early warning strategies.
[0098] The early warning methods include generating voice prompts, suggesting air traffic control instructions, and providing emergency procedure guidance, all displayed through a visual interface to show the risk distribution. Risk distribution can be dynamically displayed using 3D airspace situation maps, heat maps, and alarm markers. The visual interface uses color-coding to indicate areas of risk when displaying airspace. The 3D situation map dynamically overlays weather cloud layers, aircraft tracks, and risk heat maps to assist controllers in making rapid decisions.
[0099] In some examples disclosed herein, flashing alerts and a list of anticipated conflict points are generated on controller terminals under low-risk conditions; heading adjustment suggestions, altitude layer allocation schemes, and voice alarms are generated under medium-risk conditions; and emergency communication links are activated under high-risk conditions, automatically sending avoidance instructions to affected aircraft and initiating airspace traffic control contingency plans. The tiered strategy matches different risk levels: low-risk situations only alert to potential conflicts, medium-risk situations provide control suggestions, and high-risk situations automatically trigger emergency instructions, achieving precise resource allocation and rapid response while avoiding excessive alarms that could disrupt normal operations.
[0100] S5: Optimize the risk assessment model based on early warning feedback data and manual correction records.
[0101] The early warning feedback data includes the controller's response to the early warning, the actual changes in airspace status after the early warning is triggered, the early warning response delay, and the system's false alarm and missed alarm data. Based on the feedback data after the early warning is issued, the system can optimize the emergency procedure guidelines generated when such early warnings occur again.
[0102] In some embodiments of this disclosure, step S5 may include the following steps:
[0103] S5-1: Record the controller's operation records and the actual risk evolution results after the warning is triggered.
[0104] S5-2: Adjust the threshold parameters and weight allocation of the risk assessment model based on false alarms and missed alarms in the feedback data.
[0105] S5-3: Saves historical model versions and verifies performance improvements after parameter adjustments, supports model rollback functionality.
[0106] In this embodiment, multimodal data fusion technology significantly improves the real-time performance and accuracy of airspace risk perception, supporting dynamic assessment of complex scenarios such as flight conflicts, weather threats, and traffic overload. The system employs an adaptive risk assessment model that automatically optimizes early warning strategies based on environmental changes, reducing the need for manual intervention. Through a multi-level early warning output mechanism, it provides controllers with intuitive visual guidance and decision support, effectively shortening emergency response time. Simultaneously, its modular design is compatible with existing air traffic control facilities, supporting flexible expansion to multi-node deployment, and significantly improving airspace resource utilization efficiency and aircraft operational safety.
[0107] Figure 2 This is a structural block diagram of an air traffic control and airspace risk early warning system in some embodiments of this disclosure. For example... Figure 2 As shown, the air traffic control airspace risk early warning system includes:
[0108] The data acquisition module 100 is used to acquire multi-source heterogeneous airspace data, which includes radar monitoring data, automatic dependent surveillance broadcast signal data, meteorological sensor data, flight plan data and airport operation status data.
[0109] The data processing module 200 is used to perform spatiotemporal alignment on multi-source heterogeneous spatial data to obtain a fused data stream, and to correct, denoise and convert the fused data stream through outlier detection and interpolation compensation methods.
[0110] The model processing module 300 is used to process the fused data stream after correction, denoising and format conversion using a risk assessment model to obtain the comprehensive risk level of the current airspace, and trigger the corresponding early warning strategy based on the comprehensive risk level. The comprehensive risk level is one of high risk state, medium risk state and low risk state.
[0111] The early warning module 400 is used to issue early warnings based on early warning strategies. The early warning methods include generating voice prompts, control instruction suggestions, and emergency procedure guidance, and displaying the risk distribution through a visual interface.
[0112] The model optimization module 500 is used to optimize the risk assessment model based on early warning feedback data and manual correction records. The early warning feedback data includes the controller's response to the early warning, the actual changes in the airspace status after the early warning is triggered, the early warning response delay, and system false alarm and missed alarm data.
[0113] In some embodiments of this disclosure, radar monitoring data includes the aircraft's position, altitude, and velocity vector;
[0114] The signal data of Automatic Dependent Surveillance-Broadcast includes flight identification code, heading angle, and emergency status code;
[0115] Meteorological sensor data includes wind speed, visibility, cumulonimbus cloud distribution, and turbulence intensity;
[0116] Flight plan data includes waypoints, estimated arrival time, and remaining fuel.
[0117] Airport operational status data includes runway occupancy, parking stand status, and ground support resource availability.
[0118] In some embodiments of this disclosure, the data processing module 200 is used to perform consistency verification on radar monitoring data and automatic dependent surveillance signal data in the fused data stream, and remove abnormal data points with spatial position deviations exceeding a threshold; the data processing module 200 is also used to smooth short-term fluctuations in meteorological sensor data in the fused data stream using the Kalman filter method, and to supplement missing values caused by communication interruptions by using adjacent node data; the data processing module 200 is also used to perform logical verification on time conflict items in flight plan data in the fused data stream, and to correct unreachable waypoints by associating with airport operation status data; the data processing module 200 is also used to convert the fused data after removing abnormal data points with spatial position deviations exceeding the threshold, supplementing missing values caused by communication interruptions, and correcting unreachable waypoints into the input format corresponding to the risk assessment model.
[0119] In some embodiments of this disclosure, the model processing module 300 is used to statistically analyze the number and speed distribution of aircraft within a preset airspace grid using a flow density calculation sub-model in the risk assessment model; the model processing module 300 is also used to calculate potential conflict points within a preset time period based on aircraft trajectory prediction results using a conflict probability prediction sub-model in the risk assessment model; the model processing module 300 is also used to predict the feasibility of aircraft detour paths using a meteorological threat assessment sub-model in the risk assessment model combined with a cumulonimbus cloud movement model and turbulence influence range; the model processing module 300 is also used to determine the comprehensive risk level of the current airspace based on the number and speed distribution of aircraft within the preset airspace grid, potential conflict points within a preset time period, and the feasibility of aircraft detour paths.
[0120] In some embodiments of this disclosure, the warning module 400 is used to generate flashing prompts and a list of expected conflict points on the controller terminal under low-risk conditions; the warning module 400 is also used to generate heading adjustment suggestions, altitude layer allocation schemes and voice alarms under medium-risk conditions; the warning module 400 is also used to activate the emergency communication link under high-risk conditions, automatically send avoidance instructions to affected aircraft and activate the airspace traffic control plan.
[0121] In some embodiments of this disclosure, the model optimization module 500 is used to record the controller's operation record and the actual risk evolution result after the warning is triggered; the model optimization module 500 is also used to adjust the threshold parameters and weight allocation of the risk assessment model according to the false alarm and missed alarm cases in the feedback data; the model optimization module 500 is also used to save the historical model version of the risk assessment model and verify the performance improvement of the risk assessment model after parameter adjustment.
[0122] In some embodiments of this disclosure, the system adopts a distributed architecture including a cloud central node and edge computing nodes;
[0123] Among them, the cloud-based central node is used for global risk assessment and cross-regional collaborative early warning;
[0124] Edge computing nodes are deployed in each air traffic control branch to perform localized real-time data processing and preliminary risk assessment;
[0125] The central node and edge nodes synchronize data through an encrypted private network, and a consistency protocol is used to ensure the credibility of the risk assessment results.
[0126] The cloud-based central node is used to coordinate overall risk assessment, while edge nodes are used to process local real-time data. The encrypted private network ensures data synchronization security and balances computing efficiency with cross-regional collaboration capabilities, making this solution suitable for large-scale airspace network management.
[0127] It should be noted that the specific implementation of the air traffic control airspace risk warning system in this disclosure is similar to the specific implementation of the air traffic control airspace risk warning method in this disclosure, and the technical effects of the air traffic control airspace risk warning system in this disclosure are similar to the technical effects of the air traffic control airspace risk warning method in this disclosure. For details, please refer to the description of the air traffic control airspace risk warning method section. In order to reduce redundancy, it will not be repeated.
[0128] In addition, embodiments of this disclosure also provide an electronic device, including:
[0129] Memory, used to store computer programs;
[0130] A processor is configured to execute a computer program stored in the memory, and when the computer program is executed, to implement the air traffic control and airspace risk warning method described in any of the above embodiments of this disclosure.
[0131] Below, for reference Figure 3 To describe an electronic device according to embodiments of this disclosure. For example... Figure 3 As shown, the electronic device includes one or more processors and memory.
[0132] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.
[0133] The memory can store one or more computer program products, and the memory can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program products can be stored on the computer-readable storage medium, and the processor can run the computer program products to implement the air traffic control airspace risk warning methods of the various embodiments of this disclosure described above, and / or other desired functions.
[0134] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).
[0135] In addition, the input device may also include, for example, a keyboard, a mouse, etc.
[0136] This output device can output various information to the outside, including determined distance information, direction information, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.
[0137] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.
[0138] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products, including computer program instructions that, when executed by a processor, cause the processor to perform the steps in the air traffic control and airspace risk warning methods according to various embodiments of this disclosure as described in the foregoing portions of this specification.
[0139] The computer program product can be written in any combination of one or more programming languages to perform the operations of the embodiments of this disclosure. The programming languages include object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0140] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the air traffic control airspace risk warning method according to various embodiments of this disclosure as described in the foregoing portion of this specification.
[0141] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0142] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.
[0143] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.
[0144] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.
[0145] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.
[0146] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions to this disclosure.
[0147] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.
[0148] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.
Claims
1. A method for early warning of airspace risks in air traffic control, characterized in that, include: Acquire multi-source heterogeneous airspace data, wherein the multi-source heterogeneous airspace data includes radar monitoring data, signal data from automatic dependent surveillance broadcast, meteorological sensor data, flight plan data, and airport operation status data; The multi-source heterogeneous spatial data is spatiotemporally aligned to obtain a fused data stream, and the fused data stream is corrected, denoised, and converted in format using outlier detection and interpolation compensation methods; The fused data stream after correction, denoising, and format conversion is processed using a risk assessment model to obtain the comprehensive risk level of the current airspace, and a corresponding early warning strategy is triggered based on the comprehensive risk level, wherein the comprehensive risk level is one of high-risk state, medium-risk state, and low-risk state; The warning is issued based on the aforementioned warning strategy, wherein the warning methods include generating voice prompts, control instruction suggestions, and emergency procedure guidance, and displaying the risk distribution through a visual interface; The risk assessment model is optimized based on early warning feedback data and manual correction records. The early warning feedback data includes the controller's response to the early warning, the actual changes in airspace status after the early warning is triggered, the early warning response delay, and system false alarm and missed alarm data. The radar monitoring data includes the aircraft's position, altitude, and velocity vector; The signal data of the automatic dependent surveillance broadcast includes flight identification code, heading angle and emergency status code; The meteorological sensor data includes wind speed, visibility, cumulonimbus cloud distribution, and turbulence intensity; The flight plan data includes waypoints, estimated arrival time, and remaining fuel. The airport operational status data includes runway occupancy, parking stand status, and ground support resource availability. The method of correcting, denoising and converting the fused data stream by outlier detection and interpolation compensation includes: verifying the consistency between the radar monitoring data and the signal data of the automatic correlation surveillance broadcast in the fused data stream, and removing outlier data points whose spatial position deviation exceeds a threshold. The meteorological sensor data in the fused data stream is smoothed by using the Kalman filter method to smooth short-term fluctuations, and missing values caused by communication interruptions are filled in by data from adjacent nodes. Logical verification is performed on time conflict items in the flight plan data of the fused data stream, and unreachable waypoints are corrected by associating them with airport operation status data; The abnormal data points with spatial location deviations exceeding the threshold, the missing values caused by communication interruptions, and the fused data after correcting unreachable waypoints are converted into the input format corresponding to the risk assessment model. The process of using a risk assessment model to process the fused data stream after correction, denoising, and format conversion yields the comprehensive risk level of the current airspace, including: The flow density calculation sub-model in the risk assessment model is used to statistically analyze the number and speed distribution of aircraft within a preset airspace grid. The conflict probability prediction sub-model in the risk assessment model is used to calculate potential conflict points within a preset time period based on aircraft trajectory prediction results. The feasibility of aircraft detour routes can be predicted by combining the meteorological threat assessment sub-model in the risk assessment model with the cumulonimbus cloud movement model and the turbulence influence range. Based on the number and speed distribution of aircraft within the preset airspace grid, potential conflict points within the preset future time period, and the feasibility of the aircraft detour routes, the overall risk level of the current airspace is determined.
2. The method according to claim 1, characterized in that, The early warning based on the aforementioned early warning strategy includes: generating flashing prompts and a list of expected conflict points on the controller's terminal under low-risk conditions; generating heading adjustment suggestions, altitude layer allocation schemes, and voice alarms under medium-risk conditions; and activating emergency communication links under high-risk conditions to automatically send avoidance instructions to affected aircraft and initiate airspace traffic control contingency plans.
3. The method according to claim 1, characterized in that, The optimization of the early warning strategy based on early warning feedback data and manual correction records includes: recording controller operation records and actual risk evolution results after the early warning is triggered; adjusting the threshold parameters and weight allocation of the risk assessment model according to false alarm and missed alarm cases in the feedback data; saving the historical model version of the risk assessment model and verifying the performance improvement of the risk assessment model after parameter adjustment.
4. A system for early warning of airspace risks in air traffic control, characterized in that, include: The data acquisition module is used to acquire multi-source heterogeneous airspace data, which includes radar monitoring data, automatic dependent surveillance (ADS) signal data, meteorological sensor data, flight plan data, and airport operation status data. The radar monitoring data includes the aircraft's position, altitude, and velocity vector; the ADS signal data includes flight identification codes, heading angles, and emergency status codes; the meteorological sensor data includes wind speed, visibility, cumulonimbus cloud distribution, and turbulence intensity; the flight plan data includes waypoints, estimated arrival times, and remaining fuel; and the airport operation status data includes runway occupancy, parking stand status, and ground support resource availability. The data processing module is used to perform spatiotemporal alignment on the multi-source heterogeneous airspace data to obtain a fused data stream, and to correct, denoise, and convert the fused data stream using outlier detection and interpolation compensation methods. This includes verifying the consistency between the radar monitoring data and the automatic dependent surveillance broadcast (ADS-B) signal data in the fused data stream, removing outlier data points with spatial position deviations exceeding a threshold; smoothing short-term fluctuations in the meteorological sensor data in the fused data stream using Kalman filtering, and supplementing missing values caused by communication interruptions using adjacent node data; logically verifying time conflict items in the flight plan data in the fused data stream, and correcting unreachable waypoints by associating them with airport operation status data; and converting the fused data after removing outlier data points with spatial position deviations exceeding the threshold, supplementing missing values caused by communication interruptions, and correcting unreachable waypoints into the input format corresponding to the risk assessment model. The model processing module is used to process the fused data stream after correction, denoising, and format conversion using a risk assessment model to obtain the comprehensive risk level of the current airspace. This includes: using the flow density calculation sub-model in the risk assessment model to statistically analyze the number and speed distribution of aircraft within a preset airspace grid; using the conflict probability prediction sub-model in the risk assessment model to calculate potential conflict points within a preset future time period based on aircraft trajectory inference results; using the meteorological threat assessment sub-model in the risk assessment model, combined with a cumulonimbus cloud movement model and turbulence influence range, to predict the feasibility of aircraft detour routes; determining the comprehensive risk level of the current airspace based on the number and speed distribution of aircraft within the preset airspace grid, the potential conflict points within the preset future time period, and the feasibility of aircraft detour routes; and triggering corresponding early warning strategies based on the comprehensive risk level, wherein the comprehensive risk level is one of a high-risk state, a medium-risk state, or a low-risk state. The early warning module is used to issue early warnings based on the early warning strategy. The early warning methods include generating voice prompts, control instruction suggestions, and emergency procedure guidance, and displaying the risk distribution through a visual interface. The model optimization module is used to optimize the risk assessment model based on early warning feedback data and manual correction records. The early warning feedback data includes the controller's response to the early warning, the actual changes in airspace status after the early warning is triggered, the early warning response delay, and system false alarm and missed alarm data.
5. The system according to claim 4, characterized in that, The system adopts a distributed architecture including a cloud central node and edge computing nodes; the cloud central node is used for global risk assessment and cross-regional collaborative early warning. The edge computing nodes are deployed in each air traffic control branch to perform localized real-time data processing and preliminary risk assessment. The central node and the edge nodes synchronize data through an encrypted private network and use a consistency protocol to ensure the credibility of the risk assessment results.
6. An electronic device, characterized in that, include: Memory, used to store computer program products; A processor for executing a computer program product stored in the memory, wherein when the computer program product is executed, it implements the method described in any one of claims 1-3.
7. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1-3.