Data analysis system for aviation low-altitude airspace

Abstract

The invention provides a data analysis system for an aviation low-altitude airspace, and relates to the technical field of aviation low-altitude airspace management, and the system comprises a data collection module which is used for collecting real-time data of the low-altitude airspace through a plurality of different types of sensors and a data platform, and a data standardization and fusion module which employs a multi-dimensional data fusion method to analyze the real-time data of the low-altitude airspace. And the advanced data analysis and intelligent reasoning module analyzes the fused data in real time and predicts potential airspace conflicts and safety risks of the aircraft in the low-altitude airspace. According to the data analysis system for the aviation low-altitude airspace, real-time data are collected through various sensors and a data platform, and comprehensiveness and diversity of data sources are ensured. Through a multi-dimensional data fusion method of the data standardization and fusion module, data from different sources can be effectively integrated on a unified platform.

Description

Technical Field

[0001] This invention relates to the field of aviation low-altitude airspace management technology, specifically to a data analysis system for aviation low-altitude airspace. Background Technology

[0002] With the development of civil aviation and the rise of the low-altitude economy, low-altitude airspace management has gradually gained importance. In recent years, the frequent occurrence of low-altitude flight activities has led to a continuous increase in the demand for monitoring and management of low-altitude airspace. Currently, low-altitude airspace management technologies mainly rely on various monitoring methods, including radar, remote sensing equipment, and air traffic management systems (ATMS). Radar systems are typically used to monitor aircraft activity within the airspace, providing information such as flight paths and speeds through real-time aircraft tracking. Remote sensing technology collects data via satellites or drones to analyze the relationship between aircraft and the ground environment, aiding in decision-making. Air traffic management systems, through real-time data collection and analysis of aircraft, schedule flight activities within the airspace.

[0003] Furthermore, with the development of information technology in recent years, data analysis technology has been gradually introduced into the management of low-altitude airspace. Big data, cloud computing, and artificial intelligence technologies are applied to data processing and analysis in low-altitude airspace, improving the efficiency and accuracy of data processing. Big data technology can help managers better understand the trends and changes in flight activities within the airspace by collecting and analyzing large amounts of aircraft data. Cloud computing provides powerful computing capabilities and storage resources, making real-time data analysis and storage possible. Artificial intelligence technology, especially machine learning algorithms, can automatically identify abnormal behaviors in flight activities during the analysis process, providing early warnings of potential safety risks. In addition, flight data visualization technology is also being gradually applied to the management of low-altitude airspace, allowing managers to more intuitively grasp the flight status within the airspace.

[0004] The biggest drawback of existing technologies lies in their insufficient data integration and real-time processing capabilities. Low-altitude airspace data comes from numerous sources, including radar data, remote sensing data, and real-time data transmitted from aircraft, distributed across different systems and platforms. Although various data technologies are becoming increasingly mature, the lack of unified standards and interfaces for data from different sources makes data integration difficult. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a data analysis system for low-altitude airspace. The technical problem this invention aims to solve is: how to improve the safety and management efficiency of low-altitude airspace through real-time data acquisition, standardization and fusion, multi-dimensional data analysis, and automated decision-making algorithms.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a data analysis system for low-altitude airspace, comprising:

[0007] The data acquisition module is used to collect real-time data from the low-altitude airspace through multiple different types of sensors and data platforms;

[0008] The data standardization and fusion module employs a multi-dimensional data fusion method.

[0009] The advanced data analysis and intelligent reasoning module performs real-time analysis of the fused data to predict potential airspace conflicts and safety risks for aircraft in low-altitude airspace.

[0010] The dynamic response and decision support module is used to generate targeted airspace scheduling schemes, flight path optimization schemes, and aircraft priority adjustment strategies.

[0011] The adaptive learning and feedback module dynamically adjusts decision-making strategies.

[0012] Preferably, the real-time data includes radar data, remote sensing data, real-time data transmitted by the aircraft, and environmental perception data.

[0013] Preferably, the data standardization and fusion module preprocesses and standardizes heterogeneous data from different data sources, and then interacts and integrates the data through an interface protocol. The preprocessing includes: format conversion, timestamp alignment, missing data imputation, standardization, and outlier detection.

[0014] Preferably, the advanced data analysis and intelligent reasoning module includes the following steps:

[0015] S1. Extract features of flight path, speed, altitude, airspace density, aircraft spacing, and weather conditions from real-time data, use statistical analysis methods to identify and quantify key features affecting aircraft conflicts, and construct a risk assessment model;

[0016] S2. Based on historical and real-time data, calculate the collision risk between aircraft and other aircraft, generate a risk map of potential airspace conflicts, and assist decision-makers in intervention;

[0017] S3. When the risk of conflict is high, use automated decision-making algorithms to generate airspace adjustment plans and schedule the paths or priorities of aircraft.

[0018] Preferably, the risk assessment model is:

[0019]

[0020] R c It represents the overall conflict risk between aircraft, indicating the degree of risk of potential conflict between aircraft, d ijd is the distance between aircraft i and aircraft j. The smaller the distance between the aircraft, the greater the possibility of a collision. max It represents the maximum safe distance between aircraft, used to normalize distances and ensure that the quantification of conflict risk is relative. (P) ij V represents the probability of a collision between aircraft i and aircraft j, typically calculated using historical and real-time data. ij It is the relative speed between aircraft i and aircraft j. The higher the speed, the higher the probability of a collision.

[0021] Preferably, the risk map uses heat map technology, which sets color markers based on the conflict risk values ​​between aircraft, making the visualization of high-risk areas more intuitive.

[0022] Preferably, the airspace adjustment scheme includes: path adjustment, altitude adjustment, speed adjustment, time adjustment, emergency avoidance and intervention, airspace replanning and route integration.

[0023] Preferably, the airspace scheduling scheme generation step includes:

[0024] a. Automatically identify areas of potential airspace conflict between aircraft within the airspace, and analyze the aircraft's path, speed, and heading data in real time;

[0025] b. After identifying areas with high conflict risk, generate targeted airspace scheduling plans to ensure that aircraft can avoid potential conflicts when entering the area.

[0026] This invention provides a data analysis system for low-altitude airspace. It has the following beneficial effects:

[0027] This low-altitude airspace data analysis system collects real-time data through multiple sensors and data platforms, ensuring the comprehensiveness and diversity of data sources. Through multi-dimensional data fusion methods using data standardization and fusion modules, data from different sources can be effectively integrated on a unified platform. This enables the system to comprehensively grasp the flight status, airspace density, and environmental changes in low-altitude airspace, thus providing more accurate information for real-time decision-making. Especially when dealing with critical factors such as flight path, speed, and altitude, the system can identify potential airspace conflicts and safety risks, provide early warnings, and make real-time adjustments to ensure flight safety.

[0028] The advanced data analytics and intelligent reasoning module combines real-time and historical data to construct a detailed airspace conflict risk map through a risk assessment model, helping decision-makers intuitively identify high-risk areas. Furthermore, automated decision-making algorithms generate airspace adjustment plans, effectively avoiding aircraft conflicts and ensuring flight safety. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the structure for realizing the invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Example 1

[0032] like Figure 1 As shown, this embodiment of the invention provides a data analysis system for low-altitude airspace, including a data acquisition module for collecting real-time data of low-altitude airspace through multiple different types of sensors and data platforms. The real-time data includes radar data, remote sensing data, real-time transmission data from aircraft, and environmental perception data.

[0033] The data standardization and fusion module adopts a multi-dimensional data fusion method. It preprocesses and standardizes heterogeneous data from different data sources, and then interacts and integrates the data through interface protocols. The preprocessing includes: format conversion, timestamp alignment, missing data imputation, standardization and outlier detection.

[0034] The advanced data analysis and intelligent reasoning module performs real-time analysis of the fused data to predict potential airspace conflicts and safety risks for aircraft in low-altitude airspace. The advanced data analysis and intelligent reasoning module includes the following steps:

[0035] S1. Extract features of flight path, speed, altitude, airspace density, aircraft spacing, and weather conditions from real-time data, use statistical analysis methods to identify and quantify key features affecting aircraft conflicts, and construct a risk assessment model.

[0036] S2. Based on historical and real-time data, the collision risk between aircraft and other aircraft is calculated, generating a risk map of potential airspace conflicts to assist decision-makers in intervention. The risk map uses heatmap technology, which assigns colors based on the conflict risk values ​​between aircraft, making the visualization of high-risk areas more intuitive.

[0037] S3. When the risk of conflict is high, use automated decision-making algorithms to generate airspace adjustment plans and schedule aircraft paths or priorities. The risk assessment model is as follows:

[0038]

[0039] Rc It represents the overall conflict risk between aircraft, indicating the degree of risk of potential conflict between aircraft, d ij d is the distance between aircraft i and aircraft j. The smaller the distance between the aircraft, the greater the possibility of a collision. max It represents the maximum safe distance between aircraft, used to normalize distances and ensure that the quantification of conflict risk is relative. (P) ij V represents the probability of a collision between aircraft i and aircraft j, typically calculated using historical and real-time data. ij It is the relative speed between aircraft i and aircraft j. The higher the speed, the higher the probability of a collision.

[0040] The dynamic response and decision support module generates targeted airspace scheduling schemes, flight path optimization schemes, and aircraft priority adjustment strategies. Airspace adjustment schemes include: path adjustment, altitude adjustment, speed adjustment, time adjustment, emergency avoidance and intervention, airspace replanning, and route integration. The airspace scheduling scheme generation steps include:

[0041] a. Automatically identify areas of potential airspace conflict between aircraft within the airspace, and analyze the aircraft's path, speed, and heading data in real time.

[0042] b. After identifying areas with high conflict risk, generate targeted airspace scheduling plans to ensure that aircraft can avoid potential conflicts when entering the area.

[0043] The adaptive learning and feedback module dynamically adjusts decision-making strategies.

[0044] Example 2

[0045] The advanced data analysis and intelligent reasoning module of this invention analyzes real-time data from low-altitude airspace to predict potential airspace conflicts and safety risks between aircraft, and generates targeted airspace adjustment plans based on the analysis results. The implementation of this module is described in detail below:

[0046] S1. Data Extraction and Feature Construction

[0047] In this step, the system extracts the following feature information from the aircraft's real-time data:

[0048] Flight path: Information such as the aircraft's current position, flight direction, and heading angle are obtained through sensor and radar data. This data helps determine the aircraft's trajectory and potential conflict paths.

[0049] Speed: Acquiring real-time speed data of aircraft helps predict their approach speeds to other aircraft. The higher the speed, the greater the risk of conflict.

[0050] Altitude: Changes in an aircraft's altitude can help assess the risk of conflict in the vertical direction, especially when multiple aircraft are flying at similar altitudes.

[0051] Airspace density: The number of aircraft in a specific area. The higher the airspace density, the smaller the relative distance between aircraft, and the higher the possibility of conflict.

[0052] Aircraft spacing: Calculate the horizontal and vertical distances between aircraft. This parameter is a key feature in conflict prediction. Smaller spacing means a higher risk of conflict.

[0053] Meteorological conditions: Real-time meteorological data (such as wind speed, airflow, temperature, etc.) have a direct impact on the movement of aircraft, so meteorological data is crucial for assessing conflict risk.

[0054] By extracting these feature data, the system uses statistical analysis methods and machine learning techniques to analyze and quantify the key factors affecting aircraft conflicts, and builds a real-time risk assessment model.

[0055] S2. Risk Map Generation and Conflict Prediction

[0056] In this step, the system calculates the collision risk between aircraft based on historical and real-time data, and generates a risk map of potential airspace conflicts:

[0057] Collision risk calculation between aircraft:

[0058] The system calculates the collision risk between the aircraft and other aircraft based on real-time data. The calculation method considers multiple factors such as the distance between aircraft, relative speed, altitude difference, and aircraft type.

[0059] When the distance between aircraft is small, the risk of collision is high;

[0060] If the relative speed of the aircraft is high, the likelihood of a conflict increases;

[0061] The difference in flight altitude of aircraft also affects the risk of collision, especially when aircraft are at similar altitudes, the possibility of vertical collision is higher.

[0062] Generate a conflict risk heatmap:

[0063] Based on the risk calculations described above, the system generates a conflict risk heatmap. The heatmap uses different colors to represent different risk levels:

[0064] Green areas indicate low-risk areas;

[0065] Yellow areas indicate medium-risk areas;

[0066] The red area indicates a high-risk area.

[0067] In this way, the risk of aircraft conflict in the airspace can be displayed in a visual way, helping decision-makers to intuitively identify high-risk areas and intervene.

[0068] S3. Automated decision-making and airspace adjustment scheme generation

[0069] In this step, when the system identifies a high risk of aircraft conflict in certain areas, the automated decision-making algorithm generates an airspace adjustment plan based on the risk assessment results:

[0070] Flight path adjustment:

[0071] When an aircraft enters a high-risk area, the system automatically adjusts the aircraft's path based on the aircraft's real-time status, airspace density, and conflict prediction results. Path adjustment may include:

[0072] Change the aircraft's course to avoid the conflict zone;

[0073] Adjust the flight altitude of the aircraft to increase the vertical distance between them;

[0074] Calculate the aircraft's detour route to avoid collisions with other aircraft.

[0075] Priority adjustment:

[0076] When multiple aircraft are in the same airspace and the risk of conflict is high, the system will adjust the aircraft's priority based on the aircraft's mission urgency, priority, and airspace resource usage. If an aircraft has a higher priority, the system will automatically adjust the path or speed of lower-priority aircraft so that higher-priority aircraft can pass through the airspace smoothly.

[0077] Airspace resource scheduling:

[0078] In situations of limited airspace resources, the system optimizes airspace scheduling to reduce congestion. For example, it adjusts aircraft takeoff and landing times or flight schedules to ensure safe passage. The system can also temporarily close or re-allocate airspace based on aircraft path adjustments and airspace usage to prevent conflicts.

[0079] Decision-making push and intervention suggestions:

[0080] When the risk of conflict is high, the system will automatically send adjustment suggestions to airspace management personnel and provide intervention measures. For example, it may adjust the takeoff or landing sequence of aircraft, change flight paths, or suggest delaying the flights of certain aircraft.

[0081] Data Examples and Implementation

[0082] Suppose the system is analyzing an airspace containing three aircraft (A, B, and C). The following is their real-time data:

[0083] Aircraft A: Current position (1000, 1500), speed 30 m / s, flight altitude 1000 m;

[0084] Aircraft B: Current position (1200, 1500), speed 28 m / s, flight altitude 1050 m;

[0085] Aircraft C: Current position (1000, 1600), speed 32 m / s, flight altitude 1100 m.

[0086] Based on this data, the system calculates the risk of conflict between aircraft A and B:

[0087] Distance: 200 meters (horizontal distance);

[0088] Maximum safe distance: 500 meters;

[0089] Relative speed: 2 m / s (based on speed difference);

[0090] Collision probability: 0.8 (calculated based on historical data).

[0091] Based on this risk data, the system marks the conflict risk between aircraft A and B as a high-risk area (red) and generates the following adjustment plan:

[0092] Path adjustment: Adjust the heading of aircraft B by 10 degrees to avoid the conflict area;

[0093] Altitude adjustment: Increase the altitude of aircraft A by 500 meters to ensure a safe vertical distance between aircraft;

[0094] Priority Adjustment: If aircraft A is a higher priority aircraft, the system may suggest adjusting the flight time or path of aircraft B.

[0095] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data analysis system for low-altitude airspace, characterized in that, include: The data acquisition module is used to collect real-time data from the low-altitude airspace through multiple different types of sensors and data platforms; The data standardization and fusion module employs a multi-dimensional data fusion method. The advanced data analysis and intelligent reasoning module performs real-time analysis of the fused data to predict potential airspace conflicts and safety risks for aircraft in low-altitude airspace. The dynamic response and decision support module is used to generate targeted airspace scheduling schemes, flight path optimization schemes, and aircraft priority adjustment strategies. The adaptive learning and feedback module dynamically adjusts decision-making strategies.

2. The data analysis system for low-altitude airspace according to claim 1, characterized in that: The real-time data includes radar data, remote sensing data, real-time data transmitted by the aircraft, and environmental perception data.

3. The data analysis system for low-altitude airspace according to claim 1, characterized in that: The data standardization and fusion module preprocesses and standardizes heterogeneous data from different data sources, and then interacts and integrates the data through an interface protocol. The preprocessing includes: format conversion, timestamp alignment, missing data imputation, standardization, and outlier detection.

4. The data analysis system for low-altitude airspace according to claim 1, characterized in that: The advanced data analysis and intelligent reasoning module includes the following steps: S1. Extract features of flight path, speed, altitude, airspace density, aircraft spacing, and weather conditions from real-time data, use statistical analysis methods to identify and quantify key features affecting aircraft conflicts, and construct a risk assessment model; S2. Based on historical and real-time data, calculate the collision risk between aircraft and other aircraft, generate a risk map of potential airspace conflicts, and assist decision-makers in intervention; S3. When the risk of conflict is high, use automated decision-making algorithms to generate airspace adjustment plans and schedule the paths or priorities of aircraft.

5. The data analysis system for low-altitude airspace according to claim 4, characterized in that: The risk assessment model is as follows: R c It is the overall risk of conflict between aircraft, d ij d is the distance between aircraft i and aircraft j. max It is the maximum safe distance between aircraft, P ij V is the probability of a collision between aircraft i and aircraft j. ij It is the relative velocity between aircraft i and aircraft j.

6. The data analysis system for low-altitude airspace according to claim 5, characterized in that: The risk map uses heat map technology, which sets color labels based on the conflict risk values ​​between aircraft.

7. The data analysis system for low-altitude airspace according to claim 6, characterized in that: The airspace adjustment schemes include: path adjustment, altitude adjustment, speed adjustment, time adjustment, emergency avoidance and intervention, airspace replanning and route integration.

8. The data analysis system for low-altitude airspace according to claim 4, characterized in that: The steps for generating the airspace scheduling scheme include: a. Automatically identify areas of potential airspace conflict between aircraft within the airspace, and analyze the aircraft's path, speed, and heading data in real time; b. After identifying areas with high conflict risk, generate targeted airspace scheduling plans to ensure that aircraft can avoid potential conflicts when entering the area.

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