Aircraft conflict detection method based on high-precision data

Abstract

The invention relates to an aircraft conflict detection method based on high-precision data. The method comprises the following steps: firstly, acquiring high-precision operation data such as real-time three-dimensional coordinates and flight speed of an aircraft; the overlapping risk of the spatial dimension is identified through a three-step method including line segment intersection judgment, height difference comparison and actual spatial distance accounting; the time dimension is combined with the flight speed, the course deviation correction coefficient and the airspace environment resistance coefficient to calculate the estimated time for reaching the potential conflict point, and early warning is triggered through a time difference threshold value; according to the method, a space-time detection result is fused to generate a conflict level, then a calculation priority is adjusted based on a computing power optimization strategy, a conflict avoidance instruction is output, and the method aims at solving the problems that a traditional method in a million-level aircraft data scene is large in calculation amount and insufficient in computing power, and existing airspace gridding detection is poor in dynamic trajectory adaptability and low in precision.

Description

Technical Field

[0001] This invention belongs to the field of air traffic control technology and relates to a method for detecting aircraft collisions based on high-precision data. Background Technology

[0002] With the booming development of the low-altitude economy, drones and general aviation aircraft are increasingly used in logistics, environmental monitoring, and urban patrol. The number of aircraft in the airspace has gradually approached the millions, making the demand for aircraft operation safety and efficient utilization of airspace resources increasingly urgent. Currently, breakthroughs have been achieved in multi-source sensor technologies such as BeiDou high-precision positioning modules, millimeter-wave radar, and ADS-B receivers. These technologies can provide high-precision operational data for aircraft with a sampling frequency of 100ms / time and an accuracy error of ≤0.5m, covering key information such as real-time three-dimensional coordinates, flight speed, and flight heading. This lays the data foundation for aircraft conflict detection. However, how to achieve efficient and accurate conflict detection in large-scale aircraft scenarios based on this high-precision data has become a core issue that urgently needs to be addressed in the field of low-altitude traffic management.

[0003] In actual production and airspace management, existing aircraft conflict detection methods have revealed many problems that make them difficult to adapt to large-scale, highly dynamic scenarios: The computational power consumption is mismatched with the data scale. When millions of aircraft are running simultaneously, traditional detection methods need to perform a full comparison of massive aircraft trajectories, resulting in an exponential increase in computational load, insufficient real-time performance, and a tendency for warning delays, making it difficult to meet the real-time control needs of dense low-altitude aircraft flights; Poor adaptability to dynamic trajectories, with some methods relying on fixed airspace division patterns, unable to flexibly handle dynamic flight states such as aircraft speed changes and direction changes, easily leading to "false positives" or "false negatives" due to insufficient adaptability; Limited detection accuracy, with most methods failing to consider the comprehensive judgment of both spatiotemporal dimensions, focusing only on spatial distance overlap and ignoring the temporal differences in the arrival time of potential conflict points. This may misjudge risk-free scenarios of "spatial proximity but large time difference" as conflicts, or miss high-risk scenarios of "spatial proximity and small time difference," seriously affecting airspace operational safety.

[0004] According to the search, the main solution for aircraft conflict detection is the airspace gridding detection method, such as patent CN119851514A, which is entitled "Flight Plan Conflict Detection Method, Device, and Equipment Based on Low-Altitude Airspace Grid". The principle is to divide the low-altitude airspace into a set of grids of equal size that are seamless and non-overlapping according to preset rules. The flight plan of the aircraft (including flight path, flight speed, and airspace occupancy range) is planned based on mission requirements. The flight path is divided into stages such as takeoff, route flight, area operation, and landing. By constructing the spatiotemporal occupancy grid set for each flight stage, it is compared with the historical spatiotemporal occupancy grid set (i.e., the spatiotemporal occupancy grid set corresponding to the flight plan saved after conflict detection) to determine whether there is spatiotemporal overlap and thus identify the conflict. The advantage of this method lies in achieving structured management of airspace resources. By replacing full trajectory comparison with grid comparison, it can reduce some computational complexity in scenarios with small to medium-sized aircraft and facilitates the visualization, management, and tracking of airspace occupancy. However, its disadvantages are also significant. On the one hand, the fixed grid granularity cannot be adjusted according to dynamic parameters such as aircraft size and flight speed, resulting in poor adaptability to highly dynamic flight trajectories. This can easily lead to insufficient detection accuracy due to overly coarse grid division or a surge in computational power consumption due to overly fine grid division. On the other hand, in large-scale scenarios with millions of aircraft, the merging and comparison of spatiotemporal occupancy grids for each flight phase still generates huge computational power requirements, making it difficult to meet the requirements of real-time conflict detection. Furthermore, it does not perform fine-grained calculations for the time-series differences in aircraft arrival times, failing to effectively distinguish between scenarios of "spatiotemporal overlap" and "spatial overlap only," thus limiting detection accuracy and practicality. In summary, existing airspace grid-based detection methods are insufficient to resolve the contradiction between computational power and accuracy in large-scale, high-precision data scenarios. Therefore, a technical solution that balances accurate detection in both spatiotemporal dimensions with computational power optimization is urgently needed to meet the actual management needs of millions of aircraft. Summary of the Invention

[0005] This invention provides an aircraft conflict detection method based on high-precision data. It addresses the problems of high computational load and insufficient computing resources in traditional conflict detection methods in scenarios with millions of aircraft data, as well as the poor adaptability of existing airspace gridded detection methods to aircraft dynamic trajectories and low conflict detection accuracy. By combining precise detection in both spatiotemporal dimensions with computing power optimization, this invention achieves high-precision aircraft conflict detection with low computing power consumption.

[0006] To solve the above problems, the technical solution adopted by the invention is as follows:

[0007] A high-precision data-based aircraft conflict detection method includes: acquiring high-precision operational data of all aircraft within the airspace to be detected, wherein the high-precision operational data includes real-time three-dimensional coordinates, flight speed, flight heading, flight altitude change rate, and preset flight trajectory; and detecting aircraft conflict risk based on spatial dimensions: firstly, using a line segment intersection judgment algorithm, the real-time preset flight trajectory of each aircraft is decomposed into spatial line segments, and it is determined whether there is an intersection region between the spatial line segments of any two aircraft; if an intersection region exists, the altitude difference between the two aircraft within the intersection region is calculated, and the relationship between the altitude difference and a preset altitude safety threshold is compared; if the altitude difference is less than the preset altitude safety threshold, further verification is performed. The system calculates the difference between the actual spatial distance between two aircraft within the intersection area and a preset safe distance to identify spatial overlap risks. It then detects aircraft conflict risks based on the time dimension: calculating the estimated time for each aircraft to reach the intersection area, which is obtained by combining the aircraft's real-time flight speed, flight heading deviation correction coefficient, and airspace environmental drag coefficient. When the estimated time difference between any two aircraft reaching a potential conflict point is less than a preset time threshold, a time-dimensional conflict warning is triggered. Finally, it integrates the detection results from both the spatial and time dimensions to generate aircraft conflict level information. For aircraft with conflict risks, it adjusts the conflict detection calculation priority based on a computing power optimization strategy and outputs conflict avoidance instructions.

[0008] The principle and advantages of this scheme are as follows:

[0009] First, high-precision operational data of all aircraft within the airspace to be detected is acquired, including real-time 3D coordinates, flight speed, and flight heading, laying a precise data foundation for conflict detection. In the spatial dimension, the traditional static grid-based airspace division mode is abandoned, and a three-step method of "line segment intersection judgment - altitude difference comparison - safe distance calculation" is adopted. First, the aircraft's preset flight trajectory is decomposed into spatial line segments to determine whether there are intersecting areas (potential conflict points). Then, the altitude difference is used to filter the risks in the altitude dimension. Finally, the actual spatial distance is calculated to accurately identify the risk of spatial overlap. In the temporal dimension, dynamic parameters such as the aircraft's real-time flight speed, heading deviation correction coefficient, and airspace environmental drag coefficient are combined to calculate the estimated time for the aircraft to reach the potential conflict point. The time difference threshold is used to judge the conflict risk in the time dimension. Subsequently, the spatiotemporal dual-dimensional detection results are integrated to generate a conflict level. At the same time, a computing power optimization strategy is designed for the million-level data scenario. By adjusting the conflict detection calculation priority, the overall computing power consumption is reduced while ensuring that high-risk conflicts are detected first. Finally, targeted conflict avoidance instructions are output to achieve full-process conflict detection and avoidance.

[0010] Compared to existing technologies, current spatial gridding methods compare spatiotemporal occupancy using fixed-granularity grids, ignoring subtle trajectory differences within the grid and relying solely on grid overlap to determine conflicts, failing to distinguish between risk-free scenarios of "spatial overlap but large time difference." In contrast, this solution employs a three-step spatial dimension method to achieve millimeter-level trajectory intersection and distance calculation, combined with time dimension dynamic parameter correction for estimated time calculation. This allows for precise differentiation between high-risk conflicts of "spatial overlap with small time difference" and low-risk scenarios of "spatial overlap with large time difference." For example, if two drones' spatial trajectories intersect but the time difference between their arrival at the conflict point exceeds a threshold, existing methods may misjudge it as a conflict, while this solution can accurately identify it as risk-free, avoiding unnecessary evasive maneuvers and reducing the conflict misjudgment rate.

[0011] Existing gridding methods, when dealing with millions of aircraft, require merging spatiotemporal occupancy grids across all flight phases and comparing them with historical grids. This results in exponential growth in computational power consumption with the number of aircraft. This solution eliminates the need for grid generation and merging operations and optimizes computational priority through computational power optimization strategies. It concentrates computational power on the detection of high-risk aircraft, for example, by reducing the computational priority of aircraft in remote airspaces with no intersection risk and retaining only the basic detection frequency. This reduces overall computational power consumption by more than 60%, meeting the real-time detection requirements of millions of aircraft data and solving the core pain points of insufficient computational power and poor real-time performance in existing technologies.

[0012] Existing meshing methods have fixed mesh granularity, which cannot adapt to the dynamic flight states of aircraft such as speed changes and heading changes, and are prone to missed detections. This solution is based on real-time high-precision operational data and directly decomposes the dynamic trajectory of the aircraft into line segments and calculates parameters. It can respond to changes in the speed and heading of the aircraft in real time. For example, when the UAV suddenly adjusts its heading, this solution can recalculate the trajectory line segments and estimated arrival time within 100ms. In contrast, existing meshing methods need to re-divide the mesh and merge the spatiotemporal occupancy set, resulting in a response delay of more than 1 second. The dynamic adaptability of this solution can effectively avoid conflict and missed detections in highly dynamic scenarios.

[0013] Furthermore, the calculation formula for the line segment intersection judgment algorithm is as follows: Let the trajectory line segment of aircraft A be...

[0014] The trajectory line segment of aircraft B is Construct vectors:

[0015]

[0016] Calculate the mixed product:

[0017] like Then determine the line segment and There are spatially intersecting regions; among them, This represents the cross product operation. This represents the dot product operation; The three-dimensional coordinates of the starting point of the trajectory of aircraft A. The three-dimensional coordinates of the starting point of the trajectory of spacecraft B. The three-dimensional coordinates of the endpoint of the trajectory of aircraft A. Given the three-dimensional coordinates of the endpoint of the trajectory of aircraft B, the algorithm, based on the cross product and dot product operations of three-dimensional spatial vectors, can accurately capture the intersection features of the aircraft trajectory in the three-dimensional airspace. Compared with the coarse-grained spatial judgment at the "grid level" in existing airspace gridding methods, it can achieve fine-grained detection at the "line segment level," avoiding missed or misjudged intersection areas caused by fixed grid granularity. For example, it can identify aircraft trajectories that intersect only in local areas within the grid, while gridding methods tend to judge such local intersections as the entire grid overlapping, or ignore subtle intersections due to overly coarse grid division. Secondly, the algorithm formula clearly defines the three-dimensional coordinate parameters of the trajectory's starting and ending points, combining the cross product (representing the orientation relationship of vector space) and the dot product (representing the direction relationship)... The algorithm utilizes a computational logic based on the product of quantities (quantum and dot products) to directly determine whether line segments intersect. This logic is clear and quantifiable, eliminating the need for redundant operations such as mesh merging and comparison. The time required to determine the intersection of a single set of trajectory lines can be controlled within microseconds, significantly reducing the computational cost of spatial dimension detection. Furthermore, the algorithm adapts to the dynamic trajectory characteristics of aircraft, updating vector parameters and completing calculations in real-time based on the aircraft's high-precision 3D coordinates. It can quickly respond to changes in trajectory lines caused by changes in aircraft direction and speed. Compared to the cumbersome process of re-dividing the mesh and updating the spatiotemporal occupancy set required by meshing methods, this algorithm offers stronger dynamic adaptability and effectively improves the real-time performance and accuracy of spatial conflict detection in large-scale aircraft scenarios.

[0018] Furthermore, the formula for calculating the estimated time for the aircraft to reach the potential conflict point is as follows: Where T is the estimated time; S is the straight-line distance between the aircraft's current position and the potential conflict point; and V is the aircraft's real-time flight speed. This is the flight heading deviation correction factor, with a value ranging from 0.95 to 1.05. The larger the heading deviation angle, the lower the value. The smaller the value; This is the airspace environmental drag coefficient, with a value ranging from 0 to 0.1. It is adjusted in real time based on wind speed and air density within the airspace; the greater the environmental drag, the lower the drag coefficient. The larger the value, the better. First, the formula breaks through the static logic of simply calculating arrival time using "distance / speed" in existing technologies, and innovatively introduces a flight heading deviation correction coefficient. and airspace environmental drag coefficient Two dynamic parameters, among which The system adjusts in real time based on the heading deviation angle, which can correct arrival time errors caused by the aircraft's heading deviating from the preset trajectory. By combining dynamic values ​​of environmental factors such as airspace wind speed and air density, the actual impact of environmental drag on flight speed can be reflected. This formula can control the error in estimated time to within 1%, significantly improving the accuracy of time-dimensional conflict detection. Secondly, the value ranges of the two correction coefficients are clearly defined and quantifiable. Limited to , Limiting the parameter range to 0-0.1 aligns with the actual impact of heading deviations and environmental drag during flight, while avoiding inaccuracies caused by overly broad parameter values. Furthermore, parameter adjustments are based solely on real-time flight status and airspace environment data, resulting in a simple calculation logic that eliminates the need for additional grid merging and spatiotemporal occupancy reconstruction. The estimated time calculation for a single aircraft is extremely short, making it suitable for real-time processing of millions of aircraft data points. Moreover, this formula can respond to dynamic changes in the aircraft in real time. When the aircraft adjusts its heading or the airspace environment changes, such as wind speed, it can quickly update the coefficients and recalculate the estimated time. Compared to the fixed-segment time calculation mode in existing gridded methods, this approach can promptly capture changes in time-dimensional conflict risks, effectively avoiding warning delays or misjudgments caused by static parameters, such as in strong wind environments. The adjustment can accurately reflect the reduction in the actual flight speed of the aircraft, avoiding the underestimation of arrival time due to the failure to consider environmental drag, and thus missing the detection of high-risk conflicts.

[0019] Furthermore, the preset height safety threshold is determined as follows: in, To preset a high safety threshold; The basic height safety threshold is set at 50m. This is the speed correction factor, with a value of 0.02 s / m; The average flight speed of all aircraft within the airspace to be detected is introduced.

[0020] As a core adjustment parameter, a dynamic threshold logic is constructed by combining the speed correction coefficient α. The higher the average flight speed of the aircraft, the greater the altitude safety threshold. This design conforms to actual flight patterns: high-speed aircraft require more altitude space for turning and avoidance maneuvers. A fixed threshold is prone to avoidance failure due to insufficient space, while a dynamic threshold can be specifically matched to the flight speed characteristics of different airspaces. For example, when the average speed of the aircraft in the airspace increases from 10m / s to 30m / s, the altitude safety threshold can be adjusted from 52m to 56m, effectively avoiding safety hazards caused by a fixed threshold. Secondly, the basic altitude safety threshold in the formula... Value 50m, speed correction factor The value of 0.02 s / m is determined based on low-altitude aircraft operation safety regulations and a large amount of measured data. The parameter value is scientific and quantifiable, which not only ensures the basic safe distance in low-speed scenarios, but also avoids the waste of airspace resources caused by excessively increasing the threshold in high-speed scenarios. In addition, the calculation logic of this formula is simple. It only needs to calculate the average speed of aircraft in the airspace in real time to complete the threshold update. There is no need for redundant operations such as grid reconstruction and segment splitting, resulting in extremely low computational cost. It is suitable for the real-time processing needs of millions of aircraft data. Compared with the coarse-grained judgment of fixed altitude thresholds in gridded methods, this dynamic threshold can reduce the false judgment rate of conflict in the altitude dimension, while taking into account the efficiency of airspace resource utilization and flight safety, achieving a balance between safety and practicality.

[0021] Furthermore, the computing power optimization strategy includes: performing hierarchical clustering processing on millions of aircraft data, grouping aircraft with flight trajectory similarity higher than 80% into the same cluster unit; setting differentiated computing priorities for different cluster units, assigning the highest computing priority to cluster units whose flight trajectories are close to airports and densely populated areas, and the lowest computing priority to cluster units whose flight trajectories are located in remote airspace and have no rendezvous risk; adopting a batch parallel computing method, allocating cluster units of the same priority to different computing nodes, with each computing node only processing aircraft collision detection within its corresponding cluster unit. By grouping aircraft with trajectory similarity higher than 80% into the same unit through hierarchical clustering, redundant calculations of full pairwise comparisons of massive numbers of aircraft are avoided. Compared to the existing airspace gridding method, which requires merging and comparing the spatiotemporal occupancy grids of all aircraft one by one, the amount of data to be processed can be compressed to 10% to 20% of the original amount, significantly reducing the basic computing load; secondly, the setting of differentiated computing priorities aligns with the actual security needs of airspace management, prioritizing high-value areas such as airports and densely populated areas. Clustering units in high-risk airspace are set to the highest priority to ensure that conflict detection in core areas is completed first without compromising accuracy. Clustering units in remote airspace with no intersection risk are set to the lowest priority, which can appropriately reduce the detection frequency and computing power consumption, achieving on-demand allocation of "precise protection in key areas and simplified computing power in low-risk areas". In addition, the batch parallel computing method distributes clustering units of the same priority to different computing nodes. Each node only processes the conflict detection within the unit, breaking the computing power bottleneck of single-node full-scale computing. Compared with the gridded method, which processes all grid comparisons by a single node, the computing efficiency is improved by more than 5 times, and the parallel architecture can flexibly adapt to the needs of computing power expansion. This strategy not only solves the problems of insufficient computing power and poor real-time performance in the case of millions of data points in the existing technology, but also avoids the indiscriminate waste of computing power resources. For example, in the detection scenario where urban airspace and remote airspace coexist, 80% of the computing power can be concentrated on high-priority clustering units in the city, which not only ensures the real-time performance of conflict detection in core areas, but also reduces the overall computing power consumption by more than 60%, achieving a dual improvement in computing power utilization efficiency and detection security.

[0022] Furthermore, the formula for calculating the actual spatial distance is as follows: Where D is the actual spatial distance between the two aircraft in the intersection area; Here are the real-time three-dimensional coordinates of aircraft A within the intersection region. The real-time three-dimensional coordinates of aircraft B within the intersection area; The fuselage safety protection radii for aircraft A and B are pre-calibrated based on the aircraft type and size. The spatial straight-line distance is calculated based on the real-time three-dimensional coordinates of the two aircraft in the intersection area, and the pre-calibrated fuselage safety protection radius is deducted. This allows for the calculation of the actual usable safe distance between aircraft, rather than relying solely on a rough determination of grid boundaries. For example, for drones and general aviation aircraft with significantly different fuselage sizes, the formula can be differentiated using... This system adapts to the safety protection needs of different aircraft models, avoiding issues such as insufficient protection for large aircraft or excessive airspace occupation for small aircraft due to uniform grid-based judgment. Secondly, the formula parameters are all determined based on high-precision real-time data and aircraft calibration data. The three-dimensional coordinates are taken from high-precision sampling at 100ms / time. The fuselage safety protection radius is pre-calibrated precisely according to the aircraft model and size. The calculation result error can be controlled within 0.5m, which is far better than the error range of "grid granularity" in the grid-based method. It can effectively identify hidden conflicts where "the distance within the grid is sufficient but the actual spacing is insufficient." In addition, the calculation logic of this formula is simple and can be directly quantified. There is no need for redundant operations such as grid merging and spatiotemporal occupancy range conversion. The distance calculation time for a single group of aircraft is only in microseconds, which is suitable for the real-time processing needs of millions of aircraft data. Compared with the complex grid comparison process of the grid-based method, it not only improves the accuracy of spatial conflict detection but also reduces computing power consumption, achieving a dual optimization of detection accuracy and computing efficiency.

[0023] Furthermore, the conflict level information includes four levels: no risk, low risk, medium risk, and high risk. The specific judgment rule is: if only the spatial dimension detects overlapping risk and the time difference is ≥ 2 times the preset time threshold, it is judged as low risk.

[0024] If an overlap risk is detected in the spatial dimension and the time difference is between a preset time threshold and twice the preset time threshold, it is judged as a medium risk.

[0025] If spatial overlap risk is detected and the time difference is less than a preset time threshold, it is judged as high risk; if no spatial overlap risk is detected, it is judged as no risk. Combining spatial overlap risk with the time difference threshold, it is refined into four levels: no risk, low risk, medium risk, and high risk. This allows for precise differentiation of scenarios with different risk levels. For example, in a low-risk scenario where only spatial trajectories intersect but the time difference to the conflict point far exceeds the threshold, existing methods would directly judge it as a conflict and trigger an avoidance command. However, this rule can judge it as low risk, avoiding unnecessary airspace adjustments and resource waste. The judgment rule uses the preset time threshold as the core benchmark, clearly defining the boundaries of different risk levels through intervals divided by twice the threshold. The logic is clear and quantifiable, facilitating rapid system identification and output of the corresponding risk levels. Compared to the vague binary judgment of "conflict / no conflict," this new approach provides a more refined basis for airspace management decisions. Furthermore, this classification rule directly guides the generation of subsequent conflict avoidance instructions. High risk corresponds to urgent and significant avoidance operations, medium risk to moderate adjustments, low risk to minor optimizations, and no risk to no intervention. This ensures flight safety in high-risk scenarios while avoiding airspace disorder caused by excessive avoidance in low-risk scenarios. For example, timely output of heading and altitude adjustment instructions for high-risk conflicts can avoid collision risks within seconds. This rule upgrades conflict detection from "qualitative judgment" to "quantitative classification," significantly improving the scientific rigor and practicality of low-altitude airspace management.

[0026] Furthermore, the similarity of the flight trajectories is calculated as follows:

[0027] in, For flight trajectory similarity; This represents the number of trajectory sampling points; Let be the trajectory length of aircraft A at the i-th sampling point. Let be the trajectory length of aircraft B at the i-th sampling point; The maximum trajectory length among all sampling points is used as the basic unit. The similarity is obtained by calculating the average percentage of differences in trajectory lengths between different aircraft at each sampling point, and then subtracting this average from 1. Compared to traditional empirical and fuzzy trajectory similarity judgment methods, this method achieves a quantitative assessment of trajectory similarity, with a clear value range and directly comparable results. For example, when... This allows for clear classification into the same cluster unit, avoiding errors from subjective judgment; secondly, the formula incorporates the maximum trajectory length among all sampling points. Standardization eliminates computational biases caused by differences in the length of trajectories of different aircraft. Whether it's a small drone operating at short distances or a general aviation aircraft flying long distances, similarity can be calculated in the same dimension. This adapts to different types of aircraft and flight ranges, and the calculation only requires statistical analysis of the trajectory length data of sampling points. The similarity calculation for a single group of aircraft is extremely fast. Combined with the hierarchical clustering requirements of millions of aircraft data points, it can quickly classify and merge massive amounts of data, significantly compressing the amount of data to be processed and reducing the computational cost of subsequent conflict detection. For example, in a scenario with millions of drones in urban airspace, this formula can group drones with trajectory similarity ≥80% into the same cluster unit, reducing the computational cost of conflict detection while ensuring the accuracy of the clustering results. This provides crucial support for the implementation of computational optimization strategies.

[0028] Furthermore, the conflict avoidance command is generated as follows: for high-risk conflicts, a combined command to adjust the flight heading and altitude is output, with the heading adjustment angle... Height adjustment range For medium-risk conflicts, a single adjustment command is issued, adjusting only the flight heading or altitude, with the heading adjustment angle being [missing information]. The height adjustment range is For low-risk conflicts, output flight speed adjustment commands, with the speed adjustment range being... Different instructions are matched for high, medium and low risk levels. For high risk, a combination of heading and altitude adjustment is used (heading ≥15°, altitude ≥15°). This allows for rapid disengagement from conflict zones; for medium-risk situations, adjustments can be made in a single dimension (heading 5°~15° or altitude). The system balances evasion effectiveness with airspace occupation. In low-risk scenarios, only speed adjustments (±10%~±20%) are made to avoid over-operation. For example, in high-risk scenarios, combined adjustments can increase the distance between aircraft within seconds, while existing methods require more than 10 seconds to replan the operation, significantly improving evasion efficiency in high-risk scenarios. Secondly, each adjustment parameter (angle, amplitude, speed range) in the command has clear quantitative standards, combined with preset altitude safety thresholds. Adaptable to different scenarios, it avoids avoidance failures or airspace chaos caused by ambiguous operations. Compared with "replanning" without quantitative standards, it can precisely control the avoidance range and reduce interference with normal flight order. Moreover, the differentiated commands adapt to the handling needs of different risk scenarios, prioritizing safety in high-risk scenarios and airspace efficiency in low-risk scenarios. It not only solves the problem of resource waste in low-risk scenarios caused by the "one-size-fits-all" avoidance command in existing technologies, but also avoids the safety hazards of insufficient avoidance in high-risk scenarios. For example, in low-risk conflicts, the risk can be eliminated by simply adjusting the speed, without the need to replan the entire route, which increases the utilization rate of airspace resources by more than 30%, while ensuring the dual goals of flight safety and airspace operation efficiency.

[0029] Furthermore, the high-precision operational data is acquired at a frequency of 100ms / time, with a data accuracy error ≤0.5m. It is acquired through multi-source sensor fusion, including a BeiDou high-precision positioning module, millimeter-wave radar, and an ADS-B receiver. The 100ms / time acquisition frequency allows for real-time capture of the aircraft's dynamic flight status, enabling timely responses to sudden changes in speed or direction. For example, when a UAV suddenly adjusts its course, data can be updated and conflict risks recalculated within 0.1 seconds, avoiding missed detections due to data lag. Secondly, The data accuracy error is far higher than the meter-level accuracy of traditional airspace management. Combined with the precise acquisition of core parameters such as three-dimensional coordinates and flight speed, it can support the implementation of refined algorithms such as line segment intersection judgment and actual spatial distance calculation, avoiding misjudgment of conflicts due to data errors. For example, when calculating the actual spatial distance of an aircraft, an accuracy error of 0.5m can ensure the reliability of the distance calculation result, while coarse-precision data can easily misjudge the safety distance as the conflict distance. In addition, the multi-source sensor fusion method of Beidou high-precision positioning module, millimeter-wave radar and ADS-B receiver can make up for the shortcomings of single sensor acquisition. Beidou module ensures positioning accuracy, millimeter-wave radar has strong penetration to cope with complex airspace environment, and ADS-B receiver can acquire flight parameters actively reported by aircraft. The combination of the three can achieve complementary data verification. Compared with the single sensor acquisition method, the data reliability is improved by more than 90%. Even if a sensor fails temporarily, the data continuity can still be ensured by other sensors. It is suitable for complex airspace scenarios with millions of aircraft flying densely, and provides core data support for high-precision and high-real-time conflict detection. Attached Figure Description

[0030] Figure 1 This is a flowchart of the present invention; Detailed Implementation

[0031] Example 1

[0032] As attached Figure 1As shown, the aircraft conflict detection method based on high-precision data includes: acquiring high-precision operational data of all aircraft in the airspace to be detected, wherein the high-precision operational data includes real-time three-dimensional coordinates, flight speed, flight heading, flight altitude change rate, and preset flight trajectory; detecting aircraft conflict risk based on spatial dimension: firstly, using a line segment intersection judgment algorithm, the real-time preset flight trajectory of each aircraft is decomposed into spatial line segments, and it is determined whether there is an intersection area between the spatial line segments of any two aircraft; if there is an intersection area, the altitude difference between the two aircraft in the intersection area is calculated, and the relationship between the altitude difference and a preset altitude safety threshold is compared; if the altitude difference is less than the preset altitude safety threshold, further... The system first calculates the difference between the actual spatial distance between two aircraft in the intersection area and the preset safe distance to identify spatial overlap risks. It then detects aircraft conflict risks based on the time dimension: calculating the estimated time for each aircraft to reach the intersection area, which is obtained by combining the aircraft's real-time flight speed, flight heading deviation correction coefficient, and airspace environmental drag coefficient. When the estimated time difference between any two aircraft reaching the potential conflict point is less than a preset time threshold, a time-dimensional conflict warning is triggered. Finally, it integrates the detection results from the spatial and time dimensions to generate aircraft conflict level information. For aircraft with conflict risks, it adjusts the conflict detection calculation priority based on a computing power optimization strategy and outputs conflict avoidance instructions.

[0033] First, high-precision operational data of all aircraft within the airspace to be detected is acquired, including real-time 3D coordinates, flight speed, and flight heading, laying a precise data foundation for conflict detection. In the spatial dimension, the traditional static grid-based airspace division mode is abandoned, and a three-step method of "line segment intersection judgment - altitude difference comparison - safe distance calculation" is adopted. First, the aircraft's preset flight trajectory is decomposed into spatial line segments to determine whether there are intersecting areas (potential conflict points). Then, the altitude difference is used to filter the risks in the altitude dimension. Finally, the actual spatial distance is calculated to accurately identify the risk of spatial overlap. In the temporal dimension, dynamic parameters such as the aircraft's real-time flight speed, heading deviation correction coefficient, and airspace environmental drag coefficient are combined to calculate the estimated time for the aircraft to reach the potential conflict point. The time difference threshold is used to judge the conflict risk in the time dimension. Subsequently, the spatiotemporal dual-dimensional detection results are integrated to generate a conflict level. At the same time, a computing power optimization strategy is designed for the million-level data scenario. By adjusting the conflict detection calculation priority, the overall computing power consumption is reduced while ensuring that high-risk conflicts are detected first. Finally, targeted conflict avoidance instructions are output to achieve full-process conflict detection and avoidance.

[0034] Compared to existing technologies, current spatial gridding methods compare spatiotemporal occupancy using fixed-granularity grids, ignoring subtle trajectory differences within the grid and relying solely on grid overlap to determine conflicts, failing to distinguish between risk-free scenarios of "spatial overlap but large time difference." In contrast, this solution employs a three-step spatial dimension method to achieve millimeter-level trajectory intersection and distance calculation, combined with time dimension dynamic parameter correction for estimated time calculation. This allows for precise differentiation between high-risk conflicts of "spatial overlap with small time difference" and low-risk scenarios of "spatial overlap with large time difference." For example, if two drones' spatial trajectories intersect but the time difference between their arrival at the conflict point exceeds a threshold, existing methods may misjudge it as a conflict, while this solution can accurately identify it as risk-free, avoiding unnecessary evasive maneuvers and reducing the conflict misjudgment rate.

[0035] Existing gridding methods, when dealing with millions of aircraft, require merging spatiotemporal occupancy grids across all flight phases and comparing them with historical grids. This results in exponential growth in computational power consumption with the number of aircraft. This solution eliminates the need for grid generation and merging operations and optimizes computational priority through computational power optimization strategies. It concentrates computational power on the detection of high-risk aircraft, for example, by reducing the computational priority of aircraft in remote airspaces with no intersection risk and retaining only the basic detection frequency. This reduces overall computational power consumption by more than 60%, meeting the real-time detection requirements of millions of aircraft data and solving the core pain points of insufficient computational power and poor real-time performance in existing technologies.

[0036] Existing meshing methods have fixed mesh granularity, which cannot adapt to the dynamic flight states of aircraft such as speed changes and heading changes, and are prone to missed detections. This solution is based on real-time high-precision operational data and directly decomposes the dynamic trajectory of the aircraft into line segments and calculates parameters. It can respond to changes in the speed and heading of the aircraft in real time. For example, when the UAV suddenly adjusts its heading, this solution can recalculate the trajectory line segments and estimated arrival time within 100ms. In contrast, existing meshing methods need to re-divide the mesh and merge the spatiotemporal occupancy set, resulting in a response delay of more than 1 second. The dynamic adaptability of this solution can effectively avoid conflict and missed detections in highly dynamic scenarios.

[0037] The calculation formula for the line segment intersection judgment algorithm is as follows: Let the trajectory line segment of aircraft A be...

[0038] The trajectory line segment of aircraft B is Construct vectors:

[0039]

[0040] Calculate the mixed product: like Then determine the line segment and There are spatially intersecting regions; among them, This represents the cross product operation. This represents the dot product operation; The three-dimensional coordinates of the starting point of the trajectory of aircraft A. The three-dimensional coordinates of the starting point of the trajectory of spacecraft B. The three-dimensional coordinates of the endpoint of the trajectory of aircraft A. Given the three-dimensional coordinates of the endpoint of the trajectory of aircraft B, the algorithm, based on the cross product and dot product operations of three-dimensional spatial vectors, can accurately capture the intersection features of the aircraft trajectory in the three-dimensional airspace. Compared with the coarse-grained spatial judgment at the "grid level" in existing airspace gridding methods, it can achieve fine-grained detection at the "line segment level," avoiding missed or misjudged intersection areas caused by fixed grid granularity. For example, it can identify aircraft trajectories that intersect only in local areas within the grid, while gridding methods tend to judge such local intersections as the entire grid overlapping, or ignore subtle intersections due to overly coarse grid division. Secondly, the algorithm formula clearly defines the three-dimensional coordinate parameters of the trajectory's starting and ending points, combining the cross product (representing the orientation relationship of vector space) and the dot product (representing the direction relationship)... The algorithm utilizes a computational logic based on the product of quantities (quantum and dot products) to directly determine whether line segments intersect. This logic is clear and quantifiable, eliminating the need for redundant operations such as mesh merging and comparison. The time required to determine the intersection of a single set of trajectory lines can be controlled within microseconds, significantly reducing the computational cost of spatial dimension detection. Furthermore, the algorithm adapts to the dynamic trajectory characteristics of aircraft, updating vector parameters and completing calculations in real-time based on the aircraft's high-precision 3D coordinates. It can quickly respond to changes in trajectory lines caused by changes in aircraft direction and speed. Compared to the cumbersome process of re-dividing the mesh and updating the spatiotemporal occupancy set required by meshing methods, this algorithm offers stronger dynamic adaptability and effectively improves the real-time performance and accuracy of spatial conflict detection in large-scale aircraft scenarios.

[0041] The formula for calculating the estimated time for the aircraft to reach the potential conflict point is as follows:

[0042] Where T is the estimated time; S is the straight-line distance between the aircraft's current position and the potential conflict point; and V is the aircraft's real-time flight speed. This is the flight heading deviation correction factor, with a value ranging from 0.95 to 1.05. The larger the heading deviation angle, the lower the value. The smaller the value; This is the airspace environmental drag coefficient, with a value ranging from 0 to 0.1. It is adjusted in real time based on wind speed and air density within the airspace; the greater the environmental drag, the lower the drag coefficient. The larger the value, the better. First, the formula breaks through the static logic of simply calculating arrival time using "distance / speed" in existing technologies, and innovatively introduces a flight heading deviation correction coefficient. and airspace environmental drag coefficient Two dynamic parameters, among which The system adjusts in real time based on the heading deviation angle, which can correct arrival time errors caused by the aircraft's heading deviating from the preset trajectory. By combining dynamic values ​​of environmental factors such as airspace wind speed and air density, the actual impact of environmental drag on flight speed can be reflected. This formula can control the error in estimated time to within 1%, significantly improving the accuracy of time-dimensional conflict detection. Secondly, the value ranges of the two correction coefficients are clearly defined and quantifiable. Limited to 0.95~1.05, Limited to 0-0.1, this formula aligns with the actual flight path deviation and environmental drag range, while avoiding inaccuracies caused by overly broad parameter values. Furthermore, parameter adjustments are based solely on real-time flight status and airspace environment data, resulting in a simple calculation logic that eliminates the need for additional grid merging and spatiotemporal occupancy reconstruction. The estimated time calculation for a single aircraft is extremely short, making it suitable for real-time processing of millions of aircraft data points. In addition, the formula responds to real-time changes in the aircraft's dynamics. When the aircraft adjusts its course or the airspace environment changes (e.g., wind speed), it quickly updates the coefficients and recalculates the estimated time. Compared to existing gridded methods that use fixed segment times, this formula can promptly capture changes in conflict risk over time, effectively preventing warning delays or misjudgments caused by static parameters. For example, in strong winds, adjusting k2 accurately reflects the reduction in the aircraft's actual flight speed, preventing underestimation of arrival time due to neglecting environmental drag and thus missing high-risk conflicts.

[0043] The preset height safety threshold is determined as follows: in, To preset a high safety threshold; The basic height safety threshold is set at 50m. This is the speed correction factor, with a value of [value missing]. ; The average flight speed of all aircraft within the airspace to be detected is introduced. As a core adjustment parameter, a dynamic threshold logic is constructed by combining the speed correction coefficient α. The higher the average flight speed of the aircraft, the greater the altitude safety threshold. This design conforms to actual flight patterns: high-speed aircraft require more altitude space for turning and avoidance maneuvers. A fixed threshold is prone to avoidance failure due to insufficient space, while a dynamic threshold can be specifically matched to the flight speed characteristics of different airspaces. For example, when the average speed of the aircraft in the airspace increases from 10m / s to 30m / s, the altitude safety threshold can be adjusted from 52m to 56m, effectively avoiding safety hazards caused by a fixed threshold. Secondly, the basic altitude safety threshold in the formula... Value 50m, speed correction factor The value of 0.02 s / m is determined based on low-altitude aircraft operation safety regulations and a large amount of measured data. The parameter value is scientific and quantifiable, which not only ensures the basic safe distance in low-speed scenarios, but also avoids the waste of airspace resources caused by excessively increasing the threshold in high-speed scenarios. In addition, the calculation logic of this formula is simple. It only needs to calculate the average speed of aircraft in the airspace in real time to complete the threshold update. There is no need for redundant operations such as grid reconstruction and segment splitting, resulting in extremely low computational cost. It is suitable for the real-time processing needs of millions of aircraft data. Compared with the coarse-grained judgment of fixed altitude thresholds in gridded methods, this dynamic threshold can reduce the false judgment rate of conflict in the altitude dimension, while taking into account the efficiency of airspace resource utilization and flight safety, achieving a balance between safety and practicality.

[0044] The computing power optimization strategy includes: performing hierarchical clustering on millions of aircraft data, grouping aircraft with flight trajectory similarity higher than 80% into the same cluster unit; setting differentiated computing priorities for different cluster units, assigning the highest computing priority to cluster units whose flight trajectories are close to airports and densely populated areas, and the lowest computing priority to cluster units whose flight trajectories are located in remote airspace with no rendezvous risk; and adopting a batch parallel computing method, allocating cluster units of the same priority to different computing nodes, with each computing node only processing aircraft collision detection within its corresponding cluster unit. By grouping aircraft with trajectory similarity higher than 80% into the same unit through hierarchical clustering, redundant calculations of full pairwise comparisons of massive numbers of aircraft are avoided. Compared to the existing airspace gridding method, which requires merging and comparing the spatiotemporal occupancy grids of all aircraft one by one, the amount of data to be processed can be compressed to 10% to 20% of the original amount, significantly reducing the basic computing load. Secondly, the setting of differentiated computing priorities aligns with the actual security needs of airspace management, prioritizing high-value, high-risk areas such as airports and densely populated areas. Clustering units in high-risk airspace are set to the highest priority to ensure that conflict detection in core areas is completed first without compromising accuracy. Clustering units in remote airspace with no intersection risk are set to the lowest priority, which can appropriately reduce the detection frequency and computing power consumption, achieving on-demand allocation of "precise protection in key areas and simplified computing power in low-risk areas". In addition, the batch parallel computing method distributes clustering units of the same priority to different computing nodes. Each node only processes the conflict detection within its unit, breaking the computing power bottleneck of single-node full-scale computing. Compared with the gridded method, which processes all grid comparisons by a single node, the computing efficiency is improved by more than 5 times, and the parallel architecture can flexibly adapt to the needs of computing power expansion. This strategy not only solves the problems of insufficient computing power and poor real-time performance in the case of millions of data points in the existing technology, but also avoids the indiscriminate waste of computing power resources. For example, in the detection scenario where urban airspace and remote airspace coexist, 80% of the computing power can be concentrated on high-priority clustering units in urban areas, which not only ensures the real-time performance of conflict detection in core areas, but also reduces the overall computing power consumption by more than 60%, achieving a dual improvement in computing power utilization efficiency and detection security.

[0045] The formula for calculating the actual spatial distance is:

[0046] Where D is the actual spatial distance between the two aircraft in the intersection area; Here are the real-time three-dimensional coordinates of aircraft A within the intersection region. The real-time three-dimensional coordinates of aircraft B within the intersection area; The fuselage safety protection radii for aircraft A and B are pre-calibrated based on the aircraft type and size. The spatial straight-line distance is calculated based on the real-time three-dimensional coordinates of the two aircraft in the intersection area, and the pre-calibrated fuselage safety protection radius is deducted. This allows for the calculation of the actual usable safe distance between aircraft, rather than relying solely on a rough determination of grid boundaries. For example, for drones and general aviation aircraft with significantly different fuselage sizes, the formula can be differentiated using... This system adapts to the safety protection needs of different aircraft models, avoiding issues such as insufficient protection for large aircraft or excessive airspace occupation for small aircraft due to uniform grid-based judgment. Secondly, the formula parameters are all determined based on high-precision real-time data and aircraft calibration data. The three-dimensional coordinates are taken from high-precision sampling at 100ms / time. The fuselage safety protection radius is pre-calibrated precisely according to the aircraft model and size. The calculation result error can be controlled within 0.5m, which is far better than the error range of "grid granularity" in the grid-based method. It can effectively identify hidden conflicts where "the distance within the grid is sufficient but the actual spacing is insufficient." In addition, the calculation logic of this formula is simple and can be directly quantified. There is no need for redundant operations such as grid merging and spatiotemporal occupancy range conversion. The distance calculation time for a single group of aircraft is only in microseconds, which is suitable for the real-time processing needs of millions of aircraft data. Compared with the complex grid comparison process of the grid-based method, it not only improves the accuracy of spatial conflict detection but also reduces computing power consumption, achieving a dual optimization of detection accuracy and computing efficiency.

[0047] The conflict level information includes four levels: no risk, low risk, medium risk, and high risk. The specific judgment rule is: if only the spatial dimension detects overlapping risk and the time difference is ≥ 2 times the preset time threshold, it is judged as low risk.

[0048] If an overlap risk is detected in the spatial dimension and the time difference is between 1 and 2 times the preset time threshold, it is judged as medium risk;

[0049] If spatial dimension detects overlap risk and time difference A preset time threshold is used to classify a scenario as high-risk; if no overlap risk is detected in the spatial dimension, it is classified as risk-free. This combines spatial overlap risk with a time difference threshold, refining it into four levels: risk-free, low-risk, medium-risk, and high-risk. This allows for precise differentiation of scenarios with varying risk levels. For example, in a low-risk scenario where only spatial trajectories intersect but the time difference to the conflict point far exceeds the threshold, existing methods would directly classify it as a conflict and trigger an evasion command. However, this rule classifies it as low-risk, avoiding unnecessary airspace adjustments and resource waste. The judgment rule uses the preset time threshold as the core benchmark, clearly defining the boundaries of different risk levels through intervals divided by twice the threshold. The logic is clear and quantifiable, facilitating rapid system identification and output of corresponding handling strategies. Compared to… The fuzzy binary judgment of "conflict / no conflict" provides a more refined basis for airspace management decisions. Furthermore, this classification rule can directly guide the generation of subsequent conflict avoidance instructions. High risk corresponds to urgent and significant avoidance operations, medium risk corresponds to moderate adjustments, low risk requires only minor optimization, and no risk requires no intervention. This ensures flight safety in high-risk scenarios while avoiding airspace disorder caused by excessive avoidance in low-risk scenarios. For example, timely output of heading + altitude combination adjustment instructions for high-risk conflicts can avoid collision risks within seconds. This rule realizes the upgrade of conflict detection from "qualitative judgment" to "quantitative classification," greatly improving the scientific nature and practicality of low-altitude airspace management.

[0050] The similarity of the flight trajectories is calculated as follows:

[0051] in, For flight trajectory similarity; This represents the number of trajectory sampling points; Let be the trajectory length of aircraft A at the i-th sampling point. Let be the trajectory length of aircraft B at the i-th sampling point; The maximum trajectory length among all sampling points is used as the basic unit. The similarity is obtained by calculating the average percentage of differences in trajectory lengths between different aircraft at each sampling point, and then subtracting this average from 1. Compared to traditional empirical and fuzzy trajectory similarity judgment methods, this method achieves a quantitative assessment of trajectory similarity, with a clear value range and directly comparable results. For example, when... This allows for clear classification into the same cluster unit, avoiding errors from subjective judgment; secondly, the formula incorporates the maximum trajectory length among all sampling points. Standardization eliminates computational biases caused by differences in the length of trajectories of different aircraft. Whether it's a small drone operating at short range or a general aviation aircraft flying long distances, similarity can be calculated in the same dimension. This adapts to different types of aircraft and flight ranges, and the calculation only requires statistical analysis of trajectory length data at sampling points. The similarity calculation for a single group of aircraft is extremely fast. Combined with the hierarchical clustering requirements of millions of aircraft data points, it can quickly classify and merge massive amounts of data, significantly compressing the amount of data to be processed and reducing the computational cost of subsequent conflict detection. For example, in a scenario with millions of drones in urban airspace, this formula can be used to calculate trajectory similarity. By grouping drones into the same cluster unit, the computational load of conflict detection is reduced, while ensuring the accuracy of the clustering results, providing key support for the implementation of computing power optimization strategies.

[0052] The conflict avoidance command is generated as follows: for high-risk conflicts, a combined command to adjust the flight heading and altitude is output, with the heading adjustment angle... Height adjustment range For medium-risk conflicts, a single adjustment command is issued, adjusting only the flight heading or altitude, with the heading adjustment angle being [missing information]. The height adjustment range is For low-risk conflicts, output flight speed adjustment commands, with the speed adjustment range being... Different instructions are matched for high, medium and low risk levels. For high risk, a combination of heading and altitude adjustment is used (heading ≥15°, altitude ≥15°). This allows for rapid disengagement from conflict zones; medium-risk situations require adjustments based on a single dimension (heading). or height

[0053] The system balances evasion effectiveness with airspace occupation. In low-risk scenarios, only speed adjustments (±10%~±20%) are made to avoid over-operation. For example, in high-risk scenarios, combined adjustments can increase the distance between aircraft within seconds, while existing methods require more than 10 seconds to replan the operation, significantly improving evasion efficiency in high-risk scenarios. Secondly, each adjustment parameter (angle, amplitude, speed range) in the command has a clear quantitative standard, which, combined with the preset altitude safety threshold H_safe, adapts to different scenarios, avoiding evasion failure or airspace chaos caused by ambiguous operations. Compared to the unquantified "replanning" method, this system significantly improves evasion efficiency in high-risk scenarios. The "planning" function can precisely control the avoidance range, reducing interference with normal flight order. Furthermore, differentiated instructions are adapted to the handling needs of different risk scenarios, prioritizing safety in high-risk situations and airspace efficiency in low-risk situations. This not only solves the problem of resource waste in low-risk scenarios caused by the "one-size-fits-all" avoidance instructions in existing technologies, but also avoids the safety hazards of insufficient avoidance in high-risk scenarios. For example, in low-risk conflicts, the risk can be eliminated simply by adjusting the speed, without the need to replan the entire flight path, thereby increasing airspace resource utilization by more than 30% and ensuring the dual goals of flight safety and airspace operation efficiency.

[0054] The high-precision operational data is acquired at a frequency of 100ms / time, with a data accuracy error ≤0.5m. It is acquired through multi-source sensor fusion, including a BeiDou high-precision positioning module, millimeter-wave radar, and an ADS-B receiver. The 100ms / time acquisition frequency allows for real-time capture of the aircraft's dynamic flight status, enabling timely responses to sudden changes in speed or direction. For example, when a UAV suddenly adjusts its course, data can be updated and conflict risks recalculated within 0.1 seconds, avoiding missed judgments due to data lag. Furthermore, the ≤0.5m data accuracy error is significantly higher than the meter-level accuracy of traditional airspace management. Combined with the precise acquisition of core parameters such as three-dimensional coordinates and flight speed, it supports the implementation of refined algorithms for line segment intersection judgment and actual spatial distance calculation, preventing misjudgments of conflicts due to data errors. When calculating the actual spatial distance of aircraft, a precision error of 0.5m ensures the reliability of the distance calculation results, while coarse-precision data can easily misjudge the safety distance as the conflict distance. In addition, the multi-source sensor fusion method of Beidou high-precision positioning module, millimeter-wave radar and ADS-B receiver can make up for the shortcomings of single sensor acquisition. Beidou module ensures positioning accuracy, millimeter-wave radar has strong penetration to cope with complex airspace environment, and ADS-B receiver can acquire flight parameters actively reported by aircraft. The combination of the three achieves complementary data verification. Compared with the single sensor acquisition method, the data reliability is improved by more than 90%. Even if a sensor fails temporarily, the data continuity can still be ensured by other sensors. It is suitable for complex airspace scenarios with millions of aircraft flying densely, and provides core data support for high precision and high real-time performance of conflict detection.

[0055] This embodiment addresses the operational scenarios of millions of low-altitude aircraft, including UAVs and general aviation aircraft, by providing a comprehensive conflict detection method encompassing "high-precision data acquisition – spatiotemporal dual-dimensional conflict detection – computing power optimization – conflict avoidance." It fundamentally solves the problems of high computing power consumption, poor dynamic adaptability, and low detection accuracy associated with traditional airspace grid-based detection. The application scenario for this embodiment is urban low-altitude control areas, covering airport perimeters, central urban areas, and suburban airspace. The number of aircraft to be detected is 1 million per day, with a data acquisition frequency of 100ms / time and a positioning accuracy of ≤0.5m. Ultimately, it achieves a conflict detection false positive rate of ≤5%, a reduction in computing power consumption of over 60%, and a conflict response latency of ≤100ms.

[0056] Step 1: Acquisition and preprocessing of high-precision operational data

[0057] Data collection

[0058] High-precision operational data of all aircraft within the airspace to be detected are obtained through multi-source sensor fusion. The sensor combination and functions are as follows:

[0059] Beidou high-precision positioning module: adopts Beidou-3 global satellite navigation system, outputs real-time three-dimensional coordinates of the aircraft including longitude, latitude and altitude, coordinate accuracy error ≤0.5m, sampling frequency 100ms / time;

[0060] Millimeter-wave radar: Deployed in urban high-rise buildings and airspace monitoring base stations, it collects data on aircraft flight speed, heading, and rate of change of altitude, with high speed measurement accuracy. Angular measurement accuracy ;

[0061] ADS-B receiver: Receives the preset flight trajectory actively reported by the aircraft, including takeoff point, landing point, work area coordinates, and planned route. The trajectory data format is a point sequence in the WGS-84 coordinate system.

[0062] Data preprocessing

[0063] The collected multi-source data is standardized, spatiotemporally aligned, and subjected to noise filtering.

[0064] Format standardization: All data are uniformly converted to the ECEF (Earth-centered Earth-fixed) coordinate system, the flight speed unit is uniformly set to m / s, and the flight heading is set with due north as 0° and clockwise as positive, ranging from 0° to 360°.

[0065] Spatiotemporal alignment: Based on the sampling timestamp of the BeiDou module, the asynchronous data of the millimeter-wave radar and ADS-B receiver are interpolated to a time granularity of 100ms to ensure that the data of each sensor at the same timestamp are matched.

[0066] Noise filtering: The Kalman filter algorithm is used to remove random noise from the 3D coordinates and flight velocity. Filtering parameters are set as follows: process noise covariance. ,correspond Coordinates, velocity, heading, and observation noise covariance After preprocessing, a structured high-precision operational dataset is obtained. Each data entry contains the following fields: spacecraft ID, sampling timestamp, and real-time 3D coordinates. Flight speed Flight heading Flight altitude change rate Preset flight path, point sequence .

[0067] Spatial Dimension Conflict Risk Monitoring

[0068] Spatial dimension detection employs a three-step method: "line segment intersection judgment - height difference comparison - safety distance calculation," implemented as follows:

[0069] Trajectory segment decomposition

[0070] The preset flight trajectory of each aircraft is broken down into spatial line segments with a granularity of 500m. For example, the preset trajectory of aircraft A is... ,like to If the straight-line distance is 1200m, then it can be divided into 3 line segments: Each line segment is assigned a unique identifier: aircraft ID + line segment number.

[0071] Line segment intersection judgment

[0072] The vector hybrid product algorithm is used to determine whether the trajectory segments of any two aircraft intersect, taking the trajectory segment of aircraft A as an example. The line segment of aircraft B For example:

[0073] Construct vectors:

[0074] Trajectory vector:

[0075] ;

[0076] Starting point difference vector

[0077] , Coordinates of the starting point of line segment B ;

[0078] Endpoint Difference Vector , Coordinates of the endpoint of line segment B .

[0079] Calculate the mixed product:

[0080] , For dot product, Cross product;

[0081] Similarly, calculate ;

[0082] Intersection determination: If Then determine and There are spatially intersecting regions; record the three-dimensional coordinate range of the intersecting regions. .

[0083] Height difference comparison

[0084] Calculate the altitude difference between the two aircraft in the intersection area. ( Let A be the average height of A in the intersecting region. The average height of B in the intersecting region, and compared with a preset height safety threshold.

[0085] Comparison:

[0086] Preset height safety threshold calculation formula: ;

[0087] Basic height safety threshold ;

[0088] Speed ​​correction factor Actual measurements show that for every 1 m / s increase in speed, the safe height needs to be increased. ;

[0089] Average velocity in the airspace : Real-time statistics of the average flight speed of all aircraft within the monitored airspace. For example, if there are 1000 aircraft in the airspace at a certain time, with speeds ranging from 5 to 30 m / s, the average speed is calculated. ,but

[0090] .

[0091] like If there is no conflict in the determination of the height dimension; Then proceed to the safe distance calculation step.

[0092] Actual spatial distance calculation

[0093] The formula for calculating the actual spatial distance between two aircraft within their intersection area is: Parameter explanation: The real-time three-dimensional coordinates of spacecraft A at the center of the intersection region are collected in real time by the BeiDou module. The real-time three-dimensional coordinates of spacecraft B at the center of the intersection region; These are the fuselage safety protection radii for aircraft A and B, respectively, specified according to aircraft type: small unmanned aerial vehicles (UAVs). medium-sized drones General aviation aircraft .like If D ≥ 0, it is determined that there is a risk of spatial overlap; if D ≥ 0, it is determined that there is no conflict in spatial dimensions.

[0094] Step 3: Time-based conflict risk detection

[0095] Identification of potential conflict points

[0096] Take the geometric center of the spatially intersecting region as the potential conflict point. Calculation formula:

[0097]

[0098] The estimated time T for each aircraft to reach the conflict point C is calculated using the following formula: ;

[0099] S is the straight-line spatial distance from the current position of the aircraft to C, calculated from real-time three-dimensional coordinates: ;

[0100] V represents the real-time flight speed of the aircraft, collected by millimeter-wave radar.

[0101] This is the flight heading deviation correction factor, with a value ranging from 0.95 to 1.05. The calculation formula is as follows:

[0102] θ is the actual heading. For the target heading towards the point of conflict, the deviation angle The range is 0° to 50°, for example, when the deviation is 10°. ;

[0103] Airspace environmental drag coefficient, with a value ranging from 0 to 0.1, is adjusted based on real-time meteorological data, as shown in Table 1.

[0104] Table 1 Airspace Environmental Resistance Coefficient Value Standard

[0105]

[0106] Time difference determination

[0107] Calculate the time difference between the arrival of the two aircraft at the point of conflict. Preset time threshold

[0108] Based on the minimum response time for aircraft avoidance maneuvers:

[0109] like There is no conflict in the time dimension;

[0110] like This triggers a time-dimension conflict warning.

[0111] Conflict level generation and computing power optimization

[0112] Conflict level determination

[0113] By integrating the spatiotemporal dual-dimensional detection results, the conflict level is divided into four levels, and the judgment rules and handling logic are shown in Table 2:

[0114] Table 2. Aircraft Conflict Level Determination Table

[0115]

[0116] Implementation of computing power optimization strategies

[0117] For millions of aircraft data points, a computing power optimization scheme of "hierarchical clustering - differentiated prioritization - parallel computing" is adopted:

[0118] Flight trajectory similarity calculation and clustering

[0119] Trajectory similarity calculation formula:

[0120] N is the number of trajectory sampling points, which is set to 10 (one sampling point every 100ms, covering a 1s trajectory).

[0121] Let be the trajectory length (cumulative flight distance) of aircraft A at the i-th sampling point;

[0122] Let be the trajectory length of aircraft B at the i-th sampling point;

[0123] The maximum trajectory length among all sampling points is normalized to eliminate trajectory length differences. The aircraft are grouped into the same cluster unit. For example, delivery drones in the central urban area have high trajectory similarity and are grouped into cluster unit 1; inspection drones in the suburbs are grouped into cluster unit 2.

[0124] Differential calculation priority settings are shown in Table 3.

[0125] Table 3 shows the configuration table for differentiated processing of clustering units.

[0126]

[0127] Batch parallel computing

[0128] Clustering units of the same priority are assigned to different computing nodes (using a distributed cluster with a total of 100 computing nodes):

[0129] Highest priority units: assigned to nodes 1-60, with each node processing 1-2 clustering units;

[0130] Medium priority units: assigned to nodes 61~90, with each node processing 3~4 clustering units;

[0131] Lowest priority units: assigned to nodes 91-100, each node processes 10-20 clustering units. Each computing node only handles collision detection of aircraft within the unit, avoiding full comparison, reducing the computational load of a single node to 1 / 10 of the original.

[0132] Conflict avoidance command generation and issuance

[0133] Differentiated avoidance instructions are generated based on the conflict level, and the quantification standards for instruction parameters are as follows:

[0134] High-risk conflict

[0135] Output heading + altitude combination adjustment command:

[0136] Heading adjustment angle Prioritize adjustments in the opposite direction of the conflict point;

[0137] Height adjustment range ,For example When adjusting, the range is ≥65m.

[0138] Command issuance delay The aircraft is required to complete the adjustment within 5 seconds.

[0139] Medium risk conflict

[0140] Output a single adjustment command (choose one):

[0141] Course adjustment: Adjust the direction to deviate from the conflict point;

[0142] Height adjustment: For example, 32.5–65m;

[0143] Command issuance delay The aircraft is required to complete the adjustment within 10 seconds.

[0144] Low-risk conflict

[0145] Output speed adjustment command:

[0146] Speed ​​adjustment range Prioritize reducing speed to increase the time difference;

[0147] For example, if the original speed of the aircraft is 20m / s, it is adjusted to... ;

[0148] Command issuance delay The aircraft is required to complete the adjustment within 15 seconds.

[0149] Command issuance and feedback

[0150] The avoidance command is sent to the aircraft's flight control system via the ADS-B broadcast channel. After the flight control system executes the command, it provides real-time feedback on the adjusted flight status, such as coordinates, speed, and heading. The detection module recalculates the conflict risk until the conflict is resolved.

[0151] The above are merely embodiments of the present invention. Commonly known structures and characteristics of the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the description of specific embodiments in the specification can be used to interpret the content of the claims.

Claims

1. A method for detecting aircraft collisions based on high-precision data, characterized in that, include: The system acquires high-precision operational data for all aircraft within the airspace to be detected. This high-precision operational data includes real-time three-dimensional coordinates, flight speed, flight heading, flight altitude change rate, and preset flight trajectory. Based on the spatial dimension, the system detects aircraft conflict risks: First, using a line segment intersection judgment algorithm, the real-time preset flight trajectory of each aircraft is decomposed into spatial line segments. It then determines whether any two aircraft's spatial line segments intersect. If an intersection exists, the system calculates the altitude difference between the two aircraft within the intersection area and compares this altitude difference with a preset altitude safety threshold. If the altitude difference is less than the preset altitude safety threshold, the system further calculates the difference between the actual spatial distance between the two aircraft within the intersection area and the preset safety distance to identify spatial overlap risks. Based on the time dimension, the system detects aircraft conflict risks by calculating the estimated time for each aircraft to reach the intersection area. This estimated time is calculated by combining the aircraft's real-time flight speed, flight heading deviation correction coefficient, and airspace environmental drag coefficient. When the estimated time difference between any two aircraft reaching the potential conflict point is less than a preset time threshold, a time-dimensional conflict warning is triggered. By integrating detection results from both spatial and temporal dimensions, aircraft conflict level information is generated. For aircraft at risk of conflict, the priority of conflict detection calculation is adjusted based on a computing power optimization strategy, and conflict avoidance instructions are output.

2. The aircraft collision detection method based on high-precision data according to claim 1, characterized in that, The calculation formula for the line segment intersection judgment algorithm is as follows: Let the trajectory line segment of aircraft A be... : The trajectory line segment of aircraft B is ; Construct vectors: Calculate the mixed product: ;like Then determine the line segment and There exist spatially intersecting regions; where × represents the cross product operation and ・ represents the dot product operation. The three-dimensional coordinates of the starting point of the trajectory of aircraft A. The three-dimensional coordinates of the starting point of the trajectory of spacecraft B. The three-dimensional coordinates of the endpoint of the trajectory of aircraft A. The coordinates of the endpoint of the trajectory of spacecraft B are shown.

3. The aircraft collision detection method based on high-precision data according to claim 1, characterized in that, The formula for calculating the estimated time for the aircraft to reach the potential conflict point is as follows: Where T is the estimated time; S is the straight-line distance between the aircraft's current position and the potential conflict point; and V is the aircraft's real-time flight speed. This is the flight heading deviation correction factor, with a value range of [value range missing]. The larger the heading deviation angle, The smaller the value; This is the airspace environmental drag coefficient, with a value ranging from 0 to 0.

1. It is adjusted in real time based on wind speed and air density within the airspace; the greater the environmental drag, the lower the drag coefficient. The larger the value, the better.

4. The aircraft collision detection method based on high-precision data according to claim 1, characterized in that, The preset height safety threshold is determined as follows: in, To preset a high safety threshold; The basic height safety threshold is set at 50m. This is the speed correction factor, with a value of [value missing]. ; The average flight speed of all aircraft within the airspace to be detected.

5. The aircraft collision detection method based on high-precision data according to claim 1, characterized in that, The computing power optimization strategy includes: performing hierarchical clustering processing on millions of aircraft data, grouping aircraft with flight trajectory similarity higher than 80% into the same cluster unit; setting differentiated computing priorities for different cluster units, setting the highest computing priority for cluster units whose flight trajectories are close to airports or densely populated areas, and setting the lowest computing priority for cluster units whose flight trajectories are located in remote airspace and have no intersection risk; and adopting a batch parallel computing method, allocating cluster units of the same priority to different computing nodes, with each computing node only processing aircraft collision detection within its corresponding cluster unit.

6. The aircraft collision detection method based on high-precision data according to claim 1, characterized in that, The formula for calculating the actual spatial distance is: Where D is the actual spatial distance between the two aircraft in the intersection area; The real-time three-dimensional coordinates of spacecraft A within the intersection region. The real-time three-dimensional coordinates of aircraft B within the intersection area; These are the fuselage safety protection radii for aircraft A and B, respectively, which are pre-calibrated based on the aircraft type and size.

7. The aircraft collision detection method based on high-precision data according to claim 1, characterized in that, The conflict level information includes four levels: no risk, low risk, medium risk, and high risk. The specific judgment rule is: if only the spatial dimension detects overlapping risk and the time difference is ≥ 2 times the preset time threshold, it is judged as low risk. If an overlap risk is detected in the spatial dimension and the time difference is between 1 and 2 times the preset time threshold, it is judged as medium risk; If an overlap risk is detected in the spatial dimension and the time difference is less than a preset time threshold, it is judged as high risk. If no overlap risk is detected in the spatial dimension, it is determined to be risk-free.

8. The aircraft collision detection method based on high-precision data according to claim 5, characterized in that, The similarity of the flight trajectories is calculated as follows: in, For flight trajectory similarity; This represents the number of trajectory sampling points; Let be the trajectory length of aircraft A at the i-th sampling point. Let be the trajectory length of aircraft B at the i-th sampling point; This represents the maximum trajectory length among all sampling points.

9. The aircraft collision detection method based on high-precision data according to claim 1, characterized in that, The conflict avoidance command is generated as follows: for high-risk conflicts, a combined command to adjust the flight heading and altitude is output, with the heading adjustment angle... Height adjustment range For medium-risk conflicts, a single adjustment command is issued, adjusting only the flight heading or altitude, with the heading adjustment angle being [missing information]. The height adjustment range is For low-risk conflicts, output flight speed adjustment commands, with the speed adjustment range being... .

10. The aircraft collision detection method based on high-precision data according to claim 1, characterized in that, The high-precision operational data is acquired at a frequency of 100ms / time, and the data accuracy error is... The data is obtained through multi-source sensor fusion, and the multi-source sensors include a Beidou high-precision positioning module, a millimeter-wave radar, and an ADS-B receiver.

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