Method and system for determining minimum safety separation of mixed traffic flow facing complex airspace

Abstract

The application discloses a complex airspace-oriented mixed traffic flow minimum safety interval determination method and system, and relates to the technical field of air traffic management. The method comprises the following steps: acquiring real-time flight parameters and planned flight parameters of an aircraft, calculating a real-time behavior deviation amount, and performing time series analysis to obtain a deviation evolution trend; clustering analysis is performed on the aircraft according to the behavior deviation amount, a normal aircraft group and an abnormal aircraft individual are divided, and a behavior deviation degree of the abnormal aircraft individual and the normal aircraft group is calculated; a safety interval compensation coefficient for the abnormal aircraft individual is generated according to the behavior deviation degree and the deviation evolution trend; and the minimum safety interval of the abnormal aircraft individual and surrounding aircraft is determined based on a basic safety interval, and control is performed. The application realizes accurate identification and differentiated interval control of abnormal aircraft, and improves operation efficiency while ensuring airspace safety.

Description

Technical Field

[0001] This invention relates to the field of air traffic management technology, specifically to a method and system for determining the minimum safe interval for mixed traffic flows in complex airspace. Background Technology

[0002] With the rapid development and widespread application of unmanned aerial vehicle (UAV) technology, mixed traffic flows involving various types of aircraft, such as drones and general aviation aircraft, have become the norm in complex airspaces. Against this backdrop, air traffic patterns have become more complex and dynamic, with aircraft flight behavior exhibiting high heterogeneity and uncertainty. To ensure the safety and efficiency of airspace operations, precise and dynamic management of safety intervals between different types of aircraft in mixed traffic flows has become a critical technical problem urgently needing to be solved in the field of air traffic management.

[0003] In existing technologies, the minimum safe separation between aircraft is typically set as a fixed value based on their type, flight phase, or airspace class. However, in complex airspace containing a large number of UAVs, the flight behavior of aircraft can deviate significantly due to external disturbances, differences in equipment performance, or varying levels of pilot skill. Fixed safe separation standards are insufficient to effectively identify such behavioral anomalies and respond dynamically. Furthermore, most existing methods rely on a single time-section of flight status for conflict detection, lacking prediction and consideration of the evolution trend of aircraft behavioral deviations. This can lead to situations where, when flight conditions change abruptly, excessively large separations may reduce airspace capacity, while insufficient separations may create safety hazards. Summary of the Invention

[0004] This invention addresses the technical problems of existing technologies that, due to the use of fixed safety interval standards, cannot dynamically respond to aircraft behavior deviations, have difficulty accurately identifying and predicting individual aircraft behavior anomalies, and cannot balance airspace safety and operational efficiency in complex airspace environments. It provides a method and system for determining the minimum safety interval for mixed traffic flows in complex airspaces.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, the present invention provides a method for determining the minimum safe interval for mixed traffic flows in complex airspace, comprising: The real-time flight parameters and planned flight parameters of each aircraft in the airspace are obtained, the real-time behavior deviation of each aircraft is calculated, and the behavior deviation is analyzed over time to obtain the deviation evolution trend of each aircraft. Cluster analysis is performed on the aircraft in the airspace based on the behavioral deviation of each aircraft to divide them into groups of normal aircraft and individual abnormal aircraft, and the behavioral deviation between the individual abnormal aircraft and the group of normal aircraft is calculated. Based on the behavioral deviation and the trend of the deviation evolution, a safety interval compensation coefficient is dynamically generated for the individual abnormal aircraft. Based on the basic safety interval and the safety interval compensation coefficient, the minimum safety interval between the abnormal aircraft and surrounding aircraft is determined, and flight interval control is performed according to the minimum safety interval.

[0006] Secondly, the present invention provides a minimum safety interval determination system for mixed traffic flows in complex airspace, comprising: The deviation calculation and analysis module is used to obtain the real-time flight parameters and planned flight parameters of each aircraft in the airspace, calculate the real-time behavior deviation of each aircraft, and perform time series analysis on the behavior deviation to obtain the deviation evolution trend of each aircraft. The clustering and deviation assessment module is used to perform cluster analysis on aircraft in the airspace based on the behavioral deviation of each aircraft, divide the aircraft into groups of normal aircraft and individuals of abnormal aircraft, and calculate the behavioral deviation between the individuals of abnormal aircraft and the groups of normal aircraft. The compensation coefficient generation module is used to dynamically generate a safety interval compensation coefficient for the individual abnormal aircraft based on the behavior deviation and the deviation evolution trend. The dynamic interval control module is used to determine the minimum safe interval between the abnormal aircraft and surrounding aircraft based on the basic safety interval and the safety interval compensation coefficient, and to control the flight interval according to the minimum safe interval.

[0007] The beneficial effects of this invention are: Compared to existing technologies, this invention firstly calculates the real-time behavioral deviation of aircraft and performs time-series analysis, effectively predicting future behavioral trends and providing a forward-looking basis for dynamic adjustment of safety intervals. Secondly, by using cluster analysis to group aircraft within the airspace, it accurately identifies individuals with abnormal behavior and quantifies the behavioral deviation between these abnormal individuals and the normal group, achieving an objective assessment of individual flight risks. Thirdly, by comprehensively considering behavioral deviation and its evolution trend, a safety interval compensation coefficient is dynamically generated, ensuring that safety interval adjustments take into account both the current risk level and future risk evolution, achieving differentiated and dynamic control of abnormal aircraft individuals. Finally, this invention ensures airspace safety while avoiding the waste of airspace resources caused by applying uniform conservative intervals to normal aircraft groups, improving the operational efficiency and safety of mixed traffic flows in complex airspace environments. Attached Figure Description

[0008] Figure 1A flowchart illustrating the method for determining the minimum safe interval of mixed traffic flow in complex airspace provided by the present invention. Figure 2 This is a schematic diagram of the system for determining the minimum safe interval of mixed traffic flow in complex airspace provided by the present invention.

[0009] In the attached diagram, the components represented by each number are as follows: Deviation calculation and analysis module 11, clustering and deviation evaluation module 12, compensation coefficient generation module 13, dynamic interval control module 14. Detailed Implementation

[0010] Example 1, as Figure 1 As shown, embodiments of the present invention provide a method for determining the minimum safe interval for mixed traffic flows in complex airspace, including: S10: Obtain the real-time flight parameters and planned flight parameters of each aircraft in the airspace, calculate the real-time behavior deviation of each aircraft, and perform time series analysis on the behavior deviation to obtain the deviation evolution trend of each aircraft. First, the real-time flight parameters and planned flight parameters of each aircraft in the airspace are obtained, and the real-time behavior deviation of each aircraft is calculated accordingly. By analyzing the time series of the behavior deviation, the deviation evolution trend of each aircraft is obtained.

[0011] Specifically, "aircraft within the airspace" refers to all aircraft operating in a specific complex airspace, including manned aircraft flying along predetermined routes, unmanned aerial vehicles (UAVs) performing specific missions, and general aviation aircraft. Real-time flight parameters of an aircraft are data collected in real-time by air traffic control surveillance equipment such as radar, Automatic Dependent Surveillance-Broadcast (ADS-B), or BeiDou Navigation Satellite System (BDS), used to characterize the aircraft's actual operating status at the current moment. Preferably, these parameters include the aircraft's real-time position coordinates, real-time ground speed or airspeed, and real-time heading angle. Planned flight parameters of an aircraft are data extracted from flight plans or pre-set mission routes, used to characterize the aircraft's expected standard operating status at the corresponding time or segment. Preferably, these parameters include planned position coordinates, planned speed, and planned heading.

[0012] Based on the real-time flight parameters and planned flight parameters of each aircraft, the real-time behavior deviation of each aircraft can be calculated. This real-time behavior deviation is a comprehensive quantitative indicator that represents the overall degree of deviation between the actual flight state of the aircraft at the current moment and its predetermined flight plan.

[0013] Specifically, the real-time behavioral deviation of each aircraft is calculated, including: Obtain the aircraft's real-time position, real-time speed, and real-time heading, as well as the corresponding planned position, planned speed, and planned heading; Calculate the position deviation, velocity deviation, and heading deviation; The position deviation, velocity deviation, and heading deviation are assigned first, second, and third weights, respectively, and then weighted and summed to obtain the real-time behavior deviation of the aircraft.

[0014] When calculating the real-time behavioral deviation of an aircraft, it is first necessary to obtain the aircraft's real-time operational parameters and their corresponding planned parameters across multiple key dimensions. Specifically, the real-time position, real-time speed, and real-time heading of the aircraft are acquired through air traffic control monitoring equipment. Simultaneously, the planned position, planned speed, and planned heading of the aircraft at the same moment or corresponding flight segment are extracted from the planned flight parameters. Obtaining these parameters provides the foundational data for quantifying the difference between the aircraft's actual performance and expected state in the three core dimensions of spatial position, velocity, and flight direction.

[0015] Secondly, based on the above parameters, the specific deviations of the aircraft in three dimensions are calculated respectively. Specifically, preferably, the position deviation is obtained by calculating the Euclidean distance between the real-time position coordinates and the planned position coordinates, and is used to characterize the degree of deviation of the aircraft in spatial positioning. The velocity deviation is obtained by calculating the magnitude of the difference between the real-time velocity vector and the planned velocity vector, and is used to characterize the degree of deviation of the aircraft in terms of speed. The heading deviation is obtained by calculating the absolute value of the angle between the real-time heading angle and the planned heading angle, and is used to characterize the degree of deviation of the aircraft in its direction of travel.

[0016] To integrate the deviations from the three dimensions—position deviation, velocity deviation, and heading deviation—into a single real-time behavioral deviation measure that comprehensively reflects the overall deviation of the aircraft's behavior, weighting coefficients are needed to fuse the deviations of each dimension. Specifically, a first weight is pre-assigned to the position deviation, a second weight to the velocity deviation, and a third weight to the heading deviation. The specific values ​​for these weights can be differentiated and dynamically adjusted based on the sensitivity of each deviation to different aircraft types, flight phases, or airspace environments.

[0017] For example, in scenarios with extremely high spatial positioning accuracy requirements, such as terminal areas, a larger first weight can be assigned to the position deviation, such as setting the first weight to 0.6, while the second and third weights are set to 0.2, to reflect the dominant role of position accuracy in the overall evaluation. During the en route cruise phase, the requirements for heading stability are relatively higher. In this case, a larger third weight can be assigned to the heading deviation, such as setting the third weight to 0.5, while the first and second weights are set to 0.3 and 0.2 respectively. For aircraft types such as drones that are more sensitive to speed control, a higher second weight can be assigned to the speed deviation, for example, adjusting the second weight to 0.5, to ensure that speed anomalies can be detected promptly.

[0018] The above methods can flexibly adapt to the security control requirements of different operating scenarios and improve the rationality and accuracy of real-time behavior deviation calculation.

[0019] Finally, the weighted position deviation, velocity deviation, and heading deviation are summed. Specifically, the real-time behavior deviation is calculated as follows: Real-time behavior deviation = Position deviation × First weight + Velocity deviation × Second weight + Heading deviation × Third weight. This real-time behavior deviation, as a comprehensive quantitative indicator, represents the overall deviation of the aircraft's actual flight state from its planned flight status in multiple dimensions at the current moment. It provides a unified input basis for subsequent cluster analysis, deviation assessment, and dynamic adjustment of safety intervals.

[0020] Furthermore, in order to better understand the dynamic evolution of aircraft behavior deviations, it is necessary to collect the behavior deviations of each aircraft over multiple consecutive time windows to form a set of time series data, and then analyze and process this time series to ultimately determine the deviation evolution trend of each aircraft.

[0021] Specifically, time series analysis is performed on the behavioral deviations to obtain the deviation evolution trend for each aircraft, including: Collect the behavioral deviation of each aircraft over the past N time windows to form the time series of that aircraft; Perform linear fitting on the time series and calculate the slope of the fitted line; When the slope is positive and the absolute value is greater than the first threshold, the trend of the judgment deviation is to continue to increase. When the slope is negative and the absolute value is greater than the second threshold, the trend of the judgment deviation is to converge. Otherwise, the deviation is judged to be a stable fluctuation.

[0022] First, historical behavioral deviation data for each aircraft is collected over N consecutive, equally timed windows. The number of N time windows can be set based on the update frequency of air traffic control surveillance data and the required lead time for prediction; for example, data points from the past 10 radar scan cycles or the past 30 seconds can be used. The collected behavioral deviation values, arranged in chronological order, constitute a time series specific to that aircraft, reflecting the trajectory of the aircraft's behavioral deviation over time.

[0023] Secondly, linear fitting is performed on the aforementioned time series corresponding to each aircraft. Specifically, the purpose of this linear fitting is to find a straight line that best represents the overall trend of the time series, i.e., a trend line. Preferably, the slope of this trend line can be calculated by applying mathematical methods such as the least squares method. The slope obtained in the final calculation is a key mathematical feature; its magnitude represents the average rate of change of the behavioral deviation within a unit time window, and its positive or negative sign represents the direction of change.

[0024] Furthermore, the evolution trend of the deviation is qualitatively determined based on the calculated slope value. The determination process introduces a first threshold and a second threshold as judgment criteria. These first and second thresholds are key parameters used to define whether the rate of change of the deviation has reached a significant level, representing the boundary values ​​distinguishing between normal fluctuations and trend changes. They are positive numbers pre-set based on historical statistical data or airspace safety operation requirements. The first and second thresholds are determined through statistical analysis of the rate of change of behavioral deviations in adjacent time windows within historical normal operation data. A large number of time series of aircraft behavioral deviations under normal operating conditions are collected, and the rate of change between adjacent time windows is calculated. The 80th quantile of the absolute value of the rate of change is taken as the first threshold, and the 20th quantile is taken as the second threshold. For example, the first threshold can be set to 0.05, indicating that if the average increase in behavioral deviation within a unit time window exceeds 0.05, it is judged as a significant expanding trend; the second threshold can be set to 0.03, indicating that if the average decrease in behavioral deviation within a unit time window exceeds 0.03, it is judged as a significant converging trend.

[0025] Specifically, when the calculated slope is positive and the absolute value of the positive slope is greater than a preset first threshold, it indicates that the aircraft's behavioral deviation is increasing over time, and the deviation evolution trend is determined to be continuously expanding. When the calculated slope is negative and the absolute value of the negative slope is greater than a preset second threshold, it indicates that the aircraft's behavioral deviation is decreasing over time, meaning the aircraft is attempting to return to normal flight status, and the deviation evolution trend is determined to be converging.

[0026] Furthermore, if the calculated slope does not meet any of the above conditions, such as the slope being close to zero, or the absolute value of the slope being too small to exceed the corresponding threshold, it means that the aircraft's behavioral deviation has not undergone a significant directional change in the time dimension, but only fluctuates around a certain level. In this case, the deviation evolution trend of the aircraft is determined to be a stable fluctuation.

[0027] By conducting time-series analysis and trend determination of behavioral deviations as described above, we can proactively grasp the future behavioral development direction of each aircraft, providing a predictive basis for subsequent targeted adjustments to safety intervals.

[0028] S20: Based on the behavioral deviation of each aircraft, perform cluster analysis on the aircraft in the airspace to divide the normal aircraft group into abnormal aircraft individuals, and calculate the behavioral deviation between the abnormal aircraft individuals and the normal aircraft group. Secondly, cluster analysis is performed on aircraft within the airspace based on their behavioral deviations to separate groups of normal aircraft from individual abnormal aircraft. Because the actual flight behavior of aircraft within the airspace is influenced by multiple factors such as differences in aircraft performance, weather disturbances, and changes in flight intent, different individual aircraft may exhibit different behavioral characteristics under the same spatiotemporal environment, resulting in a discrete distribution of their corresponding real-time behavioral deviations. Therefore, cluster analysis is necessary to perform on all aircraft within the airspace based on their real-time behavioral deviations and spatial distribution. This cluster analysis process automatically groups aircraft with similar behavioral characteristics and spatial clustering into one category, forming one or more aircraft groups with similar operational states. This allows for the effective identification of the dominant normal flight pattern from the mixed traffic flow.

[0029] Through the above cluster analysis, aircraft belonging to large groups with consistent behavioral characteristics can be classified as normal aircraft groups, while aircraft that cannot be classified into any significant group or belong to a very small group can be classified as abnormal aircraft individuals.

[0030] Specifically, cluster analysis is performed on aircraft within the airspace based on the behavioral deviations of each aircraft, including: The real-time position coordinates of each aircraft in the airspace are mapped to a preset planar coordinate system to obtain the planar coordinate point corresponding to each aircraft. For each aircraft, its real-time behavior deviation is used as the terrain height value of the corresponding plane coordinate point, and the plane coordinate points and terrain height values ​​of all aircraft are integrated to generate an airspace aircraft terrain map. In the airspace topographic map, the coordinate point corresponding to the aircraft with the smallest behavioral deviation and located in the core area of ​​the airspace is determined as the starting point for simulating water level rise. Starting from the initial point, the water level rise process is simulated with a preset step size. When the water level submerges a new plane coordinate point corresponding to a new aircraft, the height difference between the terrain height value of the new plane coordinate point and the terrain height value of the initial point is calculated. Determine whether the height difference does not exceed the preset height difference threshold. If the height difference does not exceed the preset height difference threshold, continue simulating the water level rise process. If the height difference exceeds the preset height difference threshold, a watershed will be generated at the new plane coordinate point, and all aircraft that have been submerged in this water level simulation will be clustered into a candidate aircraft group. After generating a group of aircraft, remove the planar coordinates of all aircraft in the group from the airspace aircraft topography map, and update the planar coordinates of the watershed to the starting point of the next water level simulation. Repeat the water level simulation and clustering process until all plane coordinate points corresponding to all aircraft in the airspace aircraft topographic map have been processed, resulting in several candidate aircraft groups.

[0031] When performing cluster analysis on aircraft in the airspace based on the behavioral deviation of each aircraft, this method adopts a clustering algorithm based on topographic simulation. This algorithm can construct a model with topographic features by mapping and fusing the spatial location of the aircraft with the behavioral deviation, and automatically divide the aircraft group by simulating the process of rising water level.

[0032] Specifically, the cluster analysis process includes the following steps.

[0033] First, the real-time position coordinates of all aircraft within the airspace, such as latitude and longitude coordinates, are mapped to a pre-defined two-dimensional plane coordinate system through projection transformation, thus assigning each aircraft a unique plane coordinate point. The pre-defined two-dimensional plane coordinate system is a uniform-area or conformal projection coordinate system selected based on the airspace coverage and analysis accuracy requirements, such as the universal transverse Mercator projection coordinate system or the Lambert conformal conic projection coordinate system. Each aircraft's plane coordinate point is a pair of abscissa and ordinate values ​​obtained by projecting its original latitude and longitude coordinates, representing the aircraft's specific location on the two-dimensional plane. This mapping process transforms the spatial distribution of aircraft in the real airspace into a point set distribution on a plane, providing a foundation for subsequent terrain construction and spatial analysis.

[0034] Secondly, for each aircraft, the real-time behavior deviation calculated in the preceding steps is used as the terrain height value of its corresponding planar coordinate point. Specifically, the larger the real-time behavior deviation, the higher the terrain height value of that point. By integrating the planar coordinate points of all aircraft and their corresponding terrain height values, an airspace aircraft terrain map can be generated. In this airspace aircraft terrain map, aircraft with adjacent planar locations and similar real-time behavior deviation values ​​will exhibit similar landform features in the constructed terrain model. Specifically, multiple aircraft spatially clustered with relatively small behavior deviations collectively form a relatively low-lying area, similar to a basin or valley in a terrain map; while multiple aircraft spatially clustered with relatively large behavior deviations collectively form a relatively high-lying area, similar to a ridge or peak in a terrain map.

[0035] In this way, the inherent consistency of the spatial distribution and behavioral characteristics of the aircraft group is intuitively transformed into continuous geomorphic units in the airspace aircraft topographic map, thus providing a visual data foundation for subsequent cluster analysis based on simulated water level rise.

[0036] Next, the clustering process for simulating water level rise begins. In the generated airspace aircraft topographic map, a starting point for simulating water level rise is first determined. This starting point is selected as the planar coordinate point corresponding to the aircraft with the smallest behavioral deviation located in the core area of ​​the airspace. This point is chosen as the starting point because it represents the benchmark point in the current airspace where behavior is most consistent with the plan and the state is most stable. Simulating water level rise from this lowest point helps to prioritize the formation of a group of aircraft around the most stable core.

[0037] Secondly, starting from this initial point, the simulated water level is gradually increased in increments of a preset step size. The preset step size is the incremental unit for each simulated water level rise, representing the granularity of the water level change. It is set based on the distribution range of aircraft behavior deviations within the airspace and the required clustering accuracy, and is typically 1% to 5% of the range of all aircraft behavior deviations. For example, if the minimum aircraft behavior deviation in the airspace is 0.1, the maximum is 2.3, and the range is 2.2, the step size is 2% of the range, i.e., 0.044, rounded to 0.05. During the simulated water level rise, it is necessary to monitor in real time whether any new aircraft-corresponding planar coordinate points are submerged. When a new planar coordinate point is submerged, the terrain height value of that new planar coordinate point, i.e., the real-time behavior deviation of the aircraft, is calculated, along with the terrain height value at the starting point.

[0038] The calculated height difference is then evaluated. Specifically, it is determined whether the height difference does not exceed a pre-set height difference threshold. This height difference threshold is a key clustering parameter, representing the maximum tolerable difference in behavioral deviation between aircraft allowed to be grouped into the same cluster. This height difference threshold is set based on the safety tolerance of airspace operations and the statistical distribution characteristics of historical flight data; for example, it can be set to 0.5. If the calculated height difference does not exceed the preset height difference threshold, it means that the newly submerged aircraft has a high similarity in behavioral characteristics to the core of the cluster represented by the starting point. Therefore, it is allowed to join the current cluster and continue to simulate water level rise at the current step size, attempting to submerge more points.

[0039] Furthermore, if the calculated height difference exceeds a preset height difference threshold, it indicates that the newly submerged aircraft differs significantly from the current group in behavioral characteristics and is unsuitable for inclusion in the same group. In this case, the algorithm generates a watershed at the new planar coordinate point as the boundary for group division. Simultaneously, all submerged aircraft from the starting point until the watershed is generated during this water level simulation are grouped together to form a candidate aircraft group.

[0040] After generating a candidate aircraft group, in order to further segment the remaining unclustered aircraft, it is necessary to remove the planar coordinates corresponding to all aircraft in the group from the airspace aircraft topographic map. Then, the planar coordinates at the watershed are updated as the starting point for the next water level simulation, serving as the benchmark for the next round of clustering.

[0041] Finally, the entire process from selecting the starting point, simulating water level rise, generating the watershed, forming clusters, and removing clustered points is repeated until all plane coordinates corresponding to all aircraft in the airspace aircraft topographic map have been processed. Through this series of iterative processes, several candidate aircraft clusters can be obtained. The aircraft in each candidate aircraft cluster are spatially adjacent and have a high degree of consistency in behavioral deviation.

[0042] Specifically, the group of normal aircraft is divided into groups of abnormal aircraft, including: Count the number of aircraft included in each candidate aircraft group; Candidate aircraft groups whose number of aircraft exceeds a preset group size threshold are classified as normal aircraft groups; Candidate aircraft groups with fewer aircraft than a preset group size threshold are identified as abnormal aircraft individuals.

[0043] After obtaining several candidate aircraft groups through the aforementioned cluster analysis, it is necessary to further distinguish between normal aircraft groups and abnormal aircraft individuals from these candidate aircraft groups.

[0044] First, statistics are compiled for each candidate aircraft group to calculate the total number of aircraft contained within each group, thus obtaining the aircraft count. This count forms the basis for assessing group size and is used to determine the proportion of the group in airspace traffic flow.

[0045] Secondly, a preset group size threshold is introduced as a standard for determining normal groups. This preset group size threshold is an integer pre-set based on airspace traffic density, historical operational data, or safety control requirements. It is used to distinguish between mainstream and marginal behavior groups. The preset group size threshold is dynamically determined based on the total number of aircraft in the current airspace, ranging from 10% to 20% of the total number of aircraft, with a minimum value of 3. For example, if the total number of aircraft in the airspace is 30, 15% is taken as the threshold, i.e., 4.5, rounded up to 5 aircraft. For any candidate aircraft group, if the number of aircraft counted exceeds the preset group size threshold, it indicates that the candidate aircraft group represents the common behavior pattern of most aircraft in the airspace, possessing statistical universality and representativeness. Therefore, the candidate aircraft group is determined to be a normal aircraft group.

[0046] Finally, for all candidate aircraft groups that were not classified as normal aircraft groups, the aircraft were identified as anomalous aircraft individuals. Specifically, among the several candidate aircraft groups formed by the aforementioned clustering analysis, the candidate aircraft groups with fewer than a preset group size threshold were classified as anomalous aircraft groups, and each aircraft individual within such a group was considered an anomalous aircraft individual.

[0047] The above classification method can effectively distinguish between small groups consisting of only a few aircraft whose behavior patterns are not statistically representative and large groups consisting of many aircraft that represent the mainstream operation mode of the airspace. This allows for the accurate identification of potential risk targets that require key attention and differentiated management from complex mixed traffic flows.

[0048] Furthermore, the behavioral deviation between an anomalous aircraft and a group of normal aircraft is calculated. This behavioral deviation is a quantitative similarity metric used to characterize the degree of difference between the comprehensive behavioral characteristics of a single anomalous aircraft and the overall behavioral characteristics of one or more normal aircraft groups. The greater the behavioral deviation, the more significant the difference between the anomalous aircraft's behavioral pattern and the mainstream behavioral pattern of the normal group, the higher the degree of deviation from the norm in its operational state, and the greater the potential disturbance or safety risk to airspace operation order. Conversely, the smaller the behavioral deviation, the closer the anomalous aircraft's behavioral pattern is to that of the normal group. Although it may be identified as an anomalous individual due to failing the clustering criteria, its actual operational state is not fundamentally different from that of normal aircraft.

[0049] Specifically, the deviation of the behavior of individual anomalous aircraft from that of a group of normal aircraft is calculated, including: Calculate the mean vector and covariance matrix of the behavioral deviations of all aircraft within a normal aircraft group; For each anomalous aircraft, calculate the Mahalanobis distance between its behavioral deviation vector and mean vector, which is used as the behavioral deviation of the anomalous individual from the normal group. When there are multiple normal aircraft groups, the minimum deviation of the abnormal individual's behavior from each normal group is taken as the final deviation of the abnormal individual's behavior.

[0050] First, statistical features are extracted for each set of aircraft identified as a normal aircraft group. Specifically, the mean vector and covariance matrix of the behavioral deviations of all aircraft within the normal aircraft group at the same time need to be calculated. The mean vector represents the average behavioral level of the normal aircraft group across multiple dimensions and is the central point of the group's behavioral pattern; the covariance matrix characterizes the correlation of behavioral deviations among the aircraft within the normal aircraft group and the fluctuation amplitude across various dimensions, reflecting the internal consistency and dispersion of the group's behavior.

[0051] Secondly, for each individual identified as an anomalous aircraft, its own behavioral deviation vector is constructed, with the same dimension as the mean vector used by the normal aircraft group. Then, the Mahalanobis distance between the anomalous individual's behavioral deviation vector and the normal group's mean vector is calculated. Mahalanobis distance is a metric that considers data distribution characteristics. By introducing a covariance matrix to decorrelate and scale normalize each dimension, it can more accurately measure the degree of difference between a sample point and a known distribution. The calculated Mahalanobis distance is used as the behavioral deviation degree between the anomalous individual and the current normal group. Specifically, the larger the calculated Mahalanobis distance, the greater the behavioral deviation, indicating that the anomalous individual's behavioral characteristics deviate further from the mainstream characteristics of the group.

[0052] Finally, considering that multiple normal aircraft groups may coexist in complex airspace, such as aircraft with different headings or altitudes forming groups with different behavioral characteristics, each anomalous aircraft needs to be compared with all normal aircraft groups. The Mahalanobis distance between the anomalous aircraft and each normal aircraft group is calculated sequentially, resulting in multiple behavioral deviation values. To comprehensively assess the maximum probability of the anomalous aircraft deviating from its normal operating pattern, the minimum value among the multiple behavioral deviation values ​​is taken as the final behavioral deviation value for the anomalous aircraft.

[0053] The reason for using the minimum value is that it represents the smallest statistical distance between the abnormal individual and a normal group, that is, the normal behavioral pattern that the abnormal individual is most likely to belong to or is closest to. This avoids overestimating the degree of abnormality due to interference from a distant normal group, making the assessment of deviation more objective and accurate.

[0054] S30: Dynamically generate safety interval compensation coefficients for individual abnormal aircraft based on behavioral deviation and deviation evolution trends; Furthermore, based on the aforementioned behavioral deviation and deviation evolution trend, a safety interval compensation coefficient is dynamically generated for individual anomalous aircraft. Behavioral deviation is a quantitative statistical distance indicator representing the degree of difference between the overall behavioral characteristics of an anomalous aircraft at the current moment and the mainstream behavioral pattern of a normal aircraft group. The larger the value, the more significantly the aircraft's operational state deviates from the norm, and the higher the potential risk of conflict. Deviation evolution trend is a qualitative trend description, representing the direction and rate of change of the behavioral deviation of an anomalous aircraft over a future period, specifically including three states: continuous expansion, convergence, and stable fluctuation.

[0055] By combining the two key parameters that respectively characterize the current risk status and the future risk evolution, a dynamically adjusted safety interval compensation coefficient can be generated for each individual abnormal aircraft. This safety interval compensation coefficient will be used to correct its basic safety interval, thereby achieving differentiated and forward-looking interval control.

[0056] Specifically, based on the degree of behavioral deviation and the trend of deviation evolution, a safety interval compensation coefficient is dynamically generated for individual abnormal aircraft, including: Establish a deviation-basic compensation coefficient mapping table. In the deviation-basic compensation coefficient mapping table, the behavioral deviation and the basic compensation coefficient are positively correlated. The trend correction factor is determined based on the trend of deviation evolution. When the evolution trend is continuous expansion, the trend correction factor is greater than 1; when the evolution trend is convergence, the trend correction factor is less than 1; when the evolution trend is stable fluctuation, the trend correction factor is equal to 1. Multiply the basic compensation coefficient by the trend correction factor to obtain the safety interval compensation coefficient.

[0057] First, a deviation-baseline compensation coefficient mapping table is established. This mapping table is a pre-defined set of correspondences, containing baseline compensation coefficients corresponding to behavioral deviations within different ranges. In this mapping, behavioral deviation and baseline compensation coefficients are positively correlated. This means that when the behavioral deviation value of an abnormal aircraft individual is small, it indicates that its behavior is not significantly different from the normal group, and the mapped baseline compensation coefficient is correspondingly small; conversely, when the behavioral deviation value is large, it indicates that its behavior is significantly abnormal, and the mapped baseline compensation coefficient is correspondingly large. Through this deviation-baseline compensation coefficient mapping table, the quantified deviation can be quickly and reasonably converted into a preliminary compensation base.

[0058] For example, the behavioral deviation can be divided into three intervals. When the behavioral deviation is less than 1.0, the corresponding basic compensation coefficient is set to 1.1; when the behavioral deviation is greater than or equal to 1.0 and less than 2.5, the corresponding basic compensation coefficient is set to 1.3; and when the behavioral deviation is greater than or equal to 2.5, the corresponding basic compensation coefficient is set to 1.6.

[0059] Secondly, a trend correction factor is determined based on the deviation evolution trend of the individual abnormal aircraft. This trend correction factor is a multiplier used to dynamically adjust the compensation coefficient, and its value is determined based on the qualitative result of the deviation evolution trend. Specifically, the rules are as follows: when the deviation evolution trend of the aircraft is determined to be continuously expanding, it means that its future risk level may further increase, therefore a trend correction factor greater than 1 needs to be applied to amplify the compensation effect; for example, the trend correction factor is set to 1.3. When the evolution trend is determined to be converging, it means that the aircraft is recovering to normal on its own, and the future risk level may decrease; therefore, a trend correction factor less than 1 is applied to appropriately reduce the compensation intensity; for example, the trend correction factor is set to 0.8. When the evolution trend is determined to be stable fluctuation, it means that the future risk level will not change significantly; therefore, the trend correction factor is set to 1, and the basic compensation coefficient is not adjusted.

[0060] Finally, the obtained basic compensation coefficient and trend correction factor are multiplied. Specifically, the safety interval compensation coefficient = basic compensation coefficient × trend correction factor. Through this calculation process, the resulting safety interval compensation coefficient reflects both the degree of abnormality of the current behavior of the abnormal aircraft and its future behavior trend, thus providing more accurate guidance for the dynamic adjustment of subsequent safety intervals.

[0061] S40: Based on the basic safety interval and the safety interval compensation coefficient, determine the minimum safety interval between the abnormal aircraft and the surrounding aircraft, and control the flight interval according to the minimum safety interval.

[0062] Finally, based on the basic safety interval and the safety interval compensation coefficient obtained from the aforementioned calculation, the minimum safety interval between the abnormal aircraft and surrounding aircraft is determined.

[0063] The basic safety interval is preset according to the combination of aircraft types, or calculated in real time based on the aircraft's maximum speed, minimum turning radius, and communication delay time.

[0064] Specifically, the basic safety separation is the nominal distance or time interval required to ensure flight safety before considering abnormal individual aircraft behavior. It is preset according to the combination of aircraft types or calculated in real time based on the aircraft's maximum speed, minimum turning radius, and communication delay time. This basic safety separation represents the minimum safe distance or minimum time interval that two aircraft must adhere to to avoid conflict under standard operating conditions.

[0065] The determined minimum safe interval is compared with the actual interval between current aircraft. When the actual interval is less than the minimum safe interval, a speed adjustment command or heading adjustment command is sent to the abnormal aircraft to increase the actual interval to be no less than the minimum safe interval. When the actual interval is greater than or equal to the minimum safe interval, the current flight status is maintained.

[0066] In addition, the method also includes: Calculate the behavioral consistency index of a normal aircraft group. The consistency index is negatively correlated with the dispersion of the behavioral deviation of each aircraft in the group. When the consistency index is higher than the first threshold, an attenuation factor is applied to the safety interval compensation coefficient; When the consistency index falls below the second threshold, a first enhancement factor is applied to the safety interval compensation coefficient, and a second enhancement factor is applied to the basic safety interval of all aircraft.

[0067] To further improve the safety and efficiency of airspace operations, this method also introduces a dynamic evaluation mechanism for the internal operational order of normal aircraft groups, and makes adaptive adjustments to the safety interval based on the evaluation results, specifically including the following steps.

[0068] First, the behavioral consistency index of a normal aircraft group is calculated. This behavioral consistency index is a numerical value used to quantify the degree of coordination in the operational states of aircraft within a normal aircraft group. Its calculation method is negatively correlated with the dispersion of behavioral deviations among all aircraft in the group. Optionally, the behavioral consistency index is equal to the negative logarithm of the determinant of the covariance matrix of behavioral deviations of all aircraft in the group. Specifically, when the behavioral deviations of each aircraft in the group are very close and the dispersion is small, it indicates that the group operates with high coordination and good order, and the calculated behavioral consistency index value is high. Conversely, when the behavioral deviations of each aircraft in the group differ greatly and the dispersion is high, it indicates that the operational state within the group is relatively scattered and the order is poor, and the calculated behavioral consistency index value is low.

[0069] Secondly, a first threshold is introduced to evaluate the aforementioned behavioral consistency indicators. This first threshold is a critical value used to define whether a normal aircraft group is in a highly coordinated state. It represents a quantitative boundary indicating good operational order within the group and is set based on statistical characteristics of good group order in historical operational data or empirical values ​​for airspace capacity optimization; for example, it can be set to 0.8. When the calculated behavioral consistency index is higher than the first threshold, it indicates that the current normal aircraft group is operating in good order, and the aircraft exhibit highly coordinated behavioral characteristics. In this case, due to the group's strong inherent stability and anti-interference capabilities, the control intensity for abnormal individuals can be appropriately relaxed. Therefore, a decay factor is applied to the safety interval compensation coefficient generated for abnormal aircraft individuals, for example, multiplying it by a coefficient less than 1, such as 0.9, to appropriately reduce the interval amplification requirement for that abnormal individual, thereby improving airspace utilization while ensuring safety.

[0070] Finally, a second threshold is introduced to evaluate the behavioral consistency index from another perspective. This second threshold is a critical value used to define whether a group of normal aircraft is in a state of disorder. It represents the quantitative boundary of deteriorating operational order within the group and is set based on statistical characteristics of disordered group behavior in historical operational data or empirical values ​​for safety risk management; for example, it can be set to 0.3. When the calculated behavioral consistency index is lower than the second threshold, it indicates that the operational order within the current normal aircraft group is poor, with significant behavioral differences between aircraft, and the group itself is in a relatively disordered state. In this situation, the safety foundation of the entire airspace is weakened, requiring an increase in the safety alert level for all aircraft. Therefore, a dual enhancement measure is adopted. On the one hand, a first enhancement factor is applied to the safety separation compensation coefficient for individual abnormal aircraft, for example, multiplying it by a coefficient greater than 1, such as 1.2, to further increase the distance between abnormal individuals and the group. On the other hand, a second enhancement factor, such as 1.1, is also applied to the basic safety separation of all aircraft, including those within the normal group, to improve the overall separation standard within the airspace, thereby comprehensively strengthening the safety margin and addressing the systemic risks brought about by group disorder.

[0071] The attenuation factor is set to 0.9, indicating a 10% reduction in compensation intensity when the group is highly coordinated; the first enhancement factor is set to 1.2, indicating a 20% increase in compensation intensity when the group is disordered; and the second enhancement factor is set to 1.1, indicating an overall 10% increase in the basic safety separation of all aircraft. The specific values ​​of the above factors are set according to airspace safety margin requirements and management strategy preferences, and can be adjusted within the range of 0.8 to 1.3.

[0072] In summary, the embodiments of this application have at least the following technical effects: This invention, by introducing real-time behavioral deviation calculation and time series analysis, can proactively predict the evolution trend of future aircraft behavior, providing a time-dimensional predictive basis for the dynamic adjustment of safety intervals and overcoming the limitations of traditional methods that rely solely on instantaneous states for conflict detection. Secondly, through terrain-simulation-based clustering analysis, it achieves precise segmentation of normal aircraft groups and abnormal aircraft individuals within the airspace, and uses Mahalanobis distance to quantify the behavioral deviation between individuals and groups, improving the accuracy of identifying potential risk objects in complex airspace. Furthermore, by integrating current behavioral deviation and future deviation evolution trends, it dynamically generates differentiated safety interval compensation coefficients, ensuring that the interval control strategy for abnormal individuals reflects both their current risk level and their future evolution direction, achieving precise and forward-looking control measures. Furthermore, this invention introduces a dynamic adjustment mechanism based on the consistency of normal group behavior, which can adaptively adjust the interval compensation coefficient and the global basic safety interval according to the operational order within the group. When the group order is good, it releases airspace capacity and enhances safety margin when the group order is disordered, thus achieving a dynamic balance and collaborative optimization of safety and efficiency in a complex and ever-changing mixed traffic flow environment.

[0073] Example 2, as Figure 2 As shown, based on the same inventive concept as the method for determining the minimum safe interval of mixed traffic flow in complex airspace provided in Embodiment 1, this embodiment of the invention also provides a system for determining the minimum safe interval of mixed traffic flow in complex airspace, including: The deviation calculation and analysis module 11 is used to obtain the real-time flight parameters and planned flight parameters of each aircraft in the airspace, calculate the real-time behavior deviation of each aircraft, and perform time series analysis on the behavior deviation to obtain the deviation evolution trend of each aircraft. The clustering and deviation assessment module 12 is used to perform clustering analysis on aircraft in the airspace based on the behavioral deviation of each aircraft, divide the normal aircraft group into abnormal aircraft individuals, and calculate the behavioral deviation between the abnormal aircraft individuals and the normal aircraft group. The compensation coefficient generation module 13 is used to dynamically generate a safety interval compensation coefficient for individual abnormal aircraft based on the behavioral deviation and the trend of deviation evolution. The dynamic interval control module 14 is used to determine the minimum safe interval between the abnormal aircraft and the surrounding aircraft based on the basic safety interval and the safety interval compensation coefficient, and to control the flight interval according to the minimum safe interval.

[0074] The deviation calculation and analysis module 11 is specifically used for: Specifically, the real-time behavioral deviation of each aircraft is calculated, including: Obtain the aircraft's real-time position, real-time speed, and real-time heading, as well as the corresponding planned position, planned speed, and planned heading; Calculate the position deviation, velocity deviation, and heading deviation; The position deviation, velocity deviation, and heading deviation are assigned first, second, and third weights, respectively, and then weighted and summed to obtain the real-time behavior deviation of the aircraft.

[0075] Specifically, time series analysis is performed on the behavioral deviations to obtain the deviation evolution trend for each aircraft, including: Collect the behavioral deviation of each aircraft over the past N time windows to form the time series of that aircraft; Perform linear fitting on the time series and calculate the slope of the fitted line; When the slope is positive and the absolute value is greater than the first threshold, the trend of the judgment deviation is to continue to increase. When the slope is negative and the absolute value is greater than the second threshold, the trend of the judgment deviation is to converge. Otherwise, the deviation is judged to be a stable fluctuation.

[0076] Among them, the clustering and deviation evaluation module 12 is specifically used for: Cluster analysis of aircraft within the airspace is performed based on the behavioral deviations of each aircraft, including: The real-time position coordinates of each aircraft in the airspace are mapped to a preset planar coordinate system to obtain the planar coordinate point corresponding to each aircraft. For each aircraft, its real-time behavior deviation is used as the terrain height value of the corresponding plane coordinate point, and the plane coordinate points and terrain height values ​​of all aircraft are integrated to generate an airspace aircraft terrain map. In the airspace topographic map, the coordinate point corresponding to the aircraft with the smallest behavioral deviation and located in the core area of ​​the airspace is determined as the starting point for simulating water level rise. Starting from the initial point, the water level rise process is simulated with a preset step size. When the water level submerges a new plane coordinate point corresponding to a new aircraft, the height difference between the terrain height value of the new plane coordinate point and the terrain height value of the initial point is calculated. Determine whether the height difference does not exceed the preset height difference threshold. If the height difference does not exceed the preset height difference threshold, continue simulating the water level rise process. If the height difference exceeds the preset height difference threshold, a watershed will be generated at the new plane coordinate point, and all aircraft that have been submerged in this water level simulation will be clustered into a candidate aircraft group. After generating a group of aircraft, remove the planar coordinates of all aircraft in the group from the airspace aircraft topography map, and update the planar coordinates of the watershed to the starting point of the next water level simulation. Repeat the water level simulation and clustering process until all plane coordinate points corresponding to all aircraft in the airspace aircraft topographic map have been processed, resulting in several candidate aircraft groups.

[0077] Specifically, the group of normal aircraft is divided into groups of abnormal aircraft, including: Count the number of aircraft included in each candidate aircraft group; Candidate aircraft groups whose number of aircraft exceeds a preset group size threshold are classified as normal aircraft groups; Candidate aircraft groups with fewer aircraft than a preset group size threshold are identified as abnormal aircraft individuals.

[0078] Specifically, the deviation of the behavior of individual anomalous aircraft from that of a group of normal aircraft is calculated, including: Calculate the mean vector and covariance matrix of the behavioral deviations of all aircraft within a normal aircraft group; For each anomalous aircraft, calculate the Mahalanobis distance between its behavioral deviation vector and mean vector, which is used as the behavioral deviation of the anomalous individual from the normal group. When there are multiple normal aircraft groups, the minimum deviation of the abnormal individual's behavior from each normal group is taken as the final deviation of the abnormal individual's behavior.

[0079] The compensation coefficient generation module 13 is specifically used for: Based on the behavioral deviation and its evolution trend, a safety interval compensation coefficient is dynamically generated for individual abnormal aircraft, including: Establish a deviation-basic compensation coefficient mapping table. In the deviation-basic compensation coefficient mapping table, the behavioral deviation and the basic compensation coefficient are positively correlated. The trend correction factor is determined based on the trend of deviation evolution. When the evolution trend is continuous expansion, the trend correction factor is greater than 1; when the evolution trend is convergence, the trend correction factor is less than 1; when the evolution trend is stable fluctuation, the trend correction factor is equal to 1. Multiply the basic compensation coefficient by the trend correction factor to obtain the safety interval compensation coefficient.

[0080] The dynamic interval control module 14 is specifically used for: The basic safety interval is preset according to the combination of aircraft types, or calculated in real time based on the aircraft's maximum speed, minimum turning radius, and communication delay time.

[0081] Specifically, it also includes: Calculate the behavioral consistency index of a normal aircraft group. The consistency index is negatively correlated with the dispersion of the behavioral deviation of each aircraft in the group. When the consistency index is higher than the first threshold, an attenuation factor is applied to the safety interval compensation coefficient; When the consistency index falls below the second threshold, a first enhancement factor is applied to the safety interval compensation coefficient, and a second enhancement factor is applied to the basic safety interval of all aircraft.

Claims

1. A method for determining the minimum safe interval for mixed traffic flow in complex airspace, characterized in that, include: The real-time flight parameters and planned flight parameters of each aircraft in the airspace are obtained, the real-time behavior deviation of each aircraft is calculated, and the behavior deviation is analyzed over time to obtain the deviation evolution trend of each aircraft. Cluster analysis is performed on the aircraft in the airspace based on the behavioral deviation of each aircraft to divide them into groups of normal aircraft and individual abnormal aircraft, and the behavioral deviation between the individual abnormal aircraft and the group of normal aircraft is calculated. Based on the behavioral deviation and the trend of the deviation evolution, a safety interval compensation coefficient is dynamically generated for the individual abnormal aircraft. Based on the basic safety interval and the safety interval compensation coefficient, the minimum safety interval between the abnormal aircraft and surrounding aircraft is determined, and flight interval control is performed according to the minimum safety interval.

2. The method for determining the minimum safe interval for mixed traffic flow in complex airspace according to claim 1, characterized in that, Calculate the real-time behavioral deviation for each aircraft, including: Obtain the aircraft's real-time position, real-time speed, and real-time heading, as well as the corresponding planned position, planned speed, and planned heading; Calculate the position deviation, velocity deviation, and heading deviation; The position deviation, velocity deviation, and heading deviation are assigned a first weight, a second weight, and a third weight, respectively, and then weighted and summed to obtain the real-time behavior deviation of the aircraft.

3. The method for determining the minimum safe interval for mixed traffic flow in complex airspace according to claim 1, characterized in that, Time series analysis was performed on the behavioral deviations to obtain the deviation evolution trend for each aircraft, including: Collect the behavioral deviation of each aircraft over the past N time windows to form the time series of that aircraft; Perform linear fitting on the time series and calculate the slope of the fitted line; When the slope is positive and the absolute value is greater than the first threshold, the deviation is determined to be continuously expanding. When the slope is negative and the absolute value is greater than the second threshold, the deviation evolution trend is determined to be convergent. Otherwise, the deviation is judged to be a stable fluctuation.

4. The method for determining the minimum safe interval for mixed traffic flow in complex airspace according to claim 1, characterized in that, Cluster analysis of aircraft within the airspace is performed based on the behavioral deviations of each aircraft, including: The real-time position coordinates of each aircraft in the airspace are mapped to a preset planar coordinate system to obtain the planar coordinate point corresponding to each aircraft. For each aircraft, its real-time behavior deviation is used as the terrain height value of the corresponding plane coordinate point, and the plane coordinate points and terrain height values ​​of all aircraft are integrated to generate an airspace aircraft terrain map. In the airspace topographic map, the coordinate point corresponding to the aircraft with the smallest behavioral deviation and located in the core area of ​​the airspace is determined as the starting point for simulating water level rise. Starting from the starting point, the water level rise process is simulated with a preset step size. When the water level submerges a new plane coordinate point corresponding to a new aircraft, the height difference between the terrain height value of the new plane coordinate point and the terrain height value of the starting point is calculated. Determine whether the height difference does not exceed a preset height difference threshold. If the height difference does not exceed the preset height difference threshold, continue simulating the water level rise process. If the height difference exceeds the preset height difference threshold, a watershed is generated at the new plane coordinate point, and all aircraft that have been submerged in this water level simulation are clustered into a candidate aircraft group. After generating a group of aircraft, remove the plane coordinates of all aircraft in the group from the airspace aircraft topographic map, and update the plane coordinates of the watershed to the starting point of the next water level simulation. Repeat the water level simulation and clustering process until all plane coordinate points corresponding to all aircraft in the airspace aircraft topographic map have been processed, resulting in several candidate aircraft groups.

5. The method for determining the minimum safe interval for mixed traffic flow in complex airspace according to claim 4, characterized in that, The group of normal aircraft is divided into groups of abnormal aircraft, including: Count the number of aircraft included in each candidate aircraft group; Candidate aircraft groups whose number of aircraft exceeds a preset group size threshold are classified as normal aircraft groups; Candidate aircraft groups with fewer aircraft than the preset group size threshold are identified as abnormal aircraft individuals.

6. The method for determining the minimum safe interval for mixed traffic flow in complex airspace according to claim 1, characterized in that, Calculating the behavioral deviation between the abnormal aircraft individual and the normal aircraft group includes: Calculate the mean vector and covariance matrix of the behavioral deviations of all aircraft within a normal aircraft group; For each anomalous aircraft, the Mahalanobis distance between its behavioral deviation vector and the mean vector is calculated as the behavioral deviation degree between the anomalous individual and the normal group. When there are multiple normal aircraft groups, the minimum deviation of the abnormal individual's behavior from each normal group is taken as the final deviation of the abnormal individual's behavior.

7. The method for determining the minimum safe interval for mixed traffic flow in complex airspace according to claim 1, characterized in that, Based on the behavioral deviation and the trend of the deviation, a safety interval compensation coefficient is dynamically generated for the individual abnormal aircraft, including: A deviation-basic compensation coefficient mapping table is established, in which the behavioral deviation and the basic compensation coefficient are positively correlated; The trend correction factor is determined based on the trend of deviation evolution. When the evolution trend is continuous expansion, the trend correction factor is greater than 1; when the evolution trend is convergence, the trend correction factor is less than 1; when the evolution trend is stable fluctuation, the trend correction factor is equal to 1. The safety interval compensation coefficient is obtained by multiplying the basic compensation coefficient by the trend correction factor.

8. The method for determining the minimum safe interval for mixed traffic flow in complex airspace according to claim 1, characterized in that, The basic safety interval is preset according to the combination of aircraft types, or calculated in real time based on the aircraft's maximum speed, minimum turning radius, and communication delay time.

9. The method for determining the minimum safe interval for mixed traffic flow in complex airspace according to claim 1, characterized in that, Also includes: Calculate the behavioral consistency index of a normal aircraft group, wherein the consistency index is negatively correlated with the dispersion of the behavioral deviation of each aircraft in the group; When the consistency index is higher than the first threshold, an attenuation factor is applied to the safety interval compensation coefficient; When the consistency index is lower than the second threshold, a first enhancement factor is applied to the safety interval compensation coefficient, and a second enhancement factor is applied to the basic safety interval of all aircraft.

10. A system for determining the minimum safe interval for mixed traffic flow in complex airspace, characterized in that, The method for determining the minimum safe interval for mixed traffic flow in complex airspace as described in any one of claims 1-9 includes: The deviation calculation and analysis module is used to obtain the real-time flight parameters and planned flight parameters of each aircraft in the airspace, calculate the real-time behavior deviation of each aircraft, and perform time series analysis on the behavior deviation to obtain the deviation evolution trend of each aircraft. The clustering and deviation assessment module is used to perform cluster analysis on aircraft in the airspace based on the behavioral deviation of each aircraft, divide the aircraft into groups of normal aircraft and individuals of abnormal aircraft, and calculate the behavioral deviation between the individuals of abnormal aircraft and the groups of normal aircraft. The compensation coefficient generation module is used to dynamically generate a safety interval compensation coefficient for the individual abnormal aircraft based on the behavior deviation and the deviation evolution trend. The dynamic interval control module is used to determine the minimum safe interval between the abnormal aircraft and surrounding aircraft based on the basic safety interval and the safety interval compensation coefficient, and to control the flight interval according to the minimum safe interval.

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