An aircraft cluster security situation awareness method in an autonomous operation mode

Abstract

The application discloses an aircraft cluster security situation awareness method in an autonomous operation mode, is oriented to a high-altitude civil aircraft operation scene and belongs to the field of aviation operation safety. First, in order to meet the requirement of risk management refinement among aircrafts in the autonomous operation mode, communication navigation surveillance factors influencing flight path uncertainty are comprehensively considered, errors caused by CNS performance are introduced into a traditional collision model, traditional aircraft edge connection judgment basis is optimized, and an aircraft cluster situation network is constructed. Second, based on the constructed situation network, combined with aircraft cluster space structure representation and traffic flow parameter information, a cluster risk evaluation index is designed, cluster risk modes are further divided, and an air traffic risk situation network is formed. Finally, based on the cluster structure in the risk situation network, the time sequence distribution, survival characteristics and space operation preference of aircraft clusters in each risk mode are discussed, and the space-time evolution characteristics of the risk situation network are mined. The application can realize refined determination of conflict risks among aircrafts and mine space-time evolution characteristics of aircraft clusters, thereby providing theoretical support for the autonomous operation mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of civil aviation operation safety, and particularly relates to an aircraft cluster security situation awareness method in autonomous operation mode. BACKGROUND

[0002] In order to cope with the future four-dimensional track operation and free route airspace operation mode, adapt to the future high-density flight operation of the aviation environment, it is urgent to reflect the potential flight conflict model in the autonomous operation mode, and it is urgent to enhance the current air traffic situation awareness capability and improve the overall operation safety level of airspace by using new technologies and new means.

[0003] At present, the research on air traffic safety situation awareness mainly starts from the perspective of evaluating the severity of specific conflicts and measuring the stability of the macro system, and is divided into two types: risk analysis based on traffic situation measure and risk analysis based on complex network theory. In the risk analysis based on traffic situation measure, the research is often aimed at specific conflict events for measurement, and the safety level of air traffic situation is described. However, the description of specific conflict events will make the air traffic situation awareness lack of globality. In recent years, graph theory and complex network theory have been introduced into the research on air traffic risk situation, which provides a new research idea and method for air traffic operation situation. In the risk analysis based on complex network theory, the commonality of such research is to regard the aircraft in the airspace as network nodes and the conflict or proximity relationship between the aircraft as edges. The specific difference lies in the edge rule between the aircraft and the selected network evaluation index. However, this kind of research has the following problems: first, the connection between the aircraft is mostly implemented according to the given distance, and the distance is often subjective and does not meet the requirements of fine determination of the connection between the aircraft in the autonomous operation mode; second, the analysis of the evolution characteristics of the risk situation network is from the time dimension to explore the survival evolution law of the risk situation network. There is a gap in the research on the complex mechanism of the spatial operation of the aircraft flow from the spatial angle.

[0004] Therefore, it is necessary to provide an aircraft cluster security situation awareness method in autonomous operation mode to solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide an aircraft cluster security situation awareness method in autonomous operation mode, to propose a risk situation network model suitable for autonomous operation mode, to fine determine the conflict between the aircraft, and to combine the risk situation evolution characteristic analysis method of survival characteristics and spatial operation hot spot distribution to explore the evolution mechanism of the aircraft cluster from the time and space angles, and to realize the security situation awareness of the aircraft cluster in the autonomous operation condition.

[0006] To achieve the above purpose, the present application provides an aircraft cluster security situation awareness method in autonomous operation mode, comprising the following steps:

[0007] Step 1: Analyze the impact mechanism of CNS performance on track uncertainty, introduce the error caused by CNS performance into the traditional collision model, and construct a situation network model suitable for autonomous operation mode;

[0008] Step 2: Combine the spatial structure representation of aircraft cluster and traffic flow parameter information to design cluster risk evaluation index, divide cluster risk mode, and form air traffic risk situation network;

[0009] Step 3: Considering the significant spatio-temporal characteristics of aircraft cluster behavior, survival characteristics analysis and spatial hotspot analysis method are used to mine the spatio-temporal evolution characteristics of risk situation network.

[0010] Step 1 specifically includes:

[0011] Step 1.1: Analyze the characteristics of autonomous operation mode.

[0012] Step 1.2: Derive the aircraft positioning error caused by RCP and RSP based on the known principle of RNP-induced aircraft positioning error.

[0013] Step 1.3: Integrate the error distribution caused by CNS performance, combine the traditional collision model, and derive the collision probability calculation model under the condition of autonomous operation. Aircraft pairs with collision probability greater than the safety target level are determined as conflict aircraft pairs, and edges are established according to the conflict relationship to construct a situation network.

[0014] In step 1.1, autonomous operation mode has the following characteristics: flight pilots select the optimal track according to the actual use of airspace, and aircraft no longer determine risk based on traditional control-led mode and fixed distance between aircraft, but conduct detailed risk assessment based on aircraft performance and actual operating conditions, thereby maximizing airspace traffic growth while ensuring safety.

[0015] Step 2 specifically includes:

[0016] Step 2.1: Combine the spatial structure representation of aircraft cluster and traffic flow parameter information to design cluster risk evaluation index.

[0017] Step 2.2: Calculate the risk vector of each cluster and divide the risk mode by combining K-means clustering algorithm.

[0018] Step 3 specifically includes:

[0019] Step 3.1: From the time dimension, analyze the survival characteristics of aircraft cluster in each risk mode.

[0020] Step 3.2: From the spatial dimension, understand the running preferences of aircraft cluster in each risk mode.

[0021] The beneficial effects of this invention are as follows: This invention can achieve refined assessment of the risk of conflict between aircraft and explore the spatiotemporal evolution characteristics of aircraft clusters, providing theoretical support for autonomous operation modes. Attached Figure Description

[0022] Figure 1 : A flowchart of a method for security situation awareness of aircraft clusters under autonomous operation mode.

[0023] Figure 2 A collision model diagram of a method for aircraft cluster security situation awareness under autonomous operation mode.

[0024] Figure 3 A risk situation network diagram of a method for aircraft cluster security situation awareness under autonomous operation mode.

[0025] Figure 4 A schematic diagram of kernel density analysis for a method of aircraft cluster security situation awareness under autonomous operation mode. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings.

[0027] This invention provides a method for security situation awareness of aircraft clusters in autonomous operation mode, such as... Figure 1 As shown: The specific steps are as follows:

[0028] Step 1: Analyze the impact mechanism of CNS performance on trajectory uncertainty, introduce the error caused by CNS performance into the traditional collision model, and construct a situational network model suitable for use in autonomous operation mode;

[0029] Step 1.1: Analyze the impact mechanism of CNS performance on track uncertainty. Accuracy has a direct impact on navigation error. An arbitrary RNPa indicates that the flight track must remain within an mile radius of the planned path 95% of the time. Furthermore, the aircraft positioning error caused by RNP satisfies e N : Right now:

[0030]

[0031] Solving for the results It can be known The value of is only related to 'a', i.e., it is an RNP type.

[0032] The main difference between different types of RCP lies in transmission time. (Analog to e) NThe definition and solving method of RCPa can be considered as the flight path within 95% of the time to be within the planned path v x a mile, where v x represents the lateral component of the speed, and a represents the transmission time. According to the type selection of RCP and the error range of 95%, the following formula is obtained:

[0033]

[0034] The solution is It can be seen that only v x and a are related, that is, the airspeed of the aircraft and the type of RCP.

[0035] The main difference between different types of RSP is the reaction time. For e S , the analysis can refer to RCP and RNP, that is, any RSPa can be considered as the flight path within 95% of the time to be within the planned path v x b n mile, where v x represents the lateral component of the speed of the aircraft, and b represents the reaction time corresponding to RSPa. Similarly, the following formula also exists:

[0036]

[0037] The solution is It can be seen that only v x and b are related, that is, the airspeed of the aircraft and the type of RSP;

[0038] Step 1.2: Derivation of collision probability calculation model under autonomous operation condition. The aircraft positioning error e CNS caused by CNS performance CNS : Where Assume that the aircraft position deviation random variable grows along the x-axis, y-axis and z-axis in the coordinate system and the errors are independent of each other, then represents that the variable obeys the normal distribution with expectation 0 and variance σ 2 . Under the performance conditions of RCPn1, RNPn2 and RSPn3:

[0039]

[0040] The conflict probability is equivalent to the integral of the flight path error probability density function of aircraft B in the fusion protection zone of aircraft A. The collision model is shown in Figure 2 , and the calculation formula is as follows:

[0041]

[0042] where P conflict is the conflict probability of two aircrafts; f is the probability density function of the track error transferred to random aircraft B; Z area is the fusion protection area; x R , y R , z R , x S , y S , z S are the positions of the reference aircraft A and the random aircraft B, respectively.

[0043] Since the aircraft position deviation random variables grow along the x-axis, y-axis and z-axis in the coordinate system and the errors are mutually independent, the calculation process of the probability density function of the normal random variable (x, y) along the horizontal axis x and the vertical axis y of the coordinate system and the conflict probability is simplified as follows:

[0044]

[0045] In the formula: f xy (x, y) is the probability density function of the two-dimensional random variable (x, y) along the horizontal axis x and the vertical axis y of the coordinate system; P xy is the instantaneous conflict probability along the horizontal axis x and the vertical axis y of the coordinate system; u x , u y are the relative positions of the two aircrafts along the horizontal axis x and the vertical axis y of the coordinate system, respectively. are the variances of the two aircrafts along the x-axis and y-axis directions, respectively.

[0046] Similarly, the calculation process of the probability density function of the normal random variable z along the z-axis direction of the coordinate system and the conflict probability is simplified as follows:

[0047]

[0048] In the formula: f z (z) is the probability density function of the random variable z along the vertical axis z of the coordinate system; P z is the instantaneous conflict probability along the vertical axis z of the coordinate system; u z is the relative position of the two aircrafts along the vertical axis z of the coordinate system; are the variances of the two aircrafts along the z-axis direction, respectively;

[0049] Then the final conflict probability: P conflict = P xy × P z .

[0050] Step 2: Combine the spatial structure representation of the aircraft cluster and the traffic flow parameter information, design the cluster risk evaluation index, divide the cluster risk mode, and form the air traffic risk situation network;

[0051] Step 2.1: The index is designed as follows.

[0052] Conflict scale: the number of aircraft in the cluster, denoted as N.

[0053] Potential conflict number: the number of edges in the cluster network, denoted as E.

[0054] Conflict density: the proportion of potential conflict pairs among all possible conflict pairs, denoted as

[0055] Average conflict duration: the conflict duration refers to the time required for the aircraft with conflict relationship to fly to the cancellation of the edge under the condition that the current flight state remains unchanged. Then the average conflict duration refers to the average time required for all aircraft with conflict relationship in the aircraft cluster to fly to the non-conflict relationship, that is:

[0056]

[0057] wherein represents the average conflict duration; (v i ,v j ) represents the edge relationship between nodes i and j, 1, 0 represents the edge or no edge; T c,ij represents the conflict duration between aircraft i and j; E is the number of network edges.

[0058] Average dispersion risk intensity: first, the relative motion state between two aircrafts is reflected by further calculating the spatial proximity rate V ij (t) between aircrafts.

[0059]

[0060] wherein V ij (t) is greater than 0, which means that aircraft i and j are in the divergent state at t time; otherwise, that is, in the convergent state; d ij (t) represents the distance between aircraft i and j at t time.

[0061] Based on the spatial proximity rate, the dispersion risk intensity R ij is calculated as follows:

[0062]

[0063] Since V ij (t) has positive and negative two cases, considering the properties of the exponential function, it is ensured that the risk intensity of the aircraft with large spatial proximity degree is greater than that of the aircraft with small spatial proximity degree under the same spatial proximity rate. The spatial proximity rate adjustment coefficients a and b are introduced to adjust V ij(t) scaled to the positive range. So the mean vergence strength

[0064]

[0065] Step 2.2: Calculate the risk vector of each cluster, combine the K-means clustering algorithm to divide the risk pattern, form the air traffic risk situation network, as shown in Figure 3 .

[0066] Step 3: Use the survival characteristics analysis and spatial hotspot analysis method to mine the spatio-temporal evolution characteristics of the risk situation network;

[0067] Step 3.1: Analyze the change of the number of aircraft clusters of different risk levels within 24 hours in 1h intervals, which illustrates the time sequence evolution characteristics of aircraft clusters of different risk patterns; statistics the duration sample of each risk pattern cluster within the corresponding time, combine the distribution function to fit the duration sample distribution; statistics the multi-day aircraft cluster survival cycle sample data, estimate the average life cycle of each mode; use the Kaplan-Meier method to calculate the survival rate curve of each mode; calculate the quartile of the survival time of each risk mode; use the Nelson-Aalen method to obtain the cumulative risk curve of each risk mode.

[0068] Step 3.2: Discretize the airspace into 300m x 300m grids, combine the kernel density statistical method, the principle of which is shown in Figure 4 . Set the search radius to 3000m, select the Gaussian kernel as the kernel function, collect the trajectory set of each risk pattern aircraft cluster within a certain period of time, and statistically analyze the spatial operation hotspot distribution of each risk pattern aircraft cluster; combine the distribution of key waypoints in the study area to define and analyze the sector operation hotspot in detail; further analyze the distribution of operation hotspots in three-dimensional space, divide the study area into layers according to a certain height, and statistics the trajectory set of each risk pattern aircraft cluster in each height section, and observe the spatial operation hotspot distribution of each height section.

[0069] Among them, the kernel density statistics is specifically introduced: the kernel density method is a common spatial geographic analysis method, which can present the spatial distribution of point feature clusters. Kernel density estimation places a smooth, symmetric kernel around each data point, then adds the value of all kernels in the entire study area to obtain the density estimate of each location, generating a smooth surface. This process can convert discrete objects into continuous fields, and realize the visualization of unit density. The kernel density statistical method focuses on the calculation of kernel density value, and its calculation formula is:

[0070]

[0071] Among them, the kernel density statistics is specifically introduced: the kernel density method is a common spatial geographic analysis method, which can present the spatial distribution of point feature clusters. Kernel density estimation places a smooth, symmetric kernel around each data point, then adds the value of all kernels in the entire study area to obtain the density estimate of each location, generating a smooth surface. This process can convert discrete objects into continuous fields, and realize the visualization of unit density. The kernel density statistical method focuses on the calculation of kernel density value, and its calculation formula is: idenotes the kernel density at point i, r is the bandwidth, d ij K is the kernel function; the kernel function K is a function defined on two input data points that returns a similarity or inner product of the two points.

Claims

1. A method for security situation awareness of aircraft clusters under autonomous operation mode, characterized in that: Includes the following steps: Step 1: Analyze the impact mechanism of CNS performance on trajectory uncertainty, introduce the error caused by CNS performance into the traditional collision model, and construct a situational network model suitable for use in autonomous operation mode; Step 2: Combining the spatial structure representation of aircraft clusters and traffic flow parameter information, design cluster risk assessment indicators, classify cluster risk patterns, and form an air traffic risk situation network; Step 3: Considering the significant spatiotemporal characteristics of aircraft swarm behavior, survival characteristic analysis and spatial hotspot analysis methods are used to explore the spatiotemporal evolution characteristics of the risk situation network; Step 1 specifically includes: Step 1.1: Analyze the operational characteristics of the autonomous operation mode; Step 1.2: Based on the known principle of aircraft positioning error caused by RNP, derive the aircraft positioning errors caused by RCP and RSP; Step 1.3: Based on the error distribution caused by the CNS performance and combined with the traditional collision model, derive a collision probability calculation model suitable for autonomous operation conditions. Determine aircraft pairs with a collision probability greater than the safety target level as conflict aircraft pairs, establish connections based on the conflict relationship, and construct a situational network. Step 2 specifically includes: Step 2.1: Design cluster risk assessment indicators by combining the spatial structure representation of aircraft clusters and traffic flow parameter information; Step 2.2: Calculate the risk vector for each cluster and use the K-means clustering algorithm to classify risk patterns; In step 2.1, the cluster risk assessment indicators include: Conflict size: The number of aircraft in the cluster, denoted as N; Potential conflict number: The number of edges in a clustered network, denoted as E; Conflict density: The proportion of potentially conflicting pairs among all possible conflicting pairs, denoted as . Average conflict duration: Conflict duration refers to the time required for an aircraft with a conflicting relationship to fly to a point where the conflict is resolved and the connection is canceled, while maintaining its current flight state. in Indicates the average duration of conflict; (v i ,v j The symbol ) represents the connection between nodes i and j, where 1 and 0 represent whether there is a connection or not; T c,ij Indicates the duration of the conflict between aircraft i and j; Average clustering / dispersion risk intensity: First, by further calculating the spatial proximity rate V between aircraft. ij (t) is used to reflect the relative motion state between the two aircraft; Where V ij If (t) is greater than 0, it means that aircraft i and j are in a divergent state at time t; otherwise, they are in a convergent state; d ij (t) represents the distance between aircraft i and j at time t; Based on spatial proximity, the aggregation and dispersion risk intensity R is proposed. ij The calculation method is as follows: The introduction of α and β spatial proximity adjustment coefficients is to adjust V ij (t) is reasonably scaled down to a positive range, so the average aggregation intensity 2. The method for aircraft cluster security situation awareness in autonomous operation mode according to claim 1, characterized in that, A situational network is a network based on graph theory, where active aircraft are treated as nodes and aircraft with conflict relationships are connected by edges to generate a situational network.

3. The method for aircraft cluster security situation awareness in autonomous operation mode according to claim 1, characterized in that, In step 1.1, the autonomous operation mode has the following characteristics: the pilots autonomously select the optimal flight path based on the actual use of the airspace. The risk between aircraft is no longer determined by the traditional control-led mode and the fixed distance between aircraft, but by a detailed risk assessment based on the performance of the aircraft and the actual operating conditions, so as to maximize the growth of airspace traffic while ensuring safety. In step 1.2, the aircraft positioning error caused by RCP and RSP is derived as follows: Accuracy directly affects navigation error. An arbitrary RNPa indicates that the flight path remains within a nautical mile to the left or right of the planned path 95% of the time. Furthermore, the aircraft positioning error caused by RNP satisfies e. N : Right now: Solving for σ N =0.5102a, therefore σ N The value of depends only on 'a'; The difference between different types of RCP lies in the transmission time; analogous to e N The definition and solution method are as follows: It is assumed that any RCPb represents the flight path being approximately v to the left or right of the planned path 95% of the time. x Within the range of b nautical miles; based on the selection of RCP type and a 95% error range, the following formula exists: Solving for σ, we get C =0.5102v x b, we know σ C Only with v x It is related to b; The difference between different types of RSP lies in their response time, for e S The analysis references RCP and RNP, meaning that any RSPc indicates that the flight path must be on either side of the predetermined centerline 95% of the time. x c′ nautical miles, of which v x The lateral component represents the aircraft's velocity; similarly, the following formula also exists: Solving for σ S =0.5102v x c′, we know σ S Only with v x Related to c′; In step 1.3, the collision probability calculation model under autonomous operation conditions is as follows: Aircraft positioning error due to CNS performance e CNS e CNS : in If the random variable of aircraft position deviation grows along the x-axis, y-axis, and z-axis in the coordinate system and the errors are independent, then under the performance conditions of RCPb, RNPa, and RSPc: Since the random variable of aircraft position deviation grows along the x-axis, y-axis, and z-axis in the coordinate system and the errors are independent of each other, the calculation process of the probability density function and the collision probability of the normally distributed random variable (x, y) along the horizontal axis x and vertical axis y of the coordinate system is simplified as follows: In the formula: f xy (x,y) is the probability density function of the two-dimensional random variable (x,y) distributed along the horizontal axis x and the vertical axis y of the coordinate system; P xy u represents the instantaneous collision probability along the horizontal axis x and the vertical axis y of the coordinate system. x u y These represent the relative positions of the two aircraft along the horizontal axis x and the vertical axis y of the coordinate system. These are the variances of the two aircraft along the x-axis and y-axis, respectively; The simplified calculation process for the probability density function and conflict probability of a normally distributed random variable z along the z-axis is as follows: In the formula: f z (z) is the probability density function of the random variable z along the vertical axis z of the coordinate system; P z u is the instantaneous collision probability along the vertical axis z of the coordinate system; z Let z be the relative position of the two aircraft along the vertical axis z of the coordinate system; These are the variances of the two aircraft along the z-axis; The final probability of conflict is: P conflict =P xy ×P z .

4. The method for aircraft cluster security situation awareness in autonomous operation mode according to claim 1, characterized in that, Step 3 specifically includes: Step 3.1: Analyze the survival characteristics of aircraft clusters under different risk modes from a time perspective; Step 3.2: From a spatial perspective, understand the operational preferences of aircraft clusters under different risk modes.

5. The method for aircraft cluster security situation awareness in autonomous operation mode according to claim 4, characterized in that, Step 3.1, the survival characteristic analysis includes: This study analyzes the changes in the number of aircraft clusters at different risk levels over a 24-hour period to illustrate the temporal evolution characteristics of aircraft clusters under different risk modes. It statistically analyzes the duration samples of each risk mode cluster within the corresponding time period and fits the duration sample distribution using a distribution function. It also statistically analyzes multi-day cluster survival cycle sample data to estimate the average lifespan of each mode. The survival rate curves for each mode are calculated using the Kaplan-Meier method. The survival time quartiles for each risk mode are calculated. Finally, the cumulative risk curves for each risk mode are obtained using the Nelson-Aalen method. Step 3.2, understanding space operation preferences specifically includes: The airspace was discretized into a 300m×300m grid. Using the kernel density statistical method, a search radius of 3000m was set, and a Gaussian kernel was selected as the kernel function. Trajectory sets of aircraft clusters of various risk modes within a specific time period were collected, and the spatial operational hotspot distribution of each risk mode aircraft cluster was statistically analyzed. The sector operational hotspots were defined and analyzed in detail based on the distribution of key waypoints within the study area. The distribution of operational hotspots in three-dimensional space was further analyzed. The study area was layered according to specific altitudes, and the trajectory sets of each risk mode aircraft cluster in each altitude segment were statistically analyzed to observe the spatial operational hotspot distribution in each altitude segment.

6. The method for aircraft cluster security situation awareness in autonomous operation mode according to claim 5, characterized in that, A detailed introduction to kernel density statistics: The kernel density method is used to present the spatial distribution of point feature clusters and hotspots. The key is the calculation of the kernel density value, and its calculation formula is as follows: Among them O i Let r represent the kernel density at point i, r be the bandwidth, and d be the kernel density at point i. ij Let p be the distance between research object i and research object j, p be the number of research objects j within the bandwidth r, and K be the kernel function; the kernel function K is a function defined on two input data points, which returns the similarity or inner product of the two points.

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