Low-altitude route conflict and dynamic early warning data processing method and system, terminal and medium

Abstract

The invention relates to the field of low-altitude navigation, and particularly provides a low-altitude route conflict and dynamic early warning data processing method and system, a terminal and a medium, and the method comprises the steps: constructing a comprehensive state vector fusing task roles and formation constraints; spatial domain situation structured perception is realized through dynamic spatial domain gridding modeling and spatial domain state attribute updating; performing grid-level macroscopic conflict identification based on airspace state attributes, and screening potential conflict hotspot regions; for aircrafts in the hot spot area, trajectory prediction of intention and dynamics fusion is carried out based on the comprehensive state vector, and conflict risk probability assessment is carried out by adopting space-time probability envelope; and when the conflicting parties belong to the same group, a collaborative collision avoidance strategy taking formation maintenance as a primary target is generated, the strategy preferentially tries to integrally and uniformly avoid, and if the strategy fails, differential instruction distribution based on task role priority is started. According to the method, the high efficiency and the real-time performance of conflict early warning and releasing are improved.

Description

Technical Field

[0001] This invention relates to the field of low-altitude navigation, specifically to a method, system, terminal, and medium for processing low-altitude flight path conflict and dynamic early warning data. Background Technology

[0002] With the booming development of the low-altitude economy, drones, electric vertical takeoff and landing (eVTOL) aircraft, and other aircraft are increasingly being used collaboratively in areas such as urban public safety, emergency rescue, and fire monitoring. In these missions, multiple drones often need to form collaborative task groups to perform complex tasks such as patrolling, search and rescue, and fire monitoring in dense urban airspace. This places high demands on the safety, real-time performance, and coordination of low-altitude traffic management.

[0003] Most existing solutions treat each aircraft in the airspace as an independent entity, basing conflict detection and resolution decisions solely on geometric position and trajectory, completely ignoring the mission logic between aircraft. When UAVs belonging to the same formation are forced to break formation due to collision avoidance commands, it directly leads to the interruption or failure of collaborative missions, failing to meet the mission requirements of collaborative operations. Furthermore, these solutions typically employ global, pairwise precise conflict detection, with computational complexity increasing exponentially with the number of aircraft, resulting in significant system latency and hindering efficient real-time early warning. Moreover, the collision avoidance strategy generation methods either plan a uniform detour path for all conflicting aircraft or generate independent resolution commands for each aircraft, failing to incorporate mission priority concepts and thus unable to make optimal decisions when conflict is unavoidable. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method, system, terminal, and medium for processing low-altitude flight path conflict and dynamic early warning data, thereby improving the efficiency and real-time nature of conflict early warning and resolution.

[0005] In a first aspect, the technical solution of the present invention provides a method for processing low-altitude flight path conflict and dynamic early warning data. This method is used for collaborative task formation, where aircraft within the collaborative task formation are assigned task role attributes and formation constraint information. The method includes the following steps: Collect real-time flight data and construct a comprehensive state vector for each aircraft based on the real-time flight data. The comprehensive state vector includes position, speed, heading, flight intention, as well as its mission role attributes and formation constraint information. The target low-altitude airspace is divided into a three-dimensional dynamic grid, and the integrated state vector is mapped to the corresponding grid cell to update the airspace state attribute of the grid cell. Based on the latest airspace state attributes of the grid cells, potential conflict hotspots are identified. For aircraft within the potential conflict hotspots, trajectory prediction and conflict judgment are performed based on their comprehensive state vectors. When it is determined that the conflicting aircraft belong to the same cooperative mission group, a system collision avoidance strategy is generated, including: initially generating a unified avoidance path applicable to the entire group; if the unified avoidance path is successfully generated, the unified avoidance path is used as the final collision avoidance strategy; otherwise, collision avoidance instructions are generated for different aircraft in the group, wherein the instructions generated for aircraft with higher mission role priority deviate less from the original planned route than the instructions generated for aircraft with lower mission role priority. Based on the conflict assessment results and / or the generated collaborative collision avoidance strategy, generate early warning information and collision avoidance instructions.

[0006] Secondly, the technical solution of the present invention provides a low-altitude flight path conflict and dynamic early warning data processing system. This system is used for collaborative task formation, where aircraft within the collaborative task formation are assigned task role attributes and formation constraint information. The system includes: The flight data acquisition module is used to collect real-time flight data and construct a comprehensive state vector for each aircraft based on the real-time flight data. The comprehensive state vector includes position, speed, heading, flight intention, and mission role attributes and formation constraint information when it belongs to a cooperative mission group. The airspace state attribute update module is used to divide the target low-altitude airspace into a three-dimensional dynamic grid and map the comprehensive state vector to the corresponding grid cell to update the airspace state attribute of the grid cell. The conflict determination module is used to identify potential conflict hotspot areas based on the latest airspace state attributes of the grid cells, and to predict the trajectories and determine conflicts between aircraft within the potential conflict hotspot areas based on their comprehensive state vectors. The collision avoidance strategy generation module is used to generate a system collision avoidance strategy when it is determined that the aircraft involved in the conflict belong to the same cooperative mission group. This includes: initially generating a unified avoidance path applicable to the entire group; if the unified avoidance path is successfully generated, then the unified avoidance path is used as the final collision avoidance strategy; otherwise, collision avoidance instructions are generated for different aircraft in the group. The instructions generated for aircraft with higher mission priority deviate less from the original planned route than the instructions generated for aircraft with lower mission priority. The warning information and collision avoidance command generation module is used to generate warning information and collision avoidance commands based on the conflict judgment results and / or the generated cooperative collision avoidance strategy.

[0007] Thirdly, the technical solution of the present invention provides a terminal, comprising: Memory, used to store the low-altitude flight path conflict and dynamic early warning data processing program; A processor is configured to implement the steps of the low-altitude flight path conflict and dynamic early warning data processing method as described above when executing the low-altitude flight path conflict and dynamic early warning data processing program.

[0008] Fourthly, the present invention provides a computer-readable storage medium storing a low-altitude flight path conflict and dynamic early warning data processing program, wherein when the low-altitude flight path conflict and dynamic early warning data processing program is executed by a processor, it implements the steps of the low-altitude flight path conflict and dynamic early warning data processing method described in any of the above claims.

[0009] As can be seen from the above technical solutions, this application has the following advantages: (1) By constructing a comprehensive state vector for aircraft that includes “mission role attributes and formation constraint information” and integrating this information throughout the entire process of conflict detection and resolution decision-making, the system can identify cooperative formations and prioritize trying to maintain a unified path of formation when generating collision avoidance strategies, and only initiate differentiated resolution based on mission priority when necessary, thereby reducing the interference of collision avoidance maneuvers on the execution of cooperative missions. (2) By using dynamic airspace grid modeling, the global monitoring problem is transformed into focused monitoring of local "potential conflict hotspots". This eliminates the need for the system to perform high-load precision calculations on all aircraft in the airspace. Instead, the computing resources are concentrated on high-risk areas, thereby reducing the computational complexity of the system and ensuring the real-time performance of early warnings in high-density and high-dynamic scenarios. (3) When uniform avoidance is not feasible, priority ranking based on mission role attributes ensures that core mission aircraft with higher priority can obtain better right of way when resources are scarce, thereby improving the robustness and intelligence of the entire low-altitude mission system. (4) By constructing an accurate state vector through multi-source data fusion and combining it with the spatiotemporal probability envelope to conduct a probabilistic assessment of conflict risk, the reliance on inertial and deterministic trajectory prediction is reduced, and potential conflicts can be identified earlier and more reliably, providing stronger protection for the safe flight of UAVs in uncertain environments such as buildings and signal blockage. Attached Figure Description

[0010] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1This is a schematic flowchart of a low-altitude flight path conflict and dynamic early warning data processing method provided in an embodiment of the present invention.

[0012] Figure 2 This is a schematic block diagram of a low-altitude flight path conflict and dynamic early warning data processing system provided in an embodiment of the present invention.

[0013] Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this application and in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0016] Figure 1 This is a schematic flowchart illustrating a low-altitude flight path conflict and dynamic early warning data processing method provided in an embodiment of the present invention. Figure 1 The executing entity can be a low-altitude flight path conflict and dynamic early warning data processing system. The low-altitude flight path conflict and dynamic early warning data processing method provided in this embodiment of the invention is executed by computer equipment; correspondingly, the low-altitude flight path conflict and dynamic early warning data processing system runs on the computer equipment. Depending on different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0017] The method in this embodiment is used for collaborative task grouping, where aircraft within the collaborative task group are assigned task role attributes and formation constraint information, such as... Figure 1 As shown, the method includes the following steps.

[0018] S1. Collect real-time flight data and construct a comprehensive state vector for each aircraft based on the real-time flight data. The comprehensive state vector includes position, speed, heading, flight intention, and mission role attributes and formation constraint information when it belongs to a cooperative mission group.

[0019] S101 collects real-time flight data from multiple heterogeneous data sources, including Automatic Dependent Surveillance-Broadcast (ADS-B), cellular network link data, flight dynamics plan data, and low-altitude meteorological data.

[0020] Asynchronous real-time flight data streams are collected in parallel from multiple heterogeneous data sources via a dedicated data interface. These include: an aircraft broadcast automatic dependent surveillance (ADS) data stream providing aircraft identification, latitude and longitude, barometric altitude, ground speed, and heading angle; a cellular network link data stream providing aircraft identification, latitude and longitude, altitude, timestamp, and signal strength; flight plan and dynamic mission data providing aircraft identification, planned 4D routes, destination, mission type, and mission priority; and low-altitude meteorological data providing gridded wind speed, wind direction, temperature, and air pressure information within the target airspace.

[0021] S102 performs preprocessing on the collected data, including time synchronization, coordinate unification, and validity verification.

[0022] Time synchronization refers to using a unified system clock as a reference, attaching a high-precision timestamp to all data, and interpolating or extrapolating the data to synchronize them to the same processing cycle.

[0023] Coordinate unification refers to the unified conversion of location information from different data sources to three-dimensional coordinates under the same geographic coordinate system.

[0024] Validity verification refers to removing obviously abnormal outlier data based on physical rules such as velocity and acceleration thresholds and data correlation.

[0025] S103, based on the preprocessed data, constructs a comprehensive state vector for each aircraft.

[0026] Based on preprocessed and spatiotemporally aligned data, a unique comprehensive state vector is constructed for each tracked aircraft. This vector is a structured data object whose fields include at least: aircraft unique identifier, three-dimensional position coordinates, three-dimensional velocity vector, heading angle, valid data timestamp, flight intent information, and its mission role attributes and formation constraint information.

[0027] The flight intention information includes the coordinates of the next waypoint, the coordinates of the destination, and the mission priority; the formation constraint information is the tolerance of the relative positional relationship between the aircraft in the formation.

[0028] S2, the target low-altitude airspace is divided into a three-dimensional dynamic grid, and the integrated state vector is mapped to the corresponding grid cell to update the airspace state attribute of the grid cell.

[0029] S201. Initialize a three-dimensional grid space based on the geographical range and altitude layer of the target low-altitude airspace.

[0030] Based on the geographical range and altitude layer of the target low-altitude airspace, a three-dimensional grid space is initialized. The size of the grid cells is configurable and can be dynamically adjusted according to the real-time density of aircraft within the airspace; in areas with high aircraft density, a finer grid partitioning strategy is used.

[0031] S202, for each aircraft, determine its current grid cell based on the three-dimensional position coordinates in its integrated state vector.

[0032] For each aircraft, its current grid cell is determined through spatial geometric calculations based on its three-dimensional position coordinates in its integrated state vector.

[0033] Specifically, assuming the target low-altitude airspace has been initialized as a three-dimensional grid space, its geographical extent is defined as: eastward range Northward range Height range .

[0034] The size of a single grid cell is the eastward length of the grid. Grid northward length Grid height .

[0035] For any aircraft, given the three-dimensional position coordinates in its integrated state vector. The index of its corresponding grid cell is calculated through the following steps. .

[0036] a) Calculate the relative displacement of this position with respect to the origin of the spatial domain, expressed as:

[0037]

[0038]

[0039] b) Remove the relative position by the grid size and round the result down to obtain the index number of the grid where the aircraft is located, represented as:

[0040]

[0041]

[0042] S203, update the relevant information in the aircraft's integrated state vector to the airspace state attribute of its grid cell; wherein, the airspace state attribute includes at least the traffic flow density calculated based on the number of unique aircraft identifiers in the grid cell, and the cooperative mission situation information used to identify whether there are aircraft with mission role attributes in the grid cell.

[0043] The relevant information in the aircraft's integrated state vector is updated in the airspace state attribute of its corresponding grid cell. This airspace state attribute is a dynamically updated dataset, and its update logic includes: Aircraft List: Adds the aircraft's unique identifier to the current aircraft list of the grid cell; Traffic flow density: Based on the number of unique aircraft identifiers in a grid cell, the traffic flow density of that cell is calculated and updated in real time. Specifically, the volume of the grid cell is calculated based on its horizontal area and height. Then, the number of unique aircraft identifiers currently mapped to that grid cell is divided by the volume, and the result is converted to the corresponding order of magnitude to obtain the traffic flow density. Cooperative mission situation: If the integrated state vector of the aircraft contains mission role attributes and formation constraint information, then the grid cell is marked as having a cooperative mission aircraft, and its group identifier can be further recorded.

[0044] S3 identifies potential conflict hotspots based on the latest airspace state attributes of the grid cells. For aircraft within these potential conflict hotspots, trajectory prediction and conflict assessment are performed based on their integrated state vectors.

[0045] The process is divided into two phases: the first phase identifies potential conflict hotspots, and the second phase determines the conflict between aircraft.

[0046] S301, based on the latest spatial state attributes of the grid cells, identifies potential conflict hotspot areas, specifically including the following sub-steps.

[0047] S301.1 Calculate density conflict factor based on traffic density in airspace state attributes.

[0048] Density conflict factor Traffic flow density, derived from airspace state attributes, is set as follows: This represents the real-time traffic flow density of the grid. If the maximum allowable density is preset, then It can be calculated as . For grid cell indexing.

[0049] S301.2 Calculate the cooperative conflict factor based on the cooperative task situation information in the spatial state attributes.

[0050] Collaborative conflict factors Using the cooperative mission situation information in the airspace state attributes, when this information indicates the presence of aircraft from different cooperative mission formations within the grid, the probability of conflict increases significantly due to the independence of flight intentions. Therefore, a definition can be made. If all aircraft within the grid belong to the same group or are not in a group, then .

[0051] S301.3, velocity convergence factor calculated based on the divergence of the three-dimensional velocity vectors of all aircraft within the grid cell.

[0052] velocity convergence factor This is a conflict factor based on velocity field divergence. This factor assesses the convergence of traffic flow by calculating the divergence of the three-dimensional velocity vectors of all aircraft within the grid. The calculation formula is as follows:

[0053] In the formula, the negative divergence value indicates that the velocity field is converging, which foreshadows the potential risk of conflict.

[0054] S301.4, the density conflict factor, cooperative conflict factor, and velocity convergence factor are weighted and summed to obtain the multidimensional conflict potential field value. , represents,

[0055] In the formula, , , These are the weighting coefficients for each factor.

[0056] S301.5 performs spatiotemporal clustering analysis on the calculated conflict potential field values, and aggregates and marks grid cells whose conflict potential field values ​​exceed the primary threshold and are spatially adjacent as a potential conflict hotspot region.

[0057] The entire spatial domain is considered as a four-dimensional spacetime field (three-dimensional space + conflict potential field value). Density-based clustering algorithms such as DBSCAN are used to cluster conflict potential field values ​​exceeding a primary threshold. Cluster analysis of the grid cells can aggregate spatially adjacent grids with similar potential field values ​​into a continuous potential conflict hotspot region, which can effectively identify macro-risk areas with irregular shapes.

[0058] For each candidate region generated by clustering, calculate the average conflict potential field of all grid cells within it. When this average exceeds a higher, dynamically adjusted confirmation threshold... At this point, the area is officially designated as a potential conflict hotspot, and subsequent Level 2 conflict detection is activated. Threshold It can be dynamically adjusted based on the global spatial load.

[0059] S302, for aircraft in potential conflict hotspot areas, performs trajectory prediction and conflict judgment between aircraft based on their comprehensive state vectors, specifically including the following sub-steps.

[0060] S302.1, based on the three-dimensional position coordinates, three-dimensional velocity vector, heading angle and flight intention information in the integrated state vector, predict the four-dimensional trajectory of the aircraft in the future preset time period.

[0061] S302.1a treats the aircraft as a point mass and constructs its state space. This state space includes the aircraft's position, speed, heading, and pitch angle.

[0062] state space Its components are, in order, eastward position, northward position, altitude, airspeed, heading angle, and pitch angle.

[0063] S302.1b, Initiate the waypoint tracking controller, which calculates a virtual control input vector to guide the aircraft toward the next waypoint based on the coordinates of the next waypoint in the flight intention information. .

[0064] The waypoint tracking controller calculates the virtual control input vector using the following control law. .

[0065] Heading instructions: .in, Current heading and next waypoint The azimuth of the connecting line, It controls the gain.

[0066] Pitch command: .in, It is the target pitch angle required to reach the next waypoint.

[0067] Acceleration command: .in, That's the cruising speed.

[0068] S302.1c, Initiate the formation holding controller, which calculates a virtual control input vector for maintaining formation based on the lead aircraft's current state, the specified formation offset, and the current motion trend. .

[0069] In a collaborative mission formation, the lead aircraft is the one whose flight path serves as a reference point for other members (wingmen). It can be a specific aircraft designated within the formation, or a virtual reference point formed by the geometric center of the formation.

[0070] Obtain the lead aircraft at the current moment state vector Extract its current position from it. and velocity vector The formation offset is defined in the current aircraft (wingman) formation constraint information. Calculate the desired formation position of the wingmen. :

[0071] in, It follows the lead aircraft's current heading A changing rotation matrix.

[0072] Calculate the relative positional error between the wingman and the lead aircraft. .

[0073] Calculate the relative speed error between the wingman and the lead aircraft. .

[0074] Combining position and velocity errors to construct an extended error vector This vector describes both the static positional deviation and the dynamic movement trend deviation of the wingman within the formation.

[0075] Based on the extended error vector, the virtual control input vector for formation holding is calculated using a feedback control law. The specific implementation method is proportional-derivative (PD) control:

[0076] in, and These are the proportional and derivative control gain matrices, respectively. This control law drives the wingman to synchronize its speed with the lead aircraft while returning to the desired formation position, thereby achieving stable and smooth formation maintenance.

[0077] S302.1d, will , The final virtual control input vector is obtained by weighted fusion. This vector contains tangential acceleration commands, heading rate of change commands, and pitch rate of change commands.

[0078]

[0079] and These are time-varying fusion weighting coefficients related to the task phase.

[0080] Final virtual control input vector The components are, in order, tangential acceleration command, heading angle rate of change command, and pitch angle rate of change command.

[0081] S302.1e, the state space and the final virtual control input vector By inputting a pre-built aircraft kinematic model, the model is solved numerically to obtain the predicted four-dimensional trajectory. The aircraft kinematic model is represented as follows:

[0082] In the formula, It is a nonlinear function that describes the kinematics of an aircraft.

[0083] In a specific embodiment, the function The specific form is defined by the following system of equations:

[0084] The model takes into account three-dimensional position, velocity, heading, and pitch, and its control inputs directly affect the rates of change of velocity, heading, and pitch.

[0085] S302.2, based on the predicted four-dimensional trajectory, constructs a spatiotemporal probability envelope for each aircraft's predicted four-dimensional trajectory, and determines the risk of conflict by calculating the overlap probability of the spatiotemporal probability envelopes of two aircraft within a preset future time period.

[0086] S302.2a, quantitative modeling of the initial state uncertainty and process uncertainty affecting location prediction.

[0087] For initial state uncertainty, use an initial covariance matrix. The matrix is ​​characterized by the measurement error of the sensing system.

[0088] For process model uncertainties, a process noise covariance matrix is ​​used. The matrix is ​​characterized to encompass the effects of unmodeled dynamics, control errors, and external disturbances such as wind fields.

[0089] S302.2b, along the predicted four-dimensional trajectory, an unscented transformation is used to propagate the initial state uncertainty and process uncertainty in order to calculate the covariance matrix of the predicted position at each future time.

[0090] Along the predicted trajectory The initial uncertainty is propagated by linearizing the nonlinear dynamic model. Alternatively, an unscented transformation can be used to calculate future times. Predicted location covariance matrix Its propagation process is described by the following equation:

[0091] in, Let be the state transition matrix of the dynamic system.

[0092] S302.2c, Based on the predicted position covariance matrix at each time point and the preset confidence level, a confidence ellipsoid sequence that evolves over time is constructed, which is the spatiotemporal probability envelope.

[0093] Based on the predicted location covariance matrix The spatiotemporal probability envelope At any time It is defined as a confidence ellipsoid. This ellipsoid consists of all points that satisfy the following inequality. constitute:

[0094] In the formula, It has 3 degrees of freedom and a confidence level of The critical value of the chi-square distribution.

[0095] S302.2d determines the risk of conflict by calculating the probability of overlap of the spatiotemporal probability envelopes of two aircraft within a preset future time period.

[0096] Conflict risk between aircraft A and B Quantified as their spatiotemporal probability envelope in the future period of time The integral of the probability of overlap within the interior:

[0097] in, These represent the times of aircraft A and B, respectively. The spatiotemporal probability envelope, It represents probability.

[0098] Overlapping probability By calculating the relative position vectors of the two aircraft The probability of falling into the joint danger zone S defined by the safety interval is used as an approximation.

[0099] S4. When it is determined that the conflicting aircraft belong to the same cooperative mission group, a system collision avoidance strategy is generated, including: initially generating a unified avoidance path applicable to the entire group; if the unified avoidance path is successfully generated, the unified avoidance path is used as the final collision avoidance strategy; otherwise, collision avoidance instructions are generated for different aircraft in the group, wherein the instructions generated for aircraft with higher mission role priority deviate less from the original planned route than the instructions generated for aircraft with lower mission role priority.

[0100] S401, initially generate a unified avoidance path applicable to the entire collaborative task group. This path must meet the conditions of maintaining a safe distance from conflicting targets and maintaining the group formation constraints.

[0101] Using the overall geometric center of the collaborative task formation or a designated lead aircraft as a reference point, the overall path planner is invoked. This planner searches the airspace for a new path suitable for the entire formation, while satisfying collision avoidance constraints (i.e., maintaining a safe distance from the conflicting party). This path planning problem can be modeled as a constrained optimization problem, with the objective function being to minimize the overall path deviation. The constraints include: maintaining a safe distance from the predicted position of the conflicting target; and the new path satisfying the formation's formation constraints, meaning that the relative positions of the aircraft within the formation must remain within a preset tolerance range while maneuvering along the new path.

[0102] S402, verifying the feasibility of a unified avoidance path for all aircraft in the formation.

[0103] Based on the formation constraint information, the individual trajectories of all aircraft within the formation flying along the unified path are calculated. These individual trajectories are verified to ensure they all comply with airspace flight rules and do not intersect with any conflicting targets. If all individual trajectories pass verification, the unified avoidance path is considered successfully generated and is output as the final collision avoidance strategy.

[0104] S403 If the verification is successful, the unified avoidance path will be used as the final collision avoidance strategy.

[0105] S404 If verification fails, the differentiated instruction generation process is executed, including: prioritizing the aircraft in the group according to the mission role attributes, fixing the routes of high-priority aircraft in descending order of priority, and planning local avoidance routes for low-priority aircraft that conflict with them, until the conflict is resolved.

[0106] Iterative decomposition includes the following steps: a) Select the aircraft with the highest current priority from the list and keep its original planned route unchanged; b) Planning low-priority routes: For aircraft with lower priority that conflict with aircraft on fixed routes, the local path planning algorithm is invoked to generate local avoidance routes that allow them to avoid conflict. c) Conflict resolution check: Check whether all conflicts between aircraft in the formation and between them and external conflict targets have been resolved after the above assignment. d) Circular assignment: If the conflict is not completely resolved, select the next highest priority aircraft from the priority list, fix its current route, and repeat steps b and c until all conflicts are resolved or the list has been traversed.

[0107] S5 generates warning information and collision avoidance instructions based on the conflict judgment result and / or the generated cooperative collision avoidance strategy.

[0108] S501: Generate early warning information classification and content.

[0109] The system generates warning messages of different levels and contents based on the conflict risk level.

[0110] Level 1 Warning (Monitoring Level): This level of warning is generated when an aircraft enters a potential conflict hotspot area, but micro-conflict detection has not yet determined an immediate risk. The warning information includes: the aircraft identification, the warning type "Entering a monitored area", the risk level "Low", and the recommended action "Remain vigilant and continue monitoring".

[0111] Level 2 Warning (Alert Level): This level of warning is generated when the micro-conflict detection calculates a conflict risk probability that exceeds the monitoring threshold but is below the alarm threshold. The warning information includes: the aircraft identification of the conflicting parties, the predicted closest point information, the conflict risk probability value, the risk level "Medium", and the suggested action "Prepare to adjust the flight path".

[0112] Level 3 Warning (Alarm Level): This level of warning is generated when the probability of conflict exceeds the alarm threshold, or when the system has generated a cooperative collision avoidance strategy. The warning information includes: the identification of the conflicting aircraft, the expected conflict countdown, the risk level of "high," and specific collision avoidance instructions or strategy descriptions. For example: "Aircraft UAV01 and UAV02 are expected to collide in 45 seconds. Please execute the climb command to an altitude of 150 meters."

[0113] S502: Collision avoidance instruction encoding and encapsulation.

[0114] For collision avoidance operations that need to be executed immediately, the system will translate the cooperative collision avoidance strategy into specific instructions that can be directly understood and executed by the aircraft's flight control system or the operator.

[0115] Collision avoidance commands are encapsulated into a standardized instruction set, which mainly includes: Horizontal maneuver commands: such as "turn left 30 degrees" or "fly to waypoint (WPT_AVOID_001)"; Vertical maneuver commands: such as "climb to an altitude of 150 meters" or "descend to an altitude of 100 meters"; Speed ​​maneuvering commands: such as "reduce to 8 m / s"; Compound instructions: Combinations of the above instructions, such as "turn right 15 degrees and climb 20 meters".

[0116] Command parameters: Each command comes with precise parameters, such as target heading angle, target altitude, target speed, command duration, or execution until a specific condition is met.

[0117] Strategy Context: For coordinated formations, the instructions will include the formation identifier and strategy type to facilitate the coordinated understanding and execution of aircraft within the formation.

[0118] S503: Information distribution and response confirmation.

[0119] The generated warning information and collision avoidance instructions are distributed to the target terminal through a low-latency communication link.

[0120] The distribution targets include ground control stations, autonomous flight control systems, and regional air traffic management positions for the aircraft involved in the conflict. On the control station interface, different levels of warning information are displayed in different colors and highlighted on the electronic map and aircraft status list. Collision avoidance commands are prompted to the operator for confirmation and execution via pop-up windows or voice prompts. The system requires the recipient to send a confirmation acknowledgment upon receiving the command. For automatically executed systems, the flight control system must provide feedback on the execution status after executing the command, forming a closed-loop command and control system to ensure the effective implementation of collision avoidance actions.

[0121] The foregoing has described in detail an embodiment of a method for processing low-altitude flight path conflict and dynamic early warning data. Based on the low-altitude flight path conflict and dynamic early warning data processing method described in the above embodiment, this invention also provides a low-altitude flight path conflict and dynamic early warning data processing system corresponding to the method.

[0122] Figure 2 This is a schematic block diagram of a low-altitude flight path conflict and dynamic early warning data processing system provided in an embodiment of the present invention. In this embodiment, the low-altitude flight path conflict and dynamic early warning data processing system 200 can be divided into multiple functional modules according to its functions, such as... Figure 2 As shown. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and which are stored in memory.

[0123] The flight data acquisition module 210 is used to collect real-time flight data and construct a comprehensive state vector for each aircraft based on the real-time flight data. The comprehensive state vector includes position, speed, heading, flight intention, and mission role attributes and formation constraint information when it belongs to a cooperative mission group.

[0124] The airspace state attribute update module 220 is used to divide the target low-altitude airspace into a three-dimensional dynamic grid and map the comprehensive state vector to the corresponding grid cell to update the airspace state attribute of the grid cell.

[0125] The conflict determination module 230 is used to identify potential conflict hotspot areas based on the latest airspace state attributes of the grid cells, and to predict the trajectories and determine conflicts between aircraft within the potential conflict hotspot areas based on their comprehensive state vectors.

[0126] The collision avoidance strategy generation module 240 is used to generate a system collision avoidance strategy when it is determined that the aircraft involved in the conflict belong to the same cooperative mission group. This includes: initially generating a unified avoidance path applicable to the entire group; if the unified avoidance path is successfully generated, then the unified avoidance path is used as the final collision avoidance strategy; otherwise, collision avoidance instructions are generated for different aircraft in the group. The instructions generated for aircraft with higher mission priority deviate less from the original planned route than the instructions generated for aircraft with lower mission priority.

[0127] The warning information and collision avoidance command generation module 250 is used to generate warning information and collision avoidance commands based on the conflict judgment result and / or the generated cooperative collision avoidance strategy.

[0128] The low-altitude flight path conflict and dynamic early warning data processing system of this embodiment is used to implement the aforementioned low-altitude flight path conflict and dynamic early warning data processing method. Therefore, the specific implementation of this system can be found in the embodiment section of the low-altitude flight path conflict and dynamic early warning data processing method above. Thus, the specific implementation can be referred to the description of the corresponding embodiments, and will not be elaborated here.

[0129] Furthermore, since the low-altitude flight path conflict and dynamic early warning data processing system in this embodiment is used to implement the aforementioned low-altitude flight path conflict and dynamic early warning data processing method, its function corresponds to the function of the above method, and will not be described again here.

[0130] Figure 3 This is a schematic diagram of a terminal 300 provided in an embodiment of the present invention, including: a processor 310, a memory 320, and a communication unit 330. The processor 310 is used to implement the flow steps of the low-altitude flight path conflict and dynamic early warning data processing method embodiment when implementing the low-altitude flight path conflict and dynamic early warning data processing program stored in the memory 320.

[0131] This invention also provides a computer storage medium, which may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The computer storage medium stores a low-altitude flight path conflict and dynamic early warning data processing program. When executed by a processor, the low-altitude flight path conflict and dynamic early warning data processing program implements the flow steps of an embodiment of the low-altitude flight path conflict and dynamic early warning data processing method.

[0132] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing low-altitude flight path conflict and dynamic early warning data, characterized in that, This method is used for collaborative mission formation, where aircraft within the formation are assigned mission role attributes and formation constraint information; the method includes the following steps: Collect real-time flight data and construct a comprehensive state vector for each aircraft based on the real-time flight data. The comprehensive state vector includes position, speed, heading, flight intention, as well as its mission role attributes and formation constraint information. The target low-altitude airspace is divided into a three-dimensional dynamic grid, and the integrated state vector is mapped to the corresponding grid cell to update the airspace state attribute of the grid cell. Based on the latest airspace state attributes of the grid cells, potential conflict hotspots are identified. For aircraft within the potential conflict hotspots, trajectory prediction and conflict judgment are performed based on their comprehensive state vectors. When it is determined that the conflicting aircraft belong to the same cooperative mission group, a system collision avoidance strategy is generated, including: initially generating a unified avoidance path applicable to the entire group; if the unified avoidance path is successfully generated, the unified avoidance path is used as the final collision avoidance strategy; otherwise, collision avoidance instructions are generated for different aircraft in the group, wherein the instructions generated for aircraft with higher mission role priority deviate less from the original planned route than the instructions generated for aircraft with lower mission role priority. Based on the conflict assessment results and / or the generated collaborative collision avoidance strategy, generate early warning information and collision avoidance instructions.

2. The method for processing low-altitude flight path conflict and dynamic early warning data according to claim 1, characterized in that, Collect real-time flight data and construct a comprehensive state vector for each aircraft based on the real-time flight data, specifically including: Real-time flight data is collected from multiple heterogeneous data sources, including Automatic Dependent Surveillance-Broadcast (ADS-B), cellular network link data, flight dynamics plan data, and low-altitude meteorological data. The collected data is preprocessed, including time synchronization, coordinate unification, and validity verification. Based on the preprocessed data, a comprehensive state vector is constructed for each aircraft. The comprehensive state vector includes the aircraft's unique identifier, three-dimensional position coordinates, three-dimensional velocity vector, heading angle, valid data timestamp, flight intention information, and its mission role attributes and formation constraint information. Among them, the flight intention information includes the coordinates of the next waypoint, the coordinates of the destination, and the mission priority; the formation constraint information is the tolerance of the relative positional relationship between the aircraft in the formation.

3. The method for processing low-altitude flight path conflict and dynamic early warning data according to claim 2, characterized in that, The target low-altitude airspace is divided into a three-dimensional dynamic mesh, and the integrated state vector is mapped to the corresponding mesh cell to update the airspace state attributes of the mesh cell. Specifically, this includes: A three-dimensional grid space is initialized based on the geographical range and altitude layer of the target low-altitude airspace; For each aircraft, its current grid cell is determined based on its three-dimensional position coordinates in its integrated state vector; The relevant information in the aircraft's integrated state vector is updated to the airspace state attribute of its grid cell; wherein the airspace state attribute includes at least the traffic flow density calculated based on the number of unique aircraft identifiers in the grid cell, and the cooperative mission situation information used to identify whether there are aircraft with mission role attributes in the grid cell.

4. The method for processing low-altitude flight path conflict and dynamic early warning data according to claim 3, characterized in that, Based on the latest spatial state attributes of the grid cells, potential conflict hotspots are identified, specifically including: Calculate the density conflict factor based on traffic density in airspace state attributes; Calculate the cooperative conflict factor based on the cooperative task situation information in the spatial state attributes; Velocity convergence factor calculated based on the divergence of the three-dimensional velocity vectors of all aircraft within the grid cell; The multidimensional conflict potential field value is obtained by weighted summation of density conflict factor, cooperative conflict factor and velocity convergence factor. Spatiotemporal clustering analysis was performed on the calculated conflict potential field values. Grid cells whose conflict potential field values ​​exceeded the primary threshold and were spatially adjacent were aggregated and marked as a potential conflict hotspot region.

5. The method for processing low-altitude flight path conflict and dynamic early warning data according to claim 4, characterized in that, For aircraft within potential conflict hotspots, trajectory prediction and conflict assessment are performed based on their integrated state vectors, specifically including: Based on the three-dimensional position coordinates, three-dimensional velocity vector, heading angle, and flight intention information in the integrated state vector, the four-dimensional trajectory of the aircraft in the future within a preset time period is predicted, including: a) Treat the aircraft as a point mass and construct its state space. This state space includes the aircraft's position, speed, heading, and pitch angle; b) Activate the waypoint tracking controller, which calculates a virtual control input vector to guide the aircraft toward the next waypoint based on the coordinates of the next waypoint in the flight intention information. ; c) Activate the formation holding controller, which calculates a virtual control input vector for maintaining formation based on the lead aircraft's current state, the specified formation offset, and the current motion trend. ; d) will , The final virtual control input vector is obtained by weighted fusion. This vector contains tangential acceleration commands, heading rate of change commands, and pitch rate of change commands; e) The state space and the final virtual control input vector By inputting a pre-built aircraft kinematic model, the model is solved numerically to obtain the predicted four-dimensional trajectory. The aircraft kinematic model is represented as follows: In the formula, It is a nonlinear function describing the kinematics of an aircraft; Based on the predicted four-dimensional trajectory, a spatiotemporal probability envelope is constructed for each aircraft's predicted four-dimensional trajectory. The risk of conflict is determined by calculating the probability of overlap between the spatiotemporal probability envelopes of two aircraft within a preset future time period.

6. The method for processing low-altitude flight path conflict and dynamic early warning data according to claim 5, characterized in that, A spatiotemporal probability envelope is constructed for the predicted four-dimensional trajectory of each aircraft, specifically including: Quantitative modeling is performed to address the initial state and process uncertainties affecting location prediction; Along the predicted four-dimensional trajectory, an unscented transformation is used to propagate the initial state uncertainty and process uncertainty in order to calculate the covariance matrix of the predicted position at each future time. Based on the predicted position covariance matrix at each time point and the preset confidence level, a confidence ellipsoid sequence that evolves over time is constructed, which is the spatiotemporal probability envelope.

7. The method for processing low-altitude flight path conflict and dynamic early warning data according to claim 6, characterized in that, A unified avoidance path applicable to the entire formation is initially generated. If the unified avoidance path is successfully generated, it will be used as the final collision avoidance strategy; otherwise, collision avoidance instructions will be generated separately for each aircraft within the formation, specifically including: A preliminary unified avoidance path is generated for the entire collaborative task group. This path must meet the conditions of maintaining a safe distance from the conflicting target and maintaining the group formation constraints. Verify the feasibility of a unified avoidance path for all aircraft in the formation; If the verification is successful, the unified avoidance path will be used as the final collision avoidance strategy. If the verification fails, the differentiated instruction generation process is executed, including: prioritizing the aircraft in the group according to the mission role attributes, fixing the routes of high-priority aircraft in descending order of priority, and planning local avoidance routes for low-priority aircraft that conflict with them, until the conflict is resolved.

8. A low-altitude flight path conflict and dynamic early warning data processing system, characterized in that, This system is used for collaborative mission formation, where aircraft within the formation are assigned mission role attributes and formation constraint information; the system includes: The flight data acquisition module is used to collect real-time flight data and construct a comprehensive state vector for each aircraft based on the real-time flight data. The comprehensive state vector includes position, speed, heading, flight intention, and mission role attributes and formation constraint information when it belongs to a cooperative mission group. The airspace state attribute update module is used to divide the target low-altitude airspace into a three-dimensional dynamic grid and map the comprehensive state vector to the corresponding grid cell to update the airspace state attribute of the grid cell. The conflict determination module is used to identify potential conflict hotspot areas based on the latest airspace state attributes of the grid cells, and to predict the trajectories and determine conflicts between aircraft within the potential conflict hotspot areas based on their comprehensive state vectors. The collision avoidance strategy generation module is used to generate a system collision avoidance strategy when it is determined that the aircraft involved in the conflict belong to the same cooperative mission group. This includes: initially generating a unified avoidance path applicable to the entire group; if the unified avoidance path is successfully generated, then the unified avoidance path is used as the final collision avoidance strategy; otherwise, collision avoidance instructions are generated for different aircraft in the group. The instructions generated for aircraft with higher mission priority deviate less from the original planned route than the instructions generated for aircraft with lower mission priority. The warning information and collision avoidance command generation module is used to generate warning information and collision avoidance commands based on the conflict judgment results and / or the generated cooperative collision avoidance strategy.

9. A terminal, characterized in that, include: Memory, used to store the low-altitude flight path conflict and dynamic early warning data processing program; A processor is configured to implement the steps of the low-altitude flight path conflict and dynamic early warning data processing method as described in any one of claims 1 to 7 when executing the low-altitude flight path conflict and dynamic early warning data processing program.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores a low-altitude flight path conflict and dynamic early warning data processing program, which, when executed by a processor, implements the steps of the low-altitude flight path conflict and dynamic early warning data processing method as described in any one of claims 1 to 7.

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