A TCAS simulation method and system based on dynamic decision and real-time interaction

Abstract

This application relates to the field of dynamic decision-making and real-time interaction technology, providing a TCAS simulation method and system for a full-motion simulator based on dynamic decision-making and real-time interaction, solving the problem of low accuracy in dynamic prediction and avoidance decisions for flight conflicts. The method includes: acquiring the position coordinates, velocity, and heading angle of multiple flight targets in a three-dimensional airspace; discretizing the three-dimensional airspace into dynamically updated grid cells based on the position coordinates, and calculating the risk potential energy value of each grid cell according to the velocity and heading angle; during the simulation, dynamically predicting the probability of conflict occurrence within a set time period based on the risk potential energy value using a Monte Carlo algorithm, and generating an avoidance command set; after the simulation, generating a conflict report in a real-time interactive display interface based on the avoidance command set, the conflict report including the number of conflicts, the avoidance success rate, and the pilot's response time. This application improves the accuracy of dynamic prediction and avoidance decisions for flight conflicts in high-density airspace.

Description

Technical Field

[0001] This application relates to the field of dynamic decision-making and real-time interaction technology, and in particular to a TCAS simulation method and system for a fully dynamic simulator based on dynamic decision-making and real-time interaction. Background Technology

[0002] In the field of aviation safety, simulation technology for airborne collision avoidance systems needs to dynamically simulate complex airspace environments, including scenarios such as real-time interaction of multiple flying targets and changes in weather conditions, to provide a highly realistic training environment. This technology must support real-time response to pilot actions, dynamically generate conflict warnings and avoidance instructions, and be able to quantitatively evaluate training effectiveness, thereby effectively improving pilots' emergency decision-making capabilities.

[0003] Currently, mainstream airborne collision avoidance simulation technology employs static logic simulation, generating collision avoidance commands through pre-programmed conflict scenarios and fixed rules. This approach, based on preset flight trajectories and conflict conditions, triggers warning and avoidance commands according to predetermined logic during simulation, thus meeting basic training needs.

[0004] Static logic simulation relies on pre-defined scenarios and struggles to adapt to dynamic changes during flight, such as multi-aircraft coordination or sudden weather interference. The collision avoidance commands it generates lack flexibility and cannot be adjusted according to real-time airspace conditions, resulting in insufficient realism in the training scenarios. Furthermore, this approach has limited ability to simulate complex conflicts, making it difficult to meet the requirements of high-precision training. Summary of the Invention

[0005] This application provides a TCAS simulation method and system for a full-motion simulator based on dynamic decision-making and real-time interaction, in order to solve the problem of low accuracy in dynamic prediction and avoidance decision-making of flight conflicts in high-density airspace in the prior art.

[0006] Firstly, this application provides a Total Motion Simulation Method (TCAS) based on dynamic decision-making and real-time interaction, comprising:

[0007] Collect the position coordinates, velocity, and orientation angle of multiple flying targets in a three-dimensional airspace;

[0008] Based on the position coordinates, the three-dimensional spatial domain is discretized into dynamically updated grid cells, and the risk potential energy value of each grid cell is calculated according to the movement speed and the direction angle.

[0009] During the simulation, based on the risk potential energy value, the probability value of conflict occurrence within a set time period is dynamically predicted using the Monte Carlo algorithm, and an avoidance instruction set is generated based on the probability value of conflict occurrence.

[0010] After the simulation ends, a conflict report is generated in the real-time interactive display interface according to the avoidance instruction set. The conflict report includes the number of conflicts, the avoidance success rate, and the pilot response time.

[0011] Optionally, during the simulation process, based on the risk potential energy value, the probability value of conflict occurrence within a future set time period is dynamically predicted using a Monte Carlo algorithm, and an avoidance instruction set is generated based on the conflict occurrence probability value, including:

[0012] The risk potential value of each grid cell was simulated multiple times using the Monte Carlo algorithm;

[0013] The number of times each grid cell overlaps in all stochastic evolution simulations is counted. Based on the ratio of the number of overlaps to the total number of stochastic evolution simulations, the probability of conflict occurring in each grid cell within a set future time period is calculated.

[0014] When the probability of a collision in any grid cell exceeds a preset warning threshold, a set of avoidance instructions is generated.

[0015] Optionally, the step of performing multiple stochastic evolution simulations of the risk potential value of each grid cell using the Monte Carlo algorithm includes:

[0016] Obtain the real-time position value of each flight target at the current moment, and determine the unit where each flight target is located based on the real-time position value of each flight target;

[0017] Based on the magnitude ratio of the risk potential energy values ​​among the units where each flight target is located, a corresponding stochastic evolution weight is assigned to each flight target.

[0018] In each simulation, the random offset direction of each flight target is determined based on the direction angle of each flight target and the corresponding random evolution weight;

[0019] Based on the random offset direction, the displacement is calculated according to a fixed time step, and the position of each flying target is updated based on the displacement.

[0020] When at least two flying targets are determined to be in the same grid cell based on the updated positions, the grid cells are marked as having overlapping positions to complete a single full simulation.

[0021] Execute the complete simulation process independently at least N times, where N is an integer greater than or equal to 1000.

[0022] Optionally, generating the random offset direction of each flight target based on the direction angle of each flight target and the corresponding random evolution weight includes:

[0023] For each flight target, the following procedure is performed: multiply the preset basic angle deviation value by the corresponding stochastic evolution weight to obtain the allowable offset angle value;

[0024] An angle interval is constructed with the direction angle as the center. The lower limit of the angle interval is the difference between the direction angle and the allowable offset angle value, and the upper limit of the angle interval is the sum of the direction angle and the allowable offset angle value.

[0025] Within the angle range, a random angle value is generated, and the random angle value is rounded according to a preset precision.

[0026] The random offset direction is determined based on the rounded random angle value.

[0027] Optionally, generating a conflict report in the real-time interactive display interface based on the avoidance instruction set includes:

[0028] Record the specific instruction content of each generated avoidance instruction set;

[0029] Monitor the actual flight trajectory after the pilot executes the specific instructions;

[0030] The actual flight trajectory is compared and analyzed with the expected trajectory of the avoidance command set;

[0031] Based on the comparative analysis results, the conflict report is output in the form of visual charts in the real-time interactive display interface.

[0032] Optionally, the step of comparing and analyzing the actual flight trajectory with the expected trajectory of the avoidance command set includes:

[0033] The spatiotemporal coordinate sequence of the actual flight trajectory is paired with the trajectory points with the same timestamp in the spatiotemporal coordinate sequence of the expected trajectory to generate a set of trajectory point pairs.

[0034] For each pair of trajectory points in the set of trajectory point pairs, calculate the planar distance of the horizontal position value and the vertical distance of the height value;

[0035] When the planar distance or the vertical distance exceeds a preset error threshold, the trajectory point pair is marked as an anomaly.

[0036] The avoidance success rate is calculated based on the ratio of the number of abnormal points to the total number of trajectory point pairs.

[0037] The difference between the timestamp of the first trajectory point pair marked as an anomaly and the timestamp of the avoidance command generation is used as the response delay time.

[0038] The avoidance success rate, response delay time, and maximum position deviation value are combined to obtain comparative analysis results.

[0039] Optionally, the step of discretizing the three-dimensional spatial domain into dynamically updated grid cells based on the position coordinates, and calculating the risk potential energy value of each grid cell according to the motion velocity and the direction angle, includes:

[0040] The three-dimensional airspace is divided into grid cells of fixed size, and the grid cell where each flying target is located is determined according to the position coordinates.

[0041] Based on the motion speed and the direction angle, calculate the migration probability of each flying target moving to an adjacent grid cell within a set time step;

[0042] The risk potential energy value of each grid cell is calculated by combining the number of flying targets in each grid cell, the migration probability, and the relative speed difference between flying targets.

[0043] Secondly, this application provides a Total Motion Simulator (TCAS) simulation system based on dynamic decision-making and real-time interaction, comprising:

[0044] The acquisition module is used to acquire the position coordinates, velocity, and orientation angle of multiple flying targets in the three-dimensional airspace;

[0045] The discrete module is used to discretize the three-dimensional spatial domain into dynamically updated grid cells based on the position coordinates, and calculate the risk potential energy value of each grid cell according to the motion speed and the direction angle.

[0046] The prediction module is used to dynamically predict the probability of conflict occurrence within a set time period in the future based on the risk potential energy value during the simulation process using the Monte Carlo algorithm, and generate an avoidance instruction set based on the probability of conflict occurrence.

[0047] The generation module is used to generate a conflict report in the real-time interactive display interface after the simulation is completed, based on the avoidance instruction set. The conflict report includes the number of conflicts, the avoidance success rate, and the pilot response time.

[0048] Thirdly, this application provides a computing device including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the TCAS simulation method for a fully dynamic simulator based on dynamic decision-making and real-time interaction as described in any of the first aspects.

[0049] Fourthly, this application provides a computer storage medium storing computer program instructions thereon, which, when executed by a processor, implement the TCAS simulation method for a fully dynamic simulator based on dynamic decision-making and real-time interaction as described in any one of the first aspects.

[0050] This application provides a Total Motion Simulation (TCAS) method for a fully dynamic simulator based on dynamic decision-making and real-time interaction. The method includes: acquiring the position coordinates, velocity, and heading angle of multiple flying targets in a three-dimensional airspace; discretizing the three-dimensional airspace into dynamically updated grid cells based on the position coordinates; calculating the risk potential energy value of each grid cell based on the velocity and heading angle; dynamically predicting the probability of conflict occurrence within a future set time period based on the risk potential energy value using a Monte Carlo algorithm; generating an avoidance command set based on the probability of conflict occurrence; and generating a conflict report in a real-time interactive display interface based on the avoidance command set after the simulation ends. The conflict report includes the number of conflicts, the avoidance success rate, and the pilot's response time.

[0051] The technical solution provided in this application has the following beneficial effects:

[0052] This application acquires real-time dynamic data of flight targets, providing accurate input for subsequent airspace analysis and conflict prediction, ensuring synchronization between the simulation environment and actual flight conditions. It transforms continuous airspace into quantifiable, discrete units, achieving real-time assessment of airspace status through dynamic updates of the grid and risk potential energy values, laying the foundation for conflict prediction. Utilizing probabilistic simulation methods, it predicts the distribution of potential conflicts in future time periods, improving the accuracy and foresight of conflict early warning and adapting to complex and ever-changing airspace environments. Based on the probabilistic prediction results, it dynamically generates avoidance strategies, providing a set of instructions for altitude, speed, or heading adjustments, ensuring the scientific nature and flexibility of avoidance decisions. By quantitatively analyzing the number of conflicts, avoidance success rate, and pilot response time, it provides intuitive data support for training effectiveness evaluation, helping to optimize training programs.

[0053] Furthermore, during the simulation process, this application also performs multiple random evolution simulations based on the risk potential energy value of the grid cells using the Monte Carlo algorithm, counts the number of times each cell's position overlaps, and calculates the probability value of conflict occurrence in future time periods; when the conflict probability of any cell exceeds the warning threshold, a set of avoidance instructions containing altitude, speed, or heading adjustment strategies is generated.

[0054] Furthermore, through dynamic probability prediction and stochastic evolution simulation, high-precision conflict early warning is achieved, and the generated avoidance instruction set can adapt to complex airspace changes, improving the realism of training scenarios and the reliability of decision-making.

[0055] These or other aspects of this application will become more apparent from the description of the following embodiments. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart illustrating a Total Motion Simulator (TCAS) simulation method based on dynamic decision-making and real-time interaction, provided for embodiments of this application;

[0058] Figure 2 A schematic diagram of the structure of a Total Motion Simulator (TCAS) simulation system based on dynamic decision-making and real-time interaction, provided for an embodiment of this application;

[0059] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0061] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0062] Current airborne collision avoidance simulation technology mainly relies on static logic pre-set scenarios. Its conflict rules and avoidance commands are generated based on fixed programming. Although it can complete basic training tasks, it has limitations: static simulation cannot respond in real time to the pilot's dynamic operations and changes in the airspace environment (such as multi-aircraft coordinated maneuvers or sudden weather disturbances), resulting in a disconnect between training scenarios and actual flight; at the same time, the pre-set logic is difficult to simulate complex air situations (such as multi-target cross-collision), and the generated avoidance commands lack flexibility, limiting the realism of training. The root cause of this problem lies in the fact that existing technologies statically and discretize the airspace state, failing to establish a closed-loop relationship between the dynamic environment and real-time decision-making.

[0063] To address the aforementioned shortcomings, this application proposes a TCAS (Total Collision Avoidance and Responsiveness) simulation method for full-motion simulators based on dynamic decision-making and real-time interaction. Its core lies in constructing a dynamic response mechanism through real-time airspace gridding and Monte Carlo probabilistic prediction. Specifically, the three-dimensional airspace is discretized into dynamically updated grid cells, the risk potential value of each cell is calculated in real time, and the probability distribution of future conflicts is predicted based on Monte Carlo stochastic evolution simulation. Avoidance command sets are then dynamically generated according to probability thresholds. This method overcomes the rigid constraints of static logic, achieving three improvements through real-time quantification of airspace states and probabilistic decision-making: first, it dynamically responds to pilot operations and airspace changes, supporting simulations of complex scenarios such as multi-aircraft collaboration; second, it generates differentiated avoidance strategies through probabilistic prediction, improving the flexibility and adaptability of commands; and third, it optimizes model parameters through closed-loop feedback training data, continuously improving simulation accuracy. Therefore, this solution fundamentally solves the problems of insufficient dynamic interactivity and low scenario complexity in static simulations, providing technical support for high-fidelity training.

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] Figure 1 A flowchart of a TCAS simulation method for a fully motion simulator based on dynamic decision-making and real-time interaction, provided in this application embodiment, is shown below. Figure 1 As shown, the method includes:

[0066] Step 101: Collect the position coordinates, speed, and direction angle of multiple flying targets in the three-dimensional airspace.

[0067] In step 101, the position coordinates represent the real-time position data of the flying target in three-dimensional space, including the horizontal position value and the vertical altitude value, used to determine the spatial distribution of the flying target. The velocity represents the current speed of the flying target's movement, used to calculate its movement trend and potential conflict risk. The azimuth angle represents the angle between the flying target's direction of movement and the reference direction, used to determine the direction of its flight path.

[0068] In this embodiment, the three-dimensional position coordinates, velocity, and heading angle data of all flying targets are acquired in real time through sensors or simulation data interfaces installed in the simulation system. Position coordinates are used to determine the specific location of the flying target in the airspace, while velocity and heading angle are used to analyze its motion trend. After being processed in a unified format, this data is transmitted to the next stage for airspace gridding analysis.

[0069] For example, in the simulation system, the position coordinates of flight target A are updated in real time through the simulation data interface. The system records its horizontal position and altitude values, while also acquiring its velocity and heading angle. Data for flight targets B and C are acquired in the same way. All data, after verification, is used to construct a dynamic airspace model.

[0070] Step 102: Discretize the three-dimensional spatial domain into dynamically updated grid cells based on the position coordinates, and calculate the risk potential energy value of each grid cell according to the motion speed and the direction angle.

[0071] In step 102, a grid cell represents dividing the three-dimensional airspace into fixed-size three-dimensional blocks, each block being used for independent conflict risk analysis. The risk potential energy value represents a quantitative indicator reflecting the potential conflict risk within the grid cell, determined by the velocity and heading angle of the flying target.

[0072] In this embodiment, the three-dimensional airspace is divided into multiple identical three-dimensional grid cells, the position of which is defined by its three-dimensional coordinate range. The grid cell in which the flying target is located is determined based on its real-time position coordinates. The probability of the flying target moving to an adjacent grid cell within a set time step is calculated based on its velocity and direction angle. The risk potential energy value of each grid cell is calculated by combining the number of flying targets in the current grid cell, the probability of movement of adjacent targets, and their relative velocity differences.

[0073] For example, airspace is divided into cubic grid cells with fixed side lengths, and flight target A is located within a certain cell. Based on its velocity and heading angle, the probability of it moving to an adjacent cell in the next time step is calculated. Combining the motion data of flight targets B and C, the risk potential value of that cell is calculated for subsequent conflict prediction.

[0074] Step 103: During the simulation, based on the risk potential energy value, the probability value of conflict occurrence within a set future time period is dynamically predicted using the Monte Carlo algorithm, and an avoidance instruction set is generated based on the probability value of conflict occurrence.

[0075] In step 103, the dynamics are manifested as follows: the grid cells are dynamically updated, therefore the risk potential energy value is dynamically updated, and thus the collision probability value is also updated. The collision probability value represents the likelihood of a collision occurring in the grid cells within a future time period, calculated through random simulation. The avoidance instruction set represents a set of instructions containing altitude, speed, or direction adjustment strategies to avoid potential collisions.

[0076] In this embodiment, the risk potential energy value of each grid cell is subjected to multiple stochastic evolution simulations. In each simulation, a random offset direction is generated based on the flight target's velocity and heading angle, its position is updated at fixed time steps, and it is detected whether the position overlaps with other flight targets. The number of position overlaps of grid cells in all simulations is counted, and the probability value of conflict occurrence is calculated. When the probability value exceeds a warning threshold, a set of avoidance instructions containing altitude, velocity, or direction adjustment strategies is generated.

[0077] For example, multiple random simulations are performed on the grid cell containing flight target A to predict its future position changes. After counting the number of position overlaps, the probability of a conflict is calculated. If the probability exceeds a threshold, the system generates avoidance commands such as "increase altitude" or "decelerate" for the pilot to execute.

[0078] Step 104: After the simulation ends, generate a conflict report in the real-time interactive display interface according to the avoidance instruction set. The conflict report includes the number of conflicts, the avoidance success rate, and the pilot response time.

[0079] In step 104, the conflict report presents summary data including indicators such as the number of conflicts, avoidance success rate, and pilot response time, used to evaluate training effectiveness.

[0080] In this embodiment, the execution result of each avoidance command is recorded, including pilot operation data and actual avoidance effect. The expected trajectory of the command is compared with the actual flight trajectory to count the number of conflicts and the avoidance success rate. The response time from command generation to pilot execution is calculated. After all data is summarized, a conflict report is displayed in chart form on the interactive interface.

[0081] For example, after the simulation, the system statistically analyzes the execution of avoidance commands for flight target A, examines the difference between its actual trajectory and the expected trajectory, and calculates the avoidance success rate and response time. The final report displays the number of conflicts and pilot performance during this training session.

[0082] This method acquires flight target data in real time, dynamically divides the airspace into grids, calculates risk potential energy values, generates avoidance commands using probabilistic prediction, and finally outputs a training evaluation report. It achieves high-precision conflict early warning and flexible avoidance decision-making in complex airspace environments, improving the realism and effectiveness of simulation training.

[0083] To address the insufficient dynamic response capability of existing air collision avoidance simulation technologies, in some embodiments, step 103 involves: during the simulation process, dynamically predicting the probability of conflict occurrence within a set future time period based on the risk potential energy value using a Monte Carlo algorithm, and generating an avoidance instruction set based on the probability of conflict occurrence, including:

[0084] Step 201: Perform multiple stochastic evolution simulations of the risk potential value of each grid cell using the Monte Carlo algorithm.

[0085] In step 201, stochastic evolution simulation refers to the process of repeatedly simulating and extrapolating the future trajectory of a flight target based on the current airspace state using probabilistic methods. Each simulation generates possible movement paths based on the flight target's motion characteristics.

[0086] In this embodiment, the system uses the risk potential energy value of each grid cell as a basis to perform multiple independent simulations of the flying target within each cell. In each simulation, based on the flying target's velocity and heading angle, an offset direction is randomly generated within its possible movement range, and the target position is updated at fixed time steps, recording the state of the grid cell after the movement. By repeating this process extensively, a statistical sample of the flying target distribution over future time periods is formed.

[0087] Step 202: Count the number of times each grid cell overlaps in all random evolution simulations, and calculate the probability of conflict occurring in each grid cell within a set time period in the future based on the ratio of the number of times the position overlaps to the total number of random evolution simulations.

[0088] In step 202, the number of position overlaps refers to the number of times two or more flight target positions appear simultaneously within the same grid cell during the Monte Carlo stochastic evolution simulation, used to quantify the probability of a collision occurring in that cell. The total number of stochastic evolution simulations represents the preset total number of simulations (e.g., 1000) to ensure statistical reliability. Its value is determined comprehensively based on the computational accuracy requirements and system performance; the more simulations, the closer the probability calculation results are to the true distribution.

[0089] In this embodiment, the system counts the number of times the flight target positions overlap in each grid cell across all simulations, and uses the ratio of this number to the total number of simulations as the probability value of a collision. For example, if a cell overlaps 200 times in 1000 simulations, its probability value of a collision is 0.2. This probability value is used to quantify the risk of a collision.

[0090] Step 203: When the probability value of a collision in any grid cell exceeds a preset warning threshold, generate a set of avoidance instructions.

[0091] In step 203, the preset warning threshold represents a pre-set critical value for conflict risk (e.g., 0.25). When the probability of a conflict occurring in a grid cell exceeds this threshold, it is determined that an avoidance command needs to be generated. The threshold value is optimized based on safety standards and historical data.

[0092] In this embodiment, when the probability of a conflict in a certain grid cell exceeds a preset warning threshold, the system generates differentiated avoidance commands based on the relative position and motion state of the flying target. For example, it generates an "increase altitude" command for approaching flying targets and an "decelerate" command for targets at the same altitude, ensuring the targeting and operability of the commands.

[0093] Here is a specific example:

[0094] In the simulation training scenario, flight targets A, B, and C simultaneously enter the same airspace. The system first collects the real-time position coordinates, speed, and heading angle data of the three targets, with flight target A located in grid cell G5. The system performs multiple random evolution simulations on this cell. In each simulation, based on flight target A's speed of 500 km / h and heading angle of 30 degrees, an offset angle is randomly generated within a 15-degree range to the left and right of its direction of movement. The displacement is calculated using a fixed time step of 10 seconds to update its predicted position. Simultaneously, flight target B moves towards an adjacent cell at a speed of 600 km / h and a heading angle of 45 degrees, while flight target C moves at a speed of 550 km / h and a heading angle of 60 degrees. After 1000 independent simulations, it is found that flight targets A and B overlap 180 times and C overlap 120 times within cell G5. The maximum value of 180 times is taken as the number of overlaps in this cell. According to the formula for calculating the probability of conflict, i.e., the number of overlaps divided by the total number of simulations, the probability of conflict in cell G5 is 0.18. The system's preset warning threshold is 0.15. Since 0.18 exceeds this threshold, the decision module generates a set of targeted avoidance instructions, including an instruction to ascend 50 meters for flight target A and an instruction to turn 10 degrees to the left for flight target B.

[0095] In this embodiment, real-time conflict warning and flexible avoidance in complex airspace environments are achieved through dynamic probability prediction and targeted instruction generation, thereby improving the realism of simulation training and the reliability of decision-making.

[0096] To address the issue of insufficient dynamic prediction accuracy in existing aerial collision avoidance simulations, in some embodiments, step 201: performing multiple stochastic evolution simulations of the risk potential value of each grid cell using the Monte Carlo algorithm, includes:

[0097] Step 301: Obtain the real-time position value of each flight target at the current moment, and determine the unit where each flight target is located based on the real-time position value of each flight target.

[0098] In step 301, the real-time position value contains the specific coordinate data of the flying target in the three-dimensional airspace, used for precise positioning. The cell in question refers to the grid cell number where the flying target is currently located, determined by comparing the coordinates with the grid boundary.

[0099] In this embodiment of the application, the system first obtains the real-time three-dimensional coordinates of all flying targets, and determines the specific unit number to which each flying target belongs based on the pre-divided grid unit spatial range, so as to provide a spatial basis for subsequent weight allocation.

[0100] Step 302: Assign corresponding stochastic evolution weights to each flight target based on the magnitude ratio of the risk potential energy values ​​among the units where each flight target is located.

[0101] In step 302, the risk potential energy ratio refers to the relative relationship between the risk potential energy values ​​of adjacent grid cells. It is obtained by comparing the risk potential energy values ​​of each cell after normalization, reflecting the differences in the degree of danger in different spatial regions. This ratio originates from the grid cell risk potential energy values ​​calculated in step 102, obtained by dividing each cell's value by the sum of the values ​​of all adjacent cells. The stochastic evolution weight refers to the simulation priority coefficient assigned to the flight target based on the risk potential energy ratio. A higher weight indicates a greater range of allowed changes in the target's motion direction during the simulation, used to focus on exploring potential paths in high-conflict-risk areas. The weight value is proportional to the risk potential energy ratio of the grid cell. The stochastic evolution weight allocation assigns different simulation priorities to flight targets within a cell based on the relative magnitude of the risk potential energy values ​​of the grid cells; a higher weight value indicates a greater probability of variation in the target's motion path.

[0102] In this embodiment, the system compares the risk potential energy differences between adjacent grid cells and assigns higher weights to flight targets in high-risk cells, so that they can obtain a larger range of motion direction changes during the simulation, thereby more fully exploring potential conflict paths.

[0103] Step 303: In each simulation, the random offset direction of each flight target is determined based on the direction angle of each flight target and the corresponding random evolution weight.

[0104] In step 303, the random offset direction refers to a random angular deviation added to the original direction of motion of the flight target. The range of this deviation is determined by the random evolution weights and is used to simulate the heading uncertainty that may exist in actual flight. The offset direction is uniformly and randomly generated within the angle interval corresponding to the weights.

[0105] In this embodiment, the maximum allowable offset angle is determined based on the assigned weight value; the higher the weight, the larger the angle range. In each simulation, the system randomly selects an offset direction within this angle range as the flight direction adjustment value for this simulation.

[0106] Step 304: Based on the random offset direction, calculate the displacement according to a fixed time step, and update the position of each flying target based on the displacement.

[0107] In step 304, the displacement represents the distance the flying target moves along a random offset direction within a fixed time step, determined by the current velocity and the time step, and is used to update the target's predicted position. The calculation formula is: Displacement = Velocity × Time Step × Direction Unit Vector. The position update process refers to calculating the distance the flying target moves within the simulation period based on the new direction and the fixed time step, and updating its predicted position coordinates.

[0108] In this embodiment, the system uses the current velocity of the flying target and a randomly generated direction angle, combined with a fixed time step, to calculate the displacement using kinematic formulas, and updates the target position to the new coordinates to complete the single-step movement simulation.

[0109] Step 305: When at least two flying targets are determined to be in the same grid cell based on the updated position, mark the grid cell as having a positional overlap to complete a single full simulation process.

[0110] In step 305, position overlap refers to the state where, during the simulation, the predicted positions of two or more flying targets simultaneously fall into the same grid cell, used to identify potential conflict risks. Each overlap event is recorded and used for subsequent conflict probability statistics.

[0111] In this embodiment of the application, the system detects the new positions of all flying targets. If two or more targets are found in the same unit, the unit is marked as overlapping, and the numbers of the flying targets involved and the time of overlap are recorded.

[0112] Step 306: Execute the complete simulation process independently at least N times, where N is an integer greater than or equal to 1000.

[0113] In this embodiment of the application, the system automatically executes a complete simulation process that includes all the aforementioned sub-steps. Each simulation uses independent random parameters, and the system is executed at least 1,000 times to form a stable probability distribution.

[0114] Here is a specific example:

[0115] During simulation training, the system detected that flight targets D, E, and F simultaneously entered the airspace grid region numbered G12. Flight target D was located at the center of cell G12, with a real-time speed of 480 km / h and a heading angle of 25 degrees; flight target E approached from the adjacent cell G11 at a speed of 520 km / h and a heading angle of 40 degrees; flight target F moved from cell G13 at a speed of 500 km / h and a heading angle of 55 degrees. The system first calculated the risk potential energy value for each cell, with a value of 0.35 for cell G12, 0.28 for G11, and 0.31 for G13. Based on the risk potential energy value ratio, flight targets D were assigned a stochastic evolution weight of 0.38, E 0.32, and F 0.30. In the first simulation, the system, based on the weight value of 0.38 for flight target D, generated a random offset direction of 8 degrees to the left from its original direction of 25 degrees. Calculating the displacement at a fixed step size of 10 seconds, the system obtained a displacement of 1333 meters and updated its predicted position to the edge of cell G12. Flight target E obtained a direction of 6 degrees to the right, with a displacement of 1444 meters, and entered cell G12. Flight target F obtained a direction of 5 degrees to the left, with a displacement of 1389 meters, remaining in cell G13. At this point, flight targets D and E were detected to overlap within cell G12, and the system recorded this overlap event. In the second simulation, flight target D obtained a direction of 4 degrees to the right and remained in cell G12, while E obtained a direction of 9 degrees to the left and left cell G12, without any overlap. After repeating this process 1000 times independently, statistics showed that flight targets D and E overlapped 210 times and D and F overlapped 150 times within cell G12. The maximum value of 210 times was taken as the number of overlaps in that cell. Based on the formula that the probability of a collision is equal to the number of overlapping positions divided by the total number of simulations, where the number of overlapping positions is 210 and the total number of simulations is 1000, the probability of a collision for element G12 is calculated to be 0.21.

[0116] In this embodiment, by using weighted simulation and a large number of repeated random evolutions, a comprehensive detection of potential conflicts in complex airspace environments is achieved, providing a highly reliable probabilistic basis for avoidance decisions and effectively improving the realism and security of simulation training.

[0117] To address the accuracy control issue in dynamic path simulation of flight targets, in some embodiments, step 303: generating the random offset direction of each flight target based on its orientation angle and the corresponding random evolution weights, includes:

[0118] Step 401: For each flight target, perform the following process: multiply the preset basic angle deviation value with the corresponding random evolution weight to obtain the allowable offset angle value.

[0119] In step 401, the basic angle deviation value represents a pre-set standard angle variation range reference value, used to control the basic change range of the flight target's motion direction. The allowable offset angle value represents the actual allowable offset angle range after weight adjustment, reflecting the allowable directional change limit of the flight target in the simulation.

[0120] In this embodiment, the system multiplies the preset base angle deviation value with the stochastic evolution weight corresponding to the flight target to obtain the maximum angle offset value allowed for the target in the current simulation. The larger the weight, the larger the allowed offset angle.

[0121] Step 402: Construct an angle interval centered on the direction angle. The lower limit of the angle interval is the difference between the direction angle and the allowable offset angle value, and the upper limit of the angle interval is the sum of the direction angle and the allowable offset angle value.

[0122] In step 402, the angle interval represents the range of directional changes formed by extending the allowable offset angle values ​​to the left and right sides with the original direction angle of the flight target as the center.

[0123] In this embodiment of the application, the system uses the current direction angle of the flying target as a reference, subtracts the allowable offset angle value to the left to obtain the lower limit of the interval, and adds the allowable offset angle value to the right to obtain the upper limit of the interval, thus constructing a complete range of offset directions.

[0124] Step 403: Within the angle range, generate random angle values ​​and round the random angle values ​​according to a preset precision.

[0125] In step 403, the random angle value represents a specific direction angle randomly selected through uniform distribution within the angle range. Rounding means rounding the randomly generated angle value to the nearest whole number according to the minimum precision unit required by the system.

[0126] In this embodiment of the application, the system randomly selects an angle value within a defined angle range, and then rounds the value according to a preset accuracy requirement to ensure that the generated angle meets the system's processing accuracy.

[0127] Step 404: Determine the random offset direction based on the rounded random angle value.

[0128] In this embodiment of the application, the system uses the rounded random angle value as the actual offset direction of the flying target in this simulation for subsequent displacement calculation.

[0129] Here is a specific example:

[0130] During simulation training, the system generates the random offset direction for flight target D as follows: First, a base angle deviation value of 20 degrees is set. Based on the random evolution weight of 0.38 assigned to flight target D, the allowable offset angle value is calculated as 20 degrees multiplied by 0.38, which equals 7.6 degrees, rounded to 8 degrees. Centered on the current direction angle of flight target D (25 degrees), an angle range is constructed from 25 degrees minus 8 degrees to 25 degrees plus 8 degrees, i.e., 17 degrees to 33 degrees. Within this range, the system randomly generates an angle value of 29.7 degrees, rounded to 30 degrees with a precision of 1 degree. Based on the rounding result, the random offset direction of flight target D is determined to be 5 degrees to the right. Simultaneously, the random offset direction for flight target E is generated as follows: the base angle deviation value of 20 degrees is multiplied by a weight of 0.32 to obtain an allowable offset angle value of 6.4 degrees, rounded to 6 degrees. An angle range of 34 degrees to 46 degrees is constructed with a direction angle of 40 degrees. A random value of 42.5 degrees is generated and rounded to 43 degrees, ultimately determining an offset direction of 3 degrees to the right. The processing of the flying target F is similar. 20 degrees is multiplied by a weight of 0.3 to obtain an allowable offset angle value of 6 degrees. An angle range of 49 degrees to 61 degrees is constructed with a direction angle of 55 degrees. 57.2 degrees is randomly generated and rounded to 57 degrees to determine the offset direction of 2 degrees to the right.

[0131] In this embodiment of the application, by controlling the offset angle with weight adjustment and generating random directions, a scientific simulation of the flight target's motion path is achieved. This ensures both the full exploration of high-risk targets and the rationality of the simulation process, providing a reliable path evolution basis for conflict prediction.

[0132] To address the real-time and visualization issues in flight training effectiveness evaluation, in some embodiments, step 104: generating a conflict report in a real-time interactive display interface based on the avoidance instruction set, includes:

[0133] Step 501: Record the specific instruction content in each generated avoidance instruction set.

[0134] In step 501, the specific instruction content refers to the detailed avoidance operation requirements generated by the system based on the conflict prediction results. It includes specific adjustment types such as "ascend / descend altitude value", "accelerate / decelerate to target speed value" or "turn left / right to specified azimuth angle", as well as the corresponding adjustment range value and execution time requirements, which are used to guide the pilot to make precise adjustments to flight attitude and trajectory.

[0135] In this embodiment, the system fully records the details of each generated avoidance instruction, including the adjustment type, magnitude, and execution time limit, forming an instruction execution list to provide benchmark data for subsequent trajectory comparison.

[0136] Step 502: Monitor the actual flight trajectory after the pilot executes the specific instructions.

[0137] In step 502, the actual flight trajectory represents the actual movement path data of the target after the pilot executes the avoidance command, and consists of a sequence of position coordinates.

[0138] In this embodiment of the application, the system continuously collects the real-time location information of the flying target through the positioning device and records its movement trajectory at fixed time intervals to ensure that the data completely corresponds to the command execution period.

[0139] Step 503: Compare and analyze the actual flight trajectory with the expected trajectory of the avoidance command set.

[0140] In step 503, the expected trajectory represents the ideal flight path calculated based on the theoretical requirements of the avoidance command, and is used to compare it with the actual trajectory.

[0141] In this embodiment, the system calculates the theoretical flight path based on the adjustment requirements of the avoidance command and the initial state of the flight target using a kinematic model, which serves as a reference standard for evaluating the accuracy of the pilot's operation.

[0142] Step 504: Based on the comparative analysis results, output the conflict report in the form of visual charts in the real-time interactive display interface.

[0143] In step 504, the visualization charts represent a comprehensive report that presents the analysis results graphically, including trajectory comparison charts and key indicator displays.

[0144] In this embodiment, the system overlays the actual trajectory with the expected trajectory, uses different colors to mark the deviation area, and displays core indicators such as the number of conflicts and instruction execution delay time in the form of bar charts and line charts.

[0145] Here is a specific example:

[0146] During simulation training, after detecting a potential conflict risk between flight targets A and B, the system generated an avoidance instruction set requiring flight target A to ascend 80 meters within 5 seconds and flight target B to decelerate to 85% of its original speed within 3 seconds. The system fully recorded the content of these two specific instructions, including the type, magnitude, and time requirements of the adjustment. Subsequently, the pilot began to execute the instructions, and the system continuously collected the actual trajectory of flight target A, showing that it ascended 75 meters within 6 seconds and flight target B decelerated to 82% of its original speed within 4 seconds. The system compared and analyzed the actual trajectory with the expected trajectory. The altitude deviation of flight target A was calculated by subtracting the expected altitude of 80 meters from the actual altitude of 75 meters, resulting in a deviation value of 5 meters. The speed deviation of flight target B was calculated by subtracting the expected speed after deceleration from the actual speed after deceleration, resulting in a deviation value of 3%. Based on this comparative data, the system generates a visualization report on the interactive interface, displaying the expected and actual trajectories using a two-color curve overlay. The trajectory of flight target A is marked in red with a 5-meter height deviation area, while the trajectory of flight target B is marked in yellow with a 3% speed deviation area. The report also shows that during this training, the command response delay for flight target A was 1 second, and the delay for flight target B was 1.5 seconds. The overall avoidance success rate was assessed as good.

[0147] In this embodiment of the application, by recording and comparing the entire process of instruction execution, an objective quantitative evaluation of training effectiveness is achieved, which intuitively shows the difference between the pilot's operation and theoretical requirements, provides a clear basis for training improvement, and effectively improves training quality and efficiency.

[0148] To address the accuracy issue in the quantitative evaluation of avoidance effectiveness during flight training, in some embodiments, step 503, which involves comparing and analyzing the actual flight trajectory with the expected trajectory of the avoidance instruction set, includes:

[0149] Step 601: Pair the spatiotemporal coordinate sequence of the actual flight trajectory with the trajectory points with the same timestamp in the spatiotemporal coordinate sequence of the expected trajectory to generate a set of trajectory point pairs.

[0150] In step 601, the trajectory point pair set represents a pairing set formed by matching the actual flight trajectory and the expected trajectory one-to-one according to the position data at the same time point, which is used to accurately compare the differences in flight status at each moment.

[0151] In this embodiment of the application, the system uses a timestamp matching algorithm to align the actual collected trajectory points with the theoretically calculated expected trajectory points in chronological order, ensuring that each comparison point represents two states at the same moment.

[0152] Step 602: For each trajectory point pair in the set of trajectory point pairs, calculate the planar distance of the horizontal position value and the vertical distance of the height value.

[0153] In step 602, the planar distance represents the horizontal positional deviation of the trajectory point, reflecting the degree of deviation of the flight target in longitude and latitude. The vertical distance represents the vertical positional deviation of the trajectory point, reflecting the degree of deviation of the flight target in altitude.

[0154] In this embodiment of the application, the system calculates the difference between the straight-line distance on the horizontal plane and the height axis for each pair of trajectory points, thus comprehensively quantifying the positional deviation.

[0155] Step 603: When the planar distance or the vertical distance exceeds a preset error threshold, mark the trajectory point pair as an anomaly.

[0156] In step 603, anomalies refer to trajectory points in the actual flight path that exceed the allowable error range, reflecting the specific moments when the avoidance operation did not meet expectations.

[0157] In this embodiment of the application, the system compares the calculated planar distance and vertical distance with a preset safety threshold. If either dimension exceeds the threshold, the point in time is marked as abnormal, and the abnormality type and deviation amount are recorded.

[0158] Step 604: Calculate the avoidance success rate based on the ratio of the number of abnormal points to the total number of trajectory point pairs.

[0159] In step 604, the avoidance success rate represents the proportion of trajectory points that successfully meet the expected requirements out of the total trajectory points, reflecting the overall avoidance effect.

[0160] In this embodiment of the application, the system counts the number of unmarked points among all trajectory points, divides it by the total number of trajectory points, and obtains the avoidance success rate index.

[0161] Step 605: Detect the difference between the timestamp of the first trajectory point pair marked as an anomaly and the timestamp of the avoidance command generation, and use it as the response delay time.

[0162] In step 605, the response delay time represents the time difference between the issuance of the command and the start of effective execution by the pilot, reflecting the speed of operational response.

[0163] In this embodiment of the application, the system identifies the timestamp of the first abnormal point, subtracts the timestamp of the instruction generation, and obtains the delay time from when the pilot receives the instruction to when he begins to execute it.

[0164] Step 606: Combine the avoidance success rate, the response delay time, and the maximum position deviation value to obtain a comparative analysis result.

[0165] In step 606, the maximum position deviation value refers to the maximum horizontal distance or maximum altitude difference between the actual flight trajectory and the expected trajectory among all trajectory point pairs. Specifically, it is obtained as follows: During the comparative analysis, the system records the calculated planar distance and vertical distance values ​​for each trajectory point pair. Then, it filters out the maximum value of the planar distance and the maximum value of the vertical distance from all trajectory point pairs, and takes the larger of these two maximum values ​​as the final maximum position deviation value. For example, when the maximum planar distance is 210 meters and the maximum vertical distance is 4 meters, the maximum position deviation value is 210 meters.

[0166] In this embodiment, the system iterates through the deviation data of all abnormal points, extracts the maximum values ​​in the plane and vertical directions, and uses them together with the avoidance success rate and response delay time to form the final evaluation result.

[0167] Here is a specific example:

[0168] During simulation training, the system performs a full-process evaluation of avoidance maneuvers for flight targets A and B. First, a set of trajectory point pairs is established, pairing the actual position data of flight target A every 0.2 seconds with the corresponding time point position of the expected trajectory, forming a total of 30 trajectory point pairs. For each trajectory point pair, the horizontal distance and altitude difference are calculated. The horizontal distance is calculated using the difference in latitude and longitude coordinates between the two points, while the altitude difference is directly subtracted. A horizontal error threshold of 10 meters and an altitude error threshold of 5 meters are set. Analysis reveals that an altitude deviation of 6 meters, exceeding the threshold, begins at the 6th pair of points, marking it as the first anomaly. Its timestamp differs from the command generation timestamp by 1.2 seconds, identifying it as a response delay. Further analysis of the remaining trajectory points reveals 5 altitude anomalies and 2 horizontal anomalies. The avoidance success rate is calculated by dividing the number of unmarked anomaly points (23) by the total number of trajectory points (30), yielding an avoidance success rate of approximately 77%. The maximum positional deviation occurs at the 15th pair of points, with an altitude difference reaching 8 meters. The analysis process for flight target B is similar, forming 25 pairs of trajectory points. When calculating the speed deviation, the difference between the actual speed and the expected speed is divided by the expected speed. A total of 3 speed anomaly points are marked, with a response delay of 1.5 seconds and an avoidance success rate of 88%.

[0169] In this embodiment, a comprehensive and objective evaluation of the avoidance operation effect is achieved through refined trajectory comparison with spatiotemporal alignment and multi-dimensional deviation analysis, providing accurate data support for pilot training improvement and effectively enhancing training quality and flight safety.

[0170] To address the accuracy issue in dynamic assessment of airspace conflict risks, in some embodiments, step 102: discretizing the three-dimensional airspace into dynamically updated grid cells based on the position coordinates, and calculating the risk potential energy value of each grid cell according to the motion velocity and the direction angle, includes:

[0171] Step 701: Divide the three-dimensional airspace into grid cells of fixed size, and determine the grid cell where each flying target is located based on the position coordinates.

[0172] In step 701, the fixed size is represented by the grid cell, which divides the three-dimensional space into multiple cubic blocks of the same size, and each block is defined by a three-dimensional coordinate range.

[0173] In this embodiment of the application, the system uniformly divides the entire airspace according to a preset division rule, and quickly locates the specific grid cell in which the flight target is located based on its real-time position coordinates, providing a spatial reference for risk analysis.

[0174] Step 702: Based on the motion speed and the direction angle, calculate the migration probability of each flying target moving to an adjacent grid cell within a set time step.

[0175] In step 702, the migration probability represents the likelihood that the flying target will move to an adjacent grid cell in the next time step, and is determined by the velocity and orientation angle.

[0176] In this embodiment of the application, the system calculates the probability distribution of the target’s movement to adjacent units in front, behind, left, right, up, and down within a set time based on the target’s current motion state, combined with its motion direction and speed, thus reflecting the target’s motion trend.

[0177] Step 703: Calculate the risk potential energy value of each grid cell by combining the number of flying targets in each grid cell, the migration probability, and the relative motion speed difference between flying targets.

[0178] In step 703, the relative velocity difference represents the degree of difference in velocity between different flying targets within the same grid cell, reflecting the potential collision risk.

[0179] In this embodiment of the application, the system counts the number of flying targets in each grid cell, combines the migration probability of each target, calculates the speed difference between targets, and obtains the real-time risk potential value of the cell by weighted fusion of these factors.

[0180] Here is a specific example:

[0181] During simulation training, the system calculates the risk potential energy value for grid cell G5. First, it is determined that there are two flying targets, A and B, within this cell. Target A has a velocity of 500 units and an orientation angle of 30 degrees. Based on its motion state, the system calculates that within the next time step of 10 seconds, the probability of it migrating to the adjacent cell G6 to the right is 0.65, and the probability of it migrating to the cell G8 directly in front is 0.25. Target B has a velocity of 600 units and an orientation angle of 210 degrees. The calculated probability of it migrating to the cell G4 to the left is 0.7, and the probability of it migrating to the cell G2 to the rear is 0.2. The system calculates the relative velocity difference between the two targets to be 100 units. According to the risk potential value calculation formula, the weighting coefficients for the number of targets within the unit are 0.4, the migration probability difference is 0.3, and the velocity difference is 0.3. Specifically, the calculation process is as follows: multiply the number of targets (2) by 0.4 to get 0.8; multiply the migration probability difference (0.05) by 0.3 to get 0.015; and divide the velocity difference (100) by 1000 and multiply by 0.3 to get 0.03. Adding these three together yields a risk potential value of 0.845 for unit G5. Simultaneously, the system calculates the risk potential value for adjacent units G6 to be 0.52 and for G8 to be 0.31.

[0182] In this embodiment, grid-based spatial management and multi-factor fusion calculation are used to achieve a refined and dynamic assessment of airspace risks, providing a reliable basis for conflict early warning and improving the accuracy and timeliness of flight safety monitoring.

[0183] Figure 2 A schematic diagram of a Total Motion Simulator (TCAS) simulation system based on dynamic decision-making and real-time interaction, provided as an embodiment of this application, is shown below. Figure 2 As shown, the system includes:

[0184] The acquisition module 21 is used to acquire the position coordinates, motion speed and direction angle of multiple flying targets in the three-dimensional airspace.

[0185] Discretization module 22 is used to discretize the three-dimensional spatial domain into dynamically updated grid cells based on the position coordinates, and calculate the risk potential energy value of each grid cell according to the motion speed and the direction angle.

[0186] The prediction module 23 is used to dynamically predict the probability value of conflict occurrence within a set time period in the future based on the risk potential energy value during the simulation process using the Monte Carlo algorithm, and generate an avoidance instruction set based on the probability value of conflict occurrence.

[0187] The generation module 24 is used to generate a conflict report in the real-time interactive display interface after the simulation is completed, based on the avoidance instruction set. The conflict report includes the number of conflicts, the avoidance success rate, and the pilot response time.

[0188] Figure 2 The TCAS simulation system, a fully dynamic simulator based on dynamic decision-making and real-time interaction, can execute... Figure 1 The implementation principle and technical effects of the TCAS simulation method for a fully motion simulator based on dynamic decision-making and real-time interaction described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit performs operations in the TCAS simulation system for a fully motion simulator based on dynamic decision-making and real-time interaction described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0189] In one possible design, Figure 2 The TCAS simulation system, a fully dynamic simulator based on dynamic decision-making and real-time interaction, as shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0190] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0191] The processing component 32 is used to perform the above. Figure 1 The embodiment describes a TCAS simulation method for a fully dynamic simulator based on dynamic decision-making and real-time interaction.

[0192] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0193] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0194] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0195] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0196] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0197] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0198] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a TCAS simulation method for a fully dynamic simulator based on dynamic decision-making and real-time interaction.

[0199] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0200] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0201] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A Total Motion Simulation Method (TCAS) based on dynamic decision-making and real-time interaction for a fully motion simulator, characterized in that, include: Collect the position coordinates, velocity, and orientation angle of multiple flying targets in a three-dimensional airspace; Based on the position coordinates, the three-dimensional spatial domain is discretized into dynamically updated grid cells, and the risk potential energy value of each grid cell is calculated according to the movement speed and the direction angle. During the simulation, based on the risk potential energy value, the probability value of conflict occurrence within a set time period is dynamically predicted using the Monte Carlo algorithm, and an avoidance instruction set is generated based on the probability value of conflict occurrence. After the simulation ends, a conflict report is generated in the real-time interactive display interface according to the avoidance instruction set. The conflict report includes the number of conflicts, the avoidance success rate, and the pilot response time. During the simulation, based on the risk potential energy value, the probability of conflict occurrence within a set future time period is dynamically predicted using a Monte Carlo algorithm. Based on the conflict occurrence probability value, an avoidance instruction set is generated, including: The risk potential value of each grid cell was simulated multiple times using the Monte Carlo algorithm; The number of times each grid cell overlaps in all stochastic evolution simulations is counted. Based on the ratio of the number of overlaps to the total number of stochastic evolution simulations, the probability of conflict occurring in each grid cell within a set future time period is calculated. When the probability of a collision in any grid cell exceeds a preset warning threshold, a set of avoidance instructions is generated. The process of discretizing the three-dimensional spatial domain into dynamically updated grid cells based on the position coordinates, and calculating the risk potential energy value of each grid cell according to the motion velocity and the direction angle, includes: The three-dimensional airspace is divided into grid cells of fixed size, and the grid cell where each flying target is located is determined according to the position coordinates. Based on the motion speed and the direction angle, calculate the migration probability of each flight target moving to an adjacent grid cell within a set time step; The risk potential energy value of each grid cell is calculated by combining the number of flying targets in each grid cell, the migration probability, and the relative speed difference between flying targets.

2. The TCAS simulation method for a fully motion simulator based on dynamic decision-making and real-time interaction as described in claim 1, characterized in that, The process of performing multiple stochastic evolution simulations of the risk potential value of each grid cell using the Monte Carlo algorithm includes: Obtain the real-time position value of each flight target at the current moment, and determine the unit where each flight target is located based on the real-time position value of each flight target; Based on the magnitude ratio of the risk potential energy values ​​among the units where each flight target is located, a corresponding stochastic evolution weight is assigned to each flight target. In each simulation, the random offset direction of each flight target is determined based on the direction angle of each flight target and the corresponding random evolution weight; Based on the random offset direction, the displacement is calculated according to a fixed time step, and the position of each flying target is updated based on the displacement. When at least two flying targets are determined to be in the same grid cell based on the updated positions, the grid cells are marked as having overlapping positions to complete a single full simulation. Execute the complete simulation process independently at least N times, where N is an integer greater than or equal to 1000.

3. The TCAS simulation method for a fully motion simulator based on dynamic decision-making and real-time interaction as described in claim 2, characterized in that, The generation of random offset directions for each flight target based on its heading angle and corresponding random evolution weights includes: For each flight target, the following procedure is performed: multiply the preset basic angle deviation value by the corresponding stochastic evolution weight to obtain the allowable offset angle value; An angle interval is constructed with the direction angle as the center. The lower limit of the angle interval is the difference between the direction angle and the allowable offset angle value, and the upper limit of the angle interval is the sum of the direction angle and the allowable offset angle value. Within the angle range, a random angle value is generated, and the random angle value is rounded according to a preset precision. The random offset direction is determined based on the rounded random angle value.

4. The TCAS simulation method for a fully motion simulator based on dynamic decision-making and real-time interaction as described in claim 1, characterized in that, The step of generating a conflict report in the real-time interactive display interface based on the avoidance instruction set includes: Record the specific instruction content of each generated avoidance instruction set; Monitor the actual flight trajectory after the pilot executes the specific instructions; The actual flight trajectory is compared and analyzed with the expected trajectory of the avoidance command set; Based on the comparative analysis results, the conflict report is output in the form of visual charts in the real-time interactive display interface.

5. The TCAS simulation method for a fully motion simulator based on dynamic decision-making and real-time interaction according to claim 4, characterized in that, The step of comparing and analyzing the actual flight trajectory with the expected trajectory of the avoidance command set includes: The spatiotemporal coordinate sequence of the actual flight trajectory is paired with the trajectory points with the same timestamp in the spatiotemporal coordinate sequence of the expected trajectory to generate a set of trajectory point pairs. For each pair of trajectory points in the set of trajectory point pairs, calculate the planar distance of the horizontal position value and the vertical distance of the height value; When the planar distance or the vertical distance exceeds a preset error threshold, the trajectory point pair is marked as an anomaly. The avoidance success rate is calculated based on the ratio of the number of abnormal points to the total number of trajectory point pairs. The difference between the timestamp of the first trajectory point pair marked as an anomaly and the timestamp of the avoidance command generation is used as the response delay time. The avoidance success rate, response delay time, and maximum position deviation value are combined to obtain comparative analysis results.

6. A Total Motion Simulation System (TCAS) based on dynamic decision-making and real-time interaction, characterized in that, include: The acquisition module is used to acquire the position coordinates, velocity, and orientation angle of multiple flying targets in the three-dimensional airspace; The discrete module is used to discretize the three-dimensional spatial domain into dynamically updated grid cells based on the position coordinates, and calculate the risk potential energy value of each grid cell according to the motion speed and the direction angle. The prediction module is used to dynamically predict the probability of conflict occurrence within a set time period in the future based on the risk potential energy value during the simulation process using the Monte Carlo algorithm, and generate an avoidance instruction set based on the probability of conflict occurrence. The generation module is used to generate a conflict report in the real-time interactive display interface after the simulation is completed, based on the avoidance instruction set. The conflict report includes the number of conflicts, the avoidance success rate, and the pilot response time. During the simulation, based on the risk potential energy value, the probability of conflict occurrence within a set future time period is dynamically predicted using a Monte Carlo algorithm. Based on the conflict occurrence probability value, an avoidance instruction set is generated, including: The risk potential value of each grid cell was simulated multiple times using the Monte Carlo algorithm; The number of times each grid cell overlaps in all stochastic evolution simulations is counted. Based on the ratio of the number of overlaps to the total number of stochastic evolution simulations, the probability of conflict occurring in each grid cell within a set future time period is calculated. When the probability of a collision in any grid cell exceeds a preset warning threshold, a set of avoidance instructions is generated. The process of discretizing the three-dimensional spatial domain into dynamically updated grid cells based on the position coordinates, and calculating the risk potential energy value of each grid cell according to the motion velocity and the direction angle, includes: The three-dimensional airspace is divided into grid cells of fixed size, and the grid cell where each flying target is located is determined according to the position coordinates. Based on the motion speed and the direction angle, calculate the migration probability of each flight target moving to an adjacent grid cell within a set time step; The risk potential energy value of each grid cell is calculated by combining the number of flying targets in each grid cell, the migration probability, and the relative speed difference between flying targets.

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