Aircraft adaptive separation control method and system

Abstract

The present invention discloses an aircraft adaptive interval control method and system, which relates to the field of air traffic management technology. An aircraft adaptive interval control system includes: a flight conflict basic unit module, a flight conflict scenario generation module, a flight conflict resolution module and a flight conflict learning module. The present invention constructs an aircraft flight conflict scenario training set by constructing an aircraft basic flight unit and generating a flight trajectory vector set, combined with a flight conflict generation model; using the training set, an aircraft autonomous interval control model is trained through an improved Markov decision model, thereby achieving effective strategy output when a flight conflict occurs; by analyzing the test results and updating the model, the adaptability and accuracy of the aircraft autonomous interval control model are improved. This process not only improves the flight safety of aircraft in complex environments, but also enhances the model's ability to predict and handle flight conflicts.

Description

Technical Field

[0001] The present invention relates to the technical field of air traffic management, and in particular to a method and system for adaptive aircraft separation control. Background Art

[0002] The adaptive aircraft spacing control method is of great significance in modern air traffic management. It not only improves flight safety, but also improves the efficiency of airspace utilization. It can adjust the spacing between aircraft in real time to ensure that no collision occurs at any stage of flight. The main difficulties faced by the adaptive aircraft spacing control method include reaction time and decision-making requirements for route changes. First, the reaction time requirement is extremely high. Aircraft must respond to emergencies within milliseconds to ensure safe spacing. Second, route change decisions need to comprehensively consider many factors, such as airspace restrictions, the dynamics of other aircraft, flight efficiency, etc. The decision-making process is complex and needs to be completed quickly. In addition, aircraft must also have a high degree of reliability and fault tolerance to cope with various uncertainties and abnormal situations to ensure flight safety and efficient use of airspace. Summary of the Invention

[0003] The present invention aims to provide an aircraft adaptive separation control method and system.

[0004] An aircraft adaptive separation control method comprises the following steps:

[0005] Step S1: Construct the basic flight unit F of the aircraft n , F n =(A n , B n , C n , D n ); where n represents the aircraft certification number, belonging to aircraft H n ; A n Indicates the aircraft flight direction angle, B n Indicates the aircraft flight speed, C n Indicates the minimum unit time of aircraft flight, D n Indicates the flight time of an aircraft unit; using the basic flight unit F of the aircraft n For aircraft H n Randomly generate aircraft flight trajectory vectors and obtain the aircraft flight trajectory vector set X n ;

[0006] Step S2: Generate a model based on the flight conflict and the flight trajectory vector set X of all aircraft nGenerate aircraft flight conflict scenarios; combine the generated aircraft flight conflict scenarios to obtain an aircraft flight conflict scenario training set; the flight conflict generation model includes a path generation layer, a path selection layer, a path optimization layer, and a result output layer, which is constructed based on the CNN model and is used to construct and screen aircraft flight conflict scenarios;

[0007] Step S3: Using the aircraft flight conflict scenario training set, the aircraft autonomous separation control model is trained. The aircraft autonomous separation control model includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer. It is constructed based on the improved Markov decision model and is used to output strategies when aircraft flight conflicts occur.

[0008] Step S4: For aircraft H n A simulation test is performed on the aircraft autonomous separation control model to obtain an aircraft flight conflict simulation test result; the aircraft autonomous separation control model is updated according to the aircraft flight conflict simulation test result to obtain a target aircraft autonomous separation control model.

[0009] As a preferred technical solution of the present invention, the flight conflict generation model in step S2 includes a path generation layer, a path selection layer, a path optimization layer and a result output layer;

[0010] The path generation layer is used to generate the path from the aircraft H n In the random algorithm, I aircraft H is selected n Perform path selection to obtain an aircraft simulation generation group;

[0011] The path selection layer is used to select the aircraft H n Vector set of aircraft flight tracks X n A preset number of aircraft flight trajectory vectors are randomly selected and spliced to obtain the aircraft simulation flight trajectory G n ;

[0012] The path optimization layer is used to generate conflict scenarios based on the aircraft simulation generation group to obtain aircraft flight conflict scenarios;

[0013] The result output layer is used to traverse several aircraft simulation generation groups to obtain the aircraft flight conflict scenario training set.

[0014] As a preferred technical solution of the present invention, the specific steps of generating a conflict scenario in the path optimization layer include:

[0015] Step E1: Generate the corresponding aircraft simulation flight trajectory G according to the aircraft simulation generation group n , get the aircraft simulation flight trajectory set;

[0016] Step E2: generating a conflict scenario for the aircraft simulated flight trajectory set to obtain the aircraft flight conflict scenario to be optimized;

[0017] Step E3: Feature screening is performed on the aircraft flight conflict scenario to be optimized to obtain the aircraft flight conflict scenario;

[0018] The specific steps for training the path optimization layer include:

[0019] Collect several groups of standard aircraft flight conflict scenario training samples; combine several groups of standard aircraft flight conflict scenario training samples to obtain an aircraft flight conflict scenario training set; use the CNN model to construct an initial path optimization layer; use the aircraft flight conflict scenario training set to train the initial path optimization layer to obtain a path optimization layer to be evaluated; perform model evaluation on the path optimization layer to be evaluated to obtain a model evaluation result of the path optimization layer to be evaluated; if the model evaluation result of the path optimization layer to be evaluated is passed, the path optimization layer to be evaluated is used as the path optimization layer; otherwise, continue model training using the aircraft flight conflict scenario training set.

[0020] As a preferred technical solution of the present invention, the aircraft autonomous separation control model in step S3 includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer;

[0021] The data extraction layer is used to obtain the flight data of the aircraft to which the current aircraft belongs and obtain the flight data of the aircraft to be analyzed;

[0022] The aircraft separation perception layer is used to obtain the aircraft flight data of the disturbing aircraft within the preset separation perception range, and obtain the flight data of the disturbing aircraft to be analyzed;

[0023] The initial separation control strategy calculation layer is used to perform strategy analysis based on the flight data of the aircraft to be analyzed and the flight data of the disturbing aircraft to be analyzed, and obtain the set of aircraft control strategies to be evaluated. The initial separation control strategy calculation layer is trained and constructed using the improved Markov decision model and the aircraft flight conflict scenario training set.

[0024] The feedback monitoring layer is used to make predictions based on the aircraft control strategy set to be evaluated and obtain aircraft separation control feedback results; based on the aircraft separation control feedback results, the aircraft control strategy set to be evaluated is judged and the optimal aircraft adaptive control strategy in the aircraft control strategy set to be evaluated is obtained;

[0025] The control strategy output layer is used to output the aircraft adaptive control strategy.

[0026] As a preferred technical solution of the present invention, the specific steps of training the initial interval control strategy calculation layer include:

[0027] Build digital twin models of training aircraft and aircraft conflicts; deploy the aircraft flight conflict scenario training set in the aircraft conflict digital twin model; input the training aircraft into the aircraft conflict digital twin model to simulate aircraft flight conflicts;

[0028] The Markov decision model is used to generate K aircraft separation control strategy individuals L for simulating aircraft flight conflicts. k , k=1, 2, ..., K; K aircraft separation control strategy individuals L k Combine and obtain the iterative population of aircraft separation control strategy; when performing population iteration, set the maximum number of iterations;

[0029] When performing population iteration, calculate the aircraft interval control strategy individual L in the aircraft interval control strategy iteration population k Interval control time T k , interval control time T k Obtained by weighted addition of dodge time and return time; the interval control time T k The reciprocal of the aircraft separation control strategy L k The fitness Y k ;

[0030] At each population iteration, all fitness Y is retained k The individual L of the aircraft separation control strategy with a fitness greater than the preset optimal fitness k , and the remaining aircraft interval control strategy individuals L in the aircraft interval control strategy iteration population k The Markov decision model is used to update and obtain the new aircraft separation control strategy individual L k ; All aircraft separation control strategy individuals L that are greater than the preset optimal fitness k and all new aircraft separation control strategy individuals L k Combining, obtaining updated aircraft separation control strategy iteration population, realizing the update of aircraft separation control strategy iteration population;

[0031] When the maximum number of iterations is reached, all aircraft separation control strategy individuals L whose fitness is greater than the preset optimal fitness are output. k , as a set of candidate aircraft control strategies for training aircraft;

[0032] Collect candidate aircraft control strategy sets for several groups of training aircraft; combine the candidate aircraft control strategy sets for several groups of training aircraft to obtain an aircraft control strategy training set; use the aircraft control strategy training set to perform model training on the initial interval control strategy calculation layer to obtain the initial interval control strategy calculation layer to be evaluated; perform model evaluation on the initial interval control strategy calculation layer to be evaluated to obtain a model evaluation result of the initial interval control strategy calculation layer to be evaluated; if the model evaluation result of the initial interval control strategy calculation layer to be evaluated is passed, the trained initial interval control strategy calculation layer to be evaluated is used as the initial interval control strategy calculation layer; otherwise, continue model training using the aircraft control strategy training set.

[0033] As a preferred technical solution of the present invention, the specific steps of training the feedback monitoring layer include:

[0034] Collecting several sets of aircraft separation control strategy prediction samples with target values, where the target values are quantitative evaluation results of the aircraft separation control strategies; combining the several sets of aircraft separation control strategy prediction samples with target values to obtain an aircraft control strategy feedback training set;

[0035] The feedback monitoring layer is trained using the aircraft control strategy feedback training set to obtain the initial feedback monitoring layer; the model of the initial feedback monitoring layer is evaluated to obtain the model evaluation result of the initial feedback monitoring layer; if the model evaluation result of the initial feedback monitoring layer is passed, the initial feedback monitoring layer is used as the feedback monitoring layer; otherwise, the model training is continued using the aircraft control strategy feedback training set.

[0036] As a preferred technical solution of the present invention, the specific steps of step S4 include:

[0037] Step S41: Collect aircraft H n Using the aircraft autonomous separation control model to simulate the control simulation data of aircraft adaptive separation control P n And record the corresponding aircraft flight conflict simulation test results;

[0038] Step S42: Using the control simulation data P n Build a target aircraft flight conflict training set based on the corresponding aircraft flight conflict simulation test results;

[0039] Step S43: Parameters of the aircraft autonomous separation control model are updated using the target aircraft flight conflict training set to obtain the target aircraft autonomous separation control model.

[0040] An aircraft adaptive separation control system, comprising:

[0041] The flight conflict basic unit module includes a building unit for building the basic flight unit F of the aircraft n , F n =(A n , B n , C n , D n ); where n represents the aircraft certification number, belonging to aircraft H n ; A n Indicates the aircraft flight direction angle, B n Indicates the aircraft flight speed, C n Indicates the minimum unit time of aircraft flight, D n Indicates the flight time of an aircraft unit; using the basic flight unit F of the aircraft n For aircraft H n Randomly generate aircraft flight trajectory vectors and obtain the aircraft flight trajectory vector set X n ;

[0042] The flight conflict scenario generation module includes a generation unit for generating a flight conflict model and a flight trajectory vector set X of all aircraft based on the flight conflict generation model. n Generate aircraft flight conflict scenarios; combine the generated aircraft flight conflict scenarios to obtain an aircraft flight conflict scenario training set; the flight conflict generation model includes a path generation layer, a path selection layer, a path optimization layer, and a result output layer, which is constructed based on the CNN model and is used to construct and screen aircraft flight conflict scenarios;

[0043] The flight conflict resolution module includes a training unit for training an autonomous aircraft separation control model using a training set of aircraft flight conflict scenarios. The autonomous aircraft separation control model includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer. It is constructed based on an improved Markov decision model and is used to output strategies when aircraft flight conflicts occur.

[0044] Flight conflict learning module, including learning units for aircraft H n A simulation test is performed on the aircraft autonomous separation control model to obtain an aircraft flight conflict simulation test result; the aircraft autonomous separation control model is updated according to the aircraft flight conflict simulation test result to obtain a target aircraft autonomous separation control model.

[0045] The present invention has the following advantages:

[0046] 1. The present invention constructs a training set of aircraft flight conflict scenarios by building basic aircraft flight units and generating a set of flight trajectory vectors. This training set is then used to train an autonomous aircraft separation control model using an improved Markov decision model, enabling effective strategy output when flight conflicts occur. Simulation tests further validate the model's effectiveness, and the model is updated through analysis of test results, improving the adaptability and accuracy of the autonomous aircraft separation control model. This process not only enhances aircraft flight safety in complex environments but also strengthens the model's ability to predict and handle flight conflicts, thereby providing a more scientific and efficient solution for aircraft separation management.

[0047] 2. The present invention realizes effective management of aircraft flight conflicts by constructing an autonomous aircraft separation control model, including a data extraction layer, an separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer. By utilizing an improved Markov decision model and a flight conflict scenario training set, the separation control strategy can be generated and iteratively optimized. By simulating conflicts and evaluating fitness, the optimal aircraft adaptive control strategy is ultimately output. This process not only improves the autonomous separation control capability of aircraft in complex flight environments, but also enhances the adaptability and accuracy of the model, thereby improving flight safety. In addition, through the digital twin model and iterative population method, the model can continuously learn and optimize, providing a more scientific and efficient solution for aircraft separation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of the structure of an aircraft adaptive separation control system adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0050] Example 1, an aircraft adaptive separation control method, comprising the following steps:

[0051] Step S1: Construct the basic flight unit F of the aircraft n , F n =(A n , B n , C n , D n ); where n represents the aircraft certification number, belonging to aircraft H n ; A n Indicates the aircraft flight direction angle, B n Indicates the aircraft flight speed, Cn Indicates the minimum unit time of aircraft flight, D n Indicates the flight time of an aircraft unit; using the basic flight unit F of the aircraft n For aircraft H n Randomly generate aircraft flight trajectory vectors and obtain the aircraft flight trajectory vector set X n ;

[0052] By defining clear flight units such as direction angle, speed, minimum unit time and unit flight duration, the flight path of an aircraft can be calculated and predicted more accurately, which is of great significance for optimizing flight plans, reducing flight time and fuel consumption. The flight trajectory vector generated based on detailed flight unit information can help the air traffic management system more effectively monitor and manage aircraft flows in the airspace, avoid flight conflicts and ensure flight safety. It also provides a basis for the automatic generation of aircraft flight trajectories and helps realize intelligent flight control, such as automatic obstacle avoidance and intelligent scheduling, thereby improving the operating efficiency and service quality of the entire aviation system. At the same time, various simulated flight trajectories can be flexibly generated to provide theoretical support for subsequent steps such as model training.

[0053] Step S2: Generate a model based on the flight conflict and the flight trajectory vector set X of all aircraft n Generate aircraft flight conflict scenarios; combine the generated aircraft flight conflict scenarios to obtain an aircraft flight conflict scenario training set; the flight conflict generation model includes a path generation layer, a path selection layer, a path optimization layer, and a result output layer, which is constructed based on the CNN model and is used to construct and screen aircraft flight conflict scenarios;

[0054] The flight conflict generation model in step S2 includes a path generation layer, a path selection layer, a path optimization layer and a result output layer;

[0055] The path generation layer is used to generate the path from the aircraft H n In the random algorithm, I aircraft H is selected n Perform path selection to obtain an aircraft simulation generation group;

[0056] The path selection layer is used to target aircraft H n Vector set of aircraft flight tracks X n A preset number of aircraft flight trajectory vectors are randomly selected and spliced to obtain the aircraft simulation flight trajectory G n ;

[0057] The path optimization layer is used to generate conflict scenarios based on the aircraft simulation generation group to obtain aircraft flight conflict scenarios;

[0058] The result output layer is used to traverse several aircraft simulation generation groups to obtain the aircraft flight conflict scenario training set;

[0059] The specific steps for generating conflict scenarios in the path optimization layer include:

[0060] Step E1: Generate the corresponding aircraft simulation flight trajectory G according to the aircraft simulation generation group n , obtain the aircraft simulation flight trajectory set;

[0061] Step E2: generating a conflict scenario for the aircraft simulated flight trajectory set to obtain the aircraft flight conflict scenario to be optimized;

[0062] Step E3: Feature screening is performed on the aircraft flight conflict scenario to be optimized to obtain the aircraft flight conflict scenario;

[0063] The specific steps for training the path optimization layer include:

[0064] Collect several sets of standard aircraft flight conflict scenario training samples; combine several sets of standard aircraft flight conflict scenario training samples to obtain an aircraft flight conflict scenario training set; use the CNN model to construct an initial path optimization layer; use the aircraft flight conflict scenario training set to train the initial path optimization layer to obtain a path optimization layer to be evaluated; perform model evaluation on the path optimization layer to be evaluated to obtain a model evaluation result of the path optimization layer to be evaluated; if the model evaluation result of the path optimization layer to be evaluated is passed, then use the path optimization layer to be evaluated as the path optimization layer; otherwise, continue model training using the aircraft flight conflict scenario training set;

[0065] By generating and optimizing simulated aircraft flight trajectories, potential flight conflict points can be discovered in advance, allowing measures to be taken to avoid collision risks during actual flight, greatly improving flight safety. The path selection layer and the path optimization layer work together to comprehensively consider multiple objectives during the path optimization process, screening out conflict paths that meet the conflict criteria and reducing meaningless simulated flight path conflicts. Using a CNN model to train the path optimization layer can improve the accuracy and robustness of the model through continuous learning and iteration, better coping with complex flight environments.

[0066] Step S3: Using the aircraft flight conflict scenario training set, the aircraft autonomous separation control model is trained. The aircraft autonomous separation control model includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer. It is constructed based on the improved Markov decision model and is used to output strategies when aircraft flight conflicts occur.

[0067] In step S3, the aircraft autonomous separation control model includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer;

[0068] The data extraction layer is used to obtain the flight data of the aircraft to which the current aircraft belongs and obtain the flight data of the aircraft to be analyzed;

[0069] The aircraft separation perception layer is used to obtain the aircraft flight data of the disturbing aircraft within the preset separation perception range, and obtain the flight data of the disturbing aircraft to be analyzed;

[0070] The initial interval control strategy calculation layer is used to perform strategy analysis based on the flight data of the aircraft to be analyzed and the flight data of the disturbing aircraft to be analyzed, and obtain the set of aircraft control strategies to be evaluated. The initial interval control strategy calculation layer is trained and constructed using the improved Markov decision model and the aircraft flight conflict scenario training set. The real-time digital twin model is constructed using the flight data of the aircraft to be analyzed and the flight data of the disturbing aircraft to be analyzed for strategy generation.

[0071] The feedback monitoring layer is used to make predictions based on the aircraft control strategy set to be evaluated and obtain aircraft separation control feedback results; based on the aircraft separation control feedback results, the aircraft control strategy set to be evaluated is judged and the optimal aircraft adaptive control strategy in the aircraft control strategy set to be evaluated is obtained;

[0072] The control strategy output layer is used to output the aircraft adaptive control strategy;

[0073] The aircraft separation perception layer can obtain flight data of surrounding disturbing aircraft in real time, promptly detect potential flight conflicts, and improve flight safety. Through the feedback monitoring layer and the control strategy output layer, the model can adaptively adjust the separation control strategy according to the current flight conditions to avoid flight conflicts and ensure flight safety. The initial separation control strategy calculation layer is based on an improved Markov decision model and can comprehensively consider multiple objectives to generate the optimal separation control strategy. The feedback monitoring layer can dynamically adjust the control strategy according to the actual flight situation to ensure that the flight path and interval are always in the optimal state. The data extraction layer and the aircraft separation perception layer can collect a large amount of flight data. By analyzing this data, flight patterns and regularities can be discovered, providing a scientific basis for flight planning and air traffic management.

[0074] The specific steps for training the initial interval control strategy calculation layer include:

[0075] Construct training aircraft and aircraft conflict digital twin models; deploy the aircraft flight conflict scenario training set in the aircraft conflict digital twin model; input the training aircraft into the aircraft conflict digital twin model to simulate aircraft flight conflicts; use the aircraft flight conflict scenario training set to randomly generate multiple flight conflict scenarios and deploy them in the aircraft conflict digital twin model;

[0076] The Markov decision model is used to generate K aircraft separation control strategy individuals L for simulating aircraft flight conflicts. k , k=1, 2, ..., K; K aircraft separation control strategy individuals L k The combination obtains the iterative population of the aircraft separation control strategy; when performing population iteration, the maximum number of iterations is set; the maximum number of iterations is set by professional technicians based on actual conditions;

[0077] When performing population iteration, calculate the aircraft interval control strategy individual L in the aircraft interval control strategy iteration population k Interval control time T k , interval control time T k Obtained by weighted addition of dodge time and return time; the interval control time T k The reciprocal of the aircraft separation control strategy L k The fitness Y k ;

[0078] At each population iteration, all fitness Y is retained k The individual L of the aircraft separation control strategy with a fitness greater than the preset optimal fitness k , and the remaining aircraft interval control strategy individuals L in the aircraft interval control strategy iteration population k The Markov decision model is used to update and obtain the new aircraft separation control strategy individual L k ; All aircraft separation control strategy individuals L that are greater than the preset optimal fitness k and all new aircraft separation control strategy individuals L k The combination obtains the updated iterative population of aircraft separation control strategy, thereby updating the iterative population of aircraft separation control strategy; the preset optimal fitness is set by professional and technical personnel according to actual conditions;

[0079] When the maximum number of iterations is reached, all aircraft separation control strategy individuals L whose fitness is greater than the preset optimal fitness are output. k , as a set of candidate aircraft control strategies for training aircraft;

[0080] Collect candidate aircraft control strategy sets for several groups of training aircraft; combine the candidate aircraft control strategy sets for several groups of training aircraft to obtain an aircraft control strategy training set; use the aircraft control strategy training set to perform model training on the initial interval control strategy calculation layer to obtain the initial interval control strategy calculation layer to be evaluated; perform model evaluation on the initial interval control strategy calculation layer to be evaluated to obtain a model evaluation result of the initial interval control strategy calculation layer to be evaluated; if the model evaluation result of the initial interval control strategy calculation layer to be evaluated is passed, use the trained initial interval control strategy calculation layer to be evaluated as the initial interval control strategy calculation layer; otherwise, continue model training using the aircraft control strategy training set;

[0081] By building a digital twin model of aircraft-to-aircraft conflicts and simulating flight conflicts within the model, potential flight conflicts can be detected and predicted more accurately, allowing effective avoidance measures to be taken. The model can adjust separation control strategies in real time during flight, responding promptly to emergencies and ensuring flight safety. It can automatically generate and adjust flight separation strategies, reducing the need for human intervention and improving the efficiency of air traffic management. It can dynamically adjust flight separation strategies based on real-time flight data and environmental changes, enhancing flexibility and adaptability. An improved optimization algorithm is used to screen aircraft control strategies while retaining the best solutions in the population, improving convergence speed.

[0082] The specific steps of training the feedback monitoring layer include:

[0083] Collecting several sets of aircraft separation control strategy prediction samples with target values, where the target values are quantitative evaluation results of the aircraft separation control strategies; combining the several sets of aircraft separation control strategy prediction samples with target values to obtain an aircraft control strategy feedback training set;

[0084] The feedback monitoring layer is trained using the aircraft control strategy feedback training set to obtain an initial feedback monitoring layer; a model evaluation is performed on the initial feedback monitoring layer to obtain an initial feedback monitoring layer model evaluation result; if the initial feedback monitoring layer model evaluation result is passed, the initial feedback monitoring layer is used as the feedback monitoring layer; otherwise, the model training is continued using the aircraft control strategy feedback training set;

[0085] By training the feedback monitoring layer, flight conflicts can be better predicted and assessed, enabling effective control strategies to be implemented, reducing the risk of mid-air collisions and ensuring flight safety. Through simulation testing and model evaluation, the autonomous separation control model can be continuously updated and optimized, improving the model's ability to predict and handle flight conflicts, thereby enhancing the autonomous separation control capabilities of aircraft.

[0086] Step S4: For aircraft H nConduct simulation tests with the aircraft autonomous separation control model to obtain aircraft flight conflict simulation test results; update the aircraft autonomous separation control model based on the aircraft flight conflict simulation test results to obtain a target aircraft autonomous separation control model;

[0087] The specific steps of step S4 include:

[0088] Step S41: Collect aircraft H n Using the aircraft autonomous separation control model to simulate the control simulation data of aircraft adaptive separation control P n And record the corresponding aircraft flight conflict simulation test results; control simulation data P n Based on flight data and control strategy generation, the results of aircraft flight conflict simulation tests are evaluated and determined by professional technicians based on actual conditions;

[0089] Step S42: Using the control simulation data P n Build a target aircraft flight conflict training set based on the corresponding aircraft flight conflict simulation test results;

[0090] Step S43: Parameters of the aircraft autonomous separation control model are updated using the target aircraft flight conflict training set to obtain the target aircraft autonomous separation control model;

[0091] By collecting and analyzing the simulation data P n The corresponding aircraft flight conflict simulation test results can be used to optimize the model based on actual data, build a more targeted aircraft autonomous separation control model, and build an aircraft autonomous separation control model that is more suitable for different aircraft to improve safety when encountering flight conflicts; ensure that its performance in actual applications is always in the best state.

[0092] Example 2, an aircraft adaptive separation control system, see Figure 1 Shown, including:

[0093] The flight conflict basic unit module includes a building unit for building the basic flight unit F of the aircraft n , F n =(A n , B n , C n , D n ); where n represents the aircraft certification number, belonging to aircraft H n ; A n Indicates the aircraft flight direction angle, B n Indicates the aircraft flight speed, C n Indicates the minimum unit time of aircraft flight, D nIndicates the flight time of an aircraft unit; using the basic flight unit F of the aircraft n For aircraft H n Randomly generate aircraft flight trajectory vectors and obtain the aircraft flight trajectory vector set X n ;

[0094] The flight conflict scenario generation module includes a generation unit for generating a flight conflict model and a flight trajectory vector set X of all aircraft based on the flight conflict generation model. n Generate aircraft flight conflict scenarios; combine the generated aircraft flight conflict scenarios to obtain an aircraft flight conflict scenario training set; the flight conflict generation model includes a path generation layer, a path selection layer, a path optimization layer, and a result output layer, which is constructed based on the CNN model and is used to construct and screen aircraft flight conflict scenarios;

[0095] The flight conflict resolution module includes a training unit for training an autonomous aircraft separation control model using a training set of aircraft flight conflict scenarios. The autonomous aircraft separation control model includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer. It is constructed based on an improved Markov decision model and is used to output strategies when aircraft flight conflicts occur.

[0096] Flight conflict learning module, including learning units for aircraft H n A simulation test is performed on the aircraft autonomous separation control model to obtain an aircraft flight conflict simulation test result; the aircraft autonomous separation control model is updated according to the aircraft flight conflict simulation test result to obtain a target aircraft autonomous separation control model.

[0097] It should be understood that those skilled in the art may make improvements or modifications based on the above description, and all such improvements and modifications shall fall within the scope of protection of the appended claims. Any portion of this specification not described in detail is prior art known to those skilled in the art.

Claims

1. An aircraft adaptive separation control method, characterized in that: The following steps are involved: Step S1: Construct the basic flight unit F of the aircraft n , F n =(A n , B n , C n , D n ); where n represents the aircraft certification number, belonging to aircraft H n ; A n Indicates the aircraft flight direction angle, B n Indicates the aircraft flight speed, C n Indicates the minimum unit time of aircraft flight, D n Indicates the flight time of an aircraft unit; using the basic flight unit F of the aircraft n For aircraft H n Randomly generate aircraft flight trajectory vectors and obtain the aircraft flight trajectory vector set X n ; Step S2: Generate a model based on the flight conflict and the flight trajectory vector set X of all aircraft n Generate aircraft flight conflict scenarios; combine the generated aircraft flight conflict scenarios to obtain an aircraft flight conflict scenario training set; the flight conflict generation model includes a path generation layer, a path selection layer, a path optimization layer, and a result output layer, which is constructed based on the CNN model and is used to construct and screen aircraft flight conflict scenarios; Step S3: Using the aircraft flight conflict scenario training set, the aircraft autonomous separation control model is trained. The aircraft autonomous separation control model includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer. It is constructed based on the improved Markov decision model and is used to output strategies when aircraft flight conflicts occur. Step S4: For aircraft H n Conduct simulation tests with the aircraft autonomous separation control model to obtain aircraft flight conflict simulation test results; update the aircraft autonomous separation control model based on the aircraft flight conflict simulation test results to obtain a target aircraft autonomous separation control model; The flight conflict generation model in step S2 includes a path generation layer, a path selection layer, a path optimization layer and a result output layer; The path generation layer is used to generate the path from the aircraft H n In the random algorithm, I aircraft H is selected n Perform path selection to obtain an aircraft simulation generation group; The path selection layer is used to target aircraft H n Vector set of aircraft flight tracks X n A preset number of aircraft flight trajectory vectors are randomly selected and spliced to obtain the aircraft simulation flight trajectory G n ; The path optimization layer is used to generate conflict scenarios based on the aircraft simulation generation group to obtain aircraft flight conflict scenarios; The result output layer is used to traverse several aircraft simulation generation groups to obtain the aircraft flight conflict scenario training set; The specific steps for generating conflict scenarios in the path optimization layer include: Step E1: Generate the corresponding aircraft simulation flight trajectory G according to the aircraft simulation generation group n , obtain the aircraft simulation flight trajectory set; Step E2: generating a conflict scenario for the aircraft simulated flight trajectory set to obtain the aircraft flight conflict scenario to be optimized; Step E3: Feature screening is performed on the aircraft flight conflict scenario to be optimized to obtain the aircraft flight conflict scenario; The specific steps for training the path optimization layer include: Collect several sets of standard aircraft flight conflict scenario training samples; combine several sets of standard aircraft flight conflict scenario training samples to obtain an aircraft flight conflict scenario training set; use the CNN model to construct an initial path optimization layer; use the aircraft flight conflict scenario training set to train the initial path optimization layer to obtain a path optimization layer to be evaluated; perform model evaluation on the path optimization layer to be evaluated to obtain a model evaluation result of the path optimization layer to be evaluated; if the model evaluation result of the path optimization layer to be evaluated is passed, then use the path optimization layer to be evaluated as the path optimization layer; otherwise, continue model training using the aircraft flight conflict scenario training set; In step S3, the aircraft autonomous separation control model includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer; The data extraction layer is used to obtain the flight data of the aircraft to which the current aircraft belongs and obtain the flight data of the aircraft to be analyzed; The aircraft separation perception layer is used to obtain the aircraft flight data of the disturbing aircraft within the preset separation perception range, and obtain the flight data of the disturbing aircraft to be analyzed; The initial separation control strategy calculation layer is used to perform strategy analysis based on the flight data of the aircraft to be analyzed and the flight data of the disturbing aircraft to be analyzed, and obtain the set of aircraft control strategies to be evaluated. The initial separation control strategy calculation layer is trained and constructed using the improved Markov decision model and the aircraft flight conflict scenario training set. The feedback monitoring layer is used to make predictions based on the aircraft control strategy set to be evaluated and obtain aircraft separation control feedback results; based on the aircraft separation control feedback results, the aircraft control strategy set to be evaluated is judged and the optimal aircraft adaptive control strategy in the aircraft control strategy set to be evaluated is obtained; The control strategy output layer is used to output the aircraft adaptive control strategy.

2. The method for adaptive aircraft separation control according to claim 1, characterized in that: The specific steps for training the initial interval control strategy calculation layer include: Build digital twin models of training aircraft and aircraft conflicts; deploy the aircraft flight conflict scenario training set in the aircraft conflict digital twin model; input the training aircraft into the aircraft conflict digital twin model to simulate aircraft flight conflicts; The Markov decision model is used to generate K aircraft separation control strategy individuals L for simulating aircraft flight conflicts. k , k=1, 2, ..., K; K aircraft separation control strategy individuals L k Combine and obtain the iterative population of aircraft separation control strategy; when performing population iteration, set the maximum number of iterations; When performing population iteration, calculate the aircraft interval control strategy individual L in the aircraft interval control strategy iteration population k Interval control time T k , interval control time T k Obtained by weighted addition of dodge time and return time; the interval control time T k The reciprocal of the aircraft separation control strategy L k The fitness Y k ; At each population iteration, all fitness Y is retained k The individual L of the aircraft separation control strategy with a fitness greater than the preset optimal fitness k , and the remaining aircraft interval control strategy individuals L in the aircraft interval control strategy iteration population k The Markov decision model is used to update and obtain the new aircraft separation control strategy individual L k ; All aircraft separation control strategy individuals L that are greater than the preset optimal fitness k and all new aircraft separation control strategy individuals L k Combining, obtaining updated aircraft separation control strategy iteration population, realizing the update of aircraft separation control strategy iteration population; When the maximum number of iterations is reached, all aircraft separation control strategy individuals L whose fitness is greater than the preset optimal fitness are output. k , as a set of candidate aircraft control strategies for training aircraft; Collect candidate aircraft control strategy sets for several groups of training aircraft; combine the candidate aircraft control strategy sets for several groups of training aircraft to obtain an aircraft control strategy training set; use the aircraft control strategy training set to perform model training on the initial interval control strategy calculation layer to obtain the initial interval control strategy calculation layer to be evaluated; perform model evaluation on the initial interval control strategy calculation layer to be evaluated to obtain a model evaluation result of the initial interval control strategy calculation layer to be evaluated; if the model evaluation result of the initial interval control strategy calculation layer to be evaluated is passed, the trained initial interval control strategy calculation layer to be evaluated is used as the initial interval control strategy calculation layer; otherwise, continue model training using the aircraft control strategy training set.

3. The method for adaptive aircraft separation control according to claim 2, characterized in that: The specific steps of training the feedback monitoring layer include: Collecting several sets of aircraft separation control strategy prediction samples with target values, where the target values are quantitative evaluation results of the aircraft separation control strategies; combining the several sets of aircraft separation control strategy prediction samples with target values to obtain an aircraft control strategy feedback training set; The feedback monitoring layer is trained using the aircraft control strategy feedback training set to obtain the initial feedback monitoring layer; the model of the initial feedback monitoring layer is evaluated to obtain the model evaluation result of the initial feedback monitoring layer; if the model evaluation result of the initial feedback monitoring layer is passed, the initial feedback monitoring layer is used as the feedback monitoring layer; otherwise, the model training is continued using the aircraft control strategy feedback training set.

4. The method for adaptive aircraft separation control according to claim 3, characterized in that: The specific steps of step S4 include: Step S41: Collect aircraft H n Using the aircraft autonomous separation control model to simulate the control simulation data of aircraft adaptive separation control P n And record the corresponding aircraft flight conflict simulation test results; Step S42: Using the control simulation data P n Build a target aircraft flight conflict training set based on the corresponding aircraft flight conflict simulation test results; Step S43: Parameters of the aircraft autonomous separation control model are updated using the target aircraft flight conflict training set to obtain the target aircraft autonomous separation control model.

5. An aircraft adaptive separation control system, characterized in that: The system applies the method for adaptive aircraft separation control according to any one of claims 1 to 4, including: The flight conflict basic unit module includes a building unit for building the basic flight unit F of the aircraft n , F n =(A n , B n , C n , D n ); where n represents the aircraft certification number, belonging to aircraft H n ; A n Indicates the aircraft flight direction angle, B n Indicates the aircraft flight speed, C n Indicates the minimum unit time of aircraft flight, D n Indicates the flight time of an aircraft unit; using the basic flight unit F of the aircraft n For aircraft H n Randomly generate aircraft flight trajectory vectors and obtain the aircraft flight trajectory vector set X n ; The flight conflict scenario generation module includes a generation unit for generating a flight conflict model and a flight trajectory vector set X of all aircraft based on the flight conflict generation model. n Generate aircraft flight conflict scenarios; combine the generated aircraft flight conflict scenarios to obtain an aircraft flight conflict scenario training set; the flight conflict generation model includes a path generation layer, a path selection layer, a path optimization layer, and a result output layer, which is constructed based on the CNN model and is used to construct and screen aircraft flight conflict scenarios; The flight conflict resolution module includes a training unit for training an autonomous aircraft separation control model using a training set of aircraft flight conflict scenarios. The autonomous aircraft separation control model includes a data extraction layer, an aircraft separation perception layer, an initial separation control strategy calculation layer, a feedback monitoring layer, and a control strategy output layer. It is constructed based on an improved Markov decision model and is used to output strategies when aircraft flight conflicts occur. Flight conflict learning module, including learning units for aircraft H n A simulation test is performed on the aircraft autonomous separation control model to obtain an aircraft flight conflict simulation test result; the aircraft autonomous separation control model is updated according to the aircraft flight conflict simulation test result to obtain a target aircraft autonomous separation control model.

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