Intelligent control method and system for retractable main wingspan

Abstract

The present invention discloses an intelligent control method and system for a telescopic main wing span, and relates to the field of intelligent control technology. The method includes: obtaining the flight mission of an aircraft; performing multi-feature decomposition to obtain multiple node flight missions; performing flight environment prediction to obtain the nth node predicted flight environment; performing main wing span control decision-making to generate the nth node main wing span control scheme; performing main wing span simulation control to obtain the main wing span simulation control monitoring data set; performing multi-feature compensation optimization to obtain the nth node main wing span control optimization result; and performing main wing span control on the aircraft. The method solves the technical problem that the existing telescopic main wing span control cannot accurately predict the flight environment and make control decisions, which in turn makes it difficult to adapt to the diverse flight mission requirements, resulting in poor aerodynamic performance and mission flexibility of the aircraft, and realizes dynamic adjustment control of the aircraft main wing span, achieving the technical effect of enhancing the aerodynamic performance and adaptability of the aircraft.

Description

Technical Field

[0001] The present application relates to the technical field related to intelligent control, and specifically to an intelligent control method and system for a retractable main wingspan. Background Art

[0002] With the rapid development of aerospace technology, facing the challenges of flight environment and diversified flight missions, traditional fixed-wing aircraft design often cannot meet the performance requirements under various flight conditions. Aircraft design is increasingly pursuing high efficiency, multi-task adaptability and intelligence, especially in situations that require high maneuverability and adaptability. Retractable main wingspan technology has become a key technology in modern aircraft design. By dynamically adjusting the wing area and aspect ratio, the aerodynamic performance and mission flexibility of the aircraft can be significantly improved. However, how to accurately and intelligently control the retractable main wingspan to adapt to complex and changeable flight missions and environments is a technical problem that needs to be solved urgently. Traditional main wingspan control methods often rely on preset flight modes and fixed control logic, which are difficult to meet the needs of modern aircraft for high adaptability and intelligence. Especially when performing multi-task flights, traditional methods are difficult to accurately disassemble and respond to the specific requirements of each node flight mission, resulting in limited flight performance.

[0003] Therefore, at the current stage, there is a technical problem in the retractable main wingspan control related technologies that cannot accurately predict the flight environment and make control decisions, which in turn makes it difficult to adapt to diverse flight mission requirements, resulting in poor aerodynamic performance and mission flexibility of the aircraft. Summary of the invention

[0004] The present application provides an intelligent control method and system for a retractable main wingspan, and adopts technical means such as high-precision flight mission analysis and prediction, accurate prediction of the flight environment, main wingspan simulation control and multi-feature compensation optimization, to solve the technical problem that the existing retractable main wingspan control is unable to accurately predict the flight environment and make control decisions, which in turn makes it difficult to adapt to diverse flight mission requirements, resulting in poor aerodynamic performance and mission flexibility of the aircraft. The application realizes dynamic adjustment control of the aircraft's main wingspan, achieving the technical effect of enhancing the aerodynamic performance and adaptability of the aircraft.

[0005] The present application provides an intelligent control method for a retractable main wingspan, the method comprising: obtaining a flight mission of an aircraft; performing multi-feature decomposition according to the flight mission to obtain a plurality of node flight missions; extracting an n-th node flight mission according to the plurality of node flight missions, and predicting a flight environment for the aircraft based on the n-th node flight mission to obtain an n-th node predicted flight environment, wherein n is a positive integer; making a main wingspan control decision according to the n-node flight mission and the n-node predicted flight environment to generate an n-node main wingspan control scheme; activating a flight simulation platform, performing main wingspan simulation control on the aircraft in combination with the n-node main wingspan control scheme to obtain a main wingspan simulation control monitoring data set; performing multi-feature compensation optimization on the n-node main wingspan control scheme in combination with the main wingspan simulation control monitoring data set according to a multi-factor main wingspan control compensation channel to obtain an n-node main wingspan control optimization result; performing main wingspan control on the aircraft according to the n-node main wingspan control optimization result based on the n-node flight mission.

[0006] In a possible implementation, multi-feature disassembly is performed according to the flight mission to obtain multiple node flight missions, and the following processing is performed: flight attribute disassembly is performed according to the flight mission to obtain a first-level disassembly result of the flight mission; flight status disassembly is performed according to the first-level disassembly result of the flight mission to obtain a second-level disassembly result of the flight mission; flight environment disassembly is performed according to the second-level disassembly result of the flight mission to generate the multiple node flight missions.

[0007] In a possible implementation, a main wingspan control decision is made according to the n-node flight mission and the n-node predicted flight environment, and an n-node main wingspan control scheme is generated, and the following processing is performed: main wingspan control records are traced back according to the aircraft to obtain a main wingspan control record set; main wingspans of the same group are established according to the main wingspan model information of the aircraft, wherein the main wingspans of the same group include multiple main wingspans of the same model; control records are traced back according to the main wingspans of the same group to obtain a main wingspan control record set of the same group; a main wingspan control decision model is constructed according to the main wingspan control record set; decision sensitivity of the main wingspan control decision model is optimized according to the main wingspan control record set to generate a main wingspan control decision channel; based on the n-node flight mission and the n-node predicted flight environment, the main wingspan control scheme of the n-node is obtained according to the main wingspan control decision channel.

[0008] In a possible implementation, according to a multi-factor main wingspan control compensation channel, in combination with the main wingspan simulation control monitoring data set, the n-node main wingspan control scheme is subjected to multi-feature compensation optimization to obtain the n-node main wingspan control optimization result, and the following processing is also performed: the multi-factor main wingspan control compensation channel includes a main wingspan control aerodynamic performance compensation channel, a main wingspan control sensitivity compensation channel and a main wingspan control load characteristic compensation channel; based on the main wingspan simulation control monitoring data set, according to the main wingspan control aerodynamic performance compensation channel, main wingspan control aerodynamic performance compensation parameters are obtained; based on the main wingspan A simulation control monitoring data set is used to generate a main wingspan control sensitivity compensation parameter according to the main wingspan control sensitivity compensation channel; based on the main wingspan simulation control monitoring data set, a main wingspan control load characteristic compensation parameter is generated according to the main wingspan control load characteristic compensation channel; the main wingspan control aerodynamic performance compensation parameter, the main wingspan control sensitivity compensation parameter and the main wingspan control load characteristic compensation parameter are integrated to generate a main wingspan control compensation characteristic factor; the n-th node main wingspan control scheme is optimized according to the main wingspan control compensation characteristic factor to generate the n-node main wingspan control optimization result.

[0009] In a possible implementation, based on the main wingspan simulation control monitoring data set and according to the main wingspan control aerodynamic performance compensation channel, main wingspan control aerodynamic performance compensation parameters are obtained, and the following processing is also performed: according to the main wingspan simulation control monitoring data set, main wingspan simulation aerodynamic performance data is extracted; according to the main wingspan simulation aerodynamic performance data, flight aerodynamic support evaluation is performed to obtain a flight aerodynamic support coefficient; it is determined whether the flight aerodynamic support coefficient is less than / equal to a flight aerodynamic support threshold; if the flight aerodynamic support coefficient is less than / equal to the flight aerodynamic support threshold, based on the main wingspan simulation aerodynamic performance data and the nth node main wingspan control scheme, according to the main wingspan control aerodynamic performance compensation channel, the main wingspan control aerodynamic performance compensation parameters are generated.

[0010] In a possible implementation, based on the main wingspan simulation control monitoring data set and according to the main wingspan control sensitivity compensation channel, a main wingspan control sensitivity compensation parameter is generated, and the following processing is also performed: according to the main wingspan simulation control monitoring data set, the main wingspan simulation state monitoring data is extracted; based on the n-node main wingspan control scheme, a control parameter deviation analysis is performed on the main wingspan simulation state monitoring data to obtain a main wingspan control parameter deviation analysis result; based on the n-node main wingspan control scheme, a control response delay analysis is performed on the main wingspan simulation state monitoring data to obtain a main wingspan control response delay analysis result; based on the main wingspan control parameter deviation analysis result and the main wingspan control response delay analysis result, according to the main wingspan control sensitivity compensation channel, the main wingspan control sensitivity compensation parameter is obtained.

[0011] In a possible implementation, based on the main wingspan simulation control monitoring data set and according to the main wingspan control load characteristic compensation channel, main wingspan control load characteristic compensation parameters are generated, and the following processing is also performed: according to the main wingspan simulation control monitoring data set, aircraft simulation load distribution data and aircraft simulation center of gravity distribution data are extracted; according to the aircraft simulation load distribution data, change trend identification is performed to generate a simulated load change trend identification result; according to the aircraft simulation center of gravity distribution data, change trend identification is performed to generate a simulated center of gravity change trend identification result; based on the simulated load change trend identification result, the simulated center of gravity change trend identification result and the nth node main wingspan control scheme, the main wingspan control load characteristic compensation parameters are obtained according to the main wingspan control load characteristic compensation channel.

[0012] The present application also provides an intelligent control system for a retractable main wingspan, including: a flight mission acquisition module, the flight mission acquisition module is used to obtain the flight mission of the aircraft; a flight mission feature disassembly module, the flight mission feature disassembly module is used to perform multi-feature disassembly according to the flight mission to obtain multiple node flight missions; a flight environment prediction module, the flight environment prediction module is used to extract the n-th node flight mission according to the multiple node flight missions, and predict the flight environment of the aircraft based on the n-th node flight mission to obtain the n-th node predicted flight environment, wherein n is a positive integer; a main wingspan control decision module, the main wingspan control decision module is used to make a main wingspan control decision according to the n-th node flight mission and the n-node predicted flight environment, Generate an n-node main wingspan control scheme; a main wingspan simulation control module, the main wingspan simulation control module is used to activate the flight simulation platform, and perform main wingspan simulation control on the aircraft in combination with the n-node main wingspan control scheme to obtain a main wingspan simulation control monitoring data set; a main wingspan control optimization result acquisition module, the main wingspan control optimization result acquisition module is used to perform multi-feature compensation optimization on the n-node main wingspan control scheme in combination with the main wingspan simulation control monitoring data set according to a multi-factor main wingspan control compensation channel to obtain an n-node main wingspan control optimization result; an aircraft main wingspan control module, the aircraft main wingspan control module is used to perform main wingspan control on the aircraft based on the n-node main wingspan control optimization result according to the n-node flight mission.

[0013] Through the intelligent control method and system for retractable main wingspan proposed in this application, the flight mission of the aircraft is obtained; multi-feature decomposition is performed to obtain multiple node flight missions; flight environment prediction is performed to obtain the nth node predicted flight environment; main wingspan control decision is made to generate the nth node main wingspan control plan; main wingspan simulation control is performed to obtain the main wingspan simulation control monitoring data set; multi-feature compensation optimization is performed to obtain the nth node main wingspan control optimization result; and the main wingspan of the aircraft is controlled. The technical problem that the existing retractable main wingspan control cannot accurately predict the flight environment and make control decisions, which leads to difficulty in adapting to the diverse flight mission requirements, resulting in poor aerodynamic performance and mission flexibility of the aircraft is solved, and the dynamic adjustment control of the aircraft main wingspan is realized, achieving the technical effect of enhancing the aerodynamic performance and adaptability of the aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic flow chart of an intelligent control method for a retractable main wingspan provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the structure of an intelligent control system for a retractable main wingspan provided in an embodiment of the present application.

[0017] Explanation of the reference numerals: flight mission acquisition module 10, flight mission feature disassembly module 20, flight environment prediction module 30, main wingspan control decision module 40, main wingspan simulation control module 50, main wingspan control optimization result acquisition module 60, aircraft main wingspan control module 70. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides an intelligent control method for a telescopic main wing span, such as Figure 1 As shown, the method includes:

[0022] Step S100, obtaining the flight mission of the aircraft. Obtaining the flight mission of an aircraft specifically refers to clarifying how the aircraft, when performing a specific mission, can adapt to different flight phases and environmental conditions by adjusting its retractable wingspan to achieve optimal flight performance and efficiency. Retractable wingspan is an advanced aircraft design technology that allows the aircraft to dynamically adjust the span and shape of the wings according to mission requirements and changes in the flight environment during flight, which can significantly improve the aircraft's multi-task adaptability and flight performance. When obtaining the flight mission of an aircraft, it is necessary to consider the flight environment and various stages of flight. Specifically, it is necessary to analyze various environmental conditions that may be encountered in the flight mission, such as atmospheric density, temperature, wind speed, wind direction, etc. These environmental factors will affect the aerodynamic performance and stability of the aircraft. It is necessary to optimize the aerodynamic layout of the aircraft by adjusting the retractable wingspan, and divide the flight mission into different stages, such as takeoff, climb, cruise, descent and landing. Each stage has different performance requirements for the aircraft, and it is necessary to formulate corresponding retractable wingspan adjustment plans. For example, in the takeoff and landing stages, the wing area needs to be reduced to reduce resistance; in the cruise stage, the wing area needs to be increased to increase lift.

[0023] Step S200, multi-feature decomposition is performed according to the flight mission to obtain multiple node flight missions. The complex flight mission is broken down into a series of subtasks (node ​​flight missions) with clear objectives and execution conditions, so as to better plan, execute and monitor the mission, which helps to improve the executability, manageability and flexibility of the mission. Specifically, the overall objectives, requirements, constraints, etc. of the flight mission are clarified, including the take-off point, target point, flight altitude, speed, heading, load and other information of the aircraft, and a comprehensive feature analysis of the flight mission is performed, including the time characteristics of the mission (such as take-off time, arrival time, etc.), spatial characteristics (such as flight route, altitude change, etc.), physical characteristics (such as load mass, size, etc.), technical characteristics (such as communication requirements, navigation accuracy, etc.), etc. The complex flight mission is broken down into multiple node flight missions, which may include Take-off preparation, flight phase division, execution of specific flight actions, etc. For example, a flight mission is to use an aircraft with retractable wings for high-altitude scientific exploration, which can be broken down into take-off preparation (such as aircraft inspection, fuel filling, deployment and adjustment of retractable wings, etc.), climb phase (climbing according to the preset climb trajectory and speed, while adjusting the retractable wings to optimize aerodynamic performance), cruise phase (cruising at a predetermined altitude and speed, performing scientific exploration tasks, such as taking images, collecting data, etc.), maneuver adjustment (according to the needs of the exploration mission, the aircraft makes necessary maneuver adjustments, such as changing the flight direction, altitude or speed, etc.), descent and landing (descending and landing according to the predetermined descent trajectory and speed) and other node flight tasks.

[0024] In a possible implementation, step S200 further includes step S210, performing flight attribute disassembly according to the flight mission to obtain a first-level disassembly result of the flight mission. Flight attribute disassembly specifically refers to decomposing the flight mission into several key stages according to the flight attributes, such as the take-off stage, the cruising stage, the landing stage, etc., to obtain a first-level disassembly result of the flight mission, namely, the take-off stage, which is the process of the aircraft going from a stationary state to reaching a safe take-off speed and leaving the ground; the cruising stage, which is the stage in which the aircraft continues to fly at a stable altitude and speed, and the main task is to move forward according to the predetermined route; the landing stage, which is the process of the aircraft gradually reducing its speed from the cruising altitude and finally landing safely.

[0025] It also includes step S220, performing flight status disassembly according to the first-level disassembly result of the flight mission to obtain the second-level disassembly result of the flight mission. Flight status disassembly specifically refers to further disassembly and refinement of each stage on the basis of the first-level disassembly to obtain the second-level disassembly result of the flight mission. Specifically, the take-off stage is disassembled into the ground taxiing stage (the preparation stage before the aircraft accelerates to the take-off speed on the runway), the take-off wheel rotation stage (the process of the aircraft's front wheels lifting and starting to leave the ground), and the climbing stage (the process of the aircraft continuing to climb to a safe cruising altitude); the cruising stage is disassembled into route planning (determining the specific flight route and altitude of the aircraft), speed control (adjusting the speed of the aircraft according to the flight mission requirements), and altitude maintenance (ensuring that the aircraft flies stably at a predetermined altitude); the landing stage is disassembled into the approach stage (the process of the aircraft starting to descend and preparing to enter the landing path), the leveling stage (the aircraft adjusts its attitude to slow down the descent speed when approaching the runway), and the touchdown stage (the front wheels of the aircraft touch the ground, and then the whole aircraft lands smoothly).

[0026] It also includes step S230, disassembling the flight environment according to the secondary disassembly result of the flight mission, and generating the multiple node flight missions. The flight environment disassembly is based on the secondary disassembly result, and deeply analyzes and considers various environmental factors that may be encountered during the execution of the flight mission, including meteorological conditions (such as wind speed, wind direction, temperature, humidity, cloud cover, visibility, etc.), topography (such as mountainous areas, plains, lakes, oceans, etc.), airspace restrictions (such as no-fly zones, restricted areas, route congestion, etc.), and divides the flight mission into multiple specific, interrelated but relatively independent task units according to the flight environment and control requirements, that is, multiple node flight missions. Each node task contains clear starting point, end point, flight path, flight speed, altitude, attitude and other requirements, as well as corresponding control strategies, including flight path planning, speed control, etc. Control, altitude maintenance, attitude adjustment, emergency handling, etc. For example, the node tasks in the take-off phase may include taxiing to the designated position on the ground, accelerating to the take-off speed, raising the wheels and climbing steadily, and each node task needs to consider the influence of meteorological factors such as wind speed and wind direction; the node tasks in the cruise phase may include flying according to the predetermined route, maintaining a specific speed and altitude, and performing necessary maneuvers. These tasks need to comprehensively consider factors such as terrain, airspace restrictions, etc.; the node tasks in the landing phase may include approaching to the designated altitude and speed, adjusting the attitude to prepare for landing, landing smoothly and decelerating and taxiing, and these tasks require special attention to the impact of meteorological conditions on landing safety.

[0027] Step S300, extracting the nth node flight mission according to the multiple node flight missions, and predicting the flight environment of the aircraft based on the nth node flight mission to obtain the nth node predicted flight environment, wherein n is a positive integer. From the multiple node flight missions that have been disassembled, the nth node flight mission is selected and extracted, where n is used to identify the position of the node flight mission in the entire mission sequence. The extracted nth node flight mission is deeply analyzed, and the flight environment of the aircraft is predicted. For example, based on the numerical weather forecast model, combined with historical meteorological data and real-time observation data, the flight environment is predicted, which may include atmospheric conditions (such as temperature, humidity, air pressure, wind direction and speed, etc.) and weather conditions (such as sunny days, rainy days, foggy days, etc.); using GIS technology, the terrain is three-dimensionally modeled and analyzed to predict various flight environment characteristics that may be encountered during the execution of the flight mission, which may include terrain (such as mountains, oceans, cities, etc.), airspace restrictions (such as no-fly zones, restricted areas, etc.) and possible other flight obstacles (such as bird activity areas, other aircraft activity trajectories, etc.), and finally the predicted flight environment of the nth node is obtained.

[0028] Step S400, making a main wingspan control decision based on the n-th node flight mission and the n-th node predicted flight environment, and generating an n-th node main wingspan control plan. In the process of the aircraft performing the flight mission, for the flight mission of the n-th node (i.e., a specific stage or mission point) and its predicted flight environment, the main wingspan of the aircraft is dynamically adjusted and optimized by comprehensively considering the performance parameters, flight status and environmental factors of the aircraft to generate a main wingspan control plan that adapts to the current flight environment and mission requirements. Specifically, a comprehensive evaluation is made on the n-th node predicted flight environment to obtain factors that affect the main wingspan of the aircraft, such as strong wind shear, terrain obstacles, airspace restrictions, etc., and then a main wingspan control decision is made, which may include adjusting the main wingspan according to the characteristics of the flight environment to maintain the stability and maneuverability of the aircraft, for example For example, in areas of strong wind shear, the main wingspan needs to be reduced to reduce wind resistance and crosswind effects, and in areas of complex terrain, the main wingspan needs to be increased to increase lift and maneuverability; optimize the main wingspan to improve flight efficiency, for example, in the long-distance cruise phase, the lift-to-drag ratio is optimized by adjusting the main wingspan to reduce fuel consumption and flight time; consider airspace restrictions and the activity trajectories of other aircraft to avoid conflicts with them, and if necessary, adjust the main wingspan to change the cross-sectional area and radar reflection characteristics of the aircraft; generate the main wingspan control plan for the nth node based on the main wingspan control decision results, and the main wingspan control plan specifies the adjustment range, adjustment timing, adjustment method, and corresponding flight parameter settings of the main wingspan.

[0029] In a possible implementation, step S400 further includes step S410, which performs a backtracking of the main wingspan control record according to the aircraft to obtain a main wingspan control record set. The main wingspan control records of a specific aircraft in previous flight missions are traced back and collected, which may include the adjustment of the main wingspan in different flight stages, the reasons for the adjustment (such as wind speed changes, flight altitude adjustments, etc.), the effects after the adjustment, and other information. By tracing back these records, a comprehensive main wingspan control record set can be formed. It also includes step S420, which establishes the same group of main wingspans according to the main wingspan model information of the aircraft, wherein the same group of main wingspans includes multiple main wingspans of the same model. After obtaining the main wingspan model information of a specific aircraft, a set of main wingspans of the same group can be established, including all aircraft with the same model of main wingspan, in order to expand the data sample size and improve the accuracy and reliability of subsequent analysis. Aircraft with the same main wingspan have similarities in aerodynamic characteristics, control strategies, etc. The method further includes step S430, wherein the control records are traced back according to the main wingspan of the same group to obtain a main wingspan control record set of the same group. After the main wingspan of the same group is established, the main wingspan control records of each aircraft in the same group are traced back, covering the historical control records of the entire aircraft in the same group. By collecting these records, a more comprehensive main wingspan control record set of the same group can be formed.

[0030] The method further includes step S440, wherein a main wing span control decision model is constructed according to the main wing span control record set of the same group. All relevant data are collected from the main wing span control record set of the same group, including but not limited to flight mission information, flight environment parameters (such as wind speed, wind direction, temperature, air pressure, etc.), and main wing span adjustment records (such as adjustment time, adjustment amount, adjustment effect, etc.), and data preprocessing (such as data cleaning, removing and correcting outliers, missing values, etc., and standardization processing) is performed. Then, features that affect the main wing span control decision are extracted from the original data, such as flight altitude, speed, acceleration, environmental parameters, etc., and a decision model is constructed based on machine learning (such as neural network, decision tree, etc.), and the preprocessed data set is divided into a training set, a validation set, and a test set. The decision model is trained using the training set, and the prediction performance of the model is optimized by adjusting the model parameters. The trained model is verified using the validation set data, and the main wing span control decision model is determined. Then, the performance of the final model is evaluated using the test set data, and the model is further optimized according to the evaluation results. This may include adjusting the model structure.

[0031] The method further includes step S450, optimizing the decision sensitivity of the main wingspan control decision model according to the main wingspan control record set to generate a main wingspan control decision channel. The decision sensitivity optimization aims to improve the sensitivity and responsiveness of the main wingspan control decision model to changes in input parameters, ensure that the model can make main wingspan adjustment decisions quickly and accurately under different flight conditions and mission requirements, and enable the model to better adapt to the complexity and uncertainty in actual flight. Specifically, the main wingspan control record set is deeply analyzed to identify the key factors and parameters affecting the main wingspan adjustment, evaluate the output response of the model under different input conditions, identify potential sensitivity problems and deficiencies, and use sensitivity analysis methods (such as local sensitivity analysis, global sensitivity analysis, etc.) to evaluate the degree of influence of model parameters on the output results, determine which parameters have a significant impact on the main wingspan adjustment decision, and the range and threshold of these parameters. According to the sensitivity evaluation results, the model is adjusted and optimized, and the optimized model is verified using simulation tools. The main wingspan adjustment process under different flight conditions and mission requirements is simulated to evaluate the performance of the model in the simulation environment to ensure that it has high decision sensitivity and accuracy. After completing the decision sensitivity optimization, the main wingspan control decision channel is generated to guide the control decision of the main wingspan adjustment of the aircraft.

[0032] It also includes step S460, based on the n-th node flight mission and the n-th node predicted flight environment, according to the main wingspan control decision channel, obtain the n-th node main wingspan control plan. After obtaining the main wingspan control decision channel, it can be applied to a specific flight mission. For the n-th node flight mission, it is first necessary to predict the flight environment of the node (such as wind speed, wind direction, temperature, etc.), and then, according to the requirements of the flight mission and the predicted flight environment information, input the corresponding parameters through the main wingspan control decision channel. The model will calculate and predict based on these input parameters, and output the n-th node main wingspan control plan. This plan will guide the aircraft on how to adjust the main wingspan at the n-th node to adapt to changes in the flight mission and flight environment.

[0033] Step S500, activate the flight simulation platform, perform main wingspan simulation control on the aircraft in combination with the n-th node main wingspan control scheme, and obtain a main wingspan simulation control monitoring data set. The flight simulation platform is a system that integrates high-precision physical models, real-time computing capabilities, and highly visual interfaces, and is used to simulate the behavior and performance of aircraft under various flight conditions. Activating the flight simulation platform usually includes loading the aircraft model, setting the initial flight state, configuring environmental parameters, etc. After the flight simulation platform is activated, the n-th node main wingspan control scheme is imported into the platform as an input parameter. The flight simulation platform dynamically adjusts the main wingspan of the aircraft according to the multiple flight parameters contained in the n-th node main wingspan control scheme. By simulating the aircraft's takeoff, climb, cruise, descent, and landing stages, the impact of the main wingspan changes on the aircraft performance is observed and recorded. After the aircraft is subjected to main wingspan simulation control, the flight simulation platform generates a main wingspan simulation control monitoring data set, including main wingspan simulation aerodynamics. Performance data and main wing span simulation state monitoring data. Specifically, the main wing span simulation aerodynamic performance data may include lift coefficient (the impact of main wing span changes on aircraft lift), drag coefficient (the impact of main wing span changes on aircraft drag), lift-to-drag ratio (the energy efficiency ratio of the aircraft under different main wing spans), stability (including pitch stability, roll stability, etc.), other aerodynamic parameters (such as pressure distribution, airflow velocity, etc.), etc.; the main wing span simulation state monitoring data may include flight trajectory (recording the flight path and altitude changes of the aircraft during the simulated flight), flight speed (recording the flight speed of the aircraft at different stages), attitude angle (including pitch angle, roll angle and yaw angle, reflecting the attitude changes of the aircraft), control surface deflection angle recording the deflection angle of the aircraft control surface (such as aileron, elevator, etc.), etc. After obtaining the main wing span simulation control monitoring data set, in-depth data analysis is required. By comparing the aerodynamic performance and state monitoring data under different main wing spans, the effectiveness and optimization space of the main wing span control scheme of the nth node can be evaluated.

[0034] Step S600, according to the multi-factor main wingspan control compensation channel, combined with the main wingspan simulation control monitoring data set, the n-node main wingspan control scheme is optimized with multi-feature compensation to obtain the n-node main wingspan control optimization result. The multi-factor main wingspan control compensation channel is actually a mathematical model for compensation optimization, which takes into account multiple factors that affect the main wingspan control effect, such as aerodynamic performance, structural strength, flight environment (such as wind speed, temperature, humidity, etc.), flight mission requirements, etc., and describes the relationship between these factors and the main wingspan control effect through mathematical methods, and provides compensation strategies based on these relationships. Combined with the main wingspan simulation control monitoring data set, the n-node main wingspan control scheme is optimized with multi-feature compensation. Specifically, multiple key features related to the main wingspan control effect are extracted from the main wingspan simulation control monitoring data set. These features should be able to fully reflect the performance of the aircraft under different main wingspans. The extracted feature data are compared with the multi-factor main wingspan control compensation channel. The wingspan control compensation model is matched, and the influence degree and compensation demand of each factor under the current control scheme are calculated through the model. According to the model calculation results, a multi-feature compensation strategy is formulated, which may include fine-tuning of the main wingspan adjustment amount, adjustment of the control surface deflection angle, correction of the flight attitude, etc. The purpose is to improve the overall performance and stability of the aircraft through comprehensive compensation optimization. The compensation strategy is applied to the simulated flight platform and optimized for multiple iterations. In each iteration, the compensation strategy is adjusted according to the new monitoring data set. After multi-feature compensation optimization, the optimization result of the main wingspan control of the nth node will be obtained, which not only includes the optimized main wingspan control scheme, but also may include a series of compensation measures and suggestions for specific flight conditions and mission requirements.

[0035] In a possible implementation, step S600 further includes step S610, wherein the multi-factor main wingspan control compensation channel includes a main wingspan control aerodynamic performance compensation channel, a main wingspan control sensitivity compensation channel, and a main wingspan control load characteristic compensation channel. The main wingspan control aerodynamic performance compensation channel mainly focuses on the control deviation caused by the change of aerodynamic performance of the aircraft during flight. The change of aerodynamic performance may be caused by many factors, such as flight speed, altitude, attitude angle, external airflow conditions, etc. The main wingspan control aerodynamic performance compensation channel analyzes the aerodynamic performance data of the aircraft, identifies the influence of aerodynamic performance on the main wingspan control under different flight conditions, and generates corresponding aerodynamic performance compensation parameters based on these laws to adjust the control strategy, thereby compensating for the control deviation caused by the change of aerodynamic performance and ensuring the stability and control accuracy of the aircraft; the main wingspan control sensitivity compensation channel focuses on the sensitivity of the aircraft control system. Sensitivity refers to the speed and degree of response of the control system to changes in input signals. In a complex and changeable flight environment, it is crucial to ensure that the control system has appropriate sensitivity. The main wingspan control load characteristic compensation channel focuses on the changes in the control characteristics of the aircraft when it carries different loads. The changes in load will directly affect the flight performance and stability of the aircraft, thereby placing higher requirements on the control system. This channel analyzes the control characteristic data of the aircraft under different load conditions, identifies the impact of load changes on the main wingspan control, and generates load characteristic compensation parameters based on these rules to adjust the control strategy, thereby compensating for the control deviation caused by load changes, which helps to ensure that the aircraft can maintain stable flight performance and safety under different load conditions.

[0036] The method further includes step S620, wherein the main wing span control aerodynamic performance compensation parameters are obtained based on the main wing span simulation control monitoring data set and the main wing span control aerodynamic performance compensation channel. The main wing span simulation control monitoring data set is preprocessed, including data cleaning, denoising, interpolation, etc., and the preprocessed monitoring data is input into the main wing span control aerodynamic performance compensation channel, which is based on the aerodynamic characteristic model and control theory of the aircraft, analyzes the specific impact of aerodynamic performance on the main wing span control under different flight conditions, and generates corresponding aerodynamic performance compensation parameters based on the aerodynamic performance analysis results and combined with the control theory, which may include but are not limited to control gain adjustment amount, phase compensation amount, frequency compensation amount, etc., and aims to compensate the control deviation caused by the change of aerodynamic performance by adjusting the control strategy.

[0037] It also includes step S630, based on the main wing span simulation control monitoring data set, according to the main wing span control sensitivity compensation channel, generating main wing span control sensitivity compensation parameters. The main wing span simulation control monitoring data set is preprocessed, including data cleaning, denoising, standardization, etc., to ensure the accuracy and consistency of the data, the preprocessed monitoring data is input into the main wing span control sensitivity compensation channel, and the preprocessed data is deeply analyzed using frequency response analysis, time domain analysis, etc. According to the sensitivity analysis results, a corresponding sensitivity compensation strategy is formulated, which may include adjusting control algorithm parameters, introducing filters, optimizing controller structures, etc. According to the formulated sensitivity compensation strategy, corresponding sensitivity compensation parameters are generated, which may include but are not limited to controller gain, filter cutoff frequency, phase compensation amount, etc.

[0038] The method further includes step S640, generating main wing span control load characteristic compensation parameters based on the main wing span simulation control monitoring data set and the main wing span control load characteristic compensation channel. The main wing span simulation control monitoring data set is preprocessed, including data cleaning, denoising, and standardization, to ensure the accuracy and consistency of the data, and the preprocessed monitoring data is input into the main wing span control load characteristic compensation channel to extract key parameters related to the load characteristics, such as load size, load distribution, load change rate, etc., and a corresponding load characteristic compensation strategy is designed, which may include adjusting control algorithm parameters, introducing adaptive control mechanisms, adding feedback loops, etc., to achieve dynamic compensation for load changes, and optimizing the parameters in the compensation strategy using optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.), which may include controller gain, time constant, filter parameters, etc., to generate main wing span control load characteristic compensation parameters.

[0039] It also includes step S650, which integrates the main span control aerodynamic performance compensation parameters, the main span control sensitivity compensation parameters and the main span control load characteristic compensation parameters to generate the main span control compensation characteristic factor. After obtaining the main span control aerodynamic performance compensation parameters, sensitivity compensation parameters and load characteristic compensation parameters, these parameters are integrated to generate the main span control compensation characteristic factor. The main span control compensation characteristic factor is a comprehensive indicator that reflects the control requirements and compensation requirements of the main span under different flight conditions. It also includes step S660, which optimizes the n-th node main span control scheme according to the main span control compensation characteristic factor to generate the n-th node main span control optimization result. Based on the generated main wingspan control compensation characteristic factors, the main wingspan control scheme of the nth node can be optimized. The optimization process takes into account factors such as the specific requirements of the flight mission, the predicted results of the flight environment, and the current state of the main wingspan to generate more accurate and reliable control instructions. The optimized main wingspan control scheme will fully consider the needs of aerodynamic performance compensation, sensitivity compensation, load characteristic compensation and other aspects to ensure that the aircraft can maintain stable flight performance and safety in a complex and changeable flight environment, which is the optimization result of the main wingspan control of the nth node.

[0040] In a possible implementation, step S620 further includes step S621, extracting the main wing span simulation aerodynamic performance data according to the main wing span simulation control monitoring data set. From the monitoring data set containing the main wing span under different flight conditions and operating states, the data items directly related to the aerodynamic performance are screened out, and necessary processing and analysis are performed to obtain a quantitative description of the aerodynamic performance of the main wing span. Specifically, the data items related to the aerodynamic performance are identified and extracted from the monitoring data set, which may include but are not limited to the lift coefficient, drag coefficient, lift-to-drag ratio, pressure distribution, moment coefficient, torsion angle of the main rotor, etc., reflecting the aerodynamic performance of the main wing span under different flight conditions. It also includes step S622, performing a flight aerodynamic support evaluation according to the main wing span simulation aerodynamic performance data to obtain the flight aerodynamic support coefficient. The aerodynamic performance data of the main wingspan in a simulated environment is used to evaluate the aerodynamic support capability of the aircraft during flight, and this capability is quantified as the flight aerodynamic support coefficient. An evaluation model is established based on physical simulation, and the aerodynamic performance data of the main wingspan is input into the evaluation model. By running the evaluation model, the input data is calculated and analyzed, and the flight aerodynamic support coefficient is output to evaluate the aerodynamic support capability of the aircraft during flight.

[0041] It also includes step S623, judging whether the flight aerodynamic support coefficient is less than / equal to the flight aerodynamic support threshold. The flight aerodynamic support threshold is a preset standard value used to judge whether the aerodynamic support capability of the aircraft meets the design requirements or safety standards. It also includes step S624, if the flight aerodynamic support coefficient is less than / equal to the flight aerodynamic support threshold, based on the main wing span simulation aerodynamic performance data and the n-th node main wing span control scheme, according to the main wing span control aerodynamic performance compensation channel, the main wing span control aerodynamic performance compensation parameter is generated. If the aerodynamic support capability is insufficient, the aerodynamic performance is compensated by adjusting the control parameters. Specifically, based on the main wingspan simulated aerodynamic performance data and the n-th node main wingspan control scheme, the specific reasons leading to the insufficient aerodynamic support capability are analyzed, and the main wingspan is used to control the aerodynamic performance compensation channel to design a compensation strategy, such as adjusting the controller gain, optimizing the control algorithm, changing the control structure, etc. The parameters in the compensation strategy are optimized through an optimization algorithm (such as a genetic algorithm, a particle swarm optimization algorithm, etc.), and the main wingspan control aerodynamic performance compensation parameters are generated to adjust the control system of the aircraft to improve its aerodynamic support capability.

[0042] In a possible implementation, step S630 further includes step S631, extracting the main wing span simulation state monitoring data according to the main wing span simulation control monitoring data set. From the data set containing the control process and state information of the main wing span under different simulation conditions, the data directly related to the current simulation state of the main wing span is screened out, which is mainly a real-time record of the main wing span when it is running in the simulation environment, and can reflect the various state parameters and performance characteristics of the main wing span, such as the deformation of the main wing span under different flight conditions, including wing tip deflection, torsion angle, etc.; vibration characteristic parameters such as vibration frequency and amplitude of the main wing span; stress distribution of key parts of the main wing span, etc. It also includes step S632, performing control parameter deviation analysis on the main wing span simulation state monitoring data based on the n-th node main wing span control scheme to obtain the main wing span control parameter deviation analysis results. From the control scheme of the n-th node main wingspan, the key control parameters and the expected ranges of these parameters are extracted, and the main wingspan simulation state monitoring data are compared with the expected ranges in the n-th node control scheme. On the basis of the comparison, the deviation between the actual value and the target value of the control parameter is calculated, and the calculated control parameter deviation is deeply analyzed to find out the cause of the deviation, and the main wingspan control parameter deviation analysis results are obtained, which clearly show the deviation status, cause of deviation and possible impact of each control parameter.

[0043] It also includes step S633, based on the main wing span control scheme of the nth node, the control response delay analysis is performed on the main wing span simulation state monitoring data to obtain the main wing span control response delay analysis result. Evaluate the time required for the main wing span to actually respond after the control system receives the control command, that is, the control response delay. Specifically, find the timestamp of the control command issuance and the actual response of the main wing span, use the timestamp of the actual response of the main wing span to subtract the timestamp of the control command issuance, and obtain the specific value of the control response delay. Perform an in-depth analysis on the calculated control response delay to find out the possible causes of the delay, evaluate the impact of the delay on the flight performance and stability of the main wing span, and obtain the main wing span control response delay analysis result. It also includes step S634, based on the main wing span control parameter deviation analysis result and the main wing span control response delay analysis result, according to the main wing span control sensitivity compensation channel, obtain the main wing span control sensitivity compensation parameter. The main wingspan control parameter deviation analysis results and the control response delay analysis results are comprehensively analyzed. Based on the analysis results, the strategy for main wingspan control sensitivity compensation is determined, such as adjusting control parameters, optimizing control algorithms, improving control structures, etc. The main wingspan control sensitivity compensation channel is used to calculate specific control sensitivity compensation parameters based on the analysis results. The calculated control sensitivity compensation parameters are verified and tested to ensure that they can effectively improve the control performance of the main wingspan.

[0044] In a possible implementation, step S640 further includes step S641, extracting aircraft simulated load distribution data and aircraft simulated center of gravity distribution data according to the main wingspan simulation control monitoring data set. The aircraft simulated load distribution data and aircraft simulated center of gravity distribution data are extracted from the main wingspan simulation control monitoring data set. Specifically, the aircraft simulated load distribution data reflects the distribution of loads (such as lift, drag, weight, etc.) borne by various parts of the aircraft under different flight stages and conditions. Load distribution is an important indicator for evaluating the structural strength, fatigue life and flight performance of the aircraft, and may include the load size, direction and changes of each wing surface, fuselage, tail and other parts over time or flight conditions; the aircraft simulated center of gravity distribution data describes the position change of the center of gravity of the aircraft under different flight states. The center of gravity position has an important influence on the stability and maneuverability of the aircraft and is an important parameter in flight control. It may include the center of gravity coordinates (such as the position in the three directions of X, Y, and Z) of the aircraft under different flight stages (such as take-off, climb, cruise, descent, landing, etc.) and conditions, the center of gravity offset and the changes over time or flight conditions.

[0045] The method further includes step S642, identifying the change trend according to the simulated load distribution data of the aircraft, and generating the identification result of the simulated load change trend. The simulated load distribution data of the aircraft is trend-identified by using a trend analysis method (such as time series analysis, etc.), including segmentation processing of the data, trend line fitting, trend direction judgment, etc., to clarify the change trend of the load distribution under different flight stages or conditions, including increase, decrease, fluctuation, etc., and generate the identification result of the simulated load change trend. The method further includes step S643, identifying the change trend according to the simulated center of gravity distribution data of the aircraft, and generating the identification result of the simulated center of gravity change trend. The simulated center of gravity distribution data of the aircraft is deeply analyzed to reveal the law and characteristics of its change over time, flight conditions or other related factors. Specifically, the simulated center of gravity distribution data of the aircraft is trend-identified by using a trend analysis method (such as time series analysis, etc.) to identify its change trend, including determining the change direction of the center of gravity position (such as upward, downward, left, right, etc.), the change rate, and the possible periodic fluctuations or sudden changes, and finally generating the identification result of the simulated center of gravity change trend.

[0046] The method further includes step S644, based on the simulated load change trend identification result, the simulated center of gravity change trend identification result and the n-th node main wing span control scheme, according to the main wing span control load characteristic compensation channel, obtaining the main wing span control load characteristic compensation parameter. After obtaining the simulated load and center of gravity change trend identification result, the simulated load change trend identification result and the simulated center of gravity change trend identification result are comprehensively analyzed with the n-th node main wing span control scheme, including evaluating the specific impact of the change of load and center of gravity on the flight performance, and how these impacts interact with the current control scheme, based on the analysis results, determining whether it is necessary to compensate the main wing span control system, and the specific requirements and goals of the compensation, such as adjusting the wing angle, control surface deflection, power distribution and other parameters to cope with the changes in load and center of gravity, according to the compensation requirements and goals, through the main wing span control load characteristic compensation channel, calculate the corresponding main wing span control load characteristic compensation parameters, these parameters should be able to accurately reflect the impact of the change of load and center of gravity on the flight performance, and provide effective compensation measures.

[0047] Step S700, based on the n-th node flight mission and the n-th node predicted flight environment, the n-th node main wingspan control scheme is obtained according to the main wingspan control decision channel. In the process of the aircraft performing the flight mission, for the flight mission of the n-th node (i.e., a specific stage or mission point) and the predicted flight environment, the main wingspan control decision channel is used to comprehensively consider the performance parameters, flight status and environmental factors of the aircraft to formulate a main wingspan control scheme that adapts to the current flight environment and mission requirements. Specifically, the main wingspan control decision channel is used to automatically or assist the pilot in formulating the optimal main wingspan control scheme according to the requirements of the flight mission and the flight environment, and uses the relevant data of the flight mission and the flight environment as input, and uses simulation to deeply analyze the data of the flight mission and the predicted flight environment, extract key information related to the main wingspan control, and formulate the main wingspan control scheme of the n-th node based on the data analysis results and in combination with the performance parameters and flight status of the aircraft, including clarifying the adjustment range, adjustment timing, adjustment method and corresponding flight parameter settings of the main wingspan. During the flight, operations shall be carried out according to the formulated main wingspan control plan of the nth node, and the status of the aircraft and changes in the flight environment shall be monitored in real time. If it is found that the actual situation deviates from the predicted results or new risk factors emerge, the main wingspan control plan shall be adjusted in time to ensure flight safety.

[0048] In the above, refer to Figure 1 The intelligent control method for the telescopic main wing span according to the embodiment of the present invention is described in detail. Figure 2 An intelligent control system for a retractable main wingspan according to an embodiment of the present invention is described.

[0049] The intelligent control system for the retractable main wing according to the embodiment of the present invention is used to solve the technical problem that the existing retractable main wing control cannot accurately predict the flight environment and make control decisions, which leads to difficulty in adapting to the diverse flight mission requirements, resulting in poor aerodynamic performance and mission flexibility of the aircraft, and realizes dynamic adjustment control of the aircraft main wing, achieving the technical effect of enhancing the aerodynamic performance and adaptability of the aircraft. The intelligent control system for the retractable main wing includes: a flight mission acquisition module 10, a flight mission feature disassembly module 20, a flight environment prediction module 30, a main wing control decision module 40, a main wing simulation control module 50, a main wing control optimization result acquisition module 60, and an aircraft main wing control module 70.

[0050] A flight mission acquisition module 10, the flight mission acquisition module 10 is used to obtain the flight mission of the aircraft; a flight mission feature disassembly module 20, the flight mission feature disassembly module 20 is used to perform multi-feature decomposition according to the flight mission to obtain multiple node flight missions; a flight environment prediction module 30, the flight environment prediction module 30 is used to extract the n-th node flight mission according to the multiple node flight missions, and predict the flight environment of the aircraft based on the n-th node flight mission to obtain the n-th node predicted flight environment, wherein n is a positive integer; a main wingspan control decision module 40, the main wingspan control decision module 40 is used to make a main wingspan control decision according to the n-th node flight mission and the n-node predicted flight environment, and generate an n-node main wingspan control Scheme; a main wingspan simulation control module 50, the main wingspan simulation control module 50 is used to activate the flight simulation platform, and perform main wingspan simulation control on the aircraft in combination with the n-th node main wingspan control scheme to obtain a main wingspan simulation control monitoring data set; a main wingspan control optimization result acquisition module 60, the main wingspan control optimization result acquisition module 60 is used to perform multi-feature compensation optimization on the n-th node main wingspan control scheme according to a multi-factor main wingspan control compensation channel in combination with the main wingspan simulation control monitoring data set to obtain an n-th node main wingspan control optimization result; an aircraft main wingspan control module 70, the aircraft main wingspan control module 70 is used to perform main wingspan control on the aircraft based on the n-th node flight mission and according to the n-node main wingspan control optimization result.

[0051] The specific configuration of the flight mission feature disassembly module 20 will be described in detail below. The flight mission feature disassembly module 20 further includes: performing flight attribute disassembly according to the flight mission to obtain the first-level disassembly result of the flight mission; performing flight state disassembly according to the first-level disassembly result of the flight mission to obtain the second-level disassembly result of the flight mission; performing flight environment disassembly according to the second-level disassembly result of the flight mission to generate the multiple node flight missions.

[0052] The specific configuration of the main wingspan control decision module 40 will be described in detail below. The main wingspan control decision module 40 may further include: performing main wingspan control record backtracking according to the aircraft to obtain a main wingspan control record set; establishing the same group of main wingspans according to the main wingspan model information of the aircraft, wherein the same group of main wingspans includes multiple main wingspans of the same model; performing control record backtracking according to the same group of main wingspans to obtain a main wingspan control record set of the same group; constructing a main wingspan control decision model according to the same group of main wingspan control record set; optimizing the decision sensitivity of the main wingspan control decision model according to the main wingspan control record set to generate a main wingspan control decision channel; based on the n-th node flight mission and the n-th node predicted flight environment, obtaining the n-th node main wingspan control plan according to the main wingspan control decision channel.

[0053] The specific configuration of the main wing span control optimization result acquisition module 60 will be described in detail below. The main wing span control optimization result acquisition module 60 may further include: the multi-factor main wing span control compensation channel includes a main wing span control aerodynamic performance compensation channel, a main wing span control sensitivity compensation channel and a main wing span control load characteristic compensation channel; based on the main wing span simulation control monitoring data set, according to the main wing span control aerodynamic performance compensation channel, obtain the main wing span control aerodynamic performance compensation parameter; based on the main wing span simulation control monitoring data set, according to the main wing span control sensitivity compensation channel, generate the main wing span control sensitivity compensation parameter; based on the main wing span simulation control monitoring data set, according to the main wing span control load characteristic compensation channel, generate the main wing span control load characteristic compensation parameter; fuse the main wing span control aerodynamic performance compensation parameter, the main wing span control sensitivity compensation parameter and the main wing span control load characteristic compensation parameter to generate the main wing span control compensation characteristic factor; optimize the n-th node main wing span control scheme according to the main wing span control compensation characteristic factor to generate the n-node main wing span control optimization result.

[0054] The specific configuration of the main wing span control optimization result acquisition module 60 will be described in detail below. The main wing span control optimization result acquisition module 60 further includes: extracting the main wing span simulation aerodynamic performance data according to the main wing span simulation control monitoring data set; performing flight aerodynamic support evaluation according to the main wing span simulation aerodynamic performance data to obtain the flight aerodynamic support coefficient; judging whether the flight aerodynamic support coefficient is less than / equal to the flight aerodynamic support threshold; if the flight aerodynamic support coefficient is less than / equal to the flight aerodynamic support threshold, based on the main wing span simulation aerodynamic performance data and the nth node main wing span control scheme, according to the main wing span control aerodynamic performance compensation channel, generating the main wing span control aerodynamic performance compensation parameter.

[0055] The specific configuration of the main wing span control optimization result acquisition module 60 will be described in detail below. The main wing span control optimization result acquisition module 60 further includes: extracting the main wing span simulation state monitoring data according to the main wing span simulation control monitoring data set; performing control parameter deviation analysis on the main wing span simulation state monitoring data based on the n-th node main wing span control scheme to obtain the main wing span control parameter deviation analysis result; performing control response delay analysis on the main wing span simulation state monitoring data based on the n-th node main wing span control scheme to obtain the main wing span control response delay analysis result; based on the main wing span control parameter deviation analysis result and the main wing span control response delay analysis result, according to the main wing span control sensitivity compensation channel, obtaining the main wing span control sensitivity compensation parameter.

[0056] The specific configuration of the main wingspan control optimization result acquisition module 60 will be described in detail below. The main wingspan control optimization result acquisition module 60 further includes: extracting aircraft simulated load distribution data and aircraft simulated center of gravity distribution data according to the main wingspan simulation control monitoring data set; performing change trend identification according to the aircraft simulated load distribution data to generate a simulated load change trend identification result; performing change trend identification according to the aircraft simulated center of gravity distribution data to generate a simulated center of gravity change trend identification result; based on the simulated load change trend identification result, the simulated center of gravity change trend identification result and the nth node main wingspan control scheme, according to the main wingspan control load characteristic compensation channel, obtaining the main wingspan control load characteristic compensation parameter.

[0057] The intelligent control system for a telescopic main wing provided in an embodiment of the present invention can execute the intelligent control method for a telescopic main wing provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0058] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0059] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. An intelligent control method for a telescopic main wing span, characterized in that: The method comprises: Obtain flight missions for aircraft; Perform multi-feature decomposition according to the flight mission to obtain multiple node flight missions; Extracting an nth node flight mission according to the plurality of node flight missions, and performing flight environment prediction on the aircraft based on the nth node flight mission to obtain an nth node predicted flight environment, wherein n is a positive integer; Making a main wingspan control decision according to the n-th node flight mission and the n-th node predicted flight environment, and generating a main wingspan control scheme for the n-th node; activating the flight simulation platform, performing main wingspan simulation control on the aircraft in combination with the n-th node main wingspan control scheme, and obtaining a main wingspan simulation control monitoring data set; According to the multi-factor main wingspan control compensation channel, in combination with the main wingspan simulation control monitoring data set, the n-th node main wingspan control scheme is optimized for multi-feature compensation to obtain the n-th node main wingspan control optimization result; Based on the n-node flight mission, performing main wingspan control on the aircraft according to the n-node main wingspan control optimization result; According to the multi-factor main wingspan control compensation channel, the multi-feature compensation optimization of the n-node main wingspan control scheme is performed in combination with the main wingspan simulation control monitoring data set to obtain the n-node main wingspan control optimization result, including: The multi-factor main wingspan control compensation channel includes a main wingspan control aerodynamic performance compensation channel, a main wingspan control sensitivity compensation channel and a main wingspan control load characteristic compensation channel; Based on the main wingspan simulation control monitoring data set, and according to the main wingspan control aerodynamic performance compensation channel, a main wingspan control aerodynamic performance compensation parameter is obtained; Based on the main wingspan simulation control monitoring data set, generating a main wingspan control sensitivity compensation parameter according to the main wingspan control sensitivity compensation channel; Based on the main wingspan simulation control monitoring data set, generating a main wingspan control load characteristic compensation parameter according to the main wingspan control load characteristic compensation channel; The main span control aerodynamic performance compensation parameter, the main span control sensitivity compensation parameter and the main span control load characteristic compensation parameter are integrated to generate a main span control compensation characteristic factor; The n-node main wingspan control scheme is optimized according to the main wingspan control compensation characteristic factor to generate the n-node main wingspan control optimization result.

2. The intelligent control method for telescopic main wingspan according to claim 1, characterized in that: According to the flight mission, multiple feature decomposition is performed to obtain multiple node flight missions, including: Disassemble the flight attributes according to the flight mission to obtain a first-level disassembly result of the flight mission; Perform flight status disassembly according to the first-level disassembly result of the flight mission to obtain the second-level disassembly result of the flight mission; The flight environment is disassembled according to the secondary disassembly result of the flight mission to generate the multiple node flight missions.

3. The intelligent control method for telescopic main wingspan according to claim 1, characterized in that: A main wingspan control decision is made according to the n-th node flight mission and the n-th node predicted flight environment, and a main wingspan control scheme for the n-th node is generated, including: Performing a main wingspan control record backtracking according to the aircraft to obtain a main wingspan control record set; According to the main wingspan model information of the aircraft, a main wingspan of the same group is established, wherein the main wingspan of the same group includes a plurality of main wingspans of the same model; Perform control record backtracking according to the main wingspan of the same group to obtain a control record set of the main wingspan of the same group; Constructing a main wingspan control decision model according to the main wingspan control record set of the same group; Optimizing the decision sensitivity of the main wingspan control decision model according to the main wingspan control record set to generate a main wingspan control decision channel; Based on the n-node flight mission and the n-node predicted flight environment, the n-node main wingspan control scheme is obtained according to the main wingspan control decision channel.

4. The intelligent control method for telescopic main wingspan according to claim 1, characterized in that: Based on the main wingspan simulation control monitoring data set and according to the main wingspan control aerodynamic performance compensation channel, a main wingspan control aerodynamic performance compensation parameter is obtained, including: Extracting main wing span simulation aerodynamic performance data according to the main wing span simulation control monitoring data set; Perform flight aerodynamic support evaluation based on the main wingspan simulated aerodynamic performance data to obtain a flight aerodynamic support coefficient; Determining whether the flight aerodynamic support coefficient is less than / equal to a flight aerodynamic support threshold; If the flight aerodynamic support coefficient is less than / equal to the flight aerodynamic support threshold, the main wingspan control aerodynamic performance compensation parameter is generated based on the main wingspan simulated aerodynamic performance data and the n-th node main wingspan control scheme and according to the main wingspan control aerodynamic performance compensation channel.

5. The intelligent control method for telescopic main wingspan according to claim 1, characterized in that: Based on the main wingspan simulation control monitoring data set and according to the main wingspan control sensitivity compensation channel, a main wingspan control sensitivity compensation parameter is generated, including: Extracting main wingspan simulation state monitoring data according to the main wingspan simulation control monitoring data set; Performing control parameter deviation analysis on the main wingspan simulation state monitoring data based on the n-th node main wingspan control scheme to obtain a main wingspan control parameter deviation analysis result; Performing control response delay analysis on the main wingspan simulation state monitoring data based on the n-th node main wingspan control scheme to obtain a main wingspan control response delay analysis result; Based on the main span control parameter deviation analysis result and the main span control response delay analysis result, the main span control sensitivity compensation parameter is obtained according to the main span control sensitivity compensation channel.

6. The intelligent control method for telescopic main wingspan according to claim 1, characterized in that: Based on the main wingspan simulation control monitoring data set and according to the main wingspan control load characteristic compensation channel, a main wingspan control load characteristic compensation parameter is generated, including: Extracting aircraft simulated load distribution data and aircraft simulated center of gravity distribution data according to the main wingspan simulated control monitoring data set; Perform change trend identification based on the simulated load distribution data of the aircraft to generate a simulated load change trend identification result; Perform change trend identification based on the simulated center of gravity distribution data of the aircraft to generate a simulated center of gravity change trend identification result; Based on the simulated load change trend identification result, the simulated center of gravity change trend identification result and the n-th node main wingspan control scheme, the main wingspan control load characteristic compensation parameter is obtained according to the main wingspan control load characteristic compensation channel.

7. An intelligent control system for a telescopic main wing span, characterized in that: The system is used to implement the intelligent control method for a retractable main wing span according to any one of claims 1 to 6, and the system comprises: A flight mission acquisition module, wherein the flight mission acquisition module is used to obtain the flight mission of the aircraft; A flight mission feature disassembly module, wherein the flight mission feature disassembly module is used to perform multi-feature disassembly according to the flight mission to obtain multiple node flight missions; A flight environment prediction module, the flight environment prediction module is used to extract an n-th node flight mission according to the multiple node flight missions, and perform flight environment prediction on the aircraft based on the n-th node flight mission to obtain an n-th node predicted flight environment, wherein n is a positive integer; A main wingspan control decision module, the main wingspan control decision module is used to make a main wingspan control decision according to the n-th node flight mission and the n-th node predicted flight environment, and generate an n-th node main wingspan control plan; A main wingspan simulation control module, the main wingspan simulation control module is used to activate the flight simulation platform, perform main wingspan simulation control on the aircraft in combination with the n-th node main wingspan control scheme, and obtain a main wingspan simulation control monitoring data set; A main wingspan control optimization result acquisition module, wherein the main wingspan control optimization result acquisition module is used to perform multi-feature compensation optimization on the n-node main wingspan control scheme according to a multi-factor main wingspan control compensation channel in combination with the main wingspan simulation control monitoring data set to obtain an n-node main wingspan control optimization result; An aircraft main wingspan control module, wherein the aircraft main wingspan control module is used to perform main wingspan control on the aircraft based on the n-node flight mission and according to the n-node main wingspan control optimization result.

Images