Intelligent control method and system for variable main wingspan

Abstract

An intelligent control method and system for a telescopic main wingspan, which method and system relates to the technical field of intelligent control. The method comprises: obtaining a flight task of an aircraft (S100); on the basis of the flight task, performing multi-feature decomposition to obtain a plurality of node flight tasks (S200); on the basis of the plurality of node flight tasks, extracting an nth node flight task, and on the basis of the nth node flight task, performing flight environment prediction on the aircraft, so as to obtain an nth predicted node flight environment, wherein n is a positive integer (S300); on the basis of the nth node flight task and the nth predicted node flight environment, making a decision regarding main wingspan control, and generating an nth node main wingspan control scheme (S400); activating a flight simulation platform, and performing simulated main wingspan control on the aircraft in view of the nth node main wingspan control scheme, so as to obtain a monitoring data set of the simulated main wingspan control (S500); on the basis of a multi-factor main wingspan control compensation channel and in view of the monitoring data set of the simulated main wingspan control, performing multi-feature compensation optimization on the nth node main wingspan control scheme, so as to obtain an nth node main wingspan control optimization result (S600); and on the basis of the nth node flight task, performing main wingspan control on the aircraft on the basis of the nth node main wingspan control optimization result (S700). The technical problem of an aircraft having a poor aerodynamic performance and task flexibility due to it being difficult to make existing telescopic-main-wingspan control applicable to diversified flight task requirements since the existing telescopic-main-wingspan control cannot accurately predict a flight environment and make a control decision is solved, and the dynamic adjustment and control of a main wingspan of an aircraft is realized, thereby achieving the technical effect of enhancing the aerodynamic performance and adaptability of the aircraft.

Description

Intelligent control method and system for telescopic main wingspan TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, in particular to an intelligent control method and system for telescopic main wingspan. BACKGROUND

[0002] With the rapid development of aerospace technology, the traditional fixed-wing aircraft design often cannot meet the performance requirements under various flight conditions in the face of flight environment and diversified flight tasks. Aircraft design increasingly pursues high efficiency, multi-task adaptability and intelligentization, especially in situations requiring high maneuverability and adaptability. Telescopic 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 task flexibility of the aircraft can be significantly improved. However, how to accurately and intelligently control the telescopic main wingspan to adapt to complex and changing flight tasks and environments is a technical problem that needs to be solved. Traditional main wingspan control methods often rely on preset flight modes and fixed control logic, which cannot meet the needs of modern aircraft for high adaptability and intelligentization. In particular, when performing multi-task flights, traditional methods cannot accurately decompose and respond to the specific requirements of each node flight task, resulting in limited flight performance.

[0003] Therefore, in the current telescopic main wingspan control related technology, there is a technical problem that the flight environment cannot be accurately predicted and control decisions cannot be made, which makes it difficult to adapt to diversified flight task requirements, resulting in poor aerodynamic performance and task flexibility of the aircraft. SUMMARY

[0004] The present application provides an intelligent control method and system for telescopic main wingspan, which adopts high-precision flight task analysis and prediction, accurate flight environment prediction, main wingspan simulation control and multi-feature compensation optimization, etc. The technical problems of the existing telescopic main wingspan control, such as the inability to accurately predict the flight environment and make control decisions, which makes it difficult to adapt to diversified flight task requirements, resulting in poor aerodynamic performance and task flexibility of the aircraft, are solved. The dynamic adjustment control of the main wingspan of the aircraft is realized, and the technical effect of enhancing the aerodynamic performance and adaptability of the aircraft is achieved.

[0005] The application provides an intelligent control method for telescopic main wingspan, which comprises the following steps: obtaining a flight task of an aircraft; performing multi-feature decomposition according to the flight task to obtain a plurality of node flight tasks; extracting an n th node flight task according to the plurality of node flight tasks, and performing flight environment prediction on the aircraft based on the n th node flight task to obtain an n th node predicted flight environment, wherein n is a positive integer; performing main wingspan control decision according to the n th node flight task and the n th node predicted flight environment to generate an n th node main wingspan control scheme; activating a flight simulation platform, combining the n th node main wingspan control scheme to perform main wingspan simulation control on the aircraft, and obtaining main wingspan simulation control monitoring data set; performing multi-feature compensation optimization on the n th node main wingspan control scheme according to a multi-factor main wingspan control compensation channel and combining the main wingspan simulation control monitoring data set to obtain an n th node main wingspan control optimization result; and performing main wingspan control on the aircraft according to the n th node main wingspan control optimization result based on the n th node flight task.

[0006] In possible implementation manners, according to the flight task, multi-feature decomposition is performed to obtain a plurality of node flight tasks, and the following processing is performed: flight attribute decomposition is performed according to the flight task to obtain a flight task first-level decomposition result; flight state decomposition is performed according to the flight task first-level decomposition result to obtain a flight task second-level decomposition result; and flight environment decomposition is performed according to the flight task second-level decomposition result to generate the plurality of node flight tasks.

[0007] In possible implementation manners, according to the n th node flight task and the n th node predicted flight environment, main wingspan control decision is performed to generate an n th node main wingspan control scheme, and the following processing is performed: main wingspan control record backtracking is performed according to the aircraft to obtain a main wingspan control record set; a same-group main wingspan is established according to main wingspan model information of the aircraft, wherein the same-group main wingspan comprises a plurality of same-model main wingspans; control record backtracking is performed according to the same-group main wingspan to obtain a same-group main wingspan control record set; a main wingspan control decision model is constructed according to the same-group main wingspan control record set; decision sensitivity optimization is performed on the main wingspan control decision model according to the main wingspan control record set to generate a main wingspan control decision channel; and the n th node main wingspan control scheme is obtained according to the main wingspan control decision channel based on the n th node flight task and the n th node predicted flight environment.

[0008] In a possible implementation, according to a multi-factor main wingspan control compensation channel, a multi-characteristics compensation optimization is performed on the n th node main wingspan control scheme in combination with the main wingspan simulation control monitoring data set, and an n th node main wingspan control optimization result is obtained, and the following processing is further 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, a main wingspan control aerodynamic performance compensation parameter is obtained according to the main wingspan control aerodynamic performance compensation channel; based on the main wingspan simulation control monitoring data set, a main wingspan control sensitivity compensation parameter is generated 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 fused to generate a main wingspan control compensation characteristic factor; and the n th node main wingspan control scheme is optimized according to the main wingspan control compensation characteristic factor to generate the n th node main wingspan control optimization result.

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

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

[0011] In a possible implementation, based on the main wingspan simulation control monitoring data set, the main wingspan control load feature compensation parameter is generated according to the main wingspan control load feature compensation channel, and the following processing is further 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, a change trend is identified, and a simulation load change trend identification result is generated; according to the aircraft simulation center of gravity distribution data, a change trend is identified, and a simulation center of gravity change trend identification result is generated; based on the simulation load change trend identification result, the simulation center of gravity change trend identification result and the n th node main wingspan control scheme, the main wingspan control load feature compensation parameter is obtained according to the main wingspan control load feature compensation channel.

[0012] The application also provides an intelligent control system for a telescopic main wingspan, comprising: a flight task obtaining module, configured to obtain a flight task of an aircraft; a flight task feature disassembling module, configured to disassemble multiple features according to the flight task, and obtain multiple node flight tasks; a flight environment prediction module, configured to extract an n th node flight task according to the multiple node flight tasks, and predict a flight environment of the aircraft based on the n th node flight task, and obtain an n th node predicted flight environment, wherein n is a positive integer; a main wingspan control decision module, configured to make a main wingspan control decision according to the n th node flight task and the n th node predicted flight environment, and generate an n th node main wingspan control scheme; a main wingspan simulation control module, configured to activate a flight simulation platform, and 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 obtaining module, configured to perform multi-feature compensation optimization on the n th 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, and obtain an n th node main wingspan control optimization result; and an aircraft main wingspan control module, configured to perform main wingspan control on the aircraft according to the n th node main wingspan control optimization result based on the n th node flight task.

[0013] The intelligent control method and system for telescopic main wing span are used to obtain the flight task of the aircraft, perform multi-feature disintegration to obtain multiple node flight tasks, perform flight environment prediction to obtain the n th node predicted flight environment, perform main wing span control decision to generate the n th node main wing span control scheme, perform main wing span simulation control to obtain the main wing span simulation control monitoring data set, perform multi-feature compensation optimization to obtain the n th node main wing span control optimization result, and perform main wing span control on the aircraft. The technical problem that the existing telescopic main wing span control cannot accurately predict the flight environment and perform control decision, thereby being difficult to adapt to diversified flight task requirements, and the aerodynamic performance and task flexibility of the aircraft are poor are solved, the dynamic adjustment control of the main wing span of the aircraft is realized, and the technical effects of enhancing the aerodynamic performance and adaptability of the aircraft are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. The flowcharts are used to illustrate the operations performed by the system according to the embodiments of the present disclosure in the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously according to needs. Meanwhile, other operations can be added to these processes, or a step or several steps can be removed from these processes.

[0015] FIG. 1 is a flowchart of the intelligent control method for telescopic main wing span provided by the embodiments of the present application;

[0016] FIG. 2 is a structural schematic diagram of the intelligent control system for telescopic main wing span provided by the embodiments of the present application.

[0017] The reference signs are explained as follows: flight task obtaining module 10, flight task feature disintegration module 20, flight environment prediction module 30, main wing span control decision module 40, main wing span simulation control module 50, main wing span control optimization result obtaining module 60, and aircraft main wing span control module 70. DETAILED DESCRIPTION

[0018] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described as follows.

[0019] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings, and the described embodiments should not be regarded as limiting the present application. All other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the present application.

[0020] In the following description, "some embodiments" are referred to, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict, and the term "first\second" referred to only distinguishes similar objects, and does not represent a specific order for the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules 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 understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0021] The embodiments of the present application provide an intelligent control method for telescopic main wing span, as shown in FIG. 1, the method comprises:

[0022] Step S100, obtaining the flight task of the aircraft. Obtaining the flight task of the aircraft specifically refers to determining how to adjust the telescopic wing span of the aircraft when performing a specific task to adapt to different flight stages and environmental conditions to achieve optimal flight performance and efficiency, wherein the telescopic wing span is an advanced aircraft design technology that allows the aircraft to dynamically adjust the span and shape of the wing during flight according to task requirements and changes in flight environment, which can significantly improve the multi-task adaptability and flight performance of the aircraft; When obtaining the flight task of the aircraft, the flight environment and each stage of flight need to be considered, specifically, analyzing various environmental conditions that may be encountered in the flight task, such as atmospheric density, temperature, wind speed, wind direction, etc. These environmental factors will affect the aerodynamic performance and stability of the aircraft, and need to be adjusted by adjusting the telescopic wing span to optimize the aerodynamic layout of the aircraft. The flight task is divided into different stages, such as take-off, climb, cruise, descent and landing, etc. Each stage has different performance requirements for the aircraft, and a corresponding telescopic wing adjustment scheme needs to be developed, for example, in the take-off and landing stages, the wing area needs to be reduced to reduce drag; In the cruise stage, the wing area needs to be increased to increase the lift.

[0023] Step S200, according to the flight task, multi-feature disassembly is carried out, and a plurality of node flight tasks are obtained. The complex flight task is refined into a series of subtasks (node flight tasks) with clear objectives and execution conditions, so as to better perform task planning, execution and monitoring, and help to improve the executability, manageability and flexibility of the task. Specifically, the overall objective, requirement, constraint condition and the like of the flight task are determined, including the take-off point, target point, flight height, speed, heading, load and the like of the aircraft, a comprehensive feature analysis of the flight task is performed, including the time feature (such as take-off time, arrival time and the like), space feature (such as flight route, height change and the like), physical feature (such as load mass, size and the like), technical feature (such as communication requirement, navigation accuracy and the like) and the like of the task, the complex flight task is disassembled into a plurality of node flight tasks, which can include take-off preparation, flight phase division, specific flight action execution and the like. For example, a flight task of using an aircraft with telescopic wings to conduct high-altitude scientific exploration can be disassembled into take-off preparation (such as aircraft inspection, fuel filling, telescopic wing unfolding and adjustment and the like), climbing phase (climbing according to a preset climbing trajectory and speed, while adjusting the telescopic wing to optimize the aerodynamic performance), cruising phase (cruising at a predetermined height and speed, and performing scientific exploration tasks such as image shooting and data collection), maneuvering adjustment (the aircraft performs necessary maneuvering adjustment according to the needs of the exploration task, such as changing the flight direction, height or speed and the like), descending and landing (descending and landing according to a preset descending trajectory and speed) and the like node flight tasks.

[0024] In a possible implementation, step S200 further includes step S210, flight attribute disassembly is performed according to the flight task, and a flight task first-level disassembly result is obtained. Flight attribute disassembly specifically refers to decomposing the flight task into several key stages according to flight attributes, such as the take-off stage, the cruising stage, the landing stage and the like, and obtaining the flight task first-level disassembly result, that is, including the take-off stage, the process that the aircraft reaches a safe take-off speed from a stationary state and leaves the ground; the cruising stage, the stage that the aircraft continuously flies at a stable height and speed, and the main task is to advance according to the predetermined route; and the landing stage, the process that the aircraft gradually reduces the speed from the cruising height and finally safely lands.

[0025] Further comprising a step S220 of performing flight state decomposition according to the first-level decomposition result of the flight mission to obtain a second-level decomposition result of the flight mission. The flight state decomposition specifically refers to further decomposing and refining each stage on the basis of the first-level decomposition to obtain the second-level decomposition result of the flight mission. Specifically, the take-off stage is decomposed into a ground taxi stage (a preparation stage before the aircraft accelerates to a take-off speed on the runway), a take-off wheel lifting stage (a process in which the front wheels of the aircraft are lifted to start leaving the ground), and a climbing stage (a process in which the aircraft continues to climb to a safe cruising altitude); the cruising stage is decomposed into route planning (determining the specific flight route and altitude of the aircraft), speed control (adjusting the speed of the aircraft according to the requirements of the flight mission), and altitude maintenance (ensuring that the aircraft stably flies at the predetermined altitude); and the landing stage is decomposed into an approach stage (a process in which the aircraft starts to descend and prepares to enter the landing path), a pull-in stage (a process in which the aircraft adjusts the attitude to slow down the descent speed when approaching the runway), and a ground contact stage (a process in which the front wheels of the aircraft contact the ground, and then the whole machine lands stably).

[0026] Further comprising a step S230 of performing flight environment decomposition according to the second-level decomposition result of the flight mission to generate the plurality of node flight missions. The flight environment decomposition is based on the second-level decomposition result, in-depth analysis and consideration of 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.), terrain (such as mountainous areas, plains, lakes, oceans, etc.), airspace restrictions (such as no-fly zones, restricted areas, route congestion, etc.), and the flight mission is divided into a plurality of specific, interrelated but relatively independent task units, i.e., a plurality of node flight missions, each node task contains clear starting point, end point, flight path, flight speed, altitude, attitude, etc. requirements, and corresponding control strategies, including flight path planning, speed control, altitude maintenance, attitude adjustment, emergency handling, etc. For example, the take-off stage node task may include node tasks such as taxiing to a designated location, accelerating to a take-off speed, lifting the wheels and stably climbing, each of which needs to consider the influence of wind speed, wind direction and other meteorological factors; the cruising stage node task may include node tasks such as flying according to the predetermined route, maintaining a specific speed and altitude, and performing necessary maneuvering actions, which need to consider factors such as terrain, airspace restrictions, etc.; and the landing stage node task may include node tasks such as approaching to a specified altitude and speed, adjusting the attitude to prepare for landing, stably contacting the ground and decelerating taxiing, etc., which need to pay special attention to the influence of meteorological conditions on landing safety.

[0027] Step S300, according to the plurality of node flight tasks, extract the nth node flight task, and based on the nth node flight task, predict the flight environment of the aircraft, and obtain the nth node predicted flight environment, wherein n is a positive integer. From the plurality of node flight tasks obtained by disassembling, the nth node flight task is selected and extracted, n is used to identify the position of the node flight task in the whole task sequence, the extracted nth node flight task is analyzed in depth, the flight environment of the aircraft is predicted, for example, based on a numerical weather prediction 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 wind speed, etc.) and weather conditions (such as sunny, rainy, foggy, etc.); using GIS technology, three-dimensional modeling and analysis of topography are carried out, and various flight environment characteristics that may be encountered in the flight task execution process are predicted, which may include topography (such as mountains, oceans, cities, etc.), airspace restrictions (such as no-fly zones, restricted areas, etc.), and other possible flight obstacles (such as bird activity areas, other aircraft activity tracks, etc.), and finally the nth node predicted flight environment is obtained.

[0028] Step S400, according to the nth node flight task and the nth node predicted flight environment, make a main wing span control decision, and generate a nth node main wing span control scheme. In the process of executing the flight task by the aircraft, for the flight task of the nth node (i.e. a specific stage or task point) and the flight environment predicted thereby, the performance parameters, flight state and environmental factors of the aircraft are comprehensively considered to dynamically adjust and optimize the main wing span of the aircraft, so as to generate a main wing span control scheme that adapts to the current flight environment and task demand. Specifically, the nth node predicted flight environment is comprehensively evaluated to obtain factors affecting the main wing span of the aircraft, such as strong wind shear, terrain obstacles, airspace restrictions, etc., then a main wing span control decision is made, which may include adjusting the main wing span according to the characteristics of the flight environment to maintain the stability and maneuverability of the aircraft, for example, in the strong wind shear area, the main wing span needs to be reduced to reduce the wind resistance and crosswind effect, in the complex terrain area, the main wing span needs to be increased to improve the lift and maneuverability; optimizing the main wing span to improve the flight efficiency, for example, in the long-distance cruising stage, the lift-drag ratio is optimized by adjusting the main wing span to reduce fuel consumption and flight time; considering the airspace restrictions and the activity tracks of other aircraft to avoid conflicts, if necessary, adjusting the main wing span to change the cross-sectional area and radar reflection characteristics of the aircraft; according to the main wing span control decision result, the nth node main wing span control scheme is generated, which clearly defines the adjustment range, adjustment time, adjustment method of the main wing span and the corresponding flight parameter settings, etc.

[0029] In a possible implementation, step S400 further includes step S410, obtaining a main wing span control record set by performing main wing span control record backtracking according to the aircraft. The backtracking and collection of main wing span control records of a specific aircraft in previous flight missions can include information such as adjustment of the main wing span in different flight stages, reasons for adjustment (such as wind speed change, flight height adjustment, etc.), and effects after adjustment. By backtracking these records, a comprehensive main wing span control record set can be formed. Step S420 further includes establishing a same-group main wing span according to main wing span model information of the aircraft, where the same-group main wing span includes a plurality of same-model main wing spans. After obtaining the main wing span model information of the specific aircraft, a same-group main wing span set can be established, including all aircrafts with the same model main wing span, to expand the data sample size and improve the accuracy and reliability of subsequent analysis. The aircrafts in the same-group main wing span have similarities in aerodynamic characteristics and control strategies. Step S430 further includes performing control record backtracking according to the same-group main wing span to obtain a same-group main wing span control record set. After establishing the same-group main wing span, the main wing span control records of each aircraft in the same group are backtracked, covering the historical control records of the entire same-group aircraft. By collecting these records, a more comprehensive same-group main wing span control record set can be formed.

[0030] Step S440 further includes constructing a main wing span control decision model according to the same-group main wing span control record set. All relevant data are collected from the same-group main wing span control record set, including but not limited to flight mission information, flight environment parameters (such as wind speed, wind direction, temperature, air pressure, etc.), main wing span adjustment records (such as adjustment time, adjustment amount, adjustment effect, etc.), data preprocessing (such as data cleaning, removing corrected abnormal values and missing values, standardization processing), and then extracting features that affect main wing span control decisions from the original data, such as flight height, speed, acceleration, and environment parameters. A decision model is constructed based on machine learning (such as neural network, decision tree, etc.), the preprocessed data set is divided into a training set, a validation set, and a test set, the training set is used to train the decision model, the model parameters are adjusted to optimize the prediction performance of the model, the validation set data is used to verify the trained model, the main wing span control decision model is determined, the test set data is used to evaluate the performance of the finally determined model, and the model is further optimized according to the evaluation result. It can include adjusting the model structure.

[0031] The method also includes step S450 of performing decision sensitivity optimization on 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 response capability of the main wingspan control decision model to changes in input parameters, ensuring that the model can quickly and accurately make main wingspan adjustment decisions under different flight conditions and task requirements, so that the model can better adapt to the complexity and uncertainty in actual flight. Specifically, the main wingspan control record set is analyzed in depth to identify key factors and parameters affecting main wingspan adjustment, the output response of the model under different input conditions is evaluated, potential sensitivity problems and deficiencies are identified, sensitivity analysis methods (such as local sensitivity analysis and global sensitivity analysis) are used to evaluate the influence of model parameters on output results, determine which parameters have a significant impact on main wingspan adjustment decisions, and the range and threshold of these parameters, based on the sensitivity evaluation results, the model is adjusted and optimized, the optimized model is verified using simulation tools, the main wingspan adjustment process under different flight conditions and task requirements is simulated to evaluate the performance of the model in the simulation environment, ensuring 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 decisions of the aircraft's main wingspan adjustment.

[0032] The method also includes step S460 of obtaining the nth node main wingspan control scheme based on the nth node flight task and the nth node predicted flight environment according to the main wingspan control decision channel. After obtaining the main wingspan control decision channel, it can be applied to specific flight tasks. For the nth node flight task, the flight environment of the node (such as wind speed, wind direction, temperature, etc.) needs to be predicted first, then according to the requirements of the flight task and the predicted flight environment information, the corresponding parameters are input through the main wingspan control decision channel, the model will calculate and predict based on these input parameters, and output the nth node main wingspan control scheme, which will guide the aircraft how to adjust the main wingspan at the nth node to adapt to the changes in flight task and flight environment.

[0033] Step S500, activate the flight simulation platform, combine the nth node main wingspan control scheme to carry out main wingspan simulation control on the aircraft, and obtain a main wingspan simulation control monitoring data set. The flight simulation platform is a system integrating high-precision physical models, real-time computing capabilities, and highly visual interfaces, used to simulate the behavior and performance of the 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 nth 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 nth node main wingspan control scheme. By simulating the takeoff, climb, cruise, descent, and landing stages of the aircraft, the effects of main wingspan changes on aircraft performance are observed and recorded. After the main wingspan simulation control of the aircraft, the flight simulation platform generates a main wingspan simulation control monitoring data set, including main wingspan simulation aerodynamic performance data and main wingspan simulation state monitoring data. Specifically, the main wingspan simulation aerodynamic performance data may include lift coefficient (the effect of main wingspan changes on the lift of the aircraft), drag coefficient (the effect of main wingspan changes on the drag of the aircraft), lift-drag ratio (the energy efficiency ratio of the aircraft at different main wingspans), stability (including pitch stability, roll stability, etc.), other aerodynamic parameters (such as pressure distribution, airflow speed, etc.), etc. The main wingspan simulation state monitoring data may include flight trajectory (recording the flight path and altitude changes of the aircraft during simulation), 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 surfaces (such as ailerons, elevators, etc.), etc. After obtaining the main wingspan simulation control monitoring data set, in-depth data analysis is required. By comparing the aerodynamic performance and state monitoring data at different main wingspans, the effectiveness and optimization space of the nth node main wingspan control scheme can be evaluated.

[0034] Step S600, according to the multi-factor main wingspan control compensation channel, the main wingspan simulation control monitoring data set is combined to carry out multi-feature compensation optimization on the n node main wingspan control scheme, and the n node main wingspan control optimization result is obtained. The multi-factor main wingspan control compensation channel is actually a mathematical model for compensation optimization, which considers multiple factors affecting the main wingspan control effect, such as aerodynamic performance, structural strength, flight environment (such as wind speed, temperature, humidity, etc.), flight task demand, etc. The relationship between these factors and the main wingspan control effect is described by mathematical method, and compensation strategies based on these relationships are provided. The main wingspan simulation control monitoring data set is combined to carry out multi-feature compensation optimization on the n node main wingspan control scheme. 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 is matched with the multi-factor main wingspan control compensation model, 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 result, a multi-feature compensation strategy is developed, 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 simulation flight platform for multiple iterations. In each iteration, the compensation strategy is adjusted according to the new monitoring data set. After multi-feature compensation optimization, the n node main wingspan control optimization result is 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 task requirements.

[0035] In a possible implementation, step S600 further includes step S610, 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 the aerodynamic performance of the aircraft during flight, and the change of the aerodynamic performance may be caused by various factors such as flight speed, height, attitude angle, external airflow condition, etc. The main wingspan control aerodynamic performance compensation channel identifies the influence law of the aerodynamic performance on the main wingspan control under different flight conditions by analyzing the aerodynamic performance data of the aircraft, and generates corresponding aerodynamic performance compensation parameters based on the law to adjust the control strategy, thereby compensating for the control deviation caused by the change of the 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 control system of the aircraft. Sensitivity refers to the response speed and degree of the control system to the change of the input signal, and it is crucial to ensure that the control system has appropriate sensitivity in the complex and changeable flight environment. The main wingspan control load characteristic compensation channel focuses on the change of the control characteristics of the aircraft when carrying different loads. The change of the load will directly affect the flight performance and stability of the aircraft, thereby putting higher requirements on the control system. This channel identifies the influence law of the load change on the main wingspan control by analyzing the control characteristic data of the aircraft under different load conditions, and generates load characteristic compensation parameters based on the law to adjust the control strategy, thereby compensating for the control deviation caused by the change of the load, and helping to ensure that the aircraft can maintain stable flight performance and safety under different load conditions

[0036] Step S620 is further included, based on the main wingspan simulation control monitoring data set, a main wingspan control aerodynamic performance compensation parameter is obtained according to the main wingspan control aerodynamic performance compensation channel. The pre-processing of the main wingspan simulation control monitoring data set includes data cleaning, denoising, interpolation, etc. The pre-processed monitoring data is input into the main wingspan control aerodynamic performance compensation channel, which analyzes the specific influence of the aerodynamic performance on the main wingspan control under different flight conditions based on the aerodynamic characteristics model of the aircraft and the control theory, and generates corresponding aerodynamic performance compensation parameters according to the aerodynamic performance analysis results combined with the control theory, which may include but is not limited to control gain adjustment amount, phase compensation amount, frequency compensation amount, etc., aiming to compensate for the control deviation caused by the change of the aerodynamic performance by adjusting the control strategy.

[0037] The step S630 further includes 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. The main wingspan 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 wingspan control sensitivity compensation channel. Frequency response analysis, time domain analysis, etc. are used to analyze the preprocessed data in depth. According to the sensitivity analysis result, a corresponding sensitivity compensation strategy is formulated, which may include adjusting control algorithm parameters, introducing filters, optimizing controller structure, etc. According to the formulated sensitivity compensation strategy, a corresponding sensitivity compensation parameter is generated, which may include but is not limited to controller gain, filter cutoff frequency, phase compensation amount, etc.

[0038] The step S640 further includes generating a main wingspan control load feature compensation parameter according to the main wingspan control load feature compensation channel based on the main wingspan simulation control monitoring data set. The main wingspan 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 wingspan control load feature compensation channel. Key parameters related to load features, such as load size, load distribution, load change rate, etc., are extracted. A corresponding load feature 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. Optimization algorithms such as genetic algorithms, particle swarm optimization algorithms, etc. are used to optimize the parameters in the compensation strategy, which may include controller gain, time constant, filter parameters, etc., to further generate the main wingspan control load feature compensation parameter.

[0039] The step S650 further includes fusing 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 a main wing span control compensation characteristic factor. After obtaining the main wing span control aerodynamic performance compensation parameter, the sensitivity compensation parameter and the load characteristic compensation parameter, these parameters are fused to generate a main wing span control compensation characteristic factor, which is a comprehensive index reflecting the control demand and compensation requirement of the main wing span under different flight conditions. The step S660 further includes optimizing the main wing span control scheme of the nth node according to the main wing span control compensation characteristic factor to generate the main wing span control optimization result of the nth node. Based on the generated main wing span control compensation characteristic factor, the main wing span control scheme of the nth node can be optimized, and the optimization process considers the specific requirements of the flight task, the prediction results of the flight environment and the current state of the main wing span and other factors to generate more accurate and reliable control instructions. The optimized main wing span control scheme fully considers the requirements of aerodynamic performance compensation, sensitivity compensation and 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, that is, the main wing span control optimization result of the nth node.

[0040] In a possible implementation, the step S620 further includes a step S621 of extracting 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 main wing span aerodynamic performance. Specifically, the data items related to the aerodynamic performance are identified and extracted from the monitoring data set, which can include but is not limited to lift coefficient, drag coefficient, lift-drag ratio, pressure distribution, moment coefficient, twist angle of the main rotor and the like, reflecting the aerodynamic performance of the main wing span under different flight conditions. The step S622 further includes evaluating flight aerodynamic support according to the main wing span simulation aerodynamic performance data to obtain a flight aerodynamic support coefficient. The flight aerodynamic support coefficient is obtained by using the aerodynamic performance data of the main wing span in the simulation environment to evaluate the aerodynamic support capability obtained by the aircraft during flight and quantifying this capability. An evaluation model is established based on physical simulation, the aerodynamic performance data of the main wing span is input into the evaluation model, the input data is calculated and analyzed by running the evaluation model, and the flight aerodynamic support coefficient is output, which is used to evaluate the aerodynamic support capability obtained by the aircraft during flight.

[0041] The step S623 further comprises judging whether the flight aerodynamic support coefficient is less than / equal to a flight aerodynamic support threshold value. The flight aerodynamic support threshold value is a preset standard value for judging whether the aerodynamic support capability of the aircraft meets the design requirements or safety standards. The step S624 further comprises, if the flight aerodynamic support coefficient is less than / equal to the flight aerodynamic support threshold value, generating the main wing span control aerodynamic performance compensation parameter according to the main wing span control aerodynamic performance compensation channel based on the main wing span simulation aerodynamic performance data and the n-th node main wing span control scheme. If the aerodynamic support capability is insufficient, the aerodynamic performance is compensated by adjusting the control parameter. Specifically, the specific reason for causing the insufficient aerodynamic support capability is analyzed according to the main wing span simulation aerodynamic performance data and the n-th node main wing span control scheme, the compensation strategy is designed by using the main wing span control aerodynamic performance compensation channel, such as adjusting the controller gain, optimizing the control algorithm, changing the control structure, etc., the parameters in the compensation strategy are optimized by using the optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, etc.), and the main wing span control aerodynamic performance compensation parameter is generated for adjusting the control system of the aircraft to improve the aerodynamic support capability thereof.

[0042] In a possible implementation, the step S630 further comprises a step S631 of extracting 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, mainly the real-time record of the main wing span running in the simulation environment, which can reflect various state parameters and performance characteristics of the main wing span, such as the deformation amount of the main wing span under different flight conditions, including the wing tip deflection, twist angle, etc.; vibration characteristic parameters of the main wing span, such as vibration frequency and amplitude; stress distribution of key parts of the main wing span, etc. The step S632 comprises 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 a main wing span control parameter deviation analysis result. From the n-th node main wing span control scheme, the key control parameters and the expected range of these parameters are extracted, and the main wing span simulation state monitoring data is compared with the expected range in the n-th node control scheme. Based on the comparison, the deviation between the actual value and the target value of the control parameter is calculated, the control parameter deviation obtained by calculation is analyzed in depth, the reason for the deviation is found out, and the main wing span control parameter deviation analysis result is obtained, which clearly shows the deviation of each control parameter, the reason for the deviation and the possible influence.

[0043] The step S633 further comprises: based on the n-th node main wingspan control scheme, performing control response time delay analysis on the main wingspan simulation state monitoring data to obtain a main wingspan control response time delay analysis result. The evaluation control system evaluates the time required for the main wingspan to actually respond after receiving a control instruction, that is, the control response time delay, specifically, finds the time stamps of the control instruction and the actual response of the main wingspan, subtracts the time stamp of the control instruction from the time stamp of the actual response of the main wingspan to obtain a specific value of the control response time delay, deeply analyzes the calculated control response time delay to find out the causes of the time delay, evaluates the influence of the time delay on the flight performance and stability of the main wingspan, and obtains the main wingspan control response time delay analysis result. The step S634 further comprises: based on the main wingspan control parameter deviation analysis result and the main wingspan control response time delay analysis result, obtaining the main wingspan control sensitivity compensation parameter according to the main wingspan control sensitivity compensation channel. The main wingspan control parameter deviation analysis result and the control response time delay analysis result are comprehensively analyzed, the analysis result is used to determine the strategy of main wingspan control sensitivity compensation, such as adjusting the control parameter, optimizing the control algorithm, improving the control structure, etc., the main wingspan control sensitivity compensation channel is used to calculate the specific control sensitivity compensation parameter according to the analysis result, and the calculated control sensitivity compensation parameter is verified and tested to ensure that it can effectively improve the control performance of the main wingspan.

[0044] In a possible implementation, the step S640 further comprises a step S641 of extracting aircraft simulation load distribution data and aircraft simulation center of gravity distribution data from the main wingspan simulation control monitoring data set. The aircraft simulation load distribution data and the aircraft simulation center of gravity distribution data are extracted from the main wingspan simulation control monitoring data set, specifically, the aircraft simulation load distribution data reflects the distribution of loads (such as lift, drag, weight, etc.) borne by each part of the aircraft under different flight stages and conditions, the load distribution is an important index for evaluating the structural strength, fatigue life and flight performance of the aircraft, and may include the load size, direction and change over time or flight conditions of each wing surface, fuselage, tail and the like; the aircraft simulation center of gravity distribution data describes the position change of the center of gravity of the aircraft under different flight states, the position of the center of gravity has an important influence on the stability and maneuverability of the aircraft, and is an important parameter in flight control, which may include the center of gravity coordinates (such as the positions in X, Y and Z directions) of the aircraft under different flight stages (such as take-off, climbing, cruising, descending, landing, etc.) and conditions, the center of gravity offset and the change over time or flight conditions.

[0045] Further comprising step S642, trend identification is performed on the aircraft simulated load distribution data to generate simulated load trend identification results. Trend analysis methods (such as time series analysis, etc.) are used to identify trends in the aircraft simulated load distribution data, including segmenting the data, fitting trend lines, determining trend directions, etc., to determine the trends of the load distribution under different flight stages or conditions, including increases, decreases, fluctuations, etc., and to generate simulated load trend identification results. Further comprising step S643, trend identification is performed on the aircraft simulated center of gravity distribution data to generate simulated center of gravity trend identification results. In-depth analysis is performed on the aircraft simulated center of gravity distribution data to reveal its laws and characteristics of change over time, flight conditions or other related factors. Specifically, trend analysis methods (such as time series analysis, etc.) are used to identify trends in the aircraft simulated center of gravity distribution data to identify trends, including determining the direction of change of the center of gravity position (such as up, down, left, right, etc.), the rate of change, and possible periodic fluctuations or sudden changes, and finally generating simulated center of gravity trend identification results.

[0046] Further comprising step S644, based on the simulated load trend identification results, the simulated center of gravity trend identification results, and the nth node main wing span control scheme, the main wing span control load characteristic compensation channel is compensated according to the main wing span control load characteristic compensation parameters. After obtaining the simulated load and center of gravity trend identification results, the simulated load trend identification results and the simulated center of gravity trend identification results are analyzed in combination with the nth node main wing span control scheme, including evaluating the specific effects of changes in load and center of gravity on flight performance, and how these effects interact with the current control scheme, based on the analysis results, determining whether the main wing span control system needs to be compensated, and the specific requirements and targets of the compensation, such as adjusting the wing surface angle, control surface deflection, power distribution, etc. Parameters to cope with changes in load and center of gravity, according to the compensation requirements and targets, through the main wing span control load characteristic compensation channel, the corresponding main wing span control load characteristic compensation parameters are calculated, which should be able to accurately reflect the effects of changes in load and center of gravity on flight performance, and provide effective compensation measures.

[0047] Step S700, based on the n th node flight task and the n th node predicted flight environment, obtaining the n th node main wingspan control scheme according to the main wingspan control decision channel. In the process of the aircraft executing the flight task, for the flight task and the predicted flight environment of the n th node (i.e. a specific stage or task point), through the main wingspan control decision channel, the performance parameters, flight state and environmental factors of the aircraft are comprehensively considered, and the main wingspan control scheme suitable for the current flight environment and task requirement is formulated. Specifically, the main wingspan control decision channel is used to automatically or assist the pilot to formulate the optimal main wingspan control scheme according to the requirements of the flight task and the flight environment. The relevant data of the flight task and the flight environment are used as input, the data of the flight task and the predicted flight environment are analyzed in depth by simulation, the key information related to the main wingspan control is extracted, and the main wingspan control scheme of the n th node is formulated based on the data analysis result, combined with the performance parameters and the flight state of the aircraft, including the adjustment range, the adjustment opportunity, the adjustment mode of the main wingspan and the corresponding flight parameter setting. In the flight process, the n th node main wingspan control scheme is operated according to the formulated n th node main wingspan control scheme, and the state of the aircraft and the change of the flight environment are monitored in real time. If it is found that the actual situation deviates from the predicted result or new risk factors appear, the main wingspan control scheme should be adjusted in time to ensure the flight safety.

[0048] In the foregoing, the intelligent control method for telescopic main wingspan according to the embodiment of the application is described in detail with reference to FIG. 1. Next, the intelligent control system for telescopic main wingspan according to the embodiment of the application will be described with reference to FIG. 2.

[0049] The intelligent control system for telescopic main wingspan according to the embodiment of the application is used to solve the technical problem that the existing telescopic main wingspan control cannot accurately predict the flight environment and make control decisions, which leads to the difficulty in adapting to diversified flight task requirements, and the poor aerodynamic performance and task flexibility of the aircraft. The intelligent control system for telescopic main wingspan realizes the dynamic adjustment control of the main wingspan of the aircraft, and achieves the technical effect of enhancing the aerodynamic performance and adaptability of the aircraft. The intelligent control system for telescopic main wingspan includes: a flight task obtaining module 10, a flight task feature disassembling module 20, a flight environment predicting module 30, a main wingspan control decision module 40, a main wingspan simulation control module 50, a main wingspan control optimization result obtaining module 60, and an aircraft main wingspan control module 70.

[0050] The flight task obtaining module 10 is configured to obtain a flight task of an aircraft; the flight task feature disassembling module 20 is configured to perform multi-feature disassembly according to the flight task, and obtain a plurality of node flight tasks; the flight environment prediction module 30 is configured to extract an nth node flight task according to the plurality of node flight tasks, and perform flight environment prediction on the aircraft based on the nth node flight task, and obtain an nth node predicted flight environment, wherein n is a positive integer; the main wingspan control decision module 40 is configured to perform main wingspan control decision according to the nth node flight task and the nth node predicted flight environment, and generate an nth node main wingspan control scheme; the main wingspan simulation control module 50 is configured to activate a flight simulation platform, and perform main wingspan simulation control on the aircraft in combination with the nth node main wingspan control scheme, and obtain a main wingspan simulation control monitoring data set; the main wingspan control optimization result obtaining module 60 is configured to perform multi-feature compensation optimization on the nth 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, and obtain an nth node main wingspan control optimization result; and the aircraft main wingspan control module 70 is configured to perform main wingspan control on the aircraft according to the nth node main wingspan control optimization result based on the nth node flight task.

[0051] In the following, the specific configuration of the flight task feature disassembling module 20 will be described in detail. The flight task feature disassembling module 20 further comprises: performing flight attribute disassembly according to the flight task, and obtaining a flight task first-level disassembly result; performing flight state disassembly according to the flight task first-level disassembly result, and obtaining a flight task second-level disassembly result; and performing flight environment disassembly according to the flight task second-level disassembly result, and generating the plurality of node flight tasks.

[0052] The specific configuration of the main wing span control decision module 40 will be described in detail below. The main wing span control decision module 40 can further include: obtaining a main wing span control record set according to the main wing span control record backtracking of the aircraft; establishing a same group main wing span according to the main wing span type information of the aircraft, wherein the same group main wing span includes a plurality of same type main wing spans; obtaining a same group main wing span control record set according to the control record backtracking of the same group main wing span; constructing a main wing span control decision model according to the same group main wing span control record set; performing decision sensitivity optimization on the main wing span control decision model according to the main wing span control record set, and generating a main wing span control decision channel; and obtaining the n th node main wing span control scheme according to the main wing span control decision channel based on the n th node flight task and the n th node predicted flight environment.

[0053] The specific configuration of the main wing span control optimization result obtaining module 60 will be described in detail below. The main wing span control optimization result obtaining module 60 can 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; obtaining a main wing span control aerodynamic performance compensation parameter according to the main wing span control aerodynamic performance compensation channel based on the main wing span simulation control monitoring data set; generating a main wing span control sensitivity compensation parameter according to the main wing span control sensitivity compensation channel based on the main wing span simulation control monitoring data set; generating a main wing span control load characteristic compensation parameter according to the main wing span control load characteristic compensation channel based on the main wing span simulation control monitoring data set; fusing 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 a main wing span control compensation characteristic factor; and generating the n th node main wing span control optimization result by optimizing the n th node main wing span control scheme according to the main wing span control compensation characteristic factor.

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

[0055] Next, the specific configuration of the main wingspan control optimization result obtaining module 60 will be described in detail. The main wingspan control optimization result obtaining module 60 further comprises: 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 main wingspan control parameter deviation analysis results; performing control response time delay analysis on the main wingspan simulation state monitoring data based on the n th node main wingspan control scheme, to obtain main wingspan control response time delay analysis results; and obtaining the main wingspan control sensitivity compensation parameters according to the main wingspan control sensitivity compensation channel based on the main wingspan control parameter deviation analysis results and the main wingspan control response time delay analysis results.

[0056] Next, the specific configuration of the main wingspan control optimization result obtaining module 60 will be described in detail. The main wingspan control optimization result obtaining module 60 further comprises: extracting aircraft simulation load distribution data and aircraft simulation center of gravity distribution data according to the main wingspan simulation control monitoring data set; performing change trend identification according to the aircraft simulation load distribution data, to generate simulation load change trend identification results; performing change trend identification according to the aircraft simulation center of gravity distribution data, to generate simulation center of gravity change trend identification results; and obtaining the main wingspan control load feature compensation parameters according to the main wingspan control load feature compensation channel based on the simulation load change trend identification results, the simulation center of gravity change trend identification results, and the n th node main wingspan control scheme.

[0057] The intelligent control system for the telescopic main wingspan provided in the embodiments of the present application can execute the intelligent control method for the telescopic main wingspan provided in any of the embodiments of the present application, and has the corresponding function 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, however, any number of different modules can be used and run on the user terminal and / or server, and the various units and modules are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual differentiation, and do not limit the protection scope of the present application.

[0059] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand 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 the present application shall be included in the protection scope of the present application.

Claims

1. A method for intelligent control of telescopic main wing span, characterized in that, The method comprises: obtaining a flight task of an aircraft; performing multi-feature decomposition according to the flight task to obtain a plurality of node flight tasks; extracting an n-th node flight task according to the plurality of node flight tasks, and performing flight environment prediction on the aircraft based on the n-th node flight task to obtain an n-th node predicted flight environment, wherein n is a positive integer; performing main wing span control decision according to the n-th node flight task and the n-th node predicted flight environment to generate an n-th node main wing span control scheme; activating a flight simulation platform, combining the n-th node main wing span control scheme to perform main wing span simulation control on the aircraft, and obtaining main wing span simulation control monitoring data set; performing multi-feature compensation optimization on the n-th node main wing span control scheme according to multi-factor main wing span control compensation channel and combining the main wing span simulation control monitoring data set to obtain n-th node main wing span control optimization result; performing main wing span control on the aircraft based on the n-th node flight task according to the n-th node main wing span control optimization result.

2. The intelligent control method for telescopic main wing span as claimed in claim 1, wherein, The method comprises: performing multi-feature decomposition according to the flight task to obtain a plurality of node flight tasks, comprising: performing flight attribute decomposition according to the flight task to obtain flight task first-level decomposition result; performing flight state decomposition according to the flight task first-level decomposition result to obtain flight task second-level decomposition result; 3. The intelligent control method for telescopic main wing span as claimed in claim 1, wherein, performing flight environment decomposition according to the flight task second-level decomposition result to generate the plurality of node flight tasks. The method comprises: performing main wing span control record backtracking according to the aircraft to obtain main wing span control record set; establishing same-group main wing span according to main wing span model information of the aircraft, wherein the same-group main wing span comprises a plurality of same-model main wing spans; performing control record backtracking according to the same-group main wing span to obtain same-group main wing span control record set; constructing main wing span control decision model according to the same-group main wing span control record set; performing decision sensitivity optimization on the main wing span control decision model according to the main wing span control record set to generate main wing span control decision channel; 4. The intelligent control method for telescopic main wing span as claimed in claim 1, wherein, obtaining the n-th node main wing span control scheme according to the main wing span control decision channel based on the n-th node flight task and the n-th node predicted flight environment. The method comprises: The multi-factor main wing span control compensation channel comprises main wing span control aerodynamic performance compensation channel, main wing span control sensitivity compensation channel and main wing span control load characteristic compensation channel; obtaining main wing span control aerodynamic performance compensation parameter according to the main wing span control aerodynamic performance compensation channel based on the main wing span simulation control monitoring data set; generating main wing span control sensitivity compensation parameter according to the main wing span control sensitivity compensation channel based on the main wing span simulation control monitoring data set; generating a main wingspan control load feature compensation parameter according to the main wingspan control load feature compensation channel based on the main wingspan simulation control monitoring data set; fusing the main wingspan control aerodynamic performance compensation parameter, the main wingspan control sensitivity compensation parameter and the main wingspan control load feature compensation parameter to generate a main wingspan control compensation feature factor; optimizing the n-th node main wingspan control scheme according to the main wingspan control compensation feature factor to generate an n-th node main wingspan control optimization result.

5. The intelligent control method for telescopic main wing span as claimed in claim 4, wherein, obtaining a main wingspan control aerodynamic performance compensation parameter according to the main wingspan control aerodynamic performance compensation channel based on the main wingspan simulation control monitoring data set, including: extracting main wingspan simulation aerodynamic performance data according to the main wingspan simulation control monitoring data set; obtaining a flight aerodynamic support coefficient by performing a flight aerodynamic support evaluation according to the main wingspan simulation aerodynamic performance data; judging whether the flight aerodynamic support coefficient is less than / equal to a flight aerodynamic support threshold value; if the flight aerodynamic support coefficient is less than / equal to the flight aerodynamic support threshold value, generating the main wingspan control aerodynamic performance compensation parameter according to the main wingspan control aerodynamic performance compensation channel based on the main wingspan simulation aerodynamic performance data and the n-th node main wingspan control scheme.

6. The intelligent control method for telescopic main wing span as claimed in claim 4, wherein, 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, including: extracting main wingspan simulation state monitoring data according to the main wingspan simulation control monitoring data set; obtaining a main wingspan control parameter deviation analysis result by performing a control parameter deviation analysis on the main wingspan simulation state monitoring data based on the n-th node main wingspan control scheme; obtaining a main wingspan control response time delay analysis result by performing a control response time delay analysis on the main wingspan simulation state monitoring data based on the n-th node main wingspan control scheme; obtaining the main wingspan control sensitivity compensation parameter according to the main wingspan control sensitivity compensation channel based on the main wingspan control parameter deviation analysis result and the main wingspan control response time delay analysis result.

7. The intelligent control method for telescopic main wing span as claimed in claim 4, wherein, generating a main wingspan control load feature compensation parameter according to the main wingspan control load feature compensation channel based on the main wingspan simulation control monitoring data set, including: extracting aircraft simulation load distribution data and aircraft simulation center of gravity distribution data according to the main wingspan simulation control monitoring data set; generating a simulation load trend identification result by identifying a trend of change according to the aircraft simulation load distribution data; generating a simulation center of gravity trend identification result by identifying a trend of change according to the aircraft simulation center of gravity distribution data; obtaining the main wingspan control load feature compensation parameter according to the main wingspan control load feature compensation channel based on the simulation load trend identification result, the simulation center of gravity trend identification result and the n-th node main wingspan control scheme.

8. An intelligent control system for telescopic main wing span, characterized by, The system is used to implement the intelligent control method for the telescopic main wingspan according to any one of claims 1-7, and the system comprises: The flight task obtaining module is configured to obtain a flight task of an aircraft. The flight task feature disassembling module is configured to perform multi-feature disassembling according to the flight task, and obtain a plurality of node flight tasks. The flight environment prediction module is configured to extract an n-th node flight task according to the plurality of node flight tasks, and perform flight environment prediction on the aircraft based on the n-th node flight task, and obtain an n-th node predicted flight environment, where n is a positive integer. The main wingspan control decision module is configured to perform main wingspan control decision according to the n-th node flight task and the n-th node predicted flight environment, and generate an n-th node main wingspan control scheme. The main wingspan simulation control module is configured to activate a flight simulation platform, perform main wingspan simulation control on the aircraft in combination with the n-th node main wingspan control scheme, and obtain main wingspan simulation control monitoring data sets. The main wingspan control optimization result obtaining module is configured to perform multi-feature compensation optimization on the n-th node main wingspan control scheme in combination with the main wingspan simulation control monitoring data sets according to a multi-factor main wingspan control compensation channel, and obtain an n-th node main wingspan control optimization result. The aircraft main wingspan control module is configured to perform main wingspan control on the aircraft based on the n-th node flight task according to the n-th node main wingspan control optimization result.

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