A Design Method for the Functional Logic Model of a Flight Control System

Abstract

The present invention relates to the field of flight control technology, and particularly to a method for designing a functional logic model of a flight control system. The method includes the following steps: obtaining multi-source data of an aircraft; unifying the data formats of the multi-source data of the aircraft to generate a multi-source unified data set of the aircraft; reconstructing a flight virtual scene for the multi-source unified data set of the aircraft to generate a three-dimensional flight virtual scene; analyzing the flight conditions of the aircraft for the multi-source unified data set of the aircraft to obtain flight condition data of the aircraft; constructing a flight classifier according to the flight condition data of the aircraft to obtain an aircraft flight mode classifier; and using the aircraft flight mode classifier to identify the flight mode of the aircraft for the flight motion feature data of the aircraft to generate aircraft flight mode data. The present invention improves the adaptability and optimization ability of the design of the functional logic model of the flight control system by unifying multi-source data, accurately analyzing flight conditions, and optimizing flight control strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of flight control, and particularly to a design method for the functional logic model of a flight control system. Background Art

[0002] The design of flight control systems mainly relies on traditional PID controllers, which achieve basic stability control of aircraft through simple proportional, integral, and derivative control. However, this method has limitations in complex environments and high-performance requirements. With the development of computer technology, flight control systems based on microprocessors and digital signal processors (DSPs) have emerged. These systems can implement more complex control algorithms, such as model predictive control (MPC) and nonlinear control methods, improving the dynamic response and flight stability of aircraft. With the progress of embedded systems and sensor technology, flight control systems have gradually integrated various sensors such as inertial measurement units (IMUs), global positioning systems (GPSs), and vision sensors. These sensor data fusion technologies enable flight control systems to achieve precise navigation and autonomous flight functions, such as inertial navigation, automatic takeoff and landing, and obstacle avoidance capabilities. Currently, the design of flight control systems has covered multiple levels, including hardware design optimization, selection of real-time operating systems, and refinement of control algorithms. At the same time, the introduction of artificial intelligence and machine learning technologies, such as deep reinforcement learning and neural network control, is driving the development of flight control systems towards more intelligent, adaptive, and high-performance directions. However, currently, traditional methods often rely on simple rules or empirical judgments when analyzing and identifying the flight modes and abnormal flight parameters of aircraft, lacking high-precision analysis and detection capabilities. At the same time, flight control strategies often rely on simple control logic or fixed rules, lacking pertinence and optimization, thus resulting in low adaptability and optimization capabilities in the design of the functional logic model of flight control systems. Summary of the Invention

[0003] Based on this, it is necessary to provide a design method for the functional logic model of a flight control system to solve at least one of the above technical problems.

[0004] To achieve the above object, a design method for the functional logic model of a flight control system, the method includes the following steps:

[0005] Step S1: Obtain multi-source data of the aircraft; unify the data formats of the multi-source data of the aircraft to generate a multi-source unified dataset of the aircraft; reconstruct a flight virtual scene for the multi-source unified dataset of the aircraft to generate a three-dimensional flight virtual scene;

[0006] Step S2: Analyze the flight situation of the aircraft for the multi-source unified dataset of the aircraft to obtain aircraft flight situation data; construct a flight classifier based on the aircraft flight situation data to obtain an aircraft flight mode classifier; use the aircraft flight mode classifier to identify the aircraft flight mode for the aircraft flight motion feature data and generate aircraft flight mode data; extract abnormal flight parameters from the aircraft flight mode data to generate aircraft abnormal flight parameters;

[0007] Step S3: Obtain the real-time flight position data of the aircraft; track the flight path of the aircraft for the real-time flight position data of the aircraft according to the normal flight mode data of the aircraft to generate the real-time flight path of the aircraft; construct an initial control strategy for the real-time flight path of the aircraft based on the aircraft abnormal flight parameters to obtain an initial flight control strategy; perform a stability analysis on the initial flight control strategy to generate strategy stability analysis data;

[0008] Step S4: Perform an optimal control solution on the initial flight control strategy based on the strategy stability analysis data to generate optimal strategy control data; convert the logical control instructions for the three-dimensional flight virtual scene according to the optimal strategy control data to generate aircraft flight control logical control instructions to execute the flight control system functional logic model design operation.

[0009] The present invention collects aircraft data from various sensors and devices, including GPS position data, inertial measurement unit (IMU) data, camera images, lidar data, etc. The data from different sources are subjected to format conversion and integration to form a unified data set. A three-dimensional flight virtual scene is constructed using the unified data set, which can provide a comprehensive and multi-dimensional data perspective, helping to better understand and analyze the behavior of the aircraft. The generated three-dimensional virtual scene can be used to simulate and test various flight conditions of the aircraft, improving the efficiency of aircraft design and testing. The unified data set is analyzed to extract the flight condition data of the aircraft. A classifier is constructed based on the flight condition data to identify different flight modes. The flight mode classifier is used to identify the flight mode in the flight motion feature data, generating flight mode data, and abnormal flight parameters are extracted from the flight mode data. Through flight mode recognition, different flight states of the aircraft can be automatically classified and identified, improving the accuracy of aircraft monitoring and management. Extracting abnormal flight parameters helps to detect and handle potential flight risks at an early stage, enhancing flight safety. The real-time position data of the aircraft during flight is collected, and based on the normal flight mode data, the real-time position data is subjected to flight path tracking to generate a real-time flight path. Based on the abnormal flight parameters, an initial control strategy is constructed for the real-time flight path, and the stability analysis of the initial control strategy is carried out to generate strategy stability analysis data. Real-time flight path tracking can accurately monitor the current position and path of the aircraft, facilitating the navigation and positioning of the aircraft. The construction and stability analysis of the initial control strategy can help to timely adjust the control strategy of the aircraft, ensuring the safety and stability of the flight process. Based on the strategy stability analysis data, an optimal control solution is obtained for the initial flight control strategy, generating optimal strategy control data. According to the optimal strategy control data, logical control instruction conversion is performed on the three-dimensional flight virtual scene to generate flight control logical control instructions. Through optimal control solution, a more accurate and effective flight control strategy can be formulated, enhancing the performance and safety of the aircraft. The generated flight control logical control instructions can be directly applied to the flight control system to realize the design and optimization of the functional logic model of the aircraft. Therefore, the present invention improves the adaptability and optimization ability of the design of the functional logic model of the flight control system by unifying multi-source data, accurately analyzing flight conditions, and optimizing flight control strategies.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain multi-source data of the aircraft using a distributed sensor network;

[0012] Step S12: Perform data preprocessing on the multi-source data of the aircraft to generate standard multi-source data of the aircraft, where the data preprocessing includes data cleaning, data denoising, filling of missing data values, and data standardization;

[0013] Step S13: Unify the data formats of the multi-source data of the standard aircraft to generate a multi-source unified dataset of the aircraft;

[0014] Step S14: Reconstruct the flight virtual scene for the multi-source unified dataset of the aircraft through virtual reality augmentation technology to generate a three-dimensional flight virtual scene.

[0015] By using a distributed sensor network to obtain the multi-source data of the aircraft, the present invention can cover a wider area, obtain data in more dimensions, and improve the comprehensiveness and accuracy of the data. Through preprocessing steps such as data cleaning, data denoising, and filling of missing data values, noise and outliers in the data can be effectively removed, missing data can be filled, and the integrity and reliability of the data can be ensured. Data standardization enables data from different sources and in different formats to be processed and analyzed on the same platform, eliminates the processing complexity caused by data format differences, and improves the data processing efficiency. Unifying the data formats of the multi-source data of the standard aircraft to generate a multi-source unified dataset of the aircraft further simplifies the data management and analysis process and enhances the convenience and consistency of data utilization. Reconstructing the flight virtual scene for the multi-source unified dataset of the aircraft through virtual reality augmentation technology can provide a more intuitive and realistic three-dimensional flight virtual scene, which helps to better perform data analysis and result display and improve the efficiency and effectiveness of decision-making support.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: Extract the flight environment characteristics from the multi-source unified dataset of the aircraft to obtain aircraft environment characteristic data;

[0018] Step S142: Reconstruct the environment for the aircraft environment characteristic data based on virtual reality augmentation technology to generate aircraft environment reconstruction data;

[0019] Step S143: Dynamically simulate the aircraft using the aircraft environment reconstruction data to generate aircraft dynamic simulation data;

[0020] Step S144: Perform virtual sensor fusion based on the aircraft dynamic simulation data and the aircraft environment reconstruction data to generate a three-dimensional flight virtual scene.

[0021] By extracting the flight environment features from the multi-source unified data set of the aircraft, the present invention can more accurately obtain the characteristic data of the environment where the aircraft is located. These data can include various factors such as terrain, weather, air flow, etc., ensuring that the aircraft can obtain more accurate environmental information support during actual flight. Using virtual reality enhancement technology for environmental reconstruction can not only reproduce the actual flight environment of the aircraft more vividly, but also provide additional information and interactive functions through augmented reality technology, providing a more intuitive and comprehensive understanding of the environment for aircraft operators. Through dynamic simulation of the aircraft environment reconstruction data, detailed aircraft dynamic simulation data can be generated. These data can be used to predict the behavior and performance of the aircraft under different environmental conditions, thereby improving the safety and reliability of the aircraft. The virtual sensor fusion technology can organically combine the aircraft dynamic simulation data and the environment reconstruction data to generate a three-dimensional flight virtual scene. This scene can be used not only for the training and rehearsal of aircraft operation, but also for the planning and optimization of flight missions, providing all-round support for the aircraft.

[0022] Preferably, step S2 includes the following steps:

[0023] Step S21: Analyze the flight situation of the aircraft from the multi-source unified data set of the aircraft to obtain aircraft flight situation data;

[0024] Step S22: Construct a flight classifier based on the aircraft flight situation data to obtain an aircraft flight mode classifier;

[0025] Step S23: Use the aircraft flight mode classifier to identify the aircraft flight mode of the aircraft flight motion characteristic data, and generate aircraft flight mode data, where the aircraft flight mode data includes aircraft normal flight mode data and aircraft abnormal flight mode data;

[0026] Step S24: Extract abnormal flight parameters from the aircraft abnormal flight mode data to generate aircraft abnormal flight parameters.

[0027] Through the analysis of the flight conditions of the multi-source unified data set of the aircraft, the present invention can comprehensively and accurately obtain the flight data of the aircraft under different conditions. These data can include various flight parameters such as speed, direction, attitude, etc., providing a detailed information basis for subsequent analysis and classification. Based on the flight condition data, a flight classifier is constructed, which can create a classification tool for different flight modes (such as normal flight and abnormal flight). This helps to identify and distinguish the flight modes of the aircraft in various states, improving the accuracy and efficiency of classification. Using the flight mode classifier to identify the flight mode of the flight motion feature data can automatically identify the flight mode of the aircraft. This not only improves the speed and accuracy of flight mode identification, but also can timely detect abnormal situations during flight, generating data including normal and abnormal flight modes. The abnormal flight parameter extraction process can deeply analyze the abnormal flight mode data and generate specific abnormal flight parameters. These parameters help to identify faults or abnormal behaviors existing in the aircraft during flight, providing important data support for fault diagnosis and prevention.

[0028] Preferably, step S22 includes the following steps:

[0029] Step S221: Perform data annotation on the flight condition data of the aircraft to generate flight condition annotation data of the aircraft;

[0030] Step S222: Extract visual features and motion features from the flight condition annotation data of the aircraft to obtain visual feature data and motion feature data of the aircraft;

[0031] Step S223: Merge the visual feature data and motion feature data of the aircraft to obtain a flight feature data set of the aircraft; divide the flight feature data set of the aircraft to generate a model training set and a model validation set;

[0032] Step S224: Use the convolutional neural network algorithm to train the model training set to generate flight behavior pattern training data; optimize the model parameters of the flight behavior pattern training data through the model validation set, thereby generating a flight mode classifier of the aircraft.

[0033] The present invention generates labeled data on the flight conditions of an aircraft by labeling the data on the flight conditions of the aircraft. Accurate data labeling is the basis for subsequent feature extraction and model training, ensuring the accuracy and consistency of the data and providing a reliable data source for the construction of the classifier. The visual features and motion features of the aircraft are extracted. The visual features may include the image data of the aircraft, and the motion features may include dynamic parameters such as speed and acceleration. By extracting these features, the flight state and behavior of the aircraft can be comprehensively reflected, providing rich input data for the classifier. The visual feature data and the motion feature data are combined to generate a comprehensive flight feature data set, which is divided into a model training set and a model validation set. This step ensures the integrity and diversity of the data, making the model training and validation have sufficient representativeness and generality, thereby improving the generalization ability of the classifier. The model training set is trained using the convolutional neural network algorithm to generate training data on the behavior patterns of the aircraft. The convolutional neural network has strong capabilities in processing image and time series data and can effectively capture the complex behavior patterns of the aircraft. In addition, parameter tuning is performed through the model validation set to ensure that the classifier has high accuracy and robustness.

[0034] Preferably, step S24 includes the following steps:

[0035] Step S241: Identify the abnormal types of the abnormal flight mode data of the aircraft to generate aircraft abnormal flight type data, where the abnormal type identification includes external environment abnormality of the aircraft and internal environment abnormality of the aircraft;

[0036] Step S242: When it is confirmed that the abnormal type identification of the abnormal flight mode data of the aircraft is an external environment abnormality of the aircraft, analyze the environmental impact factors of the aircraft environmental feature data to generate environmental impact factor data; extract the external abnormal environment parameters from the abnormal flight mode data of the aircraft according to the environmental impact factor data to obtain the external environment abnormal feature data of the aircraft;

[0037] Step S243: When it is confirmed that the abnormal type identification of the abnormal flight mode data of the aircraft is an internal environment abnormality of the aircraft, analyze the flight structure of the aircraft to generate internal structure data of the aircraft; perform fault troubleshooting on the internal structure data of the aircraft to generate internal structure fault data of the aircraft;

[0038] Step S244: Analyze the structural impact factors according to the internal structure fault data of the aircraft to generate structural impact factor data; extract the internal abnormal operation parameters from the abnormal flight mode data of the aircraft according to the structural impact factor data to obtain the internal operation abnormal feature data of the aircraft;

[0039] Step S245: Integrate the abnormal feature data of the external environment of the aircraft and the abnormal feature data of the internal operation of the aircraft to generate abnormal flight parameters of the aircraft.

[0040] In the present invention, by identifying the abnormal types of the abnormal flight mode data of the aircraft, the abnormalities are classified into external environment abnormalities and internal environment abnormalities. This process can accurately distinguish the sources of abnormalities and provide a basis for subsequent targeted analysis and processing. For external environment abnormalities, by analyzing the environmental impact factors of the aircraft environmental feature data, environmental impact factor data is generated, and further external abnormal environment parameters are extracted to obtain the abnormal feature data of the external environment of the aircraft. This step can deeply analyze the impact of the external environment on the flight of the aircraft and provide detailed data support for external environment abnormalities. For internal environment abnormalities, through flight structure analysis and fault troubleshooting, the internal structure data and internal structure fault data of the aircraft are generated. This process can comprehensively check the internal structure of the aircraft, identify potential faults and problems, and provide detailed data support for the handling of internal abnormalities. By analyzing the structural impact factors of the internal structure fault data of the aircraft, structural impact factor data is generated, and further internal abnormal operation parameters are extracted to obtain the abnormal feature data of the internal operation of the aircraft. This step can deeply analyze the impact of the internal structure of the aircraft on the flight behavior and provide detailed data support for internal environment abnormalities. Integrate the abnormal feature data of the external environment of the aircraft and the abnormal feature data of the internal operation of the aircraft to generate abnormal flight parameters of the aircraft. This process can comprehensively analyze the comprehensive impact of the external and internal environments on the flight of the aircraft, provide comprehensive abnormal flight parameter data, and provide a reliable basis for the fault diagnosis and maintenance of the aircraft.

[0041] Preferably, step S3 includes the following steps:

[0042] Step S31: Use GPS to obtain the real-time flight position data of the aircraft;

[0043] Step S32: Mark the real-time flight position data of the aircraft according to the normal flight mode data of the aircraft to obtain the real-time flight space coordinate data of the aircraft; Track the flight path of the aircraft through the real-time flight space coordinate data of the aircraft to generate the real-time flight path of the aircraft;

[0044] Step S33: Calculate the path deviation of the real-time flight path of the aircraft based on the abnormal flight parameters of the aircraft to obtain the abnormal deviation path of the aircraft flight;

[0045] Step S34: Control the attitude of the aircraft based on the abnormal deviation path of the aircraft flight to generate attitude control adjustment data of the aircraft; Adjust the path trajectory planning of the abnormal deviation path of the aircraft flight through the attitude control adjustment data of the aircraft to generate path trajectory planning adjustment data of the aircraft;

[0046] Step S35: Use the three-dimensional flight virtual scene to construct an initial control strategy for the aircraft attitude control adjustment data and the aircraft path trajectory planning adjustment data to obtain an initial flight control strategy; perform a stability analysis on the initial flight control strategy to generate strategy stability analysis data.

[0047] The present invention obtains the real-time flight position data of the aircraft by using the GPS technology, ensuring that the aircraft is always under monitoring during the flight, providing accurate position information for subsequent path tracking and attitude control. According to the normal flight mode data of the aircraft, mark the real-time flight position data with spatial coordinate points to generate the real-time flight space coordinate data of the aircraft, and perform flight path tracking through these data to generate the real-time flight path. This step realizes the precise monitoring of the aircraft flight path and ensures that the aircraft flies according to the predetermined path. Based on the abnormal flight parameters of the aircraft, calculate the path deviation of the real-time flight path to generate the abnormal deviation path of the aircraft flight. This process can timely detect the abnormal deviation in the aircraft flight path and provide a basis for timely abnormal warning and handling. Based on the abnormal deviation path of the aircraft flight, perform attitude control to generate the aircraft attitude control adjustment data, and perform path trajectory planning adjustment on the abnormal deviation path through these data to generate the aircraft path trajectory planning adjustment data. This process can timely adjust the flight attitude and path trajectory of the aircraft to ensure that the aircraft can return to the normal flight path and avoid potential dangers. Use the three-dimensional flight virtual scene to construct an initial control strategy for the attitude control adjustment data and the path trajectory planning adjustment data, generate an initial flight control strategy, and perform a stability analysis on this strategy to generate strategy stability analysis data. This step can ensure the reliability and stability of the initial flight control strategy and provide guarantee for the safe flight of the aircraft.

[0048] Preferably, step S35 includes the following steps:

[0049] Step S351: Perform parameter mapping on the three-dimensional flight virtual scene according to the aircraft attitude control adjustment data and the aircraft path trajectory planning adjustment data to obtain the target attitude mapping sequence and the target flight path mapping data;

[0050] Step S352: Based on the aircraft attitude control adjustment data and the aircraft path trajectory planning adjustment data, construct an initial control strategy to generate an initial flight control strategy; perform simulation control on the initial flight control strategy through a preset time step and record the obtained control strategy performance data;

[0051] Step S353: Perform a strategy stability analysis on the control strategy performance data to generate strategy stability analysis data.

[0052] The present invention generates a target attitude mapping sequence and target flight path mapping data by performing parameter mapping on a three-dimensional flight virtual scene and using aircraft attitude control adjustment data and aircraft path trajectory planning adjustment data. This step ensures that the attitude and path of the aircraft in the virtual scene are consistent with the actual flight conditions, providing an accurate simulation environment for the subsequent construction of control strategies. Based on the attitude control adjustment data and path trajectory planning adjustment data, an initial flight control strategy is constructed. Then, simulation control is performed through a preset time step, and the obtained control strategy performance data is recorded. This process can verify the performance of the initial control strategy at different time steps and ensure the effectiveness of the strategy in practical applications. The strategy stability analysis is performed on the control strategy performance data obtained from the simulation control to generate strategy stability analysis data. This analysis process can evaluate the stability and robustness of the control strategy under different flight conditions and ensure the reliability of the strategy in actual flight. Through precise parameter mapping and simulation control, it can be ensured that the control strategy is highly consistent with the actual flight conditions, improving the accuracy and reliability of the control strategy. Through stability analysis, unstable factors existing in the control strategy under different flight conditions can be identified and resolved, enhancing the stability and robustness of the strategy. The construction and verification process of the initial control strategy can continuously optimize the flight path and attitude control to ensure that the aircraft can fly safely and smoothly in various environments.

[0053] Preferably, step S353 includes the following steps:

[0054] Step S3531: Analyze the basic performance indicators of the control strategy performance data to obtain basic performance indicator data;

[0055] Step S3532: Evaluate the control effect of the initial flight control strategy through the basic performance indicator data to generate strategy control effect evaluation data;

[0056] Step S3533: Calculate the control steady-state error of the strategy control effect evaluation data to obtain strategy stability index data;

[0057] Step S3534: Perform statistical analysis on the strategy stability index data to generate strategy control statistical analysis data; perform time series analysis on the control strategy performance data to generate strategy control time series stability data;

[0058] Step S3535: Integrate the basic performance indicator data, strategy stability index data, strategy control statistical analysis data, and strategy control time series stability data to generate strategy stability analysis data.

[0059] The present invention obtains basic performance index data by analyzing the performance data of the control strategy for basic performance indexes, such as control accuracy, response time, etc. This step can intuitively evaluate the basic performance of the control strategy and provide a basis for subsequent in-depth analysis. Based on the basic performance index data, the control effect of the initial flight control strategy is evaluated to generate strategy control effect evaluation data. This process can quantitatively evaluate the effect of the control strategy in actual applications and guide the further optimization and adjustment of the strategy. The control steady-state error of the strategy control effect evaluation data is calculated to obtain strategy stability index data. This step can analyze the error situation of the control strategy in the steady state and evaluate its stability and accuracy. Statistical analysis is performed on the strategy stability index data to generate strategy control statistical analysis data; at the same time, time series analysis is performed on the control strategy performance data to generate strategy control time series stability data. These analyses can comprehensively understand the performance of the control strategy under different conditions and reveal its potential advantages and improvement spaces. The basic performance index data, strategy stability index data, strategy control statistical analysis data, and strategy control time series stability data are integrated to generate strategy stability analysis data. This step can comprehensively analyze the performance of the control strategy in multiple aspects and provide a comprehensive evaluation basis and optimization suggestions for decision-makers.

[0060] Preferably, step S4 includes the following steps:

[0061] Step S41: Perform optimal control solution on the initial flight control strategy based on the strategy stability analysis data to generate optimal strategy control data;

[0062] Step S42: Perform Monte Carlo simulation on the three-dimensional flight virtual scene according to the optimal strategy control data to generate a control strategy simulation data set;

[0063] Step S43: Based on the control strategy simulation data set, perform logical control instruction conversion to generate flight control logic control instructions for the aircraft to execute the flight control system function logic model design operation.

[0064] The present invention analyzes data based on policy stability, solves for the optimal control of the initial flight control policy, and generates optimal policy control data. This step aims to find the best control policy parameters through mathematical optimization methods to improve the control efficiency and stability of the aircraft. According to the optimal policy control data, a Monte Carlo simulation is performed on the three-dimensional flight virtual scene to generate a control policy simulation data set. The Monte Carlo simulation can simulate the influence of various random factors on the control policy, evaluate its performance in various situations, and improve the adaptability and robustness of the control policy. Based on the control policy simulation data set, logical control instruction conversion is performed to generate flight control logic control instructions for the aircraft. These instructions will be used to execute the functional logic model design of the flight control system to ensure that the aircraft can perform various flight tasks and operations as expected. Through optimal control solution, the performance of the control policy under various flight conditions can be improved, and the control accuracy and efficiency of the aircraft can be optimized. The Monte Carlo simulation can comprehensively evaluate the stability and robustness of the control policy in different random environments, providing guarantee for actual flight to cope with complex situations. Through logical control instruction conversion, it can be ensured that the aircraft executes tasks according to the designed logical model, improving the operation safety and reliability of the aircraft.

[0065] The beneficial effects of the present invention are as follows: By unifying the formats of multi-source data of the aircraft and reconstructing the scene, a three-dimensional flight virtual scene can be obtained, which helps to more accurately simulate and analyze the operating environment of the aircraft. By analyzing the flight condition data of the aircraft, an aircraft flight mode classifier can be constructed, and then the flight mode of the aircraft can be identified. This helps to understand the behavior and performance of the aircraft and provides a basis for subsequent control policies. By analyzing the flight mode data of the aircraft, abnormal flight parameters can be extracted, and these parameters can be used to detect and judge whether the aircraft is in an abnormal situation, so as to take corresponding control measures to ensure the safe operation of the aircraft. According to the real-time flight position data and normal flight mode data of the aircraft, flight path tracking can be performed, and an initial flight control policy can be constructed based on the abnormal flight parameters. This helps the aircraft to maintain stability and safety during real-time operation. Perform stability analysis on the initial flight control policy to ensure the effectiveness and safety of the control policy. Then, through optimal control solution, optimal policy control data can be obtained, thus achieving the best control performance of the aircraft. Convert the optimal policy control data into flight control logic control instructions for the aircraft to execute the functional logic model design operation of the flight control system. This helps to achieve precise control and operation of the aircraft. Therefore, the present invention improves the adaptability and optimization ability of the functional logic model design of the flight control system by unifying multi-source data, accurately analyzing flight conditions, and optimizing flight control policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the step flow of a method for designing a functional logic model of a flight control system;

[0067] Figure 2 is Figure 1 a schematic diagram of the detailed implementation steps of step S2 in

[0068] Figure 3 is Figure 1 a schematic diagram of the detailed implementation steps of step S3 in

[0069] Figure 4 is Figure 1 a schematic diagram of the detailed implementation steps of step S4 in

[0070] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0071] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0072] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0073] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed related items.

[0074] To achieve the above object, please refer to Figures 1 to 4 , a method for designing a functional logic model of a flight control system, the method comprising the following steps:

[0075] Step S1: Obtain multi-source data of the aircraft; unify the data formats of the multi-source data of the aircraft to generate a unified multi-source dataset of the aircraft; reconstruct the flight virtual scene for the unified multi-source dataset of the aircraft to generate a three-dimensional flight virtual scene;

[0076] Step S2: Analyze the flight conditions of the aircraft for the unified multi-source dataset of the aircraft to obtain flight condition data of the aircraft; construct a flight classifier based on the flight condition data of the aircraft to obtain an aircraft flight mode classifier; use the aircraft flight mode classifier to identify the flight mode of the aircraft for the flight motion feature data of the aircraft to generate aircraft flight mode data; extract abnormal flight parameters from the aircraft flight mode data to generate aircraft abnormal flight parameters;

[0077] Step S3: Obtain the real-time flight position data of the aircraft; track the flight path for the real-time flight position data of the aircraft according to the normal flight mode data of the aircraft to generate the real-time flight path of the aircraft; construct an initial control strategy for the real-time flight path of the aircraft based on the abnormal flight parameters of the aircraft to obtain an initial flight control strategy; conduct a stability analysis on the initial flight control strategy to generate strategy stability analysis data;

[0078] Step S4: Solve the optimal control for the initial flight control strategy based on the strategy stability analysis data to generate optimal strategy control data; convert the logical control instructions for the three-dimensional flight virtual scene according to the optimal strategy control data to generate aircraft flight control logical control instructions to execute the flight control system function logic model design task.

[0079] The present invention collects aircraft data from various sensors and devices, including GPS position data, inertial measurement unit (IMU) data, camera images, lidar data, etc. The data from different sources are subjected to format conversion and integration to form a unified data set. A three-dimensional flight virtual scene is constructed using the unified data set, which can provide a comprehensive and multi-dimensional data perspective, helping to better understand and analyze the behavior of the aircraft. The generated three-dimensional virtual scene can be used to simulate and test various flight conditions of the aircraft, improving the efficiency of aircraft design and testing. The unified data set is analyzed to extract the flight condition data of the aircraft. A classifier is constructed based on the flight condition data to identify different flight modes. The flight mode classifier is used to identify the flight modes in the flight motion feature data, generating flight mode data, and abnormal flight parameters are extracted from the flight mode data. Through flight mode recognition, different flight states of the aircraft can be automatically classified and identified, improving the accuracy of aircraft monitoring and management. Extracting abnormal flight parameters helps to detect and handle potential flight risks at an early stage, enhancing flight safety. Real-time position data of the aircraft during flight is collected, and based on the normal flight mode data, the real-time position data is subjected to flight path tracking to generate a real-time flight path. Based on the abnormal flight parameters, an initial control strategy is constructed for the real-time flight path, and the stability analysis of the initial control strategy is carried out to generate strategy stability analysis data. Real-time flight path tracking can accurately monitor the current position and path of the aircraft, facilitating the navigation and positioning of the aircraft. The construction and stability analysis of the initial control strategy can help to timely adjust the control strategy of the aircraft, ensuring the safety and stability of the flight process. Based on the strategy stability analysis data, an optimal control solution is obtained for the initial flight control strategy, generating optimal strategy control data. According to the optimal strategy control data, the three-dimensional flight virtual scene is subjected to logical control instruction conversion to generate flight control logic instructions. Through the optimal control solution, a more accurate and effective flight control strategy can be formulated, enhancing the performance and safety of the aircraft. The generated flight control logic instructions can be directly applied to the flight control system to realize the design and optimization of the functional logic model of the aircraft. Therefore, the present invention improves the adaptability and optimization ability of the design of the functional logic model of the flight control system by unifying multi-source data, accurately analyzing flight conditions, and optimizing flight control strategies.

[0080] In an embodiment of the present invention, referring to Figure 1 as described, it is a schematic diagram of the step flow of a method for designing a functional logic model of a flight control system of the present invention. In this example, the method for designing a functional logic model of a flight control system includes the following steps:

[0081] Step S1: Obtain multi-source data of the aircraft; unify the data formats of the multi-source data of the aircraft to generate a unified multi-source dataset of the aircraft; reconstruct the flight virtual scene for the unified multi-source dataset of the aircraft to generate a three-dimensional flight virtual scene.

[0082] In the embodiments of the present invention, various types of sensor data on the aircraft are collected, including but not limited to GPS data, IMU data, camera image data, lidar data, etc. Log data during the flight of the aircraft is obtained, including aircraft status, control commands, flight events, etc. Environmental data of the flight area of the aircraft is collected, such as weather conditions, terrain and landform data, etc. External data related to the flight of the aircraft is obtained, such as airspace control information, position data of other aircraft, etc. Noise and outliers in the original data are cleaned to ensure the accuracy and consistency of the data. Data from different sources are aligned in time and space to ensure the consistency of each data source at the same time point and position. Data in different formats are converted into a unified format, for example, the data formats of different sensors are standardized into the same data structure. Data from different data sources are fused in time and space to generate a comprehensive multi-source dataset. The data of different sensors are converted into a unified coordinate system, for example, GPS data, IMU data, etc. are converted into a geographic coordinate system or an aircraft body coordinate system. Lidar data or visual SLAM (Simultaneous Localization and Mapping) technology is used to generate point cloud data of the flight area. Multi-view image data is used for image stitching and stereo reconstruction to generate a high-precision three-dimensional model. According to the environmental data and flight log data, a three-dimensional model of the flight area is constructed, including terrain, buildings, vegetation, etc. The generated point cloud data and three-dimensional model are integrated into the virtual scene to generate a complete three-dimensional flight virtual scene. Texture mapping is performed on the three-dimensional model to increase the realism and details of the scene. The virtual scene is optimized, including reducing the number of polygons, optimizing the texture size, etc., to improve the rendering efficiency and real-time performance. The accuracy and correctness of the virtual scene are evaluated to ensure that the reconstructed three-dimensional scene is consistent with the actual flight area. The rendering performance of the virtual scene on different platforms is tested to ensure that it can run in real time in aircraft simulation training or control system testing.

[0083] Step S2: Analyze the flight conditions of the aircraft for the unified multi-source dataset of the aircraft to obtain flight condition data of the aircraft; construct a flight classifier based on the flight condition data of the aircraft to obtain an aircraft flight mode classifier; use the aircraft flight mode classifier to identify the flight mode of the aircraft for the flight motion feature data of the aircraft to generate flight mode data of the aircraft; extract abnormal flight parameters from the flight mode data of the aircraft to generate abnormal flight parameters of the aircraft.

[0084] In the embodiments of the present invention, motion data of the aircraft, such as speed, acceleration, position, attitude, etc., is extracted from a multi-source unified dataset. Environmental-related data during flight, such as weather conditions, airspace information, etc., is extracted. Statistical analysis is performed on the motion data of the aircraft to calculate the average speed, acceleration, attitude change, etc. of the aircraft in different flight phases. Data mining and machine learning techniques are used to identify typical motion patterns of the aircraft in different flight phases. Anomaly detection algorithms are used to identify anomalies in the flight data, such as sudden changes in speed, abnormal attitude changes, etc. The flight data is labeled to distinguish normal flight modes from abnormal flight modes, generating labeled data on the flight conditions of the aircraft. Computer vision techniques are used to extract visual features from the image data, such as changes in the appearance of the aircraft, environmental changes, etc. Motion features, such as speed, acceleration, attitude, etc., are extracted from the motion data. The visual feature data and the motion feature data are merged to generate a comprehensive dataset of flight characteristics of the aircraft. The flight characteristic dataset is divided into a model training set and a model validation set to ensure the balance in quantity and characteristics between the training set and the validation set. Deep learning algorithms, such as convolutional neural networks (CNNs), are used to train the model training set to generate training data on the behavior patterns of the aircraft. The model parameters are tuned through the model validation set to optimize the model performance, generating a flight mode classifier for the aircraft. The trained flight mode classifier is used to perform pattern recognition on the real-time flight motion feature data of the aircraft, classify the current flight mode of the aircraft, and generate flight mode data for the aircraft according to the recognition results, distinguishing normal flight mode data from abnormal flight mode data. Anomalous flight situations caused by external environmental factors are identified, generating external environmental anomaly feature data for the aircraft. Anomalous flight situations caused by internal faults or abnormal operations of the aircraft are identified, generating internal environmental anomaly feature data for the aircraft. The external environmental anomaly feature data is analyzed to extract external anomaly parameters affecting the flight of the aircraft. Fault troubleshooting and structural analysis are performed on the internal environmental anomaly feature data to extract internal anomaly parameters affecting the flight of the aircraft. The external environmental anomaly feature data and the internal environmental anomaly feature data are integrated to generate abnormal flight parameters for the aircraft, which are provided to the flight control system for further processing and adjustment.

[0085] Step S3: Obtain the real-time flight position data of the aircraft; perform flight path tracking on the real-time flight position data of the aircraft according to the normal flight mode data of the aircraft to generate the real-time flight path of the aircraft; construct an initial control strategy for the real-time flight path of the aircraft based on the abnormal flight parameters of the aircraft to obtain an initial flight control strategy; perform stability analysis on the initial flight control strategy to generate strategy stability analysis data;

[0086] In the embodiments of the present invention, by configuring and calibrating the GPS receiver on the aircraft, the acquisition of high-precision position data is ensured. Continuously collect the real-time GPS data of the aircraft, including longitude, latitude, altitude, etc. Clean the noise and outliers in the GPS data to ensure the accuracy of the data. Ensure the synchronization of the GPS data with other sensor data in time to guarantee the consistency of the data. Use the Kalman filtering algorithm to filter and smooth the real-time position data of the aircraft to improve the accuracy of path tracking. Based on the real-time position and speed data of the aircraft, conduct short-term trajectory prediction to enhance the stability of path tracking. Calculate the real-time flight path of the aircraft according to the GPS position data and the normal flight mode data of the aircraft. Visualize the real-time flight path data for easy monitoring and analysis. Based on the abnormal flight parameters of the aircraft, calculate the deviation of the real-time flight path of the aircraft, including position deviation and attitude deviation. Extract the abnormal features in the flight path, such as sudden altitude changes, sharp turns, etc. According to the abnormal flight parameters of the aircraft, design the initial flight control strategy, including attitude control, speed control, direction control, etc. Set the initial parameters of the control strategy, such as control gain, response time, etc., to ensure that the initial strategy can effectively handle the abnormal situation of the flight path. Simulate and test the initial flight control strategy in a simulation environment to evaluate its performance under different flight conditions. Evaluate the control effect of the initial control strategy, including path tracking accuracy, attitude stability, response speed, etc. Analyze the basic performance index data of the control strategy performance data to obtain the basic performance index data. Evaluate the control effect of the initial flight control strategy through the basic performance index data to generate the strategy control effect evaluation data. Calculate the control steady-state error of the strategy control effect evaluation data to obtain the strategy stability index data. Conduct statistical analysis on the strategy stability index data to generate the strategy control statistical analysis data. Conduct time series analysis on the control strategy performance data to generate the strategy control time series stability data. Integrate the basic performance index data, the strategy stability index data, the strategy control statistical analysis data, and the strategy control time series stability data to generate the strategy stability analysis data.

[0087] Step S4: Perform optimal control solution on the initial flight control strategy based on the strategy stability analysis data to generate optimal strategy control data; convert the logical control instructions for the three-dimensional flight virtual scene according to the optimal strategy control data to generate the flight control logic control instructions for the aircraft, so as to execute the flight control system function logic model design operation.

[0088] In the embodiments of the present invention, by analyzing data according to policy stability, the objective function of the optimal control problem is set, which usually includes minimizing path deviation, minimizing fuel consumption, maximizing stability, etc. Physical constraints and operation constraints of the aircraft are set, such as maximum acceleration, maximum speed, attitude change range, etc. The genetic algorithm is used for global optimization, which is suitable for nonlinear and multi-peak optimization problems. The particle swarm optimization algorithm is used for fast convergence, which is suitable for optimization problems in high-dimensional spaces. For problems with time-series decision-making characteristics, the dynamic programming method can be used to solve the optimal control strategy. According to the initial flight control strategy and policy stability analysis data, the parameters of the optimization algorithm are initialized. The optimization algorithm is run to iteratively solve the optimal control strategy, and the control parameters are continuously updated until convergence to the optimal solution. The effectiveness and reliability of the optimal strategy control data are verified to ensure that it can be effectively executed in practical applications. The optimal strategy control data is mapped to the control parameters of the three-dimensional flight virtual scene, such as position, speed, attitude, etc. According to the mapping result, the optimal control strategy is converted into specific flight control logic control instructions, including heading instructions, speed instructions, attitude adjustment instructions, etc. According to the functional logic model of the flight control system, the generation rules and execution processes of the logic control instructions are designed. The correctness and feasibility of the logic control instructions are verified through simulation and testing to ensure that they can be effectively executed in the three-dimensional flight virtual scene. Multiple flight scenarios and environmental conditions are set, and Monte Carlo simulation is performed on the optimal control strategy to generate a control strategy simulation data set. The simulation results are analyzed to evaluate the performance of the optimal control strategy under different scenarios and verify its robustness and adaptability. Based on the simulation data set, the generation rules of the logic control instructions are formulated to ensure the accuracy and timeliness of the instructions. According to the real-time flight data and the optimal control strategy, real-time flight control logic control instructions are generated to ensure the safety and stability of the aircraft. According to the flight control requirements of the aircraft, a functional logic model of the flight control system is constructed, including modules such as instruction reception, instruction parsing, and instruction execution. The correctness and reliability of the functional logic model are verified through simulation and testing to ensure that it can effectively execute the flight control logic control instructions.

[0089] Preferably, step S1 includes the following steps:

[0090] Step S11: Obtain multi-source data of the aircraft using a distributed sensor network;

[0091] Step S12: Perform data preprocessing on the multi-source data of the aircraft to generate standard multi-source data of the aircraft, where the data preprocessing includes data cleaning, data denoising, filling of missing data values, and data standardization;

[0092] Step S13: Unify the data formats of the standard multi-source data of the aircraft to generate a unified multi-source data set of the aircraft;

[0093] Step S14: Reconstruct the flight virtual scene for the multi-source unified dataset of the aircraft through virtual reality enhancement technology to generate a three-dimensional flight virtual scene.

[0094] In the embodiments of the present invention, multi-source data of the aircraft are collected by using a distributed sensor network (including GPS, inertial measurement unit (IMU), remote sensing sensors, etc.). These data sources can be obtained from various components and the environment of the aircraft to obtain comprehensive flight status and environmental information. The data preprocessing includes the following steps: Data cleaning: Remove outliers, incorrect data, or incomplete data from the data. Data denoising: Eliminate noise in the data to ensure data quality. Filling missing data values: Fill in the missing data points to make the dataset complete. Data standardization: Convert the data into a unified standard format or unit for subsequent processing and analysis. Format the preprocessed data uniformly to ensure the consistency and comparability of the dataset, which involves unifying the data collected by different sensors into the same data structure and data type. Use virtual reality enhancement technology, such as computer graphics and three-dimensional modeling technology, to reconstruct the flight virtual scene for the multi-source unified dataset of the aircraft. This includes: Scene modeling: Create a virtual environment around the aircraft based on information such as position, motion, and environmental parameters in the dataset. Visual enhancement: Enhance the visual effects of the virtual scene to make it as real as possible to reflect the actual flight environment. Interactive design: Ensure that users can interact and observe in the virtual scene to simulate the actual situation under different flight conditions.

[0095] Preferably, step S14 includes the following steps:

[0096] Step S141: Extract flight environment features from the multi-source unified dataset of the aircraft to obtain aircraft environment feature data;

[0097] Step S142: Reconstruct the environment based on the aircraft environment feature data through virtual reality enhancement technology to generate aircraft environment reconstruction data;

[0098] Step S143: Dynamically simulate the aircraft by using the aircraft environment reconstruction data to generate aircraft dynamic simulation data;

[0099] Step S144: Perform virtual sensor fusion based on the aircraft dynamic simulation data and the aircraft environment reconstruction data to generate a three-dimensional flight virtual scene.

[0100] In the embodiments of the present invention, by using data processing and analysis techniques, characteristic data of the environment where the aircraft is located is extracted from the multi-source unified dataset of the aircraft. These characteristic data include environmental parameters such as the position, attitude, speed, surrounding terrain, and meteorology of the aircraft. Using virtual reality enhancement techniques, such as computer graphics and 3D modeling techniques, environmental reconstruction is performed on the aircraft environmental characteristic data extracted in step S141. This process includes: creating a virtual environment around the aircraft according to the environmental characteristic data, including terrain, buildings, vegetation, etc., and making the visual effect of the virtual scene as realistic as possible to reflect the appearance and atmosphere of the actual flight environment. Using the environmental reconstruction data to perform dynamic simulation on the aircraft, simulating the movement and behavior of the aircraft in the actual environment. According to the movement characteristics of the aircraft and environmental conditions, simulating the flight path, attitude change, etc. of the aircraft. According to the movement characteristics of the aircraft and environmental conditions, simulating the flight path, attitude change, etc. of the aircraft. Performing virtual sensor fusion on the aircraft dynamic simulation data generated in step S143 and the aircraft environmental reconstruction data generated in step S142. This includes: integrating data from different sensors (such as visual sensors, radars, GPS, etc.) into a unified virtual sensor dataset. Using data fusion algorithms to fuse the information provided by different sensors to obtain more comprehensive and accurate aircraft state and environmental information.

[0101] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0102] Step S21: Analyze the flight situation of the aircraft from the multi-source unified dataset of the aircraft to obtain aircraft flight situation data;

[0103] Step S22: Construct a flight classifier based on the aircraft flight situation data to obtain an aircraft flight mode classifier;

[0104] Step S23: Use the aircraft flight mode classifier to identify the aircraft flight mode for the aircraft flight motion characteristic data, generating aircraft flight mode data, where the aircraft flight mode data includes aircraft normal flight mode data and aircraft abnormal flight mode data;

[0105] Step S24: Extract abnormal flight parameters from the aircraft abnormal flight mode data to generate aircraft abnormal flight parameters.

[0106] In the embodiments of the present invention, aircraft data from different sensors and data sources is collected, including position, speed, acceleration, attitude, battery status, etc. The data is cleaned to handle missing values and outliers to ensure data quality. Useful features are extracted from the raw data, such as the flight trajectory of the aircraft, attitude changes, flight speed changes, etc. Statistical analysis, time series analysis, or spatial analysis is performed on the extracted feature data to reveal the flight conditions and behavior patterns of the aircraft. Based on the flight condition data, normal flight and abnormal flight data are labeled. Appropriate features are selected and machine learning or deep learning models, such as support vector machines, decision trees, neural networks, etc., are trained to construct an aircraft flight mode classifier. Methods such as cross-validation are used to evaluate the performance of the model, and the model is tuned to improve the classification accuracy and generalization ability. The flight feature data of the aircraft is preprocessed by inputting it into the flight mode classifier to ensure the consistency of the data format and features. The trained classifier is used to classify the data to identify different flight modes of the aircraft, including normal flight modes and abnormal flight modes. The identified flight mode data is stored or output for subsequent anomaly detection and analysis. The abnormal flight mode data is analyzed to identify abnormal events or behaviors. Key parameters or features are extracted from the abnormal flight data, such as abnormal attitude, speed changes, energy consumption, etc. The abnormal parameters are analyzed to generate an abnormal flight report or alarm to support aircraft operation management and maintenance decisions.

[0107] Preferably, step S22 includes the following steps:

[0108] Step S221: Perform data annotation on the aircraft flight condition data to generate aircraft flight condition annotation data;

[0109] Step S222: Extract visual features and motion features from the aircraft flight condition annotation data to obtain aircraft visual feature data and aircraft motion feature data;

[0110] Step S223: Merge the aircraft visual feature data and the aircraft motion feature data to obtain an aircraft flight feature data set; divide the aircraft flight feature data set to generate a model training set and a model validation set;

[0111] Step S224: Use the convolutional neural network algorithm to train the model on the model training set to generate aircraft behavior pattern training data; tune the model parameters of the aircraft behavior pattern training data through the model validation set, thereby generating an aircraft flight mode classifier.

[0112] In the embodiments of the present invention, by preparing the collected aircraft data, including various sensor data such as position, speed, and attitude. Manually or automatically annotate the data, marking the time periods or events of normal flight and abnormal flight. Ensure the accuracy and consistency of the annotated data, and handle the existing inconsistencies or ambiguities. Use computer vision technology and motion analysis methods to extract visual features such as images or video content, and motion features such as speed and acceleration from the annotated data. Convert the extracted feature data into a format that the model can process, such as vector or matrix form. Combine the visual feature data and motion feature data according to time or event association to form a complete flight feature dataset. Divide the combined dataset into a model training set and a model validation set, usually using cross-validation or holdout set methods for division, to ensure the independence and effectiveness of model training and evaluation. Use deep learning models such as convolutional neural networks (CNNs) to train the training set and learn the behavior patterns of the aircraft. Use the validation set to evaluate the model and adjust the parameters to optimize the performance and generalization ability of the model. Finally, generate an aircraft flight mode classifier that can accurately classify the normal and abnormal flight modes of the aircraft, providing support for subsequent flight behavior analysis and anomaly detection.

[0113] Preferably, step S24 includes the following steps:

[0114] Step S241: Identify the abnormal type of the aircraft abnormal flight mode data to generate aircraft abnormal flight type data, where the abnormal type identification includes external environment abnormality of the aircraft and internal environment abnormality of the aircraft;

[0115] Step S242: When it is confirmed that the abnormal type identification of the aircraft abnormal flight mode data is an external environment abnormality of the aircraft, then analyze the environmental impact factors of the aircraft environmental feature data to generate environmental impact factor data; extract the external abnormal environment parameters from the aircraft abnormal flight mode data according to the environmental impact factor data to obtain aircraft external environment abnormal feature data;

[0116] Step S243: When it is confirmed that the abnormal type identification of the aircraft abnormal flight mode data is an internal environment abnormality of the aircraft, then analyze the flight structure of the aircraft to generate aircraft internal structure data; conduct fault troubleshooting on the aircraft internal structure data to generate aircraft internal structure fault data;

[0117] Step S244: Analyze the structural impact factors according to the aircraft internal structure fault data to generate structural impact factor data; extract the internal abnormal operation parameters from the aircraft abnormal flight mode data according to the structural impact factor data to obtain aircraft internal operation abnormal feature data;

[0118] Step S245: Integrate the abnormal feature data of the external environment of the aircraft and the abnormal feature data of the internal operation of the aircraft to generate abnormal flight parameters of the aircraft.

[0119] In the embodiments of the present invention, by using a classification algorithm or a rule engine to analyze and classify the abnormal flight mode data, the abnormal types are identified, such as external environment abnormalities or internal structure abnormalities. Analyze the external environmental conditions where the aircraft is located, including factors such as weather, air quality, and electromagnetic interference. According to the environmental impact factor data, extract the external environmental parameters that affect the normal operation of the aircraft, such as wind speed, humidity, temperature, etc. Analyze the internal structure of the aircraft, including components such as circuit boards, sensors, and motors. According to the internal structure data of the aircraft, check the internal faults that cause abnormal flight, such as sensor failures and circuit short circuits. Analyze the degree of influence of internal faults on the flight behavior of the aircraft and evaluate the influence factors of different faults on the aircraft. Combine the abnormal feature data of the external environment and the abnormal feature data of the internal operation to form complete abnormal flight parameters of the aircraft.

[0120] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0121] Step S31: Use GPS to obtain the real-time flight position data of the aircraft;

[0122] Step S32: Mark the real-time flight position data of the aircraft according to the normal flight mode data of the aircraft to obtain the real-time flight space coordinate data of the aircraft; track the flight path of the aircraft through the real-time flight space coordinate data of the aircraft to generate the real-time flight path of the aircraft;

[0123] Step S33: Calculate the path deviation of the real-time flight path of the aircraft based on the abnormal flight parameters of the aircraft to obtain the abnormal deviation path of the aircraft flight;

[0124] Step S34: Perform attitude control on the aircraft based on the abnormal deviation path of the aircraft flight to generate attitude control adjustment data of the aircraft; adjust the path trajectory planning of the abnormal deviation path of the aircraft flight through the attitude control adjustment data of the aircraft to generate path trajectory planning adjustment data of the aircraft;

[0125] Step S35: Use a three-dimensional flight virtual scene to construct an initial control strategy for the attitude control adjustment data of the aircraft and the path trajectory planning adjustment data of the aircraft to obtain an initial flight control strategy; perform stability analysis on the initial flight control strategy to generate strategy stability analysis data.

[0126] In the embodiments of the present invention, the current position information such as longitude, latitude, and altitude of the aircraft is obtained through the GPS module or system. Ensure that the position data of the aircraft can be updated in real time to reflect the current position state of the aircraft. The real-time obtained GPS data is marked and processed according to the spatial coordinate system to obtain the coordinate data of the aircraft in three-dimensional space. The tracking algorithm (such as the Kalman filter or path planning algorithm) is used to process and predict the real-time position data of the aircraft to generate the current real-time flight path of the aircraft. The abnormal flight parameter data is used to compare and analyze the normal flight path to calculate the deviation of the aircraft from the expected path. According to the abnormal deviation path data, the attitude control parameters of the aircraft are adjusted to enable it to more effectively correct the path deviation. Based on the adjusted attitude control data, the path trajectory of the aircraft is re-planned to ensure that the aircraft can fly safely along the corrected path. The attitude control and path planning adjustment process of the aircraft is simulated in a three-dimensional flight virtual scene to generate an initial flight control strategy. The engineering method or mathematical model is used to perform stability analysis on the initial control strategy to evaluate its stability and reliability under various flight conditions.

[0127] Preferably, step S35 includes the following steps:

[0128] Step S351: Perform parameter mapping on the three-dimensional flight virtual scene according to the aircraft attitude control adjustment data and the aircraft path trajectory planning adjustment data to obtain the target attitude mapping sequence and the target flight path mapping data;

[0129] Step S352: Construct an initial control strategy based on the aircraft attitude control adjustment data and the aircraft path trajectory planning adjustment data to generate an initial flight control strategy; perform simulation control on the initial flight control strategy through a preset time step and record the obtained control strategy performance data;

[0130] Step S353: Perform strategy stability analysis on the control strategy performance data to generate strategy stability analysis data.

[0131] In the embodiments of the present invention, by mapping the attitude control adjustment data and path planning adjustment data of the aircraft to the model parameters in the three-dimensional flight virtual scene, including the target attitude sequence and target flight path data of the aircraft. Simulate the flight process of the aircraft in the virtual environment to ensure the accuracy and real-time of attitude and path adjustment. According to the obtained target attitude and path data by mapping, construct the initial flight control strategy. In the virtual scene, simulate and control the flight control strategy according to the preset time step, and simulate the attitude adjustment and path planning process of the aircraft. Record the performance data during the control process, including parameters such as attitude stability and path tracking accuracy. Use engineering methods or mathematical models to analyze and evaluate the recorded performance data, and evaluate the stability and reliability of the control strategy under different conditions. Optimize the initial control strategy according to the stability analysis results to ensure that the aircraft can maintain a stable attitude and path control under various flight conditions. More specifically, use computer graphics and virtual reality technologies to map the attitude control adjustment data (such as Euler angles or quaternions) and path planning adjustment data of the aircraft to the aircraft model in the three-dimensional scene. In the virtual environment, dynamically adjust the attitude and position of the aircraft model according to the obtained target attitude sequence and flight path data by mapping, and simulate the actual flight process. Based on the attitude and path adjustment requirements of the aircraft in the virtual scene, design the initial control algorithms and strategies, such as PID controllers or model predictive control (MPC). Implement the initial control strategy in the virtual environment, simulate the movement of the aircraft through simulation software or real-time rendering engines, and record the actual attitude and path adjustment of the aircraft. Real-time record the key performance data during the control process, such as attitude stability, path deviation, response time, etc., for subsequent analysis and evaluation. Use system analysis tools or mathematical modeling techniques to quantitatively analyze the recorded performance data, and evaluate the stability and robustness of the control strategy under different conditions. Adjust and optimize the initial control strategy according to the stability analysis results, improve the control algorithm parameters or enhance the adaptability of the control strategy to improve the control accuracy and safety of the aircraft.

[0132] Preferably, step S353 includes the following steps:

[0133] Step S3531: Analyze the basic performance indicators of the control strategy performance data to obtain the basic performance indicator data;

[0134] Step S3532: Evaluate the control effect of the initial flight control strategy through the basic performance indicator data to generate the strategy control effect evaluation data;

[0135] Step S3533: Calculate the control steady-state error of the strategy control effect evaluation data to obtain the strategy stability indicator data;

[0136] Step S3534: Perform statistical analysis on the strategy stability index data to generate strategy control statistical analysis data; perform time series analysis on the control strategy performance data to generate strategy control time series stability data;

[0137] Step S3535: Integrate the basic performance indicator data, the policy stability indicator data, the policy control statistical analysis data and the policy control time series stability data to generate policy stability analysis data.

[0138] In an embodiment of the present invention, the recorded control strategy performance data includes basic performance indicators such as posture stability, path deviation, and response time. Extract key performance indicator data, such as maximum posture deviation, mean and variance of path tracking error, etc. Evaluate the effect of the control strategy based on the extracted performance indicator data, and analyze the response speed, accuracy and stability of the controller. Record various effect data obtained during the evaluation process, such as the response curve of the controller, posture stability score, etc. Calculate the error of the controller in a stable state, and evaluate the control strategy's ability to continuously control the target posture and path. Extract statistical data of steady-state errors, such as average steady-state error, error variance, etc. Use statistical methods to analyze stability indicator data and evaluate the stability and consistency of the control strategy under different conditions. Analyze the stability changes of the control strategy in time series and explore the evolution trend of the control effect over time. Integrate the data obtained from the above analyses into a complete strategy stability analysis report or data set. Generate a comprehensive stability analysis report including basic performance indicators, stability indicators, statistical analysis and timing analysis to provide a basis and reference for control strategy optimization and improvement.

[0139] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0140] Step S41: performing optimal control solution on the initial flight control strategy based on the strategy stability analysis data to generate optimal strategy control data;

[0141] Step S42: performing Monte Carlo simulation on the three-dimensional flight virtual scene according to the optimal strategy control data to generate a control strategy simulation data set;

[0142] Step S43: Perform logic control instruction conversion based on the control strategy simulation data set to generate aircraft flight control logic control instructions to execute the flight control system functional logic model design task.

[0143] In the embodiments of the present invention, an optimization algorithm (such as a genetic algorithm, a particle swarm optimization, or a simulated annealing algorithm) is used to optimize and solve the initial flight control strategy based on the data of policy stability analysis, so as to improve the efficiency and performance of the control strategy. According to the obtained optimal control parameters and policy adjustment data, an optimized optimal flight control strategy is generated. In a three-dimensional flight virtual scene, through random sampling and multiple simulations, the stability and reliability of the optimal control strategy under various flight conditions are verified and evaluated. In a three-dimensional flight virtual scene, through random sampling and multiple simulations, the stability and reliability of the optimal control strategy under various flight conditions are verified and evaluated. According to the analysis and evaluation of the simulation data set, the logical control instructions of the aircraft flight control system are designed, including functions such as attitude adjustment, flight path planning, and emergency response. The designed logical control instructions are converted into an instruction format that the flight control system can understand and execute, so as to implement various control tasks and operations of the aircraft during actual flight. Specifically, an optimization algorithm suitable for the characteristics of the problem is selected, such as a genetic algorithm, a particle swarm optimization, a simulated annealing algorithm, etc. Considering the real-time and complexity of flight control, a hybrid algorithm or an adaptive algorithm is adopted to improve the convergence speed and the quality of the solution. The key parameters of the control strategy are optimized, such as control gain, path planning parameters, or attitude adjustment strategies, to maximize the performance and stability of the control system. The obtained optimal control parameters and policy adjustment data are converted into a data format that the aircraft control system can actually execute. In the simulation environment, through random sampling and a large number of repeated simulations, various flight situations and environmental changes are covered. The control effect data during the simulation process are recorded, including indicators in multiple aspects such as attitude stability, path tracking accuracy, and response speed. The simulation data are integrated and processed to form a simulation data set containing a large number of control strategy effect evaluations for subsequent evaluation and verification. According to the analysis results and requirements of the simulation data set, specific flight control logical control instructions are designed, including various flight tasks such as takeoff, landing, heading control, and altitude control. The designed logical control instructions are converted into an instruction format that a specific flight control system can understand and execute to ensure compatibility with the hardware platform and software algorithm. The generated flight control logical control instructions are loaded into the flight control system of the aircraft to perform the actual functional logic model design operation, and the actual effects and performance of the control strategy are verified and optimized.

[0144] The beneficial effects of the present invention are as follows: By unifying the formats of multi-source data of the aircraft and reconstructing the scene, a three-dimensional flight virtual scene can be obtained, which helps to more accurately simulate and analyze the operating environment of the aircraft. By analyzing the flight situation data of the aircraft, an aircraft flight mode classifier can be constructed, and then the flight mode of the aircraft can be identified. This helps to understand the behavior and performance of the aircraft and provides a basis for subsequent control strategies. By analyzing the flight mode data of the aircraft, abnormal flight parameters can be extracted, and these parameters can be used to detect and determine whether the aircraft is in an abnormal situation, so as to take corresponding control measures to ensure the safe operation of the aircraft. According to the real-time flight position data and normal flight mode data of the aircraft, flight path tracking can be carried out, and an initial flight control strategy can be constructed based on the abnormal flight parameters. This helps the aircraft to maintain stability and safety during real-time operation. Perform a stability analysis on the initial flight control strategy to ensure the effectiveness and safety of the control strategy. Then, through optimal control solution, optimal strategy control data can be obtained, so as to achieve the best control performance of the aircraft. Convert the optimal strategy control data into aircraft flight control logic control instructions to execute the functional logic model design operation of the flight control system. This helps to achieve precise control and operation of the aircraft. Therefore, the present invention improves the adaptability and optimization ability of the functional logic model design of the flight control system by unifying multi-source data, accurately analyzing the flight situation, and optimizing the flight control strategy.

[0145] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0146] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A flight control system functional logic model design method, characterized in that: The following steps are involved: Step S1: Acquire multi-source data of aircraft; Unify the data format of multi-source data of aircraft to generate a unified multi-source data set of aircraft; reconstruct the flight virtual scene of the multi-source unified data set of aircraft to generate a three-dimensional flight virtual scene; Step S2: analyzing the aircraft flight situation of the aircraft multi-source unified data set to obtain aircraft flight situation data; constructing a flight classifier based on the aircraft flight situation data to obtain an aircraft flight mode classifier; using the aircraft flight mode classifier to identify the aircraft flight mode of the aircraft flight motion feature data to generate aircraft flight mode data; extracting abnormal flight parameters from the aircraft flight mode data to generate abnormal aircraft flight parameters; Step S3: Acquire the real-time flight position data of the aircraft; perform flight path tracking on the real-time flight position data of the aircraft according to the normal flight mode data of the aircraft to generate the real-time flight path of the aircraft; construct an initial control strategy for the real-time flight path of the aircraft based on the abnormal flight parameters of the aircraft to obtain an initial flight control strategy; perform stability analysis on the initial flight control strategy to generate strategy stability analysis data; Step S3 includes the following steps: Step S31: using GPS to obtain real-time flight position data of the aircraft; Step S32: marking the real-time flight position data of the aircraft with spatial coordinate points according to the normal flight mode data of the aircraft to obtain the real-time flight spatial coordinate data of the aircraft; tracking the flight path of the aircraft through the real-time flight spatial coordinate data of the aircraft to generate the real-time flight path of the aircraft; Step S33: Calculating the path deviation of the real-time flight path of the aircraft based on the abnormal flight parameters of the aircraft to obtain the abnormal flight deviation path of the aircraft; Step S34: performing attitude control on the aircraft based on the abnormal flight deviation path of the aircraft to generate aircraft attitude control adjustment data; performing path trajectory planning adjustment on the abnormal flight deviation path of the aircraft based on the aircraft attitude control adjustment data to generate aircraft path trajectory planning adjustment data; Step S35: constructing an initial control strategy for the aircraft attitude control adjustment data and the aircraft path trajectory planning adjustment data using the three-dimensional flight virtual scene to obtain an initial flight control strategy; performing stability analysis on the initial flight control strategy to generate strategy stability analysis data; Step S35 includes the following steps: Step S351: Parameter mapping is performed on the three-dimensional flight virtual scene according to the aircraft attitude control adjustment data and the aircraft path trajectory planning adjustment data to obtain a target attitude mapping sequence and a target flight path mapping data; Step S352: constructing an initial control strategy based on the aircraft attitude control adjustment data and the aircraft path trajectory planning adjustment data to generate an initial flight control strategy; performing simulation control on the initial flight control strategy through a preset time step, and recording the control strategy performance data; Step S353: Performing strategy stability analysis on the control strategy performance data to generate strategy stability analysis data; Step S353 includes the following steps: Step S3531: Perform basic performance index analysis on the control strategy performance data to obtain basic performance index data; Step S3532: Evaluate the control effect of the initial flight control strategy through basic performance indicator data to generate strategy control effect evaluation data; Step S3533: Calculate the control steady-state error of the strategy control effect evaluation data to obtain strategy stability index data; Step S3534: Perform statistical analysis on the strategy stability index data to generate strategy control statistical analysis data; perform time series analysis on the control strategy performance data to generate strategy control time series stability data; Step S3535: Integrate the basic performance index data, the policy stability index data, the policy control statistical analysis data and the policy control time series stability data to generate policy stability analysis data; Step S4: Based on the strategy stability analysis data, the initial flight control strategy is optimally solved to generate optimal strategy control data; according to the optimal strategy control data, the three-dimensional flight virtual scene is logically converted to generate aircraft flight control logic control instructions to execute the flight control system functional logic model design task.

2. The flight control system functional logic model design method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using a distributed sensor network to obtain multi-source data of the aircraft; Step S12: performing data preprocessing on the aircraft multi-source data to generate standard aircraft multi-source data, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: unifying the data format of the standard aircraft multi-source data to generate an aircraft multi-source unified data set; Step S14: Reconstruct the flight virtual scene of the aircraft multi-source unified data set through virtual reality enhancement technology to generate a three-dimensional flight virtual scene.

3. The flight control system functional logic model design method according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: extracting flight environment features from the aircraft multi-source unified data set to obtain aircraft environment feature data; Step S142: reconstructing the aircraft environment feature data based on virtual reality enhancement technology to generate aircraft environment reconstruction data; Step S143: dynamically simulating the aircraft using the aircraft environment reconstruction data to generate aircraft dynamic simulation data; Step S144: Perform virtual sensor fusion based on the aircraft dynamic simulation data and the aircraft environment reconstruction data to generate a three-dimensional flight virtual scene.

4. The flight control system functional logic model design method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: analyzing the aircraft flight situation on the aircraft multi-source unified data set to obtain aircraft flight situation data; Step S22: constructing a flight classifier according to the aircraft flight situation data to obtain an aircraft flight mode classifier; Step S23: using an aircraft flight mode classifier to perform aircraft flight mode recognition on the aircraft flight motion feature data to generate aircraft flight mode data, wherein the aircraft flight mode data includes aircraft normal flight mode data and aircraft abnormal flight mode data; Step S24: extracting abnormal flight parameters from the aircraft abnormal flight mode data to generate abnormal flight parameters of the aircraft.

5. The flight control system functional logic model design method according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: annotating the aircraft flight condition data to generate aircraft flight condition annotated data; Step S222: extracting visual features and motion features from the aircraft flight situation annotation data to obtain aircraft visual feature data and aircraft motion feature data; Step S223: merging the aircraft visual feature data and the aircraft motion feature data to obtain an aircraft flight feature data set; dividing the aircraft flight feature data set to generate a model training set and a model verification set; Step S224: Perform model training on the model training set using a convolutional neural network algorithm to generate aircraft behavior pattern training data; perform model parameter tuning on the aircraft behavior pattern training data using a model verification set to generate an aircraft flight pattern classifier.

6. The flight control system functional logic model design method according to claim 5, characterized in that: Step S24 includes the following steps: Step S241: performing abnormal type identification on the aircraft abnormal flight mode data to generate aircraft abnormal flight type data, wherein the abnormal type identification includes aircraft external environment abnormality and aircraft internal environment abnormality; Step S242: when it is confirmed that the abnormal type of the abnormal flight mode data of the aircraft is identified as abnormal external environment of the aircraft, an environmental impact factor analysis is performed on the aircraft environment feature data to generate environmental impact factor data; external abnormal environment parameters are extracted from the abnormal flight mode data of the aircraft according to the environmental impact factor data to obtain abnormal external environment feature data of the aircraft; Step S243: When it is confirmed that the abnormal type of the abnormal flight mode data of the aircraft is identified as an abnormal internal environment of the aircraft, a flight structure analysis is performed on the aircraft to generate aircraft internal structure data; a fault detection is performed on the aircraft internal structure data to generate aircraft internal structure fault data; Step S244: performing structural influence factor analysis based on the aircraft internal structural fault data to generate structural influence factor data; performing internal abnormal operation parameter extraction on the aircraft abnormal flight mode data based on the structural influence factor data to obtain aircraft internal operation abnormality feature data; Step S245: Integrate the abnormal characteristic data of the external environment of the aircraft and the abnormal characteristic data of the internal operation of the aircraft to generate abnormal flight parameters of the aircraft.

7. The flight control system functional logic model design method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing optimal control solution on the initial flight control strategy based on the strategy stability analysis data to generate optimal strategy control data; Step S42: performing Monte Carlo simulation on the three-dimensional flight virtual scene according to the optimal strategy control data to generate a control strategy simulation data set; Step S43: Perform logic control instruction conversion based on the control strategy simulation data set to generate aircraft flight control logic control instructions to execute the flight control system functional logic model design task.

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