Intelligent operation management system and method for aircraft flight trainer

Abstract

An intelligent operation management system for an aircraft flight trainer comprises an acquisition system, a business system, a support system and an interaction system, integrated and intelligent management of the aircraft flight trainer is realized through the acquisition system, the business system, the support system and the interaction system, and the acquisition system can acquire data of each system in real time; the support system realizes unified storage, management and calling of data through a comprehensive data service; the service system and the interaction system realize real-time monitoring, operation optimization, equipment maintenance and fault diagnosis of the aircraft flight trainer, and comprehensively improve the efficiency and safety of flight training. Meanwhile, the method based on the intelligent operation management system of the aircraft flight trainer can divide the original course hierarchy into independent training sequences with training significance, and each sequence clearly corresponds to a specific training target, so that equipment resources are fully utilized, and the training efficiency is improved. And the equipment efficiency is improved while the operation safety of the equipment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of aviation technology, and in particular to an intelligent operation management system and method for an aircraft flight training device. Background Art

[0002] With the rapid development of aviation technology and the increasing demand for flight safety, flight training devices are playing an important role in pilot training, aircraft system verification, and aircraft performance evaluation. Flight simulators are widely used in areas such as pilot skill training, flight operation evaluation, and aircraft control system verification. However, the existing management model of flight training devices mainly relies on manual operation, which has many inefficient links. Specifically,

[0003] Unintelligent scheduling and management: Existing systems generally rely on manual intervention to schedule training tasks and are unable to dynamically optimize based on factors such as pilots' real-time needs and equipment status, resulting in wasted resources and delays in training tasks.

[0004] Insufficient resource utilization: Traditional flight training management systems treat a single device failure as a complete aircraft failure. Once a failure occurs, the entire training plan is interrupted, resulting in inadequate utilization of equipment resources.

[0005] Delayed fault diagnosis: Most trainers are unable to provide immediate warnings and intelligent diagnosis when equipment failure occurs. Often, manual troubleshooting and repairs can only be performed after the failure occurs, increasing equipment downtime.

[0006] Insufficient data analysis: Traditional systems have limited status monitoring and data analysis capabilities, making it difficult to extract effective information from big data to guide equipment maintenance, pilot training effectiveness evaluation, and other aspects.

[0007] Therefore, a new, integrated intelligent management system is needed for real-time monitoring, fault prediction and diagnosis, task scheduling optimization, and data-driven decision support of aircraft flight training devices to improve training efficiency, reduce costs, and ensure flight safety. Summary of the Invention

[0008] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions.

[0009] An intelligent operation and management system for aircraft flight training devices comprises an acquisition system, a business system, a support system, and an interactive system. The business system is signal-connected to the acquisition system and the support system, respectively, and the interactive system is signal-connected to the support system. The acquisition system synchronously acquires the operating status of each system, the working status of equipment components, operating instructions, and environmental data through a flight training device interface, and performs efficient caching and storage management through a data synchronization middle layer. The business system is used to formulate training plans, realize personalized task allocation, and improve training effectiveness and equipment utilization. The support system is used to support data processing and functional services of the entire system, and the interactive system is used to synchronously display and feedback interactive information.

[0010] Preferably, the business system also includes a training sequence module, a training plan module and a fault diagnosis module. The training sequence module is used to identify flight training sequences, process course data, and store operation sequences and equipment lists; the training plan module intelligently formulates training plans based on courses, personnel information, equipment status and prediction reports to achieve personalized task allocation; the fault diagnosis module constructs fault mode norm data based on historical data and service life information, uses machine learning and neural network algorithms to diagnose equipment status and predict faults, and generates diagnostic prediction reports and repair and maintenance plan reports, and the training sequence module, training plan module and fault diagnosis module are respectively connected to the support system signals.

[0011] Preferably, the training sequence module also includes a flight training sequence identification module, an operation sequence identification module and a database creation module. The flight training sequence identification module is used to identify the flight training sequence, the operation sequence identification module identifies the flight training sequence, and intelligently formulates the training operation sequence based on the flight training sequence identification, and the database creation module is used to store the training equipment list.

[0012] Preferably, the support system also includes a fault identification and prediction module, a plan and task module, a sequence and relationship library module, a course analysis module, a permission module and a data encryption module. The fault identification and prediction module is signal-connected with the fault diagnosis module. The fault identification and prediction module analyzes the historical operation data of the flight training device based on a deep learning algorithm, identifies potential fault hazards, and predicts and diagnoses potential fault risks and probabilities of occurrence in combination with environmental data; the plan and task module is signal-connected with the training plan module respectively; the sequence and relationship library module and the course analysis module are signal-connected with the training sequence module respectively; the permission module divides users into different roles and assigns corresponding operation permissions to each role; the data encryption module uses the TLS protocol to encrypt data.

[0013] Preferably, the interactive system also includes a system management interface, a resource monitoring interface, a task synchronization interface, a maintenance information interface and an alarm and prompt interface. The system management interface is connected to the sequence and relationship library module signal; the resource monitoring interface and the task synchronization interface are connected to the planning and task module signal and the data encryption module signal respectively; the maintenance information interface and the alarm and prompt interface are connected to the fault identification and prediction module signal and the authority module signal respectively.

[0014] Preferably, the present invention further provides an intelligent operation management method for an aircraft flight training device, comprising the following steps:

[0015] S1: Acquire data. The data acquisition system synchronously obtains the operating status of each system, the working status of equipment components, operating instructions, and environmental data, and efficiently caches and stores them through the data synchronization middle layer.

[0016] S2: Data processing and analysis: First, the raw data in the data synchronization middle layer is cleaned and preprocessed. Then, the preprocessed data is formatted and time-series aligned to ensure data consistency.

[0017] S3: Data analysis: First, the collected flight training courses are analyzed and pre-processed according to the pilot training syllabus, the entire course is divided into multiple sub-training units, and then integrated to construct a flight training sequence;

[0018] S4: Establish a relational database. Based on the constructed flight training sequence, combined with the equipment list and the characteristics of each operation action in the operation sequence, establish the association between the operation sequence and the equipment, and realize the construction of the relational database.

[0019] S5: Intelligent platform establishment, based on relational database, combined with fault prediction and diagnosis, training plan management and intelligent task management, to realize the construction of intelligent operation platform of aircraft flight training device.

[0020] Preferably, the specific steps of the raw data cleaning and preprocessing are: first, using sliding mean filtering and Kalman filtering methods to remove noise data, and using statistical methods to detect outliers, while filling in missing data in network communication through interpolation algorithms to ensure data integrity and time series continuity.

[0021] Preferably, the specific steps of constructing the flight training sequence are as follows:

[0022] Step 1: Each sub-training unit is further refined into several branch tasks through flight training sequence recognition and processing;

[0023] Step 2: For each branch task, identify the training operation sequence and generate several corresponding detailed operation actions. Then, integrate each operation action to complete the flight training sequence construction.

[0024] Preferably, the specific steps of the fault prediction and diagnosis are as follows:

[0025] Step 1: Generate equipment fault identification and prediction models through the flight training device's fault diagnosis module and equipment list driver.

[0026] Step 2: Using fault identification and prediction models, the flight training device's equipment signals and environmental information are collected simultaneously. Using fault identification and prediction algorithms, potential fault hazards are identified, and the fault risk and probability of occurrence are assessed.

[0027] Step 3: Issue prompts and warnings in a timely manner through the intelligent early warning mechanism, and generate fault prediction reports, diagnosis reports and repair and maintenance plans.

[0028] Preferably, the specific steps of training plan management and intelligent task management are as follows:

[0029] Step 1: Collect course sequence, staff attendance, equipment status and task progress information for statistical analysis;

[0030] Step 2: Implement task scheduling and allocation through an intelligent task management mechanism, and intelligently evaluate the diagnostic and prediction reports generated by the fault prediction and diagnosis module;

[0031] Step 3: Execute corresponding maintenance plans or task assignments according to the scheduling arrangements to ensure the smooth and efficient implementation of the training plan.

[0032] The beneficial effects of the present invention are:

[0033] First, the present invention achieves integrated and intelligent management of aircraft flight training devices through an acquisition system, a business system, a support system, and an interactive system. The acquisition system can obtain data from each system in real time, and the support system implements unified storage, management, and access of data through comprehensive data services, ensuring efficient and coordinated operation of each module, improving the system's intelligence and data security, and providing accurate and efficient technical support for flight training, equipment maintenance, and mission management.

[0034] Second, the present invention realizes real-time monitoring, operation optimization, equipment maintenance and fault diagnosis of aircraft flight training devices through business systems and interactive systems, while supporting intelligent management of related personnel and business processes, comprehensively improving the efficiency and safety of flight training.

[0035] Third, through a hierarchical management strategy, the present invention breaks down training sequences into specific operation sequences, intelligently matching and assigning each operation to the corresponding equipment, achieving efficient coordination between training tasks and equipment resources. This not only improves the utilization rate of training equipment, but also enhances the proactiveness of equipment maintenance, reduces operating costs, and lays an important technical foundation for the development of intelligent management of flight training devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of the system of the present invention;

[0037] Figure 2 This is a detailed flow chart of the business system of the present invention;

[0038] Figure 3 A flow chart constructed for the flight training sequence of the present invention;

[0039] Figure 4 This is the principle flow chart of the fault diagnosis module;

[0040] Figure 5 This is the principle flow chart of the training plan module;

[0041] Figure 6 This is a diagram comparing the principles of the present invention and existing flight training management;

[0042] Figure 7 Flowchart of the method of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0045] Example 1:

[0046] See also Figure 1An intelligent operation and management system for aircraft flight trainers includes an acquisition system, a business system, a support system, and an interaction system. The business system is signal-connected to the acquisition system and the support system, respectively, and the interaction system is signal-connected to the support system. The acquisition system synchronously acquires course data, personnel data, equipment data, and environmental data from each subsystem via the flight trainer interface system. Course data is obtained via a course parsing module, equipment data specifically includes operational status data and equipment component operating status data, personnel data specifically includes various operator instructions, and environmental data includes temperature, wind speed, humidity, and so on. Furthermore, the interface system's reserved interfaces can collect information on associated personnel (such as instructors, trainees, and technical maintenance personnel). The system supports the import and export of static information such as equipment, courses, associated personnel, and operating specifications, enabling batch data entry, storage, and management to ensure data integrity and consistency. Users can batch import relevant information using standardized formats (such as Excel, CSV, JSON, and database interfaces), reducing manual data entry workload, and can export data at any time for analysis or backup. A data synchronization middle layer is used for efficient caching and storage management, improving data processing speed.

[0047] The business system is used to formulate training plans, implement personalized task allocation, and improve training effectiveness and equipment utilization. The support system supports the data processing and functional services of the entire system. The interactive system is used to synchronously display and feedback interactive information. Through the above solution, the interactive system is able to synchronously display and feedback interactive information, and through an intuitive graphical user interface, it can display the current task content, progress and status in real time to authenticated users (including various operators); at the same time, the content of each interactive interface covers multiple aspects such as alarm prompts, maintenance information, task synchronization, resource monitoring and system management. In addition, the interactive system integrates speech recognition (ASR) and speech synthesis (TTS) technologies to support the control of the flight training device through voice commands. Secondly, the interactive system also introduces computer vision technology to realize gesture recognition, allowing users to control the device through gesture operations, thereby significantly improving the convenience of human-computer interaction and training efficiency.

[0048] See Figure 2The business system also includes a training sequence module, a training plan module and a fault diagnosis module. The training sequence module is used to identify flight training sequences, process course data, and store operation sequences and equipment lists; the training plan module intelligently formulates training plans based on courses, personnel information, equipment status and prediction reports to achieve personalized task allocation; the fault diagnosis module constructs fault mode norm data based on historical data and service life information, uses machine learning and neural network algorithms to diagnose equipment status and predict faults, and generates diagnostic prediction reports and repair and maintenance plan reports, and the training sequence module, training plan module and fault diagnosis module are respectively connected to the support system signal.

[0049] The training sequence module also includes a flight training sequence identification module, an operation sequence identification module and a database creation module. The flight training sequence identification module is used to identify the flight training sequence, the operation sequence identification module identifies the flight training sequence and intelligently formulates the training operation sequence based on the flight training sequence identification, and the database creation module is used to store the training equipment list.

[0050] See Figure 2 and Figure 5 The training planning module also includes an information statistics module, a task scheduling module, a task allocation module, and a training planning module. Through the above technical solution, the fault diagnosis module utilizes fault prediction and diagnosis algorithms to monitor equipment status in real time, identify potential fault risks, and issue timely warnings through an intelligent early warning mechanism, generating a fault probability analysis report. This module, in conjunction with the training planning module, generates a repair and maintenance plan. The training planning module, based on statistical analysis of information such as course sequence, personnel attendance, equipment status, and task progress, combines training plan management with intelligent task management functions to achieve intelligent scheduling and unified allocation of flight training tasks, optimizing the development and execution of training plans. The training sequence module parses the course files and uses the operation sequence recognition module to convert the courses into training sequences, which are then refined into specific operations. The operations are then assigned to the corresponding equipment based on the equipment list. Finally, the generated course sequence information is used to build a relational database to provide data support for the system business development. The fault diagnosis module adopts a hierarchical management strategy. When a device fails, it will not affect the related operation actions and will not cause the system to stop training, so that the equipment resources can be fully utilized. Secondly, this system can reasonably and quickly perform task scheduling and synchronously formulate maintenance plans to ensure the subsequent repair / maintenance work of the equipment, thereby ensuring the execution of the overall training task and improving the equipment utilization efficiency while ensuring the safe operation of the equipment.

[0051] See Figure 1 and Figure 5The support system also includes a fault identification and prediction module, a planning and task module, a sequence and relationship library module, a course analysis module, a permission module and a data encryption module. The fault identification and prediction module is signal-connected to the fault diagnosis module. The fault identification and prediction module analyzes the historical operation data of the flight training device based on a deep learning algorithm to identify potential fault hazards, and predicts and diagnoses potential fault risks and probabilities of occurrence in combination with environmental data. The planning and task module is signal-connected to the training plan module respectively, and the sequence and relationship library module and the course analysis module are signal-connected to the training sequence module respectively. The permission module divides users into different roles and assigns corresponding operation permissions to each role; the data encryption module uses the TLS protocol to encrypt data.

[0052] Through the above technical solution, the fault identification and prediction module is mainly based on machine learning and deep learning algorithms. It analyzes the historical operation data, equipment list, and real-time operation data of the flight training device to identify potential fault hazards. At the same time, it combines environmental data to predict and diagnose potential fault risks and probabilities. The planning and task module mainly monitors and manages the execution of training plans, and provides decision-making assistance and suggestions for the training plan module in combination with the current task status. The sequence and relationship library module can parse and pre-process the information based on the file information analyzed by the course parsing module, and extract key data such as course structure and training requirements. Next, the operation sequence judgment algorithm is used to divide the original course level into independent and training-meaningful training sequences based on preset criteria (such as task priority, operation timing, action correlation, etc.), so as to ensure that each sequence clearly corresponds to a specific training goal. On this basis, the system further refines the training sequence into specific operation sequences. Based on the equipment list information, the system intelligently matches and assigns each operation to the corresponding equipment, achieving efficient coordination between training tasks and equipment resources, maximizing equipment utilization and ensuring the continuity of the training process; finally, the generated complete course sequence information will be cached in the database, providing accurate data support for subsequent training plan formulation, task scheduling and real-time monitoring.

[0053] See Figure 1The interactive system also includes a system management interface, a resource monitoring interface, a task synchronization interface, a maintenance information interface, and an alarm and prompt interface. The system management interface is connected to the sequence and relationship library module by signal; the resource monitoring interface and the task synchronization interface are connected to the planning and task module and the data encryption module respectively; and the maintenance information interface and the alarm and prompt interface are connected to the fault identification and prediction module and the permission module respectively. The system management interface can monitor sequence information in real time. The interactive system has a graphical interactive interface that can synchronously monitor and display the aircraft flight training device system operating status, hardware device working status, alarm and maintenance information, and training progress in real time and graphically. It also integrates an intelligent interaction module that supports gesture, voice, and other interaction methods in addition to the default touch and keyboard and mouse interaction operations. It can also control the flight training device equipment through the aircraft flight training device interface system.

[0054] See Figure 7 The present invention also provides a method for intelligent operation management system based on an aircraft flight training device, comprising the following steps:

[0055] S1: Acquire data. The data acquisition system synchronously obtains the operating status of each system, the working status of equipment components, operating instructions, and environmental data. The working status of equipment components is specifically device data, and the operating instructions include headcount data and course data. Then, the data synchronization middle layer is used for efficient caching and storage management.

[0056] S2: Data processing and analysis: First, the raw data from the data synchronization middle layer is cleaned and preprocessed in the support system. Then, the preprocessed data is format-standardized and time-series-aligned to ensure data consistency.

[0057] S3: Data analysis: First, the collected flight training courses are analyzed and pre-processed according to the pilot training syllabus, the entire course is divided into multiple sub-training units, and then integrated to construct a flight training sequence;

[0058] S4: Establish a relational database. Based on the constructed flight training sequence, combined with the equipment list and the characteristics of each operation action in the operation sequence, establish the association between the operation sequence and the equipment, and realize the construction of the relational database.

[0059] S5: Intelligent platform establishment, based on relational database, combined with fault prediction and diagnosis, training plan management and intelligent task management, to realize the construction of intelligent operation platform of aircraft flight training device.

[0060] The specific steps of the raw data cleaning and preprocessing are: first, the sliding mean filter and Kalman filter method are used to remove noise data, and the statistical method is used to detect outliers. At the same time, the interpolation algorithm is used to fill in the missing data of the network communication to ensure the integrity and time series continuity of the data.

[0061] The specific steps of constructing the flight training sequence are as follows:

[0062] Step 1: Each sub-training unit is further refined into several branch tasks through flight training sequence recognition and processing;

[0063] Step 2: For each branch task, identify the training operation sequence and generate several corresponding detailed operation actions. Then, integrate each operation action to complete the flight training sequence construction.

[0064] For specific steps, see Figure 3 First, the pilot training syllabus is parsed and preprocessed through the course parsing module. Then, through flight training sequence recognition, the training syllabus is divided into different branch sub-training units, including training 1, training 2...training n, etc. Each sub-training unit is then further refined to form different branch tasks, including task a, task b...task z, etc., thus forming a pilot training sequence. Then, through operation sequence recognition, each branch task is refined into operation actions, including operation a1, operation a2...operation zk, etc., among which operation a1, operation a2...operation zk constitute a training operation sequence, and each device corresponds to an operation action. Then, all operation actions are integrated to complete the flight training sequence construction.

[0065] The specific steps of fault prediction and diagnosis are as follows:

[0066] Step 1: Generate equipment fault identification and prediction models through the flight training device's fault diagnosis module and equipment list driver.

[0067] Step 2: Using fault identification and prediction models, the flight training device's equipment signals and environmental information are collected simultaneously. Using fault identification and prediction algorithms, potential fault hazards are identified, and the fault risk and probability of occurrence are assessed.

[0068] Step 3: Issue prompts and warnings in a timely manner through the intelligent early warning mechanism, and generate fault prediction reports, diagnosis reports and repair and maintenance plans.

[0069] Specific reference Figure 4During fault prediction and diagnosis, the fault diagnosis module of the flight trainer first obtains fault diagnosis data, and then synchronously drives the flight trainer equipment signal processor with this fault diagnosis data and the equipment list. The flight trainer equipment signal processor synchronously performs fault identification and fault prediction through the fault identification model and the fault prediction model, and when predicting faults, it is also necessary to combine external environmental information; fault identification and fault prediction are both based on corresponding fault identification algorithms and prediction algorithms, and faults and alarms / prompts are issued during the training process, and corresponding repair / maintenance plans are generated, so that personnel can understand fault information in real time and perform maintenance in a timely manner.

[0070] The specific steps for training plan management and intelligent task management are as follows:

[0071] Step 1: Statistical analysis of the collected course sequence, staff attendance, equipment status, and task progress information;

[0072] Step 2: Implement task scheduling and allocation through an intelligent task management mechanism, and intelligently evaluate the diagnostic and prediction reports generated by the fault prediction and diagnosis module;

[0073] Step 3: Execute corresponding maintenance plans or task assignments according to the scheduling arrangements to ensure the smooth and efficient implementation of the training plan.

[0074] Through this method, data collected by the acquisition system from various systems is efficiently cached and stored, preprocessed and parsed in the support system, and finally key data such as course structure and training requirements are extracted. An operation sequence judgment algorithm is used; ultimately, the generated complete course sequence information is cached in the database, providing accurate data support for subsequent training plan development, task scheduling, and real-time monitoring.

[0075] The aircraft flight training device intelligent operation management system and method provided by the present invention is more efficient than the existing flight training device management. Figure 6As shown, in the existing flight training device management system, devices are connected in an "AND" relationship. When a single device fails, the entire device fails, halting training and requiring individual repairs. However, with the flight training device intelligent operation management system and method provided by the present invention, a failure in a single training device, training operation sequence, or training sequence does not affect other training devices, training operation sequences, or training sequences. Training can continue simply by implementing task scheduling and allocation through an intelligent task management mechanism, without causing system downtime. This system enables real-time monitoring, operational optimization, equipment maintenance, and fault diagnosis of aircraft flight training devices, while also supporting intelligent management of associated personnel and business processes. Through technical means such as data collection, flight training sequence construction, fault diagnosis and prediction, and training plan allocation, the system comprehensively improves the efficiency and safety of flight training, optimizes the pilot training process, and provides efficient and intelligent management tools for airlines, training centers, and other institutions. This technology not only improves the utilization rate of training equipment, but also enhances the initiative of equipment maintenance and reduces operating costs. It lays an important technical foundation for the development of intelligent management of flight training devices, meets the development needs of civil aviation pilots' data collection, monitoring and management throughout their life cycle, and has broad application prospects.

[0076] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent operation management system for an aircraft flight training device, characterized in that: It includes an acquisition system, a business system, a support system, and an interaction system. The business system is connected to the acquisition system and the support system respectively, and the interaction system is connected to the support system. The acquisition system synchronously obtains the operating status of each system, the working status of equipment components, operating instructions, and environmental data through the flight training device interface, and performs efficient caching and storage management through the data synchronization middle layer. The business system is used to formulate training plans, realize personalized task allocation, and improve training effects and equipment utilization. The support system supports the data processing and functional services of the entire system, and the interactive system is used for synchronous display and feedback of interactive information.

2. The intelligent operation management system for aircraft flight training devices according to claim 1, characterized in that: The business system also includes a training sequence module, a training plan module, and a fault diagnosis module. The training sequence module is used to identify flight training sequences, process course data, and store operation sequences and equipment lists. The training plan module intelligently formulates training plans based on courses, personnel information, equipment status, and forecast reports to achieve personalized task allocation. The fault diagnosis module constructs fault mode norm data based on historical data and service life information, uses machine learning and neural network algorithms to diagnose equipment status and predict faults, and generates diagnostic prediction reports and repair and maintenance plan reports. The training sequence module, training plan module and fault diagnosis module are respectively connected to the support system signals.

3. The intelligent operation management system for aircraft flight training devices according to claim 2, characterized in that: The training sequence module also includes a flight training sequence identification module, an operation sequence identification module and a database creation module. The flight training sequence identification module is used to identify the flight training sequence, the operation sequence identification module identifies the flight training sequence and intelligently formulates the training operation sequence based on the flight training sequence identification, and the database creation module is used to store the training equipment list.

4. The intelligent operation management system for aircraft flight training devices according to claim 1, characterized in that: The support system also includes a fault identification and prediction module, a planning and task module, a sequence and relationship library module, a course analysis module, a permission module and a data encryption module. The fault identification and prediction module is signal-connected to the fault diagnosis module. The fault identification and prediction module analyzes the historical operation data of the flight training device based on a deep learning algorithm, identifies potential fault hazards, and predicts and diagnoses potential fault risks and probabilities of occurrence in combination with environmental data; the planning and task module is signal-connected to the training plan module respectively, and the sequence and relationship library module and the course analysis module are signal-connected to the training sequence module respectively; the permission module divides users into different roles and assigns corresponding operation permissions to each role; the data encryption module uses the TLS protocol to encrypt data.

5. The intelligent operation management system for aircraft flight training devices according to claim 1, characterized in that: The interactive system also includes a system management interface, a resource monitoring interface, a task synchronization interface, a maintenance information interface and an alarm and prompt interface. The system management interface is connected to the sequence and relationship library module signal; the resource monitoring interface and the task synchronization interface are connected to the planning and task module signal and the data encryption module signal respectively; the maintenance information interface and the alarm and prompt interface are connected to the fault identification and prediction module signal and the authority module signal respectively.

6. A method for intelligent operation management of an aircraft flight training device, characterized in that: The method for the intelligent operation management system for an aircraft flight training device according to any one of claims 1 to 5 comprises the following steps: S1: Acquire data. The data acquisition system synchronously obtains the operating status of each system, the working status of equipment components, operating instructions, and environmental data, and efficiently caches and stores them through the data synchronization middle layer. S2: Data processing and analysis: First, the raw data in the data synchronization middle layer is cleaned and preprocessed. Then, the preprocessed data is formatted and time-series aligned to ensure data consistency. S3: Data analysis: First, the collected flight training courses are analyzed and pre-processed according to the pilot training syllabus, the entire course is divided into multiple sub-training units, and then integrated to construct a flight training sequence; S4: Establish a relational database. Based on the constructed flight training sequence, combined with the equipment list and the characteristics of each operation action in the operation sequence, establish the association between the operation sequence and the equipment, and realize the construction of the relational database. S5: Intelligent platform establishment, based on relational database, combined with fault prediction and diagnosis, training plan management and intelligent task management, to realize the construction of intelligent operation platform of aircraft flight training device.

7. The intelligent operation management method of an aircraft flight training device according to claim 6, characterized in that: The specific steps of the raw data cleaning and preprocessing are: first, the sliding mean filter and Kalman filter method are used to remove noise data, and the statistical method is used to detect outliers. At the same time, the interpolation algorithm is used to fill in the missing data of the network communication to ensure the integrity and time series continuity of the data.

8. The intelligent operation management method of an aircraft flight training device according to claim 6, characterized in that: The specific steps of constructing the flight training sequence are as follows: Step 1: Each sub-training unit is further refined into several branch tasks through flight training sequence recognition and processing; Step 2: For each branch task, identify the training operation sequence and generate several corresponding detailed operation actions. Then, integrate each operation action to complete the flight training sequence construction.

9. The intelligent operation management method of an aircraft flight training device according to claim 6, characterized in that: The specific steps of fault prediction and diagnosis are as follows: Step 1: Generate equipment fault identification and prediction models through the flight training device's fault diagnosis module and equipment list driver. Step 2: Using fault identification and prediction models, the flight training device's equipment signals and environmental information are collected simultaneously. Using fault identification and prediction algorithms, potential fault hazards are identified, and the fault risk and probability of occurrence are assessed. Step 3: Issue prompts and warnings in a timely manner through the intelligent early warning mechanism, and generate fault prediction reports, diagnosis reports and repair and maintenance plans.

10. The intelligent operation management method of an aircraft flight training device according to claim 6, characterized in that: The specific steps of the training plan management and intelligent task management are as follows: Step 1: Statistical analysis of the collected course sequence, staff attendance, equipment status, and task progress information; Step 2: Implement task scheduling and allocation through an intelligent task management mechanism, and intelligently evaluate the diagnostic and prediction reports generated by the fault prediction and diagnosis module; Step 3: Execute corresponding maintenance plans or task assignments according to the scheduling arrangements to ensure the smooth and efficient implementation of the training plan.

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