Full-movement simulator training data collection and sharing system using blockchain technology

Abstract

The application discloses a full-movement simulator training data collection and sharing system applying a blockchain technology, which comprises a data collection system and a data sharing system, wherein the data collection system is connected with the data sharing system, the data collection system comprises an attendance module, a course selection module, a simulator, and a blockchain module, the simulator comprises a simulator training module and a training data collection module, and the attendance module is connected with the course selection module. The weak trust problem of participants in simulator training is integrated, valuable data in training is collected and managed, the training subjects of pilots are recorded, the logs and operations of various conditions in simulator training are recorded, the flight data of pilots during training is acquired, preparation for training quality analysis and more effective training is made, the supervision of the CAAC supervision department and the record of the flight subjects of pilots are facilitated, and a complete data collection and sharing scheme is provided for all parties.

Description

Technical Field

[0001] This invention relates to the field of simulator training technology, and in particular to a full-motion simulator training data collection and sharing system that applies blockchain technology. Background Technology

[0002] Features of consortium blockchains

[0003] Access control mainly includes the following:

[0004] • Access permission: Only nodes with this permission can access the network;

[0005] • Consensus permission: Only nodes with this permission can participate in the blockchain consensus.

[0006] • Business permissions: Set more granular permissions for on-chain operations, such as deploying contracts and managing roles;

[0007] Security and Privacy: Through access control, we strictly protect data privacy and prevent data leaks;

[0008] Data exchange can be conducted without revealing the data through cryptographic techniques;

[0009] Consensus efficiency: Based on enterprise application scenarios, some unnecessary decentralization can be sacrificed to improve consensus efficiency;

[0010] Consensus security: A secure and stable consensus algorithm that cannot be forked;

[0011] PlatONE Platform Introduction

[0012] PlatONE is a next-generation consortium blockchain platform based on privacy computing to support enterprise-level applications. The platform proposes an enterprise-grade consortium blockchain infrastructure featuring privacy computing, which can meet various needs in financial and commercial scenarios.

[0013] PlatONE currently offers a variety of innovative technologies and features, including: secure multi-party computation, homomorphic encryption and other cryptographic technologies, optimized high-efficiency consensus, high TPS, complete and easy-to-use enterprise-grade toolchains and components, optimized user / permission model, and support for multiple development languages, aiming to solve the current dilemmas in the development of consortium blockchains.

[0014] PlatONE Enterprise-level Contract Management

[0015] PlatONE utilizes system contracts to provide personalized customization services such as dynamic adjustment of system parameters and CNS (Content Management System).

[0016] System configuration parameters are managed uniformly through contracts, supporting technology upgrades and governance;

[0017] Node access management adopts the public key upload mode to avoid the problems of traditional CA certificate expiration and the risk of leakage during certificate transmission;

[0018] Supports optimized contract access control, role support, and management;

[0019] Supports CNS (ContractNameService) service, which means that transactions are no longer sent by calling the contract address in the traditional hexadecimal format, but by calling the contract name, reducing data compatibility issues caused by contract upgrades;

[0020] Supports blockchain operation and maintenance situational awareness and threat warnings. Dynamically monitors the operational status of the blockchain and smart contracts, and promptly reports on-chain security information.

[0021] The current problem to be solved: The primary purpose of a full-motion flight simulator is to train pilots to achieve, test, and maintain proficiency in aircraft operation without risk to personnel or property. Training in a flight simulator is also significantly cheaper than training in an actual aircraft. Recent statistics are much more telling. Since the introduction of highly realistic flight simulators in the 1980s, the number of accidents caused by pilot error has decreased by 70%. Therefore, less than 30% of aircraft accidents are now caused by pilot error. The real benefit of flight simulators is that they force pilots to internalize their experience, rather than simply memorizing what's on the blackboard. Simulators force pilots to learn how to remain calm and think clearly through traumatic experiences. Abnormalities can occur in flight, and pilots need to learn how to remain calm and think clearly.

[0022] The key point is that piloting is a profession where critical decision-making is inherently emotional and instinctive. That's why it's crucial to have them practice in that emotional state. Transferring simulator practice time to the real world could save thousands of lives every year.

[0023] However, the main participants in full-motion simulator training are the CAAC regulatory body, airlines, flight training centers, flight simulator manufacturers, and pilots. The CAAC monitors pilots' training completion and the quality and usage of the simulators. Airlines focus on whether pilots attend simulator training on time, the quality of the training, and the completion of training modules. Flight training centers primarily focus on training duration (for fee collection), completed training modules, simulator malfunctions, and the overall quality of simulator training. Flight simulator manufacturers are concerned with whether their simulators experience malfunctions or problems during use and the quality of simulator training. Pilots are primarily concerned with the effectiveness of their flight training.

[0024] Based on the above analysis, the main participants in full-motion simulator training have different needs, resulting in different data requirements from each party. Moreover, there is currently no complete system to collect and share this valuable training data according to their respective permissions. Summary of the Invention

[0025] The purpose of this invention is to propose a data collection and sharing system for full-motion simulator training using blockchain technology. This system utilizes blockchain technology to upload valuable data to the chain and employs a consortium blockchain mechanism to set permissions for each data item. Only the data owner has the authority to manage and use the data. This invention addresses the issue of weak trust among participants in simulator training, collecting and managing valuable training data, recording pilot training course information, logging various situations and operations during simulator training, and acquiring flight data during pilot training. This facilitates training quality analysis and prepares for more effective training. It also facilitates supervision by the CAAC (China Academy of Civil Aviation Administration) and the recording of pilot flight courses, providing a complete data collection and sharing solution for all parties involved.

[0026] To achieve the above objectives, the present invention adopts the following technical solution:

[0027] A full-motion simulator training data collection and sharing system applying blockchain technology includes a data collection system and a data sharing system, which are connected. The data collection system includes an attendance module, a course selection module, a simulator, and a blockchain module. The simulator includes a simulator training module and a training data collection module. The attendance module is connected to the course selection module, which is also connected to the simulator training module. The simulator training module is connected to the training data collection module, which is connected to the blockchain module. The data sharing system includes a distributed storage module, which is connected to the training data collection module. The data sharing system also includes a client module, a data scheduling center module, and a data node group module. The client module is connected to the distributed storage module, the data scheduling center module, and the data node group module.

[0028] Preferably, the attendance module is implemented in the following ways: computer check-in, fingerprint check-in and facial recognition, mainly to record the start time and end time of training for instructors and pilots, so that flight training centers and airlines can count training time.

[0029] Preferably, the course selection module function is made according to the training outlines of various airlines, and the flight instructors of the corresponding airlines can select training courses and import them into the simulator training module.

[0030] Preferably, the simulator training module allows instructors and pilots to directly begin training on selected courses after entering the simulator.

[0031] Preferably, the training data collection module collects data from the pilot's operations and aircraft systems during the pilot's selected course training and sends it to the distributed storage module.

[0032] Preferably, the data scheduling center module includes a data block status unit, a data block scheduling unit, a data block query function unit, and a data block structure list unit.

[0033] Preferably, the data node group module includes multiple data node units, and the data states of the multiple data node units are not completely identical. The data node group module has a data sharing function, and the method for implementing the sharing function includes the following steps:

[0034] Step 1: After the user generates massive amounts of data in the training data collection module, the distributed storage module contains the database and storage logic. The training data collection module merges small files and splits large files, dividing the data into fixed-size data file blocks. The data file blocks are then transmitted to the data scheduling center via binary file streams. The data splitting calculation method utilizes sparse matrix calculation.

[0035] Step 2: The data file block enters the data scheduling center module, initially in a "pending synchronization" state, and is recorded in the database structure list unit for data scheduling and synchronization.

[0036] Step 3: The database uses the non-relational database Redis. This database is installed on the data nodes to store data file blocks. There will be concurrent operations during data storage, so a database distributed lock is developed based on Redis. Redis is a single-process, single-threaded mode, and a queue mode is used to turn concurrent access into serial access. The distributed lock function is implemented using the Redis commands SETNX and GETSET to perform queue scheduling and data update.

[0037] Step 4: After the data file block is synchronized to the data node group module, its status is "synchronizing". Now the file block has entered one of the data node units, and the data units of other nodes have not yet been updated. The process of synchronizing all node data units is asynchronous. A cache for this data is established in the data query unit to save the correctness of the data during the synchronization delay phase.

[0038] Step 5: The data file block is synchronized to all data node units. After the data of the data node group module is updated, a data snapshot is taken and saved to the disk. A new socket thread is started. The snapshot can be sent to other data nodes through the socket thread. At this time, the snapshot is shared by all data node units. Other data node units synchronize their data. After all data node units have completed the synchronization, the status of this data file block is "synchronization completed".

[0039] Step 6: The storage order of data file blocks in each data node unit is different and basically unordered. Generating large file data requires combining them using the structural relationships in the data block structure list unit. Large files can be calculated by combining multiple small file blocks. This solves the problem of querying TB-level files in data queries.

[0040] Step 7: The engine used for massive data query is Hadoop. It can use model calculations to analyze and filter the most frequently queried data records, the average depth of data queries, the common paths for browsing data, and establish a high-speed query cache channel to accelerate the speed of data query and display.

[0041] Step 8: Throughout the data storage process, the established data index structure is the data block structure list unit, which contains pointers to all data blocks. The header of the data block file is added with a hash value generated by the blockchain module. The blockchain module hash value is bound to the data block index and uploaded to the chain. The header of the data block structure list unit file composed of data block indexes is added with a hash value generated by the blockchain module. The entire structure file is uploaded to the chain. The blockchain module not only ensures that the pointer index of a single data block is uploaded to the chain, but also ensures that the entire file structure is uploaded to the chain, thus ensuring data integrity.

[0042] Preferably, the blockchain module forms a blockchain network based on a third-party blockchain platform.

[0043] Preferably, the course selection module includes a voice wake-up unit, a course recognition unit, a security protection unit, and an intelligent robot unit. The voice wake-up unit is connected to the intelligent robot unit, the intelligent robot unit is connected to the course recognition unit, and the security protection unit is connected to the course recognition unit.

[0044] Preferably, the client unit includes a mobile login unit and a web login unit. The mobile login unit is connected to a verification unit, the verification unit is connected to an information sending unit, and the web login unit is connected to an ID input unit, which is connected to the verification unit.

[0045] Compared with the prior art, the beneficial effects of this invention are as follows:

[0046] 1. It collects and manages valuable data from flight training and uses blockchain technology to solve the problem of weak trust among participants in flight training.

[0047] 2. To address the issue of massive training data volume and alleviate the storage pressure on data nodes of various stakeholders, a massive distributed storage solution is proposed. A large amount of training data will not be directly saved to the data nodes of each party. Instead, key header data and file structure will be stored on the data nodes using technical means, without increasing the storage load on the data node servers of each party.

[0048] 3. The consortium blockchain mechanism solves the problem of data silos caused by the lack of interconnection between the systems of various entities in flight training. This invention uses blockchain technology to link all systems and data to form a complete training data chain, which improves the integrity, credibility and availability of flight training data.

[0049] 4. This invention forms a complete blockchain platform structure for flight training, encompassing flight training data collection, blockchain network data processing, distributed processing of massive amounts of data, and verification, management, storage, querying, and analysis of trusted data.

[0050] In summary, this invention addresses the issue of weak trust among participants in simulator training by collecting and managing valuable training data. It records pilots' training course details, logs and operations for various situations during simulator training, and acquires flight data during training. This facilitates training quality analysis and prepares for more effective training. It also simplifies CAAC oversight and pilot flight course recording, providing a comprehensive data collection and sharing solution for all parties. Attached Figure Description

[0051] Figure 1 This is a data collection block diagram of a full-motion simulator training data collection and sharing system using blockchain technology, as proposed in Example 1.

[0052] Figure 2 This is a data storage block diagram of a full-motion simulator training data collection and sharing system using blockchain technology, as proposed in Example 1.

[0053] Figure 3 This is a block diagram of the blockchain module of a full-motion simulator training data collection and sharing system using blockchain technology, as proposed in Example 1.

[0054] Figure 4 Here is a block diagram of a blockchain network in a full-motion simulator training data collection and sharing system using blockchain technology, as proposed in Example 1.

[0055] Figure 5 This is a schematic diagram of the structure of a full-motion simulator training data collection and sharing system using blockchain technology, as proposed in Example 2.

[0056] Figure 6 This is a schematic diagram of the structure of a full-motion simulator training data collection and sharing system using blockchain technology, as proposed in Example 3. Detailed Implementation

[0057] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0058] Example 1

[0059] Reference Figure 1-4 A full-motion simulator training data collection and sharing system applying blockchain technology includes a data collection system and a data sharing system, which are connected. The data collection system includes an attendance module, a course selection module, a simulator, and a blockchain module. The simulator includes a simulator training module and a training data collection module. The attendance module is connected to the course selection module. The attendance module functions as follows: computer check-in, fingerprint check-in, and facial recognition. It mainly records the start and end times of training for instructors and pilots, so that flight training centers and airlines can statistically analyze training time.

[0060] The course selection module is connected to the simulator training module. The course selection module is designed based on the training outlines of various airlines. Flight instructors of the corresponding airlines can select training courses and import them into the simulator training module. The simulator training module is connected to the training data collection module. The simulator training module allows instructors and pilots to directly conduct training for the selected courses after entering the simulator.

[0061] The training data collection module is connected to the blockchain module. During the pilot's selected training course, this module collects data from the pilot's operations and the aircraft's systems, and sends it to the distributed storage module. The data sharing system includes a distributed storage module. This module was chosen because it generates massive amounts of recorded data during simulator operation, which needs to be stored and traced at any time. Direct storage, which stores data directly on the hard drive, has poor scalability and flexibility, and is difficult to adapt to technological changes and maintain. Centralized storage, while having some scalability, is limited by equipment capabilities and performance constraints. Therefore, the most suitable storage method for massive amounts of data is a distributed storage module. Based on standard hardware and a distributed architecture, it achieves scalability at the thousand-node / EB level and can uniformly manage various storage types such as blocks, objects, and files. This solves the technical problems of storing massive amounts of data, offering excellent performance, flexibility, ease of maintenance, and strong scalability, laying a solid foundation for the integration of subsequent new technologies.

[0062] The distributed storage module is connected to the training data collection module; the pilot operation data mainly includes the control stick, throttle, flaps, landing gear, brakes, and various buttons, knobs and indicators on the electronic flight instrument system; the aircraft display data includes the data on the main flight display and navigation display; the aircraft overhead panel data includes the flight control panel, DC panel, navigation panel, APU panel, fuel panel, hydraulic panel and bleed air conditioning panel.

[0063] The training data collection module also records various anomalies and errors that occur in the simulator during pilot training. This information is then uploaded to the blockchain, and only simulator manufacturers and training centers have the authority to view this data.

[0064] The data sharing system also includes a client module, a data scheduling center module, and a data node group module. The client module is connected to the distributed storage module, the data scheduling center module, and the data node group module. The data scheduling center module includes a data block status unit, a data block scheduling unit, a data block query function unit, and a data block structure list unit.

[0065] The data node group module includes multiple data node units, and the data states of the multiple data node units are not completely identical. The data node group module has a data sharing function; the method for implementing the sharing function includes the following steps:

[0066] Step 1: After the user generates massive amounts of data in the training data collection module, the distributed storage module contains the database and storage logic. The training data collection module merges small files and splits large files, dividing the data into fixed-size data file blocks. The data file blocks are then transmitted to the data scheduling center via binary file streams. The data splitting calculation method utilizes sparse matrix calculation.

[0067] Step 2: The data file block enters the data scheduling center module, initially in a "pending synchronization" state, and is recorded in the database structure list unit for data scheduling and synchronization.

[0068] Step 3: The database uses the non-relational database Redis. This database is installed on the data nodes to store data file blocks. There will be concurrent operations during data storage, so a database distributed lock is developed based on Redis. Redis is a single-process, single-threaded mode, and a queue mode is used to turn concurrent access into serial access. The distributed lock function is implemented using the Redis commands SETNX and GETSET to perform queue scheduling and data update.

[0069] Step 4: After the data file block is synchronized to the data node group module, its status is "synchronizing". Now the file block has entered one of the data node units, and the data units of other nodes have not yet been updated. The process of synchronizing all node data units is asynchronous. A cache for this data is established in the data query unit to save the correctness of the data during the synchronization delay phase.

[0070] Step 5: The data file block is synchronized to all data node units. After the data of the data node group module is updated, a data snapshot is taken and saved to the disk. A new socket thread is started. The snapshot can be sent to other data nodes through the socket thread. At this time, the snapshot is shared by all data node units. Other data node units synchronize their data. After all data node units have completed the synchronization, the status of this data file block is "synchronization completed".

[0071] Step 6: The storage order of data file blocks in each data node unit is different and basically unordered. Generating large file data requires combining them using the structural relationships in the data block structure list unit. Large files can be calculated by combining multiple small file blocks. This solves the problem of querying TB-level files in data queries.

[0072] Step 7: The engine used for massive data query is Hadoop. It can use model calculations to analyze and filter the most frequently queried data records, the average depth of data queries, the common paths for browsing data, and establish a high-speed query cache channel to accelerate the speed of data query and display.

[0073] Step 8: Throughout the data storage process, the established data index structure is the data block structure list unit, which contains pointers to all data blocks. The header of the data block file is added with a hash value generated by the blockchain module. The blockchain module hash value is bound to the data block index and uploaded to the chain. The header of the data block structure list unit file composed of data block indexes is added with a hash value generated by the blockchain module. The entire structure file is uploaded to the chain. The blockchain module not only ensures that the pointer index of a single data block is uploaded to the chain, but also ensures that the entire file structure is uploaded to the chain, thus ensuring data integrity.

[0074] The blockchain module is based on a third-party blockchain platform to form a blockchain network, combined with... Figure 3 To explain in detail:

[0075] Flight training centers are responsible for recording and maintaining training data, but cannot freely view it. Airlines access their own training data for training summaries and pilot training status monitoring. Pilots can view their own training status and records through their airlines, such as training courses and durations. Airlines can use this training data to analyze and evaluate the effectiveness of pilot training. CAAC regulatory authorities can access relevant airline and pilot training records and statistics according to regulatory needs. Flight simulator manufacturers can view simulator malfunctions and errors, as well as maintenance records.

[0076] The nodes in this invention mainly include:

[0077] CAAC regulatory bodies and flight simulator manufacturers (observer nodes): responsible only for synchronizing blocks, not for producing blocks; used for stable block synchronization, and also for being designated as bootnodes by other nodes for connection; airlines and flight training centers (consensus nodes): participate in block production and block synchronization.

[0078] Blockchain Platform Accounts: In the third-party blockchain platform used in this invention, each account has an associated state and a 20-byte address. The third-party platform PlatONE supports both EVM and WASM smart contract virtual machines. The EVM virtual machine is compatible with Ethereum's Solidity smart contracts, while the WASM virtual machine supports multiple contract languages ​​such as C / C++ / Rust. WASM smart contracts support high-level language development and are compiled into WASM for execution. Transactions triggering WASM contracts are packaged by consensus nodes and repeatedly verified by all nodes in the network. The state of WASM contracts is stored in a public ledger. The development and deployment of verifiable contracts are no different from WASM contracts, and they are ultimately compiled into WASM for execution. State transitions are executed asynchronously off-chain by compute nodes. After computation, the new state and state transition proof are submitted to the chain, allowing all nodes to quickly verify correctness and update the public ledger. Verifiable contracts can support complex and computationally intensive logic without affecting the overall chain performance. Privacy contracts also support high-level language development and are compiled into the llvmir intermediate language for execution. Input data for privacy contracts is stored locally on data nodes, which perform privacy computations off-chain using secure multi-party computation and submit the results to the chain. In the third-party PlatONE consortium blockchain, access control is maintained by system contracts. Seven system contracts are deployed on the system at the initial stage for access management. Based on different entities in the blockchain system, PlatONE modularizes access management. For the different behaviors of user accounts, nodes, and smart contracts in the blockchain system, user role management, node management, and contract firewall modules are designed to control and manage access. Role management: PlatONE sets different user roles based on different permissions and manages these roles through system contracts. Users are assigned different permissions based on their roles.

[0079] Node Management: The third-party PlatONE manages nodes through a node management contract, including functions such as whether nodes can access the network, whether nodes can participate in consensus, and the maintenance of node information. According to the platform's user role settings, only three types of users, chainCreator, chainAdmin, and nodeAdmin, can set the node data in the system contract. When it is necessary to add nodes, update node status, or delete nodes, these three types of users need to call the node management contract.

[0080] Contract Firewall: Access to contracts within the third-party PlatONE is controlled by the contract firewall. Only the contract creator can configure the firewall for that contract. The contract firewall provides access control at the contract interface level, implemented through the following two lists:

[0081] ACCEPT: A list of addresses that can access the corresponding interface, equivalent to a whitelist;

[0082] REJECT: A list of addresses that are denied access to the corresponding interface, essentially a blacklist.

[0083] The specific implementation of the blockchain network for training data from the full-motion simulator is as follows: Figure 4 The following is shown and explained in detail:

[0084] Flight training centers and airlines A and B, respectively, record and synchronize training data operations according to smart contracts A and B. The flight training center, airlines A and B, complete consensus calculations based on a consensus algorithm. This consensus generates a consensus proof for each block on the blockchain, representing a valid signature from each consensus node for that block, allowing for self-verification. For example, when an airline needs to retrieve the complete record of a training session for teaching quality analysis, this operation is only permitted after reaching a consensus with the flight training company, and the operation record is simultaneously added to the blockchain. The CAAC regulatory body and flight simulator manufacturers act as observer nodes, not participating in consensus but only synchronizing blocks. The CAAC regulatory body and flight simulator manufacturers can query information relevant to themselves; for example, the CAAC regulatory body can query the training status and records of pilot C of airline A within a certain period, and the flight simulator manufacturer can query the simulator's malfunction and maintenance status, all while synchronizing block records.

[0085] The third-party PlatONE is pluggable and supports different consensus algorithms. Currently, it supports Concurrent BFT and Optimized BFT consensus. It uses VRF and probability distribution to randomly select consensus nodes, which balances decentralization and scalability well.

[0086] Concurrent BFT: Block production and block verification are carried out in parallel, which greatly improves the block production rate while ensuring 1 / 3 of the fault tolerance of BFT; in the test network, the time for all nodes to reach consensus and produce a block is 1 second.

[0087] Optimized BFT: Adds an unlocking mechanism to solve the consensus deadlock problem and supports more than 100 consensus nodes; in the test network, the time for each node to reach consensus and produce a block is 1 second.

[0088] Example 2

[0089] Reference Figure 5 The difference between this embodiment and Embodiment 1 is that the course selection module in this embodiment includes a voice wake-up unit, a course recognition unit, a security protection unit, and an intelligent robot unit. The voice wake-up unit is connected to the intelligent robot unit, the intelligent robot unit is connected to the course recognition unit, and the security protection unit is connected to the course recognition unit. In this embodiment, the student wakes up the intelligent robot unit through the voice wake-up unit, then states their needs and the course, selects the corresponding course unit through the intelligent robot unit, and sends the information to the course recognition unit. The course recognition unit sends the information to the security protection unit, which determines whether the selected course is offered by the teacher. If not, it cannot be opened; if it is, it can be opened, thus preventing students from randomly selecting courses.

[0090] Example 3

[0091] Reference Figure 6 The difference between this embodiment and embodiments 1 and 2 is that the client unit in this embodiment includes a mobile login unit and a web login unit. The mobile login unit is connected to a verification unit, and the verification unit is connected to an information sending unit. The web login unit is connected to an ID input unit, and the ID input unit is connected to the verification unit. After logging in through the mobile login unit, the user is verified by the verification unit and the information is sent to the user through the information sending unit to ensure login security and not disrupt the learning progress. Login through the web login unit requires the user to enter their account information through the ID input unit before verification and information sending.

[0092] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A full-movement simulator training data collection and sharing system using blockchain technology, characterized by, The application relates to a data collection system and a data sharing system, wherein the data collection system is connected with the data sharing system; the data collection system comprises an attendance module, a course selection module, a simulation machine, and a blockchain module; the simulation machine comprises a simulation machine training module and a training data collection module; the attendance module is connected with the course selection module; the course selection module is connected with the simulation machine training module; the simulation machine training module is connected with the training data collection module; the training data collection module is connected with the blockchain module; the data sharing system comprises a distributed storage module connected with the training data collection module; the data sharing system further comprises a client module, a data scheduling center module, and a data node group module; the client module is connected with the distributed storage module; the client module is connected with the data scheduling center module; and the data scheduling center module and the data node group module are connected. The data node group module comprises a plurality of data node units, the data states of the plurality of data node units are not completely same, the data node group module has a data sharing function, and the implementation method of the sharing function comprises the following steps: Step 1: after a user generates massive data in the training data collection module, the distributed storage module is a database, the training data collection module combines small files and divides large files, cuts the data into data file blocks of a fixed size, and transmits the data file blocks to the data scheduling center in the form of a binary file stream; and the data cutting calculation mode utilizes a sparse matrix calculation mode; Step 2: the data file blocks enter the data scheduling center module, are initially in a "to be synchronized" state, are recorded in a database structure list unit, are subjected to data scheduling, and are subjected to a synchronization process; Step 3: the database uses a non-relational database Redis, the database is installed in the data node and stores the data file blocks; concurrent operations exist in the data storage process, so that a database distributed lock is developed based on the Redis; the distributed lock function is realized by using the Redis commands SETNX and GETSET; the data is updated by queue scheduling; the Redis is in a single-process single-thread mode; and concurrent access is changed into serial access by adopting a queue mode; Step 4: after the data file blocks are synchronized to the data node group module, the state of the data file blocks is "in synchronization"; the data file blocks have entered one data node unit, other node data units have not been updated, the process of synchronizing all node data units is asynchronous, the cache of node data is established in the data query unit, and the correctness of the data is saved in the synchronization delay stage. Step 5: After the data node group module is updated, a data snapshot is taken and saved on the disk, and a socket thread is restarted to send the snapshot to other data nodes. The data snapshot is shared by all data node units, and other data node units synchronize data. After all data node units are synchronized, the status of the data file block is "synchronization complete"; Step 6: The storage order of each data node unit's data file block is different and unordered. To generate a large file data, the structural relationship in the data block structure list unit needs to be used for combination. A large file can be combined and calculated using multiple small file blocks; Step 7: The engine used for massive data query is Hadoop, which can use model calculation to analyze and filter out the most queried data records, the average depth of data query, and the commonly used path of browsing data, and establish a high-speed query cache channel to speed up the speed of data query display; Step 8: The data index structure established in the entire data storage process is the data block structure list unit and the pointer of all data blocks. The file header of the data block file increases the hash value of the block chain module, and the hash value of the block chain module is bound to the chain with the data block index. The file header of the data block structure list unit file increases the hash value of the block chain module, and the structure file is chained as a whole.

2. The full-movement simulator training data collection and sharing system using blockchain technology according to claim 1, wherein, The function of the attendance module is to record the start and end times of the training of the instructors and pilots, so that the flight training center and the airline company can calculate the training time. 3.The full-movement simulator training data collection and sharing system using blockchain technology of claim 1, wherein, The function of the course selection module is to select the training courses according to the training outline of each airline company, and the corresponding airline company flight instructors can select the training courses and import them into the simulator training module. 4.The full-movement simulator training data collection and sharing system using blockchain technology of claim 1, wherein, The function of the simulator training module is that the instructors and pilots can directly select courses for training after entering the simulator. 5.The full-movement simulator training data collection and sharing system using blockchain technology of claim 1, wherein, The training data collection module collects the pilot's operation and the data on the aircraft system during the pilot's training of the selected course, and sends them to the distributed storage module. 6.The full-movement simulator training data collection and sharing system using blockchain technology of claim 1, wherein, The data scheduling center module includes a data block state unit, a data block scheduling unit, a data block query function unit, and a data block structure list unit. 7.The full-movement simulator training data collection and sharing system using blockchain technology of claim 1, wherein, The blockchain module is based on a third-party blockchain platform to form a blockchain network. 8.The full-movement simulator training data collection and sharing system using blockchain technology of claim 1, wherein, The course selection module includes a voice wake-up unit, a course recognition unit, a security protection unit, and an intelligent robot unit. The voice wake-up unit is connected to the intelligent robot unit, the intelligent robot unit is connected to the course recognition unit, and the security protection unit is connected to the course recognition unit. 9.The full-movement simulator training data collection and sharing system using blockchain technology of claim 1, wherein, The client module includes a mobile phone mobile terminal login unit and a web login unit. The mobile phone mobile terminal login unit is connected to a verification unit, the verification unit is connected to an information sending unit, the web login unit is connected to an ID input unit, and the ID input unit is connected to the verification unit.

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