CN114493539A - Training data collecting and sharing system of full-motion simulator applying block chain technology - Google Patents
Training data collecting and sharing system of full-motion simulator applying block chain technology Download PDFInfo
- Publication number
- CN114493539A CN114493539A CN202210118299.5A CN202210118299A CN114493539A CN 114493539 A CN114493539 A CN 114493539A CN 202210118299 A CN202210118299 A CN 202210118299A CN 114493539 A CN114493539 A CN 114493539A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- training
- unit
- simulator
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012549 training Methods 0.000 title claims abstract description 132
- 238000005516 engineering process Methods 0.000 title claims abstract description 24
- 238000013480 data collection Methods 0.000 claims abstract description 31
- 238000000034 method Methods 0.000 claims description 24
- 230000006870 function Effects 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000012795 verification Methods 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 12
- 238000013500 data storage Methods 0.000 claims description 8
- 230000001360 synchronised effect Effects 0.000 claims description 8
- 230000008676 import Effects 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012821 model calculation Methods 0.000 claims description 3
- 238000004080 punching Methods 0.000 claims description 3
- VUFNLQXQSDUXKB-DOFZRALJSA-N 2-[4-[4-[bis(2-chloroethyl)amino]phenyl]butanoyloxy]ethyl (5z,8z,11z,14z)-icosa-5,8,11,14-tetraenoate Chemical compound CCCCC\C=C/C\C=C/C\C=C/C\C=C/CCCC(=O)OCCOC(=O)CCCC1=CC=C(N(CCCl)CCCl)C=C1 VUFNLQXQSDUXKB-DOFZRALJSA-N 0.000 abstract description 11
- 238000012544 monitoring process Methods 0.000 abstract description 8
- 238000004458 analytical method Methods 0.000 abstract description 4
- 238000007726 management method Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 6
- 238000012423 maintenance Methods 0.000 description 5
- 238000004088 simulation Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 210000003169 central nervous system Anatomy 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000002996 emotional effect Effects 0.000 description 2
- 230000008140 language development Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 1
- 206010000369 Accident Diseases 0.000 description 1
- 206010039897 Sedation Diseases 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000002513 implantation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 230000036280 sedation Effects 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000472 traumatic effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
- G06Q10/1091—Recording time for administrative or management purposes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
- G06F16/2308—Concurrency control
- G06F16/2336—Pessimistic concurrency control approaches, e.g. locking or multiple versions without time stamps
- G06F16/2343—Locking methods, e.g. distributed locking or locking implementation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24552—Database cache management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
- G06Q50/2057—Career enhancement or continuing education service
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Educational Administration (AREA)
- Strategic Management (AREA)
- Educational Technology (AREA)
- Tourism & Hospitality (AREA)
- Computer Security & Cryptography (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Computational Linguistics (AREA)
- Bioethics (AREA)
- Primary Health Care (AREA)
- Computing Systems (AREA)
Abstract
The invention discloses a training data collecting and sharing system of a full-motion simulator applying a block chain technology, which comprises a data collecting system and a data sharing system, wherein the data collecting system is connected with the data sharing system, the data collecting system comprises an attendance checking module, a course selecting module, a simulator and a block chain module, the simulator comprises a simulator training module and a training data collecting module, and the attendance checking module is connected with the course selecting module. The problem of weak trust of participants in the training of the analog machine is integrated, valuable data in the training is collected and managed, the training subject condition of the pilot is recorded, logs and operations of various conditions in the training of the analog machine are recorded, flight data during the training of the pilot is obtained, so that the training quality analysis is performed, more effective training is prepared, the monitoring of a CAAC (computer aided education) monitoring department and the recording of the flight subjects of the pilot are facilitated, and a complete data collection and sharing scheme is provided for all parties.
Description
Technical Field
The invention relates to the technical field of simulator training, in particular to a training data collecting and sharing system of a full-motion simulator applying a block chain technology.
Background
Characteristics of federation chains
The authority control mainly comprises the following steps:
grant permission: the node with the authority can access the network;
consensus rights: the node with the authority can participate in the consensus of the block chain;
service rights: performing finer-grained permission setting on operations on the chain, such as contract deployment, role management and the like;
safety and privacy: through the authority control, the data privacy is strictly protected, and the data leakage is avoided;
data interaction is carried out on the premise of not revealing data through a cryptography technology;
consensus efficiency: based on an enterprise application scene, the unneeded decentralization degree of a part can be sacrificed, and the consensus efficiency is improved;
the consensus is safe, the consensus algorithm is safe and stable, and the consensus cannot be branched;
PlatONE platform introduction
PlatONE is a new generation alliance blockchain platform based on privacy computing to support enterprise-level applications; platform proposesAAn enterprise cascading alliance chain infrastructure with privacy computing as a characteristic can meet various demand scenes such as financial business and the like.
Currently, PlatONE provides a number of innovative technologies and functions, including: the method has the characteristics of safe multi-party calculation, homomorphic encryption and other cryptographic technology implantation, optimized efficient consensus, high TPS, complete and easy-to-use enterprise-level tool chains and components, optimized user/authority models, multi-development language support and the like, and aims to solve the dilemma in the development of the current alliance chain.
PlatONE enterprise-level contract management
PlatONE realizes personalized customization services such as CNS (central nervous system) by dynamically adjusting system parameters in a system contract mode:
the system configuration parameters are uniformly managed through contracts, and technical upgrading and treatment are supported;
the node access management adopts an uploading public key mode, so that the overdue problem of the traditional CA certificate and the leakage risk in the certificate transmission process are avoided;
supporting optimized contract authority control, role support and management;
the method supports the CNS (ContractNameService), and reduces the data compatibility problem caused by contract upgrading by calling the sending transaction through the contract name instead of the traditional contract address in the hexadecimal format;
and block chaining operation and maintenance situation awareness and threat warning are supported. And dynamically monitoring the running states of the block chain and the intelligent contract and reporting the safety condition information on the chain in time.
The problems to be solved at present are as follows: the primary purpose of a full-motion flight simulator is to train the pilot to achieve, test and maintain the proficiency of aircraft operation without personnel or property hazards. Training in flight simulators is also much less expensive than training in actual flying airplanes. The latest statistics are much brighter. Since the introduction of highly realistic flight simulators in the 1980's, the number of accidents caused by pilot error has decreased by 70%. Thus, now less than 30% of aircraft accidents are caused by false decisions by pilots. The real benefit of the flight simulator is that it forces pilots to internalize their experience, not just to remember the content on the blackboard. The simulator forces the pilot to learn how to remain sedated and to think clearly through a traumatic experience. Anomalies can occur during flight, and pilots need to learn how to maintain sedation and clear thinking.
Critically, the pilot is an important decision-making occupational that is inherently emotional and instinctive. This is why it is important to have them practice in that emotional state. Shifting the time of simulator practice into the real world can save thousands of lives each year.
However, the main participants of the training of the full-motion simulator are the CAAC supervision department, the airline company, the flight training center, the flight simulation machine manufacturer and the pilot, the CAAC supervision department needs to monitor the conditions of the subjects trained and completed by the pilot, and the using condition and the quality of the simulator; an airline company pays attention to whether the pilot carries out the training of the simulation machine on time, how to the quality of the training, the completion condition of the training subjects and the like; the flight training center mainly focuses on the training duration (for collecting expenses) of the pilot, which subjects are trained, whether the simulator breaks down, the training quality of the simulator and the like; flight simulator manufacturers are concerned about whether faults and problems occur in the use process of self-produced flight simulators and how the training quality of the simulators is. The pilot mainly focuses on how effective the pilot trains the flight.
Based on the above analysis, the main participants of the training of the full-motion simulator have different appeals, so that the data required by each party is different, and a complete system for collecting and sharing the valuable training data according to the respective authority is not available at present.
Disclosure of Invention
The invention aims to provide a training data collecting and sharing system of a fully-automatic simulator applying a block chain technology, which links valuable data by using the block chain technology, sets the authority of the data by using a mechanism of a alliance chain, and only the owner of the data has authority management and use data. The monitoring of CAAC monitoring departments and the recording of the flight subjects of pilots are facilitated, and a complete data collection and sharing scheme is provided for all parties.
In order to achieve the purpose, the invention adopts the following technical scheme:
the utility model provides an use full-motion analog machine training data collection and shared system of block chain technique, includes data collection system and data shared system, the data collection system is connected with the data shared system, the data collection system includes attendance module, course selection module, analog machine, block chain module, including analog machine training module and training data collection module in the analog machine, the attendance module is connected with course selection module, course selection module is connected with analog machine training module, analog machine training module is connected with training data collection module, training data collection module is connected with block chain module, the data shared system includes distributing type storage module, distributing type storage module is connected with training data collection module, the data shared system still includes client module, the data sharing system is connected with client module, The system comprises a data dispatching center module and a data node group module, wherein the client module is connected with the distributed storage module, the client module is connected with the data dispatching center module, and the data dispatching center module is connected with the data node group module.
Preferably, the attendance module is implemented in a manner of: the computer check-in, fingerprint card punching and face recognition mainly record the training starting time and the training finishing time of instructors and pilots so that the flight training center and the airline company can count the training time.
Preferably, the course selection module function is manufactured according to training outlines of all airlines, and flight instructors of corresponding airlines can select training courses by themselves and import the training courses into the simulator training module.
Preferably, the function of the simulator training module is that the trainer and pilot can directly perform the selected course of training after entering the simulator.
Preferably, the training data collection module collects data on the pilot's operations and aircraft systems and sends the data to the distributed storage module during training of the selected course by the pilot.
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.
Preferably, the data node group module includes a plurality of data node units, data states of the plurality of data node units are not completely the same, the data node group module has a data sharing function, and the implementation method of the sharing function includes the following steps:
step1, after the user generates mass data in the training data collection module, the distributed storage module is provided with a database and storage logic, the training data collection module merges small files, divides large files, divides the data into data file blocks with fixed size, transmits the data file blocks to the data dispatching center in a binary file stream mode, and the data division calculation mode utilizes a sparse matrix calculation mode.
And Step 2, the data file block enters a data scheduling center module, is initially in a state of waiting for synchronization, is recorded in a database structure list unit, and is subjected to data scheduling and synchronization.
And Step 3, the database uses a non-relational database Redis, the database is installed on a data node, a data file block is stored, and concurrent operation exists in the data storage process, so that a distributed lock of the database is developed based on the Redis, the Redis is in a single-process and single-thread mode, the concurrent access is changed into serial access by adopting a queue mode, the distributed lock function is realized by utilizing commands SETNX and GETSET of the Redis, and the data is scheduled and updated by utilizing the queue.
And Step 5, synchronizing the data file block to all the data node units, performing data snapshot after the data of the data node group module is updated, storing the snapshot on a disk, restarting a socket thread, sending the snapshot to other data nodes through the socket thread, wherein the snapshot is shared by all the data node units, the other data node units perform data synchronization, and the state of the data file block is in a 'synchronization completion' state after the synchronization of all the data node units is completed.
And Step 6, the storage sequence of the data file blocks of each data node unit is different and basically unordered, the generation of the large file data needs to be combined by using the structural relationship in the data block structure list unit, and the large file can be obtained by using a plurality of small file blocks for combination calculation. Solving the problem of inquiring the files at TB level in the data inquiry.
And Step 7, an engine used for mass data query is Hadoop, and the engine can analyze and screen the data records with the most queried contents of the data, the average depth of the data query, the common path of browsing the data, establish a high-speed query cache channel and accelerate the speed of data query and display by utilizing model calculation.
Step 8, establishing a data index structure in the whole data storage process, namely a data block structure list unit and pointers of all data blocks; the hash value generated by the block chain module is added to the file header of the data block file, the block chain module hash value and the data block index are bound and linked, the hash value generated by the block chain module is added to the file header of the data block structure list unit file formed by the data block indexes, the structure file is linked integrally, and the block chain module not only ensures the pointer index of a single data block to be linked, but also ensures the structural linking of all files, so that the integrity of data is ensured.
Preferably, the blockchain module forms a blockchain network based on a third party blockchain platform.
Preferably, the course selection module includes voice awakening unit, course identification unit, safety protection unit, intelligent robot unit, the voice awakening unit is connected with the intelligent robot unit, the intelligent robot unit is connected with the course identification unit, the safety protection unit is connected with the course identification unit.
Preferably, the client unit includes a mobile phone terminal login unit and a web terminal login unit, the mobile phone terminal login unit is connected with a verification unit, the verification unit is connected with an information sending unit, the web terminal login unit is connected with an ID input unit, and the ID input unit is connected with the verification unit.
Compared with the prior art, the invention has the beneficial effects that:
1. valuable data in flight training is collected and managed, and the problem of weak trust of participants in the flight training is solved by using a block chain technology.
2. Aiming at the problem of huge training data volume, the storage pressure of each beneficiary data node is reduced, a massive distributed storage scheme is provided, a large amount of training data cannot be directly stored in each data node, key head data and a file structure are stored on the data nodes by using a technical means, and the storage load of each data node server cannot be increased.
3. The problem that systems of all subjects are not interconnected to form a data island in flight training is solved by adopting a mechanism of a alliance chain, all the systems are associated with a data application blockchain technology to form a complete training data chain, and the integrity, the reliability and the usability of flight training data are improved.
4. The invention forms a complete flight training blockchain platform structure from flight training data collection, blockchain network data processing, mass data distributed processing, and verification, management, evidence storage, query and analysis of trusted data.
In conclusion, the problem of weak trust of participants in the training of the simulation machine is integrated, valuable data in the training are collected and managed, the training subject condition of the pilot is recorded, logs and operations of various conditions in the training of the simulation machine are recorded, and flight data during the training of the pilot are obtained so as to analyze the training quality and prepare for adopting more effective training. The monitoring of CAAC monitoring departments and the recording of the flight subjects of pilots are facilitated, and a complete data collection and sharing scheme is provided for all parties.
Drawings
Fig. 1 is a block diagram of data collection of a training data collection and sharing system of a full-motion simulator applying block chain technology in embodiment 1;
FIG. 2 is a block diagram of data storage of a training data collection and sharing system of a full motion simulator using blockchain technology as proposed in example 1;
fig. 3 is a block chain module block diagram of a training data collection and sharing system of a fully-automatic simulation machine applying block chain technology in embodiment 1;
fig. 4 is a block chain network block diagram in the training data collection and sharing system of the full-motion simulator applying the block chain technology in embodiment 1;
fig. 5 is a schematic structural diagram of a training data collection and sharing system of a full-motion simulator applying the blockchain technique according to embodiment 2;
fig. 6 is a schematic structural diagram of a training data collection and sharing system of a full-motion simulator applying the blockchain technique according to embodiment 3.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1
Referring to fig. 1-4, a training data collecting and sharing system of a full-motion simulator applying a block chain technology, includes a data collecting system and a data sharing system, the data collecting system is connected with the data sharing system, the data collecting system includes an attendance module, a course selection module, a simulator, and a block chain module, the simulator includes a simulator training module and a training data collecting module, the attendance module is connected with the course selection module, and the function implementation manner of the attendance module is as follows: the computer check-in, fingerprint card punching and face recognition mainly record the training starting time and the training finishing time of instructors and pilots so that the flight training center and the airline company can count the training time.
The course selection module is connected with the simulator training module, the course selection module is manufactured according to the training outline of each airline company, and a flight instructor of the corresponding airline company can select a training course by self and import the training course into the simulator training module; the simulator training module is connected with the training data collecting module, and the simulator training module has the function that a teacher and a pilot can directly train selected courses after entering the simulator.
The training data collection module is connected with the blockchain module, collects the operation of the pilot and the data on the aircraft system in the course of training the selected course by the pilot and sends the collected data to the distributed storage module; the data sharing system comprises a distributed storage module, the distributed storage module is selected, mass recorded data can be generated in the running process of the simulator, the recorded data needs to be stored and can be traced at any time, the data is directly stored in a hard disk by using a direct storage mode data storage technology, the expansibility and the flexibility are poor, and the technical transformation and the maintenance are not easy to perform; the centralized storage mode has certain expansibility limited by equipment capacity, limited performance and limited expansion capacity, so that the most suitable storage mode of mass data is a distributed storage module, thousand-node/EB-level expansion is realized based on standard hardware and a distributed architecture, various types of storage such as blocks, objects, files and the like can be uniformly managed, the storage technical problem of the mass data is solved, the performance is excellent, the flexibility and the easiness in maintenance are realized, the expansion capacity is very strong, and a foundation can be laid for subsequent new technology access.
The distributed storage module is connected with the training data collection module; the pilot operation data mainly comprises various buttons, knobs and indication data on a steering column, an accelerator, a flap, a landing gear, a brake and an electronic flight instrument system; the data displayed by the aircraft includes data on the primary flight display and the navigation display; the aircraft head roof data comprises a flight control panel, a direct current panel, a navigation panel, an APU panel, a fuel panel, a hydraulic panel and a bleed air conditioner panel.
The training data collection module also records various abnormal and error information of the simulator during the training process of the pilot, and then the information is uploaded to the block chain, and only the simulator manufacturer and the training center can have the authority to see the data.
The data sharing system further comprises a client module, a data scheduling center module and a data node group module, wherein the client module is connected with the distributed storage module, the client module is connected with the data scheduling center module, the data scheduling center module is connected with the data node group module, and the data scheduling center module comprises a data block state unit, a data block scheduling unit, a data block query function unit and a data block structure list unit.
The data node group module comprises a plurality of data node units, the data states of the data node units are not completely the same, and the data node group module has a data sharing function; the implementation method of the sharing function comprises the following steps:
step1, after the user generates mass data in the training data collection module, the distributed storage module is provided with a database and storage logic, the training data collection module merges small files, divides large files, divides the data into data file blocks with fixed size, transmits the data file blocks to the data dispatching center in a binary file stream mode, and the data division calculation mode utilizes a sparse matrix calculation mode.
And Step 2, the data file block enters a data scheduling center module, is initially in a state of waiting for synchronization, is recorded in a database structure list unit, and is subjected to data scheduling and synchronization.
And Step 3, the database uses a non-relational database Redis, the database is installed on a data node, a data file block is stored, and concurrent operation exists in the data storage process, so that a distributed lock of the database is developed based on the Redis, the Redis is in a single-process and single-thread mode, the concurrent access is changed into serial access by adopting a queue mode, the distributed lock function is realized by utilizing commands SETNX and GETSET of the Redis, and the data is scheduled and updated by utilizing the queue.
And Step 5, synchronizing the data file block to all the data node units, performing data snapshot after the data of the data node group module is updated, storing the snapshot on a disk, restarting a socket thread, sending the snapshot to other data nodes through the socket thread, wherein the snapshot is shared by all the data node units, the other data node units perform data synchronization, and the state of the data file block is in a 'synchronization completion' state after the synchronization of all the data node units is completed.
And Step 6, the storage sequence of the data file blocks of each data node unit is different and basically unordered, the generation of the large file data needs to be combined by using the structural relationship in the data block structure list unit, and the large file can be obtained by using a plurality of small file blocks for combination calculation. Solving the problem of inquiring the files at TB level in the data inquiry.
And Step 7, an engine used for mass data query is Hadoop, and the engine can analyze and screen the data records with the most queried contents of the data, the average depth of the data query, the common path of browsing the data, establish a high-speed query cache channel and accelerate the speed of data query and display by utilizing model calculation.
Step 8, establishing a data index structure in the whole data storage process, namely a data block structure list unit and pointers of all data blocks; the hash value generated by the block chain module is added to the file header of the data block file, the block chain module hash value and the data block index are bound and linked, the hash value generated by the block chain module is added to the file header of the data block structure list unit file formed by the data block indexes, the structure file is linked integrally, and the block chain module not only ensures the pointer index of a single data block to be linked, but also ensures the structural linking of all files, so that the integrity of data is ensured.
The block chain module is a block chain network formed based on a third-party block chain platform, and is specifically explained with reference to fig. 3:
the flight training center is responsible for recording and maintaining the training data but cannot check the flight training data at will; the airline company calls the training data of the company to conduct training summary and pilot training state monitoring, the pilot can check training conditions and records of the pilot through the airline company driver, such as training courses and training duration, and the airline company can analyze and evaluate the training effect of the pilot by using the training data; the CAAC supervision department can call training records and statistical data of relevant airlines and pilots according to supervision requirements; the flight simulator manufacturer can review simulator fault and error information, as well as maintenance records.
The nodes in the invention mainly comprise:
CAAC regulatory department and flight simulator manufacturers (observer nodes): only takes charge of the synchronous block and does not participate in the block output; for stabilizing the sync blocks and also for connections designated as bootnodes by other nodes; airline and flight training center (consensus node): participating in the out-of-block, and sync block.
Blockchain platform account: in the third-party blockchain platform adopted by the invention, each account hasAA state (state) associated therewith anda20 bytes of address (address); the third party PlatONE simultaneously supports two types of intelligent contract virtual machines, namely an EVM (event virtual machine) and a WASM (Wide area network), wherein the EVM virtual machine is compatible with the safety intelligent contract of the Ethengfang, and the WASM virtual machine can support various contract languages such as C/C + +/Rust and the like; the WASM intelligent contract supports high-level language development and is compiled into WASM to be executed; the transaction triggering the WASM contract is packaged by the consensus node, and the whole network node repeatedly executes verification; the state of the WASM contract is stored in a public ledger; the development and the release of the contract can be verified to be not different from those of the WASM contract, and the contract is finally compiled into the WASM for execution; can be testedThe state conversion of the certification contract is asynchronously executed by the computing node under the chain, after the computation is completed, a new state and a state conversion certification are submitted to the chain, and the whole network node can quickly verify the correctness and update the new state into a public account book; verifiable contracts can support complex and heavy computational logic without affecting the performance of the entire chain; the privacy contract also supports high-level language development and is compiled into llvmir intermediate language for execution; the input data of the privacy contract is stored in the local data node, the data node performs privacy calculation in a safe multi-party calculation mode under the chain, and a calculation result is submitted to the chain; in a third party platform alliance chain, authority control is maintained by a system contract, and 7 system contracts are deployed on a system at the beginning of the chain and used for authority management; according to different entity objects in the blockchain system, the third party platform carries out modularized splitting on the authority management. Aiming at different behaviors of three types of entities, namely a user account, a node and an intelligent contract in a block chain system, a user role management module, a node management module and a contract firewall module are respectively designed to control and manage the authority; and (3) role management: the third party platform sets different user roles according to different authorities, and manages the roles of the users in a system contract mode. According to different roles, users are endowed with different rights in the system.
Node management: the third party platform manages the nodes through a node management contract, and comprises functions of judging whether the nodes can be accessed to a network, judging whether the nodes can participate in consensus, maintaining node information and the like; according to the setting of the user roles of the platform, only three types of users, namely chainCreator, chainAdmin and nodeAdmin, can set node data in a system contract, and when nodes need to be added, the node state needs to be updated and the nodes need to be deleted, the three types of roles are needed to call the node management contract.
Contract firewall: the calling authority of the contract in the third party PlatONE is controlled by a contract firewall, and only a creator of the contract can set the firewall of the contract; the contract firewall has contract interface level access control, and is realized by the following two lists:
ACCEPT: the address list of the corresponding interface can be accessed, which is equivalent to a white list;
REJECT: denying access to the list of addresses of the corresponding interface is equivalent to a black list.
The specific implementation of the training data block chain network of the full-motion simulator is shown in fig. 4 and explained specifically:
the flight training center and the airline A, B respectively perform block output and synchronization on training data operation records according to intelligent contracts A, B; the flight training center and the airline A, B respectively complete consensus calculation according to a consensus algorithm, and the consensus generates a consensus certificate for each block on each chain, namely effective signatures of the consensus nodes of the block, so that the block can be self-verified; for example, when an airline company needs to call a complete record of a certain training for teaching quality analysis, after the airline company completes consensus with a flight training company, the operation is allowed, and the operation record is logged into a blockchain; CAAC supervision department and flight simulator manufacturer are observer nodes, do not participate in consensus, and only carry out block synchronization; the CAAC supervision department and the flight simulator manufacturer can inquire information related to the CAAC supervision department and the flight simulator manufacturer, for example, the CAAC supervision department inquires the training condition and record of a pilot C of an airline company a in a certain period of time, and the flight simulator manufacturer inquires the fault maintenance condition of the simulator and simultaneously performs synchronous block recording.
The third party platform can support different consensus algorithms in a plug-in mode, currently supports Concurrent BFT and Optimized BFT consensus, randomly selects a consensus node by adopting VRF and a probability distribution mode, and well balances the decentralization and expandability problems.
Current BFT: block output and block verification are performed in parallel, so that the fault tolerance of BFT 1/3 is guaranteed, and the block output rate is greatly improved; in the test network, the time for each node to agree on and find a block is 1 s.
Optimized BFT: an unlocking mechanism is added to solve the problem of consensus deadlock, and more than 100 consensus nodes are supported; in the test network, the time for each node to agree on and find a block is 1 s.
Example 2
Referring to fig. 5, a difference between this embodiment and embodiment 1 is that the course selection module in this embodiment includes a voice wakeup unit, a course identification unit, a security protection unit, and an intelligent robot unit, where the voice wakeup unit is connected to the intelligent robot unit, the intelligent robot unit is connected to the course identification unit, and the security protection unit is connected to the course identification unit; in this embodiment, the student awakens up intelligent robot unit through the pronunciation unit of awakening up, then says demand and course, select corresponding course unit and with information transmission to course recognition unit through intelligent robot unit, course recognition unit is with information transmission to the safety protection unit, confirm selected course through the safety protection unit, whether for mr's course of starting, if then can't open, if then can open, if can open, so can not make the student break in disorder the course.
Example 3
Referring to fig. 6, this embodiment is different from embodiments 1 and 2 in that the client unit includes a mobile phone login unit and a web login unit, the mobile phone 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; after logging in, the mobile phone mobile terminal login unit passes through the verification unit for verification, and the information sending unit sends the information to the login user after hearing the information, so that the login security is ensured, and the learning progress is not disturbed; the login of the web terminal requires the ID input unit to input an account number, and then authentication and information transmission are carried out.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. The training data collecting and sharing system of the full-motion simulator applying the block chain technology is characterized by comprising a data collecting system and a data sharing system, wherein the data collecting system is connected with the data sharing system, the data collecting system comprises an attendance checking module, a course selecting module, a simulator and a block chain module, the simulator comprises a simulator training module and a training data collecting module, the attendance checking module is connected with the course selecting module, the course selecting module is connected with the simulator training module, the simulator training module is connected with the training data collecting module, the training data collecting module is connected with the block chain module, the data sharing system comprises a distributed storage module, the distributed storage module is connected with the training data collecting module, the data sharing system further comprises a client module, a data collecting module and a block chain module, The system comprises a data dispatching center module and a data node group module, wherein the client module is connected with the distributed storage module, the client module is connected with the data dispatching center module, and the data dispatching center module is connected with the data node group module.
2. The system for collecting and sharing training data of a full-motion simulator applying a blockchain technology according to claim 1, wherein the attendance module is implemented in a manner of: the computer check-in, fingerprint card punching and face recognition mainly record the training starting time and the training finishing time of instructors and pilots so that the flight training center and the airline company can count the training time.
3. The system for collecting and sharing training data of a fully-automatic simulator using blockchain technology as claimed in claim 1, wherein the course selection module function is made according to the training outline of each airline company, and the flight instructor of the corresponding airline company can select the training course by himself or herself and import it into the simulator training module.
4. The system of claim 1, wherein the simulator training module functions to allow instructors and pilots to perform selected training sessions directly after entering the simulator.
5. The system for full-motion simulator training data collection and sharing using blockchain techniques of claim 1, wherein the training data collection module collects the pilot's operations and data on the aircraft systems during the selected course training and sends the collected data to the distributed storage module.
6. The system for collecting and sharing training data of a fully-automatic simulator applying blockchain technology of claim 1, wherein the data scheduling center module comprises 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 training data collecting and sharing system for the full-motion simulator applying the blockchain technology as claimed in claim 1, wherein the data node group module comprises a plurality of data node units, data states of the plurality of data node units are not identical, the data node group module has a data sharing function, and the implementation method of the sharing function comprises the following steps:
step1, after a user generates mass data in a training data collection module, a database and storage logic are arranged on a distributed storage module, the training data collection module merges small files, divides the large files, divides the data into data file blocks with fixed sizes, transmits the data file blocks to a data scheduling center in a binary file stream mode, and the data division calculation mode utilizes a sparse matrix calculation mode;
and Step 2, the data file block enters a data scheduling center module, is initially in a state of waiting for synchronization, is recorded in a database structure list unit, and is subjected to data scheduling and synchronization.
Step 3, the database uses a non-relational database Redis, the database is installed on a data node, a data file block is stored, and concurrent operation exists in the data storage process, so that a distributed lock of the database is developed based on the Redis, the Redis is in a single-process and single-thread mode, the concurrent access is changed into serial access by adopting a queue mode, the distributed lock function is realized by utilizing commands SETNX and GETSET of the Redis, and the data is scheduled and updated by the queue;
step 4, after the data file block is synchronized to the data node group module, the state is in a 'synchronous' state; the file block enters one of the data node units, other node data units are not updated, the process of synchronizing all the node data units is carried out asynchronously, a cache of the data is established in the data query unit, and the correctness of the data is saved in the synchronization delay stage;
step 5, synchronizing a data file block to all data node units, performing data snapshot after the data of the data node group module is updated, storing the snapshot on a disk, restarting a socket thread, sending the snapshot to other data nodes through the socket thread, wherein the snapshot is shared by all the data node units, the other data node units perform data synchronization, and the state of the data file block is in a 'synchronization completion' state after the synchronization of all the data node units is completed;
step 6, the storage sequence of the data file blocks of each data node unit is different and basically unordered, the generation of large file data needs to be combined by using the structural relationship in the data block structure list unit, and the large file can be obtained by using a plurality of small file blocks for combination calculation, so that the problem that TB-level files are not inquired in data inquiry is solved;
step 7, an engine used for mass data query is Hadoop, and the engine can analyze and screen the data records with the most queried contents of the data, the average depth of the data query, the common path of browsing the data, establish a high-speed query cache channel and accelerate the speed of data query and display by utilizing model calculation;
step 8, establishing a data index structure in the whole data storage process, namely a data block structure list unit and pointers of all data blocks; the hash value generated by the block chain module is added to the file header of the data block file, the block chain module hash value and the data block index are bound and linked, the hash value generated by the block chain module is added to the file header of the data block structure list unit file formed by the data block indexes, the structure file is linked integrally, and the block chain module not only ensures the pointer index of a single data block to be linked, but also ensures the structural linking of all files, so that the integrity of data is ensured.
8. The system of claim 1, wherein the blockchain modules are third party based blockchain platforms forming a blockchain network.
9. The system of claim 1, wherein the course selection module comprises 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.
10. The system for collecting and sharing training data of a fully-automatic simulator using blockchain technology as claimed in claim 1, wherein the client unit comprises a mobile phone login unit and a web login unit, the mobile phone login unit is connected with a verification unit, the verification unit is connected with an information sending unit, the web login unit is connected with an ID input unit, and the ID input unit is connected with the verification unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210118299.5A CN114493539A (en) | 2022-02-08 | 2022-02-08 | Training data collecting and sharing system of full-motion simulator applying block chain technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210118299.5A CN114493539A (en) | 2022-02-08 | 2022-02-08 | Training data collecting and sharing system of full-motion simulator applying block chain technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114493539A true CN114493539A (en) | 2022-05-13 |
Family
ID=81478666
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210118299.5A Pending CN114493539A (en) | 2022-02-08 | 2022-02-08 | Training data collecting and sharing system of full-motion simulator applying block chain technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114493539A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114898600A (en) * | 2022-07-13 | 2022-08-12 | 安胜（天津）飞行模拟系统有限公司 | UAM four-dimensional track sharing and management method based on block chain technology architecture |
| CN114912856A (en) * | 2022-07-19 | 2022-08-16 | 安胜（天津）飞行模拟系统有限公司 | Flight simulator maintenance method based on block chain |
-
2022
- 2022-02-08 CN CN202210118299.5A patent/CN114493539A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114898600A (en) * | 2022-07-13 | 2022-08-12 | 安胜（天津）飞行模拟系统有限公司 | UAM four-dimensional track sharing and management method based on block chain technology architecture |
| CN114898600B (en) * | 2022-07-13 | 2022-10-21 | 安胜（天津）飞行模拟系统有限公司 | UAM four-dimensional track sharing and management method based on block chain technology architecture |
| CN114912856A (en) * | 2022-07-19 | 2022-08-16 | 安胜（天津）飞行模拟系统有限公司 | Flight simulator maintenance method based on block chain |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114493539A (en) | Training data collecting and sharing system of full-motion simulator applying block chain technology | |
| US20100145774A1 (en) | Method for voting preparation and implementation by means of an automated operational information system | |
| CN110694256A (en) | Novel emergency computer war game deduction system and method | |
| US20150066342A1 (en) | Flight scheduling, dispatch and check-in | |
| CN110570705B (en) | Multi-navigation management simulation system combined training method based on self-adaptive grouping | |
| KR20190124045A (en) | System for training and education using augmented reality and method for training and education using therof | |
| CN109379233A (en) | A kind of cloud emulation platform model safety safeguards system, method, server and terminal | |
| CN113469633A (en) | Safety supervision smart cloud platform | |
| WO2015066542A1 (en) | Video role-play learning system and process | |
| EP1873724A1 (en) | Automated operation and information system for managing a vote process preparation and running | |
| Johnson et al. | Modeling student performance in diagnostic tasks: A decade of evolution | |
| CN116469287A (en) | Real training system and method for emergency exercise of civil aviation passenger cabin in meta-space vision field | |
| CN111739376A (en) | Airport controller simulation training system | |
| CN106991626A (en) | A kind of educational administration management system based on Quality Culture | |
| CA3235482A1 (en) | System and method for predicting performance by clustering psychometric data using artificial intelligence | |
| CN111814306B (en) | City heterogeneous simulation system | |
| KR20230091456A (en) | Functional level evaluation system of chung-mu plan government's three major function | |
| CN110097337A (en) | A kind of teaching of comprehensive intelligent text and Students'Management System | |
| CN112231792B (en) | BIM-based vehicle-ground transmission construction monitoring system and working method thereof | |
| Chalaris et al. | Process’ standardization and change management in higher education. The case of TEI of Athens | |
| CN112991113B (en) | Online live course arranging system and method | |
| Yang | Application of Computer Technology in the Management of College Students | |
| Zhang | Design of Multichannel Resource Adaptive Scheduling Algorithm for English Online Translation Teaching | |
| Zheng et al. | Research on Architecture and Key Technologies of Big Data Intelligent Analysis Platform for Computer Network Virtual Experiment | |
| Zi | Network Platform Education Resource Development Process Construction Based on Cloud Computing Technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |