351 results about "Flight training" patented technology

The invention relates to the technical field of flight training evaluation, and discloses an electroencephalogram feature and physiological index fused flight training evaluation system. The system comprises a physiological signal acquisition module which synchronously captures multichannel electroencephalogram original signals and body surface physiological index data, and the body surface physiological index data comprises an electrocardiograph R-R interval sequence, respiratory wave frequency amplitude and galvanic skin response amplitude; the multi-modal fusion module is used for analyzing an electrocardiograph R-R interval sequence to generate a heart rate variability feature vector and establishing dynamic association mapping of an electroencephalogram entropy value and a physiological feature vector; the cognitive state modeling module is used for generating a cognitive load index according to the dynamic association mapping and constructing a cognitive stability quantization matrix; the self-adaptive feedback module is used for receiving related data and dynamically adjusting simulated flight scene parameters; and the evaluation output module is used for integrating the data to generate a comprehensive training evaluation report containing a neurophysiological coordination degree score and an operation accuracy rating. According to the system, comprehensive evaluation and dynamic training adjustment of the cognitive state of the pilot are realized.

The invention relates to the field of flight training data processing, in particular to a flight training regulation and control method and system, and the method comprises the steps: collecting a physiological signal, an electroencephalogram signal and a visual signal of a pilot, and generating comprehensive feature data through multi-modal feature fusion; time dependence analysis is carried out through a recurrent neural network, a comprehensive pressure trend is generated in combination with historical pressure data, the psychological pressure of the pilot is further evaluated through a deep learning model, and the training intensity is adjusted based on real-time feedback data; in the virtual training, the situation is dynamically adjusted based on reinforcement learning, and a personalized training scheme is generated in combination with personalized analysis, so that the training effect of the pilot is optimized; according to the invention, comprehensive monitoring and personalized training regulation and control of the physiological state and psychological stress of the pilot are realized, and the adaptability and safety of flight training are improved.

The invention relates to a method for evaluating the situation awareness ability of a pilot based on a Ti-SA model. The Ti-SA model comprises an eye movement trace multi-dimensional attention enhancement module, an AOI multi-scale comprehensive feature module and a fusion module. The evaluation method comprises the steps of collecting eye movement trace time sequence data and flight training videos of a pilot at continuous moments in real time, and obtaining corresponding AOI fixation time sequence data based on the eye movement trace time sequence data, the flight training videos and a preset AOI subdivision area; preprocessing the eye movement trace time sequence data and the AOI fixation time sequence data; inputting the preprocessed eye movement trace time sequence data into an eye movement trace multi-dimensional attention enhancement module to obtain a category probability value corresponding to the eye movement trace time sequence data; inputting the preprocessed AOI fixation time sequence data into an AOI multi-scale comprehensive feature module to obtain a category probability value corresponding to an AOI fusion feature; and inputting the two category probability values into a fusion module to obtain a predicted situation awareness level.

The invention discloses a pilot mental load assessment system based on emotion calculation. The pilot mental load assessment system comprises an information acquisition and processing part and a psychological assessment and intervention report generation part. Specifically, a wearable physiological signal acquisition device is provided for a pilot in traditional simulated flight training, electroencephalogram, electrocardio and skin electrical signals of the pilot are acquired in the training process, and the emotion level of the pilot during training is judged through comprehensive processing of a common person emotion model and a data algorithm, so that the training accuracy of the pilot is improved. According to the judgment result, mental load and pressure level indexes of the pilot at a certain moment in training are obtained through comprehensive analysis, and the analyzed oscillogram of each index changing according to the time axis is taken as an output result; and finally generating physiological data, flight training data and an emotion evaluation result in training collected in training, and formulating a targeted training intervention scheme according to the individual emotion state of the pilot. According to the method, the psychological quality problem of the pilot can be well corrected, and finally an ideal training effect is achieved.

The invention relates to the field of electroencephalogram signal processing, and particularly discloses a multi-mode electroencephalogram characteristic cognitive ability evaluation system based on a neural network, comprising: an acquisition module used for synchronously acquiring whole brain signals and eye movement data during pilot simulation training; the data preprocessing module is used for primarily processing the received whole brain signals and the eye movement data, monitoring the primarily processed data in real time by utilizing a preset monitoring mechanism, and triggering to generate interaction test starting signals if preset symbolic fluctuation occurs in the whole brain signals or the eye movement data; and the test interaction sub-module is used for generating arithmetic test questions according to a preset rule after receiving the interaction test starting signal, and synchronously recording answer correlation parameters of the pilot. By adopting the technical scheme of the invention, the deep-level reaction mechanism of the brain under the stimulation of the special complex cognitive task of flight training can be fully considered, and the cognitive load change of the pilot in the simulated training can be comprehensively and accurately reflected.

The invention relates to a pilot SA ability evaluation system based on eye movement tracking and space directional flight simulation, and the system comprises a space directional flight simulator which is used for simulating a real flight environment; the eye tracker is used for recording an eyeball movement track of a pilot in real time in a simulated real flight environment to obtain eye movement track time sequence data and shooting a flight training video; the microprocessor evaluation module comprises a time sequence data processing module and a Ti-SA model; the time sequence data processing module obtains corresponding AOI fixation time sequence data based on the eye movement trace time sequence data, the flight training video and a preset AOI subdivision area; the Ti-SA model is used for predicting a current SA capability value of a pilot based on the eye movement trace time sequence data and the AOI fixation time sequence data and judging whether the predicted SA capability value is lower than a preset SA threshold value or not, and if the predicted SA capability value is lower than the preset SA threshold value, an early warning instruction is sent to the early warning and voice broadcasting module; and the early warning and voice broadcasting module is used for early warning when the SA capability value is lower than a preset SA threshold value.

The invention relates to the technical field of TEM models, provides a pilot dynamic evaluation method, system and device based on a TEM model and a storage medium, and solves the problems of low accuracy of flight training evaluation and poor adaptability of a training scheme. The method comprises the steps that flight control, physiological monitoring and cockpit voice data are acquired, and a multi-modal data stream is formed through time synchronization; performing threat perception, error management and non-technical skill three-level evaluation on the feature vector by using a TEM model, generating corresponding indexes, and fusing the indexes into a comprehensive feature vector; respectively processing the time sequence and cognitive features by means of a dual-channel long-short-term memory network, and extracting deep features; performing decision tracing by adopting an SHAP value so as to locate a capability defect node; finally, defect types are matched through a personalized improvement scheme recommendation mechanism, and self-adaptive training content is generated. According to the invention, the accuracy of flight training evaluation and the adaptability of the training scheme are improved.

The invention, which relates to the technical field of aviation training and simulation, discloses a virtual low-altitude air traffic controller AI model training system comprising a data acquisition and preprocessing module, an AI core model module and a communication and interaction module. The data acquisition and preprocessing module is used for collecting data from a simulated low-altitude radar, low-altitude weather, low-altitude geographic information and a low-altitude flight plan data source, and performing cleaning, denoising, format conversion and integration processing on the data to generate preprocessed data used by the AI core model. According to the virtual low-altitude air traffic controller AI model training system provided by the invention, the effect and quality of low-altitude flight training of a pilot are remarkably improved, and the pilot can perform a large amount of practical operation and emergency treatment training in a safe environment by simulating various real low-altitude flight scenes and complex conditions; the coping ability of the system to low-altitude flight rules, control programs and various complex conditions is enhanced, the training period is shortened, and the training cost is reduced.

A training method and system based on VR visual simulation for a new aerial vehicle. A VR display system is combined with real flight. An aerial vehicle as a real aerial vehicle flies in a relatively safe state, such as hovering or moving at a low speed. At the same time, a pilot wears a virtual display device such as VR glasses in the VR display system, and a corresponding flying state after the pilot performs an operation is displayed by means of the VR display system. The flying state of the aerial vehicle does not respond completely in accordance with the operation of the pilot, in order to simulate flying states involving a relatively high risk, e.g., scenarios such as a relatively high altitude and a relatively high speed. Thus, a real environmental state is achieved to provide a pilot with a real hovering experience, and the flight training of the pilot is also completed under a relatively safe condition.

The invention relates to the technical field of flight training, in particular to a flight training system. The flight joystick receives operation action data input by a target user based on a preset training instruction; the button control table receives button action data input by a target user based on a preset training instruction; the electronic equipment receives parameter setting data which is input by a target user based on a preset training instruction and is used for setting flight parameters; the multi-axis gravity seat performs six-degree-of-freedom movement based on the operation action data, the button action data and the parameter setting data so as to simulate the flight attitude of the aircraft; and the electronic equipment generates a target virtual driving scene based on the operation action data, the button action data and the parameter setting data, and controls the dynamic scene display equipment to display the target virtual driving scene. The target virtual driving scene is combined with the actual operation process. And the training effect is improved in the flight training process.

The invention relates to the technical field of artificial intelligence and flight training, and particularly discloses an intelligent training method and system for consciousness of a pilot. The method comprises the following steps: constructing a multi-modal flight scene and an original task structure chart, and extracting a flight task target and a semantic tag set; guiding a pilot to carry out natural language expression, collecting a voice stream and combining a task stage to align a text; comparing the language expression with task semantics by using a language model, and identifying expression differences; generating a feedback statement based on the difference, and pushing the feedback statement to the pilot; updating a task flow and reconstructing an execution script; finally, training records are counted, the capacity label weight is updated, and a capacity growth trend graph is generated. According to the method, accurate identification of pilot task understanding deviation and language expression defects can be realized, a scene training process dynamically adaptive to a cognitive state is constructed, and scene awareness and language expression ability of a pilot are remarkably improved.

The invention discloses a flight training digital twin system, which comprises a data acquisition module (for collecting real-time data of an aircraft and an environment to enable virtual and actual conditions to be consistent), a model and simulation module (for creating an aircraft, an operating environment and an airborne equipment simulation model), and an intelligent analysis and control module (for analyzing operation and equipment states through big data, and analyzing and analyzing the real-time data of the aircraft and the environment to enable virtual and actual conditions to be consistent). Feedback and optimization suggestions are generated), and an interaction and training module (based on real-time data scoring feedback and immersive interaction-aid operation). According to the system, states and behaviors of physical entities such as an aircraft, a pilot and an environment are mapped to a virtual space through a high-precision virtual simulation technology, and a real-time interaction and dynamic feedback simulation training platform is constructed. The system integrates multi-dimensional data, can be linked with physical equipment, and assists a pilot in improving skills and enhancing the coping ability of complex conditions in a safe and controllable virtual environment by simulating a real scene and providing a personalized scheme, intelligent decision and risk prediction.

The invention provides a flight environment virtual simulation restoration method and system based on flight data, relates to the technical field of virtual reality and flight simulation, and solves the problem of how to accurately and smoothly restore real flight data to a virtual simulation environment. The method comprises the following steps: acquiring real flight data, converting the real flight data into a data format which can be identified by a UE rendering engine, analyzing and extracting key flight parameters, and smoothing process data corresponding to the key flight parameters, so that the process data smoothly changes when the flight environment of an aircraft is virtually simulated and updated; according to the smoothed process data, updating the aircraft pose in the UE virtual environment of the UE rendering engine to realize the reduction of the flight path; meanwhile, a world space UI is created, and instrument information corresponding to the flight path is updated and displayed in the world space UI. According to the invention, the flight path and instrument display under the simulation condition are close to the real flight condition, and the authenticity and immersion of flight training are effectively improved.

The invention relates to the technical field of operation training evaluation, in particular to a training quality evaluation method and system based on pilot attention distribution. The method comprises the following steps: collecting data required by pilot attention distribution training quality evaluation according to a specific flight mission scene; based on the collected data, calculating a standard attention distribution model of the pilot in a specific flight mission scene, and generating attention distribution and actual attention distribution of the standard attention distribution model; and comparing the attention distribution of the standard attention distribution model with the actual attention distribution, evaluating the training quality of the attention distribution of the pilot in the specific flight mission scene, and forming and displaying an evaluation result. According to the method, the attention distribution mode of the pilot is quantified, the eye movement tracking technology and task-related subjective and objective data are combined, the performance of the pilot in simulator training can be objectively and accurately evaluated, and the scientificity, fairness and safety of flight training are improved.

The invention relates to the technical field of running big data, provides a flight training adaptive optimization method, system and device based on running big data, and a storage medium, and solves the problems of low control precision and poor stability of high maneuvering flight training. The method comprises the following steps: acquiring attitude time sequence data, structural deformation monitoring data and historical operation data of a flight target in a maneuvering state in a current period; performing environmental interference suppression on the attitude time sequence data, and extracting motion characteristic parameters; converting the structural deformation monitoring data into deformation compensation data, and fusing the motion characteristic parameters with the deformation compensation data by using an attitude fusion algorithm; based on historical operation data, multi-period feature matching is carried out on the fusion result of the current period, and six-dimensional attitude information of the flight target is generated; and generating an optimization control instruction through the adaptive control strategy, wherein the optimization control instruction is used for optimizing the response performance of the flight target. According to the invention, the control precision and stability of high-maneuverability flight training are improved.

The invention discloses a flight training quality evaluation method based on DTW and K-means clustering, and belongs to the technical field of pilot training, and the method comprises the following steps: S1, data collection and processing; s2, data preprocessing and standard template generation; s3, DTW similarity measurement and mahalanobis distance weighting are carried out; s4, performing similarity measurement and clustering analysis; s5, generating a personalized training scheme; therefore, the invention provides a scientific, objective and efficient flight trainee quality evaluation method, the landing process of the pilot is subjected to comprehensive quantitative analysis in a data-driven manner, a more accurate and personalized flight training scheme is provided, the flight level of the flight trainee is finally improved, and the risk of flight accidents is reduced.

The invention provides a QAR data-based main pilot effective manipulation analysis method and system, and belongs to the technical field of flight training. Comprises: acquiring flight decoding data; performing initialization correction on the joystick force in the flight decoding data, and limiting a data analysis range; constructing a control stick data analysis logic, and executing the control stick data analysis logic in a limited data analysis range to analyze the initialized and corrected control stick force so as to judge whether the main pilot is effectively manipulated or not; matching the flight decoding data with the task book information to judge whether the control rate of the main pilot reaches the standard or not; and according to the effective control of the main pilot and the standard control rate, the actual effective control condition of the main pilot is judged. The method can accurately quantify the manipulation performance of the pilot and the flight condition of the captain of the instructor, and provides scientific guidance for the evaluation of the driving level of the pilot.

The invention provides an aircraft simulation model checking method. The aircraft simulation model checking method comprises the following steps: S1, determining a flight mode of an aircraft operated by a pilot and recording flight data; s2, after the flight data are obtained, an airplane simulation model is established in combination with existing appearance parameters of an airplane; s3, carrying out correction and compatibility detection on the flight data and the simulation data; s4, determining and identifying an aerodynamic derivative needing to be corrected according to a compatibility detection result, and correcting the aerodynamic derivative; and S5, when the corrected data accords with the specification, exporting a final corrected aerodynamic model parameter table, and generating a debugging report. According to the airplane simulation model checking method, airplane digitization can be faster and more efficient, and the method has extremely high replicability and can be applied to various aircrafts such as manned aerial vehicles and unmanned aerial vehicles. The flight simulation model created by the method can also be applied to national defense digital software such as war simulation, flight training machines and the like, and has very practical contributions to national safety and military strength.

A rotorcraft flight training method for a rotorcraft comprising at least two engines and at least one rotor providing lift helping to keep the rotorcraft in the air. The method comprises at least the following steps: flying the rotorcraft in a mode wherein each engine supplies driving power to the rotor; and activating a first command to start a training phase simulating asymmetric flight, the training phase comprising a first flight phase comprising a first control operation, with a controller, controlling the at least two engines and a first display operation for displaying, with a display, information carrying at least two simulated powers.

The invention discloses a multi-degree-of-freedom aircraft driving simulation training platform. The platform comprises a multi-degree-of-freedom motion platform; the control device is used for acquiring the longitudinal and lateral translation acceleration signals and the lifting and translation acceleration signals, performing wash-out and advanced compensation on the longitudinal and lateral translation acceleration signals and the lifting and translation angular velocity signals to respectively form a displacement signal, an attitude signal, a yaw angle signal and a lifting signal, and outputting the displacement signal, the attitude signal, the yaw angle signal and the lifting signal. The minimum error cost function of the simulation scene feeling model and the motion platform feeling model is taken as a target during wash-out; and controlling the multi-degree-of-freedom motion platform according to the displacement signal, the attitude signal, the yaw angle signal and the lifting signal. According to the method, the acceleration signal output by the scene simulation software is subjected to washout and advanced compensation, so that the error cost function between the simulation scene feeling model and the motion platform feeling model is minimized, the effect of high consistency of the simulation scene and the motion state is realized, and the flight training effect is powerfully improved.

The invention discloses an eVTOL flight trainee training system based on a virtual low-altitude environment, which relates to the technical field of flight training and comprises a data layer, a business logic layer and a user interface layer. The data layer comprises a data source, a data synchronization center, a data transmission protocol, a data receiving end and a data storage module, the data source comprises flight student training equipment and sensor data of a low-altitude aircraft, and the data synchronization center adopts a distributed architecture to coordinate multi-component data synchronization. The invention further discloses an eVTOL flight trainee training method based on the virtual low-altitude environment. According to the eVTOL flight trainee training system and method based on the virtual low-altitude environment provided by the invention, through innovative architecture design and function implementation, the problems of integration, scheduling, synchronization, communication and the like of various elements in the virtual environment are effectively solved, an efficient and high-quality training platform is provided for eVTOL flight trainees, flight talents can be cultivated, and the eVTOL flight trainees can be trained. The application of the aircraft in the traffic field is promoted.

The invention discloses a general aviation flight training risk assessment method, system and device and a storage medium. The method comprises the following steps: acquiring survey report information of a flight training unsafe event, and dividing the survey report information into a model corpus and an instance corpus; forming a model corpus containing labels; constructing a risk identification model based on a deep learning algorithm; constructing a flight training risk evolution analysis network; calculating node parameters of accident cause factors according to a flight training risk evolution analysis network; and sorting the risk nodes of the flight training unsafe events by integrating the node parameters, thereby completing the steps of risk assessment of general aviation flight training and the like. The method has the advantages that the accident cause theory, deep learning and the complex network form a hybrid model to be applied to general aviation flight training risk assessment, subjectivity can be reduced, influence factors of flight training unsafe events can be effectively assessed, and research results can provide scientific basis for formulating systems such as general aviation flight training safety management.

The invention discloses an unmanned aerial vehicle moving obstacle ring training system and method, the system comprises an unmanned aerial vehicle moving obstacle ring training support and an unmanned aerial vehicle fixed obstacle ring training support, and the unmanned aerial vehicle moving obstacle ring training support comprises a supporting support assembly, a horizontal rotating shaft assembly, a vertical pendulum shaft assembly and a ring assembly; the unmanned aerial vehicle moving obstacle ring training support and the unmanned aerial vehicle fixed obstacle ring training support form an unmanned aerial vehicle flight training competition path place used for flight training or competition of the unmanned aerial vehicle. According to the technical scheme, the system and method are specially designed for unmanned aerial vehicle flight competition and training, based on the structural design of the unmanned aerial vehicle moving obstacle ring training support, a plurality of unmanned aerial vehicle accompanying flying rings can be arranged, different motion combinations of the unmanned aerial vehicle accompanying flying rings are controlled through linkage programming, different difficulty training environments are adapted, and the training efficiency is improved. The complexity of flight is increased, omnibearing training from a foundation to a high order is provided for an unmanned aerial vehicle pilot, and comprehensive improvement of the skill of the pilot is facilitated.

The invention relates to the technical field of flight training, and particularly discloses an intelligent auxiliary training method and system for the driving skill of a helicopter pilot, and the method comprises the steps: deploying a plurality of sensors in a simulated helicopter cockpit for collecting operation, physiology, eye movement and other original data; after data standardization preprocessing, constructing a trainee training state comprehensive data model by using a multi-modal data fusion algorithm; dynamically generating a simulated flight scene by using VR and AR; carrying out comprehensive evaluation by adopting a multi-modal evaluation model based on deep learning in combination with multi-source data and student scene performance, and generating a multi-dimensional report; an evaluation result is fed back in real time through multiple channels such as voice and touch, and customized training resources are pushed; and formulating an exclusive training plan by using a personalized learning path planning algorithm based on multiple training data. Through multi-source data fusion and an intelligent algorithm, the training state of the trainee is comprehensively and accurately evaluated, a personalized training scheme is provided, and the driving skill training efficiency and quality of the helicopter pilot are effectively improved.

The invention belongs to the technical field of aviation flight training, particularly relates to a multi-module collaborative flight simulator control panel and method, and solves the problems that an existing system is low in interaction efficiency, subjective in evaluation, lagged in early warning and time-consuming in comment. The system depends on traditional IOS hardware, and is provided with an intelligent voice interaction module which recognizes teacher voice through ASR, generates an instruction through intention recognition and three-level slot completion, and cooperates with double-layer flight stage recognition logic to guarantee interaction accuracy; the flight quality monitoring module operates a rule engine and an enhanced LSTM neural network in parallel, and outputs a comprehensive flight quality score in combination with an adaptive fusion algorithm; the real-time reminding module dynamically adjusts the early warning priority according to the event level and the flight stage, and multi-mode early warning is provided; and the intelligent comment module fuses the multi-source data and generates a personalized structured report according to the priority of the template. According to the invention, intelligent interaction, quantitative evaluation and automatic commenting of training are realized, and the training efficiency and evaluation objectivity are improved.

The invention relates to the technical field of aviation safety and flight training data analysis, provides a training priority determination method and system based on aviation event data statistical analysis, electronic equipment and a storage medium, and solves the problems of poor accuracy of flight training subject design and poor risk pertinence. The method comprises the following steps: extracting aviation unsafe event data of a plurality of heterogeneous data sources, extracting threat, error and unexpected aircraft states, and judging a unit management state; a group decision is combined with a distributed scoring node and block chain consensus mechanism, three-level scoring is performed on various threats from the training effect, the occurrence possibility and the consequence severity, and a threat scoring matrix is formed; the unit management rate and the actual occurrence frequency of each threat are counted, a comprehensive risk value is calculated, all threats are ranked to determine training priorities, and the threats with high priorities and related errors are embedded into the scene design of a flight training subject. According to the invention, the accuracy of flight training subject design is improved.

The invention provides an aviation trajectory prediction method based on multi-flight training and transposition input, a medium, a device and a program product. The method comprises the steps of collecting a flight data set and performing data preprocessing; constructing a Transform model based on transpose input, and training the model by using the preprocessed data; the trained Transform model is utilized to carry out trajectory prediction; and the trajectory prediction result is visualized. According to the method, multi-flight input is realized through data preprocessing for training, only effective flight data is utilized, the data volume is greatly reduced, and convenience is provided for subsequent training; and meanwhile, the Transform model based on transpose input can better capture dependency among data during trajectory prediction, so that the problem of complex dependency in time sequence data is solved, mutual influence among different variables can be identified under the condition of multiple variables, and the prediction accuracy and the generalization ability of the model are improved.

Techniques for adaptive machine learning training via in-flight feature modification are described. A training monitor captures training data during the training of a machine learning model, and a metric generator creates metrics such as feature importance metrics based on the data. A rule evaluation engine determines whether any modification conditions are met for any of the features based on the metrics, and based on such a determination can cause the in-flight training job to be modified.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a design and development method of a multi-mode high-simulation training integrated platform, which constructs a highly realistic virtual substation environment by combining a virtual reality technology and multi-sensor simulation, and is used for unmanned aerial vehicle inspection training and operator skill improvement. The platform comprises a scene simulation core module, an environment simulation core module, an unmanned aerial vehicle sensor simulation core module and an unmanned aerial vehicle dynamics simulation core module, real perception and reaction data are provided, and the actual flight behavior and performance of the unmanned aerial vehicle are accurately reflected. Besides, the platform is also integrated with an immersive training system of the operator, and comprehensive evaluation and improvement of the performance of the operator are realized through multi-scene simulation flight training, an intelligent scoring system and a high-simulation visualization function. According to the invention, a reliable tool is provided for the development and test of the unmanned aerial vehicle, and a safe and efficient solution is provided for the training and evaluation of operators.

The invention relates to the technical field of space flight and aviation, in particular to a low-delay communication control system and method for an analog machine in a fixed flight training course. The control system comprises a multi-source sensor fusion module, a data simplification processing module, a dynamic time delay optimization communication framework, a distributed intelligent decision-making terminal, a virtual-real mapping synchronous controller, a milli delay regulation and control module and an intelligent sensing alarm interaction module. According to the invention, 4G, 5G and satellite double links are integrated, an optimal link is dynamically selected by adopting a Q-Learning algorithm, millisecond-level time delay control is realized, transmission reliability is improved through a packet loss rate compensation algorithm, multi-sensor data is fused based on an FPGA hardware accelerator and an EKF algorithm, and a data compression ratio is improved in combination with Huffman coding and Kalman filtering. The bandwidth requirement is greatly reduced, through software and hardware collaborative optimization, the end-to-end time delay is reduced to be within hundreds of milliseconds, the virtual-real synchronization precision is improved to the centimeter level, and the decision response speed is improved by two times.