237 results about "Virtual training" patented technology

The invention discloses a child electric motorcycle intelligent safety control system based on the Internet of Things, and relates to the technical field of electric motorcycle safety control. The problem of insufficient driving risk perception and control response of children is solved. The method comprises the following steps: firstly, fusing obstacle trajectory prediction, a road adhesion coefficient and an environment contour, constructing a dynamic risk vector field, and generating a collision probability and out-of-control coefficient fused risk assessment matrix; then, the matching degree of handle operation and vehicle body response is calculated through cross attention, and a third-level behavior label is output; according to the behavior level and the risk direction, anti-sideslip control or progressive power limitation is executed, and intervention characteristics are recorded; and finally, constructing a virtual training task by using parent operation data, extracting a personalized control threshold, dynamically optimizing system parameters, and realizing a child driving behavior-oriented closed-loop safety control system.

The invention discloses an interactive control method for virtual simulation training of electric power operation and maintenance, and particularly relates to the technical field of electric power operation and maintenance simulation, which comprises the following steps of: acquiring operation behavior data of students, performing real-time capability grading on the students by utilizing a multi-dimensional capability evaluation model, and performing real-time capability grading on the students; a difference adaptation parameter model is constructed in combination with the difference between the virtual device and the target actual device, training content with corresponding difficulty is matched from a preset training task library, a knowledge graph of students is constructed, and a personalized training path is generated through a path recommendation engine. Iteratively updating the training difficulty and the recommended path according to the newly generated behavior data and the performance of the trainee in the aspect of the difference adaptive capacity, constructing a difference adaptive parameter model, bringing the difference into a training task adjustment and capacity evaluation mechanism, dynamically adjusting the training task parameters to fit the actual equipment complexity, and evaluating the performance of the trainee in the aspect of the difference adaptive capacity. And a migration barrier between virtual training and real operation and maintenance is broken through.

The invention discloses a PICC virtuality and reality combined simulation training system and method, solves the problems that puncture simulation training is not real enough and students cannot perform intensive training aiming at a puncture technology due to the fact that an existing venipuncture simulation platform lacks necessary puncture force feedback, and belongs to the technical field of virtual training. The system is composed of a personal information management module, a simulation training terminal, a virtual model management module and a man-machine interaction module, and student operation information is collected in real time based on a multi-dimensional sensor; the virtual model management module reconstructs real anatomical structures such as blood vessels and skin of a human body through a high-precision 3D modeling technology, a physical engine is matched to provide a vivid puncture hand feeling, students can experience a scene very close to real operation, and the simulation training terminal can simulate the real operation through a force feedback technology in a virtual scene. The trainee can feel the touch similar to that of real puncture, so that the understanding of the trainee on the puncture process and the control of the trainee on the puncture strength and angle are improved.

The invention provides a mine safety training method and system based on digital twinning and virtual reality, and the method comprises the steps: obtaining laser scanning point cloud and geological exploration data of an underground roadway of a mine, and constructing a high-precision virtual underground environment model based on the data; on the basis of the virtual underground environment model, combined with explosion condition judgment, disaster simulation is triggered, and a virtual training environment is generated by applying real-time rendering and dynamic labeling technologies; obtaining operation behavior data of trainees in the virtual training environment, and evaluating the operation behavior data to obtain a behavior evaluation result; and based on the behavior evaluation result and the environmental parameter change data, constructing and training an evaluation model by utilizing machine learning, and generating customized training content in combination with geological conditions and mining process characteristics of different mining areas. According to the method, a high-reduction-degree virtual scene can be constructed, the problem that a traditional training scene is single is solved, quantitative evaluation of the training effect is achieved, and training pertinence and effectiveness are improved.

The invention relates to the technical field of medical simulation training, in particular to an ultrasonic operation intelligent training method and device based on multiple modalities, and the method comprises the steps: obtaining real-time six-degree-of-freedom pose data of an ultrasonic probe held by an operator, and generating an ultrasonic image in real time through a first deep learning model in cooperation with scene parameters; wherein the first deep learning model is trained to learn and establish a continuous mapping relation from an ultrasonic probe pose space to an ultrasonic image space; performing section classification on the ultrasound image by using a second deep learning model, and determining deviation information for a non-standard section; and based on the real-time six-degree-of-freedom pose data and a division result of a preset standard section and an ultrasonic probe pose, generating visual guide information for guiding an operator to adjust the probe pose, and displaying the visual guide information. In this way, the problems that an existing virtual training image is discontinuous and guiding is inaccurate are solved, and meanwhile the training efficiency and the reality sense of ultrasonic operation are remarkably improved.

The invention provides a power equipment three-dimensional dynamic updating method and system for virtual reality training, and the method comprises the steps: collecting the working state data of power equipment in real time through a sensor; processing the working state data based on a preset function model, and calculating the physical state change of the power equipment; according to the physical state change, adjusting physical attributes of the three-dimensional model of the power equipment in real time so as to simulate physical effects of thermal expansion, stress concentration or structural deformation; loading the adjusted three-dimensional model into a virtual reality environment, and performing real-time display through a virtual reality rendering platform; and after the user executes the interactive operation through the virtual reality equipment, the working state of the power equipment is updated, and the influence of the operation on the three-dimensional model of the equipment is dynamically fed back, so that a virtual-real linkage closed-loop training process is formed. The virtual model can change in real time along with the collected data, and the technical verisimilitude and operability of virtual training are remarkably improved.

The invention discloses a VR-based power distribution network maintenance virtual training system and method in the technical field of electric power. A VR-based power distribution network maintenance virtual training system comprises a modeling module, a database module, a training platform module, an operation learning module, a comprehensive training module and a training evaluation module. A three-dimensional virtual environment is created for trainees through the VR technology, so that the trainees can feel the actual operation scene and the fault condition of the power distribution network in an immersive manner; students can deeply understand the structure and the working principle of the power distribution network and various phenomena and details when faults occur from different angles; in the virtual training environment, the trainee can perform various dangerous operations and fault processing exercises without causing any actual threat to the safety of the trainee. In combination with actual cases and data in a power distribution network fault database, the system can intelligently adjust the difficulty of training contents and fault scenes, so that the training contents and the fault scenes are matched with the ability of students.

The invention discloses a new energy automobile battery pack thermal management system multi-target adaptive control method, which comprises the following steps: constructing a thermal resistance-thermal capacity dynamic network model of a battery module and a cooling flow channel, and constructing a reinforcement learning virtual training environment; constructing a multi-target composite reward function; constructing a neural network model based on depth deterministic strategy gradient, and guiding gradient updating of strategy network parameters by using a multi-target composite reward function until the neural network model converges; solidifying the trained and converged strategy network parameters, deploying the solidified strategy network parameters to a vehicle thermal management controller, and outputting the optimal cooling liquid flow rate under the current working condition; and mapping the optimal cooling liquid flow rate into an electronic water pump rotating speed instruction. The method can solve the problems that in the prior art, due to the fact that the calculation load is large, the requirement for millisecond-level real-time response under the extreme working condition is difficult to meet, limitation is caused by linear deduction of temperature distribution only depending on head and tail modules, and a dynamic adjustment strategy cannot be fed back in real time.

The invention discloses a thoracoscope minimally invasive cardiac surgery navigation system based on multi-modal image fusion, and the system comprises the steps: constructing a 5D heart digital twin containing an anatomical structure, a soft tissue boundary, metabolic activity and electrophysiological information through fusing preoperative CT, MRI, PET and intraoperative ultrasonic images; real-time dynamic registration is realized by using an electrocardio gating driven biomechanical model; superposing the multi-color coding navigation information to the visual field of the thoracoscope through a wavelength selection type spectroscope; a dynamic risk scoring model is constructed based on the distance, the speed and the tissue vulnerability, and three-level grading intervention is triggered; a miniature force feedback actuator is integrated to realize closed-loop control; and pre-operation virtual drilling and post-operation federal learning optimization are supported. The system remarkably improves the surgical precision and safety, and is suitable for high-difficulty heart minimally invasive surgeries such as mitral valve repair and atrial fibrillation ablation.

The invention provides an intelligent drill evaluation method and system based on a large model and RAG, a medium, a program product and a terminal. The method comprises the steps that a virtual training scene is constructed based on a large language model; the trainee carries out dialogue interaction with the virtual customer portrait in the virtual training scene, and dialogue interaction data is collected in real time; and analyzing and feeding back the virtual customer and the dialogue interaction data by adopting RAG, and evaluating the comprehensive ability of the trainee based on a multi-dimensional evaluation index. According to the invention, trainees are helped to identify portrait characteristics of virtual customers, determine questions possibly concerned by the portrait customers and provide answering strategies; and the professional ability of the trainee is comprehensively evaluated from multiple dimensions of answer fluency, objection question processing, portrait question pertinence, customer emotion processing, insurance professional knowledge and the like, so that the accuracy and fairness of evaluation are ensured. According to the method, virtual customer portrait analysis is combined with insurance industry information and enterprise private domain knowledge, the professional ability and service quality of trainees can be comprehensively improved, and a multi-dimensional evaluation mechanism is of great significance to improvement of customer service quality and satisfaction.

The invention discloses an intelligent driving school mode virtual driving scene generation method based on an AI model, relates to the technical field of virtual driving scene generation, and is used for solving the problems of feedback lag and poor individualized adaptability of a training scheme caused by difficulty in dynamically adapting to the current stress level and operation habit of a driver. The method comprises the steps of monitoring the instantaneous speed and direction control amplitude of a simulated vehicle in real time, calculating a longitudinal G value and a steering change rate, collecting physiological data of a tested driver to analyze heart rate variability and cognitive load degree, generating driving behavior characteristics and stress characteristics by using an LSTM model, and judging whether to trigger a specific scene or not through a support vector machine based on the characteristics. The method comprises the following steps of: acquiring response time and misoperation frequency, generating a risk response score through fusion function joint quantification, and judging a driving level grade according to a score threshold value, thereby realizing screening and marking of tested drivers, dynamically adapting to actual performance, and improving pertinence and effectiveness of virtual training.

The invention relates to an AR projection interaction treadmill virtual scene generation method and device, equipment and a medium. The method comprises the following steps: acquiring motion inertia data and running belt motion parameters of a user on a running machine; respectively carrying out motion state extraction and gait phase extraction according to the motion inertia data and the running belt motion parameters to obtain a current motion state vector and gait phase information of the user; inputting the motion state vector and the gait phase information into a pre-trained motion state prediction model to obtain a preset predicted motion state of a plurality of frames in the future; based on the predicted motion state, performing layered scene generation through a scene generator to obtain a corresponding virtual scene; and performing projection mapping transformation processing on the virtual scene, and obtaining real-time AR projection data in combination with low-delay compensation. By adopting the method, a highly immersive and interactive virtual training environment can be provided in an AR projection mode.

The invention relates to the technical field of agents, in particular to an agent behavior generation system and method supporting skill drilling, and the method comprises the steps: obtaining environment data and interaction object features of an agent, constructing a multi-dimensional environment model, and carrying out the real-time updating; in a multi-interaction object and multi-device interaction scene, distributed control and collaborative learning are adopted to realize behavior coordination and information sharing among intelligent agents. Constructing an adversarial and collaborative training mechanism, and enabling the intelligent agent to optimize skills in an interaction process; and based on deep reinforcement learning, in combination with preset skill rules, interaction objects and environment data, intelligent agent behaviors are generated, including self actions, sound and picture contents, target reasoning, rule selection, task planning and exception handling. According to the method, the adaptability and decision-making ability of the intelligent agent in a complex environment are improved, the interaction intelligence and the skill drilling effect are enhanced, and the method is suitable for the fields of virtual training, intelligent interaction, automatic control and the like.

The invention discloses a power distribution network fault maintenance VR scene processing method and training system in the technical field of electric power, and the method comprises the following steps: collecting the fault types of a previous power distribution network, building a power distribution network fault database, and carrying out the training classification; establishing a power distribution network fault simulation unit, and performing power distribution network fault scene simulation according to the data of the power distribution network fault data; the VR device generates a corresponding virtual training scene, and the student wears the VR device to prepare for training; real-time data transmission is carried out during VR equipment operation, and student operation data is compared with internal data of a power distribution network fault database. Through the virtual training scene generated by the VR equipment, the real-time data transmission during the operation of the VR equipment and the comparison with the internal data of the power distribution network fault database, operation feedback can be provided for students in time, the students are helped to rapidly improve the fault maintenance skills, and errors and mistakes in actual work are reduced.

The invention relates to the field of virtual reality, in particular to an adaptive virtual reality cognitive training method and system. The method comprises the steps of obtaining a user individual information set and a real-time multi-modal physiological data stream, performing real environment three-dimensional reconstruction and relative and friend face modeling based on the user individual information set, and generating an individual virtual training scene; based on the real-time multi-modal physiological data flow and the individualized virtual training scene, nerve-behavior feature extraction and digital twinning dynamic modeling are carried out, and a user cognitive state evaluation information set is generated; based on the user cognitive state evaluation information set, training task parameter real-time optimization and virtual scene element dynamic adjustment are carried out through an adaptive strategy, and a cognitive enhancement intervention instruction set is generated; and based on the cognitive enhancement intervention instruction set, performing neural regulation intervention and training data closed-loop feedback, and generating and outputting an individualized cognitive training report. According to the method and the device, accurate self-adaptive intervention is realized in a virtual reality cognitive training process.

The invention provides a GIS equipment partial discharge detection system based on deep learning, and the system comprises an edge layer which is used for collecting sensing data collected by each sensor node, extracting a sensing feature vector, and transmitting a compressed waveform fragment; the fog calculation layer is used for determining defect positions by using a time difference of arrival algorithm according to the sensing data uploaded by the edge layer, and obtaining defect types and confidence coefficients according to the compressed waveform fragments and the sensing feature vectors in combination with a double-flow deformable attention convolutional network model; and the cloud platform is used for updating parameters of the double-flow deformable attention convolutional network model by adopting a meta-learning method according to the defect position, the defect type and the confidence which are uploaded by the fog computing layer in combination with virtual training data generated by the digital twin simulation model, and sending the updated parameters to the fog computing layer. The GIS equipment partial discharge detection system provided by the invention is light in weight and low in cost.

The invention relates to the technical field of artificial intelligence and automatic driving, in particular to a reinforcement learning-based virtual edge scene automatic generation system and a reinforcement learning-based virtual edge scene automatic generation method, comprising a generative adversarial network module for generating a virtual scene by using a dual-channel generator and a discriminator, an interaction module for inputting the scene into a driving strategy model and obtaining an evaluation result, the reinforcement learning module automatically adjusts scene generation weight, reduces manual intervention and improves training efficiency and generation quality, the scene screening module evaluates and screens a high-quality scene based on a deep neural network, the scene reconstruction module adaptively reconstructs a low-quality area, and the multi-modal fusion module integrates multi-source environment constraint data. An innovative double-channel confrontation generation mechanism optimizes scene authenticity and dangerousness at the same time, the quality and practicability of the generated scene are improved, the system fuses multidisciplinary knowledge such as the generative confrontation network, reinforcement learning and the deep neural network, and an efficient and practical virtual training scene is provided for automatic driving.

The invention relates to an acupuncture rehabilitation training data processing method based on the combination of VR and digital therapy, and the method comprises the steps: obtaining an acupuncture stimulation signal and a VR training interaction signal, carrying out the synchronous association of the two signals based on a unified time reference, and dynamically determining a signal processing priority according to the state of a patient when the acupuncture operation and a virtual training task conflict, carrying out backtracking calibration on the offset signal; the task action rhythm, force and completion condition of a patient are monitored in real time in VR rehabilitation training, data of abnormal behaviors are collected and recorded, whether the abnormal behaviors are caused by acupuncture stimulation or not is analyzed by combining acupuncture stimulation parameters and time sequence information, and data confirmed to belong to acupuncture effect interference are marked as independent samples; real-time subjective feedback information of a patient is collected in a VR scene, subjective indexes of comfort, aching feeling and fatigue are associated with physiological and behavior data, analysis weights of corresponding data segments are adjusted according to feedback, and an individualized rehabilitation data processing strategy of the patient is formed based on historical feedback data.

The invention belongs to the technical field of tunnel construction, and particularly relates to a tunnel construction ventilation intelligent regulation and control method based on electrical automation. Distributed sensor arrays are deployed on a tunnel driving working face, a haulage roadway and a fan station through a multi-source heterogeneous sensor network, ventilation loads, environmental parameters, equipment states and construction event signals are collected in real time, and real-time and accurate original data support is provided for the system; a three-dimensional fluid mechanics-particulate matter diffusion coupling simulation model of a tunnel ventilation system is constructed, then a digital twin is used as a virtual training environment, an optimization instruction is converted into an electrical control signal, and a closed-loop PID-fuzzy coincidence controller dynamically compensates execution delay, controls operation of a fan and an air valve, and finally performs dynamic simulation on the tunnel ventilation system. And an execution result is fed back to the digital twin body for model calibration, so that finer and more efficient ventilation control is realized, the use problem in a high-altitude and alpine region environment is solved, the continuous effectiveness of ventilation regulation and control is effectively guaranteed, and the overall performance is improved.

The invention discloses a safety production virtual training method and system for a mine underground scene, and the method comprises the steps: creating a model library, and storing a preset part model through the model library; based on the mine underground mine hole excavation design drawing, the spatial orientation of each component model is obtained, and a virtual practical training scene of the mine underground is generated; based on a received practical training instruction, a virtual practical training scene is obtained from the scene library according to the practical training instruction, and the virtual practical training scene is displayed on the interaction module; adjusting and transforming the virtual practical training scene based on an operation feedback signal of a practical training miner, labeling the component model with a high danger coefficient in the virtual practical training scene, judging the safety condition of each component model under the mine, calculating the average labeling number in the virtual practical training scene, and comprehensively judging the safety coefficient of the mine. The system has the advantages that the mine underground environment is simulated through the virtual reality technology, miners are helped to carry out safety operation training under the risk-free condition, and the safety consciousness and the emergency response capacity of the miners are improved.

The invention relates to the technical field of virtual training, in particular to a firefighter search, rescue and escape virtual training method and system, and the system comprises a virtual scene dynamic construction module which abstracts a room and a corridor intersection into nodes according to a planar graph of a building, abstracts a door and a channel into edges with geometric distance weights, and carries out the virtual scene dynamic construction module; and responding to a fire spreading or structure collapse event in the virtual training, adjusting the weight of a corresponding edge to be infinite, obtaining a shortest path, and establishing a reference rescue path set. According to the method, through dynamic analysis of building plan structure information, rooms and corridor intersections are abstracted as nodes, doors and channels are abstracted as edges with geometric weights, the accessibility of the edges is adjusted in real time after fire spreading or structure collapse occurs, impassable conditions are introduced during path calculation, and the path calculation accuracy is improved. And the real-time adaptability and rescue simulation precision of the path generation process are improved.

The invention discloses a VR fire-fighting training system, method and device based on Unity, and the system comprises a scene simulation module, a user interaction module, a personalized feedback module and the like, the system simulates a plurality of immersive fire virtual training scenes based on a Unity engine, and deploys a VR fire-fighting training system; receiving a user gesture or a controller instruction by means of VR equipment, and guiding the user to perform interactive training in the immersive fire virtual training scene; in user interaction training, the system records and intelligently evaluates user interaction training performance, and individually adjusts and optimizes an immersive fire virtual training scene according to the user performance. According to the invention, a vivid fire environment is created, a user can feel the temperature, smoke and sound of a fire scene in an immersive manner, a strong crisis feeling is formed, and the highly immersive experience can help the user to master fire-fighting knowledge more quickly and make a correct judgment when a fire occurs in reality.

The invention discloses a ship positioning virtual training system based on a sextant simulator. Comprising a 3D modeling reproduction module, a data setting module for setting virtual positioning practice data, a simulation scene construction module for setting a marine virtual training scene, an interactive training module and a positioning model construction module for acquiring an astronomical positioning model and a landmark positioning model. And the comprehensive evaluation storage module is used for acquiring result data and operation grade scores of each operation training stage, and obtaining a virtual training result of ship navigation positioning according to the constructed astronomical positioning model or landmark positioning model based on the result data of each operation training stage. The method solves the problems that traditional manipulation training generally depends on actual ship training, but has certain limitation and problems, due to the reasons that actual ship training is high in cost and high in safety risk, complex navigation environments cannot be truly represented and the like, diversified and flexible operation training scenes are difficult to provide, and the training efficiency is high. And sailors cannot effectively understand and master ship positioning principles, skills and experiences in various complex navigation environments.

The embodiment of the invention discloses a fine tuning method for a large language model, and the method comprises the steps: carrying out the preliminary fine tuning training of a pre-trained large language model through employing a real training sample, and generating a virtual training sample through employing the large language model which is subjected to the preliminary fine tuning training; and then the virtual training sample is used for carrying out advanced fine tuning training on the large language model, so that the large language model further consolidates the learned knowledge, and the accuracy of the output result of the large language model can be improved on the basis of not needing additional manual annotation cost.

The invention relates to the technical field of medical training, and discloses a sputum suction operation training method and system based on a virtual reality technology. The method comprises the steps that historical operation records of a trainee are collected, key operation nodes are extracted, and an airway state model of a virtual patient is constructed based on the nodes; secondly, setting a virtual sputum suction equipment model, performing a multi-working-condition simulation test on the virtual sputum suction equipment model to obtain operation response characteristics, and mapping the equipment model into the virtual airway state model; then calculating a secretion distribution parameter of a virtual airway in a training period and a theoretical clearance load of virtual equipment, and combining operation response characteristics to obtain an operation intensity parameter; generating an operation triggering instruction sequence based on the operation intensity parameter, and performing staged operation control on the virtual equipment according to the sequence; and finally, carrying out quantitative evaluation on the operation effect of the virtual equipment in the virtual airway. According to the method, a targeted virtual training scene can be constructed, a real operation response is restored, and accurate tracking and effect quantitative evaluation of the operation process are realized.

The embodiment of the invention discloses a cerebral apoplexy upper limb rehabilitation training system and method based on dynamic reward feedback. A data acquisition module is used for acquiring physiological signals, motion signals, emotional state data and training performance data; the motion intention decoding module is used for performing motion intention feature extraction and reliability evaluation on the physiological signals according to an improved MREE-Net + + fusion algorithm, performing weighted fusion after feature weights are adjusted according to a reliability result, obtaining a unified motion intention feature vector, performing motion intention decoding, and obtaining a motion intention intensity index and a motion intention vector; the dynamic reward decision module is used for processing the facial micro-expression data according to an improved VGG-Face model to obtain an emotion titer, and obtaining an emotion awakening degree according to the voice signal; a reward action is generated according to an improved deep reinforcement learning algorithm; and the adaptive training regulation and control module is used for generating a personalized virtual training scene according to the generative adversarial network and regulating and controlling the training intensity according to the fatigue index. The rehabilitation training effect can be improved.

The invention provides an aircraft refueling virtual-real combined practical training method and platform based on AR and scrapped equipment, and relates to the technical field of aircraft refueling training, and the method comprises the steps that a processing module loads digital twin scene data according to a training subject; the AR display module carries out space registration and superposition on the virtual airplane model, the virtual instrument interface and real scrapped refueling equipment; collecting operation action data of the trainee in real time; inputting the operation action data into the digital twin model, updating the state information of the virtual refueling operation, and sending the state information to an AR (Augmented Reality) display module; the AR display module displays the state information in an overlapping manner; when the butt joint operation is carried out, the relative position and posture of the physical joint and the physical refueling panel are monitored in real time, the correctness of the operation is judged, and the result is displayed through the AR display module; and the processing module records, analyzes and generates a quantitative evaluation report. The method can effectively solve the problems that pure virtual training lacks sense of reality, and pure physical training is high in cost and high in risk.

The invention provides an anesthesia risk simulation method and simulator based on virtual reality, and relates to the technical field of virtual reality, and the method comprises the steps: constructing a risk event sample database in anesthesia; carrying out generative learning by utilizing the risk event sample database, and embedding an emergency generative model into a virtual reality simulation platform; collecting initial force feedback data of initial interaction operation of a user according to a force feedback interaction module in the simulation scene, and calling an emergency generative model to simulate a risk event chain based on the initial force feedback data in the simulation scene; recording operation track data of the user in the simulation scene under the risk event chain; and performing simulation operation scoring on the user, and outputting a simulation operation scoring report. Through the method and the device, the technical problem that the actual combat ability of a user to cope with a complex clinical risk event is further affected due to the fact that a virtual training platform is seriously insufficient in the aspects of simulation authenticity, skill evaluation accuracy and the like in the prior art can be solved.

The invention belongs to the technical field of bioengineering, and particularly relates to a biomass fermentation safety reinforcement learning control method and system, and the method comprises the steps: training an initial strategy network through imitation learning by employing historical data, and providing safety weight initialization for a reinforcement learning agent; and constructing a virtual training environment fusing the mechanism model and the data-driven residual error correction network, applying domain randomization, training an agent in the environment to maximize long-term accumulated rewards, and obtaining a robust control strategy. A training strategy is deployed to a real fermentation system, a control instruction is corrected in real time through an independent safety layer according to a hard constraint rule to ensure operation safety, and meanwhile online fine tuning and updating are conducted on the strategy based on actual operation data. According to the method, safe and smooth transition from virtual training to practical application is realized, and the final ethanol concentration and the overall control performance in the fermentation process are remarkably improved.

The invention relates to the technical field of virtual reality simulation training, in particular to a shooting simulation training method and system based on virtual reality, and the method comprises the steps that a virtual training space is constructed, and dynamic light and shadow and terrain disturbance variables are injected into a visual environment model; loading a physical attribute template by the trajectory simulation engine; and loading a standard attitude sequence into the instrument action library. In training, the system captures real-time posture, instrument operation and environment interaction data of a trainee, inputs the data into a trajectory simulation engine to generate a trajectory prediction data set containing a speed curve and a deflection angle, compares the trajectory prediction data set with a standard posture frame by frame, and generates an action analysis report. According to the method, the authenticity and adaptability of training are enhanced by simulating continuously changing environmental physical interference; through full-process trajectory simulation and action traceability, accurate evaluation and deviation correction guidance of shooting operation details are realized.