111 results about "Virtual training" patented technology

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 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 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 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 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 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.

The invention provides a goaf tunnel surrounding rock stability progressive evaluation method and system, and relates to the technical field of geotechnical engineering. The method comprises the following steps: constructing a virtual training sample set by utilizing an optimization process fused with active learning based on goaf and tunnel engineering key parameters and a Knoth time function; establishing a two-stage prediction architecture comprising a rapid screening model and an accurate rechecking model; carrying out risk primary judgment and shunting on the target section by utilizing a rapid screening model; for a section needing to be rechecked, outputting a surrounding rock safety coefficient and a probability prediction value of convergence displacement by using an accurate rechecking model in combination with Bayesian inference; outputting a differentiated support scheme in combination with the multi-level decision matrix; and executing parameter reverse reconstruction and model increment evolution based on the form deviation index of the field actual measurement data and the prediction data. The problem that the stability of the surrounding rock is difficult to accurately quantify under the small sample condition is solved, and self-adaptive evolution of an evaluation system is realized.

The invention relates to an unmanned ship intelligent navigation method and system based on deep reinforcement learning, and the method employs the construction of a virtual training environment to cooperate with the real-time sensing data collected by a real unmanned ship sensing system, thereby greatly reducing the training cost and risk. The autonomous navigation intelligent agent trained by using a deep reinforcement learning algorithm improves the adaptive and decision-making ability of the unmanned ship to a complex environment, reduces the dependence on a communication link, and improves the motion precision and stability of the unmanned ship.

The invention relates to the technical field of exercise rehabilitation, and discloses a hemodialysis patient exercise rehabilitation virtual training platform which comprises a dialysis state acquisition module used for acquiring dialysis parameters reflecting the dialysis tolerance state of a patient in real time in the hemodialysis process; the mapping module is connected with the dialysis state acquisition module and is used for mapping the dialysis parameters into virtual training constraint parameters; according to the hemodialysis patient exercise rehabilitation virtual training platform, dialysis parameters reflecting the dialysis tolerance state of a patient are collected in real time in the hemodialysis process, the dialysis parameters are mapped into virtual training constraint parameters, the action amplitude, the action rhythm and the training process in virtual training are controlled to be executed, and the hemodialysis patient exercise rehabilitation virtual training platform has the advantages that the training efficiency is improved; the virtual training content can be dynamically adjusted along with the change of the hemodialysis state, so that potential safety hazards caused by adopting a fixed training scheme are avoided, and the safety of exercise rehabilitation training during dialysis is improved.

The invention relates to the technical field of environment modeling, in particular to an underground safety production simulation modeling method based on the VR technology, and the method comprises the steps: obtaining all RGB images of an underground scene and corresponding depth images, and the attitude information and position coordinates of a camera when each frame of RGB image is shot, so as to generate a dense point cloud, and constructing a triangular mesh model; calculating the screening value of each point cloud, and obtaining the feature point cloud of each triangular patch; and obtaining all candidate images of each triangular patch, determining a mapping deviation value, a texture distortion degree and a quality evaluation value of each triangular patch in each candidate image, screening the candidate images of each triangular patch, performing texture mapping on the triangular mesh model, and importing the texture mapping result into a VR engine to construct a virtual underground environment. According to the method, the overall effect of texture mapping of the model can be improved, so that a highly vivid virtual underground scene is constructed, and the immersion and reality of underground scene safety production virtual practical training are enhanced.

The application relates to the technical field of virtual training, and provides a simulation data analysis method and system for virtual training of first aid personnel, which is applied to a simulation server and comprises the following steps: obtaining target information and first aid information of the first aid personnel on a target virtual patient; the first aid information comprises original time stamps of each first aid event in multiple first aid events and transmission delay time lengths of each first aid event; for a target first aid event in the multiple first aid events, original time stamps of the target first aid event and transmission delay time lengths of the target first aid event are determined according to the target information, the original time stamps of the target first aid event and the transmission delay time lengths of the target first aid event; the target first aid event is any one of the multiple first aid events; the adjusted time stamps of the multiple first aid events are arranged in chronological order to obtain training process data of virtual training of the first aid personnel. The simulation data analysis timing accuracy of virtual training of the first aid personnel can be improved.

A simulation method and related equipment for machining process training are disclosed. The method includes: acquiring engineering data and process data of the target workpiece; extracting features from the engineering data and combining them with the process data to construct a case resource library; performing semantic association analysis between the pre-set knowledge resource library and the machining cases in the case resource library to generate a process knowledge network; constructing a virtual training environment based on the process knowledge network; collecting interaction data of learners in the virtual training environment and evaluating the interaction data to obtain evaluation results; and generating guidance animations based on the evaluation results and the process knowledge network. This application can improve the learning efficiency of simulation training.

1. The name of the design product: visual smart glasses. 2. The use of the design product: data model visualization, virtual training and other fields, with the characteristics of being able to enhance the display output of the real environment. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention provides a ship steel plate manual gas cutting VR simulation method and computer equipment. According to the method, the three-dimensional steel plate grid model is constructed, the grid density is adjusted in a self-adaptive mode, the deformation and damage conditions of the steel plate in the gas cutting process can be accurately simulated, the simulation precision and authenticity are improved, meanwhile, a heat-force-phase change coupled life-death unit simulation solving algorithm is adopted, and the simulation precision is improved. The multi-physical field coupling effects of heat, force, phase change and the like in the gas cutting process can be comprehensively considered, and the damage and failure behaviors of the steel plate can be more accurately simulated. By establishing a three-dimensional cutting scene VR hardware environment and accessing a VR helmet and a motion capture device, immersive virtual training experience is realized, students can practice repeatedly in a safe and pollution-free environment, and the training cost and the safety risk are reduced.

This application relates to a method and system for training neurosurgical specialist nurses based on a virtual teaching and research room. The method includes: acquiring patient data and clinical scenario parameters corresponding to preset neurosurgical training tasks from a medical database; constructing a virtual training environment using virtual simulation technology based on the patient data and clinical scenario parameters; collecting user interaction behavior data in real time through a sensor network deployed in the environment; analyzing the behavior data in real time and driving the virtual environment to generate real-time feedback effects; identifying key operation points using a pre-trained LSTM analysis model based on the behavior data and feedback effects; and generating a training feedback report based on the key operation points. This method can improve the realism of neurosurgical specialist nurses' operations in the virtual environment, the assessability of the training process, and the real-time nature of feedback, effectively assisting in professional skills training and assessment.

To provide a method for training a neural network for actuating a technical device in a virtual training environment.SOLUTION: In a computing system 2, a method includes establishing a first data link between a neural network 8 and a first simulation 14a of a technical device and training the neural network by actuating the first simulation. Once a first training goal has been achieved, the first data link is broken, and a second data link is established between the neural network and a second simulation of the technical device to train the neural network by actuating the second simulation 14b. The second simulation is configured to be more realistic than the first simulation and requires more mathematical operations than the first simulation. The second simulation trains the neural network.SELECTED DRAWING: Figure 1

The application discloses a new energy automobile battery pack thermal management system multi-objective adaptive control method, comprising: constructing a thermal resistance-thermal capacity dynamic network model of a battery module and a cooling flow channel, and building a reinforcement learning virtual training environment; constructing a multi-objective composite reward function; constructing a neural network model based on a deep deterministic policy gradient, using the multi-objective composite reward function to guide the gradient update of the policy network parameters until the neural network model converges; solidifying the trained and converged policy network parameters, deploying them into a vehicle thermal management controller, and outputting the optimal coolant flow rate under the current working condition; and mapping the optimal coolant flow rate to an electronic water pump speed instruction. The application can solve the problems that the prior art is difficult to meet the millisecond-level real-time response demand under extreme working conditions due to large calculation load, is limited by relying only on linear deduction of the temperature distribution of the first and last modules, and cannot dynamically adjust the strategy in real time.

The invention relates to the technical field of power distribution operation simulation, and discloses a power distribution operation three-dimensional virtual model library construction method based on a domain knowledge graph. According to the method, real-time interactive feedback is realized by constructing a power distribution field knowledge graph and performing semantic enhancement, performing semantic annotation and feature extraction on a three-dimensional model, constructing an intelligent model library and maintaining consistency, intelligently generating an operation scene and performing compliance optimization, and entity and model semantic mapping is established by adopting a semantic topology energy quantification algorithm; evaluating scene quality by using a process constraint optimization function; according to the method, the problems of semantic association deficiency, low scene generation efficiency and poor compliance of a traditional model library are solved, the model retrieval accuracy, the scene generation efficiency and the virtual training effectiveness are improved, and the method is suitable for power distribution operation virtual simulation and personnel training.

The invention relates to the technical field of artificial intelligence, in particular to a papermaking employee virtual practical training method and system combined with a large model. According to the papermaking employee virtual practical training method and system combined with the large model, immersive interaction with a high-fidelity virtual papermaking production line is achieved through data collection, virtual simulation and large model interaction and reasoning. According to the method, a large-scale language model is introduced, so that the method has strong natural language understanding, deep reasoning and multi-modal information fusion capabilities, complex and changeable virtual environment states can be analyzed in real time, and intelligent diagnosis and guidance are performed in combination with natural language questions of employees; and even non-preset or multi-factor coupled fault scenes which cannot be processed by a traditional rule system can be processed, man-machine dialogue interaction similar to experienced experts is realized, and the intelligent level of the system on solving complex problems is remarkably improved.

This invention relates to the field of equipment training technology, and discloses an electro-dynamic feedback device and design method for virtual training, including the following steps: Designing an arm-worn force feedback device based on a DC servo motor; establishing a mapping relationship between virtual environment interaction information and physical feedback force by deriving a mathematical model of feedback force and servo motor rotation angle; designing a phase displacement-torque dual closed-loop control system, controlling the servo motor rotation angle through a phase displacement closed loop and controlling the servo motor output torque through a torque closed loop to achieve precise control of the feedback force amplitude; using the mathematical model and the dual closed-loop control system, driving the arm-worn force feedback device to generate feedback force matching the virtual training scene, achieving rapid response to virtual collisions, and keeping the feedback force and rotation angle error within a preset threshold to enhance the realism and immersion of virtual training. Establishing a mapping relationship between virtual interaction and physical feedback enables precise control of the servo motor rotation angle and output torque.

This invention discloses a sensor node selection method and system based on hybrid deep reinforcement learning. The method includes: establishing a target state model and an observation model, and outputting the target state estimate and the covariance of the estimation error; generating virtual training samples between adjacent real observation times; establishing a node selection optimization model; mapping the node selection optimization model to a reinforcement learning problem, constructing a state space, action space, and reward function; constructing a hybrid deep Q-network (Hybrid-DQN), and training the Hybrid-DQN based on a hybrid experience replay mechanism that includes virtual training samples and online real interaction samples; outputting node activation decisions, acquiring the observation data of the activated nodes and performing target state estimation, and outputting the node selection result. This invention can reduce the computational overhead of node selection while meeting tracking accuracy requirements, and the node selection decision time can be compressed to the millisecond level, adapting to the dynamic tracking needs of underwater targets.

The invention provides a transformer fault maintenance virtual training method and system based on multi-modal cooperation. The method comprises the following steps: constructing a multi-modal knowledge graph of transformer fault data; pre-testing the trainee by adopting a preset collaborative training system, determining the initial proficiency of the trainee, and determining an eye movement triggering threshold of the trainee by adopting a preset fault complexity optimization algorithm and a preset eye movement triggering threshold algorithm; according to the eye movement data of the trainee and the eye movement triggering threshold value of the trainee, fault associated data in the multi-mode knowledge graph are called, a preset virtual-real feedback intensity algorithm is adopted, fault interaction data of the trainee and the collaborative training system are obtained, and then a personalized training scheme is generated. According to the invention, trainees are assisted to construct a complete fault maintenance knowledge framework, knowledge fragmentation is avoided, association understanding is enhanced, the problem of missing practical operation is solved, a personalized training scheme is generated, the training pertinence and efficiency are improved, and the skill improvement period is shortened.

This invention discloses a tactile-visual collaborative realistic simulation method for intravenous puncture in virtual training for intravenous therapy nurses, belonging to the field of medical virtual simulation training technology. Addressing the problems of single tactile feedback and poor coordination between tactile and visual feedback in existing systems, this invention uses layered collision modeling to detect the needle tip's level in real time and trigger events; it generates multi-level tactile and visual blood return feedback signals based on the needle tip level and outputs them synchronously based on the same event. This invention allows trainees to simultaneously experience the dual sensations of "feeling emptiness" and "stable blood return within the needle tube" during puncture, achieving close coordination between tactile and visual feedback and helping trainees establish correct neuromuscular memory. This invention can be widely applied in the field of virtual simulation training for nursing skills, significantly improving the realism of training and operational recognition.

The invention discloses an interaction control method for a VR immersive training platform, and particularly relates to the technical field of virtual reality interaction control, and the method comprises the following steps: continuously collecting the information of the hand of a trainer in a virtual training scene through a virtual reality interaction device, and carrying out the time sequence integration, thereby forming an interaction perception data sequence; performing action interval division on the massage operation, extracting a movement track, a force application direction, a force application change and rhythm characteristics, and constructing a manipulation parameter model; mapping the actual hand action position to the virtual human body meridian and acupoint model to obtain a meridian and acupoint mapping result; comparing with a massage function specification to generate a multi-dimensional interaction judgment parameter; according to the invention, through continuous acquisition and sequential integration of massage interaction data, a manipulation parameter model is constructed and meridian point mapping is completed, and position and manipulation performance are compared with a power specification to generate multi-dimensional determination parameters, quantitative evaluation and targeted feedback are realized, and normalization and consistency of massage training and overall improvement of a teaching effect are improved.

The invention belongs to the technical field of intelligent police affairs, and discloses a VR glasses type on-site law enforcement multifunctional support system, which comprises a law enforcement data middle station, a three-dimensional scene construction engine and a VR application service module. The law enforcement data middle station is configured to access heterogeneous law enforcement data sources such as image acquisition equipment, an information database, a command and dispatch system and the like, and performs cleaning, standardization and fusion processing on the data. And the three-dimensional scene construction engine receives the processed field image data and generates a three-dimensional virtual scene model. And the VR application service module fuses the three-dimensional model and heterogeneous law enforcement data, realizes information association by creating data anchor points, and provides a high-fidelity virtual training environment and an immersive three-dimensional redisk environment for the VR terminal. According to the method, rapid and automatic construction from a real law enforcement site to an immersive VR environment is realized, and the technical problems that traditional law enforcement training and redisk depend on two-dimensional information and the VR content customization cost is high are solved.

The invention discloses a traffic law enforcement virtual simulation training method based on a dynamic scene and cognitive evaluation, and the method comprises the steps: generating an initialization parameter according to a trainee portrait and a training target, and driving an emergence type scene generation engine to synthesize a dynamic three-dimensional virtual training scene containing composite illegal elements; in scene operation, multi-modal interaction behaviors and physiological response data of the trainees are synchronously collected to form a data stream; through real-time analysis of the data flow, a cognitive behavior analysis report for evaluating attention, cognitive load and decision intention is generated; constructing a space-time knowledge graph based on the student operation sequence, performing automatic reasoning and contradiction detection in combination with formalized legal rules, and outputting an evidence chain logic verification result; and fusing the report and the result to carry out multi-dimensional weighted evaluation, and generating a comprehensive score and personalized feedback. According to the method, normal form transformation from preset process training to open cognitive evaluation is realized, and the authenticity, pertinence and evaluation depth of training are improved.