1172 results about "Simulation training" patented technology

The invention belongs to the technical field of robot control, and discloses a high-gear-ratio humanoid robot robust walking control method and system based on potential dynamics self-adaption, and the method comprises the steps: designing a control strategy training frame based on deep reinforcement learning, and carrying out the optimization through an Actor-Critic structure and a PPO algorithm; constructing a potential dynamic adaptive network LDAN, and extracting environment and ontology dynamic parameters through a variational auto-encoder; designing a multi-dimensional reward function; constructing a periodic gait library by using von Mises distribution and motion capture data; gradually introducing terrain disturbance and dynamic change through curriculum type simulation training; the trained strategy and the LDAN module are deployed on the high-gear-ratio driven humanoid robot, the problems that the high-gear-ratio humanoid robot is poor in motion stability, poor in adaptability and insufficient in action expression in a variable environment are solved, and the high-gear-ratio driven humanoid robot has the advantages of being high in robustness, high in natural expressivity and high in migration ability.

The invention provides a PICC puncture virtual simulation training system and method constructed based on XR. A nursing skill simulation training platform and an intelligent training room based on augmented reality (XR) combine a simulation medical technology, an artificial intelligence large model, XR intelligent hardware, intelligent agent application, space calculation, computer vision and a mainstream technology of Internet of Things in scheme planning and design. According to the PICC puncture virtual simulation training platform, the ground practice, standardization, high virtual simulation and adaptive application of PICC puncture virtual simulation training are realized, and the intelligent development process and teaching concept of PICC puncture teaching are improved. According to the PICC puncture teaching system, intelligent virtual tutors, puncture teaching holographic three-dimensional sharing, virtual reality fusion technologies and the like are provided, an interactive teaching mode can be brought to PICC puncture teaching, the interest of students in teaching and clear operation guidance are improved, the theory and practice are fused, and the clinical cooperation ability is improved.

The invention relates to the technical field of robot training. The robot self-adaptive training method based on reinforcement learning comprises the steps that task sub-target information is generated through a high-level strategy network, the task sub-target information is input into a low-level execution network, an action control instruction is generated according to the task sub-target information, interaction feedback information is collected in the execution process, and the action control instruction is sent to a robot through a robot. Calculating a reward value according to the interaction feedback information, carrying out association processing on the reward value and the scene complexity parameter, executing a dynamic reward shaping operation, generating an adjusted reward signal, generating a strategy model optimized by meta-learning based on the adjusted reward signal, loading the strategy model in a simulation environment, and carrying out dynamic reward shaping. A target strategy model optimized through simulation training is generated, the target strategy model is loaded to the robot, and the robot is controlled to execute task operation in the actual interaction scene. The method has the effect of realizing adaptive task learning of the robot in a multi-interaction scene.

The invention discloses a quadruped robot motion control method based on adaptive deep reinforcement learning. The method comprises the following steps: S1, determining a network model, a composite reward function, a state space and a bionic action generation mechanism of a simulation training environment; the state space provides environment information input, the network model processes the input information and generates a decision, the bionic action mechanism executes a specific decision behavior, and the composite reward function evaluates a behavior effect and optimizes a decision direction; s2, constructing a simulation training environment of the quadruped robot, wherein the simulation environment comprises quadruped robot model information and simulation environment information; s3, training the network model by using a deep reinforcement learning algorithm based on robot model information and simulated environment information to obtain a trained motion control strategy; and S4, verifying the feasibility of utilizing the trained motion control strategy by controlling the motion of the quadruped robot in a real environment. According to the invention, the self-adaptive capability to a complex environment is obviously improved.

The invention provides an automatic history fitting method for gas drive numerical reservoir simulation, which comprises the following steps of: 1, establishing a gas drive numerical simulation mathematical model, and correcting the gas drive numerical simulation mathematical model by using a gas drive PVT phase state fitting experiment; 2, solving the corrected gas drive numerical simulation mathematical model obtained in the step 1, performing parameter analysis, and screening main control factors influencing accumulated oil production; 3, generating a numerical simulation training sample set based on an orthogonal experiment, and obtaining the main control factor parameter initial value constraint screened out in the step 2 through a machine learning algorithm; step 4, establishing a multi-parameter fitting objective function for realizing historical fitting; and step 5, using the main control factor parameter initial value constraint obtained in the step 3, performing iterative calculation through a projection gradient method, fitting the target function established in the step 4, and terminating when a convergence condition is satisfied. According to the method provided by the invention, the constraint parameter values can be adaptively calculated, automatic historical fitting is completed, and the definition of the fitting parameter range does not depend on empirical and experimental determination any more.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention discloses an aircraft control simulation platform construction method, and the method comprises the steps: constructing a terrain multi-dimensional model, fusing a cross-scale meteorological particle system, and dynamically obtaining an environment disturbance factor; a space obstacle dynamic database is generated in combination with millimeter-level space positioning data, and virtual collision detection is carried out; performing eye movement-gesture collaborative decision, and identifying a control instruction with high confidence; the six-degree-of-freedom kinetic equation is coupled with an unreal engine interface in real time, and a high-fidelity instrument is driven to feed back; generating a training defect thermodynamic diagram, triggering an adaptive optimization strategy and iterating platform parameters; the invention provides a solution integrating multi-modal high-precision data acquisition, dynamic environment-physical rule coupling, intelligent decision and closed-loop optimization so as to realize aircraft control simulation training with high immersion degree, high fidelity and high adaptability.

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 discloses a deep reinforcement learning robot-assisted ultrasonic scanning system and method based on security constraints. The invention discloses a deep reinforcement learning mechanical arm ultrasonic scanning method and system based on security constraints. A scanning system comprising a mechanical arm, an ultrasonic probe, a joint torque and visual sensor and a control unit is constructed. The environment state space covers the joint angle, the tail end pose, the contact force, the scanning path and the safety state variable of the mechanical arm, and the action space is a tail end linear speed instruction of the mechanical arm. A deep reinforcement learning model is built based on a near-end strategy optimization (PPO) algorithm, and a reward function including image quality, task completion reward and multiple penalty terms is designed. A control barrier function (CBFs) is used for implementing safety constraint on motion of the mechanical arm, and the safety constraint is fused into deep reinforcement learning training through a penalty function method. A model is trained in a simulation environment simulating an actual scanning scene and then deployed to an actual system, and a mechanical arm is controlled to scan and monitor safety constraints according to a real-time environment state. The mechanical arm can efficiently execute the ultrasonic scanning task under the condition that the safety constraint is met, and a reliable scheme is provided for ultrasonic scanning automation. Meanwhile, the system is provided with a data acquisition module, a state construction module, a reward calculation module, a security constraint module, a control module and the like, and the model performance can be improved through simulation training.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention relates to a simulation dialogue partner training system based on a large model. The simulation dialogue partner training system comprises a product knowledge base module, a question and answer library construction and management module, a pair training simulation module, a stage control module, a supernatural voice dialogue module, a verbal skill prompt module, a real-time evaluation module and a dialogue summary report module. The intelligent partner training system for customer communication training has high simulation dialogue ability, flexible configuration ability and a perfect evaluation feedback mechanism, can effectively replace a traditional scripted training or real person simulation partner training mode, and improves the communication actual combat ability of front-line personnel such as sales personnel and customer service personnel. According to the invention, low-efficiency links in a traditional training mode are replaced by an intelligent means, the training quality, the standardization degree and the reproducibility are improved, an enterprise is finally assisted to create a more professional and efficient customer front-line team, and the system can be widely applied to scenes such as enterprise training, new employee on-duty training and product popularization preparation.

The invention relates to the technical field of multi-agent cooperation, provides a multi-agent cooperative unmanned aerial vehicle cluster simulation training method, system and device, and a storage medium, and solves the problems of low penetration success rate and poor formation cooperation efficiency of an unmanned aerial vehicle cluster. The method comprises the following steps: collecting pigeon flock biological motion data, cluster trajectory data when an unmanned aerial vehicle cluster executes a historical penetration task, and magnetic field gradient data of a simulation environment; constructing a three-dimensional path library, and calculating geomagnetic course angle deviation; on the basis of the geomagnetic course angle deviation and the three-dimensional path library, a multi-target collaborative path is generated in combination with a swarm intelligent optimization model; generating a flight control instruction based on the multi-target cooperation path in combination with the real-time state data of the unmanned aerial vehicle cluster; and loading the flight control instruction to a simulation model of the unmanned aerial vehicle cluster, and driving the unmanned aerial vehicle cluster to carry out cooperative defense penetration training in a simulation environment. According to the invention, the penetration success rate and formation cooperation efficiency of the unmanned aerial vehicle cluster in a complex electromagnetic environment are improved.

The invention provides a mixed reality industrial simulation training system which is composed of a space fusion perception unit, a multi-physics field simulation unit, a cooperative training management unit and a self-adaptive teaching engine. The space fusion sensing unit is composed of a real-time environment modeling and positioning subsystem and a physical interaction detection algorithm, and the real-time environment modeling and positioning subsystem dynamically constructs a three-dimensional space coordinate system of a real industrial scene through a visual sensor array and a radio frequency scanning device; generating a pose matrix of the virtual equipment model in the perspective display interface; the physical interaction detection algorithm realizes contact force feedback between virtual and real objects based on dynamic solution of collision volume, and when an operator triggers a virtual switch through a handheld controller, a real equipment control signal is synchronously driven; the multi-physics field simulation unit comprises a physical engine core module and a fault injection interface.

The invention belongs to the technical field of analog simulation, and particularly discloses an intelligent agent-based dynamic confrontation simulation system, which comprises a data acquisition module, an intelligent analysis module, a decision generation module, an intelligent agent behavior self-adaption module, a training evaluation module and a multi-mode man-machine interaction module, physiological, action, voice and eye movement data of trainees are collected in real time through a multi-modal sensor, and a tactical intention is recognized and a dynamic three-dimensional battlefield situation thermodynamic diagram is generated in combination with virtual battlefield environment parameters; an agent coping strategy is generated based on reinforcement learning and a decision tree, and an agent is driven to carry out real-time confrontation; and performing multi-dimensional quantitative evaluation on the whole training process through a training evaluation module, and performing closed-loop optimization on an agent decision and strategy library based on an evaluation result. The method supports various natural interaction modes such as voice, gestures and eye movement, remarkably improves the fidelity, intelligence and training efficiency of simulation training, and is suitable for the field of military training and tactical drilling.

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 relates to the technical field of occupational simulation training evaluation, and discloses a system for college student occupational scene simulation training evaluation. The system comprises a task analysis unit, a dynamic behavior modeling unit, a multi-mode interaction management unit, a state evolution tracking unit, a strategy adaptation unit and a comprehensive evaluation generation unit. The system receives the initial scene description and generates a structured task framework, and behavior parameters are configured for student roles; constructing a multi-modal interaction atlas by fusing aligned voice, action and sight line data in real time; identifying a key decision node, and dynamically tracking a team state evolution path; matching and adjusting strategies in a behavior rule base according to the evolution sequence, and outputting an adaptive intervention scheme; and finally generating a multi-dimensional structured evaluation report. According to the system, deep insight and dynamic and objective intelligent evaluation of complex interaction behaviors in the simulation training process are realized.

Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used to in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior.

The invention relates to an unmanned aerial vehicle simulation training method for video guidance and track deviation evaluation based on deep learning. The method specifically comprises the following steps: S1, data acquisition and input; s2, generating and prompting a path visible area; s3, track deviation evaluation of image and flight control fusion; s4, trajectory behavior modeling and score analysis; s5, training a feedback output mechanism; and S6, carrying out personalized training recommendation and playback analysis. According to the invention, space-level deviation evaluation is realized by fusing the image and the flight control data, and the judgment is more accurate; the real-time visual feedback and voice prompt system can improve the training instantaneity and the interaction experience; a Transform-based trajectory scoring mechanism is close to the nature of an operation behavior, and multi-dimension evaluation is carried out; differentiated task recommendation is supported, and teaching pertinence and training efficiency are improved; the whole process is visual and can be repeated, and the teaching closed-loop design is optimized.

The invention discloses an unmanned aerial vehicle autonomous positioning navigation method and device based on a cross-view azimuth regression network. The method comprises the steps of 1, constructing a cross-view-angle data set which is oriented to a cross-view-angle scene and contains azimuth information, 2, constructing an unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene, and 3, training and verifying the unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene by using a training set and a verification set. And 4, carrying out regression performance test on the unmanned aerial vehicle position heading regression model facing the cross-view scene by using the test set. The method only depends on a visual image, can reduce the dependence degree of unmanned aerial vehicle positioning navigation on a sensor, improves the survivability in an extreme environment, uses a cross-visual-angle remote sensing map to construct simulation training data and generate the absolute orientation regression capability, can eliminate the drift problem, and also has natural advantages in the aspect of migration from simulation to a real scene. The model is light in weight and is more suitable for unmanned aerial vehicle continuous positioning and directional navigation scenes with limited computing power resources.

The invention relates to the technical field of digital twinborn simulation, and particularly discloses a digital twinborn simulation training system for the whole process of administrative law enforcement, and the system comprises an intelligent sensing layer which is used for collecting the multi-modal data of a law enforcement site in real time, carrying out the data transmission, preprocessing and encryption, and obtaining the monitoring data of the emotional response and physiological state of a law enforcement officer; the twinborn modeling layer is used for constructing a digital twinborn model of a law enforcement scene based on a quantum neural network and simulating a multi-agent game process in a law enforcement process; the scene generation layer is used for intelligently generating a law enforcement scene in combination with legal provisions and cases, simulating the evolution process of emergencies and performing rule constraint on physical interaction in a virtual environment; according to the simulation training system, various dynamic changes in a complex law enforcement environment can be reflected more accurately, and a more vivid and efficient training environment is provided for administrative personnel, so that the law enforcement ability and the ability of coping with complex conditions of the law enforcement personnel are effectively improved.

The invention discloses an application method, system and device of a dynamic scene generation technology in regulation and control operation simulation training and a medium, and belongs to the technical field of power system regulation and control operation simulation training. Generating a standardized scene parameter matrix containing power grid operation parameters and meteorological parameters; generating a dynamic simulation scene according to the standardized scene parameter matrix; simulation deduction is carried out, a simulation result is generated, and evaluation and feedback are carried out. According to the method, a highly realistic dynamic scene is generated through multi-source data fusion and the generative adversarial network, and scene parameters are optimized in combination with reinforcement learning, so that the scene fidelity and the emergency disposal capability are remarkably improved. The high-precision simulation platform supports second-level real-time simulation and hour-level continuous deduction, and the training period is shortened and the resource utilization rate is improved in combination with dynamic scoring and a self-adaptive difficulty adjustment mechanism.

The invention discloses a rail transit dispatching simulation practical training and automatic examination system integrated with emergency assistance, and relates to the technical field of simulation practical training, and the system comprises a data collection module which is used for collecting basic data, operation data and historical emergency event data of a rail transit system; and the simulation environment construction module is used for constructing a high-fidelity rail transit simulation environment by applying a digital twin technology based on the data acquired by the data acquisition module, mapping a geometric field model, a physical field model, a behavior field model and a rule field model which are required by virtual equipment, constructing a four-model mapping rule base and fusing a multi-field model. According to the invention, the simulation environment construction module relies on a digital twinning technology and real acquired data, combines geometric, physical, behavioral and regular four-field models and a mapping rule base, constructs a high-fidelity simulation environment and meets diversified practical training requirements, and the emergency aid decision-making module introduces a quantum computing technology and can quickly process mass data in a complex scene.

The invention discloses a neurosurgical operation virtual simulation training system based on multi-modal data fusion, and relates to the technical field of operation simulation. The data acquisition and preprocessing module acquires data such as images and force feedback, and standardized storage is performed after filtering and registration; the multi-modal data fusion module performs weighted fusion on the data by using an attention algorithm to generate a three-dimensional model; the virtual operation scene construction module generates a high-fidelity environment through ray tracing and a physical engine; the surgical operation interaction simulation module realizes accurate interaction through positioning tracking and force feedback; the training evaluation and feedback module quantifies operation performance and provides guidance; and the system management and iteration module guarantees operation and supports data updating and optimization. According to the method, a high-reality scene is constructed through multi-modal fusion, interaction and multi-dimensional evaluation help skill improvement, dynamic difficulty and personalized paths are adaptive to different users, collaborative training and privacy data collaboration are supported, operation training standardization is promoted, and the risk of errors in an operation is reduced.

The invention relates to the technical field of aircraft simulation training, and discloses a multi-mode man-machine interaction aircraft simulation training system. A multi-modal feature extraction module of the system generates a multi-modal feature map including a flight environment situation map and an equipment operation health map through a hierarchical feature extraction network; the incremental analysis module divides an incremental data set, updates flight state prediction model parameters through an incremental clustering algorithm and outputs an incremental state prediction result; the parameter optimization layer initializes a tabu search population according to a prediction result, and iteratively optimizes parameters of an operation rule base through a dynamic efficiency evaluation matrix; the decision generation module fuses the optimized rule base parameters and the real-time multi-modal feature map, and generates a flight operation instruction sequence based on dynamic confidence score; and the execution optimization layer analyzes the instruction sequence, satisfies constraint conditions through a constraint projection algorithm, and completes instruction sequence integer programming in combination with the operation portrait deviation. The system improves the interaction accuracy and scene adaptability of simulation training.

The invention belongs to the technical field of flight simulation training equipment control, and particularly relates to a full-motion flight simulator teacher seat adaptive control method, system and equipment. The problem that in the prior art, passive safety protection exists, high-frequency vibration interference exists, and dynamic working condition self-adaptive capacity is lacked, so that the posture imbalance risk of a teacher is caused is solved. The method comprises the steps that platform motion data, teacher skeleton key points and pressure distribution data are collected in real time, the current flight working condition is recognized after multi-source fusion processing, and the weight proportion of all data sources in deviation calculation is dynamically determined; by calculating the deviation between the weighted current posture of the instructor and the standard posture, a multi-direction self-adaptive adjustment instruction is generated to drive the seat to conduct displacement and angle adjustment, closed-loop adjustment is conducted on the basis of the residual deviation till the precision requirement is met, and active safety protection and intelligent self-adaptive adjustment of the seat are achieved.

The invention relates to the technical field of intelligent teaching simulation training, and discloses a multi-mode interactive teaching simulation training system. According to the system, voice, action and visual data are obtained and fused to form a unified operation behavior flow, and the unified operation behavior flow is disassembled into task sequences according to knowledge points. And extracting an interaction quality feature vector from each task segment, inputting the interaction quality feature vector into an evaluation network constructed based on a knowledge graph, and obtaining a mastering state evaluation result through layer-by-layer mapping with a standard vector. And performing link type fusion on the results to generate a skill mastering degree total score and defect distribution map, and further adaptively generating a training intervention scheme comprising a knowledge point backtracking path, an interaction complexity step and an auxiliary strategy according to the map. According to the system, refined and structured evaluation of complex operation skills is realized, and accurate personalized training guidance can be dynamically provided.

The invention discloses a network attack path automatic generation and defense strategy optimization method, system and device and a medium, and relates to the technical field of network security, and the method comprises the steps: constructing a topology mapping model, obtaining a network architecture, an asset list and a dependency relationship, constructing a visual topology chart, building a vulnerability association analysis model, and combining a vulnerability scanning result. The method comprises the steps of obtaining a vulnerability knowledge graph, optimizing a path calculation process based on the vulnerability knowledge graph, establishing a path derivation model, generating an attack chain by applying a path derivation algorithm, optimizing defense logic, formulating an optimization step model, and establishing a real-time simulation feedback model based on the generated attack chain. And performing simulation implementation on the defense strategy through a multi-level simulation training scheme, and dynamically optimizing the defense strategy according to feedback data. According to the method, the crossing from passive protection to active prediction and from single-point defense to global optimization is realized, and the accuracy and adaptive capacity of network defense are remarkably improved.

The invention provides a sound source localization rehabilitation training method, system and device and a medium. The sound source localization rehabilitation training method comprises self-adaptive training and scene simulation training, the self-adaptive training comprises the steps of providing a training task in the period, and the training task in the period comprises the step of selecting training difficulty; according to the training difficulty, sound stimulation is presented in different spatial orientations, and the training difficulty can be adjusted according to a certain logic; the training difficulty of the next period of training is adaptively adjusted according to the correct rate p of one training group of the subject until the preset training amount is reached, and the difficulty setting includes but is not limited to scene setting, analog input information amount and signal-to-noise ratio adjustment. According to the scheme, a hearing-impaired person can perform rehabilitation training in a home environment, the need of manual intervention is reduced, and the training convenience and flexibility are improved. Through adaptive training, on the basis of ensuring scientificity, accuracy and effect of a training plan, the positioning ability of the hearing-impaired person is enhanced, and the integration of the hearing-impaired person into the society is accelerated.

In order to overcome the defects that in the prior art, driving simulation training duration statistics is extensive, training quality cannot be effectively evaluated, and system fault interference is likely to happen, the invention provides a driving simulation training effective class hour evaluation method and device, and the method comprises the following steps: S1, collecting operation parameters of a driving simulator and current training scene information; s2, dividing the collected data into independent time slices according to a preset time interval, and calculating an operation characteristic index in each time slice; s3, performing effective class hour and comprehensive quality evaluation by utilizing an intelligent evaluation module, wherein whether the operation characteristic indexes in each time slice are effective or not is preliminarily judged on the basis of the basic threshold parameters; in combination with current training scene information, querying a scene-operation rule base, dynamically adjusting a basic threshold parameter, and re-evaluating whether the operation characteristic index in each time slice is valid or not; and performing quality evaluation and effective class hour evaluation based on a multi-dimensional data fusion algorithm, and outputting an accumulated effective class hour and comprehensive quality evaluation report.

The invention provides a breast endoscopic surgery simulation training system. The breast endoscopic surgery simulation training system comprises a hardware end and a software end, the hardware end comprises a simulation human body model, and the software end is provided with a 3D mesh model corresponding to the simulation human body model; the simulation surgical instrument comprises a simulation electrotome, a simulation endoscope, simulation grasping forceps and a simulation incision protection sleeve, and the software end is provided with an electrotome model, an endoscope model, a grasping forceps model and an incision protection sleeve model which correspond to one another; the pose detection device is used for detecting the current pose of the simulation surgical instrument in real time and sending the current pose to the software end, so that interaction between the simulation surgical instrument and the simulation human body model is mapped to interaction between the instrument model and the 3D mesh model; the software end further comprises a display screen, and the display screen is used for displaying interaction between the instrument model and the 3D mesh model in real time. According to the scheme, breast endoscopic surgery training, teaching and research which are closer to a real surgery scene can be provided, so that the safety and the operation precision in an actual breast endoscopic surgery are improved.