273 results about "Training evaluation" patented technology

The invention relates to the technical field of training assistance, in particular to a training quality prediction method and system based on training damage analysis, and the method comprises the steps: multi-modal data collection and accumulative superposition, causal graph construction, attitude risk modeling, data enhancement, dynamic prediction and training plan optimization. According to the method, daily exercise data of a user is continuously collected, a chronic load accumulative calculation model is established, and a time sequence analysis method is adopted to perform long-term tracking and accumulative calculation on core indexes such as exercise amount, heart rate variability and joint activity, so that a complete personal exercise state database is formed; the chronic load accumulation mechanism breaks through the limitation of traditional single training evaluation, comprehensive monitoring and long-term trend analysis of the motion state of the user are achieved, a potential risk mode can be found earlier, a reliable data basis is provided for personalized risk evaluation and scientific training optimization, and the method is suitable for popularization and application. The problems that a traditional motion monitoring system is single in data and one-sided in evaluation are effectively solved.

The invention discloses an AI agent marketing training evaluation method based on capability evolution, and particularly relates to the technical field of artificial intelligence marketing training evaluation. Constructing a multi-stage capability assessment model, dividing a marketing task into stage sub-tasks, and extracting capability assessment factors corresponding to the stage sub-tasks; generating a capability state vector sequence of the AI agent based on a mapping relation between the observable behavior variable and a capability factor; constructing a capability evolution path model through a gated recursive network and periodic position coding, and capturing a capability growth trend of the capability evolution path model; further generating an evolution evaluation curve reflecting growth speed and inflection point characteristics based on a nonlinear mapping relation between the capability path and a task completion stage; a derivative analysis and ability contribution degree evaluation mechanism is used to identify ability bottleneck points, and a corresponding training strategy adjustment instruction is generated; according to the method, dynamic evaluation, bottleneck positioning and strategy optimization in the AI training process can be realized, and the agent training efficiency and the task completion quality are improved.

The invention relates to the technical field of vocal music training evaluation, and discloses a vocal music training intelligent evaluation method and system based on voice analysis. The method comprises the following steps: firstly, acquiring vocal music training original data, and processing and aligning multi-modal data by using a transfer learning small sample completion method when the data is insufficient; constructing a pronunciation stability evaluation model and a comprehensive pronunciation performance evaluation model; constructing a pronunciation scene simulator for different vocal music styles; the evaluation parameters are analyzed through a Shapley value decomposition algorithm, and a lightweight index system is constructed; and establishing a training scheme optimization agent model by using Gaussian process regression and a Bayesian optimization algorithm. The system comprises a data acquisition and completion module, an evaluation module, a simulation module, an analysis module, an optimization module and the like. The vocal music training system can comprehensively and accurately evaluate vocal music training, adapts to various styles, optimizes a training scheme, and assists a singer to improve the singing level through real-time feedback and visual display.

The invention discloses an equipment digital inspection evaluation method and system based on big data, and belongs to the field of artificial intelligence, and the method comprises the steps: S1, collecting multi-source heterogeneous data; s2, unifying timestamps by adopting a time synchronization technology, and realizing accurate alignment of multi-source data; s3, cleaning and structuring data by using a distributed stream processing framework, eliminating invalid redundancy, and completing coding standardization; s4, constructing a graph neural network model based on equipment topology, and extracting a state feature vector; s5, combining with historical fault labels, applying a reinforcement learning training evaluation strategy, and improving prediction accuracy and inspection efficiency; s6, dynamically sorting the inspection tasks according to a strategy, and determining a priority and a path in combination with the risk and the dependency relationship; s7, generating a digital inspection evaluation report fusing real-time analysis and a model result; and S8, performing digital signature and timestamp registration through an intelligent contract uplink evidence storage, and ensuring that a result is verifiable, traceable and non-tampered. The beneficial effects are that inspection efficiency is improved and data safety and reliability are ensured.

The invention discloses a live-action interaction safety teaching management method and device based on VR technology and a medium, and relates to the technical field of personnel training management, and the method comprises the steps: collecting three-dimensional point cloud data and a panoramic image of a target teaching environment, and constructing a three-dimensional space structure body with a semantic tag; generating a VR twinning environment with interaction detection capability, and configuring a standard operation track and a teaching content script; acquiring student operation data, constructing a behavior vector sequence and generating an actual behavior track; performing comprehensive scoring based on the behavior deviation function and the operation accuracy, and generating a personalized training scheduling plan; analyzing and optimizing a teaching script structure according to the bottleneck factor; and finally, according to the comprehensive score and the risk record, determining a post adaptation level, and filing a training evaluation report. According to the method, behavior tracking refinement, training feedback individuation and teaching content dynamic optimization are realized.

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 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.

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 relates to the technical field of artificial intelligence, in particular to a training evaluation and examination system based on artificial intelligence, and the system comprises a data collection and processing unit which collects user behavior data and environment state data; the task dynamic weight distribution unit combines a space-time cross attention mechanism with a lightweight space-time gating mechanism, introduces an assessment item type impact factor to calculate a dynamic distribution weight item, and constructs a dynamic assessment loss function; the task feedback association adjustment unit divides user feedback information into structured scores and unstructured text suggestions, updates dynamic allocation weight items in combination with a feedback memory and attenuation mechanism, and constructs a dynamic assessment feedback loss function; and the visual interface display unit displays the task score. According to the artificial intelligence-based training evaluation assessment system, a dynamic assessment loss function is constructed based on combination of a space-time cross attention mechanism and a lightweight time-space gating mechanism and is applied to artificial intelligence training evaluation assessment, so that the evaluation assessment system with dynamic feedback is realized.

Apparatuses, systems, program products, and method are disclosed for detecting suitability of machine learning models for datasets. An apparatus includes a training evaluation module configured to calculate a first statistical data signature for a training data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes an inference evaluation module configured to calculate a second statistical data signature for an inference data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes a score module configured to calculate a suitability score describing the suitability of a training data set to an inference data set as a function of a first and a second statistical data signature. An apparatus includes an action module configured to perform an action related to a machine learning system in response to a suitability score satisfying an unsuitability threshold.

The invention relates to the technical field of model construction, can be applied to business scenes such as financial science and technology, and discloses a double-track evaluation driven optimization processing method, device and equipment and a medium, and the method comprises the steps: constructing a double-track evaluation system, analyzing training data, generating optimized training data, and building a target model; generating a capability index by utilizing a double-track evaluation system and multi-role agent simulation, and generating an alignment analysis result in combination with reinforcement learning performance; obtaining real service feedback indexes to form a training evaluation index set, and determining an attribution link; and generating training adjustment information based on the attribution link and driving the target model to be retrained to obtain an optimized target model for processing the service input data and outputting a service processing result. According to the method, the evaluation result and the service feedback are linked to realize a training closed loop, so that the capability gap of the model can be identified and corrected, and the compliance, the reliability and the application effect of the model in the actual service are improved.

The invention discloses a practical training evaluation method and system based on multi-modal data fusion. The method comprises the following steps: firstly, constructing a practical training task topology based on a knowledge graph, mapping elements such as knowledge points into nodes, and defining a relationship through directed edges; in response to task starting, dynamically deploying a multi-modal data fusion agent pre-loaded with a targeted model and a rule; in the operation process, the agent synchronously collects and processes original data from the machine vision and sensor network in real time, and a standardized feature flow is generated; then, on-line space-time correlation and reasoning are carried out on the multi-modal features based on a fusion strategy, and a task scene understanding model is dynamically constructed and maintained; after the task is completed, packaging a program code, a report and process abstract data exported by the model to form an enhanced practical training result package; and finally, multi-dimensional automatic comparison is carried out by calling the rule base and the case model, and an evaluation result is generated. According to the invention, intelligent perception and comprehensive evaluation of the whole practical training operation process are realized.

The invention relates to the technical field of rehabilitation training, and particularly provides a knee bending training evaluation method and system based on WiFi signals, and the method comprises the steps: collecting actual channel state information peak values and knee bending action time information corresponding to multiple knee bending actions; for each knee bending action, calculating an actual knee bending angle according to a corresponding actual channel state information peak value, a preset channel state information peak value corresponding to the preset knee bending angle and a preset channel state information initial value, and then calculating an actual knee bending speed according to the actual knee bending angle and knee bending action time information; evaluating knee bending action stability according to all actual knee bending angles; acquiring a knee bending training score according to the knee bending action stability, all the actual knee bending angles and all the actual knee bending speeds; according to the method, non-contact and accurate evaluation of knee bending training without image acquisition can be realized.

The invention discloses an energy consumption optimal processor frequency prediction method and system based on a support vector machine. The method comprises the steps of obtaining original data of performance monitoring counter values, running time, processor energy consumption, processor frequency and processor power of different programs under different parallelism degrees, different scales and different processor frequencies; constructing a data set and a target vector for the original data according to preset self-defined operation time and energy consumption constraint conditions; designing a target function; performing parameter tuning and training evaluation on the support vector machine model; and performing processor frequency prediction on the target program by using the trained support vector machine model to obtain the processor frequency with optimal energy consumption. The method aims at solving the problem that energy consumption is low due to improper processor frequency setting during program running, it is guaranteed that the optimal processor frequency for application program running can be obtained before the application program is loaded through energy consumption optimal processor frequency prediction based on the support vector machine, and the program running energy efficiency is improved.

The invention provides a scoring method and system for weapon equipment training based on virtuality and reality, and belongs to the technical field of weapon equipment training, and the method comprises the steps: obtaining multi-source data in a target training task executed by a student, the multi-source data comprises student operation behavior data, weapon equipment state data, virtual environment data and student physiological monitoring data; based on the multi-source data, calculating to obtain a basic capability score value; analyzing historical training data of the trainees on the same or similar target training tasks to generate ability development factors; acquiring historical norm data matched with the attributes of the trainees and the attributes of the target training tasks, and calculating group performance reference information based on the historical norm data; based on the basic ability score value and the group performance reference information, calculating to obtain a standardized relative performance score; and generating a comprehensive capability evaluation report based on the basic capability score value, the capability development factor and the standardized relative performance score. According to the invention, the scientificity and effectiveness of training evaluation can be improved.

The invention discloses an enterprise training evaluation system and method fusing RAG and an intelligent agent, and belongs to the technical field of training evaluation of artificial intelligence. The system comprises a computing node, a storage unit, a network interaction unit, a document vectorization processing module, a dynamic question setting module, a real-time semantic scoring module, a training effect visualization module and a learning recommendation module. The method comprises the steps of document preprocessing, dynamic question setting, real-time scoring, effect visualization and learning recommendation. Through deep fusion of RAG retrieval and agent decision, personalized question setting, multi-dimensional instant scoring, long-term knowledge state tracking and early warning and personalized learning recommendation based on student knowledge states are realized, and an evaluation-feedback-optimization closed loop is formed. According to the method, the question setting pertinence, the scoring accuracy (up to 92.3%) and the training efficiency (compressing the training time by 40%) are remarkably improved, and the method is suitable for various enterprise training scenes.

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 discloses a police martial arts fighting action evaluation method and system based on peak shifting time pulses, belongs to the technical field of action behavior analysis, and solves the problem that tiny dynamic changes of joints cannot be captured in time when an existing method processes a complex motion sequence. The method comprises the steps of extracting key skeleton points from a target action video, converting serialized motion data into a discrete pulse signal set through a pulse function, and processing the pulse signal set based on a peak shifting time function. The multi-dimensional time sequence evaluation model carries out police martial arts fighting action recognition and training evaluation on the target action video based on the pulse feature set; according to the method, the time sequence relation of the skeleton points is analyzed by introducing the peak shifting time function, the time sequence and integrity of feature extraction are ensured based on the feature adaptive extraction algorithm of the flexible trigger mechanism of the water tank, and the accuracy and robustness of motion behavior evaluation are improved, so that a scientific and effective evaluation tool is provided for motion training and evaluation.

The invention discloses a badminton player action posture analysis system and method based on multi-sensor fusion, and relates to the technical field of athletic training auxiliary systems.The system comprises a wearable sensing subsystem, an environment sensing subsystem and a central processing and feedback subsystem; the wearable sensing subsystem is arranged on an inertia measurement unit and a pressure sensing insole of a body to collect movement inertia and plantar pressure data; the environment perception subsystem collects global videos through multiple cameras. The central processing and feedback subsystem performs synchronous processing on multi-source data, adopts a hierarchical fusion algorithm, restrains inertia integral drift by utilizing a plantar contact state, reconstructs a three-dimensional skeleton posture and a motion trail in combination with visual key points and skeleton restraint, drives a digital twin model, compares with a standard motion model, and performs three-dimensional motion control on the three-dimensional skeleton posture and the motion trail. Quantitative evaluation results such as joint angle deviation, time sequence difference and pressure center track are generated, and visual feedback is performed through a display device and an augmented reality terminal for badminton training evaluation and technical deviation correction.

The invention discloses a pelvic floor muscle repairing instrument, a pelvic floor muscle training indication method, pelvic floor muscle training indication equipment and a medium, and the pelvic floor muscle repairing instrument comprises a main body part which is used for being inserted into or placed in a part corresponding to pelvic floor muscle; the first electrode and the second electrode are arranged on the main body part and are used for electrically stimulating pelvic floor muscles and detecting pelvic floor muscle surface electromyogram signals after the pelvic floor muscles are electrically stimulated; the third electrode is attached to the peripheral muscle of the pelvic floor muscle during use and is used for detecting an electromyographic signal of the peripheral muscle of the pelvic floor muscle; the posture detection unit is arranged on the trunk part and is used for detecting a posture time sequence signal after the action of the trunk part is changed; the controller is electrically connected with the first electrode and the second electrode and is used for performing feature extraction on the three signals in the same time sequence, generating a fusion feature vector, determining a pelvic floor muscle training evaluation result and generating a training feedback instruction according to the pelvic floor muscle training evaluation result; the training feedback instruction is used for instructing the user to train.

The invention relates to the technical field of artificial intelligence, and discloses a man-machine cooperation-based old-age care practical training evaluation system, which comprises a sensor, data preprocessing, feature extraction and fusion, clustering analysis and practical training evaluation. According to the system, capability group division is realized under double constraints of an interactive structure and attribute similarity by using a double-graph diffusion-contour weighted K-Means clustering method; in combination with a GNN-Transformer model, through joint asymmetric loss function optimization formed by cross entropy, focus loss and passive asymmetric q-norm loss, cross-modal and time-sequence-dependent deep modeling is realized, and multi-dimensional evaluation indexes such as standardization, proficiency and humanistic care degree are output; finally, the evaluation result is visualized and fed back through a human-computer interaction interface, a perception-analysis-feedback-improvement collaborative closed loop is formed, and the scientificity and guidance of nursing training of the aged are improved.

The embodiment of the invention provides a person training evaluation method based on a virtual reality simulation environment, an edge computing device and a medium. The method comprises the following steps: acquiring initial multi-modal data evaluated by a tested person in a first virtual reality simulation environment; calculating a first evaluation result based on the skill score corresponding to each piece of initial modal data and the weight corresponding to each piece of initial modal data; adjusting training scene parameters of the first virtual reality simulation environment based on the skill scores corresponding to the initial modal data, the first evaluation result and a preset score threshold, and generating a corresponding second virtual reality simulation environment and a training scheme; the second evaluation result is obtained based on the adjusted training scene of the virtual reality simulation environment, that is, the evaluation result is obtained based on the weight and the skill score corresponding to the at least two modal data of the tested person, and the evaluation result is obtained again by adjusting the training scene parameters, so that the comprehensiveness of obtaining the evaluation result of the person is improved.

The invention discloses a training evaluation method and device for scoliosis rehabilitation training, terminal equipment and a storage medium, and the method comprises the steps: obtaining posture evaluation data, balance evaluation data and vestibular evaluation data of a scoliosis patient in each preset training period; the dynamic scoliosis angle, the static balance capacity, the dynamic scoliosis angle and the falling risk index of the scoliosis patient in the rehabilitation training process are calculated, and then a rehabilitation training report of the scoliosis patient is generated according to the dynamic scoliosis angle, the falling risk index, the static balance capacity and the dynamic balance capacity of each training period. Therefore, the overall rehabilitation training effect of the patient can be accurately evaluated, and the patient and a doctor can conveniently and visually know the rehabilitation training effect in the training process and after training is completed.

The invention discloses an fNIRS adaptive feedback emotion cognition cooperative training device and method, and relates to the technical field of emotion disorder cognition training. The device comprises a data acquisition module used for acquiring an original dual-wavelength light intensity signal from a brain; the data processing module is used for performing multi-stage preprocessing on the original dual-wavelength light intensity signal; the neural feedback module is used for calculating a multi-dimensional evaluation index according to the hemoglobin concentration data and adjusting a difficulty level and a feedback threshold value of a training task by using a dynamic threshold value control algorithm; the training module is used for training participants based on the emotion stimulation task and the cognitive training task; and the evaluation module is used for performing training evaluation according to the comparison result of the comprehensive performance score and the feedback threshold. Brain function equipment is closely combined with cognitive training and emotion stimulation tasks, dynamic evaluation and real-time feedback are added for traditional tasks, and through the brain network analysis technology, accurate evaluation of the user training effect is achieved.

The invention relates to the technical field of intelligent training and data analysis, in particular to an intelligent model construction method and system for student training data analysis. Comprising the following steps: constructing an SOP time sequence knowledge graph, obtaining a standard operation specification document and expert-level operation demonstration data, and constructing the SOP time sequence knowledge graph; real-time feature extraction: capturing a multi-modal data stream, and generating a real-time behavior feature vector; state recognition and path deduction: inputting the real-time behavior characteristics into an event classifier to recognize a key event, performing path deduction based on deterministic finite state automaton logic in an SOP time sequence knowledge graph, and determining and outputting a current state pointer; deviation analysis and judgment: extracting a compliance mask based on a current state, and injecting the compliance mask into a decoding process of the attention network; and performing feedback and adjustment to generate a corresponding deviation event signal. Through deep analysis, feedback is more targeted, and the accuracy and fineness of training evaluation are remarkably improved.

The invention provides a teaching interaction processing method and system applied to an intelligent online practical training platform. The method comprises the following steps: firstly, acquiring a training scene data set comprising operation process simulation data (corresponding to gynecological examination and related medical skill standard operation processes) and anatomical structure model data (constructed based on medical image data and comprising three-dimensional structure feature information of a female reproductive system), and then carrying out interactive content generation processing on the training scene data set; generating interactive teaching content including operation step guide information and a three-dimensional dissection display model by combining teaching logic rules, and then receiving interactive operation data (corresponding operation simulation behaviors including operation action time sequence records and spatial position coordinates) of a user on a platform; then training effect evaluation processing is carried out on the interactive operation data and the interactive teaching content to generate a training evaluation result, and finally personalized feedback content is generated according to the evaluation result and pushed to a user interface to guide a user to adjust a training behavior, so that the medical training teaching quality and efficiency are improved.

The invention provides an MR training system based on gesture tracking. The MR training system comprises a head-mounted display device, a gesture tracking module, an environment sensing module, a virtual-real fusion engine, a training evaluation module and a multi-channel feedback module. The head display device is integrated with a binocular RGB camera, an infrared ToF depth sensor and a millimeter wave radar array, the RGB camera tracks and positions a hand position through a skeleton and collects a hand image at the same time, the millimeter wave radar array captures submillimeter displacement data of hand micro-motion, and the submillimeter displacement data is transmitted to the head display device through the infrared ToF depth sensor. Transmitting the acquired hand image and displacement data to a virtual-real fusion engine; the infrared ToF depth sensor directly calculates the distance between a target object and the sensor by emitting a modulated near-infrared light pulse and measuring the time difference of light reflected to the sensor, and generates a pixel-by-pixel depth map; a skeleton model of 21 joint points of a hand is constructed, the angle variation of the associated joints is calculated in real time, holding, rotating and pressing operation actions are recognized, and the operation actions are tracked and optimized according to image data and displacement data collected by head display equipment.

The invention relates to the technical field of training evaluation, in particular to an evaluation method and system based on functional actions and physical fitness, and the method comprises the steps: collecting an image when a user does an evaluation action; the action quality score after the user completes the assessment action is calculated, the action and physical fitness of the user are assessed according to the action quality score, and the action quality score is in positive correlation with the action execution score for completing the assessment action and the balance coefficient for completing the assessment action. The balance coefficient reflects the stability of plantar pressure distribution when the user completes the evaluation action. The method not only improves the accuracy and individuation level of posture evaluation, but also can flexibly adjust scoring key points according to different users and action types. Finally, the intelligent mirror has higher adaptability, real-time performance and guidance in scenes such as rehabilitation evaluation and physical fitness detection, and the training effect of the user is remarkably improved.

The invention relates to the technical field of fire safety and simulation training, and discloses a rule-driven fire-fighting real smoke and real fire training simulation system, which comprises a data acquisition and modeling module, a rule driving module, a rendering output module and a training evaluation module. The data acquisition and modeling module is used for acquiring initial fire data and constructing a reference model of smoke diffusion and fire spreading; the rule driving module simulates the non-synchronous evolution process of the fire through a particle simulation algorithm, a time coordination rule and a feedback loop algorithm; the rendering output module is used for integrating the environment rendering data and the multi-factor propulsion mode and generating a training effect evaluation index; and the evaluation optimization module judges whether the simulation precision of the reference model reaches a preset standard or not according to the training effect evaluation index until a simulation result meeting a precision requirement is obtained. According to the invention, the reality and accuracy of fire-fighting simulation training are improved.

The invention provides an evaluation method and system for education practical training and electronic equipment. The method comprises the following steps: acquiring practical training original data in a virtual practical training environment; performing feature extraction on the practical training original data to obtain an intensity feature including operation force data and operation speed data and a spatial feature including position data and trajectory data; constructing a behavior analysis model by adopting a deep neural network, inputting the strength features and the spatial features into the behavior analysis model, and generating learning behavior features; the theoretical knowledge, the practical operation skills and the problem solving ability are evaluated through a multi-task learning framework, and evaluation indexes are generated; and based on the evaluation indexes, generating an evaluation report and performing visual display. According to the method, comprehensive feature extraction is realized, a multi-task learning framework is constructed to realize multi-dimensional evaluation, the technical problems of insufficient intelligent degree and low practicability and guidance in existing education practical training evaluation are solved, and the comprehensiveness, objectivity and accuracy of education practical training evaluation are improved.