326 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, which relates to the technical field of artificial intelligence and skill training examination, discloses a multi-crowd adaptive training evaluation system comprising the following components: a user data management module, an RAG module, an answer processing module, a score analysis module, an intensive training module and a question bank updating module. According to the method, comprehensive and accurate assessment of the user ability is realized by constructing a multi-crowd adaptive training assessment system, the system constructs the user ability portrait by collecting historical data and real-time performance of the user in multiple dimensions and utilizing advanced data processing and analysis technologies, and dynamically adjusts the training content and difficulty, so that the user ability assessment efficiency is improved. According to the method and the system, the ability level and the learning requirement of different users are adapted, the personalized training mode not only improves the participation degree and the learning efficiency of the users, but also helps the users to quickly and accurately identify the ability weakness of the users, and powerful support is provided for subsequent improvement.

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.

A method of using a computer implemented neural network for a simulation of aerodynamic performance of a technical object having a geometry, the method comprising: Training the neural network using a plurality of sets of encodings of pre-computed computational fluid dynamics, CFD, outputs, wherein the training is generated using inputs comprising: a geometry of at least one training technical object; spatial locations of input nodes of the neural network as node attributes; a relationship between the geometry of the at least one training technical object and the neural network input node locations; associated boundary conditions; operating conditions; and computed outputs comprising flow fields and aerodynamic performance parameters; the training using a loss function that evaluates an error between a neural network output and the pre-computed CFD outputs to produce a trained neural network; using the trained neural network with new inputs to generate as output a predicted aerodynamic performance of the technical object, wherein the relationship between the geometry of the at least one training technical object and the neural network input node locations comprises a vector with at least two parameters.

The invention provides a practical training method and system based on multi-modal Internet of Things perception and virtual-real symbiosis, and relates to the technical field of virtual-real symbiosis, and the method comprises the steps: collecting the multi-modal perception data of a practical training environment through a plurality of heterogeneous Internet of Things devices arranged in the practical training environment; constructing a three-dimensional digital twinborn model of the practical training environment according to the category and position information of the object in the practical training environment, the voice control instruction and the comprehensive environment parameters; the method comprises the following steps: setting training items and evaluation indexes in a virtual-real symbiotic training space, receiving a training item selection instruction input by training personnel, and loading a corresponding training item according to the selection instruction, and sending a control instruction of the practical training project to a virtual equipment model in the three-dimensional digital twinborn model and Internet of Things equipment mapped with the virtual equipment model in the practical training environment for practical training, and evaluating the practical training process and the practical training result of the practical training project according to the evaluation index after the practical training project is finished. And a training evaluation report is generated and fed back to the training management system.

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

This disclosure introduces an automated computer system designed to detect fire and smoke in video frames. Within this system, an object detection model is trained to recognize fire and smoke patterns. In a post-training evaluation phase, the system utilizes the model to determine an instance-level detection threshold from a validation set, which dictates the minimum confidence score required for a fire or smoke designation. In addition, a frame-level detection rule is defined, considering both the cumulative count of detected fire or smoke instances and their associated confidence scores. During inference, the system processes video frames through the model, designates detected patterns with bounding boxes, and subsequently applies the instance-level threshold and frame-level rule. When a sequence of frames fulfills a predetermined criterion, the system activates an alarm.

The invention discloses a spoken English man-machine conversation interaction training method and system, and relates to the technical field of spoken English training, spoken English audio signals of a user are monitored in real time through a microphone, and the frequency spectrum information of the audio signals is refined into each frame of spectrogram and cepstrum coefficient feature vectors by using Fourier transform and feature extraction technologies; therefore, the voice signal characteristics of the user can be completely captured; three core dimensions of semantic similarity, grammatical structure similarity and fluency are incorporated into an evaluation model, a comprehensive oral English training evaluation system is formed, a text sequence is generated by analyzing a time sequence dependency relationship through a convolutional neural network, and the oral English training evaluation model of a deep learning technology is combined, so that the oral English training evaluation accuracy is improved. According to the method, the expressions of the user in the aspects of semantics, grammar and fluency can be accurately measured, the oral training level of the user can be comprehensively judged by the system by comparing the comprehensive evaluation coefficient with the preset qualified threshold value, and a personalized learning guide is provided according to a result.

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 discloses a fine-grained gait sub-phase recognition method and device based on spatio-temporal feature fusion, and aims at accurately recognizing eight types of gait sub-phases when a lower limb rehabilitation exoskeleton robot walks. The method comprises the steps that gait multi-source data are obtained through an integrated multi-source heterogeneous sensor, a gait sub-phase label distribution algorithm is designed, and a gait sub-phase label distribution algorithm is designed; constructing a training data set in combination with a sliding overlapping window technology; designing a feature extraction module based on the heterogeneous parallel convolution architecture, and combining the feature extraction module with BiLSTM to construct a spatial-temporal feature fusion model; introducing a weighted cross entropy loss function to improve the recognition capability of the model on the phase categories of gaits with fewer samples, and carrying out training evaluation through a layered K-fold cross validation strategy; and finally, an end-to-end gait sub-phase online recognition system is constructed based on Simulink, and real-time recognition of gait sub-phases is realized. The method is superior to the prior art in the aspects of recognition precision, fine-grained analysis and real-time performance, and the perception ability of the lower limb rehabilitation exoskeleton robot to the walking mode is 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.

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.

The invention belongs to the field of aviation simulation training, and discloses a carrier landing command simulation training objective evaluation system and method suitable for approaching distance. The carrier landing command simulation training objective evaluation system comprises an automatic flight control simulation program, a scene simulation program and an objective evaluation program, the automatic flight control simulation program further comprises a flight simulation program and a pilot simulation program, and the scene simulation program further comprises a three-dimensional scene simulation program and an operation page simulation program. The objective evaluation program further comprises a carrier landing command training evaluation standard set, a carrier landing command training evaluation weight factor library and a carrier landing command training calculation matrix. According to the invention, objective evaluation of the carrier landing commander in daily training of the carrier landing commander training simulator can be supported.

The invention relates to the technical field of practical training teaching, and discloses an intelligent practical training system based on a computer neural network, and the system comprises a knowledge explanation unit which employs a neural network model to provide personalized theoretical knowledge explanation; the skill operation simulation unit is used for constructing a simulation model to realize virtualized practice of skill operation; and the practical training evaluation unit is used for accurately evaluating the practical training performance of the user by using a deep learning technology. Meanwhile, the system is provided with a data acquisition unit which is responsible for acquiring user learning behaviors, operation simulation and practical training result data and transmitting the data into each model to acquire an output result. And finally, an intelligent feedback unit provides personalized practical training feedback and suggestions for the user through a defined intelligent feedback algorithm according to a model output result. According to the system, theoretical knowledge explanation and skill operation simulation are integrated, intelligentization and individuation of practical teaching are realized, and the practical teaching effect is effectively improved.

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 relates to the technical field of text recognition, and discloses a text recognition method and system for an electric power audit service. The method comprises the following steps: acquiring and preprocessing historical data of an electric power auditing service; using an OCR technology to convert historical data into a digital sequence training set; constructing a BiLSTM model, setting an attention mechanism layer in the model, performing training evaluation on the model, and adjusting parameters based on reliable indexes to control a loss function to obtain a final model; and deploying the model in a power auditing system to realize text recognition. According to the method, the input sequence can be processed from two directions through the BiLSTM model, so that richer text information is captured, features and semanteme in the text are effectively extracted, important information of related features and semanteme is automatically concerned, irrelevant information is ignored, automatic recognition of the text and extraction of key information are achieved, and the recognition efficiency of the text is improved. And the text recognition efficiency for processing the power audit service is 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.