127 results about "Training evaluation" patented technology

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 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 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.

This application discloses a method and apparatus for constructing a multi-scale adaptive gating MambaPlus network, belonging to the field of artificial intelligence and machine learning technology. The method includes: initializing the network configuration and constructing the basic structure; preprocessing the input data to generate a standard dataset; mapping the input data to the hidden space via an input mapping layer, and extracting backbone features from the Mamba backbone; constructing at least two parallel scale branches in the hidden space to obtain a multi-scale feature set; inputting the backbone features and multi-scale features into an adaptive gating module, dynamically allocating weights and adaptively fusing them through a hierarchical gating mechanism to generate fused features; further enhancing the features through cross-scale attention and feedforward enhancement, and then superimposing the residuals to generate the final discriminative features; finally, completing category prediction and model training evaluation. This application, while retaining the advantages of Mamba's long-range dependency modeling, addresses the problems of insufficient utilization of multi-scale information, poor adaptive feature fusion, and low robustness in complex scenarios.

The invention discloses a multi-sensor motion evaluation method and system for special training of lower limbs. The method comprises the following steps: step S100, collecting data of various sensors of the lower limbs; step S200, based on the collected data, carrying out gait and support state identification; step S300, judging the state of foot varus and valgus; step S400, calculating a foot stability index; step S500, calculating a lower limb exercise ability index; step S600, carrying out special exercise evaluation; and step S700, generating training feedback. Compared with an optical system, the core advantages of the system in training are reflected in that the system can be used for real training instead of a forced performance type test; the device is suitable for long-distance and continuous special training; the technical stability and injury risk can be directly reflected; and the training evaluation cost and the use threshold are obviously reduced.

This application discloses a training method, device, electronic device, and storage medium based on a large language model. The method includes pre-configuring training modes, which at least include open-ended training and restricted training. The open-ended training and restricted training can run independently or be switched on demand. Responding to the input of the user to be trained, the method selects the training mode and simultaneously conducts training based on a fallback process, outputting the final training evaluation result. The fallback process ensures the continuity of the large language model training process. This application provides a training method including open-ended training, process tree restricted training, and a fallback process. The open-ended training integrates a knowledge base and a large evaluation model; the process tree restricted training includes configurable nodes and eight core functions; and the fallback process has model monitoring, self-interruption, and dialogue switching capabilities, achieving integrated training with flexible interaction, standardized control, and stable operation.

The invention discloses a satellite orbit single-point state forecasting method based on deep learning. An orbit precision ephemeris is used as training data, a six-dimensional position speed state at a current moment is used as input, a state at a next moment is used as a target value, a prediction network in which LSTM and a one-dimensional CNN are connected in series is constructed, and a CBAM attention module is introduced to realize adaptive weighting of channel and time sequence features. And designing a weighted loss function, carrying out weighted fusion on a mean square error of the model to a real ephemeris, an HPOP equal-step prediction result and a residual mean square error output by the model, and improving prediction rationality through physical constraints. And training and evaluating through the training set, the verification set and the test set, and outputting a predicted ephemeris. According to the method, a historical sequence or TLE is not needed, high-precision and rapid forecasting can be achieved only through a single-point state, and unmodeled perturbative force is implicitly compensated.

The invention provides a model training method combining parameter adjustment record screening and a large model and a related product. According to one specific mode of the method, simplified historical parameter adjustment records are obtained through screening from historical parameter adjustment records. And analyzing a change rule of the simplified historical parameter adjustment record by using the large model, outputting a next round of hyper-parameter configuration, and performing a next round of training by using the next round of hyper-parameter configuration. Therefore, the method can reduce the number of tokens for large model analysis, improve the speed of generating the next-round hyper-parameter configuration, enable the next-round hyper-parameter configuration to be explained, and determine the feature distance between two hyper-parameter configurations through the feature sensitivity of each hyper-parameter based on the target machine learning model to the training evaluation result. And parameter adjustment records are screened based on the characteristic distance between the hyper-parameter configurations, so that high-value simplified parameter adjustment records can be obtained. The lower round of hyper-parameter configuration obtained by utilizing a large model and based on a more representative high-value simplified parameter adjustment record is easier to understand and more stable.

This invention discloses a method and system for parallel training, evaluation, and control based on a digital twin using an equipment bus. It unifies the time-stamping and state-space mapping of multi-bus observation data, and solves for a unified state estimate based on quality indicators, fusion residuals, and cross-bus physical consistency constraints. This drives the state evolution of the digital twin, executing the Lyapunov descent admission condition under synchronization error and end-to-end delay constraints. Further, multi-scenario parallel simulations are conducted to obtain an upper bound on the risk probability and construct a training evaluation function. When the evaluation is below a threshold or the risk exceeds the limit, only the affected control components are subject to safe feasible region projection correction, and closed-loop control commands are output. This invention improves state estimation accuracy, synchronization stability, risk prediction capability, and training control safety.

The invention provides an adaptive large model hint attack and security assessment method based on feedback learning, and belongs to the technical field of artificial intelligence security. The method comprises the following steps: constructing an attack library containing a plurality of text transformation attack strategies, and obtaining expression data of the attack strategies on a target model; the success rate, the average score and the R value and the Q value of the attack strategy are calculated through the data, and the optimal attack strategy is selected in a descending order according to the Q value; applying the strategy to an original malicious prompt to generate adversarial input, submitting the adversarial input to a target model, judging whether an attack is successful or not through a pre-training evaluation model score, if yes, re-selecting the strategy, if yes, updating statistical data, re-calculating the score, and iteratively trying the strategy according to a Q value until a preset condition is met; and finally recording a test process and generating a report. According to the method, the security evaluation of the large model is realized, the success rate and efficiency of attack testing are improved, an empirical basis is provided for the design of a large model defense mechanism, and the application security of the large model is ensured.

The present application relates to big data processing and intelligent analysis technical field, specifically to a kind of electronic pricing scale big data acquisition processing and intelligent analysis method based on NFC and AI;Its core is: through pre-training evaluation model to original transaction data stream is inferred, according to the preset value and risk threshold value division processing level;Based on the level, in combination with topological strategy library and system load, according to utility and cost weight selection optimal processing topology and execute;Loss function is constructed by collecting processing delay, resource consumption and analysis efficiency, and threshold value and weight parameter are reversely dynamically updated;The present application realizes the change from rigid processing to dynamic stratification and adaptive topology optimization based on data instantaneous value and risk.

The application discloses a kind of fNIRS adaptive feedback emotion cognitive collaborative training device and method, it is related to emotional disorder cognitive training technical field.The device includes: data acquisition module, for obtaining original double-wavelength light intensity signal from brain;Data processing module, for the original double-wavelength light intensity signal is carried out multistage pretreatment;Neural feedback module, for calculating multidimensional evaluation index according to hemoglobin concentration data, and using dynamic threshold control algorithm adjusts the difficulty level and feedback threshold of training task;Training module, for training participant based on emotional stimulation task and cognitive training task;Evaluation module, for training evaluation according to comprehensive performance score and feedback threshold comparison result.The present application closely combines brain function equipment with cognitive training and emotional stimulation task, adds dynamic evaluation and real-time feedback to traditional task, and realizes the precise evaluation of user training effect by brain network analysis technology.

The invention provides a personalized training subject adaptive generation and recommendation method for an unmanned aerial vehicle driver, and the method comprises the steps: collecting environment dimensions and physiological and psychological state data through a flight simulator and wearable sensing equipment, carrying out the time-space alignment and normalization processing, discretizing situation parameters through the combination of the statistical features of a sliding time window and a clustering algorithm, and carrying out the adaptive generation and recommendation of a personalized training subject. Forming a standardized situation mode label set; performing off-line modeling on a situation compensation function library by utilizing historical behavior data and situation characteristics to realize accurate evaluation on various situation influence coefficients; in the real-time evaluation, the skill score and the situation adjustment amount are dynamically fused through the gating proportion parameter, personalized correction is carried out based on the personal historical offset characteristics, the stable and adaptive calibrated skill score is output, and the fairness, accuracy and individualization level of training evaluation are effectively improved.

The invention discloses a military crane chassis fault reproduction and practical training system based on working condition simulation, and relates to the technical field of military equipment practical training, and the system comprises an entity chassis building unit which selects and uses entity parts of subsystems of a military automobile crane to be arranged and installed according to a real automobile, retains a key relation, presets an interface, defines a safety threshold value and the like; the dual-mode fault generation unit constructs an initial library, and an intelligent agent is embedded to generate a fault; the multi-dimensional data acquisition unit responds to the trigger signal to acquire multiple types of data; the fault diagnosis practical operation unit is used for trainees to operate; the training evaluation and fault iteration unit generates a report and feeds back optimization suggestions; according to the invention, a dual-mode fault generation unit is constructed, an initial fault library and an intelligent agent exploration capability are integrated, a screening algorithm is matched, and multiple fault scenes are provided; a complete practical training system is built, multi-dimensional data acquisition is achieved, practical operation and evaluation iteration are standardized, the practical training quality is improved through closed-loop design, and the military crane chassis fault practical training requirements are met.

The invention relates to the technical field of physical training evaluation, in particular to a physical training evaluation method and system based on motion recognition, and the method comprises the steps: positioning a motion reversing key moment and an image frame according to knee-shoulder angular velocity reversal, constructing a shoulder-knee connection path, and extracting a pixel length to form a time sequence table; non-smooth mutation is monitored, a standard trend is compared to generate an action offset list, a rhythm fluctuation time period is identified based on key frame interval change, offset and fluctuation information is analyzed in a cross mode, and a training action identification evaluation result is obtained according to distribution characteristics of overlapped interval parts. According to the method, action conversion is recognized through angular velocity conversion, a shoulder-knee track is constructed to extract extension length, offset is judged based on sudden change and combined with rhythm trend, concentrated features of overlapped interval parts are analyzed in a cross mode, synchronous offset and rhythm interference are accurately extracted, action continuity and stability discrimination capability is improved, and key state recognition resolution is enhanced. And fine tracking and effective early warning of the complex action flow are realized.

The application discloses a kind of based on patrol trolley robot police training intelligent tracking evaluation method and system, it is related to intelligent monitoring technical field.The method includes: demarcate training area and robot activity area in training site, and patrol trolley robot is based on laser radar and dynamic window method (DWA) in activity area autonomous movement and real-time obstacle avoidance;Through the AI vision module and laser radar carried, the preset training team is accurately identified and locked, and the pan-tilt and the moving chassis are controlled to realize stable follow-up shooting, and training video is collected in real time.Further, based on the multi-angle video shot, using the action recognition algorithm based on skeleton key point detection, the team action specification of training team and the action specification of individual student are intelligently evaluated, and the evaluation report containing quantitative score, deviation positioning and rectification suggestion is automatically output.The application solves the problem that traditional police training evaluation relies on manual, strong subjectivity and low efficiency, realizes the automation tracking of training process, multi-angle recording and intelligent quantitative evaluation.

The embodiment of the invention discloses an image processing method and device for scoliosis training, a medium and a product, and belongs to the technical field of image processing. The method comprises the following steps: in response to a first trigger operation, determining a scoliosis identifier of a target object, and displaying a reference video corresponding to the scoliosis identifier of the target object; acquiring a training video frame corresponding to the current reference video frame; determining a plurality of reference triangles corresponding to the demonstration action displayed by the current reference video frame and a plurality of training triangles corresponding to the current training action in the training video frame; determining target similarities between the plurality of reference triangles and corresponding triangles in the plurality of training triangles, wherein the target similarities comprise orientation similarities and shape similarities; and according to all the target similarities, determining and displaying training evaluation information corresponding to the current training action. According to the technical scheme, the scoliosis training experience of the user can be remarkably improved.

The invention discloses a three-dimensional power grid training simulation method based on a virtual reality technology, and relates to the technical field of power grid training simulation, and the method comprises the following steps: initializing a virtual reality environment, loading a mountainous area power grid three-dimensional model, and generating a power transmission line node; setting training environment parameters; calculating an effective wind speed at each line node according to the terrain gradient; calculating the line galloping amplitude in real time; dynamically rendering a line galloping effect in a VR environment, and visually displaying a dangerous section through color coding; students can deploy anti-galloping devices interactively, and the system updates the damping effect in real time and recalculates the galloping amplitude. The system continuously monitors the safety state, provides a real-time alarm and generates a training evaluation report. According to the invention, a terrain-adaptive galloping amplitude real-time calculation algorithm is introduced, and a virtual reality training system deeply fused with a real geographical environment is constructed, so that simulation and visualization of the galloping behavior of the high-voltage transmission line in a mountainous complex terrain can be realized.

The invention discloses a training evaluation method, device, equipment and program product. According to the scheme, the method comprises the steps of performing semantic analysis on training requirement description input by a user based on a natural language processing technology, and constructing an evaluation dimension system comprising a plurality of evaluation dimensions and logic relationships among the evaluation dimensions; for each evaluation dimension in the evaluation dimension system, selecting an adaptive target evaluation model from a plurality of corresponding candidate evaluation models to generate a model scheduling strategy for mapping each evaluation dimension to the corresponding target evaluation model; wherein the candidate evaluation model is used for executing evaluation calculation of the evaluation dimension to which the candidate evaluation model belongs. And according to the model scheduling strategy, calling a corresponding target evaluation model to evaluate the training process data, and obtaining an evaluation result corresponding to each evaluation dimension. And according to the logic relationship in the evaluation dimension system, integrating the evaluation result of each evaluation dimension, and generating a target evaluation result for the training process data.

The application relates to the technical field of debugging training, in particular to an automatic adjustment debugging training system based on a PLC, which comprises a modeling module, a connecting module and a control module. The modeling module is used for acquiring operation parameters in multiple thermal processes of a thermal power plant, modeling the multiple thermal processes according to the operation parameters, and obtaining corresponding system simulation models. The connecting module is used for connecting the system simulation models with a controller of a user through a preset channel. The control module comprises a touch operation screen, the touch operation screen is used for operating the system simulation models, a training evaluation value is calculated according to an operation flow of a trainee on the system simulation models, and a training level of the current trainee is judged according to the training evaluation value. The application solves the technical problem that the debugging of an automatic adjustment system is all conducted by hiring factory-outside experts, which not only costs a lot but also cannot be permanent, and the debugging process has an influence on equipment.

The invention provides a training evaluation method and device for balloon blocking hemostasis, a medium and a product, and relates to the technical field of intelligent teaching. And respectively acquiring static pressure data reflecting the mechanical characteristics of the blood vessel wall and dynamic pressure data reflecting the hemodynamic characteristics. On the basis, the system combines specific preset bleeding case data, and determines hemostasis pressure ranges and partial hemostasis pressure ranges corresponding to different areas of the aorta under specific operation conditions through calculation. When a user operates, the system can call the matched pressure range standard according to the current real-time blocking position and the target operation combination parameter, and compares the collected real-time blocking pressure with the matched pressure range standard. According to the scheme, the technical problem that an existing simulation training system is difficult to adapt to complex fluid mechanics changes of a simulated person in different physiological states is solved.

The application discloses a motion training evaluation system and method based on cooperation of an event camera and a visual camera, and is applied to the field of computer vision, and aims at the problems that an existing training evaluation system based on an optical camera cannot capture fast motion trajectories and limb shapes, cannot effectively evaluate action quality, and cannot provide effective training guidance; the application provides continuous human motion events in the form of event streams, can achieve millisecond-level motion response, is not affected by motion blur effects of high-speed moving objects, and can provide a higher dynamic range, and can provide more effective motion training evaluation in scenes with strong light, backlight, and sharp changes in brightness. Through cooperation of the visual camera and the event camera, not only the accuracy of fast actions can be evaluated, but also standard action teaching and training in the form of visual scene interaction can be performed.

The invention provides a risk control model construction method and system based on feature normalization enhancement, and the method comprises seven main steps: obtaining multi-source heterogeneous data, carrying out feature extraction and fusion, building a dynamic adaptation mechanism, carrying out distribution offset detection and parameter updating, carrying out multi-granularity feature normalization, carrying out feature feedback optimization, and carrying out model training evaluation. The feature distinction degree and the stability of the risk control model are remarkably improved.

The invention discloses a labor education interactive teaching system, belongs to the technical field of education, and aims to solve the problems of rigid evaluation and non-personalized guidance of traditional labor skill training. The system comprises a multi-modal data acquisition module, a baseline management module, a contradiction analysis module, a strategy generation module and a multi-modal interaction terminal module. According to the method, a personal dynamic ability baseline is established, an operation-state contradiction is diagnosed based on deep coupling of real-time behavior data and physiological data, and a composite guidance strategy containing body relaxation guidance or step-by-step technology guidance can be generated in a targeted manner. According to the system, visual, auditory and tactile feedbacks are output in real time through the multi-mode interaction terminal, and the accuracy of labor skill teaching, the fairness of evaluation and the skill generalization ability of a learner in a high-voltage situation are remarkably improved.

The application provides a pilot attention allocation training method, device and equipment and a storage medium, and belongs to the field of flight training. The method comprises the following steps: in the case that a pilot wears a head-mounted eye tracker for training, obtaining a fixation point video of the pilot and recording the triggering conditions of observable items; dividing the fixation point video into interest areas, performing data analysis on the interest areas, and determining the access times, Pearson correlation coefficients, pupil diameter change scatter plots, Markov transition matrices, access sequences and time lengths, in-cabin and out-of-cabin access times, cockpit saccade mode models and the pupil diameter change rates of the pilot; further determining the attention transfer portrait and the attention allocation score of the pilot; and adjusting the attention allocation training bias of the pilot according to the attention allocation score and the attention transfer portrait. In this way, the limitations of traditional subjective judgment can be overcome, and the training evaluation is more scientific and operable.

The application discloses a kind of dystonia evaluation system and method based on wearable inertial sensor.The system includes: data acquisition module, including wearable inertial sensor and portable muscle strength tester, for the synchronous acquisition of subject's multidimensional motion data and muscle strength data;Data preprocessing module is used to optimize the data collected;Characteristic processing module is used to extract multidimensional features from the processed data, and then complete feature dimension reduction through principal component analysis to obtain a core feature subset;Evaluation model module is based on machine learning algorithm Training evaluation model, the core feature subset is used as model input, to realize the accurate classification of dystonia type;Result output module is used to generate quantitative evaluation report, to provide objective data support for clinical diagnosis and treatment plan formulation.The application solves the problem that traditional evaluation relies on subjective experience and lacks quantitative standard, and improves the accuracy and standardization of dystonia evaluation.

An exercise system is disclosed that includes a walking exercise device having a pedal stepped on by a patient and one or more linked pedal drives driving the pedal, and a system control device that performs a patient walking training in accordance with a patient's individual training plan, the system control device includes: an index prediction unit that determines a walking evaluation index of a patient based on basic information including patient personal information and initial walking state information; a training planning unit having a self-discipline training planning model for establishing a patient individual-targeted training plan performed by the walking exercise unit on the basis of the result of the walking training; and a training evaluation unit that evaluates a training result on the basis of training result data of a rehabilitation training result of the patient performed by the walking exercise unit.

The application relates to the technical field of massage training, and discloses a pediatric traditional Chinese medicine acupoint massage training system, which comprises a nursing terminal interaction module, a training data acquisition module, an intelligent evaluation module and a management terminal interaction module. The real-time adjustment amount is generated by corresponding training standards of the training case selected by nursing staff and the massage data collected when the nursing staff is trained, the real-time adjustment amount is used for real-time reminding and correction of wrong methods, the accuracy of acupoint massage is improved, the efficiency of learning massage of the nursing staff is improved, the comprehensive evaluation coefficient is obtained by evaluating the massage data when the nursing staff is trained, the subjectivity of the traditional evaluation mode is avoided, the objectivity of the training evaluation is improved, the nursing staff is rated and the rating is predicted according to the comprehensive evaluation coefficient, the training report of the massage training process of the nursing staff is generated, and the training report is checked by a management doctor and the nursing staff.