22 results about "Continuous training" patented technology

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

The invention relates to an intelligent agent continuous training and effect evaluation closed-loop method and system fusing work order feedback, and a medium, and relates to the technical field of deep learning. The agent continuous training and effect evaluation closed-loop method comprises the following steps: receiving and analyzing work order feedback information, and recording the number of error information and information storage duration in combination with a preset structure sample label; dynamically judging the number of error information and the information storage duration, triggering a retraining rule of an original agent model, distributing candidate information weights, and screening a target sample set; directionally training and optimizing the original agent model according to a retraining rule in combination with the target sample set, and generating a new agent model; constructing a historical work order problem set, comparing and verifying a new agent model in combination with an original agent model, generating an agent evaluation table, and correcting a candidate information weight; by accurately focusing the weak link, the continuous and efficient enhancement of the model capability is realized, so that the answer accuracy of the intelligent agent is remarkably improved, and the AI illusion is effectively inhibited.

The invention belongs to the technical field of water area rescue practical training, and provides a torrent water channel and urban inland inundation combined practical training system which comprises a torrent water pump room, a water distribution pool, a training water channel, a training pool and an urban inland inundation training area. The training water channel communicates with the water distribution pool and the training pool. The urban inland inundation training area is communicated with the water distribution pool and the training pool. According to the system, a torrent water pump house, a water distribution pool, a training water channel, a training water pool and an urban inland inundation training area are integrated into an organic whole, and a parallel water channel is designed, so that a single system can provide two differentiated training environments of torrent rescue training and urban inland inundation rescue training at the same time. Flow regime areas such as a boiling line, a rolling flow, a covering flow, a smile flow, a rugosa flow and a vortex flow are arranged in the training water channel, and the most typical and dangerous water flow form in a natural river is systematically and completely reproduced. Therefore, learning and training personnel can learn and master skills of identifying and coping with various complex water flow forms in a continuous training water channel.

The invention provides a progressive respiratory training device which comprises a shell, an air inlet and an air outlet are formed in the two ends of the shell respectively, an impeller is further rotatably arranged in the shell, a propelling disc is slidably arranged in the shell, the propelling disc is connected with the impeller through a resistance mechanism, and a driving plate is hinged to the interior of the air outlet through a torsional spring. The driving plate is connected with the propelling disc through a propelling mechanism so that rotation of the driving plate can be converted into driving of the propelling disc to slide by a specified distance, and a reset mechanism connected with the propelling disc is arranged on the shell. According to the device, the training intensity can be automatically and gradually improved, manual adjustment of a patient or a medical worker is not needed, the labor intensity of the worker is greatly reduced, meanwhile, training interruption caused by manual adjustment is avoided, the training continuity is guaranteed, and when the breathing intensity of the patient cannot control the rotation driving angle of the driving plate, the driving angle is adjusted. Therefore, the strength can be kept for continuous training, the maximum training strength can be quickly achieved, and the training effect is improved.

This invention discloses a mechanical equipment fault monitoring system and method based on multidimensional data-driven approaches, belonging to the field of fault prediction technology. The method specifically includes: collecting multidimensional data of the mechanical equipment, preprocessing the collected multidimensional data, extracting features from the preprocessed multidimensional data, introducing a time-frequency locality adjustment mechanism to obtain first local weight coefficients, weighting the first local weight coefficients using a likelihood function to obtain second local weight coefficients, weighting and fusing the extracted features of the preprocessed multidimensional data, training a fault prediction model using common fault data of the mechanical equipment, and predicting faults of the mechanical equipment based on the trained fault prediction model and the features of the weighted and fused multidimensional data. This invention, through continuous training and optimization of the model, adapts to the operating characteristics of the equipment under different working conditions, automatically adjusts the fault prediction model, and possesses strong adaptive capabilities.

The application relates to the technical field of large model training strategies, in particular to a distributed fault tolerance and stability guarantee method for large model training, which comprises the following steps: acquiring model training related data in a large model training process; analyzing the abnormality degree of the model training state in a single training cycle; analyzing the stability change degree of the training state of the model in a continuous training process; and evaluating whether the model parameters of a single training cycle are saved by a checkpoint. The application aims to adjust the saving strategy of the checkpoint by analyzing the model training state, so as to improve the efficiency of breakpoint resuming training of the model.

The utility model discloses a knee joint loosening training device which comprises an inclined plate, a positioning ring fixedly connected to the inclined plate, a rotating shaft installed in the positioning ring through a bearing, an L-shaped supporting plate fixedly connected to the outer surface of the rotating shaft, a gear fixedly connected to one end of the rotating shaft, and a reinforcing rod fixedly connected to the outer surface of the positioning ring. And one end of the reinforcing rod is fixedly connected with a limiting frame, a driving rack is slidably connected into the limiting frame, and the gear is in engaged connection with the driving rack. According to the knee joint looseness training device, a servo electric cylinder can drive a fixing plate to move, a driving rack can slide in a limiting frame, the driving rack can drive a gear to rotate, an L-shaped supporting plate can drive the shank of a patient to swing, then the knee joint is trained, the servo electric cylinder is adopted for providing power, the patient does not need to apply force, and the training efficiency is improved. The patient is more labor-saving during training, and continuous training can be carried out.

Described is a system for performing a set of machine learning model training operations that include: accessing media content items associated with interaction functions initiated by users of an interaction system, generating training data including labels for the media content items, extracting features from a media content item of the media content items, identifying additional media content items to include in the training data based on the extracted features from the media content item, processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output. The system checks whether retraining criteria has been met, and repeats the set of machine learning model training operations to retrain the machine learning model.

The present application relates to the technical field of monitoring high-risk operations, and particularly relates to a high-risk operation intelligent monitoring management system based on multi-element linkage. The present application constructs the mutual linkage of key elements of high-risk operations to achieve the technical standard of risk perception and control in the operation process at the safety level. At the same time, through an electronic fence automatic drawing algorithm and an electronic fence automatic validation algorithm, the operation risk state is determined in a limited space. Through micro-network data acquisition, deep learning and continuous training, an operation risk high-accuracy AI model is obtained to maximize the elimination of safety hazards in the work of operation personnel on the fan platform working surface and the fan tower working surface. The working state of the crane is determined by the proportion of time nodes of the crane outside the electronic fence within the preset time length, and corresponding adjustment is made when the working state is unqualified, thereby improving the working accuracy of the crane. The present application effectively monitors the operation risk and improves the work efficiency.

An object of a system according to an embodiment is to provide an optimal training plan based on an individual lifestyle and an exercise history and support continuous training.SOLUTION: A system includes an input part, an analysis part, a proposal part, a monitoring part, and an adjustment part. The input unit inputs lifestyle habit data and exercise history data. The analysis unit analyzes the information input by the input unit. The proposal unit proposes a training plan based on the result obtained by the analysis unit. The monitoring unit monitors a progress status of the training based on the plan proposed by the proposal unit. The adjustment unit adjusts the training plan based on the progress status obtained by the monitoring unit.SELECTED DRAWING: Figure 1

The invention discloses a large model training data processing method oriented to the tumor field. The method specifically comprises the following steps of: cleaning industrial acknowledged standard literatures to obtain plain texts only containing important information; for the continuous training task, segmenting the text content, and assembling into pre-training data; aiming at the alignment task, generating question and answer pairs through a rule or a general model, and assembling question and answer data; aiming at a preference fine tuning task, using a general model to generate a plurality of answers for the same question, and combining standard answers to assemble preference data. The method has the beneficial effects that indexes can be effectively quantified, so that the data production efficiency is improved by 80%; quality improvement is obtained, so that data quality is improved, and a service scene is met better; the cost is reduced, and the data production cost is remarkably reduced.

This invention discloses a subway pantograph simulation training device, comprising a support component, a simulation mechanism, and an environmental simulation component. The support component includes N-shaped frames located at the left and right ends of the simulation mechanism, with stabilizing rods fixedly connected to the left and right sides of the N-shaped frames, and stabilizing components fixedly connected to their bottom. The simulation mechanism includes a collar module located in the middle of the crossbar of the N-shaped frame, with rotating rods fixedly connected between opposing collar modules, and multiple simulation components arranged around the circumference of the collar module. This subway pantograph simulation training device, by driving the collar module to rotate via a drive module (motor, ratchet, etc.), can quickly switch between the first to fourth simulation components (new line, dusty line, worn line, damp line) without disassembling the original detection line structure, solving the pain point of existing devices being "cumbersome and time-consuming to replace," improving the efficiency of single-condition switching by more than 80%, and meeting the needs of training sites for continuous training of batches of personnel.

Smart Athletic Monitor for Continuous Training Assessment

The invention discloses a concentration training data breakpoint continuous training method supporting cross-device real-time synchronization, which relates to the technical field of cognitive training data processing, and comprises the following steps: pre-training data acquisition and structured storage, defining an acquisition dimension comprising basic training data, scene parameter data and user operation data, packaging into a training data packet by adopting a JSON format, and locally caching the training data packet; and cross-device real-time data synchronization is carried out, a training data packet is uploaded to a cloud data synchronization center through a multi-dimensional synchronization triggering mechanism, and newest training data is acquired and synchronized from the cloud when a new device logs in. According to the method, full-dimension real-time synchronization of training data is realized, cloud distributed storage and a multi-trigger condition synchronization mechanism are combined, it is ensured that the training progress, scene parameters and operation records can be immediately linked when a user seamlessly switches among multiple devices, and the problem of training splitting caused by data delay and untimely synchronization in a traditional system is thoroughly solved.

The invention discloses leg strength training equipment, and relates to the technical field of swimming training. A connecting plate is slidably connected to the upper portion of the fixing plate, a pedal is fixedly mounted above the connecting plate, a rack is fixedly mounted at the bottom of the connecting plate and meshed with a gear, the gear is connected with a first transmission wheel through a transmission mechanism, and the first transmission wheel is in transmission connection with a second transmission wheel through a first transmission belt. A ratchet wheel is fixedly installed in the center of the second transmission wheel and meshed with a pawl, and the pawl is connected with a first threaded rod through a reset spring. According to the invention, through leg straightening and power generation, the connecting plate drives the rack to move, and through transmission of structures such as the gear, the sliding plate stretches and compresses the spring to store energy. Along with continuous training, the resistance is increased, and leg kicking and power generation are simulated after leg muscle fatigue in swimming. When the legs bend and reset, the ratchet wheel and the pawl do not hinder. According to the design, resistance is changed according to leg strength, force generation and fatigue are simulated, and a user is helped to exercise and lift better.

The invention relates to the technical field of large model training strategies, in particular to a distributed fault tolerance and stability guarantee method for large model training, and the method comprises the steps: obtaining model training related data in a large model training process; analyzing the abnormal degree of the model training state in a single training period; analyzing the stability change degree of the training state of the model in the continuous training process; and evaluating whether the model parameters of a single training period are subjected to check point storage or not. The invention aims at improving the breakpoint continuous training efficiency of the model by analyzing the model training state to adaptively adjust the storage strategy of the check point.

The application discloses a label noise detection method based on a multi-time step loss sequence, utilizes a character recognition module to recognize characters from an input image; utilizes an MSL-MentorNet label noise detection module to analyze a loss sequence of the input image in a continuous training period according to a character recognition result, dynamically allocates sample weights to detect and filter noise labels, and feeds back screened data to a character recognition module to optimize model training. The application adopts an MSL-MentorNet label noise detection and noise filtering mechanism based on dynamic curriculum learning, analyzes a loss sequence of a sample in a continuous training period, utilizes a BiLSTM network to distinguish noise labels from clean difficult samples, and combines a linear scheduling strategy to gradually increase a training data amount. Based on the method of the application, the complex text image recognition accuracy can be improved, and the method is especially suitable for robust recognition and noise filtering of complex text images in scenarios such as ancient book digitization, historical document restoration, industrial OCR quality inspection and the like.

The invention relates to the technical field of AI model training, and discloses an AI model training management method supporting breakpoint continuous training and fault prediction, and the method comprises the following steps: obtaining an optimizer momentum tensor and memory allocation data in an AI model training process, calculating a momentum distribution entropy value and a memory allocation frequency, and generating a multi-dimensional risk feature vector; analyzing mutation points in the risk feature vector sequence, calculating causal association strength among different risk sources, and outputting a risk association matrix; inputting the risk incidence matrix into a risk gradient propagation algorithm, calculating a propagation path and an influence weight from the risk of the system layer to the parameter layer, and generating a parameter-level risk distribution map; and calculating the sensitivity of each parameter to a loss function, performing weighted fusion with a risk distribution diagram, and screening a high-risk key parameter set. According to the method, dynamic balance of fault prevention and resource optimization is realized, and the method is particularly suitable for a long-time training task of a large-scale AI model.

The application provides a radiotherapy breath synchronization training device, an application system and a monitoring method, and relates to the technical field of medical instruments.The training device comprises a breathing mask, a wearable monitoring belt, a first sensor module, a second sensor module, a third sensor module and a central processing communication module.The first sensor module collects a breathing airflow signal, is suitable for breast radiotherapy patients and liver radiotherapy patients;the second sensor module collects chest and abdominal movement signals respectively, obtains chest and abdominal movement amplitudes and posture information, and is suitable for lung radiotherapy patients.The central processing communication module performs time synchronization, feature extraction, individualized template establishment and matching on multiple source signals, calculates an index, identifies invalid modes, and adaptively adjusts a training rhythm, a feedback mode and a training target.The application system can generate recommended parameters according to continuous training results, realizes monitoring, intervention and clinical parameter support closed-loop management of radiotherapy breath training.

The application provides an image quality evaluation model training method and device, equipment and medium, the method comprises the following steps: dividing a plurality of selected sample images containing target objects collected into a training sample set, a test sample set and a meta-training set; constructing an initial evaluation model; replacing the initial model parameters of the initial evaluation model with the independent model parameters corresponding to any target batch meta-training set, and training the initial evaluation model using the target batch meta-training set to obtain the adjusted process model parameters corresponding to each batch meta-training set; according to the weight value matched by each process model parameter, the target model parameters are calculated, and when the preset convergence condition is met, the target evaluation model containing the target model parameters is verified using the test sample set, and the training is completed after the preset training requirement is met. The method of the application does not forget the sample characteristics trained earlier in the process of continuous training, has strong generalization ability and high accuracy.

The embodiment of the invention provides a training state determination method, a training platform, a device, equipment, a medium and a product, and the method comprises the steps: in the training process of an artificial intelligence cluster, a monitoring node can obtain and analyze each feature data packet in a feature data set. Afterwards, the monitoring node can integrate the analyzed gradient data according to the sequence of the feature data packets in the feature data set. In the method, the gradient data obtained after the monitoring nodes are integrated can be regarded as the global gradient data of the artificial intelligence cluster, and the global gradient data can describe the training state of the artificial intelligence cluster more completely and more accurately. And if so, when the artificial intelligence cluster training fails, the monitoring node can recover the artificial intelligence cluster to a normal state by using the global gradient data so as to facilitate subsequent continuous training.

Apparatuses, systems, and techniques for continuous training of an object detection / classification model are provided. A first image depicting an environment is identified. Data for an object detected in the first image is obtained based on outputs of a machine learning (ML) model. A determination is made of whether a level of confidence that the object corresponds to an object class satisfies a confidence criterion. If so, first conditions corresponding to the environment depicted in the first image are identified. Noise characteristics associated with a second image depicting the environment are determined based on a difference between the first conditions and second conditions of the second image. The first image is augmented based on the noise characteristics to generate a third image depicting the environment based on the second conditions and the detected object. Training data associated with the third image is provided to train the ML model.