49 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 discloses a weak supervision time sequence action positioning method and system for a self-supervision pseudo label optimization network, and the method comprises the steps: dividing all segments in a training video into a plurality of class clusters, and obtaining atomic action features representing global semantics; taking the atomic action features representing global semantics as action representation, mapping the video clip-level features extracted by the feature extractor into query vectors, and participating in attention calculation together with the atomic action features to generate enhanced fragment representation containing global context information; the method comprises the following steps: identifying an inconsistent region by utilizing a high-discrimination characteristic expressed by an enhanced fragment, executing pseudo-label correction by collecting pseudo-label results generated in two continuous training periods, judging whether to expand a label range and generate a pseudo-single-frame label yfp by adopting an affinity search strategy and utilizing characteristic similarity, and optimizing through consistency loss. And obtaining a positioning result. According to the method, the marking cost is remarkably reduced, background interference is effectively reduced, and the integrity and precision of action positioning are improved.

The invention relates to the technical field of power system power distribution networks, in particular to a multi-source energy storage power distribution network voltage regulation and control method based on a safety reinforcement learning algorithm. Firstly, a power distribution network voltage regulation and control model is constructed, power distribution network nodes are regarded as state nodes, the electrical connection relation forms a state space, and regulation and control operation is used as an action space. A state transition probability matrix is established by analyzing a physical relationship, and a reward function is defined in combination with security operation constraints. Then, a near-end strategy optimization algorithm is selected, the preprocessed operation state data of the power distribution network are used as input, and the intelligent agent and the environment are subjected to interactive training; during training, a reward function is utilized to guide an intelligent agent to learn a safe and effective regulation and control strategy. The average reward and the voltage qualified rate are monitored through continuous training, and the model can adapt to dynamic changes of the power distribution network after convergence. The problems that a traditional regulation and control method is difficult to adapt to complex changes of the power distribution network and does not comprehensively consider various factors are solved.

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.

Provided in the present application are an apparatus and method for training and evaluating an AI model. The method comprises: creating a process task template applied to a containerized application management platform, wherein the process task template comprises: process configuration information associated with a training and evaluation task of an AI model, and a stage flag corresponding to each step in the training and evaluation task; and in response to receiving a start command for a continuous training and evaluation task: querying the process task template, splitting the training and evaluation task into stages on the basis of the process configuration information and the stage flag, so as to construct a task in a training stage, performing product escape analysis on the basis of the task in the training stage, so as to generate a product escape analysis result associated with the task in the training stage, starting the task in the training stage on the basis of a product escape result, and when the task in the training stage reaches a preset training stage node, starting the concurrent running of one or more tasks in an evaluation stage without interrupting the task in the training stage.

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 invention provides a large language model staged pre-training method and system. The method comprises the following steps: training a Transform model by using a basic data set, optimizing a negative logarithm likelihood target, and adopting an AdamW optimizer and a cosine attenuation learning rate; continuing training by using the universal knowledge data set based on the first-stage parameters; and weighting the professional data in the training field by adopting an oversampling strategy. By structuring a training target and a data type, the model can efficiently learn language basis, general knowledge and professional skills in stages. Experiments show that according to the method, the training efficiency of the model in the basic stage is improved by 40%, the overall training time is shortened by 30%, and meanwhile the accuracy rate of tasks in the professional field is 15%-20% higher than that of traditional end-to-end training. Finally, model parameters are evaluated through professional ability, and both universal language understanding and domain specialty are achieved.

The present invention provides a method and device for planning gait trajectories for a lower-limb exoskeleton based on self-oscillation. The method comprises continuously collecting joint angle information and plantar pressure information from a plurality of walking groups of a wearer, preliminarily dividing the walking process into multiple gait phases based on the plantar pressure information, using the preliminarily acquired gait phases and corresponding joint angle information as a data set, building a convolutional neural network (CNN) model, using the data set as training data, and continuously training the CNN model to accurately acquire the starting point of each gait phase. Each gait phase is accurately acquired from the starting point, establishing an adaptive oscillator model, inputting the data set into the adaptive oscillator model, outputting a tracking phase, and performing gait trajectory planning for the lower-limb exoskeleton based on the gait phases acquired by the CNN convolutional neural network model. The predicted output trajectory is dynamically adjusted in real time online. The present invention can adapt to the requirement of non-constant walking speed and achieve dynamic gait trajectory planning.

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 an image risk classification method and apparatus based on YOLOv7. The method crawls basic NFT information through the API provided by OpenSea, obtains and partitions an open-source dataset, and uses the partitioned training and validation sets to train the model. After improving the YOLOv7 algorithm, the backbone network of the model extracts and fuses features from the images to obtain image feature maps; the head network in the model fuses features from the feature maps and uses a new GAP-FCN classification structure to obtain classification results; the loss is calculated using the obtained classification results and preset labels, and the network weights are updated in reverse. Through continuous training with multiple batches of samples, a trained model is obtained; the images from the partitioned test set or the images to be predicted are input into the trained model, and the input, extraction, and fusion are repeated to obtain the image risk classification results. The data augmentation technique used in this invention expands the dataset, increases the number of training samples, and improves data utilization.

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 invention discloses a continuous training device for a two-way memory alloy wire. The continuous training device comprises a pay-off wheel, a stretching wheel and a plurality of heating and cooling assemblies which are sequentially arranged in the advancing direction of the wire. The pay-off wheel is connected with a first motor, the stretching wheel is connected with a second motor, and the advancing speed of a wire on the stretching wheel is larger than that of the wire on the pay-off wheel; the heating and cooling assembly comprises a hollow roller and a constant tension wheel which are arranged in the wire advancing direction. Wherein a resistance wire is arranged in the hollow roller, and the constant tension wheel is arranged in the cooling box; according to the invention, the hollow roller and the constant tension wheel form the heating and cooling assembly, so that the wire can continuously advance between the two parts, and meanwhile, the wire is subjected to cold and hot variable temperature circulation, so that the cold and hot circulation training of the wire is realized, and the training efficiency is improved.

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 present invention provides a tennis transmitter, which relates to the field of transmitters and includes a launching mechanism, a ball-dialing disc, a ball storage bin, and a mounting bracket. The ball-dialing disc and the ball storage bin are installed on the mounting bracket. The ball-dialing disc is arranged at the bottom of the ball storage bin, and the outlet of the ball-dialing disc is communicated with the inlet of the launching mechanism. The ball-dialing disc is used to convey tennis balls from the ball storage bin into the launching mechanism. The launching mechanism includes a link assembly, a pitching assembly, and a limiting assembly. When it is necessary to adjust the launching angle of the launching mechanism, the second baffle in the pitching assembly rotates. At this time, the temporary storage bin rotates around the axis of the rotating shaft. Since the centers of the tennis balls at the outlet of the link assembly intersect with the axis of the rotating shaft when the tennis balls are stacked at the outlet of the link assembly, the link can change but the tennis balls are not squeezed and will not get stuck. The limiting assembly prevents the tennis balls in the temporary storage bin from falling, so that the tennis balls will not fall out when serving is not required, ensuring continuous launching of tennis balls and enabling continuous training for the trainees.

The invention provides a training evaluation device and method for an AI model. The method comprises the steps that a process task template applied to a containerized application management platform is created, wherein the process task template comprises process configuration information associated with a training evaluation task of an AI model and stage marks corresponding to all steps in the training evaluation task; and in response to a received starting command of the continuous training evaluation task, querying a process task template, performing stage splitting on the training evaluation task according to the process configuration information and the stage mark to construct a training stage task, performing product escape analysis according to the training stage task, and performing product escape analysis according to the product escape analysis. In one embodiment, the method includes receiving a training stage task, generating a product escape analysis result associated with the training stage task, initiating the training stage task based on the product escape result, and initiating concurrent operation of one or more evaluation stage tasks without interrupting the training stage task when the training stage task reaches a predetermined training stage node.

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.

A system, method and device for early detection of lower extremity injuries in patients with neuropathy who may have impaired pain sensation. The system comprises a sensor apparatus with multiple sensors that measure physiological parameters at specific locations on a patient's foot and lower limb. A connected computing entity receives and analyzes sensor data to identify potential injury markers such as temperature anomalies, tissue oxygenation changes, and swelling. Using machine learning algorithms that improve through continuous training, the system determines injury likelihood based on personalized baselines and historical data at a patient level. The computing entity generates an intuitive user interface displaying injury likelihood assessments for healthcare providers. This technology enables early intervention through non-invasive monitoring, potentially preventing serious complications in a vulnerable populations.

The present application provides a method, apparatus, and device for continuous model training based on joint metric selection, including: determining a first cross-entropy loss value for a candidate dataset for a first small language model, determining a second cross-entropy loss value for a training dataset for a second small language model, calculating a first difference between the first cross-entropy loss value and the second cross-entropy loss value, determining a first target dataset from the candidate dataset based on the first difference and a first optimal transmission distance between the candidate dataset and the training dataset, and continuously training the first small language model based on the first target dataset to obtain a target small language model. This can improve the data quality of the first target dataset used to train the first small language model to a certain extent, thereby improving the prediction accuracy of the target small language model to a certain extent.

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.

The present invention discloses a multi-stage adjustment data augmentation method for model training, which includes five modules: an information preprocessing module, a discriminator module, a training preference calculation module, a class information amplification module, and a training set update module. The steps of this method include the establishment of an online class information library and the pre-training of the model, model training and discrimination, calculation of phased training preferences, amplification of class information, and training set update and model continuous training. The present invention optimizes the data augmentation method based on the model training preferences, optimizing the problems of most current data augmentation with manually set parameters and artificial matching, making the matching between data augmentation and the model more objective. At the same time, a discriminator is introduced in the training of the model, and the training is divided into multiple stages according to the training situation of the model. The class information amount of the training set is adjusted multiple times with phased training preferences, forcing the model to pay more attention to the learning of certain classes in the next training, making the matching between the data augmentation algorithm and the model more perfect.

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 application provides a knee joint rehabilitation training method based on multi-source feedback fusion, and relates to the technical field of medical rehabilitation, and comprises the following steps: before starting single training, physiological baseline of a patient is acquired, an initial resistance characteristic model and a pain threshold reference space are established; during training, a biomechanics sub-vector, a physiological sub-vector and a feedback sub-vector are constructed for each preset control period; a sliding window is used to perform trend analysis on the physiological sub-vector, and a pain prediction index is calculated in combination with the biomechanics sub-vector; the pain prediction index, a biomechanics safety score, a physiological safety score and a subjective safety score are weighted and fused to obtain a safety comprehensive score, and a multi-level response strategy is executed according to the safety comprehensive score; and the rehabilitation effect of the last M continuous trainings is combined to determine whether to advance to the next training stage or regress to the previous training stage. The application can avoid the risk of secondary injury, shorten the rehabilitation period, be personalized, reduce adverse psychology of the patient and improve the rehabilitation effect.

The utility model discloses a badminton training device which comprises a supporting device, a mounting device and a training device, the supporting device comprises a connecting assembly, three supporting assemblies and a telescopic assembly, and the three supporting assemblies are movably connected with the connecting assembly respectively; the telescopic assembly is movably arranged at the top of the connecting assembly; the mounting device comprises a mounting assembly and a suction cup, and the mounting assembly is movably arranged on the telescopic assembly; the sucker is connected with one side of the mounting assembly; the training device comprises a mounting block and a training assembly, and the training assembly is rotatably arranged on the mounting block. Therefore, the supporting device is arranged, it is ensured that the device can be firmly fixed to the ground, a stable exercise environment is provided, the rotatable training device is further arranged, every time a practicer completes the beating action of the badminton, the device can automatically rotate by a circle, then the device is presented to the optimal position again to wait for next beating, and the training effect is good. And seamless connection continuous training experience is realized.