13 results about "Process training" patented technology

This invention discloses a development and training method and system based on artificial intelligence in the logistics field, including the following steps: receiving input of logistics professional training requirements, preprocessing and standardizing the input content, completing the training environment configuration and resource initialization, and obtaining clear training objectives and resource configuration parameters; based on the determined training objectives, collecting relevant data of logistics scenarios. This invention deploys full-process logistics professional training by constructing an edge-cloud collaborative training architecture, breaking the limitations of traditional training equipment and scenarios, improving the flexibility and practicality of training, processing training data through a combination of automated annotation and manual verification to improve data annotation efficiency and accuracy, quickly constructing high-quality standardized logistics training datasets, and building AI models for logistics scenarios through hyperparameter optimization and incremental training mechanisms to improve model adaptability and recognition capabilities, and lower the model development threshold.

The invention provides a model training method and device, a task processing method and device, equipment, a medium and a product, and the model training method comprises the steps: processing training data according to a text generation model of a current training round to obtain a prediction text set and a sample label set corresponding to the training data, determining reward values corresponding to the prediction text set in a plurality of preset optimization dimensions; according to the reward value corresponding to each preset optimization dimension, determining an advantage value of each preset optimization dimension; according to the preset dimension weight of each preset optimization dimension and the advantage value of each preset optimization dimension, determining a dimension loss value of each preset optimization dimension; and training a text generation model according to the dimension loss values of the plurality of preset optimization dimensions to obtain a text generation model of the next training round. According to the embodiment of the invention, the training precision of the text generation model can be improved.

The invention discloses a self-adaptive promotion robot driving training method based on capability grading and task decomposition, and aims to overcome the defects that in the prior art, test taking is emphasized, the capability is light, feedback is extensive, and a path is rigid. According to the method, a pyramid type three-layer teaching framework is adopted, and basic vehicle control training, atomic operation fragment strengthening and comprehensive process training are sequentially carried out. The system collects operation data in real time, constructs a dynamic ability portrait based on indexes such as vehicle speed standard deviation, calculates a comprehensive score through a weighting formula, realizes self-adaptive promotion in combination with a threshold value, and supports personalized point location marking and flexible splitting training. According to the invention, transformation from test taking to ability is realized, training precision and efficiency are improved, individual demands of trainees are adapted, teaching resource configuration is optimized, and the method is suitable for intelligent driving training scenes.

This application discloses a model training method and a cloud management platform based on a cloud management platform, which can improve model training efficiency. The method includes: a tenant sending a model training request for a specific model to the cloud management platform, whereby the tenant sets the number of queries, keys, and values ​​for the model. Based on this request, the cloud management platform determines multiple computing nodes. If the number of computing nodes exceeds the number of keys, the cloud management platform decomposes the model into multiple sub-models and deploys them on these computing nodes. These computing nodes can use these sub-models to process training data, obtaining the queries, subkeys, and subvalues. Then, these computing nodes can fuse the subkeys and subvalues ​​to obtain the keys and values. Subsequently, these computing nodes can use the queries, keys, and values ​​to complete the subsequent training of the sub-models.

This invention discloses a modular VR / AR surgical teaching system and method based on a local large-scale model. The local large-scale model training module is deployed in a local computing environment, storing structured medical knowledge and dynamically generating teaching scripts and assessment question banks. A lightweight VR / AR hardware group collects multi-dimensional user operation data and provides multi-sensory feedback. A full-process training control module, with a built-in physical simulation engine and evaluation logic, constructs a virtual surgical scene, simulates tissue mechanical changes in real time, compares user operations with standard surgical procedure models and generates error correction guidance, and generates a quantitative ability assessment report based on the entire process data. A multi-scene data mapping interface adopts a modular architecture based on a standardized intermediate communication protocol, achieving low-coupling logical isolation and dynamic integration between functional modules through a unified data exchange format and abstract interface specifications. This application adopts an edge-side deployment strategy to protect patient privacy and ensure real-time force feedback and tissue deformation rendering in the VR / AR scene.

The present application relates to a computing system, a model training method and apparatus, and a product. The computing system relates to a computing unit, and the computing unit comprises: a main board, which is configured with a central processing unit (CPU); and a base board, which is connected to the main board by means of a first communication link, wherein the base board is configured with a plurality of accelerator cards, and the plurality of accelerator cards are connected to each other by means of a second communication link. The main board is used for splitting a training task of a target model into a plurality of concurrent model training tasks and releasing same to the plurality of accelerator cards, and processing training results of the plurality of accelerator cards, so as to obtain a trained target model. The plurality of accelerator cards are used for concurrently executing the respective model training tasks thereof, so as to obtain the training results. The computing system forms an elastically scalable computing system architecture by means of modular base-board design and interconnection, such that the computing power and bandwidth of the computing system can match model training tasks at different parameter scales.

An XR-based semiconductor manufacturing process training device according to an embodiment of the present invention is configured to determine set values for parameters based on user input data, identify the spec data to which the set values belong among at least one spec data corresponding to the parameters defined in each manufacturing process, assign weights corresponding to the spec data to the set values, and calculate and display final data based on the first motion information and the weighted set values.

The invention provides a model generation method, a recommendation method and a related device. The method comprises the following steps: constructing a single upright post connecting operation process training data set; performing data treatment on the training data set in the operation process of the single upright post to obtain a treated data set; based on feature engineering, performing data feature extraction on the treated data set to obtain a single upright post connecting operation process target training data set, and determining relevance features of the extracted features; and based on the relevance features and the target training data set in the operation process of the single upright post, training to obtain an intelligent recommendation model of the full-time nodes of the operation process of the single upright post and the working parameters of the single upright post. The method is used for improving the accuracy and reliability of the intelligent recommendation model of the working procedure full-time nodes of the single connecting stand column and the working parameters of the single connecting stand column, so that the intelligent recommendation model can keep efficient and stable performance in different application scenes, and a solid foundation is provided for scientific and accurate drilling operation; and the drilling operation efficiency is further improved.

A computer-implemented integration management system for the operational onboarding of a personnel unit into an institutional IT environment, wherein the system includes the following: a storage unit that is operationally coupled to a processor unit and configured to store role definition records, operational evidence records, milestone evaluation records, and integration evaluation records; a display interface unit configured to display information on integration progress, milestone assessment results, and operational task plans on a display device; a network communication interface configured to exchange institutional system data with external computer systems, including human resources management systems, institutional platforms, and communication servers; a data interface unit for role configuration, configured to receive role definition records that include task specifications, regulatory compliance parameters, institutional resource identifiers, and operational milestone plans linked to the personnel unit; a processing unit executed by the processor unit for generating a role matrix, configured to generate a role requirement matrix record from the role definition records, wherein the role requirement matrix record includes task identifiers, responsible entity identifiers, deadline parameters, and verification check fields stored in memory; a processing unit for deriving deployment plans, configured to generate a multi-level operational deployment record from the role requirement matrix record, wherein the deployment record includes sequential operational phase records and corresponding task execution parameters; an identity provisioning control unit configured to generate digital identity records, including authentication data, institutional account identifiers, and platform access authorization records, which are linked to the personnel unit; a training data processing unit configured to store and process training module datasets that include training module identifiers, competency verification parameters, and records of training completion; a milestone evaluation unit configured to process operational evidence records, including task completion logs, institutional system usage logs, mentoring interaction logs, and training completion logs, to generate milestone evaluation scores; a processor for calculating the integration value, configured to calculate an integration value based on the milestone evaluation values, the training completion records, and the stored operational evidence records; and a decision control unit configured to compare the integration value with predefined threshold parameters and generate appropriate adjustment control signals for operational reinforcement measures.

The invention discloses a model updating method and a related product. The method comprises the steps of obtaining to-be-processed training data; the to-be-processed training data comprises unlabeled data and labeled data; performing clustering processing on unlabeled data in the to-be-processed training data to obtain multiple groups of unlabeled data clusters; generating a label for each group of label-free data clusters in the to-be-processed training data to obtain the training data after the first processing; carrying out expansion processing on data with labels in the training data processed for the first time to obtain the training data after expansion processing; and carrying out updating training on the model by utilizing the training data after expanding writing. According to the method, the dependence of traditional supervised learning on large-scale manual annotation is effectively relieved, and the annotation cost and the iteration period are remarkably reduced; meanwhile, by introducing a long-tail semantic mode in the label-free data, the generalization ability of the model for unseen expressions is enhanced, the recognition effect on low-frequency intentions is improved, the efficiency of generating new data in the model updating process is improved, and the cost is reduced.

The invention relates to an evaluation method for aviation emergency simulation rescue comprehensive training, belongs to the technical field of emergency rescue task training evaluation, and solves the problem that in the prior art, aviation emergency rescue training lacks multi-role collaborative comprehensive evaluation. A task process training evaluation model is established and used for executing task completion condition evaluation, including individual completion condition evaluation and unit completion condition evaluation, and individual completion condition evaluation and unit completion condition evaluation are obtained; s2, establishing a task cooperative training evaluation model for obtaining a unit cooperative training evaluation value; and S3, obtaining task training parameters through VR training, providing the task training parameters to the established task process training evaluation model and the task cooperative training evaluation model, obtaining individual completion condition evaluation, unit completion condition evaluation and unit cooperative training evaluation values as evaluation results, and providing the evaluation results to an analysis process and a decision process.

The application relates to a multi-label learning method, device and equipment based on sample missing label enhancement, which comprises the following steps: acquiring a training data set of a missing label sample; pre-processing the training data set to obtain a processed training set with restored real labels; learning and aggregating the processed training set by using an algorithm adaptation strategy to obtain a multi-label learning classifier; taking the classifier as a label prediction model; and inputting a sample to be predicted into the label prediction model to obtain labels corresponding to the sample to be predicted. The method realizes label information enhancement by obtaining the processed training set with restored real labels; then the processed training set is induced by using the algorithm adaptation strategy to obtain a classifier considering the class imbalance problem in the processed training set; and the label prediction model is constructed based on the classifier to solve the multi-label class imbalance problem and improve the precision and accuracy of the predicted labels.

An example apparatus includes interface circuitry to obtain data; samples to train an AI-based model; machine readable instructions; and at least one programmable circuit to at least one of instantiate or execute the machine readable instructions to: transform the data samples into features; generate hash signatures for corresponding ones of the features; group the features into clusters based on the hash signatures; generate a filtered data set by filtering out features within a cluster of features having more than a threshold number of features; and train the AI-based model based on the filtered data set.