4 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.

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.

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.