4 results about "Unified Process" patented technology

The application provides a configuration-driven DL4NLP model automatic training and deployment method, which comprises the following steps: parsing a YAML configuration file and verifying parameters to generate a unified process configuration; automatically loading data and performing preprocessing according to the configuration to generate a model input tensor; instantiating model components through a component factory and assembling the model components into a complete network model; automatically configuring a training environment to train and evaluate the model to obtain an optimal model; exporting the model into a TorchScript format binary file; and automatically generating a standardized deployment package based on a template engine. The application abstracts the whole process of model development and deployment into a configuration-driven standardized pipeline by parsing the YAML configuration file, automatically performs data loading and preprocessing, model component instantiation and assembly, training optimization and deployment package generation, and solves the problems of low development efficiency, long cycle and technical gap between training and deployment caused by repeated code writing and manual conversion and deployment for different tasks in traditional model training and deployment.

Methods and systems include converting an event log for an application into event log process information. The source code for the application is converted into source code process information. Relations between the event log process information, the source code process information, and an initial process model that is implemented by the application are identified using fuzzy logic on a set of events. A unified process model is generated using the relations. A problem in execution of the application is resolved based on the unified process model.

Methods and systems for system incident resolution include converting an event log for an application into event log process information. Source code for the application is converted into source code process information. A unified process model is generated from the event log process information, the source code process information, and an initial process model that is implemented by the application. A problem in execution of the application is resolved based on the unified process model.

The invention relates to the cross technical field of intelligent manufacturing, industrial artificial intelligence, a big data technology and high-end material engineering, in particular to a neodymium iron boron production process management method based on artificial intelligence and a big data technology. According to the method, a'big data platform + AI algorithm engine 'double-wheel-driven intelligent management system is constructed, artificial intelligence technologies such as machine learning, deep learning, knowledge graph and digital twinning are deeply fused with a mass multi-source heterogeneous big data processing technology, and the method runs through the whole process of neodymium iron boron material production. The core comprises the steps of constructing a unified process data lake based on a big data technology, and realizing fusion treatment of multi-source data; establishing a quantitative correlation model of process parameters and product performance based on a machine learning algorithm; digital expression and intelligent reasoning of process knowledge are realized based on the knowledge graph; constructing a virtual process simulation optimization platform based on a digital twinning technology; and finally, a continuous evolution closed loop of data acquisition, feature extraction, model training, intelligent decision making and feedback optimization is formed. The technical problems that in traditional neodymium iron boron production, value mining of mass data is insufficient, process decision depends on artificial experience, quality control lags behind, and the process optimization period is long are effectively solved, and fundamental transformation of the production process from experience driving to data and intelligent double-wheel driving is achieved.