3 results about "Process graph" patented technology

The application discloses a zero sample graph learning method based on a large language model, comprising: neighborhood subgraph sampling on to-be-processed graph structure data to generate graph query text; based on the graph query text, a structured prompt template containing multi-stage reasoning instructions is constructed, and the prompt template instructs the large language model to sequentially perform topological structure analysis, semantic attribute interpretation, multi-candidate answer enumeration and deep reevaluation before generating a final answer; a graph reasoning training data set is constructed; the basic large language model is fine-tuned by full parameters by using the graph reasoning training data set; the large language model is optimized by using a reinforcement learning algorithm based on group relative advantage; for a new graph task to be reasoned, the new graph task is input into the optimized large language model, and a final prediction answer containing a reasoning process is directly output. The application discards the dependence of a graph neural network, and only uses the text reasoning capability of the large language model to complete a graph task, so that the deployment complexity and the calculation cost are greatly reduced.

This application discloses a method and system for identifying connection relationships in pipe and instrument diagrams based on graph neural networks. The method includes: acquiring a pipe and instrument flow diagram to be identified; detecting components and connecting lines in the pipe and instrument flow diagram to construct graph structure data; encoding the features of nodes and edges in the graph structure data respectively, and mapping the features of nodes and edges to a high-dimensional latent space to obtain initial feature vectors; inputting the initial feature vectors into a preset graph neural network, using multiple graph convolutional layers for information propagation and aggregation, learning and updating the latent space features of topological relationships between components; based on the updated topological representation features, identifying whether the connection relationships corresponding to each edge in the graph are valid through an edge classifier, and outputting the connection relationship identification result. This invention transforms the connection relationship identification problem into a graph connectivity judgment task, significantly improving the accuracy and robustness of pipe and instrument diagram connection relationship identification in complex scenarios.

The invention relates to a multi-machine-tool thermal error prediction space-time diagram modeling method based on meta-learning, and the method comprises the following steps: data collection: collecting the thermal deformation data of a main shaft and the temperature data of each part of a machine tool at a corresponding moment within a certain time from the starting to the operation of the machine tool at an interval time; then arranging temperature and thermal deformation data into a graph format required by SGAT, dividing a data set into a meta-training set, a meta-test set and a meta-task, and then dividing a training structure into internal circulation and external training by a meta-learning method; the processed graph data is put into an SGAT-Transform, training is carried out in combination with the divided meta training set, meta test set and subtasks, and a complete MAML-SGAT-Transform model capable of adapting to unknown working conditions is obtained; and after the MAML-SGAT-Transform model is trained, rapid adaptation is carried out on a support set of a corresponding meta test set, and the robust performance of modeling under multiple working conditions of a single machine tool and different working conditions of multiple machine tools can be enhanced.