3results about How to "Improve output reliability" patented technology

The application discloses a multi-scene intelligent agent calling data processing method and system based on knowledge isomerization and belongs to the field of electric digital data processing. The method comprises the following steps: constructing an intelligent agent five-tuple description model and vectorization, realizing the capability standardization representation; accessing isomerized data through a multi-protocol gateway and pre-processing; carrying out demand deep identification based on a knowledge graph, mapping the pre-processed data to knowledge graph nodes, executing k-hop neighborhood search to extract an intention subgraph, and generating a demand feature vector through graph convolution neural network processing; a scheduling engine matches and calculates the demand feature vector and the intelligent agent capability feature vector, constructs a directed acyclic graph calling link according to the knowledge graph for a complex task; and the isomerized output is fused and the format is restructured by using evidence theory. The application realizes a complete closed loop from isomerized data input to multi-intelligent agent collaborative scheduling, and significantly improves the demand recognition accuracy and calling flexibility in multiple scenes.

The embodiment of the invention provides a time sequence signal fault prediction method and device, equipment and a storage medium, and relates to the technical field of anomaly prediction. The method comprises the following steps: generating a simulation vibration signal based on the vibration response of a gear, and inputting the simulation vibration signal into a gear fault prediction model for signal prediction to obtain a predicted vibration signal; physical information loss is calculated based on predicted displacement, predicted speed and predicted acceleration in the predicted vibration signals, predicted error loss is calculated based on the simulated vibration signals and the predicted vibration signals, a total loss value is calculated according to the predicted error loss and the physical information loss, parameter adjustment is carried out, and a trained gear fault prediction model is obtained. And inputting a to-be-predicted signal into the trained gear fault prediction model for signal prediction to obtain a reference vibration signal, and obtaining a time sequence abnormal result based on the reference vibration signal and the to-be-predicted signal. The prediction process fits the actual characteristics of the signal and the physical law of gear vibration, and the recognition accuracy and stability are improved.

The application provides an industrial cognitive base system based on multi-modal contrast learning and an execution method, wherein the industrial cognitive base system based on multi-modal contrast learning constructs a system architecture comprising a knowledge graph management module and a cross-modal encoding fusion module, dynamically injects industrial field knowledge in the form of a structured sub-graph into a multi-modal feature learning process, and combines a contrast learning mechanism with the introduction of industrial semantic constraints, so that the model obtained through final training can not only realize semantic alignment of multi-modal data, but also ensure that the unified semantic representation and reasoning results generated by the model strictly comply with pre-defined industrial logic rules, thereby effectively overcoming the defects that the simple data-driven method in the prior art may produce results contrary to industrial common sense, and significantly improving the output reliability, decision confidence and practical application value of the industrial cognitive system in key tasks such as fault diagnosis and state monitoring.