4results about How to "Avoid timeout" patented technology

The application belongs to the technical field of power system automation and artificial intelligence application, and is a power supply service AI assistant system based on whole-process time limit early warning, characterized by comprising: a work order access module, which is used for receiving fault work orders from a 95598 customer service system and an active repair system; a work order execution module, which automatically distributes work orders to the terminals of repair personnel in corresponding grids and records the time of distributing work orders; a time limit rule library, which is used for storing time limit thresholds of each link; a whole-process early warning module, which is used for monitoring the flow state of work orders in each link; and a data feedback module, which is used for recording time limit execution data of each work order in the whole process, generating an overtime risk analysis report, and providing data support for parameter optimization of the time limit rule library. The problems of insufficient whole-process time limit control of fault repair work orders, single follow-up mechanism and process overtime of repair personnel caused by on-site factors in the prior art can be solved.

This application discloses a request processing method, apparatus, electronic device, and storage medium. The request processing system includes at least two hierarchical processing systems. A data layer stores business data for the region corresponding to the hierarchical processing system. An access layer receives requests initiated by clients from the region corresponding to the hierarchical processing system. The method includes: after the access layer in the first hierarchical processing system receives a request initiated by a client from the first region, the routing layer in the first hierarchical processing system determines, among the at least two hierarchical processing systems, the target hierarchical processing system where the target business layer for processing the request is located; the routing layer in the first hierarchical processing system routes the request to the target business layer in the target hierarchical processing system, so that the target business layer obtains the corresponding business data from the target data layer in the target hierarchical processing system and processes the request. This solution achieves effective request processing while ensuring business data isolation.

This application discloses a request processing method, apparatus, electronic device, and storage medium. The request processing system includes at least two hierarchical processing systems. A data layer stores business data for the region corresponding to the hierarchical processing system. An access layer receives requests initiated by clients from the region corresponding to the hierarchical processing system. The method includes: after the access layer in the first hierarchical processing system receives a request initiated by a client from the first region, the routing layer in the first hierarchical processing system determines, among the at least two hierarchical processing systems, the target hierarchical processing system where the target business layer for processing the request is located; the routing layer in the first hierarchical processing system routes the request to the target business layer in the target hierarchical processing system, so that the target business layer obtains the corresponding business data from the target data layer in the target hierarchical processing system and processes the request. This solution achieves effective request processing while ensuring business data isolation.

This invention relates to the field of text classification technology, and more particularly to a text classification method, medium, and device. It dynamically determines the first parallel inference quantity M using computational resources, achieving a balance between computational resource utilization and inference stability. By initiating multi-threaded parallel inference, it generates M candidate output sequences containing a first intermediate semantic representation and a first text classification result, covering different inference logics of the model for the target text. This approach balances result diversity and inference efficiency, reducing classification errors caused by single-sequence inference bias. Through compliance verification and confidence assessment, it calculates a first quality score to obtain the target classification result. This not only filters sequences containing security risks or structural defects, ensuring the security of the classification process, but also distinguishes the reliability differences of the classification results, avoiding one-sided judgments that ignore reliability based solely on labels. In scenarios without real labels, it achieves objective quality quantification, ensuring the accuracy of the text classification results.