3 results about "Business goals" patented technology

The present application relates to a kind of intelligent interview evaluation and feedback system based on multi-modal data fusion, specifically relates to data processing field, by meta-learning mechanism dynamic perception interview scene and generate initial fusion weight, subsequently utilize the complex interaction relationship between modalities modeled by graph neural network to carry out fine-grained correction to weight, so as to significantly improve the accuracy and scene adaptability of multi-modal evaluation, further introduce reinforcement learning, link evaluation decision and long-term performance of talents, continuously optimize weight generation strategy, ensure that evaluation standard and business goal are aligned, finally, through closed-loop iteration mechanism, make the whole scheme can be updated automatically according to new data and performance feedback, continuous evolution, with strong self-optimizing ability and long-term robustness, realize the fundamental change from static rule to dynamic intelligent decision.

An implementation method, device and equipment for path searching along an original path as much as possible and a storage medium. In the application, the business goal of "searching along the original path as much as possible" is converted into a quantifiable priority index in the path searching algorithm. By judging the "deviation" degree of the candidate route from the original route in real time during the path searching process, and giving a lower priority to the route that deviates earlier, the search process is systematically guided to preferentially explore the route branch similar to the original path, and the optimal solution is efficiently found.

This invention provides a method and system for training large-scale models in vertical domains, belonging to the field of artificial intelligence technology. Based on a general-purpose large language model, the method sequentially constructs an instruction fine-tuning dataset through data engineering, introduces domain-supervised samples for joint optimization after multi-task pre-training to obtain a base model, employs a two-stage hybrid fine-tuning strategy to activate and consolidate domain knowledge, and finally performs dual-dimensional alignment training of business goal alignment and common sense alignment. The common sense alignment constructs training sample sets and selects appropriate optimization algorithms for four anomaly patterns in the model response: illusion, factual error, security and privacy, and timeliness issues. This invention significantly reduces the training cost of large-scale models in vertical domains, enhances the model's deep understanding of specific domain knowledge and instruction compliance capabilities, and improves the stability and security of performance in engineering applications.