2results about How to "Efficient split" patented technology

The application discloses a large-scale two-dimensional label map aggregation node optimization method, relates to the technical field of computers, and comprises the following steps: when a map interactive operation or label node data changes, the zoom level and the viewport range of a current prompt map are acquired; based on the viewport range, target label nodes in the viewport range are screened from the label node data; according to the zoom level, the basic aggregation parameters for label node aggregation are determined; and combined with the node density of the target label nodes in the viewport range, the basic aggregation parameters are dynamically adjusted to obtain the aggregation parameters for the current viewport range. The large-scale two-dimensional label map aggregation node optimization method significantly reduces the memory occupation and the calculation burden caused by repeated creation of node objects, improves the overall operation stability and the resource utilization efficiency of the system, and thus better meets the comprehensive requirements of performance, real-time performance and display effect of a large-scale two-dimensional label map application scene.

The invention discloses a knowledge-intensive multi-document question-answering method and product based on multi-agent cooperation, and the method comprises the steps: 1, dynamically generating a multi-dimensional complementary expert role set based on the multi-view agent cooperation of roles, forming a candidate strategy set based on all expert roles, and selecting an optimal strategy for integrating knowledge through a voting mechanism; 2, constructing a closed-loop process of refinement of the reflection knowledge, extracting atomic facts through a sliding window and an reflection construction mechanism, and performing a recursive process of task decomposition and knowledge distillation to obtain essence knowledge; 3, based on the optimal strategy in the step 1 and the essence knowledge obtained in the step 2, cross-document comprehensive reasoning is achieved, and a final answer is generated. According to the method, the limitation of a single role view angle is broken through, a high-quality knowledge basis is provided for subsequent reasoning, different types of knowledge-intensive cross-document tasks can be flexibly handled, dispersed and multi-source information is efficiently processed, and the method has wide application prospects and market value.