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The application discloses a kind of virtual power plant distributed resource group control group regulation systems of virtual power plant technical field, comprising: virtual power plant regulation platform, deployment is in power company dispatching center, for according to the operation of power grid, issue plan adjustment demand and real-time adjustment command;Aggregator, for the total adjustable capacity and real-time total power of distributed resource reported by distributed resource group control group regulation terminal according to the overall adjustment of distributed resource, and according to the integrated distributed resource, plan adjustment demand and real-time adjustment command are decomposed, obtain the resource regulation requirement of each described distributed resource group control group regulation terminal, and issue to corresponding distributed resource group control group regulation terminal;Distributed resource group control group regulation terminal, for real-time calculation the total adjustable capacity and real-time total power of distributed resource in current jurisdiction range and report it to aggregator, and according to resource regulation requirement, select suitable distributed resource to carry out regulation.

This application discloses a human resource allocation method and system based on multi-source employment demand, relating to the field of knowledge graph technology. The method includes: acquiring employment task types and multi-dimensional task demand information; obtaining family-of-tasks and family-of-task demand information; and extracting neighboring task groups and neighboring task demand information groups; generating multi-dimensional capability parameters; processing and acquiring human resource matching degree, matched human resource information, and matched multi-dimensional capability parameters; and extracting neighboring human resource information groups and neighboring multi-dimensional capability parameter groups; performing derivative task matching analysis to obtain derivative human resource matching degree; and optimizing human resources based on the human resource matching degree and derivative human resource matching degree to obtain optimal human resource information for allocation. This application solves the technical problems of existing human resource allocation methods lacking the ability to predict potential derivative tasks and exhibiting information silos between the human resource database and the task database, leading to low matching efficiency and accuracy.