2results about How to "Achieve prediction" patented technology

The invention relates to the technical field of vehicle control, and discloses a magnetorheological seat control method and system and electronic equipment. The method comprises the following steps: acquiring a current pavement vibration signal of a vehicle and a vibration frequency of a magnetorheological seat; if the vibration frequency of the magneto-rheological seat is lower than the preset resonance frequency, matching is conducted in a first preset relation table according to the road surface vibration signals, and the magnitude of control current of the magneto-rheological seat is determined so as to adjust the damping force of the magneto-rheological seat; and if the vibration frequency of the magneto-rheological seat is higher than the preset resonance frequency, matching is carried out in a second preset relation table according to the road surface grade and the road surface vibration signal, and the magnitude of control current of the magneto-rheological seat is determined, so that the damping force of the magneto-rheological seat is adjusted. Dynamic adjustment is carried out according to the road surface vibration signal and the seat vibration frequency, so that the seat control has the self-adaptive capacity, the damping response of the seat can be automatically adjusted according to different driving environments and road conditions, and therefore personalized comfort adjustment is achieved.

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.