5results about How to "Realize dynamic deployment" patented technology

The application provides a method and system for checking the cross-modal information consistency of a maintenance plan based on a large model, and relates to the technical field of big data analysis. The method comprises the following steps: identifying a target maintenance plan through a power large model, determining key maintenance content, an unstructured proportion, and an attachment type complexity; obtaining a text data complexity and a business risk index based on key maintenance content analysis, as a first checking difficulty coefficient and a second checking difficulty coefficient; determining a third checking difficulty coefficient according to the unstructured proportion and the attachment type complexity evaluation; taking the expected review time limit of the target maintenance plan as a constraint, formulating an adaptive checking depth and an adaptive checking breadth based on the first checking difficulty coefficient, the second checking difficulty coefficient, and the third checking difficulty coefficient; and performing cross-modal information consistency checking on the target maintenance plan according to the adaptive checking depth and the adaptive checking breadth. The dynamic allocation of resources for checking the cross-modal information consistency of a maintenance plan is achieved.

The invention relates to the technical field of resource scheduling, and discloses a resource scheduling distribution management method and system, a medium and a processor, and the method comprises the steps: obtaining heterogeneous resource information of each node under a system computing power network; acquiring quotas of various heterogeneous resources applied by the configuration file of the Pod to be scheduled in the system; according to the heterogeneous resource information of each node and the quota of various heterogeneous resources applied by the to-be-scheduled Pod, calculating a node load score and a node resource balance degree score of the to-be-scheduled Pod scheduled to each node; obtaining a scheduling scoring result of each node according to the node load score and the node resource balance degree score; and according to the scheduling score result, selecting the node with the highest scheduling score in all the nodes to schedule the current Pod to be scheduled. According to the method, two factors of node load and node resource balance degree can be comprehensively considered, various heterogeneous resources under the computing power network are managed and scheduled, and the load of each node and dynamic allocation of the resources are balanced.

The invention provides a multi-unmanned aerial vehicle task allocation system based on a two-stage multi-population wolf pack algorithm, and the system comprises an initialization module which defines the position of an individual wolf in the two-stage multi-population wolf pack algorithm as a multi-unmanned aerial vehicle task allocation scheme, and divides a wolf pack into a plurality of sub-populations, including a common group and a leader group; the wandering module is used for re-ordering task numbers by adopting different wandering frequency calculation methods for the common group and the leader group for wolf detection; the calling module is used for learning the scheme of the current first wolf at different convergence speeds by the common group and the leader group; according to the purse module, except the head wolf, the individual wolves of the common group and the leader group adopt different threshold values to carry out variation or learn the scheme of the low-fitness individual wolves; the individual wolf reconstruction module is used for reconstructing by adopting a roulette mechanism; and the sub-population adjustment module is used for starting a multi-population mechanism after reconstruction is completed, and reforming a leader group. The wolf pack algorithm is improved through multiple mechanisms, and the solving efficiency and accuracy are improved.

The application discloses an engineering safety risk perception method based on deep learning, which comprises the following steps: step one, collecting engineering safety data of a construction site; step two, performing data preprocessing to generate a standard multimodal sample set; step three, generating a risk perception tensor sequence through the improved FusionNet network from the standard multimodal sample set; step four, constructing an engineering risk perception vector sequence based on the risk perception tensor sequence; step five, inputting the engineering risk perception vector sequence and the risk perception tensor sequence into a risk reasoning model to generate a construction site risk perception result; step six, collecting safety event feedback information of the construction site, and updating the improved FusionNet network and the risk reasoning model; and step seven, redeploying the updated improved FusionNet network and the risk reasoning model. The application improves the perception ability of engineering safety risks through the improved FusionNet network.

The embodiment of the invention provides a vehicle defrosting method and electronic equipment, and the method comprises the steps: responding to a received defrosting instruction of a target structure in a vehicle, determining a target defrosting type corresponding to the defrosting instruction from a plurality of defrosting types, obtaining the layout information of a heating system in the vehicle and a heating demand value of the target structure, and on the basis of the heating system layout information, a first sub-heating area in the target structure and a first heating line corresponding to the first sub-heating area are determined, and on the basis of the target defrosting type, the first sub-heating area is heated by using a heating current value and a heating demand value corresponding to the first heating line, so that the target structure is defrosted. The technical problem that the defrosting efficiency of the vehicle is low in the related technology is solved.