3results about How to "Guaranteed refinement" patented technology

The present application belongs to the technical field of resource allocation, and particularly relates to a space resource dynamic allocation method based on an AI model and BIM operation and maintenance, comprising: obtaining a to-be-processed task and a spatial position identifier thereof, and constructing a task feature vector set; inputting the task feature vector set into a modal recognition network model to obtain a plurality of primary scheduling modes containing spatial coupling components and resource demand components through mapping; iteratively updating coupling weights between the primary scheduling modes and high-level scheduling modes until convergence, determining a current resource allocation mode based on the converged high-level scheduling modes, and determining the association strength of each task relative to the current resource allocation mode based on the coupling weights; generating a scheduling instruction set containing joint scheduling paths and independent scheduling instructions according to the association strength and in combination with the spatial position identifier, and converting the scheduling instruction set into executable control instructions; and the present application realizes fine and adaptive dynamic resource allocation based on BIM and real-time situation.

This invention relates to the field of image processing, specifically providing an image segmentation processing method, control device, and readable storage medium, aiming to solve the problem of how to achieve more accurate segmentation of subtle categories in images. To this end, the image segmentation processing method of this invention includes: extracting features from the image to be segmented to obtain extracted features and stitched features, wherein the stitched features are obtained by further extracting features from the extracted features; and selecting, according to preset conditions, to perform image segmentation using either the stitched features or the combined stitched features and extracted features, thereby obtaining a segmentation category map. This solution preserves the high resolution of the image and improves the accuracy of image segmentation. During segmentation, in addition to using the stitched features output by the feature extraction network, feature information from the feature extraction stage is also incorporated, providing more referenceable feature information when optimizing the segmentation results, further ensuring the refinement of the segmentation results.

The invention relates to the technical field of tobacco baking, and provides a baking process based on the quality of fresh tobacco leaves of Xiangyan No.7. The baking process comprises the following steps: S1, carrying out near infrared spectrum scanning on harvested fresh tobacco leaves to obtain near infrared spectrum data of each tobacco leaf; s2, dividing the near infrared spectrum data into a training set and a test set; s3, taking the near infrared spectrum data of each tobacco leaf in the training set as an input vector, inputting the input vector into a self-organizing neural network model for clustering, and dividing the tobacco leaves into three subsets; s4, aiming at the three divided subsets, baking process parameters are optimized according to a three-section baking process, and the three-section baking process comprises a yellowing period, a color fixing period and a stem drying period; and S5, performing near infrared spectrum scanning on newly harvested tobacco leaves, classifying the newly harvested tobacco leaves through the self-organizing neural network model, determining subsets to which the newly harvested tobacco leaves belong, and according to the baking process curves corresponding to the subsets, sequentially executing the baking processes of a yellowing period, a color fixing period and a stem drying period.