4results about How to "Mapping implementation" patented technology

The invention discloses a Kubernetes-based GPU fault automatic monitoring method and system, and relates to the technical field of computer monitoring, and the method comprises the steps: running a GPU detection program at each node; indexes are collected, wherein the indexes comprise XID and / or SXID error events in the system logs and the driving state and the equipment running state obtained based on the NVML; standardizing the indexes into an index data structure; generating a fault judgment result associated with the node identifier and the GPU equipment identifier according to a preset evaluation rule; and when the data is abnormal, the data is mapped as a Node Condition and / or reported to a Kubernetes Event, and a Prometheus acquisition interface is exposed to be pulled and stored. Through the technical scheme of the invention, GPU fault automatic identification and card level positioning are realized, cluster perceptibility and alarm traceability are enhanced, manual troubleshooting cost is reduced, and cluster stability is improved.

The invention relates to the technical field of DRG automatic grouping, and discloses a DRG automatic grouping method and system based on semantic information fusion, and the method comprises the steps: collecting the personal information, medical record information and diagnosis information of a patient to form electronic health record data, carrying out the semantic information extraction of the electronic health record data of the patient, and obtaining a semantic information extraction result; obtaining a segmented semantic information feature vector; semantic enhancement processing is conducted on the segmented semantic information feature vectors, and DRG grouping information of the patients is generated; and carrying out patient clustering processing based on the DRG grouping information and the personal information. According to the method, the segmented semantic information feature vectors are spliced, enhanced and subjected to norm constraint processing, disease group information representing disease diagnosis classes, processing modes and complications of patients is formed, different disease group information is mapped to corresponding DRG codes, DRG grouping information of the patients is obtained, automatic DRG grouping is achieved, and the accuracy of DRG grouping is improved. And carrying out patient clustering and batch distribution of medical resources in combination with personal information.

This invention discloses a digital twin system for multi-wire diamond wire saw cutting, comprising: a digital twin machine tool, a machine tool model built based on NX MCD software, including a worktable, two spindle rollers located below the worktable, diamond wire wound on the two spindle rollers, and guide wheels driving the spindle rollers to rotate; each moving part is equipped with an MCD signal; the diamond wire forms multiple wire saws between the two spindle rollers, and the wire saws have cutting function, wire bow visualization function, and motion visualization function; a data acquisition and transmission module, which acquires external data and transmits it to the digital twin machine tool and the MCD signals of the moving parts for mapping and association; and a data display interface, which displays the external data in text form. This invention establishes a mapping relationship between real machining process data and a virtual twin model, realizing the mapping of machining process information on the virtual twin model, which will be of great significance for understanding the multi-wire cutting process and optimizing the multi-wire cutting process technology.

The present application relates to the field of computer vision, and more particularly to a face emotion classification system based on multi-modal image recognition, comprising a multi-modal data acquisition module, a data preprocessing module, a face detection and target detection module, a feature extraction module, a global context modeling module and an emotion classification output module, wherein the present application introduces a KAN nonlinear channel mapping mechanism after the visual Transformer feature output, utilizes the function approximation capability based on the Gaussian radial basis function to perform a channel-by-channel nonlinear transformation on the multi-scale features, realizes the mapping from the linear semantic representation to the nonlinear semantic space, and thus enhances the expression capability of the model on the micro-expression changes and the fine-grained emotion features; the present application introduces a state space modeling mechanism in the global context modeling module, and combines the KAN gating nonlinear modulation mechanism to realize the linear complexity modeling on the long-range dependency relationship, and simultaneously enhances the nonlinear expression capability in the state space.