3results about How to "Avoid low recognition accuracy" patented technology

The invention discloses a merging unit error self-monitoring method, system and device, a storage medium and a program product, and relates to the technical field of data processing, and the merging unit error self-monitoring method comprises the steps: obtaining high-frequency sampling data; performing error related feature extraction based on the high-frequency sampling data to obtain error evaluation features; and performing error classification and trend prediction based on the error evaluation features to obtain an error type and an error trend. According to the method, error related feature extraction is carried out by acquiring high-frequency sampling data, so that inherent calculation errors caused by insufficient sampling are reduced, and meanwhile, the problem of low recognition precision caused by simple threshold comparison is avoided; through prediction of the error trend, advanced risk prediction of the error condition is realized, and richer and more predictive information support is provided for operation and maintenance decisions. By constructing a progressive perception decision-making system, high-precision and real-time self-monitoring of various errors is realized in a merging unit, and the use cost of the system is reduced.

The invention discloses an osteosarcoma detection method and system based on multi-target collaborative recognition, and relates to the technical field of target recognition. The method comprises the following steps: firstly, acquiring multi-modal target point data of a target image area, performing feature coding processing to obtain a multi-target point feature vector, performing adaptability verification in a multi-target point combination process, and then performing abnormal coding vector identification on a gene target point vector in a corresponding target point combination after the adaptability verification is qualified, so as to obtain a multi-target point feature vector. And finally, carrying out gene target specific binding detection and non-linear monitoring on a detection signal amplification and response process to judge whether a detection result is output, so as to realize the purpose that in the multi-target identification process, the detection result is accurately detected, and the detection accuracy is improved. Through accurate cooperative processing of serum, image and gene cross-modal data, an effective data support is provided for accurate recognition of osteosarcoma mutation sites and an osteosarcoma detection process.

This disclosure relates to the field of artificial intelligence technology, and in particular provides a cloud PC mining detection method and system based on supervised classification. The method includes collecting system-level resource utilization data of each virtual machine (VM) in a cloud PC cluster within a preset time period; extracting features from the collected system-level and process-level resource utilization data to construct a multi-dimensional feature vector characterizing the behavior patterns of the VMs; the multi-dimensional feature vector includes at least statistical features and fluctuation features of system-level resource utilization, as well as statistical features and fluctuation features of process-level resource utilization; based on the multi-dimensional feature vectors of known mining and normal cloud PC samples, for the VM to be detected, data acquisition and feature engineering steps are performed to obtain its multi-dimensional feature vector, improving the quality and efficiency of detecting illegal VMs and effectively ensuring the adaptability of the optimized method.