4results about How to "Improve self-learning ability" patented technology

The invention discloses an interactive load prediction method based on fusion of prior knowledge and data driving, and the method comprises the steps: reconstructing a load sequence into a trend load sequence and a disturbance load sequence through modal decomposition, and constructing a prior knowledge base of tag-feature weight according to SHAP interpretability analysis; and through a prior knowledge base and feature contribution analysis, channel-level weight learning is carried out by using an automatic correlation determination module, and key input features are obtained. And inputting the reconstructed load sequence and the key input features into an improved Transform model, mapping a priori knowledge base into a priori knowledge matrix, embedding the priori knowledge matrix into the model, and generating a load prediction result. And carrying out interpretability analysis on a prediction result, and feeding back and updating an analysis result to a priori knowledge base to realize dynamic collaborative updating of prediction and the knowledge base. According to the invention, organic combination of expert knowledge and a data-driven model is realized, and the precision and stability of load prediction are improved.

This invention discloses a closed-loop control method and system for prevention, intervention, and risk feedback in cerebral hemorrhage, relating to the field of cerebral hemorrhage risk intervention technology. It includes the following steps: S1, real-time acquisition of multi-source cerebrovascular monitoring data, data preprocessing, and identification of high-pressure peak events; S2, quantification of key dynamic characteristics of each peak based on the multi-source cerebrovascular monitoring data corresponding to the high-pressure peak events, and assessment of high-pressure peak abnormalities; S3, fusion of multi-source cerebrovascular monitoring data and high-pressure peak abnormality assessment results, quantification of the risk probability of each peak event, identification of physiological fluctuations and risk events, and risk warning and response; S4, real-time monitoring of multi-source cerebrovascular monitoring data after risk warning, assessment of warning accuracy, identification of missed and false alarms, and threshold optimization. This solves the technical problems of existing technologies, such as difficulty in distinguishing between physiological and pathological peaks, susceptibility to misjudgments, and impact on the timeliness and individualized management of risk intervention.

The invention discloses an intelligent coffee machine operation state monitoring method and system based on reinforcement learning, and the method comprises the following steps: S1, collecting and preprocessing data, and forming an operation parameter sequence; s2, carrying out dimension reduction processing on the operation parameters, and constructing an operation state sequence; s3, performing multi-step prediction on the running state sequence through a DLinear model, and generating a state prediction sequence by adopting a linear predictor; s4, calculating an instant reward value of each regulation and control behavior according to the running state sequence and the state prediction sequence; s5, an A3C algorithm is adopted, and a regulation and control instruction is generated and executed according to the instant reward value; s6, establishing a state transition group and writing the state transition group into an empirical data set; and S7, updating the DLinear model and A3C algorithm parameters according to the empirical data set. According to the method, the Markov model, the principal component analysis, the DLinear model and the A3C algorithm are fused, and the method has the advantages of being high in adaptability, high in prediction precision and good in stability.

This invention relates to the field of scheduling technology, specifically to an intelligent MCU scheduling method based on adaptive edge learning. The method includes the following steps: acquiring the working status data, historical scheduling behavior data, and peripheral sensing signals of the MCU running node; constructing a task running scenario feature vector and an edge classification model based on the acquired task load parameters, peripheral call indicators, power consumption sampling data, and runtime; inputting the task running scenario feature vector into the edge classification model, performing an initial scheduling strategy matching operation, generating a candidate strategy set, and outputting scheduling strategy category labels; matching the corresponding scheduling control template according to the scheduling strategy category labels, and executing the MCU task scheduling process in conjunction with a preset task queue scheduling relationship; recording the context chain of abnormal events to construct a task abnormal behavior chain; and triggering the edge learning module to correct the scheduling strategy category labels based on the task abnormal behavior chain. This invention can improve resource utilization efficiency.