12results about How to "Guaranteed Predictability" patented technology

The invention discloses a branch prediction unit power consumption management method and device, and the method comprises the steps: setting a multi-stage branch predictor and a low-power-consumption control logic coupled with the multi-stage branch predictor in a processor core micro-architecture, and enabling the multi-stage branch predictor to comprise three stages of predictors which are sequentially accessed in an assembly line, the hardware resource consumption and the prediction accuracy of each predictor are gradually increased from front to back; monitoring the prediction condition of each predictor in real time through low-power-consumption control logic, and closing all predictors behind the first predictor meeting the prediction requirement within a first preset time according to a front-to-back sequence; branch jump addresses are divided into high-order address fields and low-order address fields by BTBs in the multiple levels of predictors except the first-level predictor to be stored in different storage blocks, and label registers used for taking the high-order addresses as label indexes are arranged. The power consumption unit of each predictor can be finely controlled, the energy consumption is greatly reduced, system-level cooperation is not needed, and the universality is high.

The invention discloses a converter FPGA (Field Programmable Gate Array) small-step real-time simulation device and method based on a per unit fixed-point reconfigurable state space solution core. The upper computer processing unit carries out per-unit processing on converter model parameters according to the per-unit reference of the introduced margin coefficient, discretization and fixed-point word length configuration are completed under the preset step length, and a discrete fixed-point matrix coefficient table is generated for different switch / topology states and is downloaded to the FPGA. A universal fixed-point matrix operation array is arranged in the FPGA simulation unit, a pre-stored matrix coefficient is called according to a switch combination index obtained through real-time analysis during operation, and per unit fixed-point state updating and output inverse per unit are completed in a fixed time sequence prediction; therefore, on-line reconstruction and small-step real-time simulation of topology and parameters of the converter are realized under the condition that the FPGA bit stream is not updated.

The invention provides a lithium battery residual life prediction method and system based on data space position analysis, and the method comprises the steps: building a battery residual life space prediction model based on a convex polytope mechanism according to the basic measurement physical quantity of a lithium battery and the health condition parameter of the battery; determining the shortest length of the online trend as a spatial trend constraint according to the battery health condition parameter of the lithium battery; extracting spatial trend characteristics based on the characteristic parameters of the online operation data of the lithium battery to be detected, and determining a spatial trend straight line by combining spatial trend constraints; and determining a residual service life prediction value corresponding to the space trend characteristics of the lithium battery to be detected by integrating the battery residual service life space prediction model. By adopting the scheme, the defects of complicated operation and insufficient prediction result precision in the prior art can be overcome, and a battery residual life prediction result with high physical significance and high interpretability can be efficiently obtained.

The invention discloses a multi-layer automatic seedbed system which comprises a rack main body and is characterized in that a motor assembly seat is fixedly mounted on the lower side of the rack main body, a servo motor is fixedly mounted on the motor assembly seat, a driving gear is arranged at the driving end of the servo motor, and the driving gear is in meshed connection with a first transmission chain; the invention relates to the technical field of automatic seedbeds, and has the beneficial effects that a multi-layer metal frame forms a framework of a system, provides stable support and realizes intensive utilization of a vertical space, and a chain and chain wheel conveying system is a key for realizing automation. The system can drive the seedling raising plates to horizontally move or vertically ascend and descend among all the layers, the space efficiency is extremely high, the most prominent advantage is that through three-dimensional stacking, the production capacity of the unit occupied area is multiplied, and the system is very suitable for intensive production in environments such as greenhouses and plants with high space cost.

The application relates to a cloud service workload prediction method and system based on a convolution enhanced Transformer, which comprises the following steps: collecting cloud server workload data and preprocessing the cloud server workload data to decompose the cloud server workload data into a trend component and a residual component; constructing a workload prediction model, training the workload prediction model by using the trend component and the residual component, and obtaining a trained workload prediction model; predicting the cloud server workload by using the trained workload prediction model to obtain a workload prediction value; predicting the trend component by using a trend information capturing module to obtain a prediction result of the trend component; predicting the residual component by using a convolution enhanced Transformer encoder module to obtain a prediction result of the residual component; and fusing the prediction result of the trend component and the prediction result of the residual component by using a feature fusion module to obtain a final workload prediction value.

This invention relates to the field of medical data processing technology, specifically to a deep learning-based method for predicting adolescent anxiety. The method includes: quantifying gut microbiota characteristics, anxiety behavior characteristics, and environmental stress characteristics; calculating time-series weighting coefficients; integrating gut microbiota characteristics, anxiety behavior characteristics, environmental stress characteristics, and time-series weighting coefficients into a model; and outputting the prediction results. This invention fills the gap in dynamic biomarker modeling in existing anxiety disorder models by integrating research on the human gut microbiota with existing behavioral and environmental factors. Through deep learning, it seeks the long-term relationship between adolescent gut microbiota and anxiety symptoms, addressing the subjectivity, invasiveness, privacy risks, cost, and delays in existing anxiety disorder diagnosis, thus enabling early prevention. It also provides causal evidence for the "gut-brain axis" theory in adolescents, promoting the development of psychomicrobiome research.

The embodiment of the application relates to the technical field of data processing, in particular to a graph representation learning method and device for out-of-distribution generalization, equipment and a storage medium, aiming to obtain adaptive graph structure data representation of out-of-distribution environment and improve the accuracy of graph structure data related prediction. The method comprises the following steps: inputting an original graph data set into a graph structure data representation network, identifying stable subgraphs and noise subgraphs, performing representation processing on the identified graph structure data, obtaining vectorized representation of the stable subgraphs and vectorized representation of the noise subgraphs; simulating a multi-distribution environment, under the multi-distribution environment, performing prediction according to the vectorized representation of the stable subgraphs to obtain corresponding prediction results; performing loss function calculation on the prediction results and labels of the original graph structure data, optimizing parameters of the graph structure data representation network, and obtaining a graph structure data representation model; and performing a graph data related task through the model to obtain a target result of the graph data related task.

The application relates to the field of power electronics, in particular to a hybrid commutation method and system. The hybrid commutation method is used for the commutation of a current source type matrix converter, and the method comprises the following steps: using a 2 / 3 PWM modulation mode to alternately control the turn-on and turn-off of the upper switch tube or the lower switch tube of any two phases of A phase, B phase and C phase of the current source type matrix converter, and meanwhile making the lower switch tube or the upper switch tube of the remaining one phase in a clamping state; obtaining the voltage difference between the upper switch tube or the lower switch tube of the any two phases; when the voltage difference is greater than a preset voltage threshold, using a voltage type commutation mode to control the switching sequence of the upper switch tube or the lower switch tube of the any two phases. Sensor-free, reduced hardware cost and complexity: the method replaces real-time sensor detection through sector prediction and current change rule calculation, and high-bandwidth current sensors and precise voltage detection circuits are omitted, so that the system hardware cost is reduced.

The application relates to the cross field of artificial intelligence and environmental health, and discloses a membrane nephropathy prognosis risk stratification method based on exposure spectrum and clinical indicators. First, demographic information, clinical detection indicators and various environmental pollutant exposure data of patients are collected to construct a multi-level exposure spectrum feature, including single pollutant concentration, same type comprehensive exposure factor and pollutant synergistic interaction term. Then, the exposure spectrum and the clinical indicators are fused, various machine learning algorithms are used to construct candidate prediction models, the optimal prognosis risk stratification model is screened through cross validation and multiple performance indicators, and the SHAP value is used to quantify the contribution degree of each feature. Further, a double machine learning method is used to control confounding variables and estimate the causal effect of key pollutants on prognosis. On this basis, individual risk stratification and main risk driving factor identification of membrane nephropathy patients are realized, and scientific basis and guidance are provided for clinical decision making.

This invention relates to the field of communication status detection technology, specifically to an intelligent method and system for detecting the communication status of 5G communication projects. It includes dividing the network transmission into segments and deploying multi-segment node detection probes; establishing a communication fault analysis model to calculate the fault status of each network element node. This invention achieves multi-channel, multi-interface, and comprehensive detection of the communication status of 5G communication projects by dividing the network transmission into segments and deploying multi-segment node detection probes. It collects network communication status data from different locations across multiple segments, and simultaneously obtains parameter data relationships between different nodes through the established communication fault analysis model. It utilizes parameter values ​​fed back from probes in key deployment nodes to classify event types and match parameter value trends, and, in conjunction with a node status calculation function, calculates the corresponding fault score in real time. This allows for real-time fault prediction of the current faulty node from multiple perspectives, avoiding the limitations of prediction based on single-node data feedback and ensuring prediction effectiveness.

The application relates to the technical field of performance regulation of recycled concrete, and relates to a recycled concrete performance regulation method and system based on aggregate gradation optimization, which comprises the following steps: confirming a recycled concrete performance regulation environment based on a recycled concrete performance regulation instruction, pretreating engineering slag soil by using an aggregate pretreatment unit, grading the coarse aggregate sand and the fine aggregate sand based on an aggregate gradation optimization unit, pouring recycled lightweight aggregate concrete mixture by using a curing control unit, detecting the compressive strength, apparent density and thermal conductivity of the recycled concrete test piece by using a performance evaluation and regulation unit, and comparing and analyzing the actually measured performance index set with the preset target performance index set, so that the recycled concrete performance regulation of the engineering slag soil is realized based on the regulated recycled concrete. The application can improve the accuracy, efficiency, resource utilization rate and engineering applicability of the recycled concrete performance regulation.

The invention provides a cognitive diagnosis method and system based on causal inference, and relates to the technical field of cognitive diagnosis, and the method comprises the steps: obtaining the multi-dimensional features of a student sample and a test question sample; performing preliminary modeling on the multi-dimensional features, inputting the multi-dimensional features to a feature fusion module, and generating a potential representation in combination with an anti-factual reasoning encoder; the potential representation comprises potential feature vectors of different knowledge points; obtaining a first internal relationship between the ability and knowledge of the student and a high-order hierarchical relationship of different knowledge hierarchies by using different attention structures in the initial prediction model, and optimizing the first internal relationship and the high-order hierarchical relationship based on the potential representation to obtain optimization parameters; adjusting the parameters of the initial prediction model based on the optimization parameters to obtain a target prediction model, and inputting the multi-dimensional features of the to-be-tested student and the to-be-tested question into the target prediction model to obtain a cognitive diagnosis result and a score prediction result of the student.