5results about How to "Strong modeling ability" patented technology

This invention discloses a method, system, and storage medium for multivariate time-series feature extraction and grade prediction in flotation processes. The method includes the following steps: Step S1: Raw data input and encoding; encoding and structured input of time-series data composed of various process variables; Step S2: Extracting dynamic features; Step S3: Condition-guided encoding modeling; Step S4: Enhancing the saliency of key variables and important time segments; employing a multi-head attention mechanism to match key-value pairs generated from target-guided query vectors and multi-scale features; Step S5: Outputting a prediction module; performing feature fusion and nonlinear mapping on the attention mechanism output to output the predicted concentrate grade and recovery rate for future times. The system and storage medium are both based on the above method. This invention has advantages such as higher intelligence, better controllability, and improved prediction accuracy and model adaptability for key indicators in the flotation process.

The application discloses an intelligent temperature control method and system for an injection molding machine, which collects temperature values of a melt conveying section, a solid melting section and a solid conveying section of a material bucket of the injection molding machine, sets temperature target values of the three sections, predicts actual control values of the temperatures of the sections and a temperature target value of the whole material bucket of the injection molding machine and a temperature prediction value of the whole material bucket of the injection molding machine according to the temperature target values of the sections and temperature values collected by all the three thermocouples, outputs ideal control amounts of the temperatures of the sections of the material bucket of the injection molding machine and a temperature control amount of the whole material bucket of the injection molding machine after a temperature controller, and obtains temperature control amounts of the sections by fusing the two, acquires real-time compensation control amounts of the temperatures of the sections, and realizes decoupling compensation control on the temperature of the material bucket of the injection molding machine by combining the temperature control amounts of the sections and the real-time compensation control amounts of the temperatures. Compared with the prior art, the application can improve the accuracy, rapidity, robustness and comprehensiveness of temperature regulation of the injection molding machine.

A lithium ion battery state of health prediction method and system based on quantum gate coupling. The cycle operation data of the lithium ion battery is acquired, the health characteristics are constructed, and the time series samples are constructed. A quantum enhanced gate recurrent neural network is constructed, a plurality of parameterized quantum circuits are configured in at least one gate module, and a quantum gate coupling mechanism is established between different gate modules. The quantum feature representation output by the plurality of parameterized quantum circuits is fused with the inter-gate coupling information, and the gate function value is calculated. The gate function value is used to update the recurrent state, and finally the SOH prediction value is output. The application enhances the single gate expression capability through the structured combination of multiple quantum circuits, realizes information interaction and collaborative calculation through inter-gate coupling, and improves the nonlinear modeling capability of the model for the battery degradation process and the cross-cell generalization capability.

This application relates to the field of image recognition technology, and in particular to a method and system for detecting heat-sealing anomalies in industrial heat-sealing equipment. The method includes: acquiring the temperature matrix of the heat-sealing area of ​​the industrial heat-sealing equipment; extracting spatiotemporal feature information from the temperature matrix; training a dynamic temporal model based on the spatiotemporal feature information; using the dynamic temporal model to detect the real-time temperature matrix to determine whether abnormal pixels exist; if so, clustering and morphological processing are performed to determine whether heat-sealing anomalies exist. This application effectively overcomes the temperature baseline drift problem caused by factors such as equipment preheating, workshop airflow, and changes in ambient temperature, avoiding the high false alarm rate of traditional methods; and solves the problem of difficulty in collecting negative samples covering all defect types in industrial settings. While ensuring low resource consumption and high real-time performance, it significantly improves the accuracy, robustness, and generalization ability of detecting heat-sealing anomalies in industrial heat-sealing equipment.

The present application belongs to the cross field of biomedical signal processing and artificial intelligence, and particularly relates to a sleep staging system and method based on a double-flow parallel neural network. The system comprises a brain electrical device, a data acquisition and preprocessing module, a HypnoMamba-Dual neural network model and a downstream application module connected in sequence; the brain electrical device is a single-channel electroencephalogram signal acquisition device for acquiring original EEG signals; the data acquisition and preprocessing module is used for filtering and windowing the original EEG signals for standardized operation; the HypnoMamba-Dual neural network model is used for analyzing and calculating the preprocessed EEG signal sequence and outputting a sleep staging sequence. The double-flow architecture of the present application, especially the introduction of the "local reserved flow", provides a mechanism for the model to resist the "context smoothing effect". When the context flow tends to ignore the short N1 period, the strong instantaneous features provided by the local reserved flow can effectively correct the decision, thereby significantly improving the F1 score of the N1 period.