4results about How to "Increase dominance" patented technology

The application provides a grid synchronization control method, device, equipment and medium of a photovoltaic inverter, comprising: collecting three-phase voltage signals at a grid point of common connection in real time, and preprocessing the three-phase voltage signals to obtain q-axis voltage residuals; serializing sampling the q-axis voltage residuals to construct a short-time phase deviation time sequence; inputting the short-time phase deviation time sequence into a pre-trained recurrent neural network model to obtain a predicted phase angle increment; based on the predicted phase angle increment, combining a photovoltaic grid-connected working condition, and calculating a synchronization phase angle; and based on the synchronization phase angle, controlling the grid synchronization phase of the photovoltaic inverter. The phase deviation is predicted through the recurrent neural network, the phase error problem caused by the lag of the traditional PI control is overcome, and the accuracy and stability of the grid synchronization of the photovoltaic inverter are effectively improved.

The application discloses a kind of multimodal sentiment analysis method, device, equipment and storage medium.The method includes: constructing the sentiment analysis dataset containing text, audio and visual sequence and inputting pre-training model, extracting each modality initial feature using feature coding module, comparing learning and dynamic fusion are carried out through modal common information processing module to obtain cross-modal common feature, with the aid of modal specificity information processing module, feature enhancement and pooling are implemented to generate global enhanced features, finally, each kind of feature is spliced and regression prediction is executed to output sentiment intensity prediction value by fusion prediction module;Since the application cooperates modeling modal common and specificity information, the modal heterogeneity is considered and the average processing bottleneck is broken, different dimension features can be dynamically distinguished, the modal conflict can be effectively relieved, and the prediction accuracy and robustness of sentiment analysis can be significantly improved.

The application discloses a kind of multimodal sentiment analysis method, device, equipment and storage medium.The method includes: constructing the sentiment analysis dataset containing text, audio and visual sequence and inputting pre-training model, extracting each modality initial feature using feature coding module, comparing learning and dynamic fusion are carried out through modal common information processing module to obtain cross-modal common feature, with the aid of modal specificity information processing module, feature enhancement and pooling are implemented to generate global enhanced features, finally, each kind of feature is spliced and regression prediction is executed to output sentiment intensity prediction value by fusion prediction module;Since the application cooperates modeling modal common and specificity information, the modal heterogeneity is considered and the average processing bottleneck is broken, different dimension features can be dynamically distinguished, the modal conflict can be effectively relieved, and the prediction accuracy and robustness of sentiment analysis can be significantly improved.

This application discloses a method and system for identifying privately installed energy storage loads based on electricity consumption behavior patterns, belonging to the field of privately installed energy storage identification technology. The method includes: obtaining hierarchical load sets by performing hierarchical analysis of historical load data based on differences in identification results; reconstructing typical load curves by morphological clustering of daily load curves for each hierarchical load set; constructing baseline load curves using rigid load confidence weight coefficients and typical load curves of each hierarchical load set; obtaining energy storage behavior curves under different user behavior patterns based on the baseline load curves and typical load curves; obtaining privately installed energy storage load characteristics corresponding to the user behavior patterns based on the energy storage behavior curves; and performing privately installed energy storage load identification based on the privately installed energy storage load characteristics. The beneficial effects of this application are: improving the accuracy and comprehensiveness of privately installed energy storage load identification by performing identification of privately installed energy storage loads with different energy storage behaviors.