4results about How to "Rich learning" patented technology

The invention relates to a language learning system, which comprises a learning system, a video player and a foreign language video material, and is characterized in that the output end of the learning system is electrically connected with the video player, and the foreign language video material is in data connection with the learning system and the video player through a data memory. By adopting the translation algorithm module and the subtitle display module, an English learning program is highly simplified, English learning becomes closer to a native language learning process, information of a foreign language video material is decomposed, extracted, translated and combined, a learner can comprehensively and accurately learn the foreign language video material, and meanwhile, the learning efficiency is improved. The translated subtitle information is transmitted to the video player in real time to be displayed, so that a learner can view the translated subtitle information while watching the video, and the learning efficiency and effect are improved.

The invention relates to the technical field of health management of satellite-borne products with performance degradation characteristics, and discloses a time sequence anomaly detection method for satellite segmented telemetry based on deep learning, which comprises the following steps: firstly, acquiring satellite telemetry data, and performing segmentation and standardization processing to obtain physically continuous data segments; performing point-by-point reconstruction on the data by using a pre-trained time sequence auto-encoder model, calculating a reconstruction error and performing in-segment limited sliding window smoothing to generate an abnormal score; then determining an anomaly judgment threshold based on normal sample distribution, and defining a boundary exclusion region according to the physical boundary of the data segment; and finally, performing classification judgment on each time step by combining a threshold value and the exclusion region, and outputting a detection result. According to the method, by introducing a segmentation constraint mechanism and a boundary elimination strategy, the problem of false alarm caused by data discontinuity and equipment start-stop transition is effectively solved, and the accuracy and stability of satellite telemetry data anomaly detection are remarkably improved.

The application discloses a photovoltaic power multi-step prediction method and system based on multi-view feature extraction and multi-task learning. The method comprises the following steps: data preprocessing; long short-term memory neural network, multilayer perceptron and convolutional neural network are used to extract features from time sequence, global and local three perspectives, so that rich and comprehensive feature information is obtained; a multi-step photovoltaic prediction task is converted into multiple single-step photovoltaic prediction subtasks, and each subtask integrates feature information through a lightweight attention mechanism; each subtask uses an expert subnetwork to deepen learning of the features through a multilayer perceptron, so that a photovoltaic power multi-step prediction result is obtained; and an improved dynamic weight average method is applied to adaptively and dynamically adjust loss weights. The application can improve the overall accuracy of photovoltaic power multi-step prediction, provide support for safe and stable operation of a power grid, and has certain engineering practical value.

This invention discloses a method for implicit sentiment recognition of Weibo text that integrates multiple features and an emoji sentiment dictionary. Belonging to the field of sentiment analysis in natural language processing, the method includes the following steps: Step 1: Data collection and processing; Step 2: Assigning corresponding sentiment intensities to default emojis in Weibo posts and establishing an emoji sentiment dictionary; Step 3: Extracting character features and phonetic features from Wubi, Zhengma, and Pinyin input methods, and fusing the extracted features; Step 4: Using a Bi-GRU network to learn deeper semantic information; Step 5: Calculating the sentiment polarity of the text and combining it with the emoji sentiment dictionary to obtain the implicit sentiment classification result. This invention integrates Chinese features to identify online words, improving the accuracy of identifying words with similar shapes and sounds, and, combined with the emoji sentiment dictionary, enhances the effectiveness of identifying implicit sentiment in Weibo text.