18results about How to "Rich learning" patented technology

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention discloses an operation site safety management and control method and system based on a multi-modal large model, and the method comprises the steps: collecting site violation data, and carrying out the preprocessing, and obtaining multi-modal data; constructing a multi-modal large model; carrying out training and optimization on the multi-modal large model by utilizing the preprocessed multi-modal data; and using the optimized multi-modal large model to realize operation site safety management and control. According to the invention, a visible light image, a safety violation knowledge base and other modal data are fused, and a comprehensive safety monitoring and management platform is constructed by using a large model and a big data processing technology, so that real-time monitoring, early warning and intelligent response of potential safety hazards of a complex operation site are realized, and the safety management level of the operation site is improved.

The invention relates to the technical field of transformer detection, in particular to a millimeter wave radar-based transformer vibration anomaly detection method, which comprises the following steps of: acquiring an original reflection signal on the surface of a transformer, performing time domain and frequency domain analysis on the original reflection signal, and constructing a vibration feature vector; based on the constructed vibration feature vector, unsupervised learning is carried out on the vibration mode of the normal transformer through a deep self-encoding network, and potential features of the normal vibration mode are automatically learned; converting the new reflected signal into a new vibration feature vector, reconstructing the new vibration feature vector by using a deep self-encoding network, calculating a reconstruction error, and if the reconstruction error exceeds a predetermined threshold, judging that the vibration mode is an abnormal vibration mode; a self-adaptive threshold strategy is adopted, and a preset threshold is automatically adjusted so as to adapt to the change of the operation states of different transformer devices along with time. According to the invention, a self-adaptive threshold strategy is introduced, so that running characteristics of different transformer devices and fluctuation of an external environment can be adapted in real time.

The invention relates to a tongue feature analysis method and device, electronic equipment and a storage medium. A trained tongue detection model and a trained tongue segmentation model are fused to obtain a target segmentation model, an image data set is segmented through the target segmentation model to obtain a tongue segmentation image data set, and the tongue segmentation image data set is labeled to obtain a tongue feature classification data set. Training a tongue feature classification model based on the tongue feature classification data set to obtain a trained tongue feature classification model, obtaining to-be-detected target image data, inputting the target image data into a target segmentation model to obtain tongue segmentation image data of the to-be-detected target image data, and obtaining the tongue segmentation image data of the to-be-detected target image data. According to the method, the trained tongue feature classification model is used for performing feature analysis on the tongue segmentation image data to obtain a tongue feature analysis result, so that the training efficiency of the model is improved, and the classification stability and reliability are also remarkably improved.

The invention discloses an unmanned aerial vehicle game decision-making system based on anomaly detection. The unmanned aerial vehicle game decision-making system comprises an unmanned aerial vehicle sensor database, an upper computer and a sensor real-time data receiver, the unmanned aerial vehicle sensor database and the sensor real-time data receiver are respectively connected with the upper computer; the upper computer comprises a data preprocessing module, a pre-training module and an anomaly detection module. And the unmanned aerial vehicle sensor database stores the unmanned aerial vehicle flight state data accumulated in the historical flight process. The sensor real-time data receiver collects real-time state data of the unmanned aerial vehicle in the flight process and transmits the real-time state data to the unmanned aerial vehicle anomaly detection module of the upper computer for anomaly detection. The upper computer comprises a data preprocessing module, a pre-training module and an anomaly detection module, data transmitted by the database and the receiver are preprocessed, adversarial pre-training is carried out by using normal data in the database, and a trained model is used for carrying out anomaly detection on real-time data of the receiver. According to the method, the antagonism training framework anomaly detection model is innovatively introduced to fully learn the intrinsic characteristics of normal data, the difference between the normal data and the abnormal data is enhanced, the problem that a traditional anomaly detection system cannot completely recognize tiny anomalies is solved, and the anomaly detection precision of the unmanned aerial vehicle is remarkably improved.

The invention provides a construction method, system, equipment, medium and program product of a traditional Chinese medicine origin traceability model fusing element fingerprints and secondary metabolite outlines, and correspondingly provides a traditional Chinese medicine origin traceability method, equipment, medium and program product based on the model. Relates to the field of medicinal material identification. According to the method, element fingerprints and chemical component profiles are fused according to metallomics and metabonomics, and an element fingerprint substance and secondary metabolite profile substance combined recognition mode is innovatively put forward and is used for geographical source recognition of traditional Chinese medicine with radix peucedani as a mode. And an aggregation model is innovatively adopted, and respective optimal models of elements and chemical components are further integrated, so that the classification precision is improved, and the scientificity and accuracy of traditional Chinese medicine quality evaluation are remarkably improved.

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 artificial intelligence optimization operation systems, and particularly discloses a sequence recommendation method based on adaptive global graph modeling and intention contrast learning, the method comprises the steps of sequence global graph construction, module output fusion, sequence recommendation learning and recommendation result optimization, and the method comprises the steps of firstly obtaining a user historical behavior sequence; the method comprises the following steps: constructing an association graph, improving and optimizing the association graph to obtain and record a user behavior sequence global graph, and respectively injecting the user behavior sequence global graph into a local module and a global module of a sequence encoder, so that the outputs of the local module and the global module are fused, and the sequence encoder can comprehensively learn intentions with more expressive power, so as to carry out intention contrast learning; according to the method, the sequence recommendation of the user is comprehensively obtained, the user is predicted, the sequence recommendation of adaptive global graph modeling and intention contrast learning is completed, the recommendation effect under the noise condition and the data sparse condition is improved, the user experience is improved, and the robustness and generalization ability of the sequence recommendation method are enhanced in multiple dimensions.

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 present invention discloses a code summary generation method and device based on structural attention, which are specifically as follows: S1, splitting the nodes of an original abstract syntax tree into type nodes and value nodes to obtain a split abstract syntax tree; S2, based on the split abstract syntax tree, obtaining original edges, constructing control flow edges and data flow edges, and designing an adjacency matrix of the original edges; S3, performing joint operations on the adjacency matrix of the original edges and the control flow, and on the adjacency matrix of the original edges and the data flow, respectively, to obtain a structure-based attention score matrix; S4, constructing a code summary model based on structural attention using the structure-based attention score matrix, a sequence of pre-order traversal, an edge relationship matrix and a hierarchy matrix; S5, generating a code summary using the constructed code summary model based on structural attention. The present invention can learn the features of the source code more comprehensively, thereby better generating a code summary.

The invention provides a remote sensing image classification method and system and a storage medium, and relates to the technical field of remote sensing image classification. Preprocessing images in the remote sensing image set; extracting multilayer features of the preprocessed remote sensing image through a pre-trained CNN model; performing dimension reduction processing on the extracted multi-layer features by applying PCA, extracting non-overlapping image blocks from the features after dimension reduction, performing spatial feature extraction on the extracted image blocks by applying 3D-CNN flow, and performing spectrum feature extraction on the extracted image blocks by applying CV-CNN flow; carrying out series fusion on the spatial features and the spectrum features; the fused features are enhanced through an SE attention mechanism; and inputting the enhanced features into an SVM (Support Vector Machine) for classification. The method integrates the advantages of multiple technologies, accurately extracts features, optimizes calculation, is high in adaptability, and effectively improves the classification precision and application value of the remote sensing image.

The present invention provides a method, device and medium for classifying benign and malignant breast tumors based on ultrasound images. The method comprises: preprocessing an original ultrasound image of a breast tumor with a classification label to obtain a preprocessed image; obtaining a region of interest in the preprocessed image; performing three types of processing on the region of interest to obtain a deep residual network feature vector, a texture feature vector and a morphological feature vector respectively; performing dimensionality reduction processing on the deep residual network feature vector and the texture feature vector respectively, and then performing feature fusion on the two to obtain fused vector data; using a support vector machine classifier to learn a certain amount of fused vector data, and using a naive Bayes classifier to learn the morphological feature vector, and obtaining a tumor classification model by weighting the two classifiers; inputting the breast tumor ultrasound image to be classified into the breast tumor classification model to obtain a classification result. The method of the present invention can realize more accurate automatic classification of breast tumor ultrasound images.

The present invention discloses a network intrusion detection system that introduces sample geometry and multi-view information, including a process of constructing a highly separable metric space on a training set and classifying the network intrusion data to be detected using a K-nearest neighbor classifier. First, the collected intrusion data is subjected to a large margin criterion framework at each viewpoint; second, the density between heterogeneous sample clusters in the training set at each viewpoint is minimized; then, an adjacency matrix with label information is introduced for each viewpoint; and then, an adaptive maximization viewpoint cross-correlation method is adopted. The above steps are substituted into a unified learning framework, the optimal solution is calculated and the final metric space is obtained; finally, the network intrusion data to be detected is sent into the metric space and classified using a K-nearest neighbor classifier. The present invention makes up for the defect that the existing system cannot mine sample and viewpoint data information, and can make full use of sample geometry information and viewpoint complementary information to guide multi-viewpoint network intrusion detection tasks.

The present invention proposes a target pose estimation method, comprising the following steps: acquiring image data of a target object to be identified; fusing a multi-branch structural block of multi-scale convolution to construct a pose estimation model; inputting the image data of the target object to be identified into the pose estimation model to obtain a pose estimation value of the target object; processing the target pose estimation value by adopting a hybrid voxel grid method to obtain the volume density and color information of the voxel, and inputting the volume density and color information of the voxel into a neural radiation field model to obtain a rendering key frame; acquiring a real-time observed true value of the target object, and calculating the loss between the rendering key frame and the real-time observed true value of the target object according to a photometric loss function to obtain a loss value; the method constructs a pose estimation model by fusing the multi-branch structural block of multi-scale convolution, can replace the convolution structure of a traditional detection network and construct a pose estimation network branch, and realizes high-precision and high-efficiency pose estimation value inference.

The present invention discloses an improved deep residual network gold prospecting prediction method, which relates to the field of gold prospecting prediction. The method comprises: obtaining gold mine related data in a study area, and selecting multiple element data therein as first sample data; using an adaptive kernel density estimation method and a factor analysis method to obtain four load factors; and using multiple geological element content data containing four load factors in the study area as second sample data; using an inverse distance weighted interpolation method to construct a regular grid for the second sample data, and fusing the data in the regular grid to form a multidimensional data sample; processing the multidimensional data sample by a step-length translation method and a random discarding method to obtain a sample data subset; constructing an improved deep residual network model obtained by integrating three deep residual network sub-models; using the improved deep residual network gold prospecting prediction model to predict the sample data subsets respectively, and obtain the potential distribution area of ​​the gold mine.

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.

The utility model relates to the technical field of English teaching aids, in particular to an English tense teaching aid, which comprises a display board, a table area is arranged on one side of the display board, and a plurality of display blocks are adsorbed on one side of the display board. The English tense teaching aid has the advantages that the English tense teaching aid can be adjusted in the vertical direction and the horizontal direction through the arranged display board, so that the display board can conveniently display the English tense in different directions in a classroom, and the English tense teaching aid is convenient for teaching; tenses, subjects, assistant verbs and the like can be written into the table in the table area, so that the tense teaching aid is more intuitive and better in teaching effect, the display blocks can be divided into tense display blocks, subject display blocks, assistant verb display blocks and the like, and the display blocks can be randomly combined, so that the display is convenient, and the teaching efficiency is improved. And the writing time of a teacher can be saved, and a plurality of combinations can enable a learner to learn more comprehensively, so that the teaching efficiency is improved.

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.