35results about How to "Remove redundant information" patented technology

The invention discloses a multi-modal medical data fusion and analysis platform, and particularly relates to the technical field of medical assistance, and the platform comprises a data collection and preprocessing module which is used for integrating, cleaning and standardizing multi-source data, and is used for efficiently fusing and safely storing the multi-modal data; the data fusion and storage module supports rapid retrieval and management; and the data analysis and modeling module is used for carrying out feature extraction and pattern recognition by utilizing a machine learning algorithm and providing deep prediction analysis. The system integrates, cleans and standardizes medical data from multiple sources through the data acquisition and preprocessing module, ensures data quality and consistency, then efficiently fuses data of different modalities into a single data set through the data fusion and storage module, supports rapid retrieval and management, and finally utilizes the data analysis and modeling module to analyze and model the medical data. And deep prediction and risk assessment of diseases are provided through feature extraction, model training and prediction analysis.

The invention particularly relates to a document abstracting method based on domain knowledge and a multi-granularity graph network, which comprises the following steps: S1, constructing a knowledge coding module, carrying out domain knowledge graph transfer learning, extracting triple structure features in combination with a capsule network, and generating a text entity coding module; s2, constructing a knowledge fusion module, converting a document text into a semantic vector, and adaptively fusing the semantic vector with external knowledge; s3, constructing a word-level semantic graph, and aggregating neighborhood information through a semantic graph attention mechanism to obtain word embedding representation containing context; s4, constructing a sentence-level semantic graph, performing self-attention calculation on the word-level semantic vector in the S3 to obtain semantic node representation of the sentence-level semantic vector, and performing feature aggregation on sentence-level nodes to obtain global semantic information of the document; and S5, constructing an output layer, and predicting according to the document feature vector in the S4 to obtain a final extraction type abstract result. The method has the advantages that redundant information in the document is removed, and ambiguity of text semantics is eliminated.

The invention relates to the technical field of data processing systems for supervision or prediction, in particular to a road and bridge deck sudden collapse early warning system and method based on the Internet of Things, and the system comprises a deformation monitoring system which is used for collecting the deformation data of a road and bridge deck; the data processing system is used for processing the deformation data based on the Internet of Things so as to obtain effective data meeting a preset storage condition; and the early warning management center is used for inputting the effective data into a pre-constructed road and bridge surface collapse risk prediction model to obtain a risk assessment result and a trend prediction result of the road and bridge surface so as to carry out early warning on sudden collapse of the road and bridge surface. Therefore, the problems of low data precision, poor credibility, early warning information delay, increased safety risk and the like caused by the fact that an early warning system only depends on a single technology, is easily influenced by environmental factors and cannot comprehensively capture various abnormal signals which may predict collapse in related technologies are solved.

The invention belongs to the technical field of Internet, and particularly relates to an energy optimization scheduling method and system, a terminal and a storage medium, and the method comprises the steps: collecting real-time energy data, equipment state information and environment data of energy consumption equipment in a park; processing the real-time energy data, the equipment state information and the environment data by adopting a multi-source data fusion technology to obtain a park data set so as to generate a uniform data input format; obtaining actual demand data of the park, inputting the park data set and the actual demand data of the park into a pre-trained energy model for prediction, and obtaining prediction results of the energy demand and the equipment health state of the park at the next moment; and adjusting the operation mode and parameter of the corresponding equipment based on the prediction result of the energy demand and the equipment health state of the park at the next moment. The method has the effect of improving the energy utilization rate of the park.

The invention discloses a multi-feature electroencephalogram emotion recognition method based on a space-time attention mechanism, and the method comprises the following steps: preprocessing electroencephalogram source data, extracting a time domain feature, a frequency domain feature and a space domain feature, and generating a plurality of artificial electroencephalogram features; superposing and combining the extracted electroencephalogram features of the time domain, the frequency domain and the space domain in another dimension to form four-dimensional electroencephalogram feature data; the combined features are encoded through a space-time attention encoder to eliminate redundant information, and useful information is reserved; the method has the advantages that the electroencephalogram feature encoder based on the space-time attention is provided, input important information is paid attention to through multiple layers of space-time attention, and the purpose of eliminating redundant information is achieved; the encoder is connected with artificial features and deep learning features, so that a model can extract general emotion features through the artificial features, and then a deep similar emotion mode in electroencephalogram signals is obtained through a deep learning model.

The invention discloses a small unmanned aerial vehicle intrusion detection method based on improved YOLOv8, and relates to the technical field of unmanned aerial vehicle intrusion detection. The invention provides an improved YOLOv8 network model, an SPD-Conv layer and a DP-MobileNetv3 layer are introduced to a Backbone module of the YOLOv8 network model, the efficient feature extraction capability is maintained, the parameter quantity and calculation complexity of the model are reduced, the size of the YOLOv8 model is further reduced, redundant information in the feature extraction process is eliminated, details of a small target of an unmanned aerial vehicle are reserved, and the accuracy of feature extraction is improved. And the accuracy and detection speed of the model are improved.

The invention discloses an industrial process monitoring method based on a lightweight deep principal component analysis-auto-encoder model, and the method comprises the steps: collecting the related process variable data of an industrial process on line, carrying out the preprocessing of the collected data, inputting the preprocessed data into a trained lightweight deep principal component analysis-auto-encoder model, and carrying out the recognition of the related process variable data. The T2 statistical magnitude and the SPE statistical magnitude are obtained and compared with corresponding control limits, and a monitoring result is output; the lightweight deep principal component analysis-auto-encoder model uses a PCA dimension reduction module for dimension reduction to obtain the data score, inputs the score into the auto-encoder, and outputs the reconstruction data. According to the method, the calculation efficiency of the model is improved, compared with a traditional deep network model, the calculation speed is obviously improved, compared with a single multivariate statistical model, the strong feature extraction capacity of the deep hierarchical cascade model structure is incomparable to that of the single multivariate statistical model, and the problem of complex industrial process monitoring is effectively solved.

The invention relates to the technical field of data analysis, and provides an unstructured data processing method and device, equipment and a medium, and the method comprises the steps: obtaining unstructured data related to financial risks; pre-processing the unstructured data to obtain pre-processed unstructured data; performing feature extraction on the preprocessed unstructured data to obtain target features; performing uncertainty processing and fuzziness processing on the target features to obtain an uncertainty analysis result and a fuzziness processing result; performing context analysis on the target feature to obtain context associated information; and based on the uncertainty analysis result, the fuzziness processing result and the context association information, evaluating the financial risk to obtain a financial risk evaluation result. In the unstructured data processing method and device, the uncertainty processing and the fuzziness processing can more accurately reflect the actual situation of the financial risk, the context analysis can identify the potential association in the data, and the accuracy of the unstructured data processing is improved.

The invention discloses an unmanned aerial vehicle cooperative automatic driving vehicle BEV visual angle fusion method, and relates to the technical field of automatic driving. The method specifically includes controlling the unmanned aerial vehicle and the target vehicle to keep synchronous movement; acquiring first image data, point cloud data and second image data at the same moment, and converting the first image data, the point cloud data and the second image data into points in a world coordinate system; respectively carrying out feature extraction on the data, and correspondingly generating an image feature graph of a BEV view angle, a BEV view angle graph with height information and an RGB image feature graph; and constructing a shallow neural network to fuse the BEV view angle image with height information, the BEV view angle image feature map and the RGB image feature map into a uniformly represented multi-modal feature map, so that the autonomous vehicle adapts to a complex meteorological environment. Therefore, the self-driving automobile adapts to a complex meteorological environment, and the perception capability of the self-driving automobile to the environment is improved.

The invention discloses a parking lot management efficiency evaluation method based on parking frequency, and the method comprises the steps: collecting vehicle access time, license plate number, parking space number, vehicle type and parking lot real-time occupancy rate data through sensors, cameras and intelligent barrier gate systems which are disposed at an entrance, an exit and parking spaces of a parking lot; performing data preprocessing on the acquired data to form a standardized data set; key indexes are extracted from the data set; based on the historical data and the external influence factors, establishing a parking demand prediction model by adopting a time sequence analysis method, and predicting the parking demand in a specified time period in the future; based on the extracted key indexes, corresponding weight factors are set, a management efficiency evaluation model is constructed, and a parking lot management efficiency score is calculated; comparing the calculated efficiency score with historical data to generate an evaluation report; and according to the predicted parking demand and the evaluation report, implementing a dynamic pricing model, and optimizing the utilization rate of the parking lot.

The invention belongs to the field of biological feature recognition, and discloses a dynamic local fusion network gait recognition method and system, the dynamic local fusion network gait recognition method comprises a dynamic local fusion network model, and the dynamic local fusion network model is composed of a feature extractor, a global perception flow and a local detail flow; the method comprises the following steps: firstly, taking a skeleton sequence extracted from an attitude estimator as input, and performing spatio-temporal feature extraction through a feature extractor consisting of an ST-GCN module with a residual error and a bottleneck structure; the extracted data respectively flows into a global perception stream and a local detail stream to obtain global feature representation and local feature representation of the gait; and splicing the obtained global features and local features to obtain the gait of the target person, and obtaining a gait recognition result according to the similarity with a known skeleton sequence. The method not only pays attention to global skeleton motion information, but also performs network model modeling on local limb parts, so that gait features with higher identification degree can be extracted.

The present invention discloses a scene classification-assisted mining area remote sensing image target detection correction method. The present invention first uses a scene classification algorithm to classify mining area remote sensing images to obtain scene category prior information, and then uses the combination degree of the target and the scene to correct the detection result of the target detection algorithm based on the scene category prior information, thereby improving the accuracy and stability of the target detection algorithm.

The application discloses a variable working condition cross-component causal coupling modeling and fault diagnosis method and device, and relates to the technical field of rotating machinery fault diagnosis. The method comprises the following steps: collecting and preprocessing multi-component signals under variable rotating speed working conditions of a rotating machinery; based on multivariate Granger causal modeling, prior information and dynamic disturbance information are fused to construct a dynamic causal diagram; a double-view structure of a main view of the dynamic causal diagram and an auxiliary view of a correlation diagram is constructed, multi-scale coupling features are extracted through a multi-order spectrum filter and an attention mechanism, a symmetric InfoNCE contrast learning loss and a supervised classification loss are combined to optimize and train a model, and fault classification is realized. The device is correspondingly provided with signal acquisition, dynamic causal diagram construction, feature fusion, model training and fault prediction modules. The application improves the accuracy of fault feature representation and the robustness of diagnosis under variable working conditions, is suitable for complex industrial scenes with multi-component coupling and frequent rotating speed switching, and has strong engineering practicability.

The present invention discloses a method for detecting the intrusion of a small unmanned aerial vehicle based on an improved YOLOv8, and relates to the technical field of unmanned aerial vehicle intrusion detection. The present invention proposes an improved YOLOv8 network model, introduces an SPD-Conv layer and a DP-MobileNetv3 layer into the Backbone module of the YOLOv8 network model, reduces the number of parameters and the computational complexity of the model while maintaining efficient feature extraction capabilities, further reduces the size of the YOLOv8 model, eliminates redundant information in the feature extraction process, retains the details of the small target of the unmanned aerial vehicle, and improves the accuracy and detection speed of the model.

The present application relates to the technical field of hydroelectric generating set bolt monitoring, and particularly relates to a hydroelectric generating set bolt state dynamic monitoring method and system. The method comprises the following steps: performing resonance frequency prediction on the bolt, and performing adaptive resonance excitation to obtain a bolt resonance characteristic spectrum; performing bolt acoustic signal collection according to the bolt resonance characteristic spectrum to obtain a multi-channel original acoustic waveform set; performing working condition noise preprocessing on the multi-channel original acoustic waveform set to obtain an acoustic propagation time-domain-frequency domain matrix; performing multi-dimensional stress state mapping analysis according to the acoustic propagation time-domain-frequency domain matrix, and performing bolt loosening state recognition to obtain a bolt structure stress distribution atlas; and performing multi-source heterogeneous data fusion and edge computing according to the bolt structure stress distribution atlas to obtain a connection reliability index. The present application realizes high-precision, real-time and online monitoring of the bolt state through acoustic characteristic change analysis under a complex stress state.

The present disclosure provides a pipeline pressure prediction method, device and equipment that fuses pressure and vibration information, and relates to the field of data processing technology. The method includes: obtaining vibration signals and pressure signals corresponding to multiple detection points on the target pipeline; preprocessing the vibration signals and pressure signals to obtain a vibration matrix and pressure vector aligned in time; performing principal component analysis on the vibration matrix, and taking the principal component components corresponding to the maximum eigenvalue of a preset number as the fusion vector; taking the fusion vector and pressure vector as the input of the pipeline pressure prediction model to obtain the predicted pressure corresponding to the pressure prediction point. The technical solution in the present disclosure can improve the accuracy and efficiency of pipeline pressure prediction.

The invention discloses a method for losslessly identifying the internal quality of nuts based on voiceprint features, which specifically relates to the field of acoustic detection technology, and comprises the following steps: first, performing ensemble empirical mode decomposition on collected nut impact soundprint signals; second, using a mutual correlation coefficient method to screen intrinsic mode function components, and reconstructing a soundprint signal with noise and redundant information removed; third, extracting time domain, frequency domain, and time-frequency domain features of the reconstructed soundprint signal; fourth, using a recursive feature elimination method to screen features; fifth, constructing a support vector machine classifier, inputting the screened feature set, and classifying and identifying the internal quality of nuts; the invention utilizes voiceprint features for detection, does not need to destroy the nut shell or internal structure, ensures the integrity of the nuts, and reduces waste and loss. Compared with traditional physical or chemical detection methods, the voiceprint feature detection method has higher detection speed and efficiency, can quickly screen and classify a large number of nuts, and accurately judge the internal quality of the nuts.

The present invention relates to a lithium battery SOC estimation method for multi-feature fusion taking stress into account, which obtains lithium battery feature information under various test conditions, constructs a decomposition model, performs variable mode decomposition on the lithium battery feature information under various conditions based on the decomposition model, obtains modal components corresponding to the lithium battery feature information, constructs a screening model, screens the lithium battery feature information and its corresponding modal components based on the screening model, obtains an input feature combination set, constructs a lithium battery SOC estimation model, inputs each lithium battery feature information and modal components corresponding to the input feature combination set into the lithium battery SOC estimation model for training, outputs a lithium battery SOC estimation value, and realizes accurate prediction of the remaining power of the lithium battery under non-steady-state conditions, which not only helps to optimize battery management strategies and extend battery life, but also has important significance for improving the overall safety and reliability of electric vehicles and energy storage systems.

The application provides a video-language understanding method and system, relates to the technical field of video understanding, and obtains a video and text to be understood; based on a trained understanding model, the input video and text are processed to generate a final understanding result; wherein the understanding model comprises a video encoder and a text query component, the video encoder comprises a clustering component and a timing component; the image blocks of all video frames in the video are filtered by using the clustering component to obtain video frame embeddings after eliminating redundant information; the timing dependency between the video frame embeddings is reconstructed by using the timing component to obtain a video feature matrix; based on the video feature matrix, the embedding of the text is performed by using the text query component to obtain the final understanding result; the application comprehensively considers three challenges of information redundancy, time dependency and scene complexity, proposes an understanding model composed of three key components, each component is aimed at a specific challenge, and the video-language understanding ability of an intelligent agent is significantly improved.

The present disclosure relates to a method, device and system for transmitting image features, and to the field of communication technology. The transmission method includes: using a machine learning model to extract first feature information of an image to be processed, the first feature information including feature matrices of each channel; determining a redundant feature matrix according to the amount of information of each feature matrix; deleting the redundant feature matrix from the first feature information to generate second feature information; transmitting the encoded second feature information and related information of the redundant feature matrix to a decoding end.

The invention provides an RAG system answer generation method, device and equipment based on a large language model, and relates to the field of artificial intelligence, and the method comprises the steps: receiving a query request input by a user; obtaining a target document block according to the query request, and further generating a target prompt word; according to the target prompt word, adopting a preset large language model to generate an answer text corresponding to the query request; wherein the target document block comprises a first document block; the obtaining method of the first document block comprises the steps that keyword retrieval is conducted on a preset knowledge base according to keywords in a query request, and a target knowledge document with the keywords is obtained; and according to the query request and the target knowledge document, generating an abstract text corresponding to the target knowledge document as a first document block. The answer generation accuracy of the RAG system can be improved.

The invention discloses a rapid remote sensing image change detection method based on superpixels, and the method comprises the steps: obtaining remote sensing images of the same region in different periods, dividing the remote sensing images into a T1 period and a T2 period, and carrying out the preprocessing of the remote sensing images in the two periods; overlapping and fusing spectral features of the two periods of images to form a new data block so as to obtain super-pixel blocks with the same scale in different periods; performing principal component analysis on the obtained superpixel blocks, and extracting representative features in each superpixel block; and carrying out binary clustering analysis on the extracted features by using a k multi-mean algorithm, and dividing the extracted features into changed image blocks and unchanged image blocks to realize change detection processing. Compared with the prior art, the rapid remote sensing image change detection method based on the superpixels has the advantages that the spatial information of the image is reserved, the calculation complexity is reduced, and the change detection accuracy is improved.

The present application provides a method, device, electronic device and storage medium for generating recommendation reasons, which relates to the field of artificial intelligence technology, wherein the method includes: in response to a query request from a user side, obtaining a target vector and a target label corresponding to the query information from the user side; recalling first data from a first database according to the target vector, and recalling second data from a second database according to the target label, wherein the first database includes vectors corresponding to the doctor's non-business type data, and the second database includes the doctor's business type label data; merging the first data and the second data to obtain the target text content; and generating a recommendation reason associated with the query information and conforming to a predetermined format from the target text content based on a preset Prompt instruction. The implementation of the technical solution provided by the present application effectively improves the matching degree of the recommendation reason and improves the user's consultation experience.

The present invention provides an indoor heading estimation method, device and medium, wherein the method includes grouping IMU data to obtain local features, integrating them with preset additional features, inputting the local features into a deep learning model to obtain correlation to update the additional features, and finally reducing the dimension of the integrated features to obtain the heading estimation result. The method can carefully mine the local features of the data and capture specific information in different regions; update the additional features through correlation to achieve global feature extraction and enrich feature representation; the dimensionality reduction process can reduce the computational complexity, reduce the training and inference time, remove redundant information and noise, and improve the generalization and robustness of the model. Compared with the existing technology, it can better adapt to the complexity of the indoor environment and improve the accuracy and reliability of heading estimation.

This invention discloses a data-driven modeling method and system for air compressors, comprising: acquiring operating data of an air compressor under various operating conditions; preprocessing the operating data to obtain cleaned operating data; dividing the cleaned operating data into time windows, performing multi-order polynomial regression fitting within each time window to generate an initial fitting parameter set corresponding to each time window; constructing a fitting parameter matrix based on all initial fitting parameter sets, performing matrix decomposition operations, and extracting principal component features of the fitting parameters; establishing an air compressor performance degradation trend model based on the principal component features to describe the performance decay law of the air compressor under different operating durations; and dynamically modeling based on the performance degradation trend model and combined with real-time acquired operating data to generate a real-time performance mapping model. This achieves automatic extraction of performance degradation features from historical data and dynamic compensation modeling.

The present invention discloses an image encoding and decoding method with multi-resolution output, the method comprising: S1: extracting multi-scale features of an image and constructing a multi-scale feature library; S2: based on the multi-scale feature library, training an extreme learning machine model and a support vector machine model to predict the target existence probability and the target size; S3: when the predicted target existence probability is greater than the target existence probability threshold, selecting a multi-scale feature matching the predicted target size in the multi-scale feature library, encoding, and obtaining the encoding feature; S4: performing multi-scale expansion convolution on the encoding feature to obtain a decoded image; S5: based on the decoded image, identifying the target, and correcting the encoding and decoding system based on the identification result. The present invention selects multi-scale features of appropriate resolution for encoding according to the target size category, and corrects the encoding and decoding system according to the decoded image identification result, which is conducive to adapting to scene requirements and improving encoding and decoding efficiency and quality.

The present application relates to a battery fault prediction model training method and device, a prediction method and device, the training method includes obtaining sample data, the sample data includes time domain signals of at least two battery variables, the battery variables include current, voltage or temperature; based on the variable attention mechanism, the interaction characteristics between the time domain signals are processed to obtain a first feature vector; based on the time attention mechanism, the timing characteristics between the time domain signals are processed to obtain a second feature vector: according to the first feature vector and the second feature vector, a cross-dimensional fusion feature vector is obtained by fusion; based on the cross-dimensional fusion feature vector and the fault category label annotated by the time domain signal, the confidence network is iteratively trained to obtain the battery fault prediction model. The model obtained by the training method of the present application can improve the problem of low accuracy and precision in battery fault prediction in the prior art.

This invention provides a method and system for predicting multimodal drug interaction events based on compact representation learning. The method includes: acquiring multimodal features of each drug in a drug pair to be predicted, wherein the multimodal features include at least two of biological features, molecular structure features, and knowledge graph features; performing intramodal compact representation learning on each modal feature of each drug to eliminate redundant information within a single modality and generate compact sub-representations for each modality; using the compact sub-representations corresponding to the molecular structure features as anchors, performing cross-modal alignment of the compact sub-representations corresponding to the biological features and knowledge graph features through mutual information minimization constraints to eliminate intermodal redundant information; constructing a multimodal fusion representation for each drug based on the compact sub-representations of each modality; concatenating the multimodal fusion representations of the two drugs in the drug pair and inputting them into a classifier to output the prediction result of the interaction event type of the drug pair.

The invention relates to a neural network specific misclassification repair method based on disturbance feature recovery, belongs to the technical field of misclassification repair, and solves the problems that in the prior art, the specific category distinguishing capability is insufficient, and the classification accuracy of an original model for misclassification repair is reduced. The method comprises the following specific steps: performing initial classification on a to-be-classified image data set by using a neural network model obtained by training an original image data set; screening out image sample data classified as a category P from the to-be-classified image data set as a doubt set; based on the doubt set and the trained feature mapping model, performing disturbance feature repair on the potential features extracted by the initial feature extractor; using the trained verifier to verify whether the repaired features correspond to a specific misclassification # imgabs0 #, if yes, repairing the image sample data misclassified as a category P in the suspected set, and obtaining a repaired classification result; according to the method, the accuracy of the original model is not greatly reduced, and meanwhile, the repair of specific misclassification is completed.

The invention discloses a face action unit intensity recognition method and system based on cross attention fusion. In time domain analysis, an ordered regression neural network based on a visual self-attention model is constructed and is used for estimating the appearance sequence of facial expressions. In spatial domain analysis, a region of interest (ROI) neural network based on face landmark points is constructed for estimating the intensity of each AU from a specified local region. In addition, a cross-attention module is proposed to explore associations between global semantics from a time domain and local semantics from a spatial domain. According to the method, the organization process of training data is simplified, the calculation complexity of the model is also reduced, redundant information in space is eliminated to a certain extent, semantic association between local ROIs is also explored, and therefore the intensity recognition capacity of all AUs is effectively enhanced.