896 results about "Information representation" patented technology

The invention provides a commodity information processing and querying method and system. Belongs to the technical field of information processing. The method comprises the steps of collecting text information of commodities, preprocessing the text information of the commodities through natural language processing information, and constructing a structured commodity text database; based on the high-definition image of the commodity, key features of the commodity are extracted through machine vision and are associated with information in the text database to form multi-modal commodity information representation; the method comprises the following steps: encoding a commodity text by adopting a BERT model based on Transform, and obtaining a deep semantic representation of the text; performing feature extraction on the commodity image by using a pre-trained convolutional neural network to obtain an advanced visual feature of the image; through natural language processing and machine vision technologies, the text information and the image information of the commodity are subjected to structured and multi-modal fusion, so that the processing efficiency of the commodity information is greatly improved.

The invention relates to the field of knowledge graph completion, provides a knowledge graph completion method based on multi-modal visual angle perception and a deep neural network, and aims to solve the problems of weak multi-modal information expression ability, rough fusion mode and insufficient structural reasoning ability in the prior art. According to the method, structure information, text description and visual image information of an entity in a knowledge graph are obtained, structure, text and image modal input is constructed respectively, and a graph neural network, a pre-training language model and a visual encoder are adopted for feature coding; weighted fusion and semantic enhancement of multi-modal features are realized through a visual angle fusion mechanism and hierarchical attention processing; cross-modal contrast learning is introduced to improve modal consistency; and carrying out triple reasoning by using a uniform Transform encoder, and verifying a completion result by scores. According to the method, multi-modal semantics are effectively integrated, the entity representation capability and the triple prediction accuracy are improved, the model robustness is enhanced, and the method is suitable for application scenes such as intelligent question answering and recommendation systems and has remarkable practical value and popularization prospects.

The invention discloses a knowledge enhancement-based neurogenic bladder personalized health management method and system, and relates to the technical field of chronic disease management, and the method comprises the steps: obtaining the illness state information of a neurogenic bladder patient, and obtaining text representation and knowledge graph representation according to the preprocessed illness state information; generating disease condition comprehensive information representation of the patient through splicing and fusion; constructing a chronic disease management scheme library of the neurogenic bladder, performing graph processing on the chronic disease management scheme library, constructing a health management scheme recommendation model based on a graph neural network, taking disease condition comprehensive information representation of the patient as model input, and generating a personalized health management scheme of the neurogenic bladder patient; and according to the personalized health management scheme, regularly performing scheme evaluation, and based on an evaluation score, judging the applicability of the personalized health management scheme. According to the method, the condition characterization of the patient is enhanced by means of massive information knowledge of the knowledge graph, and intelligent recommendation and construction of personalized schemes of chronic diseases of neurogenic bladder patients are enriched.

Systems, devices and automated processes are described for automated enhancement of metadata in a database of information about movies, television shows or other media programs. Gaps or errors in metadata describing the different programs in the database can be corrected using a digital architecture in which one or more sources are queried for missing information. Queries may be directed toward a large language model (LLM) or other artificial intelligence (AI) engine, if desired, that represents information about the media programs as embedded vectors that can be compared to query data to identify additional information about the media programs.

Methods, systems, and devices for wireless communication are described. A network entity may configure a user equipment (UE) to report applicability information associated with artificial intelligence (AI) and / or machine learning (ML) functionalities and / or models. The applicability information indicates an applicability of a functionality and / or model per AI / ML-enabled feature or feature group. Based on the applicability information and a handover of the UE from a source network entity to a target network entity, the target network entity may configure a functionality and / or model. Additionally, or alternatively, the UE may be configured with a reference configuration and one or more additional configurations corresponding to respective AI / ML functionalities and / or models. The UE may apply an additional configuration based on a handover to the target network entity, the additional configuration satisfying an applicability condition, or both. Based on an indication of the applied configuration, one or more functionalities or models may be activated.

The invention discloses an industrial zero sample anomaly detection method and system based on cross-modal prompt learning, and relates to the technical field of computer vision and deep learning. The method comprises the following steps: acquiring RGB images and point cloud data containing product defects and normal states in an industrial scene, and preprocessing the point cloud data; constructing an anomaly detection model, preliminarily extracting information representation of the RGB image and the point cloud data by using the anomaly detection model, and performing further feature extraction and semantic alignment operation through prompt information; training the anomaly detection model based on a cross-modal collaborative mechanism, and testing the trained anomaly detection model by adopting a collaborative modulation strategy; and performing anomaly detection on a to-be-detected product by using the trained anomaly detection model. According to the method, the real-time defect detection precision and robustness can be remarkably improved in an industrial environment, especially under the conditions of data scarcity and complex modality.

The invention belongs to the technical field of image processing, and particularly relates to a sea temperature complementation method and system based on recursive double-flow Mama, and the method comprises the following steps: splicing a damaged SST image and a weekly average SST image as input, and outputting a predicted weekly average SST image and a predicted abnormal SST image through N times of recursive iteration of N same recursive hierarchical Mama blocks, and the two output images are added to obtain a final complemented SST image. According to the method, a recursive hierarchical Mama block for a sea surface temperature completion task is set, two parallel layers, namely a stable information representation module and an abnormal information representation module, are integrated in each block, features related to stability and features related to anomalies are extracted respectively, long-range dependency relationship modeling under large-area deficiency is achieved, and completion accuracy is improved.

The invention relates to a BOM table generation method based on full life cycle management, computer equipment and a computer readable storage medium, and the method comprises the following steps: S1, generating a client matrix according to a product design document and an engineering material, and obtaining information in the client matrix; the customer matrix is presented in the form of a two-dimensional table, column information in the two-dimensional table represents serial numbers of product parts and / or components of different assembly levels, and row information at least comprises names, numbers, specifications and models of the product parts and / or components and parent component identifiers; s2, generating an in-plant BOM matrix according to the material key information, the in-plant material number, the in-plant inventory status and the supply chain constraint condition, inputting the in-plant BOM matrix into a preset management system to generate an electronic drawing and document database, and establishing a structured D-BOM; s3, comparing the client matrix information obtained in the step S1 with the D-BOM established in the step S2; s4, carrying out adjustment and optimization on the D-BOM; and S5, importing the optimized D-BOM into a client matrix, and generating a final BOM.

The invention discloses a pediatric nursing knowledge question answering method and system based on multi-modal large model enhancement, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-modal pediatric nursing related data, extracting knowledge entities, obtaining description text data of each knowledge entity, and forming text representation of the entities; establishing triple information according to the knowledge entities to generate a pediatric nursing knowledge basic database, and extracting triads to construct a knowledge graph; extracting multi-order graph data of each entity, and learning to generate knowledge graph representation of the entity; the text representation and the knowledge graph representation are fused, comprehensive knowledge information representation of the entity is obtained, the comprehensive knowledge information representation is used for finely adjusting a preset multi-modal large model, and pediatric nursing knowledge questions and answers are conducted through the finely-adjusted multi-modal large model. According to the method, more accurate, more reliable and more intelligent knowledge questions and answers are realized in a pediatric nursing scene through deep fusion of the structured constraint of the knowledge graph and the generalization ability of the multi-modal large model.

The invention provides a multi-modal data classification method and system, computer equipment and a storage medium, and the method comprises the steps: receiving multi-modal data through edge equipment, and carrying out the preprocessing of the multi-modal data; aligning to the same latitude through linear transformation or a projection layer; a comprehensive information representation vector is generated through fusion of an attention mechanism or a Transform architecture; quantifying the multi-modal large model, and deploying the multi-modal large model in edge equipment; sending the comprehensive information representation vector to a multi-modal large model for reasoning, and processing the comprehensive information representation vector through a multi-layer Transform architecture; and generating a classification result according to task requirements. According to the method, the real-time performance, the accuracy and the resource utilization efficiency of the system are improved through localized deployment on the edge equipment, sensitive data can be processed locally, data privacy and safety are guaranteed, cross-modal feature extraction and alignment are achieved, and the recognition capability in a complex scene is improved. Dependence on large-scale annotation data is reduced, and adaptability is improved.

The invention discloses a method and a device for enhancing scene trajectory prediction by fusing video monitoring and ADS-B, which can improve the precision and reliability of airport scene situation awareness, further accurately predict the moving trajectory of airplanes and other vehicles, and provide powerful technical support for airport scene management and flight safety. The method comprises the following steps: (1) synchronizing video monitoring data and ADSB data by using a timestamp, and realizing time-space alignment of different modal data through coordinate conversion; (2) determining a corresponding relation among different data sources through a data association technology, and constructing positive and negative sample pairs; (3) pre-training the multi-source heterogeneous data by using a comparative learning algorithm to extract and fuse key features, and constructing an efficient situation information representation model; and (4) adopting a pre-trained model as the basis of trajectory feature extraction, and inputting the basis to a long short-term memory (LSTM) network to predict the trajectory of the airport scene.

The invention provides a multi-view camera image and laser radar fused road perception method and system. The method comprises the steps of obtaining depth information features of a camera aerial view and depth information features of a laser radar aerial view based on a vehicle-mounted camera multi-view image and laser radar point cloud data; obtaining camera aerial view features with depth distribution based on the depth information features of the camera aerial view and the depth information features of the laser radar aerial view; performing feature alignment on the camera aerial view features with depth distribution and the camera aerial view features without depth distribution to obtain camera aerial view image features with depth information representation; and obtaining a prediction result based on the camera aerial view image features containing the depth information representation and the depth information features of the laser radar aerial view. According to the invention, the road condition of the current scene is inferred and predicted in real time through the image of the vehicle-mounted camera during vehicle driving, and the current driving road condition is sensed in real time under the unstructured road condition with limited visual field and severe environment.

A three-dimensional data encoding method includes: calculating coefficient values from pieces of attribute information of three-dimensional points included in point cloud data; generating a second code sequence including first information and second information, the first information indicating a total number of zero coefficient values consecutive in a first code sequence in which the coefficient values are arranged, the second information indicating whether each of the coefficient values is 0, the zero coefficient values being included in the coefficient values and having a value of 0; and generating a bitstream including the second code sequence.

The invention discloses a joint multi-modal entity relationship extraction method and system based on information representation and semantic alignment, and the method comprises the steps: obtaining sample data composed of an original image and a text, and obtaining the feature representation of visual information and text information based on the sample data; aligning the feature representation of the visual information and the feature representation of the text information by using a progressive modal semantic alignment strategy; by introducing a multi-layer correlation mapping mechanism guided by fine granularity, the correlation coefficient represented by the features of the aligned visual information and text information is judged, and features irrelevant to a task core are filtered; performing visual representation and text interaction by using a multi-modal interaction module to obtain multi-modal semantic features; carrying out weighted mapping on the multi-modal semantic features by utilizing a routing weighting function, and finally obtaining multi-modal feature representation; and sending the multi-modal feature representation into a word pair relation label extractor, and extracting an entity, an entity relation and an entity attribute quintuple.

The invention relates to a wind power interval prediction method based on deep learning and Markov chain Monte Carlo. The method comprises the steps of carrying out feature extraction on historical wind power generation data by utilizing LSTM, constructing a Gaussian likelihood function based on a mean value and a variance according to hidden layer information representation, carrying out modeling processing on the Gaussian likelihood function by utilizing negative binomial distribution, optimizing hidden layer parameters of the LSTM by utilizing a loss function of a probability density function obtained through design, and obtaining hidden layer parameters of the LSTM. Obtaining a mean value and a variance of the optimized Gaussian likelihood function; a Markov chain is constructed according to the distribution characteristics of data characteristics represented by the mean value and variance of the optimized Gaussian likelihood function by adopting a Markov chain Monte Carlo algorithm to randomly generate a large number of samples, so that the stable distribution of the samples is the same as the known distribution; and taking a prediction interval with a predetermined size as a final prediction result for the distribution of the samples which most accord with the historical data characteristics. By adopting the method, the problem that a large amount of historical data cannot be effectively utilized by probability prediction can be solved.

The present disclosure provides a booklist cover generation method, apparatus, device, and storage medium, wherein the method includes: acquiring booklist information about a target booklist, wherein the target booklist indicates a book collection, and the booklist information represents introduction information for the target booklist; determining cover image description information for the target booklist that includes description information corresponding to at least one characteristic dimension of booklist attributes, based on the booklist information; generating a cover image for the target booklist based on the cover image description information.

A gear fault diagnosis method based on the joint weighted envelope anti-noise correlation of sub-signals. The method comprises: first, converting an original vibration signal sequence into envelope signals by means of a squaring-low pass filtering-square root calculation process of a signal sequence element by element; then, reconstructing the envelope signals on the basis of different time intervals, so as to obtain a series of sub-signals, and calculating a fault information representation metric of each sub-signal on the basis of L-moment theory indexes; next, assigning a weight to each sub-signal by combining the fault information representation metric and Sigmoid function transformation; then, on the basis of the envelope signals, and reconstructed sub-signals and corresponding weights thereof, calculating a joint weighted envelope anti-noise correlation function of an envelope signal sequence and the reconstructed sub-signals; and finally, determining a characteristic frequency on the basis of the reciprocal of a time interval value corresponding to a characteristic peak in a drawn graph showing the change of the joint weighted envelope anti-noise correlation function along with the time intervals, and finally recognizing a gear fault. By means of the method, a gear fault can be reliably recognized simply on the basis of time domain analysis when a signal length is limited and there is complex noise interference.

The invention discloses a mountain area lake identification method and device based on environmental characteristics. The method comprises the following steps: acquiring a plurality of remote sensing images of a target water body; calculating a comprehensive water body index based on the remote sensing image so as to enhance the spectral characteristics of the water body and obtain a spectral enhanced image; calculating topographic data based on the spectrum enhanced image, calculating a buffer area of the target water body according to the topographic data, and extracting a remote sensing image of the buffer area; and inputting the remote sensing image of the target water body, the remote sensing image of the buffer area and the topographic data into a pre-trained water body classification model to obtain a category recognition result output by the water body classification model. The technical problems that an existing method is limited by information representation capacity of traditional local spectrums and texture features, and accurate recognition of small water bodies in the alpine regions is difficult to achieve are solved, and therefore remote sensing recognition accuracy of lakes in the alpine regions is improved.

The invention discloses a cache management method and a storage medium thereof. The cache management method comprises the steps that when a target data write-in request is responded, page information of a current target block is obtained, and the page information represents a page, needing to be subjected to data read-write operation, of the current target block; according to page information, corresponding block operation is executed, the page information comprises the last page of the current target block, the first page of the current target block and the middle page of the current target block, the page information comprises block operation logic corresponding to the page information, and the block operation logic is used for indicating execution of the corresponding block operation. According to the cache management method disclosed by the invention, the page information is obtained to perform the corresponding block operation on the current target block, so that the block management of the NAND Flash memory can be dynamically realized in real time, the loss of the storage unit is considered, and the erasing and random reading and writing of the storage unit are reasonably arranged, thereby improving the durability and performance of the NAND Flash memory, and improving the user experience. And the overall efficiency of data read-write operation is improved.

The invention belongs to the technical field of power load prediction, and relates to a power load prediction method based on interpretable multi-mode enhancement, which comprises the following steps: 1, constructing a text representation mode of an original load time sequence through a multi-mode enhancement module; 2, time sequence modal information and text representation information of the load are embedded into a high-dimensional vector space through a two-channel coding module; 3, receiving an embedded vector of a time sequence mode through a multi-mode prediction module, and taking a representation vector of a multi-mode text as input; 4, realizing an interpretable multi-mode alignment module; according to the method, the effects among the multi-modal information are divided into uniqueness, redundancy and collaboration; by constructing a negative sample pair for training, alignment of multi-modal information representation is enhanced, and then the prediction performance of the model is improved.

The embodiment of the invention discloses a data annotation method and device, equipment and a medium. According to the scheme, the method comprises the steps of obtaining to-be-annotated data; obtaining to-be-labeled labeling type information for the to-be-labeled data; the annotation type information represents the type of annotation information which can be annotated by the to-be-annotated data; determining an annotation processing module corresponding to the annotation type information according to the annotation type information; the annotation processing module has a data annotation function of annotating annotation information corresponding to the annotation type information; and carrying out annotation processing on the to-be-annotated data by utilizing the annotation processing module to obtain annotated data containing annotation information.

The invention discloses a video quality evaluation method with rich information representation, which comprises the steps of spatial domain feature extraction, motion feature extraction, time sequence modeling and quality prediction, and is characterized in that a fused feature vector is input into a time domain convolutional network for time sequence modeling, and the feature dimension is reduced to 128; and finally, directly mapping the 128-dimensional features into the quality score of the video through a full connection layer. According to the method, spatial domain feature extraction, motion feature extraction, time sequence modeling and quality prediction are set, the spatial domain feature extraction part of the model considers different features represented by video frames in RGB and YIQ color spaces so as to fully extract the features of the video frames in the spatial domain, and in addition, in order to fully understand the relation between adjacent frames, the feature extraction part of the model considers the different features represented by the video frames in the RGB and YIQ color spaces. Motion information on a time sequence is supplemented by extracting motion features of a video, so that the model has better performance, the accuracy of model prediction is improved, the overall model has better performance and accuracy in video quality evaluation, and the overall effect of the model is improved.

The invention discloses a millimeter wave electrocardiogram reconstruction system and method based on adaptive MODWT and CNN-BiLSTM-CA, and the method comprises the steps: collecting a tiny phase displacement signal of a target thoracic cavity region, and carrying out the multi-band decomposition and key component adaptive screening of an original signal through combining with a multi-scale stationary wavelet decomposition algorithm; and introducing a channel attention mechanism to enhance key information representation, inputting a reconstruction signal into a deep learning model combining a convolutional neural network and a bidirectional long-short term memory network, completing time sequence modeling and nonlinear mapping, and outputting a reconstruction waveform highly consistent with a standard electrocardiogram. According to the method, the signal reduction capacity under the complex interference condition is remarkably improved, high-precision and privacy-friendly remote physiological signal monitoring can be achieved under the condition of not depending on a lead electrode, and the method is superior to a traditional baseline model in the aspects of waveform reduction precision, signal time sequence consistency, model generalization capacity and the like; the method is suitable for various application scenes such as intelligent medical treatment.

The invention provides a point cloud weak supervision semantic segmentation method based on a dual self-attention mechanism, and the method comprises the steps: obtaining partially labeled three-dimensional point cloud data which comprises space coordinate information; local coordinate features of the point cloud data are coded through a GNE module, and discriminative local information representation is generated; a DLA module is adopted to calculate feature similarity between points in the point cloud data, attention weight is generated, and point feature expression is adjusted; performing weighted aggregation on the adjusted point features through an AFAP module to generate global feature representation; the DLA module and the AFAP module are connected in series and stacked to form a DARF module, and the modeling capability of complex geometrical shapes is improved; and performing semantic segmentation on the point cloud data based on a weak supervision training strategy, and outputting a segmentation result. According to the method, the dependence on the annotation data is remarkably reduced, and the segmentation performance is improved during limited annotation.

The invention discloses a battery fault diagnosis method based on a graph structure and an LSTM, and belongs to the field of battery fault diagnosis, and the method comprises the steps: converting battery blocks of a battery module and a connection relation thereof into a graph model based on a battery pack space connection information representation mode of the graph structure, so as to effectively capture the space dependence between the battery blocks; meanwhile, by combining the graph neural network and the long-short-term memory network, the spatial-temporal characteristics of the battery state can be accurately extracted, the processing capacity of time sequence data is enhanced, and therefore the accuracy and comprehensiveness of fault diagnosis are improved; besides, aiming at the problem of lack of a confidence evaluation mechanism in the prior art, an accurate confidence evaluation module is designed, and the reliability and transparency of the decision are effectively improved by calculating the confidence score of the diagnosis result.

A blockchain-based data processing method, performed by a consensus node on a second blockchain, includes: obtaining a cross-chain message submitted by a forwarding service device, and obtaining a blockchain identification carried in the cross-chain message associated with cross-chain transaction data stored in a first blockchain; obtaining, in a second blockchain corresponding to the blockchain identification, block Merkle information corresponding to the first blockchain, and obtaining, in a block Merkle tree represented by the block Merkle information, a Merkle proof set corresponding to the cross-chain message, the Merkle proof set including node information of one or more nodes in the block Merkle tree,; verifying validity of the cross-chain message according to the Merkle proof set and the block Merkle information; and obtaining a transaction execution result corresponding to the cross-chain message in response to verification succeeds, and storing the transaction execution result into the second blockchain.

The invention discloses a PLC register point location information determination method, apparatus and device, a medium and a product. The method comprises the steps of obtaining display data of to-be-analyzed parameters and source data in a PLC; wherein the source data comprises a data set, and the data set comprises a register point location address and a stored data value; the to-be-analyzed parameters comprise all or part of parameters displayed by the HMI; determining a target data group matched with the display data from the source data; according to the target data set, point location information of the to-be-analyzed parameter is generated, and the point location information represents the corresponding relation between the to-be-analyzed parameter and the register point location address of the to-be-analyzed parameter in the PLC. According to the application, accurate positioning of the PLC register point location address can be realized without using a PLC source program or a touch screen program or depending on detailed point location information provided by a manufacturer, so that the PLC data can be directly applied to various control scenes and monitoring scenes, and a basis is provided for improving the production efficiency and reducing the production cost.

The invention relates to the technical field of remote sensing images, in particular to a remote sensing image semantic change detection method based on multi-head adaptive attention and spatial feature enhancement, based on an MAA-SFENet model architecture, a multi-head adaptive attention (MAA) and attention driven feature association (ADFC) module is integrated, the MAA focuses on capturing fine local changes, and the ADFC module focuses on capturing fine local changes. And meanwhile, the global context is fused, and the ADFC focuses on processing large-scale high-dimensional data, so that the sensitivity to a spatial variation mode is enhanced. Besides, in order to further improve the adaptability to different environments, a spatial adaptive feature modulation (SAFM) module is introduced, and spatial information representation is dynamically adjusted, so that the detection precision under a complex background is improved. Experimental results on two SCD data sets show that the MAA-SFENet is superior to other advanced methods in qualitative and quantitative evaluation. The ablation experiment further verifies the important contribution of the three modules to performance improvement. Experimental results show that the MAA-SFENet has excellent performance and reliability in the remote sensing image SCD.

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.