152 results about "Information mining" patented technology

The invention discloses a multi-modal data retrieval method and device, a storage medium and computer equipment. The method comprises the following steps: collecting multi-modal original data; on the basis of description information of metadata of original data, all metadata belonging to the same associated items and logic relations among all the metadata are obtained, a metadata chain is constructed, a distributed graph database is constructed on the basis of the metadata chain, the metadata chain is expressed in the distributed graph database in the form of a graph, the graph comprises nodes and edges, the nodes represent the metadata, and the edges represent the metadata. The edge represents a logical relationship between the metadata; and when a data retrieval instruction is received, traversing each node in the metadata chain along the logical relationship of the metadata chain in the distributed graph database, obtaining a target node matched with a data retrieval requirement corresponding to the data retrieval instruction, and returning original data corresponding to metadata represented by the target node. Multi-modal data dynamic association retrieval can be realized, and cross-modal information mining efficiency and accuracy are improved.

The invention discloses a multi-source data resource key information mining method and system, and the method comprises the steps: obtaining multi-source data of various forms and types, and carrying out the preprocessing; performing division according to data types; word frequency statistics and word correlation analysis are carried out on the character data, and numerical value meanings of the digital data are extracted through context semantic correlation; constructing a cross-modal key information data set based on the key information data set of each data type; designing a multi-branch long-short-term memory neural network model, inputting a cross-modal key information data set into the model for training, and generating key information mining models for different fields by dynamically adjusting network parameters; and selecting to carry out key information real-time extraction and updating on the optimal model, generating a knowledge graph, and carrying out visual operation. The method has the advantages that through multi-source data fusion and processing, the long-short term memory neural network model is combined, the knowledge graph is generated, and domain-specific key information mining and visualization operation are efficiently carried out.

The invention relates to the technical field of data processing, and discloses a gait rehabilitation evaluation method and system based on spatial-temporal characteristics of multi-modal data, a terminal and a storage medium, and the method comprises the steps: synchronously collecting myoelectricity, electroencephalogram and exoskeleton robot IMU signals of a dyskinesia crowd in a certain motion normal form in a specific period interval; a motion function scoring result of a doctor on a patient according to a scale is synchronously recorded, data preprocessing, channel selection and feature extraction are performed on myoelectricity, electroencephalogram and exoskeleton robot IMU signals, a model is established based on a neural network, motion evaluation is performed according to a fused feature array, and a motion function quantitative score is obtained; and the motion function state of the evaluated person is graded according to the score. According to the method, the multi-modal synchronous data rehabilitation evaluation model of the movement function of the dyskinesia crowd is constructed according to multiple information mining, and the movement function state of the evaluated person is rapidly evaluated, predicted and graded.

The invention relates to a GIS (Gas Insulated Switchgear) equipment mechanical defect diagnosis method based on a Gramb angle field and a dual-channel PCNN-Attention neural network, and belongs to the technical field of gas insulated switchgear mechanical vibration defect diagnosis. The method solves the problems that traditional diagnosis depends on artificial feature extraction, so that subjectivity is high, information mining is insufficient, and defect severity evaluation is missing. According to the technical scheme, the method comprises the steps that a one-dimensional vibration signal is converted into a GASF two-dimensional image and a GADF two-dimensional image through a GASF field so as to completely reserve time sequence topological features; carrying out data enhancement by adopting an image geometric transformation technology so as to improve the generalization ability of the model; and a dual-channel PCNN-Attention model is constructed, and synchronous intelligent identification of defect types and severity is realized through parallel feature extraction and dynamic weight optimization of an attention mechanism. According to the method, the diagnosis accuracy, reliability and adaptive capacity are improved, and support is provided for equipment state operation and maintenance.

The invention discloses a transformer substation fault detection method and related equipment, and the method comprises the steps: obtaining the real-time data of a transformer substation secondary system, forming a scene prediction model through a deep learning model and a dynamic Bayesian network, and determining a fault detection result in combination with a digital twin system. The deep learning model can deeply mine multi-source real-time data features, accurately recognize fault information in a complex scene, make up for the problem of insufficient information mining of a traditional data driving method, and improve the accuracy of fault detection and positioning. The dynamic Bayesian network carries out probability modeling on the dynamic change of the system state, and the problems that a traditional method is difficult to adapt to complex working conditions and cannot accurately simulate system evolution are solved. Meanwhile, the digital twin system realizes deep fusion analysis of multi-source data, constructs an intelligent processing closed loop from data acquisition to maintenance decision, remarkably improves the timeliness, accuracy and intelligent level of fault detection, and provides reliable guarantee for stable operation of a transformer substation.

The invention discloses an innovative resource collaborative matching method and system oriented to distributed operation, and relates to the field of data processing recommendation, and the method comprises the steps: carrying out the standardization processing of a heterogeneous resource portrait description text through a large language model, constructing and forming a resource attribute knowledge graph, generating extension description information of the resources in the target industrial chain through the large language model, and constructing and forming an industrial chain knowledge graph; performing semantic alignment and hidden associated information mining on the fused graph by using a pre-trained SACN model, and constructing a resource collaborative knowledge graph representing resource attributes and collaborative logic in an industrial chain; and performing semantic fusion on the original description text representation of the resource and the resource collaborative knowledge graph representation based on a large language model, generating comprehensive vector representation through multi-modal fusion, and outputting a resource matching recommendation result by adopting a collaborative filtering recommendation model. According to the method, the cross-organization and cross-industry-chain innovative resource collaborative knowledge graph is constructed, instant and clear demands of users are accurately responded, and the resource allocation efficiency is improved.

The invention provides a mechanical excavation operation dust fall control method and system based on AI, and relates to the technical field of artificial intelligence. According to the control method, a first dust diffusion range generated during excavation is predicted according to excavated object information, the excavation direction of an excavation part, excavation information and weather information, and a first water mist area covering the first dust diffusion range is formed by controlling a holder and a power driving part; in this way, the concentration of flying dust generated in the excavation operation process can be reduced, environmental pollution is avoided, and it is guaranteed that on-site constructors have a healthy working environment.

The invention provides a hotspot technology mining method and system based on scientific and technological literature data, and relates to the technical field of scientific and technological information mining, and the method comprises the steps: firstly obtaining a scientific and technological literature data set containing papers and patent literatures, and constructing a current technical element network through technical phrase extraction and semantic normalization processing; dividing the technical element network segment into a time-serialized technical element network segment set according to time slices, calculating nodes and edge time sequence importance indexes to judge an emerging technical cluster, carrying out dynamic graph representation learning and future associated edge prediction on the set, generating a potential technical combination prediction set, and evaluating the potential technical combination prediction set; and finally, generating an emerging technology theme list and a technology fusion direction list in combination with an emerging technology cluster node growth trend and a prediction association edge evaluation result, thereby improving the accuracy, comprehensiveness and foresight of hotspot technology mining.

The invention provides a multi-view space transcriptomics clustering method based on sharing-specific information mining. The technical problems that in an existing space domain recognition method, robustness of a single view is insufficient, multi-view information fusion is insufficient, and description of data statistical characteristics is not accurate are solved. According to the technical scheme, the method comprises the following steps: S1, acquiring and preprocessing original data of a spatial transcriptome; s2, constructing a sharing-specific decomposition coding network; s3, using a ZINB expression decoder, a structure decoder and a Student's t distribution clustering module to construct a multi-task loss function; and S4, outputting a spatial domain division result based on the trained network. According to the method, the spatial domain recognition precision and robustness are remarkably superior to those of an existing method, the data statistical characteristics and biological significance can be accurately described, stable support is provided for tissue function analysis and tumor microenvironment research, and the analysis quality and application value of spatial transcriptomics data are effectively improved.

The invention provides an information deep mining method and system based on a multi-agent cooperation framework, and the method comprises the steps: firstly receiving a to-be-mined information demand, carrying out the task decomposition and cooperative scheduling through a plan expert agent, generating a cooperative task scheduling scheme, and then carrying out the multi-source information cooperative search through a search expert agent according to the scheme, the method comprises the following steps: acquiring multi-source original information, reading an expert agent to carry out deep analysis and association extraction on the multi-source original information, generating structured association information, analyzing the expert agent to carry out multi-level deep reasoning on the structured association information to obtain a deep mining intermediate result, and finally, integrating multi-dimensional verification and fusion on the intermediate result by the expert agent to obtain a deep mining result. And generating a final information deep mining result meeting the requirement, thereby realizing automation, intelligence and high efficiency of information mining, and mining the potential value of the information.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a standard short text entity relationship extraction method based on RoBERTa and a convolutional neural network, which uses a RoBERTa pre-training language model to carry out upstream coding on a standard short text, and uses a multilayer bidirectional Transform (Trm) network node in the RoBERTa pre-training language model to carry out information mining, so as to obtain a standard short text entity relationship extraction result. And then the downstream network extracts the entities and the relationship between the entities by using the structure, semantics and other features of the short text. Due to the fact that the deep learning technology is adopted for extracting the entities and the relation between the entities, the method has good universality and transfer learning capacity, and a good extraction effect is achieved for part of standard short text templates which are not learned.

The embodiment of the invention provides a multi-modal data processing method and system based on a knowledge graph, and aims to solve the problems of semantic association deficiency and potential association mining difficulty in multi-source information processing. The method comprises the following steps: firstly, acquiring a multi-source information set containing different expression form information units and source identifiers, and performing entity mapping on the multi-source information set and a preset semantic association network to generate an information unit set with semantic association tags; performing cross-source feature association processing to obtain a comprehensive feature set; then, a semantic reasoning rule is called for association extension, and an extension feature set is generated; and finally, based on the extended feature set, generating an information processing result conforming to an application scene and feeding back the information processing result to a corresponding interface, thereby effectively integrating multi-source information and mining potential association.

The invention belongs to the field of image data information mining, and particularly relates to a tumor retrieval library construction method and system based on WSI image global features. According to the method, various types of frozen sections in different pathological states are obtained and converted into panoramic pathological scanning images, and quality control is carried out; performing pathology state labeling on the panoramic pathology scanning image and endowing a global pathology label; cutting the panoramic pathology scanning image into a plurality of color blocks at equal intervals, performing quality control to obtain a color block set, and performing internal parameter training on a pre-trained ViT model based on a DINO v2 self-supervised learning method; inputting the color block set into the trained ViT model for processing, and obtaining a descriptor matrix on a full connection layer; a geometric mean value of descriptors of all color blocks of a complete panoramic pathology scanning image and a global pathology label are taken according to the color blocks and are regarded as an instance, and all instances with known labels form a retrieval library. According to the invention, tumors can be found and traced quickly and accurately.

The invention discloses a multi-modal emotion recognition method based on DLKA and EEGNet. The multi-modal emotion recognition method comprises the following steps: performing feature extraction and preprocessing on EEG signals; a DLKA-EEGNet model is constructed, and the DLKA-EEGNet model is adopted to extract electroencephalogram characteristics; carrying out feature extraction on the EOG signal, and carrying out multi-modal feature fusion on the EOG signal and the EEG signal; and inputting the fused features into a deep learning model for emotion recognition. According to the method, a model combining DLKA and EEGNet is introduced, a brand-new multi-modal emotion recognition method is provided, a DLKA module adopts a convertible large kernel convolution strategy, a receptive field can be adjusted in a self-adaptive mode, meanwhile, the parameter quantity is reduced, the calculation complexity is reduced, the DLKA module reinforces global information mining of a deep convolutional network on EEG signals, and the recognition efficiency is improved. Therefore, the robustness and the accuracy of the emotion recognition model are improved.

The application discloses a kind of multi-source data resource key information mining method and system, comprising: obtaining each form type multi-source data, and pre-processing;According to data type, it is divided;Word frequency statistics and word association analysis are carried out to literal data, and the numerical value meaning of digital data is extracted by context semantic association;Based on each data type key information dataset, cross-modal key information dataset is constructed;Design multi-branch long short-term memory neural network model, input model is carried out training to cross-modal key information dataset, and key information mining model for different fields is generated by dynamically adjusting network parameters;Optimal model is selected to carry out key information real-time extraction and update, generate knowledge graph, and carry out visual operation.The application has the advantages that: by multi-source data fusion and processing, long short-term memory neural network model is combined and knowledge graph is generated, and field-specific key information mining and visual operation are efficiently carried out.

The present invention provides a TIN-DDM terrain complexity measurement method based on rolling ball transformation, belonging to the technical fields of seabed terrain information mining and analysis and TIN-DDM multi-scale expression technology. The present invention introduces the change in sampling point depth during the rolling ball transformation process into the construction process of the TIN-DDM sampling point TCI calculation model. The present invention clarifies the correlation between the depth change of the TIN-DDM sampling point and its TCI during the rolling ball transformation process, analyzes the selection principle of the optimal rolling ball radius, constructs and optimizes the sampling point TCI calculation model, and finally achieves an effective measurement of TIN-DDM terrain complexity by interpolating the complex field with the TCI. This solves the problem that the TCI calculation models established by the current TIN-DDM terrain complexity measurement methods are unable to effectively reflect the differences in morphological change characteristics between different terrain units by comparing the TCI values of the sampling points.

This invention discloses a method for mining alumni information based on a large-scale model, comprising the following steps: 1) acquiring raw alumni-related data; 2) preprocessing the acquired data; 3) performing natural language processing on the acquired data; 4) semantic parsing and information extraction using a large-scale model; 5) constructing a knowledge graph; storing the extracted alumni information and relationships in a graph database; and constructing a visualized alumni knowledge graph; 6) updating the knowledge graph data; and 7) providing intelligent retrieval and question-answering services for alumni information based on the knowledge graph data. This invention uses a large-scale model to perform deep semantic understanding and information extraction from unstructured text. Compared to traditional keyword-based or small-scale model-based methods, it can more accurately identify alumni identities, professional experiences, and social relationships, while reducing the false matching rate.

The application provides an atoll vegetation index prediction method, which can be applied to the technical fields of geographic information and deep learning. The method comprises the following steps: obtaining monthly normalized vegetation index data of a target atoll, corresponding marine environment data and meteorological data, and constructing a vegetation growth dataset; calculating the partial correlation of the marine environment data, the meteorological data and the normalized vegetation index change, so as to determine the key environmental data affecting the normalized vegetation index change; determining the lag response period of the normalized vegetation index to each key marine environment data; based on the lag response period, combining attention mechanism and double-time-scale information mining, constructing a double-time-phase convolution long short-term memory neural network model considering the climate time lag effect of the atoll, wherein the neural network model cell structure comprises convolution operation; training the model by using the vegetation growth dataset to obtain a trained model; and predicting the future short-term atoll vegetation normalized vegetation index change by using the trained model.

The invention discloses a multi-level CAD drawing version traceability management method and platform, and relates to the technical field of drawing management, and the method comprises the steps: introducing a quaternary code set by stage-software version-purpose-server, introducing a feature descriptor to construct a difference knowledge base in combination with directional focusing and difference codes in a version updating process, and constructing a multi-level CAD drawing version traceability database; constructing a double-layer state machine according to the characteristic difference and the updating type; and based on the quaternary coding and the state machine, constructing a micro-service architecture in the drawing management platform, receiving and decoupling a version tracing task, executing coding tracing and state layer cascade tracing, verifying a tracing result, and outputting a final version tracing result. Therefore, the technical effects of coding and state two-dimensional information mining and improvement of the accuracy, integrity and reliability of drawing version tracing are achieved.

The invention discloses a power grid dispatching data mining method, system and device and a storage medium, and the method comprises the steps: screening first power grid dispatching data through a density peak algorithm based on mixed density and micro-cluster aggregation, and obtaining second power grid dispatching data; classifying the second power grid dispatching data through a Bray-Curis algorithm to obtain third power grid dispatching data of a plurality of data types; and performing information mining on the third power grid dispatching data through a pruning tree algorithm to obtain target effective information. The efficiency and accuracy of power grid dispatching data mining are improved, so that the reliability of power grid dispatching is improved, and the method can be widely applied to the technical field of data processing.

The invention discloses an unsupervised domain adaptation method for a remote sensing time sequence with phonological feature alignment, which mainly solves the problem of poor generalization performance caused by difficulty in coping with time migration and distribution migration in a cross-domain task due to neglect of phonological information in time sequence data in the prior art. According to the scheme, the method comprises the following steps: constructing a self-adaptive phenological segmentation module based on time constraint, extracting a representative phenological stage from a satellite image time sequence, and accurately capturing key time sequence characteristics of a crop growth cycle; a comparison learning module based on phenological difference is introduced, stage-level positive and negative sample pairs are established, double loss optimization is carried out, and fine alignment of cross-regional phenological features is realized. Through phenological information mining and stage-level comparison and alignment, the precision and robustness of cross-regional crop semantic segmentation under an unsupervised condition are significantly improved, and the generalization ability of a remote sensing deep learning model in an unknown target region is effectively improved.

The present invention relates to a binocular narrow butt weld detection method based on deep learning, comprising the following steps: projecting stripe structured light and passive light by a projector, collecting weld images, and obtaining the point cloud of the welded part; performing two-dimensional data annotation based on image binarization processing and binocular consistency correction; constructing a two-dimensional weld extraction model based on spatial information mining to extract two-dimensional welds; mapping two-dimensional pixels into three-dimensional space coordinates based on a binocular vision model. Pose estimation is performed based on the point cloud of the welded part and the local neighborhood feature information of the weld points. The binocular narrow butt weld detection system of the present invention realizes accurate and efficient weld extraction; specifically, it is an accurate and reliable automatic data annotation method; specifically, it is a weld extraction network that realizes the extraction of two-dimensional coordinates of the weld to assist three-dimensional weld positioning. The present invention provides a necessary strategy for realizing more accurate and efficient narrow butt weld detection.

The embodiment of the invention discloses a clustering method and device based on GCN, equipment and a medium. The method comprises the steps of obtaining an initial sub-graph structure corresponding to each to-be-clustered sample according to feature information of a plurality of to-be-clustered samples; performing feature extraction on the initial sub-graph structure based on a classification network to obtain an attribute classification result between the neighbor node and the center node; wherein the classification network comprises a GCN network; optimizing the initial sub-graph structure according to an attribute classification result to obtain a target sub-graph structure; and clustering the to-be-clustered samples according to the target sub-graph structure to obtain a clustering result. According to the scheme, on the basis of the information mining capability of the GCN for the graph structure, the neighbor node attributes are predicted through the classification network comprising the GCN to optimize the initial sub-graph structure, so that the purer sub-graph structure is obtained, and the clustering precision and the clustering efficiency are improved.

The present invention discloses a method, device and storage medium for mining key nodes in a directed network, belonging to the field of network information mining technology. The mining method comprises: decomposing strong connectivity to obtain a strongly connected subgraph with zero in-degree, selecting a node with the smallest in-degree difference and the smallest in-degree from all strongly connected subgraphs with zero in-degree to meet the reachability condition; then using a greedy algorithm to select nodes that are farther away from the key node group and have a smaller in-degree minus out-degree and in-degree from the strongly connected subgraph with the smallest eigenvalue in each cycle and add them to the key node group; that is, using the graph theory of measurement indicators, the shortest path concept and the greedy algorithm to achieve the mining of key nodes in a large-scale directed network. Since key nodes can directly or indirectly affect all other nodes in the network, the stability of the existing complex network node importance evaluation method in the control synchronization and diffusion capabilities of the directed network is improved.

The present invention proposes a remote sensing image segmentation method and system based on an improved SwinTransformer. The encoder is composed of a Swin transformer backbone network and a global information enhancement module. The Swin transformer backbone network fully mines the global context information of the remote sensing image to obtain global feature maps of different scales and levels. The global information enhancement module is used to perform global pooling operations on the feature maps in the horizontal and vertical directions. The global information enhancement module is fused with the global feature maps output by the corresponding Swin transformer backbone network at different stages, thereby making up for the global information mining capability of the SwinTransformer backbone network limited by the window mechanism. The decoder fuses the global enhanced feature maps of different scales and levels output by the encoder through an attention mechanism, thereby retaining more local information while obtaining more extensive context information.

The invention discloses a consumer complaint information mining method, device and equipment based on a large language model, and relates to the field of product defect risk assessment and recall management. Constructing a standardized product security data set according to the multi-modal data, and performing semantic recognition and labeling on multi-modal complaint information in the data set to obtain a structured product security event information flow; in response to expiration of a preset update period, analyzing the product security event information stream and the current latest product security dynamic knowledge graph to obtain a product security event; the current latest product security dynamic knowledge graph is generated by fusing newly added product security event information flow on the basis of a historical knowledge graph; and carrying out situation awareness, risk early warning and defect correlation analysis on the product security event to finally obtain a product defect assessment result. The result is fed back to the current latest product safety dynamic knowledge graph to be optimized and updated; according to the method, semantic recognition and understanding capability can be realized, and dynamic association analysis capability is improved.

The application discloses a kind of digital archives intelligent information mining method, it is related to file information processing technical field, the present application is evaluated data quality by a series of quantitative indicators including consistency index, integrity index, importance index and comprehensive importance index, which effectively reduces subjective judgment when manual screening, make data screening process more objective, reliable and scientific, simultaneously, by combining quantitative indicators with comprehensive weight, to automatically generate weighted threshold based on data-driven, the application of this weighted threshold not only can quickly process large-scale data set, to reduce the demand for a large number of manual annotation of manpower, and can effectively filter out high-quality data set, and be applied to subsequent deep learning model training, to significantly improve the output accuracy and stability of model, ensure that more accurate prediction results can be obtained, and based on this more accurate prediction results, more scientific and reasonable business decisions can be made.

The invention discloses an LLM-based open source webpage information analysis method and device, a medium and a product, and relates to the field of open source information analysis. According to the method, the semantic comprehension capability and the instruction following capability of LLM are fully utilized, an xpath expression extraction technology, a statistical algorithm extraction technology and an LLM-based retrieval enhancement generation (RAG) technology which are common in the webpage information crawling field are integrated, effective open source webpage information mining analysis is achieved, a corresponding open source information analysis application process framework is designed, and the method has the advantages of being high in practicability and easy to popularize. The method can intelligently query and generate a brief report and a research and judgment report of a hot event, provides factual reference information related to the report, displays analysis results and insight of related events, themes or individuals to a user, and helps the user to better understand conditions, so that a more scientific and reasonable decision scheme is formulated, and the method has better engineering application benefits.

The invention belongs to the technical field of data mining, is applied to the field of medical health and the field of automobile insurance, and discloses a data mining method, device and equipment and a storage medium. The method comprises the steps of obtaining original data in a data source, and performing feature extraction on the original data; performing data dimension reduction on the extracted features, and projecting high-dimensional data to a low-dimensional space; performing data mining on the data after dimension reduction, wherein the data mining method comprises clustering analysis, association rule mining, classification and regression analysis; the mined data is evaluated; and displaying an evaluated result by adopting a visual tool. According to the method, the efficiency and accuracy of data mining are improved, and information mining from mass data is facilitated.

The application discloses a market supervision internet information monitoring and analysis system, which comprises a CNN model construction module, an internet information collection module, an information preprocessing module and an information mining module; the application uses a CNN model algorithm to model sample data marked by artificial marking, and uses a model to determine illegal behaviors; through continuously accumulating sample data, repeatedly training and achieving the purpose of machine learning, the determination accuracy is gradually improved.