191 results about "Information mining" patented technology

The invention provides a traffic safety detection method and system based on big data analysis, and the method comprises the steps: obtaining a to-be-detected traffic internet-of-things data set, carrying out the fusion representation information mining of the to-be-detected traffic internet-of-things data set, and obtaining first fusion representation information; the first fusion representation information comprises multiple pieces of data dimension representation information corresponding to multiple data dimensions; indexing a plurality of historical traffic internet-of-things data sets matched with the traffic internet-of-things data set to be detected according to the first fusion representation information, and obtaining a traffic congestion level mark corresponding to each historical traffic internet-of-things data set according to second fusion representation information corresponding to each historical traffic internet-of-things data set and the traffic congestion level mark corresponding to each historical traffic internet-of-things data set; generating a traffic situation fusion template according to the first fusion representation information of the traffic internet-of-things data set to be detected; and according to the traffic situation fusion template, detecting a traffic congestion level mark of a traffic internet-of-things data set to be detected. According to the invention, the traffic jam grade marks can be efficiently and accurately obtained.

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 relates to the technical field of karst tunnel collapse early warning, in particular to a karst tunnel collapse intelligent early warning method, system and equipment based on micro-seismic multi-precursor characteristics. According to the technical scheme, the method comprises the steps of multi-scale micro-seismic monitoring network construction, multi-modal micro-seismic feature processing, quantum neural network early warning model construction and self-adaptive early warning and risk assessment. According to the invention, through omnibearing data acquisition of the multi-scale micro-seismic monitoring network, different depth sensors and an intelligent transmission mode, monitoring is accurate and stable, through combination of multi-modal feature extraction fusion, time domain, frequency domain and waveform multi-dimensional information mining, surrounding rock conditions are accurately judged, early warning analysis is enhanced by using quantum characteristics through the quantum neural network, and the early warning accuracy is improved. And self-adaptive threshold adjustment and multi-factor risk assessment are matched, accurate early warning is achieved, collapse is efficiently prevented, and tunnel construction safety is powerfully guaranteed.

The invention relates to the technical field of operation and maintenance management, in particular to a medical equipment operation and maintenance management system based on information mining analysis. According to the method, accurate screening of data abnormal features is introduced, so that the evaluation of the equipment operation state is more refined. According to the method, abnormal data can be effectively identified based on the mean offset of the data measured by the blood oxygen sensor, and the accuracy of equipment state evaluation is ensured in combination with the distribution trend of measurement errors. The influence of environmental factors on the equipment performance is deeply excavated, the dynamic change trend of the equipment performance under different environmental conditions can be identified by utilizing the correlation calculation of the environmental temperature change and the power consumption offset, and the sensitivity to the influence of external factors is improved. The evaluation mode of the operation state is more comprehensive, the subtle change trend of the equipment operation state can be accurately described through the collaborative analysis of the measured frequency fluctuation amplitude and the signal response time, and the accuracy of fault prediction is improved.

The invention relates to the technical field of reservoir prediction, in particular to a high-resolution reservoir prediction method under seismic full-wave information mining. The method comprises the following steps: acquiring seismic full-wave information data; performing frequency domain conjoint analysis on the seismic full-wave information data to generate a multi-scale full-wave feature tensor; performing seismic full-wave signal multi-scale decoupling on the multi-scale full-wave feature tensor to generate seismic multi-wave field separation data; implicit reservoir feature mapping is carried out on the seismic multi-wave-field separation data to generate initial reservoir feature mapping data; performing nonlinear correlation analysis on the initial reservoir feature mapping data to generate high-order reservoir feature data; and performing inversion linkage optimization on the high-order reservoir feature data and the seismic multi-wave-field separation data to generate reservoir dynamic response optimization data. According to the method, through multi-wave-field information extraction, high-resolution reservoir detail display, dynamic reservoir change prediction and confidence evaluation, the accuracy and resolution of reservoir prediction 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 belongs to the field of remote sensing image change detection, and particularly relates to a remote sensing image change detection network for multi-scale feature extraction and information mining. The invention proposes a change detection network MSFMF-CDNet for multi-scale feature information mining fusion, and the method comprises the steps: firstly, designing a selective convolution attention mechanism SCBAM module to improve the distinguishability of multi-scale features based on the observation of different levels of information displayed by hierarchical features; then, a CCSAM module is proposed to refine multi-scale feature global information, and finally, advanced features are utilized to guide a multi-scale fusion HFGFFM module to improve the ability of the model to identify objects of different sizes. Experiments on two common optical remote sensing image CD data sets of LEVIR-CD, WHU-CD and CDD show that compared with other common methods, stronger remote sensing image change detection CD performance is realized.

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 discloses a discrete manufacturing enterprise energy consumption prediction method suitable for dynamic production, and the method comprises the steps: collecting the time sequence energy consumption data of a discrete manufacturing enterprise, and constructing a data set, so as to obtain an energy consumption curve of the discrete manufacturing enterprise; rearranging the 1D time sequence energy consumption data into a 2D matrix by adopting a preset calculation mode; processing the 2D matrix by adopting preset convolution modules, carrying out weighted summation calculation on the data after data processing, then stacking the convolution modules and connecting the convolution modules by adopting residual errors; and inputting an output result of the convolution module into a full connection layer, and generating a prediction result at the next moment, thereby realizing energy consumption prediction of the discrete manufacturing enterprise. According to the method, the time sequence periodic information mining method is adopted to identify the energy consumption time sequence change of the discrete manufacturing enterprise caused by the dynamic change of production and equipment, so that the energy consumption prediction precision of the discrete manufacturing enterprise is improved, and the accurate prediction of the energy consumption of the discrete manufacturing enterprise with large energy consumption fluctuation and insignificant consumption rule is realized.

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.

Disclosed in the present invention are an intelligent energy consumption management method and system based on multi-dimensional refinement. The method comprises the following steps: using a machine learning algorithm to perform learning fitting on first data information and second data information to an information data association measurement and calculation model representing an association relationship between the first data information and the second data information; and on the basis of a sequence position of the current first data information in a first data information descending sequence, determining an energy consumption optimization direction for reducing energy consumption of a production line. In the present invention, measurement and calculation of energy consumption of a gypsum board production line are realized, refined management of energy consumption conditions is realized in related links such as energy consumption devices, energy consumption states, and energy consumption environments, an energy consumption optimization direction for reducing energy consumption of a production line is determined, information mining of energy consumption data is realized, and an energy consumption optimization direction mined from the energy consumption data is visually displayed.

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 relates to the technical field of information mining analysis, in particular to an information mining analysis method in an industrial scene, which comprises the following steps of: acquiring each data source, determining a data state according to a data source correlation degree and a mining difficulty coefficient, and determining a strategy analysis mode according to the data state; when the sub-data is extracted and analyzed, a processing mode is determined according to a dangerous sub-data distribution coefficient and a dangerous turbulence value to obtain a sub-data set, and the processing mode is a first extraction mode determined according to a dangerous end radiation coefficient and an in-end sub-data correlation coefficient, or a second extraction mode determined according to a dangerous gathering area category; when the associated source group is selected and analyzed, a clustering mode is determined according to the feature conversion coefficient and the number of the data sources, the data sources in the associated source group are selected according to the data flow anomaly index, and sub-data corresponding to the selected data sources serve as a sub-data set; the data mining efficiency in the industrial production process can be improved.

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 monitoring method and system based on building informatization, which relates to the field of data processing technology. In the present invention, a key information mining unit is used to perform key information mining processing on target building information model data to output a key information mining result representation vector; a key information extraction unit is used to perform at least one key information extraction processing on the key information mining result representation vector to output at least one selected key information mining result representation vector; a building quality prediction unit is used to perform building quality prediction processing on the key information mining result representation vector and each selected key information mining result representation vector to output at least two building quality prediction results; at least two building quality prediction results are fused to output a target building quality prediction result. Based on the above method, the reliability of building quality monitoring can be improved to a certain extent.

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.

The invention discloses an image rain removal method and device for unmanned aerial vehicle power grid inspection, and belongs to the technical field of unmanned aerial vehicle aerial image rain removal. The image rain removal method provided by the invention comprises the following steps: acquiring an aerial image acquired by an unmanned aerial vehicle, and performing step-by-step information mining operation on the aerial image to obtain a de-noised sub-image; the at least one level of information mining operation process comprises a spatial dimension reduction operation and a spatial dimension expansion operation which are performed in sequence; and based on the de-noised sub-image, constructing a target image after rain stripe removal, thereby completing image rain removal operation. In the feature information mining process of the aerial image, the spatial dimension reduction operation is firstly carried out, the content of rain stripe noise is greatly compressed, meanwhile, the influence on other objects in the image is relatively small, then the spatial dimension expansion operation is carried out, the sparsity and the dispersion degree of residual rain stripe information are improved, rain stripes are efficiently removed, and meanwhile the feature information mining efficiency is improved. And other image features are well reserved.

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 an electronic business card associated data analysis system and method based on artificial intelligence, and relates to the technical field of information mining. According to the method, all collected different types of electronic business cards are analyzed and standardized, user information in each electronic business card is normalized, and an electronic business card database is constructed; synthesizing the social relationships of all the users to generate a social relationship network of the users; generating mining elements of each user by utilizing emotional characteristics of each user during communication with different users in history and combining user information in different electronic business cards; potential customers matched with the real-time users are found, and electronic business cards of the potential customers are recommended to the real-time users; and visually displaying the recommendation reason of each recommended potential customer, and optimizing the mining elements of the real-time user by using the electronic business card information corresponding to the key features.