6221results about "File/folder operations" patented technology

The invention discloses a fusion method for automatic coprocessing of multi-source heterogeneous data, which comprises the following steps of: analyzing the format and semanteme of the multi-source heterogeneous data to obtain metadata features of each data source, the metadata features comprising data types, coding modes and semantic tags, and generating a standardized metadata description set; according to the metadata description set, a pre-established protocol template library is adopted, an applicable template is matched, an initial unified exchange protocol is generated, and the initial unified exchange protocol comprises a format conversion rule and a semantic mapping relation; and if the format conversion rule of the initial unified exchange protocol cannot adapt to the metadata features of the newly added data source, dynamically updating the protocol template through a machine learning algorithm to obtain an exchange protocol adaptive to the new data source.

The invention discloses a multi-source three-dimensional model lightweight processing and lossless compression supporting method, system and device and a storage medium, and relates to the three-dimensional technical field, and the method comprises the following steps: carrying out format compatibility check on an imported three-dimensional model, and analyzing the geometric structure, texture characteristics and material attributes of the model; the geometric structure of the three-dimensional model is simplified by adopting a quadratic error measurement algorithm and edge folding, the simplified model is optimized through edge segmentation operation, and a lightweight model is obtained; and compressing geometric data of the lightweight model by using a data compression algorithm, and compressing texture data through predictive coding to complete lossless compression of the three-dimensional model. According to the method, the loading speed and the real-time rendering efficiency are remarkably improved by reducing the size of the three-dimensional model file and optimizing the texture compression algorithm. The lossless compression technique ensures that a high-quality visual effect is kept in the lightweight process, the lightweight model reduces the storage space requirement, and the energy consumption of data transmission and processing is reduced.

The invention provides a data fusion sharing method for multi-source heterogeneous power big data, which comprises the following steps: when receiving multi-source heterogeneous power data, carrying out dynamic protocol adaptation, and converting multi-source heterogeneous power data formats of different protocols into a current system internal standard format; performing data quality evaluation on the data converted into the standard format in the current system, and when a quality evaluation result does not meet a quality requirement, performing data restoration; analyzing the data passing the data quality evaluation or restoration, determining the correlation among different data sources, and fusing the data according to the correlation among the different data sources; and performing distributed storage on the fused data for distributed indexing and access. By implementing the method and the system, efficient fusion and sharing of multi-source heterogeneous data can be realized, the intelligent level of data management and analysis of a power system is improved, and the requirements of an intelligent power grid on data instantaneity, accuracy and safety are met.

The invention provides an integrated metadata management method and system based on data source expandability, and relates to the technical field of data sources, and the method comprises the steps: executing real-time detection on a plurality of heterogeneous data sources through a PF4J plug-in framework, collecting connection information, and establishing a dynamic connection pool; obtaining a table structure, a field attribute and an association relationship list, and assembling the table structure, the field attribute and the association relationship list into JSON format data; performing analysis and standardization processing on the data, and constructing an association degree calculation matrix; generating a standardized data structure description file; establishing a three-layer index directory, and detecting structure change and updating; and calculating a data table reference dependency relationship, and generating a DAG data flow path. According to the method, efficient integration and management of heterogeneous data sources are realized, and the metadata processing efficiency is improved.

The invention discloses an archive management system and method based on data analysis, and particularly relates to the technical field of archive management, and the method comprises the following steps: obtaining document attribute data of archives, generating document attribute vectors, carrying out clustering analysis, finding a similar group set containing similar attributes with a current archive from all archives, and carrying out clustering analysis on the similar group set; then whether an information exposure risk investigation mechanism and a transverse association risk level are triggered or not is judged; respectively carrying out content analysis and access analysis on the current archive, then generating a dynamic desensitization level of the current archive in combination with a transverse association risk level judgment result, and applying the dynamic desensitization level to the current archive in real time; according to the method, the behavior entropy access risk index is obtained by calculating the content sensitivity, dynamically evaluating the file security level and calculating the confusion degree of the user access behavior, the risk perception capability is improved, and the desensitization level of the file is automatically adjusted according to the content sensitivity, the access risk and the overall risk level, so that the data protection is more accurate.

The invention relates to the field of standard comparison analysis, in particular to a method for automatically identifying differences of domestic and overseas standard files. A method for automatically identifying differences of standard files at home and abroad comprises the following four steps of S1, standard file analysis and information extraction, S2, semantic understanding and information integration, S3, standard difference comparison, S4, compliance analysis and suggestion, and in combination with a rule engine and a natural language generation technology, evaluating difference influences and outputting adjustment suggestions. According to the method, comprehensive analysis of the standard file is realized through multi-modal integration and structured processing, and a knowledge base with cross-language and format is constructed; clause association reasoning is realized through deep semantic analysis and knowledge graph construction; according to the method, accurate difference recognition is achieved through hybrid embedding and a dynamic threshold strategy, the accuracy and efficiency of standard comparison are remarkably improved, the manual intervention cost is reduced, and an efficient solution is provided for standard system analysis in international trade and technical cooperation.

An apparatus for data ingestion and manipulation, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a resource data file from one or more data acquisition systems, classify the resource data file to one or more educational categorizations, generate an educational module as a function of the resource data file and the classification of the educational categorizations wherein the education module comprises one or more machine learning models, retrieve a user profile of a plurality of user profiles as a function of a user input, create user-specific outputs as a function of the educational module, the user profile, and a conversational input and generate a virtual avatar model as a function of the user-specific outputs.

The invention discloses a metadata real-time self-adaptive standardization system for multi-field data sharing. The metadata real-time self-adaptive standardization system comprises a metadata information layering module, an intelligent information acquisition module, a real-time self-adaptive standardization module and a visual management module, the metadata information layering module identifies sources of various metadata, and sequentially performs cleaning, classification and layering preprocessing on the metadata; the intelligent information acquisition module uniformly acquires metadata information and converts the acquired data into standardized intermediate representation; the real-time adaptive standardization module is used for realizing rule dynamic adaptation and real-time optimization in a metadata standardization process, is internally provided with a dynamic rule library, is used for storing dynamically generated metadata mapping rules and supports automatic loading, updating and semantic extension of the rules; and the visual management module realizes management, analysis and dynamic adjustment of metadata through a visual interface. The method is high in accuracy of processing unknown data, can adapt to different data scenes in real time, and is high in semantic interoperation capability.

The embodiment of the invention provides a file resource reuse library management method and device based on a database, and efficient management of project resources is realized by constructing a distributed column family database cluster and dividing the distributed column family database cluster into a plurality of special column families. A multi-dimensional index mechanism is innovatively designed and comprises reverse indexing, reverse indexing and version linking, and flexible resource retrieval is supported. A distributed version lock and a transaction manager are introduced to process concurrent access, and a multi-user cooperation scene is intelligently processed through a conflict detector. And the system automatically processes resource import and tool chain configuration, so that the consistency and reliability of resource reuse are ensured. According to the method, the limitation of traditional file resource management is broken through, and an efficient resource reuse solution is provided for large-scale project development.

The invention belongs to the technical field of intelligent document management and auditing in the electric power industry, and provides an electric power document autonomous review method and system fusing causal reasoning and compliance review. According to the technical scheme, obtained electric power document data is subjected to semantic analysis, and semantic representation of a document is obtained; based on the semantic representation of the document, performing event extraction on each sentence recognized in the document, and encoding each event to obtain the semantic representation of the event; constructing an initial event causal chain based on the semantic representation of the event; verifying the constructed initial event causal chain by adopting a causal reasoning algorithm, comparing each event in the verified causal chain and the incidence relation between the events with a power industry compliance rule, calculating a compliance score, and calculating the incidence relation between the events of which the compliance score is lower than a set score value or the incidence relation between the events of which the compliance score is lower than the set score value; and generating a correction suggestion, and generating a review report. And the efficiency and the accuracy of document review are obviously improved.

The invention discloses a human resource archive filing management system, and relates to the technical field of data management, the system comprises an acquisition module, a processing module and a management module, the acquisition module acquires original archive data of employees, transmits the data to the processing module, classifies the original archive data according to a preset first rule, and stores the classified data to the management module; the first archive data is obtained, controlled and managed, a calling permission and a distribution strategy are generated, the first archive data is recognized through a preset second rule, second archive data is generated, corresponding processing is conducted according to the processing priority, a processing log is reserved, third archive data are obtained and transmitted to the management module, and the management module is used for managing the first archive data. A distributed cloud storage cluster and a local storage node are constructed, an overall storage architecture and a retrieval path of third archive data are optimized, archive management is refined through three-dimensional classification, data integrity is guaranteed through field-level verification and automatic completion, scene-based dynamic authority control safety is achieved, compliance risks are intelligently cleared and avoided, and efficiency is improved through mixed storage. And active management of archive management is promoted.

The invention relates to the technical field of archive management, and provides an archive resource intelligent classification and association indexing method based on multi-modal ai analysis, which comprises the following steps: preprocessing collected archive data to form a multi-modal data set comprising text data, image data and audio data; extracting feature vectors of the text data, the image data and the audio data, and fusing the feature vectors into a unified archive representation vector through a cross-modal attention fusion mechanism; constructing a hierarchical classification model, and performing intelligent classification on the archives in combination with a domain knowledge graph; the method comprises the following steps: constructing an associated index of an archive from four dimensions of entities, semantics, time and space and events, establishing a feedback mechanism, dynamically updating and optimizing the associated index, and providing multi-modal retrieval and visual display based on a classification system and the associated index. According to the intelligent classification and association indexing method for the archive resources, the problems that in an existing archive classification and indexing method, the multi-modal processing capacity is insufficient, the association mining depth is insufficient, and the efficiency is low are solved.

The invention provides an instrument panel type building engineering construction progress display method, system and device and a medium, and relates to the field of building construction management.The method comprises the steps that geographic information system map data and building information model data of an area where a building project is located are obtained and fused, and then a multi-dimensional display platform of the building engineering progress is constructed and formed; actual progress data, historical construction data and environment data of a construction site are collected, risk factors of construction tasks are analyzed, the dependency relationship between the construction tasks is analyzed through topological sorting, and a construction risk task tree is constructed; a construction risk task tree is adopted to identify potential risks of a construction task, weighted calculation is carried out on risk factors according to weight coefficients, a comprehensive risk assessment score of the construction task is generated, a construction progress sequence is optimized, and integration is carried out through an instrument panel to form a unified visual display interface. Dynamic prediction, real-time display and automatic adjustment can be realized, and the accuracy of construction progress monitoring is improved.

The invention relates to the technical field of intelligent processing of document information, discloses a method and a system for extracting RAG type data items and checking consistency for engineering technology documents, and solves the problem that an existing scheme is difficult to consider high-accuracy extraction, cross-document consistency, strong traceability and structured landing in engineering technology document data processing. The scheme comprises the following steps of: performing layout analysis and necessary OCR (Optical Character Recognition) processing on a received document, and dividing the document into multiple levels of text blocks; carrying out vectorization processing on the multi-level text blocks by adopting an embedding model subjected to two-stage fine adjustment, establishing a vector index and recalling candidate fragments to form a candidate fragment set; a cross encoder with efficient fine tuning of parameters is adopted to screen out high-correlation fragments; performing standardization processing by extracting a target variable to form a structured record; consistency checking is conducted on the target variables, and finally structured records and checking results are bound with minimum evidence fragments and corresponding position information to be exported through a format list.

The invention relates to the field of Web application, in particular to a tree structure data dynamic management method based on node hierarchical attributes and recursive updating. Comprising the following steps: S1, constructing a tree structure data system, and endowing each node with a hierarchical attribute which is used for identifying position information of the node in a tree structure; s2, receiving an operation instruction of a user for the tree structure data, wherein the operation instruction comprises node adding, node batch deleting, node moving, data exporting and importing, node querying and node quantity counting; and S3, according to different operation instructions, executing corresponding processing logic: based on node hierarchy attributes, applying a recursive update mechanism to realize dynamic management of tree structure data, and ensuring data consistency and accuracy.

The invention discloses a document auditing method, device and equipment based on a large model and a storage medium, and relates to the technical field of deep learning, and the method comprises the following steps: obtaining document auditing data from a plurality of preset data sources, and analyzing the document auditing data; the document auditing data comprises an auditing material for representing a document auditing rule, a historical document auditing case and a standard document; constructing a document auditing database based on the analyzed document auditing data, and constructing a large model fine tuning data set based on the document auditing database; performing fine tuning on a preset document auditing large model by utilizing the large model fine tuning data set to obtain a target document auditing large model; and determining a current document auditing task, and auditing a to-be-audited document of the document auditing task by using the target document auditing large model to generate an auditing report. Automation, high efficiency and accuracy of document auditing are achieved through intelligent document analysis, auditing database retrieval and compliance judgment of a fine adjustment large model.

Technology embodied in a method that includes receiving, as an input to a machine-learning model, data indicative of user-interaction of a particular user with a generative artificial intelligence (AI) system. The machine learning model is trained to identify one or more topics associated with inputs provided to the machine-learning model. The method also includes identifying a first context associated with the data indicative of the user-interaction with the generative AI system, and parsing a file system to determine that one or more folders within the file system correspond to the first context. The file system includes multiple folders each corresponding to a separate topic as identified from historical interactions of the particular user with the generative AI system. The data indicative of the interaction is augmented and provided to the generative AI system for generation of a response to the interaction.

The invention provides a conference system, an intelligent agent and a conference processing method, and the system comprises an edge device which is used for carrying out the preliminary screening and feature extraction of a conference link file, and determining a first feature; the cloud server is used for analyzing the first feature after receiving the first feature sent by the edge device, and determining a conference link and conference starting time; the clock and network state monitoring module is used for evaluating the quality of a network environment where the intelligent agent is located within a first preset time before the conference starting time after receiving the conference link and the conference starting time sent by the cloud server, and selecting an optimal network access strategy to access the conference; the knowledge graph construction module is used for constructing a first knowledge graph according to the conference data; the conference recording and analyzing module is used for performing real-time translation according to the source language and the target language of the spokesman during the conference; and analyzing the conference content by adopting the optimized pre-trained large model and a multi-modal fusion algorithm to generate a conference summary document.

The invention discloses an intelligent archive content evaluation system based on deep learning, which relates to the technical field of deep learning, and comprises the steps of inputting a standardized feature vector into a deep learning model, calculating a time sequence waveform factor and a semantic kurtosis factor in combination with a rule matching reference, and generating an evaluation confidence coefficient matrix; the cosine similarity and the Spearman correlation coefficient are included, and the comparison index threshold triggers abnormal marking and grading early warning. According to the method, a multi-model fusion deep learning evaluation model is constructed and comprises a semantic understanding model, a risk evaluation model and a value classification model, split attributes are dynamically optimized in combination with an evaluation rule decision tree, file contents can be comprehensively evaluated from multiple dimensions of semantic understanding, risk evaluation, value classification and the like, and the evaluation efficiency is improved. And an accurate comprehensive evaluation result and a rule matching reference are generated, so that the comprehensiveness and scientificity of file content evaluation are improved.

The invention discloses a BIM model-based digital twinning and panoramic camera mapping method and system, and relates to the technical field of BIM and digital twinning, and the system is mainly composed of a data acquisition module, a data preprocessing module, a feature extraction and analysis module, a coordinate conversion and matching module, a digital twinning model module and a display module. Real-time and efficient integration of the video data and the BIM model is realized, and then the digital twin model capable of accurately reflecting the current state of the building is constructed. The model is of great significance to comprehensive monitoring and management of the building environment, and the intelligent level and the operation and maintenance efficiency of the building are remarkably improved.

The invention provides a digital office data synchronization and sharing method and system based on cloud computing, and relates to the technical field of cloud computing, and the method comprises the steps: building a distributed data synchronization system on a cloud computing platform, and detecting the change of digital office data on each terminal device through a real-time monitoring mechanism; when data change is detected, triggering an incremental synchronization algorithm, and only synchronizing the changed data part; historical version information of each data change is recorded by using a block chain-based distributed account book technology, and data version tracking and backtracking are realized; constructing a role-based access control system, and allocating data access permissions to different users; a real-time collaborative editing function is realized, and multiple users are allowed to edit the same digital office data at the same time; establishing a data sharing log system, and recording data access and operation behaviors of each user; and a machine learning algorithm is utilized to analyze a user behavior mode, and related digital office data is automatically recommended.

The invention discloses a digital archive management method and system, particularly relates to the field of digital archive management, and aims to evaluate the fracture or abnormal jump degree of time data through continuous analysis of the time data in a digital archive. And carrying out integrity analysis on the spatial data in the digital archive, and evaluating the abnormal deviation or missing degree of the spatial data. And according to the evaluation results of the time data and the spatial data, screening out archives with time-space association abnormity. And based on the digital archive with the space-time association abnormity, the space-time association between the time data and the space data is evaluated by analyzing the space-time association matrix. The digital archive management model is established according to the fracture or abnormal jump degree of the time data, the abnormal deviation or missing degree of the space data and the space-time correlation of the digital archive space-time data, so that the digital archive management efficiency and the digital archive integrity are improved.

The invention belongs to archives, and particularly relates to an investment and financing project archive evidence intelligent management platform based on big data, which comprises the following steps: step 1, a system uploads and preprocesses archives through an upload and storage module, and converts project files in different formats into structured data; 2, a data processing and intelligent analysis module automatically extracts key information and performs archive classification, data matching and compliance review by using natural language processing and a machine learning algorithm on the basis of data of the uploading and storage module; 3, the system carries out automatic examination based on the compliance model, an evidence chain meeting legal requirements is generated, real-time risk assessment is carried out, and when potential risks are recognized, the system triggers early warning and provides suggestions; step 4, in combination with risk early warning and legal examination data, a system workflow and task scheduling module dynamically schedules a workflow according to a task priority and a dependency relationship; and 5, providing decision support by the system through a user interface, and helping a manager to make a precise decision.

The invention discloses a regional refined sea wave coupling numerical forecasting method based on a non-static mode, and aims to improve forecasting accuracy. The method comprises the following steps: firstly, constructing a regional refined sea wave coupling numerical forecasting system consisting of a data collection subsystem, a preprocessing subsystem, a data assimilation subsystem, a coupling mode calculation subsystem and a post-processing subsystem; the data collection subsystem obtains related data of atmosphere, ocean and sea waves; the preprocessing subsystem generates atmosphere, ocean and sea wave mode grid files and interpolates atmosphere, ocean and sea wave forecast fields to obtain preprocessed data, namely an atmosphere initial field, an atmosphere boundary field, an atmosphere forced field, an ocean initial field, an ocean boundary field and a sea wave boundary field. The data assimilation subsystem assimilates the preprocessed data to obtain assimilated data; and the coupling mode calculation subsystem performs integration on assimilated data to obtain forecast fields of atmosphere, ocean and sea waves. And the post-processing subsystem generates a forecast product according to each forecast field. The method is small in forecasting error and high in forecasting accuracy.

The invention provides a cloud computer integrated storage method and device, electronic equipment, a chip and a medium, and the method comprises the steps: determining the prediction time when the usage amount of a local storage space exceeds a preset threshold value based on local storage data, and enabling the prediction time to be obtained through the prediction of a trained target prediction model; according to the storage information configuration file, a target cloud storage space is determined from at least one cloud storage space contained in the storage information configuration file, and the cloud storage space is used for the cloud application to store data; before the prediction time, mounting the target cloud storage space to a local mounting point; and uploading the file written into the local mounting point to the target cloud storage space. According to the invention, the cloud storage space of other cloud applications can be integrated to the local cloud computer client, the integrated storage of the cloud computer client and the cloud storage space is realized, the utilization rate of the storage space is improved, elastic capacity expansion can be realized according to requirements through automatic mounting, the user experience is improved, and the capacity expansion efficiency is improved.

The invention provides a surveying and mapping result management method and system based on big data, and the method comprises the steps: obtaining geographic space surveying and mapping data through employing an unmanned plane, a vehicle-mounted laser radar and a sensor, and recording the time, position and equipment information of the surveying and mapping data in real time; a regularizer based on online linear optimization classifies the surveying and mapping data according to geographic positions, collection time and data types, and uploads the classified surveying and mapping data to a distributed storage system; performing data cleaning, format unification and quality verification on the stored surveying and mapping data to generate standardized surveying and mapping data; constructing a knowledge base based on a large language model (LLM), performing semantic analysis and feature labeling on the standardized surveying and mapping data by using geographic space operators in the knowledge base, and establishing a spatial index of the standardized surveying and mapping data; and receiving a user retrieval request, and performing privacy protection retrieval on the standardized surveying and mapping data based on the spatial index.

The invention relates to the technical field of data management, in particular to a visual data processing system applied to traditional Chinese medicine pediatrics. The system comprises the following modules: a data acquisition module used for acquiring a pediatric physiological time sequence data set; carrying out physiological event anchor point identification on the pediatric physiological time sequence data set to obtain a physiological event anchor point set; the time calibration module is used for performing noise suppression on the pediatric physiological time sequence data set according to the physiological event anchor point set to obtain a noise-suppressed physiological time sequence data set; performing time calibration on the noise suppression physiological time sequence data set to obtain a time correction physiological data set; the feature extraction module is used for extracting a tongue picture image set from the time correction physiological data set; and generating a tongue picture feature vector set based on the tongue picture image set. According to the method, the problem of dynamic association analysis caused by multi-dimensional sign data isomerism in traditional Chinese medicine pediatric clinical data processing can be effectively solved.

The invention provides a code retrieval method and device and related equipment, and relates to the technical field of retrieval. The method comprises the following steps: segmenting each source code file to obtain a plurality of code blocks of each source code file; determining description information of each code block, and determining an association relationship between any two code blocks; generating a multi-layer code knowledge graph based on the description information of each code block and the incidence relation between any two code blocks; obtaining query information of a user, and determining a first code block based on the query information; determining a second code block related to the first code block in the multi-layer code knowledge graph; and generating a retrieval result by using the first code block and the second code block. Through the technical means, the problem of low code retrieval accuracy in related technologies is solved.

Disclosed herein are methods and systems for generating metadata from content using one or more machine learning models. In an embodiment, a method may include receiving the content through a graphical user interface associated with the large language model, generating a first file by tokenizing the content into an input format for the large language model and merging the tokenized content with a content instruction, inputting the first file into the large language model, generating, using the large language model, metadata from at least the first file, the metadata reflecting a context associated with the content, generating a second file, the second file comprising the metadata, and displaying the generated metadata on the graphical user interface.

Embodiments are directed to workload allocation for file system maintenance. A file system that includes storage nodes and snapshots may be provided such that each snapshot may be associated with a plurality of data blocks. If snapshots are deleted further actions may be performed, including: determining the dead blocks associated with the deleted snapshots such that each dead block may be a data block that may be unassociated with undeleted snapshots; adding the plurality of dead blocks to dead trees located on the storage nodes; determining an urgency score based on a workload model and file system metrics; determining delete tasks based on the urgency score; determining a portion of the storage nodes based on a number of delete tasks; and executing the delete tasks on the portion storage nodes to delete the dead blocks to return storage capacity to the file system.