2847results about "File metadata searching" patented technology

An AI based system and method for processing and querying files. A method of processing files for an artificial intelligence (AI) querying service includes: hierarchically parsing the files into a set of hierarchically connected data chunks; generating metadata for each of the hierarchically connected data chunks, wherein the metadata includes hierarchical information; processing the hierarchically connected data chunks and metadata with an embedding model to generate vector embeddings that include the hierarchical information; generating textual summaries from the hierarchically connected data chunks; and storing the vector embeddings, textual summaries, and hierarchically connected data chunks for the AI querying service.

The invention belongs to the technical field of data storage, and discloses a multi-source marine environment data storage method and system based on HDF5. According to the method, a hierarchical storage framework is constructed, a domain-type-source three-level directory architecture is constructed, a space-time dimension dynamic extension mechanism is executed, and space-time composite coding is performed; designing an HDF5 nested packet storage architecture, wherein grid topological structure definition, dynamic resolution self-adaptive storage, multi-source data processing and metadata management are carried out; constructing distributed data association and indexes, including data standardization preprocessing and cross-source data association; the storage performance is optimized and expanded, wherein density sensitive blocking and layered compression are carried out. According to the method, redundant storage of a high-latitude region is greatly reduced, and dynamic adaptation of a local encryption grid is supported. According to the invention, open circulation and collaborative utilization of interdisciplinary data resources are facilitated.

The invention discloses an enterprise-level knowledge management system based on a large language model. The enterprise-level knowledge management system comprises a knowledge storage module, a knowledge analysis module and a knowledge application module, the knowledge storage module is responsible for managing and storing knowledge from different sources, supporting uploading, updating, deleting and sharing of electronic documents and multimedia documents, and is provided with authority control; the knowledge analysis module is used for carrying out deep understanding and structured processing on text and multimedia contents through a special analyzer by utilizing an advanced large language model technology; the knowledge application module provides intelligent retrieval and intelligent question and answer functions and supports advanced applications such as behavior portrait analysis and standard process supervision. According to the method and the system, the acquisition, understanding and application efficiency of enterprise knowledge is remarkably improved by fusing a large language model technology and a traditional knowledge management method, an intelligent, automatic and safe knowledge management solution is provided for an enterprise, and value mining and innovative application of enterprise knowledge assets are effectively promoted.

The invention discloses a file retrieval and management method fusing an AI large model and graph data, and belongs to the technical field of retrieval management, the retrieval and management method comprises the following specific steps: I, a user imports a multi-format file into a file retrieval and management system, and the system extracts characters and structured content through a file analysis module; iI, the file retrieval and management system gradually constructs a multi-level database based on the analyzed file content and metadata; iII, performing full-process management on the file from creation to archiving, and providing an intelligent tool to optimize a document processing process; according to the method, the automation and efficiency of file management can be greatly improved, a high-precision semantic search and sorting result is provided, complex association analysis and multi-dimensional query are supported, professional document translation requirements in an internationalized scene are met, document content management is optimized, and the knowledge precipitation capacity is improved.

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 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.

Systems / techniques that facilitate de-identified search of part designs are provided. In various embodiments, a system can access a part design file provided by a client device, wherein the part design file can comprise a computer-aided design (CAD) geometry of a part. In various aspects, the system can determine whether the part design file is subject to any manufacturing restrictions, via performance of a multi-staged search of an historical database. In various instances, the multi-staged search can be based on a cryptographic hash of the part design file, on a spatial hash of the CAD geometry, and on an anonymized feature vector extracted from the CAD geometry. In various cases, the system can return, to the client device and in response to the multi-staged search, an electronic notification indicating whether the part design file is subject to any manufacturing restrictions.

The embodiment of the invention discloses a chip register description document generation method and device, a storage medium and terminal equipment, and relates to the field of document processing. The method comprises the following steps: analyzing a register attribute table file of a chip, and determining each functional module included in the chip and a register included in each functional module according to an analysis result; querying an associated register description template document according to the model of the chip; extracting field values corresponding to fields in the register list display area and the register description display area from the register attribute table file, and filling the read field values into a register description template document to generate a register description document of the chip, the problems that in the prior art, compiling of the chip register description document is low in efficiency, prone to errors and poor in timeliness are effectively solved, and a more efficient, accurate and reliable method is provided for compiling and managing the chip document.

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.

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 discloses a small file distributed high-concurrency aggregation storage method and system, and the method comprises the steps: collecting performance index data, and transmitting the data to a resource coordination server; calculating a queuing rate; determining a target server according to the queuing rate, the file reading concurrency and the disk residual space; determining a first-level directory hash value; selecting a first-level storage directory and a second-level storage directory according to the first-level directory hash value; creating a target aggregation file in the secondary storage directory, generating a 2-byte file format field according to the compression identifier or the non-compression identifier, and generating a 4-byte file length field according to the size of the to-be-written file; writing the file format field, the file length field and the to-be-written file into a target aggregation file in sequence, and recording the initial offset of writing; and generating a file name containing the storage position information, and returning the file name to the client. According to the invention, the efficiency and effect of small file storage are optimized.

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 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 water conservancy design archive retrieval system and method based on a local lightweight large model, and the method comprises the steps: S1, constructing a Python automatic preprocessing assembly line, extracting texts for PDF and Word multi-format archives, correcting metadata, and outputting standardized data; s2, constructing a full-text retrieval and semantic retrieval dual-mode cross-document retrieval service by relying on a Weavi ate local vector database and a lightweight text embedding model; s3, analyzing a user query intention through a local large model, synchronously triggering metadata accurate retrieval and content semantic retrieval, and generating a structured result; and S4, integrating the core module into a local area network Web platform, adopting Docker containerization deployment, and combining an RBAC permission model and JWT authentication to guarantee security. The system comprises a preprocessing module, a cross-document retrieval module, an intelligent agent module and a background management module, and collaboration is achieved through a standardized API. According to the method, the problem of archive fragmentation is solved, multi-mode retrieval breaks through keyword limitation, an intelligent agent reduces manual intervention, a localized architecture prevents secret-related leakage, background management adapts to an existing I T environment, and full-process intelligent archive service is provided for water conservancy design.

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.

The invention relates to the technical field of document data retrieval, and discloses an archive data retrieval method, system and device.Firstly, multiple pieces of archive data in a system are cleaned and standardized, document data are preprocessed, and document data processing specifically comprises text data, picture data and video data; semantic representation is carried out on the processed archive data, specifically, a deep learning model is used for creating semantic vector representation for a document; the expressions of the text, the image and the video are fused into a unified vector space, data of various data types of the text, the image and the video are integrated, and cross-modal retrieval is carried out; and outputting a retrieval result according to the similarity ranking. According to the method, the query intention of the user and the content of the document can be more accurately understood, keyword extraction and sentiment analysis can be carried out, the system can be helped to more comprehensively analyze the document, and therefore the correlation of retrieval results is improved.

The invention discloses an artificial intelligence-based bidding document automatic writing method and system, and the method comprises the steps: recognizing a project bidding document through a bidding document understanding module, extracting the requirements of the bidding document from the project bidding document, automatically recognizing materials according to the extracted requirements of the bidding document, and continuously updating a material database; according to the method, related materials are automatically obtained from a material database, a first to-be-processed bidding file is automatically generated according to the requirements of the recognized bidding file, a second to-be-processed bidding file is generated after the first to-be-processed bidding file is automatically compared, optimized and modified by a learning module, and a final version of bidding file is generated after manual confirmation by a system using unit. The method automatically identifies the bid invitation file requirements, continuously updates the material database, automatically matches the bid invitation file requirements, automatically generates the bid invitation file, and is convenient to use, short in time consumption, efficient and accurate.

The invention discloses an intelligent document duplicate checking system and method based on a vector database and a large language model, and the method comprises the following steps: S1, collecting document data in various formats, and carrying out the preprocessing of the document data; s2, semantic coding is carried out through a large language model, and a document semantic vector is generated; s3, storing the document semantic vector into a vector database, constructing a vector index and recording historical query data; s4, carrying out preliminary candidate document retrieval, and carrying out approximate nearest neighbor retrieval based on outlier identification; s5, calculating the similarity between the candidate document and the document to be subjected to duplicate checking, and screening a final high-similarity document; s6, generating a duplicate checking report, and recording user operation behaviors; and S7, receiving user feedback, and dynamically updating the document semantic vector and the vector index. According to the method, efficient and accurate intelligent document duplicate checking is realized by utilizing the large language model and the vector database, the semantic matching capability is improved, the duplicate checking efficiency is optimized, and the intelligence and adaptability of a duplicate checking system are improved.

The invention provides a file metadata generation method and device based on a large model, equipment and a medium, and relates to the field of artificial intelligence, in particular to the technical fields of NLP, the large model, LLM, deep learning, big data and the like. According to the specific implementation scheme, the method comprises the steps of performing multi-modal analysis on file content of an unstructured file to obtain multi-modal information; performing semantic reasoning on the multi-modal information to obtain semantic metadata; analyzing the multi-modal information by adopting a business rule matched with a target business scene to which the unstructured file belongs, so as to obtain business metadata; and generating file metadata of the unstructured file according to the semantic metadata and the business metadata.

Disclosed in embodiments of the present application are a multi-modal retrieval model generation method and apparatus, a device, and a storage medium. The embodiments of the present application can be applied to various scenarios such as artificial intelligence, intelligent transportation, and assisted driving. The multi-modal retrieval model generation method comprises: by means of a prefix vector module, performing feature recognition on first sample image modal data to obtain a first image prefix vector; by means of a modal identification module and on the basis of the first image prefix vector, the first sample image modal data, and first sample text modal data, generating a first retrieval character; by means of a constrained decoding module, acquiring from a pre-generated database a first sample retrieval result associated with the first retrieval character; and on the basis of a first reference retrieval result and the first sample retrieval result, adjusting the model parameters corresponding to the prefix vector module and the constrained decoding module to obtain a retrieval model after the adjustment. The present application can improve the training efficiency of multi-modal retrieval models.

The invention discloses a file retrieval method and device, equipment and a storage medium, and relates to the technical field of data retrieval, and the method comprises the steps that a user mode space responds to a file retrieval instruction received at the current moment, a target retrieval strategy corresponding to the file retrieval instruction is determined, and an event filtering strategy is updated to a kernel mode space; the kernel mode space obtains a target file operation event from all file operation events captured at the current moment based on the updated event filtering strategy; and the user mode space determines metadata information of a target file corresponding to the target file operation event, updates the mixed index database based on the metadata information, and then retrieves in the updated mixed index database based on the target retrieval strategy and the file retrieval instruction to obtain a file retrieval result. According to the method, the purposes of real-time updating of the metadata information of the file system and efficient retrieval of multiple retrieval dimensions are achieved, the efficient file retrieval requirement is met, and the file retrieval efficiency is improved.

The invention belongs to the technical field of bidding and tendering information management, and particularly provides a bidding and tendering information management method and system based on an electronic bidding and tendering transaction platform, and the method mainly comprises the steps: obtaining the original data of a bidding file; performing structured extraction and version calibration on the original data of the bid invitation file to obtain a versioned key clause set; performing change relation chain construction and response feasibility verification on the versioned key clause set to obtain a dynamic association body; obtaining a bidding file; performing rule-driven equivalent matching on the dynamic association body and the bidding file to obtain a bidding response mapping table; and performing bid evaluation rule synchronization processing on the dynamic association body to obtain an adapted bid evaluation rule. The bidding and tendering information processing efficiency and accuracy are effectively improved, full-process transparent management is realized through visual presentation, human intervention risks are reduced, fairness and normalization of bidding and tendering activities are guaranteed, and the scientificity and intelligent level of bidding and tendering management is remarkably improved.

The invention provides a data classification and grading method and device based on an artificial intelligence large model technology, and relates to the field of data security. The method comprises the following steps: constructing a data classification and grading catalog according to a preset classification rule, and storing each row of the classification and grading catalog as a document record in a target vector database; respectively carrying out first retrieval operation, second retrieval operation and third retrieval operation on the data classification and grading catalog by taking the business system name, the data table name and the field name as first query conditions so as to obtain a first matching document, a second matching document and a third matching document in the target vector database; sorting the first matching document, the second matching document and the third matching document according to a preset sorting mode, and obtaining a sorting result; and inputting a sorting result into the large language model, and performing data security early warning through the large language model. According to the method and the device, the security problems that corresponding security measures cannot be taken, sensitive data is leaked and the like when a data governance engineer identifies the metadata according to the data classification and classification catalog and judges the classification and security level of the metadata are solved.

The invention relates to the field of computers, and provides a method and a device for pre-aggregating time series data. The method comprises the following steps: acquiring at least one time sequence data from a first device; the first device is a generation device of at least one time sequence data, or the first device is a node device of a time sequence database, and any time sequence data in the at least one time sequence data comprises an identifier; determining a pre-polymerization method according to the identifier of the at least one time sequence data, wherein the pre-polymerization method and the identifier of the at least one time sequence data have a preset binding relationship; processing the at least one time sequence data by using a pre-aggregation method to generate pre-aggregated data; and when the occupancy rate of the memory meets a trigger condition, writing the pre-aggregated data and the at least one time sequence data into a disk. According to the method, the time sequence data and the pre-aggregated data can be uniformly distributed, and the utilization rate of the storage space is improved.

The invention provides a medicine raw material label consistency comparison method, system and device and a medium, and belongs to the technical field of medicine raw material label identification. The edge server searches a local template library according to the template index based on the comparison picture, and obtains a template tag picture and a feature vector file; extracting a feature vector of a comparison label picture by using a reconstructed Resnet18 network, calculating the similarity between the feature vector of the template label picture and the feature vector of the comparison label picture, and obtaining a label consistency result of the template picture and the comparison picture; and carrying out result visualization display on the label consistency result. Through a traditional image processing algorithm and a deep learning network feature extraction technology, the verification workload is reduced, and the verification efficiency is improved. The feature vectors are extracted by using the reconstructed Resnet18 network, and the consistency of the tag styles can be accurately judged in combination with an SIFT key point matching algorithm.

The invention relates to the technical field of digital twinning, in particular to a steel structure building construction whole process mechanical property evaluation method based on digital twinning, which comprises the following steps of: acquiring multi-source heterogeneous steel structure state data from a stress sensor, BIM model component information and a displacement monitoring point, comparing timestamps from all data sources, and determining the state of the multi-source heterogeneous steel structure according to the comparison result; and calculating a transmission delay value of each data source relative to the unified reference time, correcting the original data timestamps one by one, and establishing a steel structure state data stream with aligned time sequences. According to the method, the timestamps of the multi-source heterogeneous data are subjected to transmission delay correction through unified reference time, the timestamps of the original data are corrected one by one, the steel structure state data streams with the aligned time sequences are generated, the time deviation among the sensors, the BIM model and the monitoring points is eliminated, and the synchronism and coherence of dynamic changes of mechanical parameters are guaranteed.

The present application relates to the field of cloud technology, and discloses a metadata access method and apparatus, a device, and a storage medium. According to the method, a global metadata cache scheme is used, and relates to a three-layer architecture, i.e., a global cache file, a cache description array, and a memory block; storage areas can be divided according to the data length of metadata in a shared memory; and when target metadata needs to be acquired for a target process, metadata can be queried layer by layer according to the three-layer architecture until target address information of target metadata that has been cached is obtained, and the target address information is returned to the target process for realizing the access of the target metadata. In this way, the metadata required by all the working processes can be accessed by means of the method, so that metadata caches of the working processes do not need to be respectively maintained by the working processes, thereby reducing the repeated metadata cache between the working processes, reducing the occupied memory resources, reducing the transmission resource occupation of the repeated metadata, and improving the resource utilization rate.

The embodiment of the invention provides a file receiving and reorganizing method and device, equipment and a storage medium. The method comprises the following steps: acquiring archive data, identifying, analyzing and uniformly formatting the archive data, screening the archive data according to a preset rule, and acquiring preprocessed archive data; and extracting metadata in the preprocessed archive data, matching the metadata with a metadata template in the format library, and storing the preprocessed archive data in a reorganization library if no metadata is missing in the preprocessed archive data. And performing four-property detection on the reorganized archive data in the reorganized library. If the reorganized archive data passes the four-property detection, the reorganized archive data is stored in an archive library through distributed storage, and if the reorganized archive data does not pass the four-property detection, the position of an error in the reorganized archive data is recorded, and a repair suggestion is provided. In this way, the efficiency and quality of archive reorganization can be improved.

This disclosure relates to approaches for managing generative artificial intelligence content. Some aspects relate to managing and enforcing licensing conditions for various actors in generative artificial intelligence, such as content creators, model owners, and platform providers. Some aspects relate to lineage tracing, in which an asset is analyzed to determine what other assets it was based on and / or to determine which other assets were generated based on a given asset. Lineage tracing can be based on one or more of metadata analysis, hash analysis (such as perception hash analysis), or vector similarity. In some implementations, a vector embedding model is used to generate vector embeddings of assets, and the vector embeddings are compared to identify similar assets.

A metadata access method, apparatus, and computer-readable storage medium for efficient metadata retrieval through cache management. The method receives metadata query requests including target index information from processes and performs matching operations on a global cache file containing records with index information and slot identifiers. Each cache description array corresponds to memory blocks caching metadata. Upon successful matching, the target cache description array is accessed using the target slot identifier. The data state of target metadata is determined from the cache description array, and target address information indicating the location of the target memory block in shared memory is obtained and returned to the requesting process, enabling efficient shared memory-based metadata access.