1917results about "File metadata searching" patented technology

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 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.

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 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 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.

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 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.

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.

The invention discloses an information processing method, and the method comprises the steps: inputting query information for a target object into an intention recognition model for intention recognition, and obtaining an information retrieval type corresponding to the query information; wherein the query information is used for querying all related log information or part of related log information of the target object; retrieving the query information through a retrieval strategy corresponding to the information retrieval type by utilizing a pre-constructed index database to obtain a retrieval result meeting an information length condition corresponding to an input requirement of a second large language model; wherein different information retrieval types correspond to different retrieval strategies; performing fusion processing on the query information and the retrieval result to generate context reference information meeting the input requirement of the second large language model; and reasoning the context reference information by using the second large language model to generate reply content corresponding to the query information.

The invention provides a star catalogue file generation method and device, a celestial body data query method and device, computer equipment and a storage medium. The star catalogue file generation method comprises the steps of obtaining star catalogue data; dividing the celestial sphere surface area corresponding to the plurality of celestial bodies into a plurality of local areas, and determining an identifier of each local area; determining an identifier matched with each celestial body data according to the right ascension and declination included in the celestial body data and the identifier of each local area in the celestial sphere surface area; sorting the celestial body data of the plurality of celestial bodies based on the identifiers corresponding to the celestial body data; and writing the sequenced celestial body data of the plurality of celestial bodies into an initial column storage file matched with the identifier to obtain a target file stored with the celestial body data, and generating file index information corresponding to the target file. The celestial body data is stored by utilizing the column type storage file, so that the problems of high maintenance cost and high use threshold caused by utilizing a database to store data are relieved while the data query efficiency is guaranteed.

The invention belongs to the technical field of archive management, and discloses an intelligent archive supervision method, device and equipment and a storage medium, and the method comprises the steps: carrying out the standardization processing of an archive feature data set which is established based on entity feature information and electronic feature information, and generating the digitalized archive information which can be analyzed; processing the parsable digital archive information through a preset rule to obtain archive metadata; constructing a dynamic supervision model based on the file metadata, and performing real-time association analysis by using the dynamic supervision model; when the correlation analysis result does not meet the preset condition, a corresponding grading response mechanism is matched according to the label attribute, and a disposal instruction containing an exception type identifier is generated; and outputting a visual supervision report based on the execution result of the disposal instruction and the historical supervision data, thereby breaking through the limitation of physical feature and electronic data splitting analysis, establishing entity-electronic feature space-time correlation mapping, and realizing accurate matching of hierarchical response strategies based on file metadata tags.

The invention provides an intelligent document management method and system, and the method comprises the steps: obtaining an uploaded file set, processing each file through a text analysis algorithm, an image analysis algorithm and a structured data analysis algorithm, generating a first feature set, and storing the first feature set in a temporary database; obtaining a distribution record from the first permission configuration file, writing the distribution record into an audit database through a log audit module, and generating a first operation traceability chain; according to the first operation tracing chain, a storage management module is called to perform fragmentation processing on the file and upload the file to cloud storage, a first storage index is generated, and the metadata table and the archive position are associated; and according to the second storage index, calling a disaster recovery backup algorithm to perform multi-region copying on the file, generating a first backup confirmation signal, and recording the first backup confirmation signal into an operation and maintenance log. According to the system and the method, intelligent classification, sensitive information protection and whole-course traceable management of the files are realized, the automation level and the safety of document management are improved, and a comprehensive solution is provided for document management.

A system and method for vulnerability detection. A method includes: tokenizing device attribute data for a device into at least one set of first tokens, wherein each of the first tokens is formatted according to a token schema; creating at least one device attribute string, each device attribute string including one of the first tokens; matching each of the at least one device attribute string to combinations of device attributes stored in a vulnerabilities database in order to identify at least one matching combination of device attributes for the device, wherein the vulnerabilities database stores mappings between combinations of device attributes and vulnerabilities, wherein each combination of device attributes in the vulnerabilities database includes second tokens formatted according to the token schema; detecting at least one vulnerability of the device based on the at least one matching combination of device attributes and the mappings in the vulnerabilities database.

The invention relates to the technical field of archive management, in particular to a visual intelligent digital archive management method and system. The method comprises the following steps: acquiring a user retrieval request; analyzing the user retrieval request, and determining retrieval information; determining an associated node of the retrieval information according to a preset knowledge graph; determining a topological structure of the associated node according to the preset knowledge graph; determining a paternal document set and a sub-document set according to the topological structure; and determining a visual evolution structure according to the association node, the paternal document set and the subsystem document set. By establishing an encrypted communication link with a user terminal, a user retrieval request is captured in real time, and the semantic ambiguity rate can be reduced when the retrieval information is determined to process terminologies in an engineering document; and according to the topological structure, the paternal document set and the sub-document set are determined, so that the fuzzy retrieval accuracy can be improved.

The invention relates to the technical field of big data, and discloses a massive small file reading optimization method, system and device and a medium, and the method comprises the following steps: scanning small files in a distributed file system by using a Spark calculation engine to obtain metadata information of the small files; based on the metadata information, small files are classified, and the small files with the same type and data relevance are classified into one group; the resource states of cluster nodes are monitored in real time, the resource states comprise the processor utilization rate, the memory occupancy rate and the I / O load, the load score of each node is calculated, and small file task quotas are dynamically allocated; executing small file merging operation in parallel by utilizing a Spark memory calculation engine to generate a merged file containing a plurality of original small files; creating and maintaining index information in an external database, and recording the position and attribute of each original small file in the combined file; and providing a read-write interface through the special service layer, and positioning and accessing specific small file contents in the combined file.

The invention relates to the field of computer software, in particular to an IPFS-based file control method and device, equipment and a medium, and the method comprises the steps: receiving a file uploading request initiated by a user through a front-end interface; basic information of the file is read, a file format is confirmed, and the file format and a preset type list are verified; performing security and integrity verification on the files passing the verification; if the verification is passed, uploading the file to an IPFS network, and obtaining a hash string returned by the IPFS as a file identifier; constructing an encrypted access URL of the file based on the server address and the hash string, and storing file metadata to a database; and receiving a file access request initiated by a user through the URL, decrypting the URL and verifying the user permission, retrieving the file from the IPFS network according to the hash string, and providing a preview or download service according to the file type. And uploading of file types which do not meet requirements is effectively avoided, and potential safety risks are reduced.

The embodiment of the invention provides a large language model retrieval enhancement generation-oriented file processing method and device, and the method comprises the steps: carrying out the information extraction and semantic information enhancement of a file according to different file types, and constructing a meta-information structure of the file in combination with an enterprise business scene; dividing the document content into a plurality of structured blocks based on the meta-information structure, and labeling the title and context information of each block; when the file is uploaded, according to the parent directory material quantity and content change of the directory where the file is located, performing dirty marking on the directory; when a data request is received, whether a dirty mark exists in a related directory or not is judged, if yes, the directory is requested to be locked, a summary is extracted from files extracted from bottom to top through a large language model, and the dirty mark is cleared after the summary is cached.

The invention discloses a heterogeneous data stream-oriented adaptive data fusion method and storage equipment, and relates to the technical field of data fusion and storage. Comprising heterogeneous data source processing, dynamic weight calculation and confidence management, time window alignment and fusion output, and hierarchical storage and migration. The method comprises the following steps of: performing uniform format processing and metadata marking on a camera video stream and a text log; dynamically calculating a data weight based on a video definition score and a log error level, and performing confidence management; then, through precise time window matching, multi-source data are fused to generate an event risk value, and an alarm or equipment control instruction is automatically triggered; and finally, dynamically storing the data in a memory and a mechanical hard disk in a hierarchical manner based on the access frequency and the data weight, and realizing cross-layer unified retrieval.

The invention relates to the technical field of information security, discloses a realization method of an electronic signature of a customs business scene and a seal management system, and aims to solve the problems of trust centralization, easy data tampering, difficult traceability, poor business adaptation and insufficient interoperability of an existing electronic signature. According to the method and the system, a distributed account book technology is introduced, decentralized creation and registration, signature generation and verification, life cycle management and on-chain recording of auditing and tracing of the electronic seal are realized, a dynamic and configurable business scene strategy engine is constructed, a customs business process is flexibly adapted, and high safety, high efficiency and high adaptability are ensured. According to the method, a decentralized trust basis is established, the tamper-proofing capability and efficiency are improved, full-chain auditing traceability and cross-mechanism interoperation are realized, and data security is guaranteed.

The invention discloses a method, a system, a medium and equipment for extracting unified aggregated text content of all types of files, and relates to the technical field of file processing, the method comprises the following steps: system initialization and configuration, signature verification, extraction judgment, proxy forwarding, content extraction, and result storage and return. A single and stable service entry point (Endpoint) and a standardized API (Application Program Interface) specification are provided for all upper-layer businesses; through a standard S3 signature verification mechanism, the security and reliability of each call are ensured; in combination with an AK / SK system, unified authority control, flow limitation and post auditing can be conveniently carried out; a service-oriented architecture is adopted, and the extraction capability of different types of files is provided by an independent micro-service module. When a new file type needs to be supported, only a new content extraction module needs to be developed and registered into the mapping table, a main process and an upper layer service do not need to be changed, and the system expansibility is extremely high. And in combination with file size judgment, executing logic of combining synchronous extraction and asynchronous extraction of the file.

The invention relates to the technical field of message recovery, and discloses an AI-based iOS device message recovery method and system, and the method comprises the steps: connecting an iOS device, positioning a database file of a message application, and obtaining a database file path list; reading an equipment storage sector, creating a physical mirror image, and analyzing a free block position mark in an APFS container super block; binary classification identification is carried out through an AI data block classification model, an SQLite page type label and a page mapping relation are obtained, deleted messages of a recombined database and a device end are determined, differential comparison is carried out on all historical backup snapshot records of an iCloud account, and cloud end deleted messages are obtained; according to the method and the system, the reorganization success rate of the database and the extraction integrity of the deleted messages are improved, and a technical solution is provided for comprehensive, accurate and efficient recovery of the iOS device messages.

Subject matter related to data management is discussed. A most recent snapshot in a snapshot chain that is not infected by malware may be identified based on mounting snapshots in the snapshot chain and determining whether the snapshots are infected. A graphical user interface showing individual snapshots in the snapshot change and indicating whether the snapshot is infected with malware may be displayed. The graphical user interface may provide a recover function for non-infected snapshots and may not enable the recover function for infected snapshots. A command to recover a non-infected snapshot in the snapshot chain may be received. Based on receiving the command, the non-infected snapshot may be recovered.