1874 results about "Data file" patented technology

The invention relates to the field of text processing, in particular to a bidding document information extraction method. Comprising the following steps: segmenting a bidding and tendering file into pages, and identifying the pages to obtain corresponding texts; generating complementary text description for images and tables in the page and adding the complementary text description to the tail of a text corresponding to the page to form an enhanced text block sequence; matching a label from the text block sequence according to a pre-constructed hierarchical label system, and generating a corresponding cue word template according to the label and a pre-constructed cue word template library; inputting the cue word template, the enhanced text block sequence and the context text abstract as a combination into a large language model to obtain a structured extraction result with a hierarchical relationship; and matching the extracted entity content with a local dictionary, carrying out aggregation arrangement on a result after the matching is passed, and outputting a structured data file. On the premise that the model does not need to be retrained, the illusion risk of the generated content is reduced.

The invention discloses a PDF engineering drawing structured recognition method and device based on multi-dimensional feature fusion and a storage medium, and belongs to the technical field of engineering drawing intelligent processing. The method comprises the following steps: PDF analysis and preprocessing: analyzing a PDF engineering drawing file, extracting a vector graph drawing instruction, text block contents and respective original coordinate information, and carrying out image rendering and standardized preprocessing on a drawing page to generate a binary image with a standard resolution; multi-dimensional feature extraction: extracting the following four types of features from the binary image: a visual depth feature, a text semantic feature, an industry specific symbol feature and a spatial topology feature between elements; feature fusion and relation reasoning: inputting the visual depth features, the text semantic features, the industry specific symbol features and the spatial topological features into a graph neural network fusion module, and constructing a graph structure taking each identified element as a node and taking a spatial topological relation between the elements as an edge, performing feature interaction and fusion among nodes through an attention mechanism, and reasoning a semantic association relationship among drawing elements; and generating structured data: classifying and grouping the drawing elements according to the semantic association relationship, and outputting a structured data file according to a predefined structured format. Therefore, the technical problems of low recognition efficiency and accuracy in the prior art are solved, and the technical effect of efficient and accurate engineering drawing structured recognition is achieved.

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

The invention discloses a row-level data recovery method and device for a damaged file of a GoldenDB database, and the method comprises the following steps: S1, scanning a to-be-recovered GoldenDB data file, and recognizing and caching all data pages; s2, on the basis of metadata of the data pages, cluster index leaf node pages containing user data are screened out from the cached data pages; s3, for each screened leaf node page, analyzing an internal record organization structure of the leaf node page, and performing row-level data recovery; the row-level data recovery comprises the steps of traversing user records according to a virtual record pointer, and performing cross check and recovery on a traversal path in combination with a page directory slot so as to extract and mark the user records one by one; and S4, outputting all the recovered row-level data.

The invention relates to a multi-agent cooperation system and method capable of realizing cross-domain mutual trust, and the system comprises a cross-domain mutual trust decentralized agent identity identification and verification system which is used for constructing an agent identity identification based on the decentralized identity of an agent and migrating the identity of the agent to the decentralized identity; the agent interaction information evidence storage system is used for performing hash anchoring on the data file of the agent, establishing a cross-agent task execution state consensus and generating interaction information evidence storage data of the agent; and the multi-agent cooperative task execution and dynamic access control system is used for executing interactive cooperation of task execution process control, dynamic access authority control and agent state management maintenance of multiple agents according to the task execution contract, the access control contract and the agent management contract. Therefore, the problems that cross-enterprise and cross-domain agent identity mutual trust cannot be established in the prior art, and serious limitation exists in the security and credibility level of the system are solved.

A system and method includes connecting an interface device to a port of a vehicle to be in communication with an electrical system of the vehicle for monitoring the vehicle, and running a selected one of multiple available calibration and / or repair programs via the interface device to generate a calibration / repair data file of the vehicle, where the data / log file is generated in one of a plurality of possible native file formats. A report generation tool acquires screen captures and / or screen recordings to document the data / information displayed during the steps of a calibration / repair procedure. An evaluation tool extracts calibration / repair data from the data / log file, where the evaluation tool is configured to extract calibration / repair diagnostic data from data / log files in each of the plurality of possible native file formats, and outputs the calibration / repair diagnostic data to a database in a common format from which detailed calibration / repair diagnostic reports are generated.

The invention relates to the technical field of power system monitoring, in particular to a power distribution terminal fault monitoring system and method. The dual-channel acquisition module is used for acquiring two analog quantities of a power distribution room and mapping the two analog quantities into a power grid environment characteristic matrix; the cyclic mapping module is used for carrying out decoupling operation and generating a noise separation feature vector; constructing a background noise dynamic reference based on the noise separation feature vector, and generating a judgment boundary threshold; performing cyclic increasing and resetting in the fault recall storage unit through a write-in pointer to form a terminal state data stream; the fault judgment module is used for comparing the amplitude of the noise separation feature vector with a judgment boundary threshold value and generating a fault locking instruction; and based on the fault locking instruction, generating a termination signal, freezing the write pointer, and extracting the partial discharge data file from the terminal state data stream. According to the method, a closed-loop monitoring system in which the environment self-adaptive reference and signal feature decoupling support each other is constructed, and a fault backtracking sample with time-space alignment and complete logic is provided.

In a blockchain, transacting a record comprises performing linear network coding on the record or on a data file corresponding to the record, to produce a plurality of coded data parts; and storing at least one of the plurality of coded data parts on the blockchain or storing a proof of knowledge on the blockchain, the proof of knowledge derived from the coded data parts. Linear network coding coefficients might be derived from cryptographic hashes of the plurality of coded data parts. An all-or-nothing transform can use the cryptographic hashes to provide the proof of knowledge.

The invention discloses a heterogeneous document structured data extraction system and method based on multi-modal fusion, and the method comprises the following steps: S1, receiving a heterogeneous document, and carrying out the preprocessing; s2, extracting visual and semantic modal information based on the visual backbone network and an OCR module, and fusing spatial coordinates, text content and layout features; s3, receiving a dynamic target mode Schema, executing field-level semantic matching, and calculating semantic similarity; s4, performing consistency verification, verifying and filling the field values, and correcting the associated fields; s5, converting the format into a data file in a specified format, and reserving fields to be mapped with an original document semantic block; s6, automatically switching a cloud mode and a local mode according to a deployment environment; and S7, outputting the data file of the target field. According to the method, high-precision and low-delay structured data extraction of the heterogeneous format document can be realized, and the automatic processing efficiency and the data credibility are improved.

Techniques for implementing a scalable automated training framework for anomaly and ransomware detection are disclosed. In some embodiments, a computer system performs operations comprising: instantiating a plurality of virtual machines, each one of the virtual machines being loaded with a corresponding file system; simulating user actions and ransomware on the virtual machines, the simulating of user actions and ransomware on the virtual machines causing changes to the corresponding file systems of the virtual machines; for each one of the plurality of virtual machines, generating a corresponding metadata file based on one or more corresponding snapshots of the virtual machine, the one or more corresponding snapshots indicating the changes to the corresponding file system of the virtual machine; and training a ransomware detection model using a machine learning algorithm and training data, the training data being based on the corresponding metadata files of the virtual machines.

According to the high-computing-power special processor architecture for the big data file and the processing method, a data preprocessing unit receives an original big data file and formats and cleans the original big data file, and a special acceleration computing chip executes a computing-intensive task and avoids the floating-point number precision problem; the parallel processing unit realizes parallel data processing through a MapReduce programming model, the cache storage architecture adopts a multi-level cache system and an LRU cache elimination algorithm to store an intermediate result, and the instruction set optimization module performs parallel operation on isomorphic continuous storage data. The method can be widely applied to multiple fields of data analysis, machine learning, high-performance calculation and the like, and has great significance in improving the data processing capacity in the big data era.

The invention relates to the technical field of embedded system construction, in particular to a software dependency relationship automatic integration method and system based on meta packaging, and the method comprises the steps: S1, creating a meta packaging framework in an embedded construction system, and defining and constructing a stage hook through a Makefile; s2, automatically deploying the pre-compiled software package and the shared dependency library to a target directory of a construction system through a resource injection script; s3, analyzing a metadata file of the pre-compiled software package, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; and S4, inversely writing the global dependency relationship list into the dependency declaration configuration of the meta-packaging by utilizing the dependency aggregation script. By implementing the scheme, the problems of dependency sequence disorder and metadata auditing failure during embedded system integration are solved, the reliability of system integration is improved, the system construction efficiency is optimized, the safety of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

A ship sub-assembly weld seam identification method, comprising: creating a point cloud weld seam identification training dataset by means of point cloud discretization, virtual sampling, weld-zone semi-automatic interactive annotation, and style transfer-based augmentation of ship sub-assembly CAD data; training a point cloud weld seam identification model, and exporting a weld seam identification model data file; using a 3D point cloud camera array as a sampling camera to collect workpiece 3D point cloud data of a sub-assembly workpiece to be welded at the production site; by means of the weld seam identification model data file, obtaining weld seam annotation position data and positioning a weld seam; and issuing a welding process plan and instruction to control a welding robotic arm and a control device therefor to complete sub-assembly workpiece welding. Also provided are a ship sub-assembly weld seam identification system, a computer device, and a computer-readable storage medium. The method overcomes the shortcomings in conventional 3D weld seam identification methods such as point cloud incompleteness and identification failures caused by steel types and the imaging angles of 3D cameras, thereby greatly improving the adaptability of automatic weld seam identification methods to complex workpieces.

The invention discloses a kernel probe generation method and device, computer equipment and a storage medium, and relates to the technical field of operating system kernel monitoring, and the kernel probe generation method comprises the steps of obtaining a target metadata file corresponding to a current operating system kernel in response to a kernel probe generation request; determining a target declarative strategy file; and determining a target program logic file corresponding to the target declarative strategy file, and generating a probe code according to the target program logic file and the target metadata file to obtain a kernel probe program. The probe program source code adaptive to the current kernel version can be dynamically generated in combination with the target metadata file and the target declarative strategy file, kernel probe development logic and underlying kernel knowledge can be decoupled through the source code generation function, and developers do not need to deeply understand kernel internal data structures and version differences; the development threshold is lower, a specific operating system or probe type is supported, and a universal Linux kernel and multiple probe types are also supported.

Techniques for automated query response determination using a hybrid AI are disclosed herein. An example computer-implemented method includes receiving a data file and a request including at least one query associated with the data file and applying a hybrid model to the data file. Applying the hybrid model includes segmenting the data file into one or more portions, embedding the one or more portions into a vector space, extracting, from the data file, data associated with one or more classifications, storing (i) the embedded portions in a first database and (ii) the extracted data in a second database, and determining a response to request queries based on the embedded portions and the extracted data, wherein the hybrid model constrains each response based on a parametric input prompt associated with the extracted data. The example computer-implemented method further includes storing one or more data objects indicating each response.

The invention relates to the technical field of cloud storage, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a hierarchical storage method, device and equipment for data files and a medium, and the method comprises the following steps: acquiring the data files in a storage unit, and extracting data characteristic parameters in the data files; performing multi-dimensional quantitative analysis on the data feature parameters to obtain a dimensional feature analysis value; identifying a dynamic weight of the data feature parameter, and performing weighted calculation on the data feature parameter according to the dimension feature analysis value and the dynamic weight to obtain a dynamic storage analysis value; identifying a data storage type label according to the dynamic storage analysis value; matching the data storage type label with strategies in a storage strategy library to obtain a target storage strategy; determining a storage hierarchy according to the target storage strategy; a storage address of the data file in the storage hierarchy is identified and the data file is stored in the storage address. The accuracy of hierarchical storage of the data files can be improved.

A computing system performs real-time replication of database management system transactions. The computing system includes a source DBMS, a replication service and a data lakehouse. The source DBMS stores at least one source transaction table recording at least one source transaction and generates at least one recovery log indicating at least one modification in the at least one source transaction table. The replication service replicates the at least one source transaction recorded in the at least one source transaction table by generating at least one data file having a first data format. The data lakehouse stores at least one lakehouse table corresponding to the at least one source transaction table and having a second data format different from the first data format, and to modify the at least one lakehouse table based on the at least one data file.

The embodiment of the invention provides a key value data processing method, a database platform, equipment, a medium and a product, and the method comprises the steps that in response to a disk refreshing instruction of a target storage unit in a target partition, a target key value pair in a memory buffer area of the target storage unit is obtained, the target key value pair is any key value pair in the memory buffer area, and the target storage unit in the target partition is stored in the memory buffer area; the target key value pair comprises a target index key and a corresponding target data value; if the target data value is greater than the set size threshold value, writing the target data value into a data file, and writing a target index key and a target file path of the target data value in the data file into a disk storage file of a target storage unit, the structure of the data file is the same as that of the disk storage file, the data file and the disk storage file are stored in the same path, the recovery mechanisms of the data file and the disk storage file are different, and the data file and the disk storage file are distinguished through the set file mark added in the metadata area. The invasion to the database is small, and the write amplification is reduced.

According to the energy storage system battery full life cycle state perception and value circulation method and system, digital twin bodies of all battery modules are generated according to battery static information, and life cycle stage identifiers are set in the digital twin bodies; training is carried out based on a federated task scheduling contract, an edge calculation unit of each energy storage power station enters a corresponding federated collaborative clustering group through dynamic matching according to the life cycle stage identifier, and a corresponding stage global sensing model and model parameters thereof are obtained; updating the corresponding stage global perception model parameters by using the global model parameter update quantity obtained by decryption; the under-chain service network writes a state evaluation result and an evaluation timestamp into a digital twinborn body of each battery module according to the updated parameters of the stage global perception model and the real-time operation data of each battery module; and monitoring the SOH of each battery module in real time to update the life cycle stage identifier, performing training based on a federated task scheduling contract, and constructing a full life cycle data file.

The present disclosure describes a method for updating a node graph data structure, comprising storing a node graph data structure comprising a plurality of entity nodes and a plurality of attribute nodes; receiving, from a data source during a plurality of time periods, a plurality of data files comprising data for a first entity; identifying a plurality of edges between a first entity node of the plurality of entity nodes that identifies the first entity and an attribute node of the plurality of attribute nodes that identifies a first attribute of the first entity, each of the plurality of edges corresponding to a value and a different time period; and updating a value stored in a data structure for an edge that corresponds to a time period associated with a data file.

The invention relates to a data processing method and device and a computer program product. The method comprises the steps of determining a physical address of a target file fragment on a storage medium; determining a target file record of the target file fragment from the metadata file, and determining a physical address of the target file record on a storage medium; and moving the target file fragment and the target file record to a target position of the storage medium based on the physical address of the target file fragment and the physical address of the target file record. Therefore, the I / O operation of the storage medium does not need to search and read the file fragments and the file records back and forth at a plurality of dispersed positions, so that the track seeking time and the data transmission delay of the storage medium are greatly reduced, and the problem of low I / O efficiency of the storage medium caused by file dispersion is directly solved; the scattered distribution state of the files and the file records on the storage medium is changed, and the layout of the files and the file records on the storage medium is optimized, so that the read-write efficiency of the storage medium is improved, and the hardware performance is brought into full play.

The described system aims to reduce or eliminate inaccuracies and hallucinations in responses generated by a machine learning model when processing user queries. The data platform parses and categorizes the text within data files to create structured textual representations. The user submits multiple prompts which are collectively assessed to refine and modify the initial queries.The modified query is used to identifying segments of data files that are most relevant to the query. These relevant portions are then compiled into a Retrieval-Augmented Generation (RAG) context block. This RAG context block is fed into a prompt response machine learning model, which processes the enriched information to generate a well-informed and accurate response to the user's query. Finally, this response is displayed back to the user through the chat interface, completing a cycle that enhances the reliability and relevance of machine-generated answers.

A method performed by an NFT platform includes generating NFT secondary file information relating to a digital file and the NFT, wherein the NFT secondary file information comprises a content ID of the digital file stored in a data storage, wherein the data storage is a permanent and immutable data storage. The method may also include the NFT platform generating an NFT metadata file comprising the content ID of the digital file stored in the data storage, storing the NFT metadata file and the NFT secondary file information in the data storage, generating a smart contract of the NFT comprising a link to a content ID of the NFT metadata file stored in the data storage, and deploying the smart contact to a distributed ledger system.

The invention relates to a software bill of material generation method and device, and belongs to the technical field of computers.The method comprises the steps that a software source address is obtained, and a software source type is recognized according to the software source address; positioning and downloading a warehouse metadata file according to a software source type; calling a corresponding parser to parse the warehouse metadata file according to the software source type to obtain software package metadata; and according to the software package metadata, extracting an original field required by the generation of the software bill of material, and converting the original field required by the generation of the software bill of material into a standard field of the software bill of material, thereby generating the software bill of material conforming to the specification of the software bill of material. According to the software bill of material generation method and device provided by the invention, the self-adaptive generation of software bills of material of different software source types can be realized, the universal adaptation capability for multi-source and multi-architecture in the software bill of material generation is improved, and the preposition, lightweight and efficient generation of the software bill of material is realized.

The embodiment of the invention discloses an application service recommendation method and device, equipment and a medium, and the method comprises the steps: generating a rule data file of each application service based on the application service information of each application service of a current platform, carrying out the preprocessing of the rule data file, and constructing a description file knowledge base; wherein the application service information comprises item description and an access interface address; constructing a recommendation agent based on a preset large model, receiving a user side input demand based on the preset large model, and decomposing the user side input demand into a plurality of subtasks; inputting the subtasks and the cue words corresponding to the subtasks into the preset large model, so as to distribute the subtasks to the corresponding recommended agents based on a preset protocol; and searching the description file knowledge base according to the corresponding recommendation agent to obtain a matched description file and an access interface address corresponding to the matched description file, and returning the matched description file and the access interface address to a user side to realize application recommendation.

The invention provides a DFT clock architecture establishment method and device and electronic equipment, and belongs to the technical field of EDA, and the DFT clock architecture establishment method comprises the steps that a structured data file corresponding to clock planning information of a chip is acquired, the structured data file at least comprises an index field used for identifying clock uniqueness and a type field used for indicating a clock type, and the index field is used for identifying the clock uniqueness; and a parameter field for indicating clock attributes; generating a corresponding clock information object based on the values of the index field, the type field and the parameter field; based on the clock information object, a first configuration file and a second configuration file are generated, the first configuration file is used for driving a DFT tool to execute circuit insertion, and the second configuration file is used for driving a comprehensive or static timing analysis tool. The problems of configuration ambiguity, floating point error, asynchronous path omission, inconsistent OCC insertion and the like caused by dispersed and unstructured DFT clock planning information and dependence on manual transmission can be solved.

The present disclosure provides a data storage method, device, electronic equipment and storage medium. The data storage method comprises: in response to a file storage request for a first target data file, obtaining the first target data file to be stored in a target storage system, the target storage system being configured with table metadata, the table metadata indicating storage branch information corresponding to the target storage system, the storage branch information comprising a main branch and at least one experimental branch, the main branch being used to mount data files that have been used for model training and the training results of which meet preset requirements, and the experimental branch being used to mount data files that have not been used for model training; based on the table metadata, determining a first target experimental branch corresponding to the file storage request, and newly creating a first target snapshot corresponding to the first target experimental branch; and based on the first target snapshot, storing the first target data file to a corresponding storage location in the target storage system. The embodiments of the present application can improve the data storage efficiency.

An apparatus for determining the condition of a printed circuit board, including a stencil database for storing a plurality of stencil data files corresponding to stencils of different dimensions, an image capturing device having an area where the printed circuit board is laid thereon for a surface profile of the printed circuit board to be captured for generating a surface data file of the printed circuit board, and a condition determination module that compares data from the surface data file with data from one or more stencil data file as a nominal reference via a matching process to determine the condition of the printed circuit board based on a set of categories. The printed circuit board is sent for further processing (i.e., stencil printing) after being determined to be in a match-successful condition where data from the surface data file falls within a tolerance of the nominal reference.

The invention discloses an intelligent image batch generation method and device, equipment and a storage medium, and the method comprises the steps: receiving an Excel data file uploaded by a user, carrying out the table structure analysis and data content extraction of the Excel data file, and obtaining target data; obtaining a design template selected by a user, and establishing association between the target data and elements in the design template by adopting a binding algorithm to obtain a binding relation mapping table; an AI image processing function is called to process the bound target data to obtain processed data, and the AI image processing function comprises at least one of AI sectional drawing, AI image sharpening, AI document generation and AI text drawing; based on the binding relation mapping table, applying the processing data to the bound design template to obtain an initial image; and performing image rendering on the initial image through a rendering engine to generate a target image. According to the method, various AI tools are integrated, so that the processing efficiency is remarkably improved.

The invention provides a user portrait method and device for driving training learning, electronic equipment and a medium, and belongs to the technical field of information recommendation, and the method comprises the steps: collecting multi-source heterogeneous data of a student, carrying out the data preprocessing, and obtaining a student data file, the multi-source heterogeneous data comprises learning stage data, capability evaluation data and basic attribute data; based on the trainee data file, extracting two-dimensional features of trainees, including obtaining learning stage features according to division of passing states and learning progress completion rates of driving test subjects, and quantifying the mastering degree of subdivided test points under weak subjects based on a weighted scoring algorithm to obtain weak subject features; and fusing the two-dimensional features and the basic attribute data, and constructing a student portrait including a learning stage label and a weak subject label. According to the invention, the accuracy of the user portrait is improved.