301 results about "Stored procedure" patented technology

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

The invention relates to the technical field of information security, in particular to a computing method and system for information security. The method comprises the following steps of: partitioning and fragmenting input data, and distributing the data to a plurality of storage nodes through randomization, distributed storage and Hash mapping to ensure the safety of data storage and the distribution flexibility; establishing an index table for all the fragments; regularly triggering a data confusion mechanism, and adjusting the storage position of data fragments through a pseudo-random function; the integrity of the data is detected by generating a check code and an erasure code, it is ensured that the data is not tampered or damaged in the transmission or storage process, and the data is recovered through a redundant copy when the data is lost; the identity verification and the data integrity when the user accesses the data are ensured through authority authentication; and monitoring storage nodes and data access behaviors in real time, detecting potential threats and taking dynamic adjustment measures. According to the invention, through a multi-level security policy, the security and fault-tolerant capability of data processing are effectively improved.

The invention discloses a container and encryption-based data behavior security management method. The method comprises the steps of preparation, data asset creation and association, data storage, data asset behavior recording, data asset behavior management and control and data asset output. The data is encrypted and stored by adopting an encryption technology, and the original data is converted into the ciphertext, so that the data is prevented from being illegally acquired in the transmission and storage process; access authority is strictly limited through RBAC control, it is ensured that only authorized users or application programs can access and operate data, and data leakage and tampering are prevented; data injection is realized by utilizing a data volume of a containerized platform, so that an application program in a container accesses external data, the data can be shared in different environments conveniently, the data can be stored persistently, and the availability of the data is ensured; the containerization expandability is convenient for expansion and adjustment when the data scale is expanded; the binding of the data ID and the comprehensive behavior record flow ensure the unique identification of the data and improve the accuracy and traceability of the data.

A system, apparatus and method for contract classification processes both primary contracts and their associated documents using context window marshalling. The computing device receives and segments each contract document into contextual chunks based on semantic boundaries, maintaining coherent meaning. A marshalling queue processes chunks sequentially, storing processed data within defined context windows, cross-references between chunks, and a running context buffer of key elements. This chunking and storing process repeats until all associated contract documents are processed. The system identifies parties and contract categories by aggregating information across processed chunks from all related documents, then determines risk parameters using a classifier that considers contractual elements, cross-document relationships, and contextual importance weights. Risk recommendations are calculated based on these parameters, identified parties, class categories, and aggregate metrics computed across document and chunk boundaries. The system ranks contracts by evaluating both individual chunk risks and whole-contract assessment, weighting risks based on contextual significance and inter-chunk dependencies across all related documents. Change recommendations are generated that correlate contractual elements with risk levels, identify high-risk chunks and their relationships, and suggest modifications considering cross-document impacts. This approach enables processing of contracts and their associated documents that exceed single context window limitations while maintaining semantic coherence and relationship preservation throughout the entire document collection.

The invention relates to the technical field of data transmission, and particularly discloses a block chain-based data security storage and verification method, which comprises the following steps: collecting biological characteristics, generating a first dynamic key, the first dynamic key being used for encrypting first data, and updating during each access to ensure the key timeliness; dividing the first data into a plurality of segments according to logic, generating segmented hash values, and storing the segmented hash values on a chain; dynamically loading the corresponding fragment according to the user permission, and realizing anonymous verification through a homomorphic encryption technology; based on the Internet of Things technology, the first positioning parameter and the first state parameter are combined, the access permission is dynamically adjusted, and a self-destruction mark is set to avoid the overtime access risk; and in the storage process of the data fragments, dynamic security scoring and path adjustment are performed through distributed storage. According to the method, the static problem in data storage and access control is solved, and a more flexible and reliable security guarantee can be provided in a dynamic access scene.

The invention relates to the technical field of product data management, in particular to a food package printing material batch traceable management method and system, and the method comprises the following steps: collecting the production time of each production batch, constructing time data which comprises year, month, day, hour, minute and second, and creating a time code based on the time data; the time code is combined with the data of the production batch. According to the method, the time data of each production batch, which is accurate to second, is collected, the unique time code is constructed, and the time code and the batch production data are closely combined, so that a traceable time stamp data packet is formed, accurate correspondence between production information and time is ensured, and more efficient information integration and storage management are realized. In the storage process, classification and indexing are carried out in combination with batch time marking data packets, a data retrieval mechanism capable of rapidly responding is established, the information positioning speed and accuracy are improved, and the efficiency and reliability of batch query and tracking are improved.

The invention discloses a power plant data node backup method and backup system based on big data, and belongs to the technical field of power plant data backup. In the process of executing file encryption, version creation and Hash calculation, an AES-256 strong encryption algorithm is selected to encrypt the content of a power plant data file; through the step, the safety and reliability of the whole power plant data backup method are remarkably improved. Firstly, the application of the AES-256 encryption algorithm ensures the confidentiality of the power plant data in the transmission and storage process, and the power plant data is not easy to leak even in the face of advanced network attack or physical storage medium loss, thereby greatly protecting the sensitive information and operation safety of the power system. The generation of the hash value not only ensures the integrity of the power plant data before and after encryption, but also provides a non-tampering proof for data verification, so that the backup and recovery process of the power plant data is more reliable and efficient.

The invention provides a data query method, system and device and a storage medium, and the method comprises the steps: defining access connection information of a new database, and adding the new database into a connection pool; defining corresponding relations between tables and fields among different databases in the connection pool, and storing the corresponding relations between the tables and the fields among the different databases in a centralized warehouse; according to a preset business requirement, a preset query statement corresponding to each table in each database is disassembled, and the preset query statement is packaged in a storage process corresponding to the table, and the storage process is a pre-compiled SQL statement; searching a data mapping rule corresponding to the query entity data in the warehouse according to the user query request, and matching a target table of at least one target database in the connection pool according to the data mapping rule; and returning a query result based on a preset query statement corresponding to the target table. Data in different databases can be integrated, and the data management efficiency is effectively improved.

To provide an information processing apparatus that can perform processing using a trained model including a large-scale language model without requiring a provider of target data to pay attention, and efficiently utilizes output data, a control method of the information processing apparatus, and a program.SOLUTION: An information processing apparatus 1 includes: a data receiving unit 30 which executes receiving processing to receive target data to be processed; an integrated processing unit 31 which executes acquisition processing to output a script including the target data and a command to a generative AI server 101 and obtain output data output by the generative AI server 101; and a data management unit 33 that can execute storage processing to store the output data in a database 90 and presentation processing to present the output data stored by the storage processing to an external terminal, such as a data user terminal 3.SELECTED DRAWING: Figure 7

Embodiments of this specification provide a shard storage method and apparatus for a graph and a subgraph sampling method and apparatus for a graph. In a distributed storage process of a graph, local identifiers of a vertex and an edge are implicitly stored, and data is stored in an ordered manner, so that the local identifiers of the vertex and the edge can be implicitly calculated. A connecting edge is stored in a CSR format, to ensure that a first-order neighbor of a node is contiguously stored in a memory. In this way, there can be a higher data loading speed and lower memory occupation.

The embodiment of the present application discloses a data processing method for retrying a failed stored procedure. The method comprises: executing a stored procedure in response to a stored procedure request, wherein the stored procedure comprises one or more ordered transactions, and when the first transaction in the one or more ordered transactions is executed, recording the marking information of the first transaction; based on an exception occurring during the execution of the stored procedure and satisfying the condition for retrying the stored procedure, retrying the transaction after the first transaction in the stored procedure according to the marking information of the first transaction.

The invention relates to the technical field of data storage management, in particular to a data management method and system for an automobile transaction platform. The method comprises the following steps: obtaining a to-be-stored data sequence corresponding to an automobile transaction platform, and carrying out index pair classification extraction and storage space limit quantification calculation to obtain a storage release memory space limit degree corresponding to each type of index field; performing sequence storage division on the corresponding to-be-stored data sequence to obtain a storage data sequence segment corresponding to each type of index field; obtaining a query access rate and a storage interval approach access degree of the corresponding sequence segment, and carrying out storage activity evaluation analysis and index storage priority measurement analysis to obtain an index storage priority measurement of each type of index fields for each sequence segment; and carrying out priority storage management on the corresponding storage data sequence segments. According to the invention, mass data in the automobile transaction platform can be managed efficiently and accurately, so that the storage process of the data is optimized.

The invention discloses a non-intrusive data information security management method and device. The method comprises the following steps: deploying database middleware between an application program and a database; the middleware manages database connection; the middleware receives an SQL request from an application program; the middleware analyzes the received SQL request; the middleware judges whether the SQL request contains a sensitive field needing to be specially processed or not; the fields are sensitive fields, and the middleware automatically applies an encryption and decryption algorithm; the modified SQL request is executed on a database so as to carry out data access operation; an execution result is returned to the application program, and the middleware decrypts the data; the Web interface provided by the middleware is used for configuring and managing encryption strategies, algorithms and secret keys; re-encrypting the existing data processed by using the old encryption scheme; and completing a data security management process. According to the method, access to sensitive fields is identified through the SQL analysis module, an encryption and decryption algorithm is automatically applied, and the safety of data in the transmission and storage process is guaranteed.

The invention relates to the technical field of data processing, and provides a cloud data archiving method and system, and the method comprises the steps: obtaining a source data set of to-be-archived data, carrying out the grade division, obtaining a data grade label, dividing the source data set into a plurality of data blocks, carrying out the data encryption of different grades, obtaining a plurality of encrypted data blocks, and storing the encrypted data blocks in a database; analyzing and coding to obtain a multi-dimensional data storage structure, uploading the multi-dimensional data storage structure to a preset cloud storage node, and generating a storage index information set based on multiple data blocks in the uploading process; and when a data recovery request is received, acquiring the data block from the cloud storage node according to the storage index information set, and performing decryption and data recombination. Through the scheme, classification, hierarchical encryption and structured archiving of the source data set are realized, the security and management efficiency of the data in a cloud storage process are improved, and the problem that the encryption level cannot be dynamically adjusted by data sensitivity or content features when a large-scale heterogeneous data source is used is solved.

The embodiment of the invention discloses an Oracle compatible method and equipment based on a database driving program, and belongs to the technical field of database migration. The problem that in the prior art, comprehensive compatibility of Oracle characteristics is difficult to achieve on the driver level is solved. Comprising the following steps: when a database is in an Oracle compatible mode, receiving a calling statement in an Oracle form at a driving layer; in the SQL statement generation and execution stage, a corresponding grammar conversion strategy is determined according to the type of a calling statement; based on a grammar conversion strategy, performing grammar conversion on the calling statement to obtain a statement supported by a target database kernel, and sending the statement to the target database kernel; in the stage of obtaining the result set, the result set returned by the kernel of the target database is analyzed, and a function return value and / or an out parameter value obtained through analysis are / is transmitted to a corresponding binding variable, so that compatibility of an Oracle function, a storage process and an anonymous block is achieved.

The invention relates to the technical field of environmental monitoring, and discloses an exclusive encrypted file format of a universal water-gas-sound-residue pollutant data acquisition instrument. File structures including a file header setting section, a data storage format definition section, a self-checking section and an encryption algorithm section are sequentially stored in a file to form an exclusive encryption file format of the universal water, gas, sound and residue pollutant data acquisition instrument, various encryption and checking measures are adopted, and the safety and integrity of data in the transmission and storage process are ensured. The method has the advantages that data are stored in the database and prevented from being tampered and stolen, data storage efficiency and retrieval speed are increased due to reasonable data storage formats and index designs, data management and analysis are facilitated, file damage or data tampering can be timely discovered through CRC32 check and data integrity check codes, data reliability is guaranteed, and data storage efficiency and retrieval speed are increased. And on the basis of an XML structured storage mode and an open file format design, subsequent addition of new pollutant parameters or function extension is facilitated, and good expandability is achieved.

The invention relates to the technical field of archive storage, and discloses an archive storage system and method based on a digital object architecture, and the system comprises a unique identification and relation analysis assembly which is used for generating a globally unique object identifier; the digital object warehouse is used for constructing digital objects; the digital object registry is used for establishing a metadata index which can be retrieved; the distributed account book module is used for forming an on-chain record; and a policy-driven engine for monitoring and receiving the broadcasted structured transactions and determining the affected context scope to execute the embedded lifecycle policy. According to the technical scheme, the electronic original text is constructed into the digital object containing the Merkel tree integrity verification data, and all key operations are recorded in the distributed account book module, so that full-chain trust guarantee of archive data from content to process is realized; therefore, the integrity of the data in the long-term storage process can be effectively verified.

The invention provides a source database storage process migration method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the grammar analysis of an original storage process in a source database, and generating a corresponding abstract syntax tree; based on a preset rewriting rule set, performing semantic equivalent transformation on the flow control syntax structure in the abstract syntax tree to generate a transformed abstract syntax tree; and generating a target script in a target execution environment from the converted abstract syntax tree, executing the target script in a script running engine, dynamically generating a standard SQL statement corresponding to the business logic defined by the original storage process, and submitting the standard SQL statement to one or more target data platforms for execution. And the business logic of the original storage process is reproduced. By introducing the target script as the intermediate execution layer, the flexibility of the migration scheme is greatly improved, so that the migration scheme is suitable for various big data platforms without the native storage process capability.

The invention discloses a multi-terminal collaborative enterprise management system and method, and relates to the technical field of intelligent management, and the method comprises the following steps: dividing port data, obtaining a login verification result, executing a first operation, setting a verification rule of the port data, verifying ownership, executing a second operation, and storing encrypted data to a cloud data storage center. The enterprise data is reasonably divided into different port data, fine management of the data is facilitated, the efficiency and the safety of data processing are improved, it is ensured that only authorized users can access the system, the safety is enhanced, data leakage and unauthorized access are prevented by setting verification rules and performing ownership verification, and the safety of the system is improved. According to the method, the data cannot be read by unauthorized persons even if the data is intercepted and captured in the transmission or storage process, and users at different places and devices share and update the data in real time due to the characteristic of multi-terminal collaboration, so that the collaboration efficiency of a team is improved.

The invention relates to a method and a device for debugging a visual storage process of an openGauss database. The method comprises the following steps: displaying a storage process source code on a graphical interface; debugging of the storage process is started, parameter information is input, server-side connection and client-side connection are obtained, the server-side connection obtains the storage process oid, and then a debugging plug-in is called to start debugging; the client connects and calls the debugging plug-in to be associated to the storage process for debugging, and obtains debugging information in real time; and presetting a breakpoint, triggering a breakpoint event by a graphical interface, and directly debugging to a line where the breakpoint is located to view information needing breakpoint debugging. According to the method, a DBEPLDEBUGGE plug-in related interface in an openGauss database is called, a graphical interface is combined to realize visual debugging of a storage process, an efficient and accurate debugging tool is provided, developers are helped to monitor parameter and variable changes in real time, and an execution process is clearly displayed, so that the debugging efficiency is improved, the error checking time is shortened, and the debugging efficiency is improved. And the development productivity and the system reliability are improved.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

An abnormal irregularity cause identifying device includes a process data acquisition unit that reads, from a storage device storing process data, the pieces of process data, an abnormality determination unit that calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, and a cause diagnosis unit that obtains accuracy of a cause of the irregularity based on a proportion of process data having the abnormality degree calculated by the abnormality determination unit satisfying a predetermined criterion to the plurality of types of pieces of process data, by using causal relation information defining a combination between the cause and the irregularity, which appears as an influence resulting from the cause, of each of the plurality of types of pieces of process data.

A data platform for managing an application as a first-class database object. The data platform includes at least one processor and a memory storing instructions that cause the at least one processor to perform operations including detecting a data request from a browser for a data object located on the data platform, executing a stored procedure, the stored procedure containing instructions that cause the at least one processor to perform additional operations including instantiating a User Defined Function (UDF) server, an application engine, and the application within a security context of the data platform based on a security policy determined by an owner of the data object. The data platform then communicates with the browser using the application engine as a proxy server.

The invention relates to the field of data processing, in particular to a data query method and system based on data de-duplication storage, and the method comprises the steps: firstly receiving a high-concurrency query request in a query process, and then calling out corresponding storage data from a database according to the high-concurrency query request. After a high-concurrency query request is sent out, the method also executes a concurrency step about a storage process, which comprises the following steps: firstly receiving heterogeneous event streams of a plurality of different data sources in real time, carrying out normalization processing on the heterogeneous event streams to obtain standard data streams, then carrying out attribution analysis on the standard data streams to obtain corresponding and unique ID-Offset mapping, and then, carrying out storage processing on the ID-Offset mapping. According to the method, the standard data stream and the ID-Offset mapping corresponding to the standard data stream are aggregated to obtain an aggregation event, and finally, the aggregation event is de-duplicated by utilizing the ID-Offset mapping based on a preset Bitmap de-duplication mechanism to obtain storage data and write the storage data into a database. The method solves the problem that the prior art is not suitable for high-concurrency storage and query.

The invention discloses a database storage method, which realizes high optimization of a storage space by finely classifying key value pairs and storing the key value pairs in different regions. The recovery frequency is dynamically adjusted, only the key value pairs marked as deleted are erased, storage fragments and the erasure frequency are reduced, the storage efficiency is improved, and the service life of storage equipment is prolonged. When the sectors are recycled, adjacent idle and invalid sectors are combined, so that the storage space utilization rate and the access efficiency are improved. When the new key value pairs are stored, the new key value pairs are preferentially distributed from the combined sector pool, the storage process is simplified, and the response speed is increased. The data access frequency and the sector erasure life are combined, the merged sectors are finely grouped, resource allocation is optimized, and the data access efficiency is improved. And meanwhile, a direct local modification technology is adopted, so that data migration and rewriting are reduced, and the updating operation efficiency is improved.

The invention relates to the technical field of large model processing, in particular to a fault tolerance method and device based on asynchronous storage check points in a large model training scenario, and the method comprises the steps: obtaining the state data of the asynchronous storage check points; converting the state data into serialized data by using a serialization tool of a deep learning framework; the serialized data is written into persistent storage through asynchronous I / O operation, the serialized data of the persistent storage is used for recovering the training task in the large model training scene, and the serialized data of the persistent storage allows data writing operation to be executed in the background. Therefore, the problems that in the related technology, in the large model training data storage process, the storage overhead is large due to frequent storage operation, the overall training progress is affected if hardware breaks down, and the training efficiency is low are solved.

The invention is suitable for the technical field of data management, and particularly relates to an AI-based government affair data management method and system, and the method comprises the steps: obtaining government affair data, defining a storage position, uploading the government affair data to the storage position, configuring a composition unit of the government affair data, creating a fingerprint number, and storing the fingerprint number in the storage position; wherein the fingerprint number is composed of numbers, the fingerprint number is divided into a plurality of sub-numbers, characters corresponding to the sub-numbers are found in each composition unit and integrated to generate unique identifiers, whether two same unique identifiers exist or not is judged, and if yes, the fingerprint number is corrected. By constructing the comparison identifier, whether the registrant has the calling permission of the target unit or not can be judged, so that private storage and anonymous calling of the government affair data are achieved, meanwhile, the government affair data can be prevented from being tampered in the storage process, and the government affair data management efficiency is further improved.

The invention provides a data processing method and device, a server and a storage medium, when the server stores target data, the target data is split into k + m sub-data according to a preset erasure code redundancy ratio, in the storage process, information of successful data writing can be fed back as long as N sub-data greater than or equal to k is successfully written, and the data writing efficiency is improved. N is greater than or equal to k, so that data recovery can be completed through the successfully stored N sub-data even if the remaining W-N sub-data are not completely written, and the reliability of the target data is guaranteed.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.