1178 results about "Data operations" patented technology

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention relates to the technical field of prediction and alarm, in particular to a school computer room data operation monitoring and early warning method and system, and the method comprises the following steps: obtaining real-time data, such as CPU utilization rate, memory occupancy rate and network delay, through a sensor, generating a parameter sequence after standardization, extracting a difference sequence through a sliding window, and recognizing the output trend risk of a continuous rising interval. And extracting a behavior frequency to generate an abnormal coefficient, carrying out weighted analysis on the coupling degree to obtain an early warning threshold, and generating an early warning instruction if the growth rate exceeds the threshold and lasts for three time slices. According to the method, difference characteristics are extracted through a standardized time sequence and a sliding window, behavior state association is analyzed in combination with a periodic frequency, coupling degree dynamic early warning is calculated, a continuous deviation trend is screened, a real-time threshold range is formed, abnormal confusion is reduced, occupation of irrelevant alarm resources is reduced, risk identification capability is improved, and stable operation of equipment is guaranteed. And hidden danger accumulation occurrence probability is reduced.

The invention relates to the technical field of artificial intelligence and big data analysis, in particular to a data operation system and method based on a knowledge graph, and the method comprises the steps: extracting an entity and semantic relationship from multi-source business data, and constructing a dynamic evolvable initial knowledge graph; node features are aggregated, and multi-dimensional situation state vectors are generated in combination with gating loop unit modeling behavior path dependence; through a structure-semantic coupling attribution scoring mechanism, a statistical information gain and semantic similarity are fused to identify a core driving factor, and a causal regression model of the factor and an operation target is established; dynamically adjusting the edge weight and the structure of the atlas in real time, and triggering a new path discovery mechanism to continuously optimize the atlas; and according to a quantitative business target, reversely extracting a high-confidence influence path from the atlas, and generating a personalized strategy combination through intervention simulation and multi-target Pareto optimization, thereby realizing intelligent recommendation and decision closed loop driven by an operation target. According to the invention, higher-precision operation situation awareness and strategy generation are realized.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data consanguinity map processing method, device, equipment and medium, and the method comprises the steps: collecting metadata change information and data operation event information, and building a mixed consanguinity map model; monitoring a metadata change event and a data operation event, and triggering incremental updating based on an event type; and performing data change influence analysis based on the updated blood relationship map model, generating an analysis result, and outputting the mixed blood relationship map model and the data change influence analysis result. According to the invention, by collecting metadata, incrementally updating the blood relationship map and carrying out data change influence analysis, full-link data tracing and influence analysis are realized, the problems of blood relationship breakage and insufficient real-time data tracing in traditional data blood relationship analysis are solved, and the accuracy of data tracing and the timeliness of business decision are improved.

Methods, apparatuses, system, devices, and computer program products for providing access to composite AI-generated data is disclosed that includes a controller of an API server, receiving from a user system, an API call that specifies a request to perform a data operation on an entity database; interpreting the API call to identify the data operation and parameters related to the scoring data; executing the data operation on the entity database according to the specified parameters; generating a response to the API call; and transmitting the response to the user system.

The invention discloses a data processing method based on computer software development, which belongs to the technical field of data processing, and comprises the following steps: S1, constructing an adaptive analysis engine driven by a knowledge graph, and carrying out multi-modal data semantic modeling and context labeling by adopting a multi-modal data feature fusion technology; and S2, designing a cross-node distributed data cleaning and dynamic fragmentation optimization strategy based on a differential privacy and feature space alignment technology, and through hierarchical deployment of a national cryptographic algorithm and homomorphic encryption, realizing data full life cycle security protection, ensuring data asset security by static storage encryption, and supporting security calculation requirements by ciphertext operation; attribute-based encryption fine-grained access control accurately matches a data use permission, and a block chain technology ensures traceability and tamper-proofing of data operation; the problem that a traditional encryption scheme is insufficient in flexibility is solved while the compliance requirement is met, and a trusted infrastructure is established for cross-domain data sharing.

The invention relates to the related technical field of data governance analysis, in particular to an intelligent data governance system based on behavior path analysis, and the system comprises a path generation module which generates a data flow path through a graph neural network according to a data behavior log, and draws up a behavior path graph; the first risk propagation path set determination module and the second risk propagation path set determination module respectively use graph diffusion and characteristic parameter marks to determine risk paths so as to operate a blocking mechanism to carry out data access management. The technical problems that data access depends on single rule matching and single-point anomaly detection, global correlation analysis on data operation behaviors is lacked, and complex attacks cannot be effectively detected are solved, the behavior path map is generated through the graph neural network, the influence range of abnormal operation is quantified by using the graph diffusion model, and the data access efficiency is improved. The technical effects of predicting the potential path of sensitive data leakage in advance, dynamically adjusting the confidence, improving the risk node identification accuracy, carrying out real-time blocking treatment and guaranteeing the data access security are achieved.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention relates to a data query analysis method based on a natural language, which comprises the following steps of: analyzing a natural language query request input by a user into a dynamic semantic intention tensor which comprises an operation type, a comparison relationship, a time constraint and a data role semantic dimension, and dynamically combining according to task semantics to form a complete data query and analysis operation expression; mapping the natural language intention to a controllable data operation space of a database, and generating a preliminary data operation chain; performing side effect perception processing on the operation tensor, predicting a potential execution consequence of the operation, generating a side effect tensor for simulating a side effect of the database operation, and if any index in the side effect tensor exceeds a preset threshold value, performing local correction on the operation tensor; according to the method, a complex natural language query task is disassembled into a plurality of minimum semantic units by adopting a step-by-step semantic resolution mechanism, each unit is mapped into a specific tensor operation, and a dependency relationship between operation units is dynamically reasoned to generate an operation execution chain.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

The invention relates to the technical field of medical data circulation and storage, in particular to a block chain-based medical data circulation and storage method, which comprises the following steps of data storage, data circulation and data auditing. The medical data storage security is remarkably improved, the blockchain storage performance bottleneck is avoided, the non-tampering property of the blockchain ensures that the data operation record is traceable in the whole process, the malicious tampering risk of internal personnel is completely eradicated, a mixed encryption mechanism is adopted, a session key is chained after being encrypted by a public key of a patient, a private key is only held by the patient, the cracking difficulty is extremely high, and the safety is high. According to the method, high-efficiency and de-intermediary data circulation is achieved, patient privacy control and compliance are enhanced, fragmented PBFT consensus is adopted, the medical transaction throughput can reach 2000 + TPS, clinical real-time requirements are met, breakthrough is achieved in the aspects of safety, efficiency and privacy protection, and a trusted infrastructure is provided for medical data value circulation.

The invention relates to the technical field of data processing, and discloses a digital intelligent water affair global data operation decision analysis comprehensive service system. The system comprises an acquisition module for acquiring standardized processing data; the construction module generates a water plant three-dimensional model and virtual representation; the storage module executes hierarchical storage to obtain quality enhancement data; the identification module constructs an index system to diagnose the operation state; the evaluation module uses a Bayesian network to check asset cost; and the generation module provides decision support and an early warning scheme. According to the invention, by constructing the global data operation decision analysis comprehensive service method, the data barrier is broken, the whole-process data integration, analysis and intelligent decision support of the water affair system from the source to the terminal are realized, and the operation efficiency and management level of the water affair system are improved.

The invention relates to a notebook software vulnerability scanning method based on a security policy. According to the method, firstly, a security policy library of a target notebook is obtained, and the security policy library comprises access control rules, data operation limiting conditions and vulnerability detection reference requirements for different software types; collecting software running data of the target notebook computer, wherein the software running data comprises current running process information, file system operation records, network connection states and software configuration parameters; performing matching analysis on the software operation data and the security policy library to generate a preliminary scanning result; evaluating the risk level of the software vulnerability according to the preliminary scanning result; and finally, based on the risk level and the repair guide terms in the security policy library, generating software vulnerability repair guide information. Therefore, notebook software vulnerabilities can be scanned comprehensively and accurately, and effective repair guidance is provided.

The invention discloses a data operation and maintenance management method and system based on edge computing, and the method comprises the steps: dividing edge nodes into a plurality of layers according to the data type information of terminal equipment, and calculating the configuration parameters of the edge nodes through employing a genetic algorithm; distributing the real-time data of the terminal device to the edge node according to the level of the edge node; establishing a self-supervised learning model of each edge node according to the configuration parameters to obtain an abnormal score of the real-time data; and establishing a multi-device association map according to the abnormal score and neighborhood information between the terminal devices, carrying out abnormal propagation analysis based on a graph neural network on the multi-device association map, identifying to obtain an abnormal propagation path, and optimizing a data stream propagated on the edge node by the real-time data and configuration parameters. By adopting the embodiment of the invention, the real-time processing and analysis capability of the data can be still ensured when the network is poor, efficient anomaly detection can be provided, and the cross-device and cross-network collaborative operation and maintenance requirements are effectively supported.

The invention discloses a performance test method and device of a storage device, an electronic device and a storage medium, and relates to the technical field of data storage, and the performance test method comprises the steps of controlling the storage device to execute data operation in response to test input; wherein the data operation comprises read operation and / or write operation; in the process of executing the data operation by the storage device, obtaining performance data of the storage device through the performance test model; wherein the performance data comprises performance data of a data link of the storage device and performance data of the storage controller, the storage controller is electrically connected with the storage device through the data link, the data link is used for transmitting data between the storage controller and the storage device, and the performance test model comprises at least one performance monitoring tool. The at least one performance monitoring tool is used for performing performance monitoring on the data link and the storage controller; and generating a performance test report of the storage device according to the performance data. The technical effect of improving the performance test efficiency of the storage device can be achieved.

The invention provides a multi-database SQL blood relationship analysis method and system based on a G4 file, and relates to the technical field of database management.The method comprises the steps that SQL statements are analyzed based on a G4 grammar file, a front-back dependency relationship is constructed through a statement dependency directed graph, grammar rules are optimized through a dialect feature matrix and a Markov decision process, and a multi-database SQL blood relationship analysis result is obtained. According to the method, the SQL data extraction operation and the circulation sequence are sequentially analyzed, the field blood relationship link diagram is generated, and the structured result is output, so that multi-database SQL grammar features can be accurately recognized, the complex SQL analysis problem is solved, the blood relationship analysis accuracy and efficiency are effectively improved, and cross-database unified blood relationship management is supported.

The embodiment of the invention provides a column storage method and device, a computer program product and a storage medium. On the premise that the online service is normally provided according to the line storage, the stock flow and the increment flow can be pulled in parallel. A column storage file and a first primary key index can be generated by pulling the stock stream. By synchronizing the data operations occurring on the target data table monitored during the pulling period to pull the incremental stream, an incremental file and a second primary key index can be generated, and it can be ensured that column storage can be kept consistent with row storage in the aspect of increment. Recording the primary key needing to be deleted for the first primary key index based on the data operation monitored during the pulling period; and in response to the ending of the pulling, combining the first primary key index and the second primary key index into a target primary key index, and correcting the target primary key index and the column storage file on the basis of the primary key needing to be deleted, so that the consistency of the column storage and the row storage in the aspect of stock can be ensured.

The invention discloses a marine meteorological observation data millisecond query system based on time sequence block indexing. The marine meteorological observation data millisecond query system comprises a data input module, a data preprocessing module, a data hierarchical modeling module, a data storage cluster, a data analysis engine, a data query engine and a data operation interface module. Based on data modeling, data thinning and data partitioning technologies, data preprocessing is carried out for different meteorological ocean space weather data, and distributed storage and access of mass data are achieved; the method comprises the following steps: inputting ocean actual observation data, forecast data or historical data, carrying out model construction and index design according to meteorological ocean space weather data characteristics, completing distributed storage, and applying a data query engine and a data analysis engine to realize rapid aggregation query of the meteorological ocean space weather data; storage and query of lattice point data are realized by using a distributed database, and the problems of scale and query performance of meteorological ocean space weather data are effectively solved.

The invention relates to an enterprise data operation multi-source data virtualization access and index automatic management and control method and system, belongs to the technical field of computers, and aims to solve the problems of index definition fragmentation, frequent logic conflicts, dependency static management and verification rule rigid coupling in the prior art. Comprising the following steps: constructing a unified virtualized data access layer to integrate heterogeneous data sources; the indexes are disassembled into semantic atoms capable of being managed in a versioning mode and registered to an atom knowledge base; dynamically constructing an index dependency relation directed acyclic graph based on semantic atom combination; dynamically binding the data quality rules with semantic atoms; and analyzing an index request through a virtualized query engine, reversely traversing atlas aggregation dependency and rules, generating and executing local query, and automatically checking result data quality at the same time. According to the method, unified index definition, dependence on automatic conduction, quality embedded verification and multi-source transparent access are realized, and the data consistency, the treatment efficiency and the system flexibility are remarkably improved.

The invention provides a robot master-slave arm teleoperation software and hardware system. The system comprises a host, a master arm, a slave arm and a scene acquisition module, the master arm is used for collecting task action data and transmitting the task action data to the host; the host processes the task action data to generate an action instruction conforming to the driven arm, and the driven arm executes a corresponding action according to the action instruction; the scene acquisition module is used for acquiring operation scene data of the driven arm and transmitting the operation scene data to the host; and the host is also used for associatively storing the task action data, the execution action data of the driven arm and the operation scene data as training data of the intelligent model with the body. The host converts the task action data collected by the driving arm into the control instruction suitable for the driven arm, teleoperation of different types of driven arms can be supported, a large amount of training data of the body-equipped intelligent model suitable for different types of driven arms with different arm lengths is conveniently obtained by collecting and associating related data, and the training efficiency is improved. The device has the function of integrating intelligent data acquisition and model verification.

The invention discloses an artificial intelligence auxiliary authentication method and system for data element right confirmation, and relates to the technical field of data element management and credible circulation. Comprising the following steps: data acquisition: acquiring a data product, an ownership statement and contract terms, and calculating an initial digital fingerprint of the data product; and ownership identification: extracting core value elements and features of the data product based on a multi-modal intelligent analysis model, quantifying value contribution weights formed by all links to the data product, and automatically generating a data element circulation graph. According to the method, the multi-modal intelligent analysis model is constructed, and the data product is deeply scanned and analyzed automatically, so that the dependence on predefined rules is reduced, and the intellectualization and automation of ownership identification are realized; by constructing an intelligent compliance verification model, data operation behaviors are monitored and compared in real time, substantive compliance control over the whole data use process is achieved, and a credible use closed loop is established.

The invention provides a data security management method and device based on a large model and a storage medium, relates to the field of data security management, and obtains access request information and identity information of a data requester and performs identity verification and auditing. Specifically, firstly, deep fusion and joint verification are carried out through an intelligent verification module so as to understand and process a complex relationship between an identity and a request intention; then, dynamically generating an NFT which has uniqueness and represents specific data access authority, and sending the NFT to a requester; when a requester subsequently initiates an actual data access operation, the authenticity and validity of the NFT are strictly verified, and all access attempts (whether the access attempts are successful or not) and permission changes are recorded on a non-tampering block chain, so that dynamic, fine and highly-credible data access authorization and full-process tracing are realized.

A method and a system for building a knowledge base for industrial use and using the knowledge base to manage and control industrial products, processes, resources and systems. The knowledge base uses semantic processing modules and artificial intelligence tools to enable continuous extraction and entry of knowledge into the knowledge base from various sources including user inputs, operation status reports, manuals, websites, diagnosis reports, sensor data, operation sequence, and other static or ephemeral sources. The knowledge base is supported by a knowledge base management system that includes a suite of tools to support knowledge entry, update, retrieval and visualization using both natural language based queries and structured queries with predefined syntax. The system also provides tools to retrieve and assemble knowledges on-the-fly to support industrial applications including to design products, services or production lines, build production process, manage scheduling and flow, monitor and optimize operations, diagnose issues, apply business logics, procure materials and supplies, manage and train workforce, and provide customer supports.

Certain aspects of the disclosure provide a question and answering system for answering questions on tabular datasets. In certain aspects, answering questions with the question and answering system includes generating a sequence of operations for answering the user question using a large language model wherein each operation of the sequence of operations encodes a data operation. Answering questions further includes generating an output based on performing the sequence of operations on a tabular dataset; determining a confidence score associated with the output; and presenting the output to the user.

The invention discloses a system and a method for supporting multi-version concurrency control in a column storage database, belongs to the technical field of databases, and aims to solve the technical problem of how to provide efficient and flexible concurrency control and data operation management. Comprising a multi-version management module for creating a transaction ID and a timestamp for each transaction and maintaining a version linked list for each data segment in a column storage database; the operation optimization module is used for performing data operation on the data in the column storage database based on the submitted transaction, performing operation according to a comparison result of a version number and a transaction timestamp when the data operation is executed, and storing the data before modification into a revocation buffer area; the transaction conflict processing module is used for detecting and processing transaction conflicts based on a preset conflict processing rule; the storage space management module divides the storage of the column storage database into table storage and local storage; and the version linked list management module is used for deleting the version information which is no longer referenced by any active transaction based on the transaction state and the version information.

In some implementations, a cybersecurity system is provided for summarizing network security investigations. The system receives a request to summarize an investigation sequence performed in response to a computer security incident, retrieves tokenized elements that correspond to the investigation sequence, and provides the tokenized elements to a large language model (LLM) for translation into a data operation format. The system receives, from the LLM, and for each tokenized element, a corresponding translated data operation. For each translated data operation, the system submits the translated data operation for execution by a data source, and receives a corresponding data operation response. The system performs a summarization process of the investigation sequence, and outputs a natural language summarization.

The invention relates to a data government method and system for digital government affairs, and relates to the technical field of digital government affairs, and the method comprises the steps: parsing SQL statements and corresponding table structures of different databases in a digital government affair system based on a constructed SQL parser to obtain data operation information; constructing a global data relation view according to the analyzed data operation information; based on the global data relationship view, deploying monitoring points on a data flow path to track data flow changes on the data flow path; when it is detected that the data flow changes, abnormal data are recognized according to the field mapping relation, the processing logic and the dependency relation recorded in the global data relation view; and repairing the identified abnormal data according to a repairing strategy. The method has the advantages that the abnormal data can be quickly recognized, and processing is carried out according to the repair strategy.

The invention provides a clinical test data security sharing method and device based on a block chain. The method is applied to the technical field of data processing, and comprises the following steps: extracting feature dimensions corresponding to various types of data, and calculating corresponding data sensitivity coefficients; determining a data sensitivity level of the corresponding data category; extracting corresponding associated metadata; selecting a corresponding encryption algorithm to encrypt the data, and generating corresponding encrypted data and a data abstract; uploading the associated metadata, the encrypted data and the data abstract to a block chain node, constructing a clinical test data sharing account book, and recording a timestamp and a node signature of data operation; extracting identity authentication information and historical access records of visitors, and calculating access credibility; judging whether the visitor is allowed to acquire the decryption key or not; after the visitor obtains the encrypted data, the integrity in the data transmission process is verified based on the non-tampering property of the block chain. Therefore, the security of clinical test data sharing based on the block chain is improved.

The invention provides a network security malicious code binary search method and system, and relates to the technical field of data processing, and the method comprises the steps: calculating the multi-dimensional structural similarity between a to-be-detected binary file and each standard binary reference file based on a function level control flow semantic feature vector; determining a finally matched standard binary reference file according to the multi-dimensional structural similarity; performing fine-grained difference analysis on the binary file to be detected and the finally matched standard binary reference file to obtain a fine-grained difference analysis result; and on the basis of a fine-grained difference analysis result, in combination with sensitive data operation behavior feature detection, judging whether the to-be-detected binary file is a maliciously tampered version, and positioning a malicious code injection point. According to the method, the defects that in the prior art, dependence on fixed features is too high, and compilation optimization is sensitive are overcome.

The embodiment of the invention provides a memory semantic processing method and device, equipment, a medium and a product. According to the method, a large memory semantic task can be split into a plurality of small sub-memory semantic tasks according to the memory semantic task and the core resource of the processor, so that the scale of the memory semantic task can be within the processing capacity of the processor, and the semantic bit width limitation of the processor can be broken through. According to the method, the sub-memory semantic tasks are allocated to the cores of the processor for parallel execution, so that the processing efficiency of the memory semantic tasks can be improved, and the problem that part of the cores are idle and the part of the cores are overloaded is avoided. In addition, by aggregating the messages corresponding to the sub-memory semantic tasks, a plurality of transmission tasks of a plurality of sub-memory semantic tasks can be completed through one-time transmission, consumption of transmission resources is reduced, and the efficiency of data operation is improved.