1607 results about "Data operations" patented technology

The invention discloses a human resource management method and system based on data security. The method comprises the steps that multi-source human resource data are collected through a standardized interface, and sensitive levels are marked in a classified mode; calling a dynamic encryption engine based on the sensitive level and the data type, performing asymmetric encryption on the structured data, and performing hybrid encryption on the unstructured data; a dynamic hierarchical access control strategy is generated in combination with user roles and service scenes, and the permission range is adjusted in real time; laws and regulations such as GDPR and CCPA are analyzed through a compliance rule base, data operation legality is automatically verified, and illegal behaviors are blocked; k-anonymity and differential privacy technologies are adopted to desensitize sensitive data, and privacy protection and data availability balance are ensured; and recording a full-process operation log based on the block chain and generating a non-tampering audit report. The system comprises a data classification acquisition module, a dynamic encryption engine module, an access control engine module and the like. According to the method, the problems of data protection rigidness, compliance response lag and privacy-utility imbalance of a traditional HR system are solved.

The invention relates to a method convenient for data consanguinity collection and analysis, which comprises the following steps of: obtaining original consanguinity data comprising a task execution log, application metadata and a cross-system dependency relationship, and carrying out standardization processing on the original consanguinity data to generate structured consanguinity information comprising an asset unique identifier, an upstream and downstream association relationship and a data operation type; structured consanguinity information is synchronously written into a graph database and a distributed data warehouse, the graph database stores real-time association topology, the distributed data warehouse stores full-amount historical versions, and a transaction consistency algorithm is adopted to ensure the atomicity of double-write operation, so that the data storage efficiency is improved. Single-asset-level consanguinity tracking is performed based on real-time topology of a graph database, global consanguinity analysis is performed based on batch computing power of a distributed data warehouse, a direct dependence path, a deep association network and a closed-loop link detection result are generated, and a closed-loop management mechanism from data acquisition, analysis to optimization is formed. And the problem that an analysis result is disjointed from an acquisition end in a traditional scheme is solved.

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 discloses a full-life-cycle auditing tracking system and method, and belongs to the technical field of data tracking backtracking, and the method specifically comprises the steps: carrying out the global unique identifier distribution of personnel configuration data during the first collection, embedding a timestamp, an operation identifier and a link identifier in each life cycle node of the data, constructing a real-time auditing model, and carrying out the real-time auditing of the data; through data change monitoring, behavior pattern recognition and an anomaly detection algorithm, each data operation is recorded in real time and compared with a preset auditing strategy, risk early warning is triggered, block chain evidence storage is established at a key operation node, a multi-block chain link relation network diagram is constructed based on multiple block chains, and when an abnormal node occurs, a data life chain is generated. Predecessor nodes of the abnormal nodes are backtracked, recursive check is carried out on each predecessor node, and an abnormal source and a responsibility subject are positioned; according to the method, the multi-step association risk can be found, the situation that tracking cannot be achieved during tracking and backtracking is prevented, the missed judgment rate is reduced, and the accuracy and efficiency of tracking and backtracking are improved.

The invention discloses an AI data warehouse full-link blood relationship tracking method and device, and relates to the technical field of data processing. The method comprises the following steps: obtaining structured log data with business terms and entity tags; establishing a knowledge graph according to the structured log data and the business document; generating blood relationship metadata according to the knowledge graph and the data job execution log; constructing a blood relationship cognition map according to the blood relationship metadata, the data operation log and the business document; generating a governance strategy vector according to the blood relationship cognitive map, the system real-time index and the service SLO, and sending the governance strategy vector to an execution engine, so that the execution engine carries out processing according to the governance strategy vector; and obtaining a governance strategy execution result, user feedback information of the governance strategy execution result and a system log generated in a governance process. According to the method, complex semantics and causal relationships behind data operation can be captured.

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 provides a safety monitoring method and system for a financial information service platform. Wherein a collaborative encryption transmission channel between a budgeting unit and an auditing department is constructed, a joint key is generated based on a multi-party computing protocol, and the ciphertext operation authority of a sensitive field is dynamically configured; performing ciphertext variance calculation and regression analysis on the encrypted budget execution record through the security operation node, generating a verifiable statistical result, binding the authority, and synchronously updating the sensitive field access frequency; a hierarchical security threat baseline is generated in combination with permission, frequency and historical query records, and cross-project high-frequency query and data operation deviation are associated; and when the query deviates from the baseline, calculating the risk confidence coefficient according to the deviation, triggering multi-party verification path adjustment and collaborative alarm, and dynamically limiting the ciphertext permission of the abnormal query. According to the technical scheme provided by the invention, cross-department data security sharing and dynamic risk collaborative prevention and control are realized.

The invention discloses a trajectory portrait generation method and system based on artificial intelligence, and the method comprises the steps: generating a structured data flow graph according to a dynamic access log, a data operation behavior record and an authority change event of an enterprise heterogeneous data source; outputting high-order semantic feature representation of data flow by using a space-time diagram neural network based on the structured data flow map; according to the high-order semantic feature representation, multi-source semantic features are fused through a cross-modal attention mechanism, and a global embedding vector of the data track is generated; and based on the global embedded vector, simulating a path evolution process of data flow by adopting a dynamic brain network evolution model, predicting a potential abnormal diffusion path through a nonlinear kinetic equation, and generating a risk-aware trajectory portrait. By utilizing the embodiment of the invention, the expression capability of the track portrait can be improved, and an advanced technical basis is provided for enterprise safety management, operation optimization and decision support.

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 discloses a data fragment storage index optimization method and system, and relates to the technical field of data fragment storage, and the method comprises the steps: determining an observation time period according to a data access mode and short-term fluctuation characteristics; monitoring data performance, and performing log data analysis; and performing data resource adjustment, and adjusting task allocation. According to the method, data loading is efficiently managed and adjusted, the user experience and reliability of a system are improved, the importance of a dynamic data management method based on the observation time period is highlighted, hot spot fragments can be recognized in time when the load is too high through setting of a dynamic threshold value, and therefore effective measures are taken, and the workload of a user is reduced. According to the method and the device, the waste of system resources is reduced, the use efficiency of the resources is improved, data operation can be completed under the condition that a main thread is not interfered, the data transmission efficiency and the storage utilization rate are improved, and the overall processing capacity and fluency of the system are improved.

The invention discloses an information security management method and system based on sensitive data, relates to the technical field of data management, and comprises the following steps: realizing quantum encryption transmission of remote high-sensitive data by combining a quantum key distribution link and a dynamic chaotic key stream; the partitioning and synchronization priority is dynamically adjusted according to the node load, the data integrity is verified through CRC, the distributed storage disaster recovery system with extremely high safety and usability is achieved, it is ensured that data can still be recovered even under extreme disasters, a zero-trust data channel based on an IPSec tunnel is constructed in the data transmission process, and the reliability of data transmission is improved. The tamper-proof, anti-counterfeiting and anti-leakage capabilities of a data access link are greatly improved, the access frequency abnormity is detected through the isolated forest model, the behavior trend is detected through the LSTM time sequence prediction model, the baseline deviation analysis is performed through user behavior portrait comparison, and the real-time, multi-modal and chained security monitoring of the user data operation behavior is realized.

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 discloses a method and system for predicting the service life of a fire valve, and relates to the field of prediction maintenance, and the method comprises the steps: obtaining a multi-dimensional monitoring data matrix which comprises the sealing performance data, operation torque data and corrosion degree data of the fire valve continuously monitored in a preset time period; determining a parameter incidence matrix according to the multi-dimensional monitoring data matrix; determining a plurality of principal component vectors and corresponding characteristic values according to the parameter incidence matrix through a principal component analysis algorithm, and determining a current health state score of the fire valve according to the characteristic values corresponding to the plurality of principal component vectors; determining a degradation law function of the fire-fighting valve according to the historical operation record of the fire-fighting valve; and predicting the service life of the fire valve according to the current health state score and the degradation law function. According to the scheme, the accuracy of life prediction of the fire valve is improved, and the problem that the accuracy of life prediction of the fire valve is low is solved.

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 discloses a heterogeneous computing system, a cache consistency maintenance method and device, equipment and a medium, and relates to the technical field of heterogeneous computing. The method comprises the steps that when a data operation request is detected, if a receiving party and a transmitting party are a cluster cache layer and a global cache layer, the protocol format of the data operation request is converted according to the mapping relation between a group consistency protocol and a global consistency protocol. And when the data is forwarded to the local cache layer, executing corresponding operation based on the memory consistency model of the calculation unit to which the data belongs, and maintaining the consistency of the local cache. And when the data is forwarded to a cluster cache layer, corresponding operation is executed based on the cluster consistency protocol, and the consistency of the cluster shared cache is maintained. And when the data is forwarded to a global cache layer, maintaining the consistency of the shared caches of the clusters based on a global consistency protocol. The method and the device can solve the problem of poor compatibility and expansibility of related technologies, can be compatible with various computing units and different memory consistency models thereof, and are easy to expand.

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.

Provided are systems and methods for AI management configured to guide interactions between end users systems and generative AI services. Various examples can manage data operations, among other options. In various embodiments, an AI management system is configured to host an interactive session via generating workflow steps for controlling interaction with AI models. The interactive session is configured to identify data management operations on user specified data sources. The AI management system can be used to analyze the data sources specified to generate a canonical data format spanning the multiple data sources, generate code for mapping the data sources into a canonical format, normalize the resulting data, cleanse the resulting data, validate the resulting data, and automatically generate code for each such function that can then be triggered by users interacting with the user interface.

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.

Methods and systems for secure, federated, and decentralized ownership, storage, sharing and usage of big data are provided. According to one example, a first server maintains access to a plurality of federated data sources including at least one local database and at least one remote database. The local database is subject to a first data policy controlled by the first server and the remote database is subject to a second data policy controlled by a remote server. The first server receives a query from a user electronic device and verifies at least one permission attribute of the user electronic device relative to the data policies. After the federated data operation query has been approved, the first server generates results of the query including data fields from the local and remote databases in compliance with the first and second data policies.

The invention discloses a traceability verification method and device for a trusted data space, and relates to the technical field of data security, and the method comprises the steps: embedding a dynamic watermark identifier in a data operation flow, and carrying out the real-time multi-dimensional traceability of data; performing cross-domain feature fusion, constructing a data evolution topological graph, and generating a credibility evaluation matrix; according to the data evolution topological graph and a credibility evaluation matrix, evaluating a traceability chain fracture risk level; inputting the feature vector of the data tampering event and the risk level into a test mapping array to obtain a verification instruction set; and according to the verification instruction set, generating a credibility calibration report and reconstructing a credible data space. The technical problems that in the prior art, a data operation process is difficult to monitor in the whole process, data is tampered easily and difficult to find in time, and tracing is difficult due to incomplete tracing information are solved, and the technical effects that the whole data operation process is monitored and managed, and data safety, integrity and traceability are improved are achieved.

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 relates to the technical field of data management and specification conversion, and provides an inter-plant standard specification conversion and application management system, a standardized configuration module is used for uniformly formulating data specifications of a plant, the data specifications comprise a unit system and a parameter naming rule, and a multi-source data conversion module is used for converting collected and input heterogeneous data of all departments into a data storage module; the data processing module automatically converts the data into effective data with the same standard and specification as the factory area and the factory building, the data verification module monitors the data quality in real time, repairs errors, establishes a data quality rule base and generates a data quality report with credibility scores, and the tracing module supports backtracking of any version of data based on data operation traces; according to the method, heterogeneous parameter semantic matching is realized by combining a Hungary algorithm, abnormal data intelligent repair is realized by low-rank matrix completion of a data verification module and a graph Laplacian regular term repair technology, and multi-plant data specification and unification, cross-department data format compatibility and continuous optimization of production data quality are realized.

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.