52 results about "Data lifecycle" patented technology

The invention discloses a data life cycle management system in a trusted data space, which belongs to the technical field of data management, and comprises the following steps of: counting data from different sources, recording various change operations of the data and generating a data log; performing preprocessing and privacy judgment on the data to obtain the type and sensitive identification of the data; the method comprises the following steps: acquiring various change operations of data, and constructing a data change graph in a directed acyclic graph form; the change operation of the data is monitored by adopting an isolated forest algorithm, and the detection of the abnormal operation is realized; privacy protection is carried out when the data is queried through a preset self-adaptive noise mechanism; performing credibility evaluation on the data according to a preset credibility evaluation index system, and dynamically adjusting the size of the set noise based on the obtained credibility score of the data; according to the method, the acquisition timestamp of the data is obtained and is compared with the current UTC time for judgment, so that a judgment result is obtained, and the data is archived or destroyed.

The invention belongs to the technical field of computer server management, and particularly discloses a laboratory collaborative environment-oriented user and data full-life-cycle server management system, which comprises a container virtualization layer, a server management layer and a server management layer, dynamic resource allocation is supported through a state saving-resource reallocation-environment recovery mechanism; the heterogeneous transaction coordination layer adopts a Saga transaction mode to coordinate a heterogeneous system so as to realize cross-system transaction consistency; the data collaboration ecological layer establishes typed management of a personal data volume, a shared software environment volume and a public data set volume, and supports software environment encapsulation sharing and versioning management; and the metadata management layer realizes real-time update and high-performance query of metadata based on a Redis hot update architecture. According to the method, the problems of inflexible resource allocation, incomplete environment isolation, dilemma in authority management and difficulty in data collaboration and recovery in the prior art are solved.

The embodiment of the invention provides a data life cycle management method and device, electronic equipment and a storage medium, and relates to the technical field of data management.The method is used for improving the flexibility and efficiency of data life cycle management.The method comprises the steps that a management rule input interface is displayed, and a rule configuration area is displayed in the management rule input interface; in response to an input operation triggered in the rule configuration area, management rule configuration information which is input by the input object and aims at the management object is obtained and loaded, the management rule configuration information comprises a strategy rule of each management operation, and the strategy rule comprises a trigger condition and an execution rule of the corresponding management operation; when it is determined that the state monitoring index of the management object meets the triggering condition of the first management operation, the first management operation is executed according to the execution rule of the first management operation, and the first management operation is any one of the management operations.

The invention provides a compliance governance evidence chain integrity analysis method based on block chain evidence storage, and relates to the technical field of block chains, and the method specifically comprises the steps: S1, collecting related transaction records and evidence storage data in block chain evidence storage records of an evidence chain through a block chain network; s2, managing the life cycle of the evidence chain data; s3, performing hash value comparative analysis on the block chain evidence storage record of the evidence chain to determine the integrity of the evidence chain; s4, carrying out traceability analysis through the evidence chain data; according to the method, the accuracy of data consistency verification can be improved, the normalization and safety of data life cycle management are enhanced, traceability analysis is performed through the causal relationship graph, and the reliability of data traceability evaluation is improved; and through multi-dimensional data analysis and standardized format output, generating an integrity evaluation report.

One example method includes receiving, at a node of a data lifecycle management system, a data stream, performing an assessment of the data stream, based on the assessment, assigning a data confidence score to the data stream, providing the data confidence score to an immutable ledger, and performing a data lifecycle operation on the data stream based on a policy to which the data confidence score corresponds. The data lifecycle operation may be performed by the node and may include data processing, data storage, data usage, data archiving, and data destruction.

The invention provides an incremental cloud learning method and system for vehicle-mounted emotion data, and the method comprises the steps: 1, collecting the multi-modal emotion data of a driver in real time through a vehicle-mounted sensor, and enabling the multi-modal emotion data to comprise a facial image, a voice signal and a physiological signal; 2, preprocessing the multi-modal data, including noise removal, feature extraction and lightweight edge model analysis, and then encrypting the multi-modal data; 3, uploading the encrypted data to a cloud end, and executing incremental learning and federated learning to update the emotion recognition model; 4, verifying the performance of the model based on a preset evaluation strategy, and issuing the updated model to the vehicle-mounted terminal through a privacy protection mechanism; and 5, implementing desensitization, encryption and compliance auditing in the full life cycle of the data to ensure the compatibility of user privacy and laws and regulations. The technical problems that in a vehicle-mounted emotion recognition system, the model precision is low, continuous learning cannot be achieved, and user privacy is difficult to guarantee are solved.

Apparatuses, systems, articles of manufacture, and methods for data lifecycle management in an edge environment are disclosed. Apparatuses and methods for data lifecycle management in an edge environment are disclosed herein. An example apparatus includes an operation executor to identify a first operation to be performed for a data object at an edge node in an edge environment and a second operation to be performed for the data object, the first operation being different than the second operation. An example apparatus includes a time parameter checker to check a first time value associated with a first operation from a data source and a second time value associated with a second operation from the data source. The operation actuator is for performing a first operation in response to a first time value and for performing a second operation in response to a second time value.

The embodiment of the invention provides a data life cycle judgment method and device, a storage medium, computer equipment and a computer program product, and belongs to the field of data life cycle safety management. The method comprises the steps that multiple pre-established life cycle stage rules are established and stored and merged into a rule base, each rule comprises at least one condition item, and each condition item at least comprises a field identifier, a comparison operator and an expected value; analyzing and normalizing the original log of the data security equipment to obtain a standardized log with a unified field structure; matching the standardized logs with life cycle stage rules in a rule base according to a preset sequence; and under the condition that the standardized log is matched with all condition items of one rule for the first time, adding a life cycle stage identifier corresponding to the rule to the standardized log, forming an enriched log, outputting the enriched log, and ending a rule matching process aiming at the standardized log. And the data life cycle division precision and accuracy are effectively improved.

This invention relates to the field of AI-based data quality assessment technology, specifically to an AI-based method and system for assessing data quality throughout its entire lifecycle. The method includes: acquiring multi-stage data samples throughout the data lifecycle; decoupling data content and metadata structure; separating the core content field from the process trajectory field; automatically constructing a multi-dimensional quality feature space in the core content field; extracting native feature points and mapping them into the space; tracking data transfer events in the process trajectory field to generate dynamic transfer trajectories; injecting the process information carried by the trajectory back into the feature space; applying process constraint offsets to the native feature points to form quality state points; and finally generating a data quality assessment value. This solution achieves dynamic quality representation throughout the data lifecycle through field decoupling and process constraint perturbation, making the assessment results more closely reflect the actual data flow state.

The application discloses a data life cycle management method and system, electronic equipment and storage medium, which are used for improving the reliability of data. The method comprises the following steps: collecting target data through an Internet of Things device, and generating a digital signature bound to the identity of the Internet of Things device; at an edge computing node, verifying and reaching a consensus on the target data and the digital signature based on a dynamic trust evaluation mechanism; recording the data digest of the target data verified by the consensus, an off-chain storage address and verification information used for proving the validity of the consensus into a blockchain; performing compliance verification on the state or operation related to the target data through a smart contract on the blockchain; based on the data entity, the Internet of Things device and the related operation event recorded in the blockchain, a multi-modal heterogeneous graph used for representing the association relationship of the whole life cycle of the target data is constructed; and in response to a query or access request for the target data, a response is provided after taking dynamic privacy protection measures.

This invention belongs to the field of data transmission technology and discloses a privacy-preserving network security data transmission method, including the following steps: S1. Data privacy preprocessing: Constructing a personalized differential privacy protection mechanism to perform local privatization processing on the original data to balance data privacy protection and availability; S2. Intelligent scheduling of transmission tasks: For scenarios with multiple network nodes and multiple transmission links, establishing a scheduling model with transmission latency and energy consumption as optimization objectives, and using a quantum particle swarm optimization algorithm that integrates chaotic perturbations to solve the model; S3. Trusted verification of transmitted data: Dividing the network into several sub-regions and constructing a tree-like key management structure, and using key priority allocation and cross-region node verification mechanisms. This solution, through a three-step collaborative mechanism, constructs an end-to-end network security protection system covering the entire data lifecycle, achieving the organic unity of personalized privacy protection, collaborative optimization of transmission efficiency and energy consumption, and decentralized trusted verification.

The application relates to the technical field of data life cycle management, and discloses a dynamic self-adaptive data life cycle management system and method, which comprises the following steps: logically binding a data object with a business genealogy log of the data object, calculating a genealogy entropy value in a non-semantic counting manner based on the log, and directly determining a life cycle strategy of the data object in a preset strategy mapping relationship according to the entropy value. The application establishes a data management path which does not depend on an external complex rule engine for event analysis, so that the management strategy can be endogenously and deterministically generated from the business history record of the data itself, and meanwhile, the application can also realize the automatic rollback of the control level of the data which is placed in a high-level control due to an accidental high-risk event after the risk of the data has actually dissipated, thereby reducing the coupling degree and maintenance complexity of the whole management system.

The invention provides a class imbalance analysis method based on data life cycle evolution. The class imbalance analysis method comprises the following steps: step 1, constructing a full life cycle evolution model of a data sample; 2, performing panoramic scanning and activeness statistical analysis on dynamic indexes of structure response characteristics in different evolution hierarchies; 3, monitoring interaction behaviors among different types of samples in real time in the dynamic propulsion process of the full life cycle evolution model; 4, constructing a number balance mechanism based on life cycle evolution abundance; and 5, training a classification model by using the weighted objective function, and applying the classification model to the test set to realize accurate classification of class imbalance data. According to the method, the optimal fusion coefficient is automatically determined through the Bayesian optimization method with Gaussian process regression as an agent model, blindness of manual parameter adjustment is avoided, and the recognition rate and the overall classification performance of minority class samples are remarkably improved.

A system and method for managing data lifecycles that can provide an adaptable data management system configured to optimize the lifecycles of data and the reliability of databases. The system can provide data management systems with the ability to attach, detach, and drop segments of data based on key partitions of the data objects to optimize database utilization. By including the key partitions to the data objects, the database including the data objects can now perform data management operations based on the key partitions. The system, based on the key partitions, can identify a location of the data object to attach row entries, detach row entries from the existing tables, and delete the detached row entries from the database altogether. The system can operate on any relational database management system.

A data lifecycle management method, device, equipment, storage medium and program product are disclosed, access heat of all data of a first storage layer in a data warehouse is acquired; wherein the data warehouse comprises at least two storage layers, and there is an association relationship between the storage layers, in which upstream data supports downstream data; cold data in the first storage layer is determined based on the access heat; associated data related to the cold data in the remaining storage layers is determined based on the association relationship; and a data cleaning operation is performed on the cold data and the associated data. By adopting the embodiment of the present application, the problems of hot data misdeletion and cold data redundancy in the data warehouse can be avoided, fine and differentiated management of data is realized, and resource waste of the data warehouse is reduced.

This invention discloses a method and system for data security encryption and multi-party computation privacy protection in e-commerce platforms, relating to the field of data security technology. It includes the following steps: Step 1, collecting the lifecycle status and multi-party computation task types of data from the e-commerce platform and participating parties, and identifying the data sensitivity level using regular expression feature extraction technology, whereby the sensitivity level includes core sensitivity and general sensitivity; Step 2, dynamically recommending suitable encryption algorithms and corresponding encrypted computation methods based on the lifecycle status, sensitivity level, and multi-party computation task type; Step 3, achieving hot switching of ciphertext between different encryption algorithms. This invention, by setting a three-dimensional parameter-driven dynamic encryption recommendation method, solves the problem of poor adaptability between existing encryption algorithms and data characteristics and computational requirements, leading to insufficient privacy protection or low computational efficiency, and achieves precise security protection throughout the entire data lifecycle.

The invention discloses a data life cycle optimization method, apparatus and device, and a computer program product. The method comprises the steps of receiving business data and metadata of the business data; according to the business data and the metadata of the business data, calculating a comprehensive value of the business data by using a preset multi-dimensional value evaluation rule; according to the comprehensive value of the business data, determining a storage strategy corresponding to the business data by using a preset dynamic storage decision strategy; and storing and managing the business data by using the storage strategy corresponding to the business data. According to the optimization method of the data life cycle, through constructing a collaborative mechanism of multi-source data unified access, multi-dimensional value quantitative evaluation and dynamic storage decision, full-link optimization of data life cycle management is realized in a vehicle and road cloud scene, and a closed-loop optimization system of data value-storage resource-service demand is formed. Data-intensive scenes such as intelligent traffic and automatic driving are assisted to realize efficient utilization and compliance operation of data assets.

Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

The application discloses a kind of warm cold storage data life cycle management method and system, it is related to storage data management technical field, the method includes: obtaining business data, business data is written into hot storage system, or directly written into warm storage system;If business data is stored in hot storage system, warm storage system periodically scans hot storage system, and then obtains business data from hot storage system;Through the AI model built-in in warm storage system, multidimensional data analysis is carried out on business data based on data content type and access frequency, and data classification label is generated for business data, data classification label includes hot data, warm data and cold data;Warm storage system is based on data classification label and preset life cycle strategy, carries out the automatic migration of business data between hot storage system, warm storage system and ice storage system, and helps to solve the problem that prior art cannot realize data intelligent classification and automatic life cycle management.

The invention provides a spacecraft general assembly data fusion processing system and method, and the system comprises a dual-core architecture which comprises a general assembly cooperation model module and a knowledge-driven engine module; the hierarchical classification database is connected with the dual-core architecture; the version penetration management module is used for establishing a product version penetration management mechanism based on a preset code; and a rule verification engine for automatically verifying the data based on a preset rule. The method comprises the following steps: carrying out collaborative sharing and intelligent decision making through a dual-core architecture, storing spacecraft general assembly data into a hierarchical classification database for standardized storage, establishing a product version penetration management mechanism, and automatically checking the data based on a preset rule obtained from a knowledge-driven engine module. According to the method, the data normalization and accuracy can be remarkably improved, the full-life-cycle accurate tracing of the data is realized, and the cross-professional collaboration efficiency is improved.

The present invention relates to the field of big data technology, particularly a geological survey, monitoring and early warning system and method, comprising: a data governance module; a data optimization module, which communicates with the data governance module to evaluate the health status of a data lake; provides metadata and data tracking capabilities; executes data management strategies to provide data lifecycle management functions; defines optimization goals and constraints to provide decision support for data management strategies and resource allocation; obtains data value scores through a data value assessment model; an intelligent analysis engine, which communicates with the data governance module and the data optimization module to select corresponding intelligent analysis methods to analyze data in the data lake according to data type; generates an analysis report and compares it with data expectations; dynamically adjusts the weight of the analysis method according to the comparison results; and a deep reinforcement learning decision system, which communicates with the data optimization module and the intelligent analysis engine to jointly ensure the quality and effective use of data.

The invention provides a power distribution cabinet intelligent operation and maintenance full-life-cycle data processing method and system, and relates to the technical field of data processing, and the method comprises the steps: 2, building a multi-dimensional data sampling window based on key state parameters in an original full-life-cycle data set, and building a topological relation between core characteristic parameters in the sampling window; step 3, based on the topological relation, performing structured topological partitioning on the data in the sampling window, and calculating the spatial shape feature similarity of data distribution of each partition; and generating a data quality calibration parameter according to the data distribution characteristics, the association strength and the shape consistency index of each partition. Real-time sensing and intelligent fault prediction of the operation state of the power distribution cabinet are realized, and the operation and maintenance efficiency and the data full-life-cycle management level are improved.

Methods, systems, and computer-readable media for a cloud-based database for spatial data lifecycle management. A spatial database receives elements of spatial data from a plurality of clients of the distributed spatial database. An individual element of the spatial data comprises one or more location values or one or more spatial objects. The spatial database stores the plurality of elements of spatial data using a plurality of storage resources. The spatial database receives a query. The spatial database determines one or more elements of spatial data matching the query from the plurality of elements of spatial data that were stored using the plurality of storage resources.

This invention relates to a secure management method for cross-domain data sharing based on blockchain and CP-ABE. The method includes: symmetric encryption of the original data to obtain ciphertext data; hierarchical CP-ABE encryption of the session key used for symmetric encryption; generation of an encrypted attribute private key by an authorized institution in the target domain based on the mapped attribute credentials; verification of the legality and validity of the encrypted attribute private key by the source domain blockchain; generation of a search trapdoor by the visitor based on the encrypted attribute private key after successful verification; retrieval of the target CP-ABE ciphertext from a cloud service provider using the search trapdoor; decryption of the target CP-ABE ciphertext in a proxy server to generate intermediate converted ciphertext; and final decryption of the intermediate converted ciphertext by the visitor to obtain the original data. This method can construct a verifiable, auditable, and high-performance security management system covering the entire data lifecycle.

This invention relates to the field of cadastral data management technology, and discloses a cadastral data management method, apparatus, device, and storage medium. By acquiring multi-source operation data from the business windows of a cadastral data system, and utilizing a Long Short-Term Memory (LSTM) network based on hardware verification data and standardized temporal operation sequences, a temporal anomaly score is calculated. Considering the attention weight at each operation moment, an enhanced anomaly score with fused attention weights is obtained. Finally, XGBoost is used to consider the impact of sensitive operations while employing a dynamic threshold judgment method to achieve alarms for cadastral data system business window operation anomalies under different states in real-world scenarios. Therefore, this invention improves the accuracy and scenario adaptability of identifying business operation anomalies in cadastral data system business windows, ensuring the accuracy, integrity, and security of cadastral data lifecycle management activities, and supporting the compliant and efficient operation of core cadastral businesses such as land ownership registration, ownership change, and land planning.

The present application relates to the technical field of power system, and more particularly to a data full life cycle management method and system, the minimum value of the power supply of the target micro-grid is obtained, and the power supply threshold of the micro-grid is compared to generate a signal of whether energy storage is possible; based on the energy storage signal, the average change of the power supply of the energy module and the fluctuation value of the power supply of the energy module are obtained, the stability coefficient of the output of the energy module is obtained, the power supply stability state of the target micro-grid is evaluated, and a first energy storage signal is generated; based on the energy storage signal, the average change of the storage power of the energy module and the fluctuation value of the storage power of the energy module are obtained, the stability coefficient of the input of the energy module is obtained, a second energy storage signal is generated, the above energy storage signal is cross-processed to obtain a regulation parameter, and the current micro-grid energy storage is adjusted based on the regulation parameter, the present application improves the timely regulation and management of the micro-grid energy storage in the full life cycle, and ensures the normal and stable operation of the power system.

The present application relates to the technical field of data processing, and discloses a data full life cycle use control method, system, device, medium and program, which solves the problem that the existing access control cannot dynamically and differentially adjust the permission and evaluate the absence according to the data contract and its performance state, data use, data secondary circulation and other factors. The method comprises the following steps: a data use direction provider initiates a request and signs a data contract; a state extraction algorithm is called to extract a state set, generate a data management and use strategy; according to the strategy and the contract, a providing side associated resource, a transmission resource and a using side associated resource are generated, and further, a usable resource is generated; the use of the usable resource is evaluated, and when it is not in compliance, the strategy is fed back and updated. The system comprises a signing system, an authorization system and a data processing system. The present application is suitable for the use control of the full life cycle of data multiple copies, realizes the dynamic differential authorization and full-process use control evaluation of the full life cycle of data multiple copies, and guarantees the compliant use of data.

The invention provides a data storage method and device, electronic equipment and a computer program product, belongs to the field of data storage, and aims to solve the problem that an existing method is difficult to consider storage resource utilization rate and data reliability at the same time. The method comprises the following steps: determining a mapping relationship between a life cycle of data and the number of erasure code check blocks according to a reliability index of a storage system and a preset data persistence requirement; obtaining an initial life cycle of to-be-stored data, determining the number of initial erasure code check blocks corresponding to the initial life cycle according to the mapping relation, encoding the to-be-stored data based on an erasure code algorithm, and storing the encoded to-be-stored data to the storage system; monitoring the remaining life cycle of the stored data, and adjusting the number of erasure code check blocks of the stored data in response to the situation that the remaining life cycle meets a preset condition.

The present application relates to the field of data processing, in particular to an object data life cycle management method, device, equipment and medium, the object data life cycle management method is executed by electronic equipment, and the object data life cycle management method includes: obtaining the first object upload time of the expired object of target bucket, using the time index prefix in the index table, multiple objects related to the first object upload time can be efficiently located, the index information of target object is accurately determined by comparing the second object upload time of these objects with the first object upload time, and the corresponding object data is deleted accordingly, the overdue object can be directly and quickly found according to the object time index to delete, and full scanning is not necessary, not only greatly improve the processing speed and efficiency of life cycle expiration deletion, but also greatly reduce the metadata processing resource occupation, improve the overall read-write performance of storage system.

The application discloses a data full-life-cycle sharing management and control method and system and a medium, relates to the technical field of data management, and comprises the following steps: acquiring a shared channel monitoring data set and a shared data real-time life-cycle characteristic, performing shared behavior compliance detection under full-life-cycle correlation, and acquiring a shared behavior compliance detection result; if the shared behavior compliance detection result is that the shared behavior is not compliant, performing hierarchical blocking management and control on the real-time data sharing channel; if the shared behavior compliance detection result is that the shared behavior is compliant, constructing a shared hidden danger evolution process model, performing dynamic reconstruction adjustment on the real-time data sharing channel, and establishing a shared channel reconstruction mechanism. The application solves the technical problem that, in the prior art, the data sharing process lacks effective management and control on the full life cycle of data, and thus the compliance of shared behavior is difficult to guarantee, and achieves the technical effects of realizing full-life-cycle compliance control of data sharing behavior and improving the safety and reliability of data sharing.