56 results about "Data life cycle" patented technology

The application discloses a multi-modal edge computing unmanned aerial vehicle real-time surveying and mapping data fusion method and system, comprising: modeling based on sensor physical characteristics, constructing a multi-source sensor dynamic time synchronization model; based on an edge computing device, through a spatial registration algorithm under kinematic constraints, dynamically calibrating the spatial coordinate system of the multi-source sensor; based on a multi-modal data fusion algorithm of adaptive weight distribution, acquiring sensor confidence and environment feature dynamic adjustment fusion weight; based on the edge computing device, performing real-time three-dimensional reconstruction, completing feature-level fusion of point cloud and image data at the unmanned aerial vehicle end; constructing a distributed edge cache mechanism, and performing data life cycle management based on an LRU algorithm.The application has the advantages that: the sensor dynamic synchronization model is innovatively constructed, the real-time three-dimensional reconstruction architecture based on the edge computing is constructed, and the spatiotemporal consistency of surveying and mapping data in a complex environment is significantly improved.

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.

According to the privacy computing data processing method in the data element market provided by the invention, the collected data is encrypted, so that the security of the data in the transmission and storage process is ensured; by adopting a safe storage mode, the safety of the data in the storage process is ensured; according to characteristics and requirements of data, privacy protection algorithms including homomorphic encryption and differential privacy are selected, and calculation and analysis of encrypted data are achieved. Healthy development of the data element market is promoted through data sharing and utilization; when data leakage occurs, measures including stopping using related data and informing a data source party are taken in time so as to reduce loss; in a data processing process, data security risk assessment is carried out regularly, and potential risk points and data leakage risks in risk assessment are identified; and when the data life cycle is ended, the data are safely deleted, so that the data are ensured not to be leaked and abused any more.

The invention provides a data life cycle autonomous management method and system based on source data feature perception. The method comprises the following steps: defining a basic attribute of target data in the storage system as a triple theta = (t, l, i); establishing a mapping protocol of the source data and the target data; monitoring the change frequency omega of the source data in real time, and executing the following cycle matching algorithm: setting that the life cycle variable and the change frequency of the source data meet an inverse relation t = k / omega, shortening t to improve the sampling precision of the digital clock when omega is increased, and prolonging t to reduce the sampling precision when omega is reduced; and automatically converting the calculated life cycle variable value into a control instruction. According to the method, the data life cycle can be converted into a dynamically programmable variable, low-power-consumption and high-precision response can be realized through hardware acceleration, or a flexible and low-cost management scheme can be realized through software scheduling.

The invention provides a configurable automatic data archiving method and system based on a storage process, and belongs to the technical field of database data life cycle management.The method comprises the following processes that firstly, an archiving task is initialized, and a configuration table archiving rule is loaded; automatically judging and creating an archiving history table; secondly, dynamically calculating an archiving reference date; archiving conditions are generated, and data archiving and source table cleaning are executed in batches; abnormities are processed through log and transaction control, and data archiving and source table cleaning are completed; and finally, task circulation and exception handling are performed, and configurable and automatic historical data management is realized. The method solves the problems that an existing data archiving scheme is high in hard coding dependency, lacks unified configuration, is high in batch processing risk, is weak in fault tolerance and traceability, and is disordered in historical table management, is suitable for batch backup and archiving of historical data and source table cleaning in a core business system, guarantees the performance of a core table, and meets the requirements of data retention compliance.

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.

The application discloses a cluster data encryption and decryption method, device and system based on double trusted binding and decryption deadline control, relates to the technical field of information security, and comprises the following steps: collecting hardware characteristic information of a cluster terminal node, extracting a certain length of bytes as original machine code after processing; obtaining a dynamic key factor, extracting a specified length of bytes as a data key after processing; splicing the original machine code and certificate validity period information, generating certificate machine code after processing; splicing the original machine code and certificate validity period information, generating a machine envelope key after processing, performing symmetric encryption on the data key to obtain a machine key; generating a key certificate; and encrypting plaintext data by using the data key to obtain data ciphertext. The application introduces a multi-hardware characteristic fusion and multi-level key encryption system, and solves the problems of device identity impersonation, key leakage and uncontrollable data life cycle in a cluster environment.

The invention relates to the technical field of data life cycle management, and discloses an insurance coupon life cycle data tracking management system, which comprises a data acquisition module, a data processing module, a tracking monitoring module, a risk assessment module, a report generation module and an optimization management module, multi-source coupon data are collected in real time through a life cycle event recording unit and a distributed collection unit in the data collection module, and a data format is standardized by using a data preprocessing unit, so that the problems of information loss and distortion caused by transmission delay and interface abnormality can be discriminated in real time, the accuracy and integrity of coupon state data are ensured, and the data processing efficiency is improved. According to the method, data acquisition errors are reduced, external environment variables and user behavior data are integrated in combination with a multi-dimensional analysis unit, risk model parameters are dynamically updated by utilizing an adaptive learning unit of an optimization management module, the card and coupon risk levels can be dynamically corrected in real time in combination with change factors, and timeliness and accuracy of risk assessment are guaranteed.

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.

The invention discloses an intelligent medical data integrated display system based on a block chain, and relates to the technical field of medical block chains, decentralized data management and dynamic access control are realized through a block chain technology, and a patient can define and update an access strategy by using a trusted terminal to ensure data privacy. Attribute-based encryption and zero-knowledge proof technologies are adopted, data encryption storage and anonymous permission verification are achieved, and safe sharing of data is guaranteed. Meanwhile, an emergency access authorization mechanism is introduced, and timeliness and safety in an emergency treatment scene are both considered. In addition, dynamic audit tracking is realized through homomorphic encryption, risks are monitored in real time, emergency response is triggered, and the safety of the data life cycle is ensured. According to the invention, the efficiency, security and privacy protection level of medical data sharing are significantly improved, and the method is suitable for a cross-institution medical cooperation scene.

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.

The invention discloses a database management method and device and a storage medium, and the method comprises the steps: determining the cycle length of a first index based on a data life cycle feature corresponding to the first index; the number of fragments of the first index in a target period is determined, and the target period is a current period or a future period; and creating the first index corresponding to the target period in the database according to the fragment number and the period length. In this way, the resource storage efficiency of the database can be improved.

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.

The application discloses a full-life-cycle security management and control system based on data grading. In the data generation, storage, transmission, use, sharing and destruction stages, the data object content features, access subject information, operation behavior records and environment information are collected to construct a unified life-cycle data object set; the data objects are subjected to feature coding and a granular ball clustering method is used to generate a granular ball set; based on the granular ball structure, sample sensitive features are subjected to compression calculation to obtain a granular ball sensitivity representation; the granular ball sensitivity is subjected to grading by using a shadow set model to divide into a high determination zone, a shadow zone and a low determination zone; the granular ball in the shadow zone is subjected to recursive subdivision to obtain a stable grading result; and a mapping relationship between the granular ball and a security policy is established according to the final grading result, and access control, transmission protection, storage protection, use audit, sharing control and destruction disposal policies are executed in different stages of the data life cycle to realize the graded security management and control of the data full life cycle.

The application discloses a short link generation method and device and a medium, and the method comprises the following steps: determining whether the corresponding long-short link mapping relationship exists in the database according to the long link; if the long-short link mapping relationship exists, returning the short link according to the long-short link mapping relationship; if the long-short link mapping relationship does not exist, converting the long link into a short link code according to the short link code generation rule and the table falling rule; determining whether the short link code record exists in the database according to the short link code; if the short link code record does not exist, generating the short link according to the short link code, determining that the short link is successfully generated, storing the long link and the short link code in the database, and returning the short link to the calling party; if the short link code record exists, generating a new long link by adding m random numbers at the tail of the long link; based on the new long link, the steps 2-4 are repeated until the short link is successfully generated. The application solves the problems of poor short link performance, easy data leakage, lack of data life cycle management and the like.

The invention discloses a cluster data encryption and decryption method, device and system based on dual trusted binding and decryption deadline management and control, and relates to the technical field of information security, the method comprises the following steps: collecting hardware feature information of a cluster terminal node, processing the hardware feature information, and extracting a byte with a certain length as an original machine code; obtaining a dynamic key factor, and extracting a byte with a specified length as a data key after a processing result; after the original machine code and the certificate validity period information are spliced, a certificate machine code is generated through processing; after the original machine code and the certificate validity period information are spliced, a machine envelope secret key is generated through processing, and the data secret key is symmetrically encrypted to obtain a machine secret key; generating a key certificate; and encrypting the plaintext data by using the data key to obtain a data ciphertext. According to the invention, by introducing a multi-hardware feature fusion and multi-level key encryption system, the problems of equipment identity counterfeiting, key leakage and uncontrollable data life cycle in a cluster environment are solved.

The invention discloses an encrypted data fragment storage, retrieval and verification method and system based on a decentralized network, and belongs to the technical field of distributed computing and data security. The method comprises the following steps: encrypting to-be-stored data based on attributes and segmenting the to-be-stored data into a plurality of fragments; embedding a unique hidden watermark associated with a fragment identifier and owner information for each fragment; chaining the fragment hash, the watermark information and the storage strategy as metadata; dynamically scheduling fragmented storage nodes through combination of an intelligent contract and node real-time reputation; a hybrid verification mechanism is adopted, the storage integrity is verified in combination with probabilistic spot check and periodic auditing, and the node reputation is updated in real time according to a verification result; and dynamically adjusting the fragment redundancy and the storage position according to the data access popularity, and realizing differential excitation linked with the data life cycle through the smart contract. The system comprises a client module, a decentralized storage node and a block chain network. According to the method, data security, reliable storage, efficient retrieval and adaptive resource optimization are realized, and the method is suitable for a decentralized storage scene.

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 invention discloses an enterprise data asset value evaluation method and device, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining original data assets of an enterprise, metadata information and feature data of each stage of a data life cycle, constructing a multi-dimensional evaluation system, and calculating an initial data asset value score. Meanwhile, multi-source scene feedback signals such as service operation, auditing and user satisfaction are received, the service scene weight is dynamically adjusted and the initial score is corrected after validity verification, and finally an accurate data asset value evaluation result is output, so that dynamic and accurate evaluation of the data asset value is realized, full life cycle characteristics and multi-source feedback of data are fully considered, and the evaluation efficiency is improved. The accuracy of enterprise data asset evaluation is improved, and powerful support is provided for management, transaction and value mining of enterprise data assets.

The invention relates to the technical field of big data storage and processing, and particularly discloses an intelligent mixed storage and cooperative processing method and system oriented to multi-source heterogeneous mass data. The method comprises the steps that firstly, original data are received and analyzed and normalized, then features are extracted from the normalized data, and the extracted features are stored in a database; and making a real-time decision according to a feature matching rule, writing the data into different underlying databases, and recording position mapping and storage strategies of the data. During query, a query request is subjected to lexical analysis and grammatical analysis, whether the query is single-library query or cross-library query is judged, different steps are executed, and finally, data migration and archiving operation is automatically executed according to a predefined data life cycle strategy. Based on the method, the system comprises a unified data access module, an intelligent routing engine module, a unified query service module, a collaborative computing engine module and a metadata and life cycle management module.

The invention discloses an intelligent memory management and data scheduling method and device for a GPNPU, a medium and equipment, and belongs to the technical field of semiconductors. The method comprises the following steps: dividing an on-chip buffer into at least two sub-regions according to data life cycles of intermediate representations, input data, output data and weights, a size relationship between the data life cycles and a storage space, a data dependency relationship between calculation steps and a distribution time period of a direct memory access storage operation and a calculation operation; acquiring data from an off-chip memory of the GPNPU, and storing the data in the first sub-region; when the data in the first sub-region is calculated, the data is acquired from the off-chip memory in parallel and stored in the second sub-region, and a data calculation result in the first sub-region is stored in the second sub-region; and when the data in the second sub-region is calculated, the calculation unit performs calculation processing by using all the data stored in the second sub-region.

The invention discloses a low-delay high-stability big data component data access method and system, and relates to the technical field of big data processing. According to the invention, the authority management module establishes fine mapping between business software and authorized data resources; the data write-in management module sorts write-in requests according to priorities through task arrangement, ensures that high-value data is written in preferentially, and distributes tasks to nodes with light loads through write-in load balancing; the data query management module realizes low-delay query through data unvarnished transmission, stops abnormal or long-time query through slow query management to guarantee system availability, and directly routes a request to a node where data is located through query load balancing; the data life cycle management module regularly marks and cleans out expired redundant data; according to the invention, the problems of resource contention, performance fluctuation and poor stability caused by disordered access to the big data component by multiple service parties are solved, and efficient, stable and controllable access to the big data component is realized.

The invention provides a data processing system, method, device and equipment and a readable storage medium. The method comprises the following steps: on the basis of a data preprocessing application deployed at an edge node, filtering, encrypting and aggregating source data in a preset time window in sequence; system indexes and logs are collected in real time, time sequence indexes are input into a pre-trained neural network model for anomaly detection, and abnormal data are traced to generate a self-healing strategy; based on a differential privacy engine, injecting Laplacian noise into the aggregated non-abnormal data; analyzing the data processing operation through a data consanguinity tracker, and constructing and updating a data consanguinity map; based on real-time load indexes collected from the edge and the cloud, tasks are dynamically allocated to edge nodes or the cloud and the number of application examples is adjusted. Compared with the prior art, the data processing efficiency is remarkably improved, a safety protection system throughout the data life cycle is constructed, and the system is endowed with intelligent fault diagnosis and self-healing capabilities.

The invention discloses a temperature and humidity data monitoring and alarming system and a method thereof. In order to solve the problems that real-time query and historical analysis interfere with each other and an alarm strategy is solidified in a high-frequency data scene in the prior art, a multi-stage data processing assembly line is designed, data are collected through a Modbus-TCP, and after being analyzed, the data are asynchronously transmitted to a Redis high-speed cache through a memory cache and an MQTT in sequence; and according to the data life cycle, the Redis hot data is filed to the MySQL regularly to realize reliable persistence. The real-time data module and the historical data module are distinguished, and the Redis and the MySQL are in butt joint respectively, so that millisecond-level real-time response and efficient historical query do not interfere with each other. Meanwhile, through cooperation of the independent webpage parameter setting module and the alarm module, remote dynamic configuration and real-time response of an alarm threshold are realized. The problem that data integrity, real-time performance and system flexibility are difficult to consider at the same time is effectively solved, and the overall performance and maintainability of a monitoring system are improved.

The present application relates to a kind of data lifecycle management method and system based on data freeze identification, first query service and second query service of independent configuration are routed to online database and nearline database respectively, and freeze label is added to online data to produce frozen data as the buffer between online data and nearline data, so that business can access historical data in real time without waiting for big data T+1 recovery data under the condition that current database structure is unchanged;Solve the problem of growing online data space, reduce the waste of hardware resources, and ensure stable operation of database;Realize the automatic filing and cleaning of online data, avoid manual operation, reduce the operation risk, and protect the stability of business.

A Data Life Cycle Templatization (DLT) engine and framework which is a plugin-based architecture that allows for a lightweight, modular approach to data management. The DLT framework operates as a lightweight library and by providing pre-built solutions that can be rapidly adapted to specific use cases, the DLT engine eliminates the need for extensive engineering and data science resources for a client. In one embodiment, a DLT engine and framework incorporates DataOPS methodologies and AI algorithms including machine learning, predictive analytics, and LLM-based user interfaces to transform maintenance strategies, optimize supply chains, and modernize data ecosystems.

The invention discloses a low-energy-consumption positioning data management system for a large-scale sensor network, relates to the technical field of data processing, and solves the technical problems that low-frequency data occupies storage resources for a long time and high-frequency data query efficiency is low due to lack of full-link metadata management and automatic data life cycle scheduling. According to the method, positioning drift data is eliminated through Kalman filtering, the data validity is improved, protocols such as scene adaptation BLE5.3 and LoRaWAN are combined, differential coding and quantitative compression are matched, the transmission data volume is reduced, the bandwidth occupancy rate is reduced, high-frequency data are subjected to secondary classification according to real-time performance, high-performance / low-performance nodes are matched respectively, and the access delay of high-frequency core data is controlled at the millisecond level; low-frequency data is subjected to near line HDD-archiving object storage and hierarchical storage, the unit storage cost is reduced, performance and economical efficiency are considered, and node performance is evaluated through response delay and storage capacity two-dimensional normalization; distributed storage is combined with a copy mechanism, and the data storage reliability is improved.

The application provides a data lifecycle monitoring and early warning method and system based on risk analysis, relates to the technical field of data security, and comprises the following steps: defining compliance criteria by constructing an operation scene semantic knowledge graph, analyzing current operation behavior and calculating a semantic risk score, and then identifying cross-stage risk correlation to generate a comprehensive risk value. When the risk value exceeds a threshold value, an early warning instruction containing positioning and repair suggestions is generated, and hierarchical control is performed, thereby realizing accurate and relevant monitoring and active early warning of data lifecycle risks.

The invention relates to the technical field of data sharing, in particular to a method and system for managing heterogeneous directories, comprising the following steps: constructing a reference directory management system and a government affair data reference directory library, the reference directory library being used for storing and managing directory data from different sources and different data structures and providing a full-range directory service, fusion, deduplication and association functions of directory data generated in multiple modes and multiple stages are achieved; the method has the beneficial effects that directory data with different sources and different data structures are stored, managed, treated, circulated and used by constructing the reference directory management system and the reference directory library. An ideal representation model is established according to the essence, correlation, development and evolution process and other information of the catalogue, and catalogue data life cycle management process links are supplemented, perfected and established.

The application provides a data management method based on a relational database and related equipment, wherein a data table primary key is set to include a start time field and an end time field, the start time field and the end time field respectively represent the start and end of a data life cycle, the content of the end time field is matched with a filtering condition, so that the data in the data table obtained by querying according to the filtering condition does not include data with content in the end time field, logical deletion is performed on the data to replace substantial deletion, and the situation of limiting a deletion operation can be addressed.