989 results about "Block (data storage)" patented technology

The invention provides a block chain-based clinical test data integrity system, and relates to the technical field of data processing, and the system comprises the following components: a multi-source heterogeneous data acquisition and preprocessing module, a multi-level encryption and under-chain data storage module, an alliance block chain network, and an alliance block chain network maintenance and verification shared distributed account book. The distributed account book only stores all digitally signed clinical test data fingerprints, metadata and operation logs, does not store original data, and is linked with the intelligent contract automatic management module, so that the clinical test data cannot be tampered in the whole life cycle from the source to archiving. Hash anchoring, digital signature and decentralized verification mechanisms of the block chain technically eradicate the possibility of unauthorized tampering of data, and ensure scientificity and reliability of clinical test results.

The invention provides an unstructured data storage optimization method and system based on information entropy and a block chain, and relates to the technical field of data storage, and the method comprises the steps: building a block chain storage network to obtain data, recognizing redundant data through dimension reduction, mapping and hyperplane construction, and removing the redundant data based on fuzzy similarity. The to-be-stored data is partitioned, the access frequency is calculated, the consistent Hash algorithm is executed to determine the storage position, the access index and the verification sequence are established, and the data integrity verification is executed, so that the data access efficiency can be improved, and the storage security can be enhanced.

The invention discloses a logistics data credible evidence storage and tracing method based on a block chain, and the method comprises the following steps: collecting original logistics data, and executing data standardization processing; constructing a layered Merkle mountain structure, and calculating to obtain a main root commitment of the layered Merkle mountain; dividing a layered Merkle mountain range structure, and calculating to obtain a windowed subtree root set; configuring a revision branch record structure, generating a collision pair by using chameleon hash, and generating a revision proof record; a multi-index root structure is constructed, a cross-root sharing mechanism is established, and a multi-dimensional tracing index set is generated; and based on the hierarchical Merkle mountain range main root commitment, the windowed subtree root set, the multi-dimensional traceability index set and the revision proof record, executing anchoring consistency verification, and outputting a credible evidence storage result and a traceability verification result. According to the invention, the logistics data evidence storage integrity and the multi-dimensional tracing efficiency are improved.

The invention relates to the technical field of computer data processing, and discloses a data segmentation and query processing system under a distributed architecture. The system comprises a data segmentation module which divides data blocks based on a self-adaptive partitioning algorithm; the distributed storage module is used for distributing data blocks by using a consistent Hash algorithm and dynamically adjusting the distribution of virtual nodes; the index construction module is used for generating a global index table through a multi-layer inverted index algorithm; the query optimization module is used for converting query statements by utilizing a query rewriting algorithm; and the result aggregation module is used for processing the sub-query results by adopting a parallel aggregation algorithm. The system can dynamically process data according to data characteristics, optimizes the data storage and query process, effectively improves the data processing efficiency and query performance of a distributed system, and is suitable for scenes such as big data storage and analysis.

The embodiment of the invention relates to the technical field of data storage, and discloses a cross-K8S cluster configuration change storage method and device based on a block chain, and the method comprises the steps: obtaining a transaction request message passing the compliance verification of an intelligent contract engine, and the transaction request message comprises a configuration change request; obtaining log information of the configuration change request in the transaction request message executed by the target K8S cluster, wherein the log information further comprises configuration change cluster difference information; the log information is stored in a fragmented mode based on the IPFS network, and content hash codes of the log information are obtained; and taking the transaction request message and the content hash code as complete transaction information, and storing the transaction request message and the content hash code in a transaction pool. Decentralized storage is achieved based on an IPFS network fragmentation storage mode, the problem that a centralized database or a log system is maliciously modified or historical records are deleted easily in centralized storage is solved, and auditing integrity and data storage safety are guaranteed.

The invention discloses a data storage method for an artificial intelligence learning mode, and relates to the technical field of computer data storage, and the method comprises the steps: 1, merging a multi-source perception stream into blocks in real time at the edge through monotone serial number writing, so as to provide a replayable time sequence; 2, asynchronous erasure coding is executed on the blocks, Merkel roots are calculated and written into a local cache, and dual guarantee of loss tolerance and integrity is achieved; 3, pushing slices and roots to object storage in sequence according to a network, and calling a time travel interface to solidify an incremental snapshot; 4, the cloud end monitors a snapshot hash event, a serial number chain is written through differential scanning, a gap is reconstructed through slices, an index is refreshed, and continuous consistency is kept; 5, the training process generates a Merkel proof online verification sample, and damaged data are immediately interpolated and repaired and an audit chain is recorded; and step 6, after training is finished, generating a leatherwise list and a frozen root, asynchronously cleaning redundant slices, updating a version table, and finally forming single-fingerprint traceable cost archiving.

The invention relates to the technical field of embedded data storage, and discloses a method and a system for prolonging the service life of a FLASH simulation EEPROM (Electrically Erasable Programmable Read-Only Memory) in a single chip microcomputer, and the method comprises the following steps: firstly, initializing the FLASH, dividing the FLASH into a main data area, a mapping table area, a backup area and a transaction log area, selecting a physical block through a dynamic wear leveling algorithm when data is written, and writing the data into a database; the method comprises the following steps of: starting a backup area or a transaction log area, safely updating a mapping table by using a transaction mechanism, performing cyclic redundancy check when reading data, if the cyclic redundancy check fails, starting cascade recovery from the backup area or the transaction log area, and finally, updating a transaction log to record operation so as to form a closed-loop management process. According to the invention, through a set of dynamic wear leveling and data classification management mechanism, global and uniform distribution of the write load in the storage array is realized, the physical block with the least erasing times is selected during writing, the access frequency of the data can be identified, and cold data which is not changed frequently can be actively migrated.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The invention relates to the field of data storage, in particular to a storage and calculation separation multi-dimensional data block indexing method based on adaptive learning. Comprising the steps of receiving original multi-dimensional data and a query load, and outputting a low-dimensional key set through a first model; the low-dimensional key set and the historical query load generate a partitioning scheme through a second model, and the partitioning scheme performs physical recombination on the original multi-dimensional data to output a plurality of physical data blocks; constructing a learning type index through the physical data blocks and the corresponding partitioning schemes; identifying hot spot and cold spot areas through the query frequency of each node of the learning type index and the real-time query load continuous monitoring index; when the access mode changes, adjusting the index structure, and outputting a self-adaptive index; and quickly positioning and reading the target data block through the self-adaptive index to complete access. According to the method, the index of adaptive learning is provided, and the index structure and the physical data layout are adjusted, so that the query accuracy and efficiency in a complex scene are improved, and the data storage overhead is reduced.

The invention is suitable for the technical field of file management, and provides a file analysis method and system and a vehicle, the file analysis method and system are applied to a file analysis system, the file analysis system runs at a browser end, and the file analysis system comprises the steps that binary header information of a to-be-analyzed file is analyzed according to the byte position, obtained in advance, of the binary header information in the to-be-analyzed file, and the binary header information of the to-be-analyzed file is obtained; binary index position information of the to-be-analyzed file is obtained; according to the binary index position information, reading and analyzing binary index data of the to-be-analyzed file to obtain index data, determining a target data block meeting a preset requirement according to basic information of each data block, and reading the target data block; and analyzing the data in the read target data block to obtain formatted target data. According to the invention, the problem that the cost of cloud data storage and data analysis is increased due to the fact that a large amount of file data is analyzed and stored at the cloud in the prior art can be solved.

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

The invention provides a dual-storage system starting method and device. An architecture in which a master control chip SOC works cooperatively with an external memory DDR, a nonvolatile memory SPI NOR and an SD card is adopted. After the SOC is powered on, a BootROM guide code is executed, a system is initialized, ID Blocks containing a DDR initialization program and an SPL secondary loading program are sequentially obtained and verified from multiple storage media, the DDR initialization program is loaded, DDR is initialized, and the SPL secondary loading program is loaded to the DDR to run. The method comprises the following steps of: firstly, loading an SD (Secure Digital) card, then loading U-Boot firmware, completing initialization of residual hardware and security mirror verification, then detecting and verifying a kernel mirror format and Header information in the SD card, then loading a Linux kernel, an equipment tree and a ramdisk, mounting rootfs to an RAM (Random Access Memory) space, and mounting the SD card as a user data storage space. According to the method, high reliability of system guiding is ensured through the SPI NOR, large-capacity storage and flexible upgrading are achieved through the SD card, and the comprehensive performance of the embedded system in the aspects of reliability, storage expansibility and cost effectiveness is remarkably improved.

The invention discloses a storage system supporting Cache and RAM double-form access and an access method thereof, and the system comprises a data storage array which is divided into a plurality of structured storage sub-blocks; the storage mode register is used for storing a configuration value, and the configuration value is used for dynamically configuring each storage sub-block to work in a Cache mode or an RAM mode, so that the data storage array works in a full Cache mode or a full RAM mode or a Cache and RAM coexistence mode; and the storage access control logic is used for arbitrating the storage access request, mapping the storage access request to the corresponding storage sub-block for read-write access according to the access address and the configuration value, activating the storage sub-block mapped by the storage access request, and inactivating the storage sub-block not mapped by the storage access request. According to the method, dynamic configuration is achieved, access of Cache and RAM forms is supported at the same time, part of or all memory banks can be configured to be Cache or RAM, the advantages of the two memory forms are utilized at the same time, parallel access of a plurality of memory sub-blocks is supported, use is flexible and efficient, and power consumption is reduced.

The invention discloses a data storage method and device and a computer storage medium, and the method comprises the steps: obtaining a system storage capacity, a new request resource capacity and a system used capacity, and obtaining a system capacity utilization rate based on the system storage capacity and the system used capacity; in response to the fact that the system capacity utilization rate reaches a threshold value and the current residual capacity does not meet the new request resource capacity, determining a storage capacity needing to be expanded based on the system storage capacity, the new request resource capacity, the used capacity and a preset target utilization rate, creating an expansion space corresponding to the storage capacity, and updating the system storage capacity; based on the updated system storage capacity, determining data to be stored of each node, and determining a data block to be migrated; migrating the to-be-migrated data block to the target node, and updating the data storage state of the current node; through the method, the change of the storage capacity can be dealt with more intelligently.

The invention discloses an airport operation and maintenance data storage method and device based on a block chain and electronic equipment, and relates to the field of airport operation and maintenance data security management.The method comprises the steps that a first hash value is calculated for collected original operation and maintenance data, and a probe private key is used for conducting double-layer digital signature, transmitting the signed operation and maintenance data packet to an intelligent operation and maintenance system by adopting a preset encryption communication channel, calling a block chain intelligent contract through the intelligent operation and maintenance system under the condition that the integrity verification of the single piece of original operation and maintenance data is passed, and verifying the legality of the double-layer digital signature; and performing structured processing on the original operation and maintenance data passing the signature verification, and storing the original operation and maintenance data to a block chain network. According to the method and the device, the technical problems that data among multi-source heterogeneous systems is insufficient in credibility in a management process of airport operation and maintenance data, the security of data transmission cannot be guaranteed, and data leakage is easily caused in related technologies are solved.

The invention relates to the technical field of data storage optimization, and discloses a solid state disk wear leveling global optimization method and system based on multi-granularity data features. The solid state disk wear leveling global optimization method specifically comprises the following steps that S101, lightweight data feature monitoring points with blocks, units and chips as basic granularity are deployed in a solid state disk controller, and original multi-dimensional data features related to wear are continuously collected and quantified in real time, all collected multi-dimensional data features are converted into dimensionless feature scores which can be transversely compared through normalization processing. According to the method, multi-dimensional feature fusion and normalization processing of three-level granularity of blocks, units and chips of a fixed hard disk are utilized, a prediction model based on a time sequence is combined, a dynamic risk thermodynamic diagram covering the whole disk is generated, a system can accurately position current and future potential abrasion hot spots, and the system reliability is improved. And passive equalization operation is converted into active risk prevention.

The embodiment of the invention provides a data processing method and device for large model parameters, equipment and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: obtaining a quantization matrix and a meta-parameter of a target large model from different partitions of a first data storage area of a memory, reading memory access granularity representing the data processing capability of a processor, and determining the size of a filling area between the insertion meta-parameter and the quantization matrix based on the memory access granularity; dividing a second data storage area different from the first data storage area in the memory, and sequentially writing the meta-parameter, preset filling information matched with the filling area in size and the quantization matrix into the second data storage area to obtain an initial data atomic block; and continuously arranging all the initial data atomic blocks in the second data storage area to obtain a target data atomic block. By optimizing the data storage mode, the memory bandwidth utilization rate of the processor in the reasoning process is improved, and then the data calculation efficiency of the processor is improved.

The utility model belongs to the technical field of servers, and discloses a cloud computing big data storage server which comprises a cabinet body, a memory placing space located in the cabinet body and a hard disk quick pop-up structure assembled in the memory placing space, and the hard disk quick pop-up structure comprises a partition plate and a hard disk support located on the left side of the partition plate. By means of the design of the hard disk rapid pop-up structure, the installation process of the hard disk is greatly simplified, a user only needs to place the hard disk on the inner wall of the hard disk support and then push the hard disk support, the action can promote the movable plate to move backwards, meanwhile, the sliding blocks smoothly move in the sliding grooves, and the hard disk can be rapidly popped up. In the moving process of the sliding block, the stopping block and the limiting plate act together to limit the moving path of the sliding block, and the sliding block gradually slides into the clamping groove along with the continuation of the pushing action, so that the positions of the movable plate and the hard disk bracket are locked, and rapid installation of the hard disk is realized.

The invention provides a hierarchical storage read cache management method and system, a storage medium and a product, and relates to the field of data storage, the method comprises the following steps: dividing all data blocks into three data hierarchies of hot data, warm data and cold data for respective storage according to access popularity, the data storage positions, the storage data proportions and the information recording precision of the three data levels are different; when the cold data is accessed, the accessed target cold data is upgraded to the temperature data, and the temperature data is updated; when the hot data is accessed, the complete metadata information of the accessed target hot data is cached in the memory, and the hot data is sorted and updated again; and when the temperature data is accessed, determining whether to replace the target hot data with the target temperature data or not according to the size relationship between the first historical access time of the accessed target temperature data and the second historical access time of the target hot data with the lowest access heat. By implementing the method, the occupation of memory resources can be reduced when the popularity of mass data is tracked.

The invention discloses a multi-source information fusion safety production risk intelligent early warning and disposal platform, which comprises a multi-source information acquisition module, an information preprocessing module, a multi-source information fusion module, an intelligent risk identification and early warning module, a disposal scheduling module and a data storage and interaction module, heterogeneous data such as sensor monitoring, video monitoring, equipment logs and manual reporting are collected and cleaned and standardized, and then a weighted fusion algorithm and a cross-modal deep learning model are adopted to realize multi-source data fusion; based on a preset risk assessment index system and the trained risk identification model, mapping risk levels by quantizing comprehensive risk scores, generating graded early warning information and pushing the graded early warning information in multiple channels; meanwhile, a disposal scheme is automatically matched, an instruction is issued, a closed loop is tracked, and data security is guaranteed in combination with distributed and block chain storage. Comprehensive perception, accurate identification, intelligent early warning and efficient disposal of safety production risks are achieved, and the intelligent and refined level of safety production management is remarkably improved.

A data storage device tracks information about the ages and read counts of blocks of memory, which are indicative of data retention and read disturb issues, respectively. The data storage device uses a machine-learning model to predict which blocks in the memory are likely to contain errors based on the tracked information, and a read scrub operation is performed only on those blocks. Limiting the number of blocks that are read scrubbed reduces read amplification and reduces the time required for the read scrub process.

The invention relates to the technical field of block chain data storage and traceability tracking, in particular to a document printing and traceability tracking system based on a block chain. Comprising an off-line certificate generation unit used for generating an off-line cryptographic certificate based on technical parameters analyzed from an external printing request; the dynamic anchoring control unit is used for monitoring system state parameters and carrying out dynamic anchoring strategy calculation based on the system state parameters so as to generate an anchoring triggering decision; the aggregation anchoring unit is used for responding to the anchoring triggering decision, aggregating a plurality of offline cryptographic certificates to generate an aggregation root certificate, and broadcasting a block chain transaction containing the aggregation root certificate; and the verification traceability unit is used for acquiring the aggregation root certification on the block chain and executing cryptographic verification in combination with the offline data to obtain a final verification result. According to the invention, the core pain point that the system is unavailable due to the performance bottleneck of the block chain in a high-concurrency scene in the prior art is solved, and the continuity and reliability of services are ensured.

The invention discloses a supply chain collaboration platform based on a hybrid block chain architecture and a data intercommunication method, and relates to the field of data processing. In the system, an edge computing device preprocesses original data; preprocessing the data, encrypting the preprocessed data based on an SM2 cryptographic algorithm, and finally uploading the encrypted data to a block chain network; the block chain network is based on a mixed block chain architecture of an alliance chain layer and a light node synchronization layer, and performs data chaining by adopting a PBFT consensus mechanism; data storage is carried out by adopting encryption fragmentation and a distributed redundancy storage mechanism; meanwhile, data desensitization processing and authority level-to-level management are carried out by adopting a cryptographic algorithm; the block chain network embeds the AI demand prediction model into a smart contract, and dynamically adjusts a production plan and logistics scheduling; the scheduling optimization module feeds back an optimized scheduling result to the smart contract based on a multi-objective optimization and closed-loop feedback mechanism; according to the invention, the whole-process data uploading and dynamic optimization of the supply chain can be realized.

The invention relates to the technical field of distributed storage, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data storage method, device, equipment and medium based on partition identifier mapping, and the method comprises the following steps: receiving a file and dividing the file into data blocks to generate block identifiers; generating a partition identifier based on the file identifier and the block identifier through hash mapping; creating a partition disk pack mapping table and writing an initial relationship; querying the mapping table to obtain a disk group, and writing the data block into a physical disk to generate a copy; monitoring the health of the disk, keeping the partition identifier and the block identifier unchanged when a fault occurs, and updating the mapping relation to a new disk group; and receiving a read-write request of the target partition identifier, querying the mapping table to obtain the target disk pack, and executing access. According to the method, the partition identification and the block identification are kept unchanged, fast fault tolerance is achieved only by updating the mapping relation, metadata updating expenditure is reduced, bottom layer change is shielded through partition identification routing, and access transparency and high availability are achieved.

The invention discloses a cross-border tax compliance management system based on a block chain and artificial intelligence, and relates to the technical field of tax management, and the system comprises a block chain data storage module which is used for storing tax data in cross-border transactions; the intelligent contract execution engine module is used for automatically adjusting a calculation rule and automatically executing calculation, declaration and payment processes of cross-border transaction tax data through an intelligent contract; the artificial intelligence compliance identification module is used for analyzing cross-border transaction data and identifying potential tax compliance risks; the multi-language and multi-regulation support module is used for holding multi-national tax regulations and automatically identifying tax requirements of different countries; the real-time monitoring and early warning module is used for monitoring the tax compliance condition in the cross-border transaction in real time and giving an early warning in time when an abnormality is found; and the user interface and report generation module is used for providing a friendly user interface and automatically generating a tax compliance report. According to the invention, automatic management and risk early warning of tax compliance can be realized.

The invention relates to the technical field of data storage, in particular to an enterprise financial data security storage management system and method based on the Internet of Things. The method comprises the following steps: acquiring an enterprise financial data transmission log based on the Internet of Things, and performing data cache chain node trust anchoring to obtain trust authentication equipment data; according to the data of the trust authentication device, analyzing a hop trust transmission node to obtain a hop trust transmission map; performing structured enhancement by using the trust authentication device data to obtain to-be-encrypted bill data; chain side security consensus packaging is carried out on the to-be-encrypted bill data to obtain a verifiable consensus block; performing multi-label traceability control filing on the verifiable consensus block to obtain a traceability label filing body; and performing jump point transmission on the traceability label archive body to an enterprise private database by using the jump point trust transmission map so as to execute a data archiving task. According to the invention, the stability and security of enterprise financial data storage are significantly enhanced.

The invention provides an encrypted data storage and key management protection method for a blue-ray disc, which relates to the technical field of data storage security and comprises the following steps of: performing multi-frequency data block division by extracting access time sequence characteristics of original data, calculating a multi-dimensional entropy value to determine an encryption algorithm parameter and a key; and extracting physical characteristic signals of the blue-ray disc to obtain sector defect distribution and evaluate reliability, thereby realizing optimal mapping storage of encrypted data and a multi-dimensional reliability sector group. According to the invention, the data storage security is effectively improved, the key management mechanism is optimized, and the physical characteristics of the blue-ray disc are fully utilized to enhance the data protection capability.

The invention discloses a water conservancy informatization management system based on a digital twinborn model, and relates to the technical field of water conservancy management, and the system comprises a block management module which is based on the equipment, terrain and hydrological information data of water conservancy facilities, management blocks are divided according to the equipment, terrain and hydrological information data characteristics of the water conservancy facilities, computing equipment is locally deployed in each management block, and the local computing equipment of the management blocks is utilized to distribute management computing tasks according to the load capacity. Management blocks of equipment, terrains and hydrological environments of water conservancy facilities are divided into a plurality of gridding node units through the block management module, data storage and calculation tasks are independently undertaken through independent monitoring management of each management block, the data load pressure of a single central server is reduced, and the management efficiency of the water conservancy facilities is improved. Response delay and fault risks caused by overload of the central server are avoided, and stable operation of the water conservancy informatization management system is guaranteed.

A method of storing data is provided. The method includes receiving a plurality of data blocks provided to a hyperscaler system. The method also includes determining a corresponding property for each data block of the plurality of data blocks. The method further includes identifying a set of data blocks from the plurality of data blocks. Each data block of the set of data blocks is associated with a first property. The method further includes storing the set of data blocks in a first zone of a zoned storage system, based on the first property.

The invention relates to the technical field of computers, and discloses a distributed block storage system performance optimization method and system.The distributed block storage system performance optimization method comprises the steps that in response to the data storage requirement of a user, the access frequency of data to be stored is analyzed to conduct dynamic blocking, and multiple pieces of block data are generated; dynamically distinguishing data storage nodes into master nodes and slave nodes according to the load state of each node in the storage system; according to the access frequency, writing each piece of block data into a corresponding master node, and copying a data increment corresponding to each master node into a corresponding slave node to obtain an updated master node and an updated slave node; in response to a data reading demand of a user, analyzing access relevance between data, and fusing the updated slave nodes to read corresponding data; according to the method and the device, the data is dynamically partitioned and hierarchically stored, the pressure of the overload main node can be shared, the load condition between the data storage nodes is balanced, and the system performance is optimized.