1473 results about "Storage management" patented technology

The invention discloses a cloud edge collaborative multi-source data fusion safety management and control system for intelligent power distribution equipment, relates to the technical field of intelligent power grids and power distribution automation, and is used for managing and controlling 10-35 kV power distribution equipment. The system comprises an edge data acquisition module, a preprocessing module, a cloud storage management module, a cloud edge collaborative scheduling module, a multi-source data fusion module, an intelligent risk assessment module, a safety control execution module, a safety protection module and a man-machine interaction module. The modules are interacted through a 5G / industrial Ethernet; the acquisition module obtains multiple parameters, the preprocessing module cleans standardized data, the cloud side performs hierarchical storage, the scheduling module allocates tasks, the fusion module integrates data, the evaluation module performs grading risk, the protection module guarantees safety, and the interaction module performs visual alarm. The method improves the power distribution data quality and risk assessment precision, optimizes the cloud edge cooperation efficiency, enhances the safety protection capability, achieves the preventive operation and maintenance of equipment, reduces the fault and power failure time, reduces the operation and maintenance cost, and provides support for the safe and efficient operation of a power distribution network.

The invention relates to the technical field of agricultural product storage management, in particular to an agricultural product storage intelligent management system based on big data, which comprises a multi-source data acquisition layer, an edge processing layer, a cloud processing layer and an execution control layer, compared with the prior art which only depends on manual recording of the picking date or simple visual sampling inspection for judging the freshness, the method has the advantages that the freshness key indexes such as the skin color degree and the texture structure of the agricultural product are synchronously and quantitatively extracted through the deep learning model in the warehousing link of the agricultural product; performing dual cross validation with the registration time to eliminate abnormal samples; the verified freshness reference data and parameters such as temperature, humidity, gas concentration and the like monitored in real time in a corresponding storage microenvironment are fused and input, and a self-adaptive shelf life prediction model based on a dynamic environmental stress factor is constructed; therefore, continuous and accurate quantification and early warning of decay processes of different types of agricultural products under various storage conditions are realized.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a data hierarchical storage method, device, equipment and medium based on access characterization. Writing the data into a matched first target storage device according to a preset device performance corresponding relation; after the data are stored, continuously monitoring subsequent access characteristics of the data, and dynamically judging whether the heat state is changed or not; and if the popularity state is changed and a specific migration condition is met, redistributing the second target storage device and completing data migration operation so as to realize automatic hierarchical storage management of cold and hot data. By acquiring the data access representation and dynamically identifying the popularity state, hierarchical migration of data with different popularity among storage devices with different performances is realized, the resource use efficiency and access performance of a storage system are improved, and the storage cost is reduced.

The invention relates to the technical field of storage optimization, in particular to a data storage management system and method based on big data, and the system comprises an efficiency monitoring module, a medium evaluation module, a decision generation module, a strategy execution module and an effect feedback module. According to the method, input and output delay, a medium health attenuation curve and a bandwidth fluctuation map are collected, a dynamic feature space is constructed, the stability of the system is judged in combination with a Lyapunov index, early warning of performance abnormity is achieved, health indexes such as an erasing cycle, a seeking error rate and a charge retention rate are fused, and the access frequency and the load pressure are combined. Calculating a compensation coefficient, improving heterogeneous medium cooperation efficiency, pre-migration evaluation cost and topology adjustment income, improving resource configuration accuracy, dynamically adjusting hot and cold data distribution and node load, enhancing system scheduling flexibility, comparing throughput, medium wear rate and synchronous delay, and quantifying stability gain brought by configuration change; and a closed-loop process from identification to feedback is constructed.

The invention relates to the technical field of data processing and storage management, and discloses a multi-source index data intelligent storage management method and system, and the method comprises the steps: building a unified data integration and processing channel, and forming a standardized index data set; meanwhile, an enterprise-level index unified management system is established, and a credible index data set is generated; the credible index data set is stored through an optimized storage strategy and a data organization model, a data consanguinity association map is constructed based on data full-link processing, and visual and traceable index data assets are obtained; according to the method, a real-time quality evaluation engine is used for carrying out continuous monitoring and intelligent warning on all links of synchronization, processing, storage and service of an index data set so as to construct a data anomaly detection and repair mechanism, and quality controllable supervision is carried out on the whole processing process of the index data set through a data quality billboard and an anomaly processing workflow; therefore, the intelligent management level and the data quality guarantee capability of multi-source index data processing and storage can be improved.

The invention relates to a construction method and device of a network security knowledge graph, equipment and a storage medium, and the method comprises the steps: obtaining multi-source data of a network security application scene, and carrying out distributed processing to obtain a security data set; performing structured storage management and security knowledge extraction on the security data set to obtain an initial knowledge graph; performing zero-day attack prediction construction based on the initial knowledge graph to obtain potential attack data; performing path fusion association on the potential attack data and the initial knowledge graph to obtain attack chain fusion information; and obtaining real-time event data of the network security application scene, and carrying out dynamic iteration updating on the initial knowledge graph by using the real-time event data and the attack chain fusion information to obtain the network security knowledge graph. According to the invention, the timeliness and practicability of the network security knowledge graph can be ensured.

The invention provides a visualization system capable of synchronously displaying multiple types of data according to time. Comprising a distributed service cluster composed of a micro-service, a user management micro-service, a data transmission micro-service, a data storage management micro-service, a data extraction micro-service, a data visualization display micro-service, a data visualization template management micro-service and a resource monitoring micro-service; the micro-services are packaged in a containerized manner and interact by adopting a lightweight communication protocol, and automatic deployment and dynamic expansion are realized through a container arrangement tool; the system is provided with a basic parameter set with customizable micro-service configuration and a graphical configuration interface which are used for cooperatively generating a system operation environment configuration strategy. The user management micro-service establishes a multi-dimensional authority system based on a role access control model, and records an operation log to realize audit tracking; the data transmission micro-service integrates a plurality of heterogeneous data access modes, carries out transmission process verification and carries out secure channel transmission through the service gateway.

The invention discloses an intelligent data storage method based on a cloud platform, relates to the technical field of data storage management, and is used for solving the problem that cold and hot data identification is lagged. According to the method, the access behavior graph is constructed, the concurrent access coupling weight is calculated, the data object with high access frequency and deep path hierarchy is identified, the structure adjustment instruction is dynamically generated, the access link complexity and index hierarchy redundancy are reduced, the response delay and the popularity prediction error before and after the structure adjustment are combined, and the access efficiency is improved. According to the method, the scheduling efficiency of the hotspot data is improved, the adaptability to the change of a storage access mode is enhanced, the dynamic adjustment capability and the structure response performance are good, and the method can be used for realizing the closed-loop linkage of the access behavior perception, the structure reconstruction and the model optimization. Therefore, the method is more suitable for a large-scale data intelligent management scene under a cloud platform.

The invention relates to the technical field of server research, development and manufacturing, and discloses an enterprise data asset intelligent management system for server research, development and manufacturing. The system comprises a data asset access module used for acquiring initial information of enterprise data assets to be managed; the multi-dimensional feature extraction module is used for extracting content, structure and semantic features of the data assets; the intelligent classification processing module performs multi-dimensional classification processing to obtain a classification result; the dynamic storage management module generates a storage strategy; and the application service output module executes application service output and displays a result. According to the system, efficient access, comprehensive feature extraction, scientific classification, dynamic storage management and convenient application service output of enterprise data assets in the research, development and manufacturing process of the server are realized, and the management level and the utilization value of the enterprise data assets are improved.

The invention discloses an unstructured storage hierarchical strategy optimization method based on machine learning, and relates to the technical field of data storage management, and the method comprises the steps: monitoring a file access event in real time, generating an access log, and extracting a multi-dimensional feature data set; utilizing the trained multi-dimensional decision tree model to distribute a corresponding storage hierarchy for the storage object to obtain a storage hierarchy decision; according to a storage level decision, distributing the storage objects to different storage layers, and carrying out resource configuration and storage operation; monitoring the access condition of the storage object in the new storage hierarchy in real time, and collecting file access performance, storage cost and response time to obtain feedback data; by monitoring the file access event in real time and extracting the multi-dimensional feature data set containing the basic attribute, the access behavior and the context information, efficient response of hotspot data and reasonable utilization of resources are ensured, and the overall performance utilization rate and the cost effectiveness of storage are remarkably improved.

A system and method for implementing a Persistent Cognitive Machine (PCMs) that extends beyond the traditional prompt-response paradigm of artificial intelligence are disclosed. A PCM maintains persistent cognitive processes regardless of external interaction, stores and organizes thoughts in a thought cache, retrieves relevant thoughts based on current stimuli, generates new thoughts through reasoning processes, and curates stored thoughts during periods of reduced external interaction. The PCM includes language and reasoning model components, a thought cache, an executive component, and an embedding system. The PCM remains continuously active, remembers previous experiences, learns from these experiences, creates new thought experiences independently, and initiates interactions without waiting for external prompts. The PCM enters sleep-like states during which it curates its thought cache, generalizes experiences, and performs other memory management functions. Applications may include but are not limited to synthetic cognitive colleagues, strategic war gaming platforms, and personal cognitive assistants.

The invention relates to the technical field of liquid caustic soda concentration regulation and control, and discloses a liquid caustic soda concentration monitoring and automatic regulating system based on Internet of Things and data fusion. A liquid caustic soda concentration data acquisition module of the system is connected with a plurality of sensors through the Internet of Things to acquire spatio-temporal information such as liquid caustic soda concentration, temperature, flow velocity and the like; the edge node operation and maintenance analysis module is used for monitoring operation and maintenance information such as memory, bandwidth and CPU utilization rate of the edge computing node; the data fusion preprocessing module performs standardization processing on the sensor data to generate a data matrix; the liquid caustic soda concentration anomaly detection module is used for extracting fusion features based on the data matrix and identifying abnormal nodes; the concentration adjustment optimization module is used for generating an optimization adjustment parameter set according to the abnormal nodes; the automatic adjusting control module is used for adjusting the valve opening and the pump speed according to the parameter set; and the distributed storage management module is used for storing sensor data and operation and maintenance information. The system realizes comprehensive monitoring and accurate adjustment of the concentration of the liquid caustic soda, and improves the operation stability and reliability of the system.

The provided is an anti-radiation low-latency chip for neural network inference acceleration. The following steps are included: deploying a neural network inference acceleration chip at a frontend of a detector, using a streaming architecture to correspond each pipeline stage to each major neural network layer, balancing a limited on-chip memory resource and support for a large-sized input, and constructing layer parallelism, channel parallelism, and convolution kernel parallelism. This application proposes to deploy an anti-radiation, low-latency, and efficient chip for convolutional neural network (CNN) inference acceleration is deployed at the frontend of the detector to improve an intelligence level of future detector hardware. To this end, a fine-grained streaming architecture, and fine-grained storage management, flexible compression and quantization, and anti-radiation digital chip design technologies are proposed to achieve a high throughput and a low on-chip memory consumption while achieving anti-radiation and low-latency inference.

The invention provides an offshore wind farm operation and maintenance data management system and method based on a block chain, and relates to the technical field of offshore wind farms. Comprising a data acquisition module, a storage management module, a block chain module, an index optimization module and a report generation module. High-efficiency management of hot, warm and cold data is realized through a multi-level storage architecture, credibility and security of the data are ensured by adopting a block chain network, query performance is optimized in combination with adaptive indexing and dynamic fragmentation, and a full-life-cycle operation and maintenance data report is generated. According to the method, the data storage efficiency can be improved, the data credibility is enhanced, the large-scale data management capability is optimized, and scientific decision support is provided for intelligent operation and maintenance of the offshore wind plant.

The invention discloses a video monitoring system based on multi-link redundant transmission and intelligent supplementary transmission, relates to the technical field of communication monitoring, and solves the problems that key frames are lost due to insufficient network link stability, transmission delay is uncontrollable due to bandwidth resource allocation rigidness, and the dependency relationship between complex systems is difficult to optimize in real time. The video monitoring system comprises a link transmission management module, an edge intelligent cache module, a dynamic supplementary transmission control module and an intelligent storage management module, the problem of network disconnection is solved through a camera device in combination with redundant backup of a cache time model, and meanwhile, automatic extraction and complementation are achieved through a protocol, and seamless connection is achieved; through memory redundancy two-stage storage, the hard disk writing speed is improved, and the blocking probability of video data is reduced.

The invention relates to a data storage method, system and device, a chip, a network interface card, equipment, a medium and a program product. The method comprises the following steps: receiving a data packet write-in request, wherein the data packet write-in request is triggered by a storage management module on a host through a memory write operation driven by a virtual storage device; triggering PCIE multicast data packet processing based on the data packet write-in request; and under the condition that a PCIE multicast data packet processing result is multicast hit, the data packet is pushed to a plurality of target ports of the PCIE switch, each target port is connected with a virtual storage device located in a data processing unit, and the virtual storage device is used for writing the data packet into a corresponding memory. The method can improve the overall performance of the system.

The invention relates to the technical field of data fusion, in particular to a textile production traceability-oriented data fusion method and system. The method comprises the following steps: obtaining textile production workshop sensing data and textile production workshop camera data, and carrying out textile production chain data stream data fusion to obtain a textile production chain data stream; performing feature cross calculation according to the textile production chain data stream to generate a processing technology enhanced feature space; performing fingerprinting on the textile production chain data stream to obtain a cross-chain time-space stamp fingerprint set; evaluating the batch production quality of the textile production line based on the camera data of the textile production workshop, and obtaining batch data of a to-be-traced production line and batch data of a to-be-stored production line; evaluating the abnormal batch dyeing optimization feasibility of the production line batch data to be traced; and transmitting the batch data of the to-be-stored production line and the batch data of the to-be-traced production line after the dyeing optimization task to a workshop warehouse management platform. The production efficiency is improved, and resource waste is reduced.

The invention provides an intelligent document management method and system, and the method comprises the steps: obtaining an uploaded file set, processing each file through a text analysis algorithm, an image analysis algorithm and a structured data analysis algorithm, generating a first feature set, and storing the first feature set in a temporary database; obtaining a distribution record from the first permission configuration file, writing the distribution record into an audit database through a log audit module, and generating a first operation traceability chain; according to the first operation tracing chain, a storage management module is called to perform fragmentation processing on the file and upload the file to cloud storage, a first storage index is generated, and the metadata table and the archive position are associated; and according to the second storage index, calling a disaster recovery backup algorithm to perform multi-region copying on the file, generating a first backup confirmation signal, and recording the first backup confirmation signal into an operation and maintenance log. According to the system and the method, intelligent classification, sensitive information protection and whole-course traceable management of the files are realized, the automation level and the safety of document management are improved, and a comprehensive solution is provided for document management.

The invention discloses a storage management method and device of a multi-control storage system, a program product and a medium, and relates to the storage technology, the storage system comprises K controllers, K is a positive integer not less than 2, the storage management method of the multi-control storage system is applied to each controller, and the method comprises the following steps: receiving a write request, and splitting the write request into sub-requests; for any sub-request, judging whether the to-be-written data block corresponding to the sub-request belongs to the duplicated data block stored in the storage equipment or not through an affiliation controller of the sub-request; if not, writing the to-be-written data block into the storage device, and updating the metadata information based on the sub-request; and if yes, updating the metadata information based on the sub-request. By applying the scheme of the invention, the repeated data deletion can be realized in the multi-control storage system, and the K controllers can be utilized, so that the system performance can be effectively exerted, and the load balance can be ensured.

The invention discloses a big data-based finance and accounting data storage management optimization method and system, and relates to the technical field of financial information management, and the method comprises the steps: collecting enterprise financial original data, carrying out the multi-dimensional cleaning and structured preprocessing, recognizing the data type based on a classification model, and generating a metadata label; executing a cold and hot hierarchical storage and load balancing strategy in combination with the access heat and the node resource state; encryption, copying and compression operations are completed before data storage, and a multi-level index is constructed to improve the access efficiency; and finally realizing long-term archiving and system operation and maintenance management. The system comprises a data acquisition module, a preprocessing module, a classification module, a storage optimization module, a data storage module, a data access module and a system management module. According to the invention, intelligent management and efficient storage of massive heterogeneous financial and accounting data are realized, and the data processing capability, security and availability are improved.

The invention discloses an intelligent stationery supply chain optimization system based on big data analysis, belongs to the field of big data analysis, and aims to solve the problem of how to construct a high-precision demand prediction model through real-time and accurate data acquisition and processing to quickly identify and deal with abnormal conditions. The system comprises a data acquisition module, a storage management module, an intelligent prediction module, a mining analysis module and a supply chain cooperation module. The Internet of Things automatically collects supply chain product data, uploads the data to the cloud platform, stores the data in a database after cleaning, and classifies and adaptively compresses the data according to access evaluation indexes. A demand prediction model is constructed based on historical data, periodical and triggered updating is carried out, and the order demand of a product is predicted. According to the method, historical data with frequent access is screened for abnormal mode identification and reason classification, dynamic adjustment and collaboration of supply chains are realized through an information sharing platform, inventory purchase is optimized, and decision support is provided for abnormal modes.

The invention discloses a customer information storage management method and system based on a block chain, and relates to the technical field of distributed data evidence storage, and the method comprises the steps: collecting terminal customer information, generating an encrypted data packet, carrying out erasure coding, and generating an encrypted data fragment set; carrying out distributed storage based on the encrypted data fragment set, and calculating a data availability commitment at the same time; executing availability sampling according to the data availability commitment, and generating a fragment validity proof; based on the fragment validity proof, performing format and validity verification to obtain a to-be-processed proof set; and generating a recursive aggregation proof according to the to-be-processed proof set, and submitting the recursive aggregation proof to the block chain to verify and update a proof storage state. And cryptographic authentication is carried out on the persistent evidence storage state of the data assets, so that the dual beneficial effects of ensuring data privacy and credible evidence storage are achieved.

The invention relates to the field of heterogeneous data storage, in particular to a heterogeneous data storage method and system for a civil aviation data platform, and the method comprises the steps: collecting metadata of each subsystem in the heterogeneous data storage system in real time, inputting the metadata into a pre-constructed metadata unified model, and carrying out standardization processing to generate a global metadata view; establishing a data traceability tracking mechanism according to the global metadata view, and mapping dispersed metadata items and data entity operation records according to the data traceability tracking mechanism to generate a traceable metadata change track; and a metadata quality label is embedded in the metadata unified model, a metadata knowledge graph is constructed according to the metadata change track and the metadata quality label, and a resource allocation strategy of the heterogeneous data storage system is dynamically optimized according to an analysis result of the metadata knowledge graph. The problem of low storage management efficiency caused by difficulty in integrating and tracing metadata in a heterogeneous data storage system in civil aviation data is solved.

The invention discloses a file metadata multi-version storage management method and device, computer equipment and a storage medium. The method comprises the steps of obtaining file metadata; in response to the received generation instruction, generating a snapshot version for the file metadata to obtain a snapshot version set; extracting a first snapshot version and a second snapshot version from the snapshot version set, updating the snapshot version set, and storing the snapshot version set in a memory area; packaging the file metadata, the first snapshot version and the second snapshot version to obtain a coded data structure, and storing the coded data structure in a memory area; storing the updated snapshot version set and the coded data structure in a storage device; querying the memory area according to the query instruction, querying the storage device if the query fails, and returning a query result; by means of the method, decoupling storage of multiple versions of file metadata can be achieved, and the system memory utilization rate and the snapshot version management efficiency can be improved.

The invention relates to the technical field of data storage and management, and discloses a self-adaptive storage capacity adjusting method and system for an industrial-grade solid state disk. The method and the system can intelligently analyze and predict future storage requirements by collecting key performance indexes and user data access modes of the solid state disk in real time, remarkably improve the efficiency of storage management, and can automatically identify behavior modes of storage use and formulate corresponding storage strategies through a machine learning algorithm, thereby improving the efficiency of storage management. According to the self-adaptive method, hot data is ensured to be quickly accessed, cold data is reasonably migrated, so that the utilization rate of storage space is optimized, in addition, the storage performance is further improved and the risk of data loss or damage is reduced by adopting a garbage collection algorithm and a TRIM command, and the self-adaptive method not only improves the response speed and the reliability of storage equipment, but also reduces the maintenance cost, and is suitable for popularization and application. And a more efficient and flexible storage solution is provided for enterprises.

The invention discloses a pseudo-ginseng seed storage method, relates to the technical field of seed treatment, and aims to solve the problems that pseudo-ginseng seeds are difficult to store, easy to lose vigor and severely mildew.The pseudo-ginseng seed storage method adopts a specially-made bionic microenvironment packaging unit to wrap the seeds, the inner layer of the specially-made bionic microenvironment packaging unit contains a moisture slow-release agent, and a bacteriostatic agent and active substances needed by germination can be intelligently released in a staged mode; the outer layer of the seed packaging box is a selective breathable film which integrates humidity and microbial contamination state indication functions and stress buffer characteristics, then the packaged seed is placed in an intelligent regulation and control storage cabin, and the temperature and humidity are accurately regulated and controlled by the storage cabin; gas components (such as low oxygen, high carbon dioxide or ozone) regulation and control, efficient air filtration and an intelligent monitoring and remote management system can be selected, the service life of the seeds can be remarkably prolonged, high germination rate and activity are kept, mildew is effectively inhibited, physiological after-ripening and germination are optimized, and intelligentization, standardization and refinement of seed storage management are achieved.

The invention relates to the technical field of software development, and discloses a development platform and a development method of a visual low-code system, and the development platform comprises a data management module which supports a plurality of database types, provides data backup, recovery, encryption, compression and caching mechanisms, and can realize the storage, management and security protection of data; the intelligent service module is used for providing code generation, error detection and repair and authority management services and integrating data mining and machine learning algorithms to realize optimization of a business process; the application development module supports multiple development modes and multi-user cooperative development, and provides intelligent form generation, intelligent code association and intelligent algorithm library functions; the dynamic display module provides a visual graphic operation interface, supports multi-terminal access and real-time data display, and can dynamically adjust service logic and respond to changes of service requirements in real time. According to the method, the development threshold is obviously reduced, and efficient, flexible and internationalized low-code development is realized.

The invention discloses an AI-based distributed big data intelligent storage management method, and relates to the technical field of storage management. The method comprises the steps of generating an initial feature matrix by obtaining observation data of all storage devices in a distributed storage cluster, and inputting the initial feature matrix into a pre-training life prediction model to obtain residual life; equipment with the service life lower than a first safety threshold value is judged as high-risk equipment, and data of the high-risk equipment is included in a to-be-migrated source data set; the devices with the service life higher than a second safety threshold value are included into a to-be-migrated target device set; and generating a migration plan according to the source and target data sets and executing migration. By predicting the residual life of the equipment, advanced identification and active data migration of the high-risk equipment are realized, the data loss risk is reduced, and the utilization rate of the health equipment and the overall cluster storage resource efficiency are improved.

The invention discloses a warehousing system for waste automobile power batteries, and relates to the technical field of warehousing management, and the system comprises a safety evaluation module which analyzes a feature data set through an artificial intelligence algorithm, and generates safety risk level labels and recommended storage environment parameters of the power batteries; the configuration scheme generation module automatically classifies and dispatches the safety risk level labels of the power batteries to the matched storage areas, configures a temperature and humidity control device according to the recommended storage environment parameters, and generates a dynamic storage configuration scheme; and the storage data recording module is used for carrying out structured integration on the state monitoring data set, the power battery tracking data, the safety risk level label and the dynamic storage configuration scheme by utilizing a multi-source data fusion algorithm to generate a storage data record. According to the invention, accurate classification and prediction are realized, so that the storage condition of the battery is dynamically adjusted without manual intervention.

The invention relates to the technical field of system application, and discloses an online document processing method and system based on structured storage, and the method comprises the steps: splitting content elements of an online document into blocks, and carrying out structured storage as independent objects; querying basic information of the document, loading data according to a document block pageBlockId list, creating a component according to the loaded data, constructing a component tree, and performing document rendering by using a lazy loading technology; the method comprises the following steps: abstracting documents and document blocks as objects, synchronizing the objects to a graphic database, modeling a relationship between the documents and the document blocks as an edge in the graphic database, and converting the relationship between the documents into a graphic data structure; when the document content changes, the storage interface is called to store the changed document block, and the online document is automatically stored, the document content is structurally stored in the block form, dynamic updating of the online document is achieved, the reference relation between the documents is stored and managed, and the relation between the documents is visually and clearly displayed.