2628 results about "Storage management" patented technology

The invention discloses an equipment fault intelligent question-answering method and system based on knowledge graph enhancement. The method comprises the following steps: constructing a knowledge graph of a professional field; developing a natural language processing module; designing an enhanced generation model; designing an interactive question and answer interface; the system comprises a user interaction layer, a language understanding processing layer, a knowledge graph query reasoning layer, a question and answer generation layer and a data storage management layer. Through the natural language processing technology, the system can quickly understand the query intention of a user, and the manual information input and interpretation time in traditional fault diagnosis is shortened, so that the overall fault response speed is increased; the large language model has deep learning and generalization capabilities, various complex and unknown fault conditions can be processed, accurate diagnosis and operation suggestions are provided, and the potential capability of the system for processing new fault types is enhanced.

The invention relates to an intelligent computing power and storage scheduling method and system for a multi-service system, and belongs to the technical field of intelligent computing resource scheduling and storage management, and the method comprises the steps: collecting the real-time monitoring data of each node in the multi-service system, and carrying out the feature extraction; predicting a computing power demand value and a storage demand layering strategy in a future preset time window; dynamically correcting the prediction result, and generating a dynamic resource demand table and a cross-service priority weight matrix; generating a task allocation scheme through a hybrid scheduling algorithm; generating a storage data migration instruction through a storage scheduling engine; executing calculation node capacity expansion, task migration and storage data redistribution operation; and based on the executed latest state snapshot of the resource pool and the scheduling failure case library, updating a weight parameter of the time sequence prediction model and a scheduling strategy rule library to obtain an updated strategy version, and applying the updated strategy version to a next scheduling period. According to the invention, efficient scheduling of resources can be realized in a multi-service system environment.

The invention discloses an optical fiber data storage management system and method based on big data, and relates to the technical field of storage management. According to the optical fiber data storage management system and method based on big data, the spatial-temporal characteristics of the historical access records are extracted through the LSTM network, the data heat value is calculated in combination with the time decay function, and the thermal distribution diagram of the data blocks is generated. And based on the thermal distribution map, constructing a storage resource allocation model by using a deep reinforcement learning algorithm, and dynamically adjusting the optical path priority and copy distribution. A data prefetching strategy is optimized through the spatio-temporal joint index and the vector similarity index, and a high-frequency data cache index distribution diagram is generated. And comprehensively considering the data access delay, the index hit rate and the copy migration frequency, obtaining an optical fiber storage efficiency index, and adjusting a storage strategy according to the index. According to the method, the dynamic scheduling and utilization efficiency of data storage resources is effectively improved, the data access delay is reduced, the copy management is optimized, and the overall performance of a storage system is remarkably improved.

The invention discloses a big data stream-oriented memory cache storage system, relates to the technical field of memory cache storage, and is used for solving the problem of poor memory cache storage management. According to the method, the access frequency, the timeliness and the service importance of the data are evaluated, the priority ranking model is constructed, the cache strategy is optimized based on the ranking result, the data storage and scheduling efficiency is improved, the write-in and read operation of the memory cache is adjusted in real time in combination with the flow prediction model, memory overload is effectively avoided, and the data storage and scheduling efficiency is improved. According to the method, non-emergency data writing is delayed by adopting a delayed writing and lazy loading strategy, data is loaded as required, meanwhile, a cache elimination mechanism is designed according to data priority, access frequency, survival time and size, data migration is carried out when a cache space is insufficient, reasonable resource allocation is ensured, and through a multi-level and dynamic cache management scheme, the cache management efficiency is improved. Therefore, the system performance, the resource utilization rate and the response speed are remarkably improved, the data processing efficiency is optimized, and cache conflicts are reduced.

The invention provides a method and system for obtaining optimal parameters of a machine tool based on an industrial mechanism AI data model, belongs to the technical field of industrial machine tool application, and solves the problems that an existing machine tool cannot obtain the optimal parameters in real time, autonomous intelligent control of the machine tool cannot be achieved and the like. Comprising the following steps: multi-dimensional high-frequency data fusion acquisition and monitoring; performing data synchronization and storage management; analyzing and mining data; mixing intelligent decision engines; constructing an industrial knowledge base and a model base; training an industrial knowledge base and a model base; intelligent decision making and optimization; equipment is automatically controlled; user visual interaction is carried out; carrying out on-line adaptive parameter optimization; continuously optimizing and iterating; and the production business system works cooperatively. According to the method, online self-adaptive parameter optimization can be realized, a closed-loop control system with rapid sensing, intelligent decision-making and accurate execution capabilities is constructed, the method develops in the direction of sensing-decision-execution full-link autonomous optimization, and finally autonomous intelligent control of the machine tool is realized.

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 provides a hazardous waste storage intelligent management system and method, and the system achieves the cooperative work of a multi-level sensor network, an intelligent data processing module, an automatic storage scheduling module, a grading early warning module, a full-life-cycle information tracking system, and a remote monitoring module. And real-time monitoring, risk early warning, automatic scheduling and whole-process tracking of the hazardous waste storage environment are realized. The sensor network collects data in a warehouse in real time, and the intelligent data processing module carries out analysis based on a machine learning algorithm and dynamically adjusts a risk early warning threshold value. The system triggers an early warning signal according to an analysis result, the storage position and the ex-warehouse sequence of the hazardous waste are optimized through an automatic scheduling module, the full-life-cycle information tracking system records operation data of the hazardous waste through RFID and a block chain technology, the non-tampering performance and traceability of the data are ensured, and the safety of the hazardous waste is improved. And the remote monitoring module allows remote operation of the system through the cloud platform. The system improves the safety, intelligence and automation level of hazardous waste warehouse management.

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 discloses an enterprise registration archive sharing and shareholder right-of-knowledge guarantee system based on full-chain data tracing and auditing, and relates to the technical field of archive management. The system comprises an identity authentication module, an archive storage management module, an archive sharing module, a shareholder right of knowledge guarantee module, a comprehensive statistics module and a system security module. According to the system, full-chain tracing of archive data is realized by utilizing a block chain technology, all change operations are completely recorded, and the transparency of archive management is remarkably improved; the security and authenticity of the shared archive are ensured through electronic signature, watermark and two-dimensional code anti-counterfeiting technologies; shareholders can track stock equity states and change records of enterprises in real time and know enterprise change information in time through a dynamic notification mechanism, so that the right of knowing of the shareholders is comprehensively guaranteed; through dynamic encryption, authority management and abnormal behavior detection, the security of file storage and access is ensured, and data leakage and tampering are effectively prevented.

The invention discloses a data management method based on a distributed storage system, relates to the technical field of data management, and is used for solving the problem of storage management behavior strategy mismatch. On the basis of dynamic perception of data access behaviors in a distributed storage system, a behavior feature vector fusing an access mutation rate, periodicity and an access span is constructed, behavior pattern recognition and strategy structure generation are completed, optimal deployment and hierarchical storage of copies are achieved through node resource state perception and construction of a cost function, and the strategy structure generation efficiency is improved. And after compression and consistency configuration are executed and the strategy falls to the ground, the compressibility and the offset trend of the behavior path are analyzed, scheduling management information is extracted, and a stable execution or strategy adjustment signal is generated, so that the problem of strategy execution mismatching caused by behavior perception deficiency in the distributed storage system is reduced, and the strategy execution efficiency is improved. The strategy closed-loop control and the resource scheduling optimization under behavior driving are realized, so that the data management efficiency and the strategy adaptation capability of the distributed storage system are improved.

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 disclosed cyber threat detection and deception system leverages metadata information collected by the data storage management system. Using the metadata collected by the data storage management system, the cyber threat detection and deception system analyzes that metadata to detect any anomalies. Once suspicious or abnormal behavior is detected in an asset, the cyber threat detection and deception system creates and deploys a cyber deception plan for that asset. The cyber deception plan is implemented by way of deploying sensors or emulation traps in any number of cyber-threat appliances within the data network. Lures or tokens are configured and deployed on the suspected assets themselves to redirect attackers to the emulation traps.

The invention relates to the technical field of large models, in particular to a large-model-oriented reasoning service system and an inter-stage resource adjustment method, and the system comprises a pre-filling processor and a decoding processor; a computing resource controller implementing computing resource control of the prefill processor and the decode processor based on a multi-process service, the multi-process service allowing a target number of streaming multiprocessors to be designated to a prefill process representing an entity of the prefill processor and a decode process representing an entity of the decode processor; and the unified storage manager is used for managing the storage utilization rate of the system and the storage access of the pre-filling processor and the decoding processor to the large model weight and the KV cache. Therefore, the problems of delay interference, low storage efficiency and the like between pre-filling and decoding in related technologies are solved.

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 discloses a large-scale sample storage management method and system based on a multi-dimensional label system. The method comprises the following steps: acquiring large-scale sample data input, and establishing a multi-dimensional label system for each piece of sample data based on a sample source, sample quality and sample feature dimensions; performing automatic auxiliary identification and classification and quality evaluation on the sample data, generating a multi-dimensional label for the sample according to a multi-dimensional label system, selecting a proper storage layer according to the multi-dimensional label of the sample, and storing the sample in a specified storage layer after the storage space is optimized; and returning a retrieval result according to a retrieval condition input by a user, and distributing the retrieved sample to a required task or analysis scene according to a requirement. According to the method, by establishing the multi-dimensional metadata labels, retrieval, positioning and management of the samples can be completed quickly and efficiently. Meanwhile, automatic auxiliary classification and recognition of the samples are achieved, and the data management efficiency is improved.

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

The invention provides an intelligent medicine storage management method and system based on digital twinning. The method comprises the following steps: firstly, generating a virtual storage space, embedding a binding relationship between a medicine batch number and a space coordinate, and then collecting temperature gradient distribution data, humidity fluctuation data and illumination intensity time sequence data of each medicine storage unit in a physical medicine warehouse; the method comprises the following steps: acquiring three kinds of data, mapping the three kinds of data to corresponding coordinate positions in a virtual storage space, generating a three-dimensional thermodynamic map based on temperature gradient distribution data and storage environment standard parameters associated with drug batch numbers, identifying an abnormal area, and reversely positioning a corresponding drug storage unit in a physical medicine warehouse according to the coordinate position of the abnormal area. The directional ventilation device is controlled to adjust the airflow circulation path of the medicine storage unit, a batch medicine migration path planning scheme is generated, the technical scheme provided by the invention realizes fine management and real-time monitoring of the medicine storage environment, and the safety and efficiency of medicine storage are significantly improved.

The invention discloses a distributed data storage management system and method, and relates to the technical field of distributed storage, and the method comprises the steps: obtaining an access log, metadata and content features of each node; data object attributes are judged, cold data are marked, and cold data distribution nodes are positioned; the method comprises the following steps: dividing cold data into permanent, periodic and sudden basic access modes through first-layer classification; generating a composite classification label through second-layer classification; matching a predefined strategy matrix according to a classification result, dynamically selecting a compression algorithm and a storage medium, and allocating bandwidth; operation auditing, compliance verification and exception handling are realized through the smart contract; and collecting strategy execution indexes in real time, and adjusting classification model parameters and resource scheduling weights. Through an intelligent cold data judgment and classification mechanism, cold data is accurately identified and managed, occupation of storage resources is reduced, and the overall utilization rate of the storage resources is improved.

The invention discloses a trusted data flow evidence storage management system based on a block chain, and relates to the technical field of data communication. The intelligent management platform is in communication connection with an evidence storage data acquisition module, an evidence storage generation module, a node priority analysis module, a flow scheduling module, a data evidence storage management module and a monitoring maintenance module, and all the modules are in electric signal connection. According to the method, the integrity and authenticity of the data in the circulation process are ensured through tampering resistance of the block chain, circulation of each data is recorded on the block chain, an open and transparent data account book is formed, the source, the flow direction and the change history of the data become traceable, the circulation path and the state of the data are clarified, and in addition, the data circulation efficiency is improved. And through log recording and transaction processing condition analysis functions, the transparency of the data circulation process is further enhanced, so that the use of the data is more standard.

The invention relates to the technical field of solid waste monitoring and management, in particular to a full-life-cycle management method and system for solid waste. The method comprises the following steps: collecting waste source monitoring data based on a sensor, performing distributed storage management according to the waste source monitoring data, and constructing a solid waste real-time management database; according to the solid waste real-time management database, waste recovery types are predicted one by one, dynamic sorting control treatment is carried out, and therefore a dynamic solid waste sorting strategy is generated; obtaining a solid waste recovery processing log; carrying out urban garbage disposal recycle bin node identification on the solid waste recycling log, and constructing an urban recycle bin distribution map; and identifying positioning information of each waste source according to the waste source monitoring data, and making a dynamic solid waste transportation decision based on the distribution map of the urban recycle bin to generate a dynamic solid waste transportation strategy. According to the invention, the solid waste recovery efficiency and real-time monitoring management are improved.

The invention discloses a video monitoring data storage management system based on big data, and particularly relates to the field of data analysis, comprising multi-source heterogeneous data acquisition, cross-layer feature fusion calculation, comprehensive index fusion, collaborative decision generation and adaptive regulation and control execution. Through multi-level data synchronous acquisition and cross-domain feature fusion, a three-dimensional tensor structure is constructed to eliminate dimensional difference, global state perception of coding, transmission and storage is realized, local optimization limitation caused by a traditional data island is solved, a dynamic weight adjustment model automatically matches an optimal strategy based on a three-order collaborative management coefficient calculated in real time, and a dynamic weight adjustment model is improved. The joint elastic regulation and control of coding parameters and network protocols and the dynamic reconstruction of storage resources are realized, and the defect of response lag of a static threshold mechanism under burst traffic is overcome.

The invention discloses an intelligent monitoring system for a village and town rain and sewage pipe network, which relates to the technical field of village and town rain and sewage management and control and comprises an equipment construction module, a data acquisition and transmission module, a storage management module, an analysis processing module, a display output module, a remote supervision module and an intelligent supervision module. According to the intelligent monitoring system for the village and town rain and sewage pipe network, monitoring point positions can be reasonably increased and monitoring equipment can be installed and deployed according to actual conditions of village and town areas, so that the running state and sewage discharge conditions of the pipe network can be comprehensively reflected, and meanwhile, an efficient data transmission and processing flow is established; the accuracy and reliability in the monitoring data transmission process are improved through a verification mechanism, meanwhile, the monitoring data are analyzed and mined by means of big data, cloud computing and other technical means, decision support is provided for managers, intelligent and automatic monitoring of a rain and sewage pipe network is promoted, and the management efficiency is improved. The problem that manual monitoring is low in working efficiency and prone to errors is solved.

The invention provides a multi-channel signal acquisition and analysis system for safe navigation of a ship, and relates to the technical field of signal acquisition, and the system comprises a signal acquisition layer which comprises signal acquisition equipment and a signal conditioning module which comprises a programmable filter, a gain amplifier and an analog-to-digital converter; the signal processing layer comprises a logic control module, and the logic control module adopts an FPGA (Field Programmable Gate Array) and a reconfigurable data flow pipeline to carry out synchronous signal processing; the data analysis layer comprises an edge processing unit, an analysis algorithm library and a real-time decision unit; and the control and communication layer comprises a system control unit, a data transmission module and a storage management unit, the system control unit performs working mode management according to an instruction of the upper computer and is provided with a resource scheduler and a power consumption manager, and the data transmission module is provided with a multiple access transmission controller, a data compression unit and an encryption transmission module. According to the invention, comprehensive integration from signal acquisition to data analysis is realized, and the safe navigation capability of the ship is greatly improved.

The invention discloses an inventory monitoring management system for a silica gel material storage center, and relates to the technical field of intelligent storage management. According to the inventory monitoring and management system for the silica gel material storage center, real-time and historical inventory data of silica gel materials are collected and stored through the inventory data module, and the inventory shortage index is analyzed by utilizing the inventory detection module in combination with a Markov chain, so that the inventory risk is evaluated. The inventory prediction module predicts future inventory demand and a replenishment trend chart based on an LSTM neural network model, and optimizes a replenishment decision; the abnormity monitoring module generates an inventory abnormity index through the inventory shortage index and the future demand quantity, and early warns abnormal conditions in time. And the inventory distribution module dynamically distributes the inventory of each warehouse according to requirements by using an inventory optimization equation, so that reasonable allocation of resources is realized. The intelligent silica gel material inventory management is realized, and the monitoring efficiency and the response speed are improved.

The invention relates to the technical field of Internet of Things, and discloses a data management method, device and equipment, and the method comprises the steps: obtaining original multi-source data of each business, obtaining an encrypted data package based on an encrypted transmission channel and the original multi-source data, writing the encrypted data package into a distributed account book, obtaining a target evidence storage record, and storing the target evidence storage record in a database; and managing the original multi-source data based on the target evidence storage record and a digital twinning algorithm. According to the method, the data is written into the distributed account book and combined with the digital twinning algorithm, a'evidence storage-simulation-management and control 'closed-loop management system is formed, the requirement of the power industry for full-life-cycle supervision of the data is met, the characteristics of an encrypted transmission channel and the distributed account book are fused, and multi-dimensional protection of the data in the links of collection, storage and management is achieved.

The invention relates to the technical field of cross-border e-commerce storage, in particular to a deep learning cross-border e-commerce storage management system, which comprises a data acquisition processing unit, a space-time diagram neural network analysis unit, an alliance management scheduling decision unit and an equipment execution feedback unit, a data acquisition and processing unit acquires and preprocesses storage data, a space-time diagram neural network analysis unit constructs a space-time diagram and predicts task requirements and equipment states, an alliance management scheduling decision-making unit constructs a dynamic alliance and generates a scheduling instruction, and an equipment execution feedback unit receives the instruction and autonomously adjusts a strategy. The problems of unreasonable equipment scheduling, poor collaboration and resource waste in traditional cross-border e-commerce warehouse management are solved, intelligent scheduling and collaborative operation of the warehouse equipment are realized, and the warehouse management efficiency and the resource utilization rate are improved.

The invention discloses a storage intelligent monitoring and checking integrated system and method based on multi-technology fusion, and particularly relates to the technical field of storage monitoring, the system obtains environmental parameters and cargo data in real time through a multi-source data acquisition module, carries out cargo identification and segmentation by using YOLOv7 and Mask R-CNN, and carries out storage monitoring and checking on the cargo. A three-dimensional model is constructed in combination with a Poisson surface reconstruction algorithm to calculate volume weight, an environment anomaly analysis module generates an early warning signal through dynamic threshold and influence level evaluation, and an intelligent decision module fuses data to realize quantity anomaly alarm and multi-level response and outputs a three-dimensional visual inventory report. According to the method, the problems of multiple blind areas, low checking efficiency, slow abnormal response and the like of traditional storage monitoring are solved, full coverage of environment monitoring, high accuracy of goods identification, low error of volume calculation and intelligence of abnormal handling are realized, and the automation and digitization levels of storage management are remarkably improved.

Systems and methods facilitate saving data storage through data storage management of storage location(s) by training, via an iterative training and testing loop, a predictive model using training data to detect data redundancies from two or more datasets stored to data storage location(s), the training includes testing the predictive model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of the target variable, where the predictive model is trained to identify data similarities among the two or more datasets. Based on any error in predicting the target variable being less than a predetermined level, the predictive model is deployed and applied to at least two datasets to quantify a percentage of similarity among the at least two datasets. If it is determined the percentage of similarity surpasses a predefined threshold percentage, then an electronic notification is transmitted.

The invention relates to the technical field of data center storage management, and discloses a data center storage space dynamic monitoring and early warning method comprising the following steps: S1, collecting and monitoring operation state data of a data center storage device in real time; s2, based on the collected historical storage data, predicting a future storage demand through a deep learning model; s3, based on the storage demand prediction result, dynamically scheduling the storage resources by adopting a reinforcement learning algorithm, and automatically adjusting the configuration of the storage device to optimize the use of the storage resources; and S4, when the storage device has a potential fault, performing early warning by using the fault prediction model. Through deep learning storage demand prediction, reinforcement learning resource scheduling optimization and intelligent fault prediction, the configuration efficiency of data center storage resources and fault early warning accuracy are improved, the storage cost and performance are optimized, and efficient and stable operation of the system is ensured.