790 results about "Storage efficiency" patented technology

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The invention belongs to the technical field of computers, and provides a video display and storage method and system based on a wayland protocol and a storage medium in order to solve the problems that an existing wayland video system is high in rendering delay, high in CPU load, low in storage efficiency and poor in cross-platform compatibility. Video frames are directly written into an Overlay layer through DRM-KMS and mixed by bypassing a synthesizer, meanwhile, CPU memory amplitude overhead is eliminated through zero copy transmission, an ISP-GPU-VPU direct transmission assembly line is constructed, full-link DMA-BUF direct transmission is achieved, and delay is greatly reduced; iSP preprocessing, GPU shader scaling and VPU coding are all executed by special hardware, so that the load is reduced, and the multi-path processing capability is improved; vPU dynamic code rate compression is utilized, segmented storage is carried out according to events / time, metadata is synchronized to a database, redundant storage is avoided, and space is saved.

The invention discloses an intelligent warehouse management method and system for hazardous wastes, and the method comprises the steps: carrying out the real-time monitoring of the state of an object in a hazardous waste warehouse through a deployment sensor network, obtaining the basic data of the position of the object and the environment parameter, and transmitting the basic data to a central processing platform, thereby obtaining a preliminary data set; performing integration processing on multi-source information by adopting a data fusion technology according to the preliminary data set, eliminating redundancy and noise, and determining a standardized data record; aiming at the standardized data record, dynamically analyzing the distribution of goods locations in the warehouse by utilizing a path optimization algorithm, and if the change of the occupation state of the goods locations is detected, adjusting a goods location allocation scheme in real time, and outputting an optimal storage location combination; according to the optimal storage position combination, a dynamic route planning scheme is generated in combination with warehouse layout information, and the optimal moving track of the operation equipment is obtained; according to the invention, intelligent management of the hazardous waste warehouse is realized, and the storage efficiency and safety are improved.

The invention discloses an unsupervised semi-pairing cross-modal retrieval method and system based on deep learning, relates to the field of artificial intelligence, and is used for solving the problems of annotation data dependence, asymmetric semantic association and high-dimensional storage efficiency. According to the method, a double-branch visual encoder and a dynamic prompt text encoder are combined, dynamic weighting of visual-text features is achieved through gating cross attention, and modal redundancy interference is restrained. An enhancement strategy is generated through low-frequency semantic guidance, and the long-tail word coverage rate is increased; a dual-stage quantitative hierarchical index is constructed, coarse-grained clustering and fine-grained product quantitative compression feature storage is adopted, and million-level data real-time retrieval is supported. A degradation aware increment maintenance mechanism monitors data distribution offset through a KL divergence threshold, and triggers index reconstruction to maintain long-term update precision. According to the method, limitation of a traditional strong pairing model is broken through, cross-modal sensitive content second-level positioning is achieved, asymmetric semantic alignment is effectively solved, and retrieval efficiency is improved.

The invention discloses an intelligent building equipment historical data storage method, and relates to the field of building equipment historical data storage, and the method comprises the following operation steps: S1, collecting and preprocessing global data; s2, intelligent storage strategy decision making; s3, designing a semantic storage structure; s4, constructing and managing a distributed storage cluster; s5, intelligently writing and indexing historical data; and S6, efficient query and deep analysis of an interface. According to the historical data storage method for the intelligent building equipment, provided by the invention, all-around improvement is realized in the aspects of data quality, storage efficiency, association capability, system reliability, operation convenience and the like through refined design of a data full link; and finally, the equipment data of the intelligent building really becomes a core asset which can be perceived, analyzed and utilized, and powerful support is provided for efficient operation, energy conservation, consumption reduction and safety guarantee of the building.

The invention relates to the technical field of direct current motor servo drive control, and provides a Zynq AXI4 bus-based real-time transmission system and a control method thereof, and the system comprises a Zynq system on chip, a DDR3 common data area, an AXI4 bus interconnection architecture and a dynamic partition manager. By implementing the scheme, firstly, the multi-channel characteristic of the AXI4 bus is utilized, special transmission channels are distributed for different types of data, and parallel data interaction is realized; secondly, a dynamic partition manager is deployed at a PL end, a DDR3 public data area is dynamically divided into a plurality of partitions in real time according to needs, multi-task parallel is achieved, and the real-time performance of the system is improved; thirdly, the read-write operation on the DDR3 in each control period is split into a plurality of state machines through precise scheduling of the state machines, the state machines are combined into a state loop to be mounted to an AXI4 bus, it is ensured that key tasks are completed on time, and the certainty of the system is improved; fourthly, the size and the number of partitions in the DDR3 are dynamically adjusted through a dynamic partition manager, and the storage efficiency is improved.

The invention discloses an anti-quantum serial digital certificate implementation method. The method comprises the following steps: generating a traditional cryptographic algorithm key pair and an anti-quantum cryptographic algorithm key pair; submitting a certificate request; splicing the traditional cryptographic algorithm public key and the anti-quantum cryptographic algorithm public key to form a dual-cryptographic algorithm public key, and generating a digital certificate according to the dual-cryptographic algorithm public key; signing the digital certificate by using a traditional cryptographic algorithm private key and an anti-quantum cryptographic algorithm private key; and splicing and combining the traditional cryptographic algorithm signature value and the anti-quantum cryptographic algorithm signature value into a dual-cryptographic algorithm signature value, putting the dual-cryptographic algorithm signature value into a signature value item of the digital certificate, so that the digital certificate forms a hybrid serial digital certificate, and returning the hybrid serial digital certificate to the client. The invention further discloses a device for implementing the quantum serial digital certificate implementation method, computer equipment and a storage medium. According to the method, the problems of poor compatibility, low storage efficiency, slow verification speed and the like of the existing digital certificate under the threat of quantum computing are solved.

The invention relates to the technical field of data processing, in particular to an HTML (Hypertext Markup Language)-based webpage word cutting annotation generation method, system and equipment, and aims to solve the problems of low storage efficiency, poor dynamic webpage adaptability, lack of fault tolerance and insufficient cross-browser compatibility of an existing word cutting annotation method. The method comprises the steps of capturing a selected area; obtaining DOM node path data and text offset, and generating positioning data; recursively storing the path data to generate a unique fingerprint identifier; storing the fingerprint data as historical fingerprint data in a JSON format; performing matching and positioning based on the historical fingerprint data; and mapping information in the historical fingerprint data to an HTML page to realize bidirectional rendering. According to the method, the label name, the class name and the hierarchical relationship of the DOM node are recorded through the node path to construct the fingerprint identifier, and cross-platform data exchange is realized through the JSON structure, so that webpage labeling which is efficient, accurate, high in fault tolerance and adaptive to a dynamic page is realized.

The invention provides a large language model reasoning method and device, electronic equipment and a storage medium, and the method comprises the steps: searching a storage space for a historical high-storage-efficiency tensor corresponding to each stored request according to a mapping table under the condition that a current request is received, and converting the historical high-storage-efficiency tensor into a historical target tensor; executing a current round of reasoning through the large language model according to the current request, obtaining an intermediate tensor corresponding to each layer of the large language model in the current round of reasoning process, and obtaining a current target tensor required by attention layer calculation of the large language model in the intermediate tensor; searching a current high-storage-efficiency tensor as a precursor of the current target tensor, storing the current request and the corresponding current high-storage-efficiency tensor in a storage space as historical intermediate data, and updating the mapping table; and inputting the historical target tensor and the current target tensor into the attention layer for calculation to obtain the reasoning text corresponding to the current request, thereby effectively reducing redundant calculation.

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 cross-system full-link metadata processing method and device.According to the method, a separated metadata storage architecture is adopted, entity attributes are stored in a JSON document form, relationships between entities are stored in an independent relationship table in a quintuple structure, and efficient storage and rapid query are achieved; through a topology driving processing framework, depth-first traversal is carried out based on a hierarchical structure of a directed acyclic graph, and multi-thread concurrent processing is started at a mode level, so that the processing efficiency of large-scale heterogeneous metadata is improved; the entity life cycle event scheduler is used for monitoring entity change events, triggering index updating, relation maintenance and other operations, and real-time response of metadata change is ensured. Meanwhile, in combination with a search index synchronization mechanism, a hierarchical entity association model, a connector abstraction factory and a fingerprint cache optimization mechanism, efficient management, real-time synchronization and deep association analysis of cross-system metadata are realized. According to the method, the storage efficiency, the real-time performance, the concurrent processing capability and the expansibility are remarkably improved.

The invention belongs to the technical field of large language models, and particularly relates to a large language model KV cache compression method based on interlayer fusion. The method comprises the following steps: inputting a to-be-processed text into a large language model to obtain a KV cache; the compression ratio of each layer of the large model is calculated, and preliminary SVD compression is carried out on KV cache of different layers of the large model; the KV cache subjected to preliminary compression is subjected to block division, and a plurality of blocks are obtained; calculating the attention score of each block; s blocks with the highest attention score are selected, and SVD reconstruction is carried out on the selected blocks; constructing a cache pool to store the reconstructed S blocks; dynamically updating the cache pool; splicing the S blocks in the cache pool to obtain a KV cache, and completing the compression of the KV cache; according to the method, the model is ensured to maintain stable prediction accuracy under diversified task scenes such as text generation and question and answer systems, and balance between storage efficiency and performance is realized.

The invention relates to the field of cold and hot data storage, in particular to a cold and hot data storage optimization method based on a large model, which comprises a business integration module, a data dependence module, a mode updating module, a cold and hot partitioning module and a file merging module, and is characterized in that the business integration module is used for collecting business data to form a training data set; the data dependency module is used for fitting business data to obtain a data consanguinity model, the mode updating module is used for predicting an access mode, the cold and hot partitioning module is used for calculating the heat of the data and performing dynamic scheduling, and the file merging module is used for automatically merging small files. A data storage structure is standardized, storage space is reduced, more efficient and more accurate cold and hot data classification can be achieved, the resource utilization rate of data storage hardware is improved, hot data response delay is reduced, the system storage utilization rate is remarkably improved, and the storage efficiency is improved.

The invention relates to a pumped storage and compressed air energy storage coupled scheduling method, which comprises the following steps of: establishing a multi-stage data system based on input data of multiple time scales; constructing a multi-time scale scheduling model; when the load of the pumped storage mixed compressed air energy storage power station is lower than a first preset threshold value, electricity is abandoned preferentially, water pumping and compression are conducted, and when the load is larger than a second preset threshold value, the pumped storage mixed compressed air energy storage power station is controlled to conduct combined energy release. The nonlinear relation between the pumped storage efficiency and the water level and the nonlinear relation between the compressed air energy storage power and the pressure are converted into linear constraints, solving is conducted by dividing the water level interval and the pressure level, and a scheduling strategy is obtained. Therefore, the problems of relatively low energy storage efficiency, insufficient scheduling refinement, relatively high system standby cost and the like caused by the problems that pumped storage water scheduling and CAES energy management are separated, a standby capacity coordination mechanism is lacked and the like in a hybrid system scheduling method in related technologies are solved.

The invention, which relates to the technical field of the logistics supply chain, discloses a digital intelligence park supply chain warehouse distribution integrated service system comprising a dynamic warehouse resource collaborative scheduling module, a multi-modal intelligent path planning module and a block chain federal learning security center module. Data are collected in real time through 5G-IoT, storage locations and equipment are dynamically allocated in combination with a quantum genetic algorithm, and the storage proportion of cold and hot regions is optimized; planning a path in real time by using an improved ant colony algorithm and a dynamic cost function, and supporting multi-carrier collaborative distribution of unmanned vehicles and unmanned aerial vehicles; a hierarchical block chain and federated learning technology is adopted to ensure data encryption storage and cross-enterprise security sharing; the system improves the storage efficiency and the inventory turnover rate, shortens the distribution time, reduces the energy consumption, guarantees the data privacy and compliance, and is suitable for the supply chain management of a modern park.

The invention belongs to the technical field of machine room power distribution, and provides a machine room power distribution equipment remote monitoring method and system, and the method comprises the steps: collecting operation data in real time through deploying edge equipment, fusing basic operation data, transient feature data and historical abnormal data, and constructing a state scoring model; utilizing a neural network training weight coefficient to realize equipment health degree dynamic evaluation; the sampling frequency is adaptively adjusted based on a scoring result, abnormal data are screened by combining multiple characteristic thresholds such as current sudden change intensity, energy sudden change quantity and temperature gradient, and only high-frequency data fragments in an abnormal time period are reserved; through an edge-cloud collaborative storage mechanism, abnormal data are locally cached after RAID redundancy check, normal data are uploaded to the cloud according to storage duration and access frequency in a grading manner, full-life-cycle traceability of the data is realized through tagging processing, anomaly detection sensitivity is improved, and storage efficiency and fault diagnosis reliability are both considered.

The invention relates to the technical field of firmware upgrading, in particular to an LZ77-based embedded firmware security upgrading method and system, which comprises the following steps: electrifying to obtain guide information and a version identifier, judging a trigger condition and writing in an upgrading mode, generating a start mark to perform LZ77 compression, executing decryption and CRC (Cyclic Redundancy Check) check, and reconstructing a firmware comparison signature. And updating the guide pointer or recovering the snapshot, and monitoring voltage evaluation abnormity to trigger recovery interruption. According to the method, the storage efficiency of the space occupied by the snapshot data is improved through compression ratio evaluation before writing, a data integrity screening mechanism is established in combination with AES encryption head decoding and CRC comparison in the data block receiving process, the data security guarantee in the firmware writing stage is enhanced, decoding recovery is achieved through an original snapshot structure under the condition that comparison fails, and the data integrity screening efficiency is improved. Power supply state dynamic evaluation is realized by combining voltage change rate analysis, snapshot recovery and firmware rollback are triggered through an abnormal trend, and the safety processing capability in a voltage abnormal environment is improved.

According to the user sensitive data protection system based on the cloud service and the block chain, an SM3 algorithm is adopted to fuse biological characteristics and dynamic tokens to generate an encryption credential, high-security identity authentication is achieved, differential fragmentation encryption is carried out on data based on semantic classification, Rabin threshold fragmentation and AES + ECC double-layer encryption is adopted for data of the highest security level, and the security level of the data is improved. A dynamic node optimization model is designed; an optimization coefficient is calculated according to the capacity, the transmission rate and the fault rate; the storage efficiency is improved by more than 40% in combination with a time period regulation factor; the node temperature and the vibration frequency are monitored in real time through a sensor; and verifying the validity period of the dynamic token and the fragment permission mark by using an intelligent contract to realize GDPR compliance fine-grained access control. The system remarkably improves the anti-attack capability and storage reliability of data, and is suitable for high-sensitivity scenes such as highway toll collection, finance and medical treatment.

The invention discloses a power system data integrity verification system based on a dynamic data block division mechanism and a block chain. The system comprises a data preprocessing module, a hash generation module, a block chain storage module and a verification processing module. The data preprocessing module divides original data into data blocks with dynamically adjustable sizes; the Hash generation module adopts an iterative Hash algorithm to generate a chain Hash structure and an integrity Hash abstract; the block chain storage module uploads the hash data to a block chain network through a smart contract; and the verification processing module outputs a verification result by recalculating and comparing the hash values. The system realizes efficient and reliable data integrity verification, and has the advantages of high storage efficiency, good verification precision and low system load.

The invention discloses a key value storage system indexing method oriented to an NVM-NVMe SSD (Non-Volatile Memory-Non-Volatile Memory Express Solid State Disk) hybrid architecture, which comprises the following steps of: constructing a heterogeneous storage architecture taking cold and hot data perception as a core driving mechanism, coordinating and managing two types of storage media, namely a non-volatile memory NVM and a solid state disk NVMe SSD, and realizing efficient identification, layered writing and dynamic migration of cold and hot data. According to the method, the characteristics of low delay, durability and byte addressing of the NVM are utilized, the frequency and I / O overhead of Flush and Compaction operations are reduced in a data write-in path, meanwhile, the problem of mixed storage of cold and hot data is avoided, and therefore the overall performance and storage efficiency of a system are remarkably improved. In addition, by introducing an asynchronous migration module, the method can dynamically adapt to the change of data popularity along with time evolution, and effectively support high performance and high availability of the key value system in long-term operation.

The invention provides a hierarchical storage method for multi-source data, a storage medium, a program product and electronic equipment, and the method comprises the steps: obtaining the multi-source data of a service, and converting the format of the multi-source data into a column storage format; performing embedded space pre-regularization processing on the multi-source data after format conversion to construct an n-dimensional simplex geometric constraint space of the multi-source data, and according to the n-dimensional simplex geometric constraint space, generating a multi-layer composite label, at least comprising a business semantic label and a cold and hot aging label, of the multi-source data; dividing the multi-source data into hot data, temperature data and cold data according to the cold and hot aging labels; the hot data, the temperature data and the cold data are stored in different storage layers respectively; and obtaining a data query request, and routing the data query request to one of the three storage layers according to the service semantic tag. The problems of semantic confusion, low storage efficiency and low access efficiency of bank data lakes in multi-source heterogeneous data fusion in the prior art are solved.

The invention discloses a message queue control method and device for asynchronous messages, equipment and a storage medium. The method comprises the following steps: acquiring at least one data packet generated by a production system through a data preprocessing unit, and for each data packet, determining an initial storage queue corresponding to the data packet based on a data packet name of the data packet; wherein the data packet name at least comprises a service type; target priority information corresponding to each initial storage queue is determined through a data analysis unit; and for each initial storage queue, adjusting the initial storage queue based on the target priority information through an information storage unit to obtain a target storage queue. And the data storage efficiency is improved.

The invention relates to the field of coal mine safety and mine system intellectualization, in particular to a multi-source information fusion supervision method for an underground coal mine drilling process, which comprises the following steps: S1, acquiring continuous synthesized monitoring pictures in the underground coal mine drilling process to form a supervision video stream; s2, performing region segmentation on each frame of synthesized monitoring picture in the supervision video stream to obtain a corresponding static UI region, a dynamic data region and a real-time video region; s3, compressing a static UI region, a dynamic data region and a real-time video region of each frame of synthesized monitoring picture in the supervision video stream by adopting corresponding compression strategies to obtain corresponding compressed data; s4, fusing the compressed data of the static UI area, the dynamic data area and the real-time video area to generate a regional compression supervision video stream; and S5, storing the regional compression supervision video stream to realize subsequent inspection and supervision. According to the invention, the storage efficiency of supervision videos and the efficiency and accuracy of coal mine underground drilling process supervision can be improved.

The invention discloses a method and a system for improving carbon dioxide mineralization and storage efficiency. The method comprises the following steps: building a microfluidic experiment platform integrated with an online CO2 mineralization and storage monitoring system; on a microfluidic experimental platform, simulating a formation temperature and pressure condition to carry out a CO2 mineralization corrosion experiment to obtain pore scale reaction kinetic parameters and a pore structure evolution rule; macromineralization efficiency data are obtained through an indoor physical simulation mineralization reaction experiment of the core scale; a CCM-GEM reaction flow numerical model is constructed; determining a Pareto optimal solution set by using a CMG-GEM reaction flow numerical model; and based on the pore scale reaction kinetic parameters, the pore structure evolution law, the macromineralization efficiency data and the Pareto optimal solution set, determining a CO2 mineralization storage efficiency improvement path of the target rock sample. According to the method, global sensitivity analysis and automatic optimization of multiple parameters are rapidly completed, the research and development period is shortened, the economic cost and the time cost are reduced, and the research efficiency is improved.

The invention relates to the technical field of user portrait information processing, and particularly provides a user real-time portrait updating method and system, and the method comprises the steps: collecting a time sequence interaction track sequence generated by a user in a current interaction scene in real time, and constructing a heterogeneous behavior data flow based on a cross-modal behavior synchronous collection technology; detecting the deviation degree of the historical behavior sequence in the unified feature space, and generating a conflict state mode label; analyzing a behavior evolution path by applying a time sequence self-attention model, and outputting multi-intention probability distribution; dynamically adjusting a confidence coefficient threshold value based on a conflict state and filtering out a noise portrait label; and triggering cross-modal joint recoding and intention re-reasoning for a prediction deviation time period, and locally updating the portrait through a feedback closed loop. Static portrait updating lag limitation is broken through, second-level intention tracking and dynamic behavior mode capturing are achieved, and portrait adaptability and storage efficiency in a high-noise scene are remarkably improved.

The invention provides a structured memory data processing method and system oriented to long time sequence interaction, and is applied to the technical field of natural language processing and artificial intelligence memory modeling. The method comprises the following steps: acquiring a real-time interaction data stream, partitioning the real-time interaction data stream into dialogue data blocks, loading a hierarchical attribute mode and a previous time step attribute tree instance, inputting serialized texts of the hierarchical attribute mode and the previous time step attribute tree instance into a generative model, generating a writing, rewriting, deleting or null operation instruction aiming at a leaf node path, analyzing, updating, generating a current attribute tree instance, and storing the current attribute tree instance; by means of the scheme, lossy compression and structured evolution of the infinite long dialogue stream can be achieved, memory forgetting is relieved on the premise that a context window is not expanded, and long-time-sequence information retrieval precision and storage efficiency are improved.

The embodiment of the invention provides a storage strategy optimization method and device based on data popularity and data consanguinity, and dynamic optimization of a storage strategy is realized through innovatively designing a multi-dimensional popularity calculation model and through node attribute analysis and a graph attention network. A directed graph structure is constructed, and a reliable dependency relationship management system is established in combination with blood relationship analysis. A hierarchical storage strategy is introduced, and the storage efficiency is ensured through heat division and dependency relationship optimization. According to the method, the defects of the traditional technology in the aspects of popularity evaluation, blood relationship analysis, storage optimization and the like are effectively overcome, and a technical guarantee is provided for software system storage management.

The invention relates to a dialogue long context precision optimization method based on abstracts and indexes, which mainly solves the problems of information loss and storage efficiency in a long dialogue scene by dynamically generating dialogue abstracts and content indexes. The technical scheme comprises the steps of collecting and preprocessing dialogue content; generating a dynamic abstract; hierarchical storage construction; constructing an intelligent index; a content backtracking mechanism; and performing adaptive optimization.

The invention discloses a data caching system and method and electronic equipment, and relates to the technical field of data storage, and the method comprises the steps: screening valid units in original input data, and carrying out centralized storage to prevent invalid data from occupying caching resources; an annular register array is segmented and spliced into a continuous data sequence covering a head-to-tail connection area, and the problem of fracture during cross-boundary reading is solved. Therefore, the problem of overflow or invalid reading caused by low storage efficiency, cross-boundary read data breakage and mismatching of read-write rhythms in the original data cache containing valid and invalid units is solved, and the effects of improving the cache utilization rate, ensuring continuous output of cross-boundary valid data and improving the data interaction stability and efficiency are achieved.

The invention discloses a lake and warehouse integrated Internet of Things time series data storage architecture system and method in the crossing field of Internet of Things time series data storage and engineering machinery intelligent technology. The system comprises a data source layer, a data access layer, a data access and preprocessing layer, a lake and warehouse integrated storage layer, a data fusion and analysis layer and an application layer. The data source layer collects multi-source heterogeneous time sequence data, after the multi-source heterogeneous time sequence data is buffered and temporarily stored by the data access layer, an adaptation module of the data access and preprocessing layer analyzes and converts the data, and a preprocessing module performs quality cleaning to form standardized data; the lake-warehouse integrated storage layer carries out classified storage according to data types; the data fusion and analysis layer extracts fusion features and constructs an equipment health state evaluation model; and the application layer realizes the functions of equipment monitoring, fault prediction, construction optimization and the like according to an analysis result, effectively solves the problems of storage, fusion and query optimization of multi-source heterogeneous time series data, improves the storage efficiency and the query speed, and assists in intelligent operation, maintenance and management of equipment.