3808 results about "Data integrity" patented technology

The invention discloses a real-time fault monitoring Internet of Things system for a chemical production equipment cluster, relates to the technical field of equipment fault monitoring control, and is used for solving the problem of poor fault monitoring precision of the chemical production equipment cluster. Comprising a data acquisition module, a comparative analysis module, an equipment abnormity determination module and a fault trend prediction module. According to the method, data integrity is ensured by collecting data of different frequencies in a layering manner, complementing and correcting abnormal values, a baseline model is established, multi-dimensional features of normal states of equipment are extracted through an auto-encoder, operation features are quantified through a Gaussian mixture model, deviation judgment is achieved, an isolated forest and principal component analysis are combined, and the accuracy of deviation judgment is improved. According to the method, an abnormal state is comprehensively judged according to the deviation degree, the isolated score and the principal component deviation degree, an alarm signal is generated, meanwhile, ARIMA and LSTM models are adopted for predicting short-term and long-term trends in a fused mode, a comprehensive predicted value is formed, control measures are automatically executed when the trends are abnormal, and the operation safety of equipment is improved.

The invention belongs to the technical field of electromechanical equipment operation and maintenance, and discloses an electromechanical equipment self-adaptive intelligent early warning system based on multi-source sensing data. The system is composed of a multi-source sensing module, an edge data acquisition and preprocessing module, a data cleaning and multi-dimensional feature extraction module, an equipment state dynamic modeling module, an intelligent fault prediction and trend analysis module, a self-adaptive early warning threshold generation and dynamic adjustment module, and an intelligent decision and remote cooperation module. The system is composed of a multi-source sensing module, an intelligent low-carbon operation and maintenance management and control module and a digital twin system integration and full-period mapping module, multiple sensors are deployed through the multi-source sensing module to acquire multi-dimensional data of equipment, cleaning and calibration are performed through the edge data acquisition and preprocessing module, deep processing is performed through the data cleaning and multi-dimensional feature extraction module, and multi-dimensional feature extraction is performed through the multi-source sensing module. The data integrity and accuracy are ensured; and data are quickly transmitted among the modules, so that the monitoring system can accurately present the running state of the equipment in real time.

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

The invention relates to the technical field of geometric quantity precision measurement based on satellite positioning, in particular to a pumped storage power station dam safety monitoring method based on Beidou positioning, and the method comprises the steps: arranging a Beidou monitoring station and base station array in a dam body and a slope region, synchronously receiving multi-band satellite signals, and carrying out the edge calculation processing; and the effective frequency band data are screened and fused by using a signal-to-noise ratio threshold to generate preprocessed observation data, and the preprocessed observation data are uploaded to a cloud server in real time through a 4G network and a Beidou short message dual-channel transmission link. A cloud server constructs a local enhanced network, ionosphere and troposphere errors are corrected by adopting a multi-base-station joint error modeling method, a closed-loop mechanism dynamically optimizes monitoring point distribution density, signal processing parameters and atmospheric refraction compensation coefficients, and data integrity is guaranteed in combination with a dual-channel redundancy check and interpolation completion technology. According to the invention, full-domain millimeter-level deformation dynamic monitoring of the dam under the complex terrain is realized, and the space-time reference uniformity, the data real-time performance and the structure safety evaluation precision are improved.

The invention provides an unstructured file parallel synchronization method and system, and relates to the technical field of file synchronization, and the method comprises the steps: carrying out the intelligent partitioning of a target file through a pre-training file partitioning model, and generating a double-layer cascade index number and a data hash value; and then, according to the data transmission priority model, transmitting the data sub-blocks to a target end database through a self-adaptive parallel transmission channel, and performing asynchronous verification. And finally, recombining the data sub-blocks based on the double-layer cascade index number and the data block association graph by utilizing a pre-training file recombining model, generating a synchronous target file, and submitting confirmation information to the block chain network. According to the method, the unstructured file synchronization efficiency can be improved, the data integrity and security are guaranteed, and the network transmission pressure and the storage cost are reduced.

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

A system for improving data integrity through blockchain-based smart contracts, consisting of: a data input module configured to receive transaction data from at least one external source; a hashing unit coupled with the data input module and configured to generate a cryptographic hash of the transaction data; a smart contract execution engine that is operationally connected to the hashing unit and configured to execute predefined conditions associated with the transaction data, wherein the smart contract execution engine is configured to trigger automated processes based on compliance with these predefined conditions; a blockchain node interface in communication with a blockchain network, configured to validate and record the hashed transaction data, with the blockchain node interface transmitting validated data entries to be appended to a decentralized ledger; a verification module configured to check the integrity and authenticity of the attached data within the blockchain network; and A data retrieval unit with an interface to the blockchain network, configured to allow authorized users to access and retrieve validated transaction data from the decentralized ledger, with permissions governed by the terms of the smart contract.

The invention relates to the technical field of environment management, in particular to a marine environment risk assessment method and system based on multi-source data fusion, and the method comprises the following steps: carrying out the assessment based on the multi-source marine environment monitoring data through the data integrity, consistency and timeliness of a plurality of data sources, and calculating efficiency scores of the plurality of data sources and adjusting the contribution proportion of the target data source to generate a multi-source data fusion model. According to the method, by analyzing the efficiency of multiple data sources, adjustment of data source contribution is achieved, dynamic adaptability and flexibility of data fusion are optimized, the accuracy and timeliness of marine environment risk assessment are improved, and detection and recognition of abnormal data of multiple environment indexes are achieved by combining analysis of water quality related indexes and temperature data; through analysis of marine development activity events, a decision basis is provided for adjustment of marine management policies, and accurate assessment of marine environment risks is realized in combination with prediction of marine biological diversity.

The invention relates to the technical field of health monitoring, in particular to a cardiovascular disease data fusion and multi-dimensional health assessment method, which comprises the following steps of: acquiring multi-source data of a patient through a sensor and wearable equipment, preprocessing the acquired data, ensuring data integrity, extracting physiological features by adopting a convolutional neural network, and evaluating the health of the patient. The method comprises the steps of extracting non-physiological data features through principal component analysis, constructing a multi-dimensional feature set, identifying cardiovascular disease related risk factors through a random forest algorithm, distributing weights through a logistic regression algorithm to generate individualized risk scores, analyzing time sequence trends of the risk factors based on a long and short-term memory network model, and generating dynamic risk scores in real time. When the score exceeds a preset threshold value, an early warning mechanism is automatically triggered, and a patient and medical staff are notified through an application program; according to the method, accurate assessment and personalized intervention of cardiovascular health are realized through multi-source data fusion and dynamic analysis, and the method has high efficiency and timeliness.

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

Disclosed systems and methods convert user-supplied textual content into animated videos. Textual input is received and processed to identify narrative elements such as characters, settings, and events. These elements are then transformed into visual scene components. Still images generated based on these components are subsequently animated in line with the narrative context. The system can automatically implement storytelling techniques adapted to incorporate neuroscience principles. The system also synthesizes speech for dialogues or narrations using voice synthesis technology that considers emotional markers, tone, and pace. Generated media and metadata are stored in a data storage system that maintains data integrity and enables efficient retrieval. Users can interact with an export interface to choose video resolution, format, and sharing options. A feedback system employing machine learning algorithms collects and analyzes user feedback for real-time adjustments to the generated animated video.

The invention discloses a fault analysis method and system for computer room equipment, and particularly relates to the technical field of computer room equipment. Accurate fault traceability and equipment positioning are realized by acquiring operation data of machine room equipment, analyzing an abnormal data mode in real time by utilizing a machine learning model, generating preliminary fault early warning and adopting a multi-source data fusion technology in combination with a historical fault database and a correlation analysis algorithm, and meanwhile, based on an equipment topological relation and service correlation analysis, the fault positioning accuracy is improved. Quantifying the influence range of the fault on the whole machine room system, calculating the influence level, and automatically generating an optimized fault processing scheme in combination with a fault case library; after the fault is processed, the equipment is continuously monitored, whether the fault is thoroughly eliminated or not is verified, and stable operation of the system is ensured; according to the method, the fault detection accuracy, the traceability and the repair efficiency of the computer room are effectively improved, the risk of false report and missing report is reduced, and the service continuity and the data integrity are improved, so that the intelligent level of operation and maintenance of the computer room is enhanced.

The present disclosure provides a system for secure compute using artificial intelligence agents. The system includes a hardware execution environment with one or more computerized processors and electronic storage media. The processors are configured to establish a secure agent comprising a secure state management module, an encrypted state transition management module, a threshold cryptography implementation, and a digital signature verification module. The system also includes a plurality of atomic agents, each configured to perform a composite task and comprising a task-specific execution module, a state management interface, and a communication module. An orchestrator agent coordinates secure execution of the atomic agents and includes a task distribution module, a result aggregation module, a workflow management module, and a security policy enforcement module. The orchestrator agent integrates encrypted outputs from the atomic agents to perform composite tasks while maintaining data integrity and security through the secure state management module.

Described herein are systems and methods for verifying the integrity of data, such as data used for training machine learning models. Some implementations are directed to verifying the provenance of datasets, the contents of datasets, or both. In some implementations, multiple filters are selected for verifying the contents of datasets. Filters can be selected based on rules, random selection, or using a machine learning model in some implementations. In some implementations, data cleaning is provided.

The invention relates to the technical field of geological disaster intelligent monitoring, in particular to a geological disaster intelligent monitoring system and method based on multiple sensors. The method comprises the following steps: covering a monitoring area through multiple sensors, and collecting and summarizing environment data; separating effective signals and noise by adopting a space-time adaptive decomposition method, completing data denoising by combining a signal-to-noise ratio dynamic threshold and a local weighted regression smoothing trend residual error, then aligning time and dimensions through linear interpolation and standardization, and integrating multi-sensor data based on an adaptive weighted fusion method; the fused data is subjected to generation and review of a to-be-reviewed mark, so that the data integrity is ensured; key features are extracted, a mixed model based on LSTM and a random forest is constructed to evaluate disaster risks, and a risk grade division rule is set; and finally, outputting fusion data and an analysis result, and generating an early warning signal and a decision suggestion for potential geological disasters. According to the invention, accurate monitoring and risk assessment of geological environment changes are realized.

The invention relates to the technical field of data verification, and discloses an integrity verification and correction method and system for flight data and a storage medium. The method comprises the following steps: performing spatial-temporal feature extraction processing on a multi-modal feature data stream, and constructing an incidence matrix according to a multi-level data mapping relationship to obtain a spatial-temporal feature association network; performing hierarchical inspection processing on the spatial-temporal feature association network to obtain abnormal mark data; performing regional recursion positioning processing on the abnormal mark data to obtain an abnormal propagation link diagram; performing correction scheme design processing on the abnormal propagation link diagram, and obtaining a data correction execution sequence based on a data reconstruction and influence evaluation optimization scheme; and performing progressive processing on the data correction execution sequence to obtain corrected flight data. According to the method, a data integrity checking and correcting mechanism is established in a distributed data acquisition environment, so that data exception can be accurately identified and positioned, and the corrected data can be ensured to meet physical constraint and space-time consistency requirements.

The invention relates to an information transmission method and device and a storage medium, and the method comprises the steps: S1, obtaining a predicted value of information transmission efficiency based on real-time network bandwidth data; s2, based on the predicted value of the information transmission efficiency, in combination with a real-time network load, carrying out capacity segmentation adjustment and sending priority configuration of the data packet, and generating data packet transmission configuration; s3, calculating data packet transmission time based on data packet transmission configuration in combination with a real-time network bandwidth, and determining a compression level of the data packet; s4, generating a hierarchical compression data packet based on a compression hierarchical calculation result; and S5, based on the hierarchical compressed data packets, performing error detection and correction in the transmission process, and performing data integrity verification on the transmitted data packets to generate an information transmission verification result. Compared with the prior art, the method has the advantages of reducing data transmission time, improving network utilization rate and the like.

The invention relates to the technical field of information security, in particular to an archive electronic information security system which comprises an archive operation behavior prediction module, an archive data encryption and security storage module, an archive security protection strategy execution module and an archive resource scheduling and security monitoring module. According to the method, through time series data analysis, potential risk behaviors can be recognized in advance, illegal access and abnormal operation can be prevented, losses caused by human errors or malicious behaviors are reduced, data integrity and confidentiality are guaranteed, archive data are stored in blocks, encryption is carried out according to sensitivity and access frequency, and the security of the archive data is improved. Multi-factor identity verification is executed, the flexibility and accuracy of protection measures are enhanced, security vulnerabilities are reduced, priority protection of sensitive archives is ensured through real-time monitoring and resource scheduling optimization, resource waste and risk exposure are avoided, and the security of the sensitive archives is improved. And the overall safety protection capability of archive management in a complex threat environment is improved by collaborative measures.

The invention provides an erasure code compatible read-write method and system based on a bidirectional data access agency, and relates to the technical field of distributed storage, and the method comprises the steps: employing multi-agent reinforcement learning to construct a heterogeneous resource portrait and optimize task allocation, combining a space-time graph convolutional network and a federated learning framework to construct a data format conversion model, and constructing an erasure code compatible read-write model; data features are analyzed through an encoder based on an attention mechanism, a fragmentation strategy is generated, self-adaptive encoding is achieved through a capsule neural network, data integrity verification and repairing are conducted through a double-layer space attention network and a graph attention network, the data access efficiency of a heterogeneous storage system is improved, data format compatibility is enhanced, and the data access efficiency is improved. And the data integrity is ensured.

The invention relates to the technical field of hard disk testing and verification, in particular to a multi-thread-based solid state disk reliability automatic testing method and system.The method comprises the steps that firstly, SMART information is deeply analyzed through microsecond-level high-granularity continuous performance monitoring, and multi-thread parallel processing is assisted; according to the method, fine performance fluctuation of the solid state disk under the concurrent load can be quickly captured, a fault mode can be identified, then early warning is realized by utilizing the extracted multi-dimensional features and a machine learning model, and a detailed fault diagnosis report is generated; and through dynamic error correction code strength verification and data integrity verification under pressure, an internal error correction mechanism of the solid state disk is actively detected and optimized. And finally, in combination with prediction reliability modeling, the system can estimate the remaining service life and predict faults, and provides product optimization suggestions for design, manufacturing and firmware optimization of the solid state disk, so that automation, intelligence and full life cycle management of the fault detection reliability of the solid state disk are realized.

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention discloses a cloud information system index collection method, system and device and a medium, and relates to the technical field of cloud information system operation and maintaining.The method comprises the steps that information collection interfaces of a cloud system are integrated, data flow information entropy is quantized, and an entropy value sequence is constructed; an interface calling serial number is traced based on a historical log, a calling chain is formed, a directed graph topological structure is established, an influence propagation path of an entropy mutation node is identified, and a collection regulation priority of an interface cluster is determined; an acquisition strategy is formulated in combination with entropy time sequence data and a load level, a state-strategy mapping relation matrix is optimized, and a time period driving type strategy adjustment parameter set is generated; and issuing the strategies in sequence, dynamically regulating and controlling the acquisition frequency, and if state transition or fitting degree abnormity occurs, triggering strategy rollback and optimizing the learning weight. According to the method, on the premise of guaranteeing the integrity of the monitoring data of the power equipment, the utilization rate of edge computing resources is remarkably optimized, and high-timeliness and high-fidelity data support is provided for safe and stable operation of a power grid.

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

The invention relates to the technical field of geological landslide monitoring, in particular to a tower footing geological landslide monitoring and early warning system and method. The method comprises the following steps: arranging an inclination angle sensor, a Beidou-GNSS module, a soil moisture content probe and a micro-seismic accelerometer on a tower footing and the periphery of the tower footing, and collecting the inclination amount, three-dimensional displacement, soil moisture content and vibration response information of the tower footing to form a monitoring original data set; timestamp unification, WGS-84 projection coordinate system conversion and multi-frequency vibration elimination are carried out on the monitored original data set, and an air-ground integrated displacement profile is constructed; the whole process realizes high integration and automation in the aspects of data acquisition, processing, fusion and transmission, so that the timeliness, accuracy and data integrity of landslide risk monitoring are ensured, and scientific decision support is provided for disaster prevention and reduction.

The invention discloses a power grid field table recognition and information extraction method based on a large model, and relates to the technical field of power grids, comprising the following steps: obtaining a table image in the power grid field, improving image definition through an image enhancement algorithm, eliminating noise and background interference, and performing tilt correction and resolution optimization on the table to obtain a table image; generating a standardized image; and analyzing the structure of the table by using a large model based on deep learning, and constructing a spatial layout model of table units. According to the method, a large model is processed by combining vision and natural language through a multi-modal fusion system, a complex table is accurately analyzed, and the analysis efficiency and accuracy are improved. Based on deep learning and logical reasoning technologies, the system can automatically detect and repair semantic and logical errors, and data integrity and reliability are ensured. And through structured conversion and standardized output, seamless joint with a scheduling platform is realized, automatic scheduling and monitoring are supported, and the power grid management efficiency and the emergency processing capability are remarkably improved.

The invention relates to the technical field of gas energy monitoring, and discloses a gas energy monitoring method based on Internet of Things, comprising the following steps: S1, multi-source data acquisition: using an intelligent sensor to acquire gas related data, and sending the data to an edge device or a cloud platform through a wireless network; s2, data preprocessing: carrying out data cleaning, denoising and feature extraction on edge equipment, executing multi-source data fusion, carrying out anomaly detection and processing, and filtering abnormal data; and S3, data transmission: performing data transmission by adopting an encrypted communication protocol, and using a data buffering and retransmission mechanism in the transmission process, the data buffering and retransmission mechanism being used for ensuring data integrity. By using a multi-source data fusion technology and an advanced data preprocessing algorithm, sensor noise and data errors are effectively reduced, the quality and consistency of gas data are remarkably improved, and the problem that the analysis accuracy is reduced due to data source diversity and complexity is solved.

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

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention discloses a safe and rapid storage method and system for image data of an imaging department, and relates to the technical field of medical image data processing. The method comprises the following steps: 1, designing a distributed storage system, and selecting and configuring storage nodes; 2, selecting and updating an encryption algorithm, and implementing file-level and block-level encryption; 3, realizing accurate data deduplication by utilizing a machine learning technology, and selecting a proper compression algorithm; 4, constructing an index system, and establishing a data warehouse and a data lake by using big data analysis and machine learning technologies; 5, establishing an image data backup and recovery mechanism; and 6, constructing a data integrity checking and monitoring system. According to the invention, by adopting the technologies of distributed storage, encryption, data deduplication, compression, big data analysis, machine learning, backup and recovery, data integrity verification, monitoring and early warning and the like, efficient storage and safety protection of image data are realized.