2457 results about "Data classification" patented technology

The invention discloses a human resource management method and system based on data security. The method comprises the steps that multi-source human resource data are collected through a standardized interface, and sensitive levels are marked in a classified mode; calling a dynamic encryption engine based on the sensitive level and the data type, performing asymmetric encryption on the structured data, and performing hybrid encryption on the unstructured data; a dynamic hierarchical access control strategy is generated in combination with user roles and service scenes, and the permission range is adjusted in real time; laws and regulations such as GDPR and CCPA are analyzed through a compliance rule base, data operation legality is automatically verified, and illegal behaviors are blocked; k-anonymity and differential privacy technologies are adopted to desensitize sensitive data, and privacy protection and data availability balance are ensured; and recording a full-process operation log based on the block chain and generating a non-tampering audit report. The system comprises a data classification acquisition module, a dynamic encryption engine module, an access control engine module and the like. According to the method, the problems of data protection rigidness, compliance response lag and privacy-utility imbalance of a traditional HR system are solved.

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention relates to the technical field of data processing, and discloses a cross-border e-commerce commodity recommendation system and method based on multi-source data fusion. The system comprises an acquisition module for performing data acquisition to obtain a cross-border unified data warehouse and federal learning cooperation data; the classification module performs text classification processing to obtain a user preference analysis result and an interpretable attention mark; a quantization module carries out quantization processing to obtain a cross-border selection feature matrix; the fusion module carries out weighted fusion processing to obtain a basic comprehensive score and a weight convergence detection result; the evaluation module performs risk evaluation processing to obtain risk probability distribution and cross-border compliance evaluation results; and the sorting module carries out real-time processing through lightweight preprocessing and a flow-type calculation pipeline to obtain a comprehensive score sorting list of cross-border selected products. The problem that a traditional cross-border e-commerce product selection method cannot effectively integrate multi-source heterogeneous data and cannot analyze user feedback text semantic information is solved.

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention relates to the technical field of network security, in particular to a network security situation awareness method and system, and the method comprises the following steps: extracting a source IP address and a target IP address based on a network behavior record, carrying out the statistics of the number of used ports, the transmission direction and the time interval value, analyzing the direction change times and the time interval difference, and screening abnormal communication pairs. And generating an abnormal communication pair set. According to the invention, through analyzing port usage, transmission direction and time interval value, deeply mining communication features, screening abnormal communication pairs and improving identification accuracy, dividing a time sequence window, analyzing rate fluctuation and frequency distribution, locking an unstable time window, combining with a multi-dimensional data classification behavior mode, and extracting a switching path and priority, a multi-dimensional data classification behavior mode is combined. Potential threat paths are identified independently, global threat situations are identified through node interaction relation statistics and correlation analysis and an expansion range, threat assessment precision and efficiency are enhanced, and comprehensive and reliable risk protection capability is provided for network managers.

The invention relates to the technical field of data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

The invention discloses a wind turbine generator data analysis and fault diagnosis method and system based on big data and artificial intelligence. According to the method, a blade image, a vibration signal, audio data and operation parameters are synchronously acquired through an unmanned aerial vehicle multi-mode sensor and a ground monitoring system, and a multi-source heterogeneous data set is constructed; after the data is classified and preprocessed, image features, vibration time-frequency domain features and operation parameter key value pairs are extracted respectively; dimensionality reduction is carried out by using an auto-encoder, feature-level space-time alignment is realized through an improved DTW algorithm, and a multi-dimensional fault feature matrix is generated; a hierarchical diagnosis model including a GRU auto-encoder, an MLP network and an attention mechanism CNN is constructed, and training is carried out by taking minimization of sub-model deviation as an optimization target; and finally, fusing multi-source features to realize fault classification, and generating a visual diagnosis report. According to the method, efficient fusion and accurate diagnosis of multi-source heterogeneous data are realized, and the accuracy and the real-time performance of fault detection of the wind turbine generator are remarkably improved.

The invention relates to a digital twin workshop management and control method and system based on data multilayer fusion, and belongs to the field of service-oriented industrial system data science, and the method comprises the steps: obtaining the global multi-source heterogeneous data of a digital twin workshop, and carrying out the data classification, data processing and data storage of the global multi-source heterogeneous data, carrying out data-level fusion to obtain digital twin workshop standard data; extracting standard data of the digital twin workshop, performing semantic association, feature extraction and feature analysis on the standard data of the digital twin workshop, and performing feature level fusion to obtain key state features of the digital twin workshop; the method comprises the following steps: constructing a decision-making model oriented to a digital twin workshop, inputting key operation features of the digital twin workshop, performing decision-making level fusion based on the decision-making model, decision-making optimization and decision-making method solution, and outputting a workshop production management and control decision. The method can provide support for digital twin workshop operation data governance analysis and production management and control accurate decision.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on a hierarchical reinforcement learning strategy. The unmanned aerial vehicle autonomous navigation system is suitable for a three-dimensional flight task in an unknown environment. The system comprises a state sensing module, a hierarchical strategy network module, a control execution module, a data classification module and a data playback module. The state sensing module extracts obstacle position information based on a deep neural network, and fuses the target, the obstacle position and the flight state to generate a state vector and a time sequence. The hierarchical strategy network adopts a high-layer DQN to generate a navigation intention, and a low-layer LSTM and PPO are combined to output a continuous control action; the control execution module adjusts the attitude of the unmanned aerial vehicle according to the instruction and performs closed-loop correction. The system introduces a double dynamic memory mechanism (DDM), improves strategy training efficiency and stability through experience classification and proportional sampling, and adopts a multi-target award function guide strategy to optimize convergence among task completion, obstacle avoidance safety and flight rationality. The system has good environmental adaptability and generalization ability, and is suitable for autonomous navigation tasks in complex scenes.

The invention relates to a method and a system for storing and backing up electric power data of a data-in-data station based on use popularity, and belongs to the technical field of data-in-data stations, and the method comprises the following steps: classifying electric power business data into hot data, temperature data and cold data based on preset access frequency, time window and data volume index; according to a classification result and a business importance degree, storing hot data in an SSD cloud disk or a memory database, storing temperature data in a distributed column database, and compressing cold data and then archiving the cold data to an object storage or a tape library; dynamically configuring differentiated backup strategies for different types of data; automatic scheduling of storage and backup is realized through a cloud platform component, and hot switching of master and slave databases, OSS archived data recovery by a DataWorks tool and a remote multi-copy recovery mechanism are supported; and migrating data in advance in combination with a historical access prediction popularity trend, and triggering cold data compression and archiving when storage resources exceed 80%. The method is suitable for power industry scenes, the storage cost is optimized, and the data calling efficiency is improved.

The invention discloses a method and a system for automatically classifying heterogeneous data based on business deep learning, particularly relates to the technical field of data classification, and is used for solving the problems of limited classification accuracy and insufficient interpretability caused by cross-modal feature entanglement in the existing method. The method comprises the following steps: decoupling and separating an independent feature and a redundant feature vector through a modal implicit feature, generating a noise correction instruction based on orthogonality residual evaluation, carrying out cross-modal noise distribution consistency correction on the redundant feature, quantifying a gradient direction conflict rate in combination with a vector space projection relation, and dynamically adjusting a back propagation path. Fusion path switching is triggered through semantic matching degree and contribution degree balance monitoring, a self-adaptive classification decision boundary function is constructed, and high-precision classification of heterogeneous data and strong generalization adaptation of service scenes are achieved.

The embodiment of the invention discloses a medical data privacy protection method and a computer device. The method comprises the following steps: firstly, acquiring medical industry laws and regulations and target medical institution data asset information; building a logic relation graph containing data classification and security level according to laws and regulations, and importing the laws and regulations information into a first graph database to obtain a laws and regulations graph; extracting a logical relationship in the data asset information, constructing a data logical relationship graph containing a library table column structure, and importing the data logical relationship graph into a second graph database to obtain an asset graph; business process information is collected, and a data flow relation is identified to perfect an asset map; analyzing the name and content of the data assets to obtain a classification and grading analysis result; on the basis of the result, constructing a heterogeneous graph neural network model by using a graph attention network, establishing a mapping relationship between the two maps, and updating the asset map; and finally, carrying out privacy protection on the data assets according to the updated asset atlas. According to the method, the accuracy and efficiency of medical data classification and grading can be improved, and the privacy protection capability is enhanced.

The invention provides a self-adaptive color depth optimization method for a liquid crystal display screen, which comprises the following steps of: acquiring manual adjustment operations of a user on brightness, contrast and color parameters of the liquid crystal display screen in different use scenes to form a user preference data set; according to the user preference data set, a data classification method is adopted to classify manual adjustment operations in different use scenes, and features of ambient light and content types are extracted to determine preference feature distribution of a user in a specific scene, and a personalized color preference model is constructed; preliminary calibration is carried out in combination with a dynamic balance algorithm, and display parameters are optimized through a continuous learning mechanism and secondary calibration. The problem that in the prior art, color adjustment of a liquid crystal display screen lacks individuation and scene self-adaption capacity is solved, continuous self-adaption of the display effect is achieved, manual intervention of a user is reduced, and the individuation degree and comfort degree of visual experience are improved.

The invention discloses a tensor depth semi-supervised learning method for high-dimensional small sample data classification. The tensor depth semi-supervised learning method comprises the steps of preprocessing original high-dimensional small sample data; constructing a deep neural network comprising a feature extraction module and a classifier module; constructing a second-order similarity matrix and a third-order similarity tensor based on the low-dimensional embedding representation obtained by pre-training; combining the second-order similarity matrix and the third-order similarity tensor to construct an objective function containing multi-order smooth constraints; then, taking the low-dimensional embedded representation obtained by pre-training as input, performing iterative optimization on the target function by adopting a gradient descent algorithm through a label propagation network formed by a full connection layer, and generating a pseudo label; and inputting the original high-dimensional small sample data, the low-dimensional embedded features and the pseudo labels into the deep neural network, iteratively updating the network in a semi-supervised mode until convergence, and outputting a final prediction result. According to the method, more accurate label propagation is realized, and the semi-supervised classification precision is improved.

The invention relates to the technical field of intelligent traffic systems, and discloses an expressway multi-source traffic data grading and classification processing system, which comprises a data acquisition standardization module for acquiring and preprocessing multi-source traffic data; the data source quality evaluation module is used for evaluating the credibility of the data source; the scene emergency degree calculation module is used for calculating the flow anomaly degree, the vehicle speed anomaly degree and the meteorological risk score and identifying an emergency event; the data grading module is used for calculating a comprehensive priority score of the data and obtaining a grading data set by adopting a threshold segmentation method; the data classification module is used for carrying out self-adaptive classification on the data; the data fusion module identifies the same traffic parameter, performs conflict detection and performs fusion processing on conflict data; the result output module is used for obtaining a processing result by adopting a grading and classification output and quality feedback mechanism; according to the invention, intelligent refined processing of the highway multi-source traffic data is realized, and the emergency response speed and the data processing accuracy are improved.

The invention relates to the technical field of section design, and discloses a shipyard steel bent frame column and beam section design simulation method and system. The method comprises the following steps: classifying and storing the historical data of the shipyard steel bent, establishing an experience library and extracting initial section parameters; inputting a parametric modeling system to generate a geometric model and a load condition; obtaining an internal force envelope value based on finite element analysis; obtaining a stress ratio and a displacement ratio according to standard combination and checking calculation; using an NSGA-II algorithm to optimize section parameters; and modeling and marking based on an optimization result, and generating a structure optimization scheme and a design drawing. The problems that in the prior art, preliminary section type selection of a steel bent frame structure lacks system optimization, a large amount of manual adjustment is needed in the design process, the overall design efficiency is low, and the material utilization rate is low are effectively solved, and automatic, refined and multi-target comprehensive optimization of steel bent frame column and beam section design is achieved.

The invention provides a traditional Chinese medicine intelligent health diagnosis system and method based on multi-source data fusion, and relates to the technical field of health diagnosis. The system comprises a database construction module which is used for collecting condition data and historical disease data of a patient and constructing a traditional Chinese medicine holographic health database. And the multi-dimensional classification module is used for establishing a multi-source data classification standard and adding category fields to the multi-source data in the traditional Chinese medicine holographic health database from multiple dimensions to obtain associated field data. The importance classification module is used for constructing an importance degree judgment standard and judging the importance degree of different associated field data to obtain importance labels, and the exclusive customization module is used for classifying health states according to different importance labels to obtain current health data and formulating exclusive treatment strategies. According to the method, the data are classified and associated from multiple dimensions, the organization and utilization efficiency of the data is improved, and the importance degree judgment method can provide a basis for priority processing and analysis of the data.

The invention relates to the technical field of artificial intelligence and remote sensing image processing, and particularly provides a hyperspectral image and laser radar data classification method based on a dynamic fusion network. The method comprises the following steps: preprocessing acquired multi-modal data, and constructing multi-scale input; a dual-scale local attention module is designed, and context information of different scales is fused in a self-adaptive weighted mode through gating soft pooling; a dynamic down-sampling feature enhancement module is designed, the down-sampling rate is dynamically adjusted according to the complexity of the feature map, and deep multi-scale interaction is carried out based on a Mama backbone; constructing a directional interactive attention module, extracting features in horizontal, vertical and diagonal directions through directional gating convolution, and capturing an anisotropic structure of a linear ground feature; through the design of a double-path classifier, fusing shallow space details and deep semantic information; and the model is trained, optimized and reasoned to obtain data classification, and the method improves the classification precision and the calculation efficiency.

The invention discloses a multi-technology fusion multi-source data grading and classifying method and system, and the method comprises the following steps: S1, determining a data source, collecting multi-source data, and carrying out the preprocessing; s2, monitoring by using a CDC technology, and extracting monitored target data; s3, performing feature extraction on the target data, and introducing an EMAP method to perform nonlinear cross projection on a multi-modal feature set; s4, inputting the high-dimensional feature vector into a hybrid classification network, and executing main component molecular space compression and unsupervised clustering; s5, mining high-frequency attribute items by adopting an FP-Growth algorithm, and constructing a privacy attribute set and a non-privacy attribute set; s6, calculating the weighted privacy degree of each privacy cluster, and performing multi-layer privacy level division through a hierarchical mapping network; and S7, carrying out encryption and desensitization processing on the data under each type of labels, and carrying out visual output. According to the invention, the intelligence and security of multi-source data classification and privacy grading are improved.

The invention is applied to the technical field of data storage, and particularly discloses a multilevel data encryption storage and access control method based on cloud computing, which comprises the following steps of: 1, data classification and multilevel encryption; step 2, constructing a layered secret key; step 3, data access control; and 4, constructing and dynamically updating the data access authority. According to the multi-level data encryption storage and access control method based on cloud computing, encryption grade division is performed on data according to the data confidentiality degree, and algorithm encryption with different intensities is performed on the data according to different encryption grades, so that encryption can be performed for different types of data, and compared with traditional single algorithm encryption, the encryption efficiency is improved. According to the technical scheme, the adaptability of data encryption can be improved, meanwhile, three levels of key structures are arranged to be matched with one another, the main key protects the middle key, the middle key isolates direct association of the main key, the working key and the data, and the exposure risk of the main key is reduced.

The invention relates to the technical field of intelligent logistics and supply chain management, and discloses a cross-border e-commerce logistics intelligent storage scheduling and transportation cooperation method based on AI path planning, and the method comprises the steps: building a monitoring module, a dynamic information capturing module, a data classification module, a data analysis module, a storage scheduling and transportation cooperation module and a management module; the monitoring module monitors storage and transportation information in real time through a sensor and monitoring equipment, the dynamic information capture module collects external environment data and internal abnormal events, the data classification module classifies the monitored data and the captured information, and the data analysis module constructs a path planning model according to the stored data of the data classification module. The system comprises a storage scheduling and transportation collaboration module, a storage scheduling and transportation collaboration module and a management module, the storage scheduling and transportation collaboration module generates an optimal path and calculates a storage efficiency index # imgabs0 #, a transportation path lifting coefficient # imgabs1 # and a storage-transportation collaboration index # imgabs2 #, the storage scheduling and transportation collaboration module optimizes the path according to a calculation result and sets an early warning mechanism, and the management module assists management personnel in monitoring and decision making through a visual interface.

The invention relates to a flying dust monitoring data processing and classifying method based on multi-source sensing fusion, and the method specifically comprises the following steps: firstly, deploying multi-source flying dust monitoring sensor nodes in a target region to collect data, carrying out the marking, and generating a data set; performing continuous wavelet transform on the acquired data, extracting a wavelet energy spectrum and a Shannon entropy, and splicing to obtain an enhanced feature tensor; secondly, through a two-stage fusion and coding strategy, frequency band energy features are extracted through wavelet packet decomposition, multi-channel cross-correlation, statistical moment and ratio features are calculated to form time sequence mode coding features, and multi-source heterogeneous feature fusion is achieved in combination with a local time sequence feature matrix; then constructing a deep learning model containing a multi-scale time sequence feature extraction and dynamic fusion module, and inputting a fusion feature matrix for training; and finally, inputting the preprocessed new monitoring data into the trained model, and outputting a dust source and pollution level classification result. The dust monitoring data classification accuracy and the dust source identification precision can be effectively improved.

The invention relates to a data lake-based industry templated modeling and dynamic access control method, which comprises the following steps of: acquiring multi-source heterogeneous data of a data center in real time, and performing data verification, key field extraction and metadata extraction through an analyzer; according to the data classification and grading key points, grading protection is carried out on the currently obtained data matching industry grading and classification template, and the protection grade is dynamically adjusted according to the data dynamic access control rule, the sensitivity and the service importance; performing threat detection and risk prediction on the abnormal behavior data by using a machine learning algorithm; constructing a distributed intelligent data lake storage architecture, and storing the currently acquired data into a data lake in a layered manner according to a classification result; and displaying the security situation and the early warning information through a visual interface. The invention also relates to a corresponding system. By adopting the industry templated modeling and dynamic access control method and system based on the data lake, the limitation of traditional data lake modeling is effectively solved.

The invention relates to the technical field of pattern recognition, in particular to a GIS ultrahigh frequency partial discharge signal separation method based on pattern recognition, which comprises the following steps: synchronously acquiring original data by using various sensors, and preliminarily eliminating interference data to obtain data to be analyzed; generating a first characteristic spectrogram and a second characteristic spectrogram based on the to-be-analyzed data, and extracting spectrogram features; according to the spectrogram characteristics, judging judgment conditions which are met by the to-be-analyzed data, wherein the judgment conditions comprise a first mode, a second mode and a third mode; if the to-be-analyzed data meets the corresponding discrimination condition, classifying the to-be-analyzed data through a corresponding data classification path; and fusing classification results obtained through the data classification paths based on confidence, outputting a classification result of the to-be-analyzed data, and separating identified real data from interference data.

The invention provides a data classification and grading method and device based on an artificial intelligence large model technology, and relates to the field of data security. The method comprises the following steps: constructing a data classification and grading catalog according to a preset classification rule, and storing each row of the classification and grading catalog as a document record in a target vector database; respectively carrying out first retrieval operation, second retrieval operation and third retrieval operation on the data classification and grading catalog by taking the business system name, the data table name and the field name as first query conditions so as to obtain a first matching document, a second matching document and a third matching document in the target vector database; sorting the first matching document, the second matching document and the third matching document according to a preset sorting mode, and obtaining a sorting result; and inputting a sorting result into the large language model, and performing data security early warning through the large language model. According to the method and the device, the security problems that corresponding security measures cannot be taken, sensitive data is leaked and the like when a data governance engineer identifies the metadata according to the data classification and classification catalog and judges the classification and security level of the metadata are solved.

The invention discloses a generation method of a generative adversarial network for planning a resource design stylized layout, and belongs to the technical field of artificial intelligence and machine learning, and the method comprises the following steps: S1, multi-source data collection: widely collecting a large number of existing planning resource samples; s2, data classification labeling; s3, constructing a generative adversarial network model; s4, performing model training; s5, style migration training; s6, optimizing the model; s7, generating a planning resource scheme; s8, optimizing the scheme; and S9, performing feedback iteration. According to the method, through integrating a technical path of multi-source design data acquisition, deep style feature extraction and generative adversarial network collaborative optimization, full-process automation from user demand input to high-quality planning resource layout generation is realized, and user personalized demands are met through a multi-scheme candidate and interactive adjustment function; and finally, constructing an incremental learning closed loop based on user feedback data, and continuously updating model parameters to adapt to an emerging design trend and an industry specification.

The invention relates to the technical field of medical data management, and discloses a medical data classification management system based on big data. A data integration and classification module of the system receives a historical medical data set and a real-time medical data stream, performs feature fusion and classification prediction through a deep learning model, and generates a theoretical classification result. And the deviation quantitative analysis module performs multi-dimensional deviation calculation on the theoretical classification result and the clinical actual classification result to generate a patient-level multi-dimensional deviation tensor. And the graph network anomaly positioning module inputs the multi-dimensional deviation tensor into a graph neural network for anomaly propagation simulation based on a medical institution topological structure, and generates an anomaly probability distribution diagram to identify a physical region where an anomaly occurs. And the adaptive decision control module dynamically allocates monitoring resources according to the abnormal probability distribution diagram, including starting high-frequency data acquisition for a high-probability abnormal region and executing an intervention test on adjacent nodes.

The invention relates to the technical field of computer data processing, provides a cross-border data compliance processing method, device and equipment, and is used for solving the problems of relatively low cross-border data processing efficiency and data processing strategy errors in related technologies. According to the embodiment of the invention, the multi-method domain and regulation knowledge graph is established in advance, laws and regulations of a plurality of regions are summarized, the multi-method domain and regulation knowledge graph can be updated in real time, and the cross-border scene template library and the data label template base library are established according to the multi-method domain and regulation knowledge graph. The cross-border scene template library and the data label template base library can be dynamically updated along with updating of the knowledge graph, and a cross-border gateway can process data by adopting a compliance strategy according to a data cross-border scene through the cross-border scene template library, so that related data management enterprises and institutions efficiently manage data outbound in a compliance manner; through the data label template library, the cross-border gateway can rapidly and accurately carry out data marking work, and the efficiency of data classification processing is improved.

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