4451 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 discloses a government affair data sharing system based on a data security law risk control mode, and relates to the technical field of data management. According to the method, full-process closed-loop management from data classification and grading, differential desensitization, law and regulation compliance review, access authority precise control to behavior trajectory real-time analysis and risk trend prediction is realized, and the risk trend prediction is realized by introducing key technologies such as a context sensitive desensitization algorithm, legal knowledge graph reasoning, behavior portraits, semantic trajectory similarity scoring and risk rise rate monitoring. The method effectively improves the recognition and response capability of the hidden data re-recognition risk, and achieves the safe, controllable, compliant and ordered intelligent risk control management of government affair data in the sharing process.

The invention relates to the technical field of environmental monitoring, in particular to a stored material controlled atmosphere maintenance early warning method and system based on environmental monitoring, and the method comprises the following steps: collecting real-time gas composition, temperature and humidity data, carrying out the time sorting of the collected data, recognizing the change trend of multiple parameters at each time point, and obtaining a parameter trend prediction result. According to the method, by combining data time sorting and change trend analysis, analyzing the relation between parameters and environment safety values and recognizing the variables exceeding the threshold value, accurate adjustment of gas, temperature and humidity is ensured, pertinence of parameter control is improved, early warning marking is carried out on the high-frequency fluctuation area through continuous monitoring and dynamic data classification, and the accuracy of the high-frequency fluctuation area is improved. The storage article quality guarantee condition monitoring capability is improved, so that article loss caused by environment change is reduced, a data analysis method and a real-time monitoring method are integrated, the operation logic of an environment controller is optimized, and the overall efficiency and the resource utilization efficiency of the system are improved.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

A federated mining method for multimodal data based on multiple security policies includes: a federated learning framework is used as a data mining model for distributed data mining; a multiple authentication mechanism is designed; a local edge node is selected to participate in a federated computation to obtain authenticated local edge node and aggregate a dataset; a multimodal data fusion and a multimodal data classification are performed on the dataset by a generalized multimodal data feature fusion model based on a multi-headed attention mechanism; and an adaptive perturbation mechanism based on cyclic correlation analysis and differential privacy is designed to add noise round by round and dynamically. A federated mining method for multimodal data is further provided.

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 data desensitization, in particular to an electronic commerce platform information security desensitization scheme analysis system based on artificial intelligence, which comprises a data classification module, an access evaluation module, a dynamic desensitization module and a security audit module. According to the method, the data of the e-commerce platform is intelligently classified and evaluated by adopting an artificial intelligence technology, dynamic desensitization and finer access control of the data are realized, various data access requirements can be dynamically met by evaluating the sensitivity level of the file and the security level of the access request, data processing is performed according to requirements, and the data processing efficiency is improved. The security and privacy of user data are effectively protected, through automation of security audit, the recognition efficiency of abnormal behaviors is improved, the transparency and traceability of overall data management are enhanced, through real-time analysis and response, the risk of data leakage is reduced, and the practical value of data and the fluency of service operation are kept.

The invention provides a new energy station operation intelligent management method and system, and relates to the technical field of new energy station operation, and the method comprises the steps: collecting various data in real time through a sensor and monitoring equipment in a new energy station; transmitting the collected data to a central data center, carrying out iterative optimization processing, and determining final data classification, cleaning and compression parameter combination so as to construct a new energy station big data platform; and according to the constructed big data platform of the new energy station, constructing a digital twinborn model of the new energy station so as to reflect the operation state, the equipment health condition and the environment change of the station in real time. Through real-time data acquisition, digital twinborn model construction and simulation analysis, automatic generation and closed-loop management of an operation optimization scheme are realized, and the operation efficiency, prediction accuracy and fault handling capacity of the new energy station are effectively improved.

The invention discloses a data security management method and system based on cloud computing, and relates to the technical field of data management, and the method comprises the following steps: S1, making a security policy; s2, performing security assessment on the cloud infrastructure; s3, performing identity authentication and access control; s4, data encryption and key management; s5, data classification and level-to-level management; s6, data leakage detection and defense; s7, performing auditing and compliance management; s8, abnormal behavior monitoring and threat response; and S9, an elastic backup and recovery mechanism. According to the data security governance method and system based on cloud computing, the elasticity and dynamic response capability of data security are remarkably improved by setting the distributed key management system and the intelligent monitoring model. The distributed key management system dispersedly stores the keys in different physical areas, so that the risk of single-point fault and attack is reduced; the AI-based intelligent behavior monitoring system analyzes user behaviors in real time, predicts abnormal operation and automatically responds.

The embodiment of the invention provides a data grading and classifying method and device, relates to the field of data processing, can realize high-quality data processing, has deep semantic understanding capability, and can construct a dynamic evolution rule system. By introducing technologies such as deep learning and a graph neural network, the intelligent level of the system is improved, and more accurate data classification and more effective knowledge management are realized.

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.

Wireless sensing using classifier probing and refinement is described. In one example, a described method comprises: computing output analytics by a classifier based on input data constructed based on raw measurement data; mapping each output analytics to a respective mapped outcome; identifying at least one reference input data each associated with a reference output analytics and a reference mapped outcome; each reference input data being one of the input data for the classifier for which a respective reference outcome is available and is different from the reference mapped outcome; and for each reference input data for the classifier: computing a respective plurality of perturbed output analytics by the classifier, generating selected perturbed input data based on the plurality of perturbed output analytics; and re-training the classifier based on the selected perturbed input data and the associated reference outcome.

The invention relates to the technical field of agricultural data security, and discloses an agricultural data security sharing method based on federated learning, through cooperative operation of a user terminal module, a data classification module, a federated learning module, an agricultural cloud database and a security protection module, a user terminal encrypts, uploads, decrypts and feeds back agricultural data through an edge computing device; the data classification module performs multi-level label labeling on data of planting industry, animal husbandry and the like by using a differential privacy technology; the federated learning module is combined with homomorphic encryption to realize local model training and parameter aggregation; the agricultural cloud database stores encrypted knowledge assets by using an IPFS technology; the security protection module integrates block chain evidence storage and a dynamic access control mechanism, the workflow comprises data encryption uploading, privacy protection classification, federated model training, solution generation and security feedback, and by adopting a federated normal form of a data immobile model, the security protection module can effectively protect the security of the original data on the premise of ensuring that the original data is not out of the domain. And value mining and safe sharing of cross-regional agricultural data are realized.

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 relates to the technical field of industrial internet data acquisition, in particular to a data acquisition method and system based on an industrial internet operating system, and the method comprises the following steps: S1, detecting and recognizing data sources of a plurality of industrial devices and sensors; s2, collecting original data from a data source in real time, and preprocessing the original data; s3, distributing part of calculation tasks to a plurality of edge nodes; s4, storing the data in a distributed database or cloud storage in a classified manner; s5, performing fragmentation processing on the data in the cache; s6, performing data synchronization and consistency verification; and S7, converting the data into a corresponding format. According to the invention, by implementing efficient data fragmentation processing, a dynamic storage strategy and secure data consistency verification, the processing speed and storage efficiency of the industrial internet data acquisition system and the security and integrity of data are remarkably improved, and the requirements of high efficiency and high reliability of modern industrial production are effectively supported.

The invention relates to the technical field of data classification management, in particular to a knowledge graph-based foreign trade data classification management system, which comprises a term word order modeling module, a graph path generation module, a path cross analysis module, a semantic category judgment module and a classification structure output module. According to the method, morpheme-level word segmentation and word order extraction are performed on foreign trade terms, an original word order template is constructed, the reduction capability of a semantic expression sequence is enhanced, a directed path is established through morpheme-level label sorting, and the level definition of a term structure and the logic relation between nodes are enhanced; a semantic intersection area is extracted through node intersection and end point frequency analysis, the accuracy of term association judgment is improved, semantic affiliation is selected according to end point label frequency, a node classification closed loop is formed by combining labels and path mapping, links of term graph modeling, path construction, intersection analysis and affiliation output are broken through, and the term association judgment accuracy is improved. And the classification management process of foreign trade data is fully improved.

The invention discloses a hierarchical use method based on sensitive data, and belongs to the technical field of data information security, and the method comprises the steps: basic data collection: obtaining overall data information which needs to be used by a demand side to the inside or the outside; identifying and preprocessing data; sensitive data storage processing: for sensitive data, storing the sensitive data in a private cloud or configuring a secret-related server; a data sensitivity evaluation model, which is used for formulating a sensitive data evaluation model according to the use route of the sensitive data through different policies; sensitive data classification processing: performing label processing on the data according to a result output by model evaluation, and providing corresponding limitation when the data is provided outwards; grading use of sensitive data; sensitive data use logs; a centralized management and control platform is used for data. According to the invention, through intelligent classification, authority control and dynamic adjustment of the access strategy, the security and compliance of the sensitive information in different use scenes are ensured.

The invention relates to the technical field of cloud computing storage, in particular to a cloud storage dynamic optimization method and system, and the method comprises the following steps: based on a cloud storage data access record, recognizing an access time interval sequence, analyzing the variation amplitude of adjacent time intervals, and calculating the standard deviation of the access time intervals; and determining the access period stability according to the standard deviation and the change amplitude, and classifying the data objects to obtain a data access period stability classification value. According to the method, task scheduling is more accurate by identifying a data access time interval sequence and changes thereof and refining data classification and storage resource allocation, a high-throughput and low-delay storage path is matched, the health condition of a storage node is evaluated, a fault node is identified in advance, data stability is guaranteed, and data copy distribution is adjusted; the storage reliability and stability are improved, the storage path for accessing the intensive data is optimized, the dynamic load balance of the storage resources is realized, and the storage efficiency is improved.

The invention relates to the technical field of point cloud data modeling, and discloses a point cloud data processing and modeling method, system and device for power distribution equipment and a storage medium, and the method comprises the steps: collecting the point cloud data of the power distribution equipment, and constructing a topological relation graph according to the topological relation of the power distribution equipment; semantic segmentation is performed on the point cloud data based on the topological relation graph, a component association model is constructed, and missing data detection is performed in combination with the topological relation graph; and generating a high-precision three-dimensional model based on the component correlation model. According to the method, semantic segmentation is guided through the topological relation graph, the accuracy of point cloud data classification is improved, and the problem of misclassification in a traditional method is avoided. Meanwhile, a part correlation model is generated based on the segmentation result, the geometric and functional connection relation between the devices is comprehensively described, and deep fusion of the device connection information and the three-dimensional geometric model is achieved.

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 the technical field of artificial intelligence and data processing, in particular to a roadbed settlement data identification method based on artificial intelligence, and the method specifically comprises the following steps: collecting time sequence monitoring data of a plurality of roadbed settlement sensors, and carrying out the manual marking; carrying out adaptive time domain segmentation normalization on the collected time sequence monitoring data, designing a conditional diffusion process, and generating enhanced data conforming to a soil mass mechanics constraint in a submerged space; constructing a roadbed settlement data classification model based on a one-dimensional convolutional neural network, and inputting the enhanced data into the model for training to obtain a trained roadbed settlement data classification model; and preprocessing new time sequence monitoring data collected by the roadbed settlement sensor, and inputting the preprocessed data into the roadbed settlement data classification model to obtain a settlement category classification result. According to the invention, the roadbed settlement data classification model based on the one-dimensional convolutional neural network is constructed and trained, so that the discrimination capability of the model can be enhanced, and the accuracy and reliability of model identification can be 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 relates to a public data automatic classification and grading method and system, and belongs to the field of data automatic classification and grading. Comprising the following steps: constructing a classification and grading vector knowledge base; for input data to be classified and graded, similarity matching is carried out on the basis of a nearest neighbor search algorithm and the content of the classification and grading vector knowledge base, and matching information of the field to which the data belongs and the grading rule is obtained; carrying out pre-training on the large model, and then carrying out adaptive fine tuning on the pre-trained large model based on the public data classification and grading vector knowledge base; and inputting data to be classified and graded and matching information of the classification and grading vector knowledge base into the large model for automatic classification and grading, and outputting a classification and grading result. According to the method, the problem that the existing classification and classification lack an automatic method and need to depend on a large amount of manual customized services is solved, automatic public data classification and classification are realized, and the generalization and efficiency of classification and classification are improved.

The invention discloses a financial data automatic filling method and device, equipment and a storage medium, and belongs to the technical field of big data. The method comprises the following steps: determining a data source corresponding to original financial data by using a pre-trained data classification model; acquiring a data processing rule engine and a target data filling template matched with the data source; performing data cleaning on the original financial data based on a data cleaning rule; performing data analysis on the standardized data by using a data analysis rule; performing association relation extraction on the target data source field and the target filling field by using a data mapping rule, and performing data field mapping according to the field association relation; and according to a data field mapping result, filling the target data source field into the target data filling template to generate a financial data report. The invention further relates to the technical field of block chains, and financial data are stored on the block chain nodes. According to the method and the device, the applicability and the operation convenience of automatic filling and reporting of financial data in a multi-source data scene are improved.

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 technology disclosed relates to detection of data traffic in computing environments, such as cloud environments. Example systems and methods detect a plurality of workloads in a virtual network in a computing environment and deploy a plurality of probe agents to the plurality of workloads. Each respective probe agent detects network traffic on a respective workload of the plurality of workloads, scans a data packet that is at least one of sent or received by the respective workload, generates a data classification relative to the data packet, and generates a scan result that includes packet payload information and an indication of the data classification. The scan results are received from the plurality of probe agents and a computing action is performed based on scan results.

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.