218 results about "Data subject" patented technology

The invention provides a block chain-based fresh product logistics full-chain traceability management and control method and system, and the method comprises the steps: obtaining environment collection data of fresh products, carrying out the encryption processing of the environment collection data, and carrying out the chaining evidence storage; according to the environment collection data subjected to the uplink evidence storage, performing score calculation on the fresh products by using a variety attenuation model to obtain quality scores; when the quality score is lower than a set threshold value or the decline rate of the quality score is greater than a set range, dynamically generating an intelligent contract based on a rule template library; executing the intelligent contract through cross-chain routing, generating cross-chain transaction data covering a production chain, a logistics chain and a sales chain, and performing credibility verification on the cross-chain transaction data by using an improved PBFT algorithm to obtain full-chain traceability management and control information of the fresh products, through deep fusion of a multi-chain collaborative architecture, a dynamic intelligent contract and a lightweight node consensus mechanism, an efficient, safe and transparent supply chain management platform can be constructed.

The invention provides a data security method and system fusing an MIN and a unified AI theory, and the method comprises the steps: S1, completing the multi-dimensional identification registration and synchronization of a data space in a multi-identification network MIN based on a data space identity identification DSID and an ontology semantic mapping frame; s2, acquiring a data acquisition record from a multi-identifier network MIN, and constructing an access control atlas in combination with a semantic vector of a data subject identifier DSID-Item; s3, analyzing the access request and verifying the authority, identifying the sensitivity level of the access request, and triggering a privacy enhancement mechanism when the sensitivity level is marked as medium-high-level privacy data; and S4, judging whether the data object reaches a value end point in the life cycle of the data object or not by taking a data space identity (DSID) registered in the multi-identifier network (MIN) as a retrieval anchor point, and if so, executing a verifiable multi-stage destruction operation. According to the invention, a full-life-cycle trusted data security technical scheme can be realized, and intelligent, explainable and efficient security management of data streams in a network space is realized.

The invention discloses a method for identifying an engineering drawing detail table and generating a BOM table, and belongs to the crossing field of automation technology and image processing, and the method comprises the steps: obtaining a scanning or electronic image of an engineering drawing; through preset datum line positioning, recursively detecting a nested rectangular region conforming to an area difference threshold value, and determining a title bar, a detail list region coordinate and a table image; identifying lines and cross points, analyzing the line and column boundaries of the table, and constructing a topological structure; performing character recognition by adopting multi-engine OCR integration; the characters and the cells are associated, a header is recognized through semantics, and analysis data subjected to integrity verification are generated; and automatically generating a structured BOM table in a preset standard format based on the data, and outputting an editable file. According to the method, full-process automation is achieved, manual intervention is not needed, and manual input cost and personal errors are greatly reduced.

A method, a system, and a computer program product for identifying sensitive data. A plurality of text portions associated with one or more data subjects is identified. A machine learning model is applied to the identified plurality of portions to extract one or more entities representative of one or more data subjects. The entities are grouped into one or more entity groups. Based on one or more entity groups, at least one data subject is identified for replacement or redaction in at least one text portion in the plurality of text portions.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data analysis method, which comprises the following steps: carrying out data cleaning and preprocessing on multi-modal data by adopting dynamic noise detection, cross-modal standardization and meta-learning driving methods; performing cross-modal feature fusion and analysis on the multi-modal data subjected to data cleaning and preprocessing through potential association between dynamic weight distribution and graph neural network mining modes to obtain a fusion feature vector with high feature association degree; obtaining a multi-modal large model through lightweight training and field fine tuning, and inputting the fusion feature vector into the multi-modal large model to obtain a multi-modal decision result; and displaying a multi-modal decision result, carrying out dynamic optimization and iteration on the multi-modal decision result, and adjusting and optimizing parameters of the multi-modal large model through a real-time evaluation and feedback mechanism. The method breaks through the efficiency and precision bottlenecks of existing multi-modal data analysis, and has the advantages of high efficiency, robustness and expandability.

The invention provides a fusion type secure storage system, electronic equipment, a data management method and a computer readable storage medium. The system comprises a hardware security layer, an encryption processing layer, a verification error correction layer, a data stream processing layer and an intelligent management layer which are in communication connection, the hardware security layer provides a physical trusted root for the system and executes a security policy; the encryption processing layer is used for carrying out encryption processing on data based on the security service provided by the physical trusted root; the verification and error correction layer is used for carrying out integrity verification and self-adaptive error correction coding on the encrypted data; the data stream processing layer is used for executing a near data calculation task on the data subjected to verification and error correction; and the intelligent management layer collects real-time state data from the hardware security layer, the encryption processing layer, the check error correction layer and the data stream processing layer, and performs dynamic strategy configuration and global optimization decision, thereby improving the active calculation, dynamic adaptation and intelligent optimization capabilities of the system.

The invention discloses a source domain irrelevant cross-domain cardiac beat recognition method and system for pseudo-label mining, and relates to the technical field of pseudo-label learning, and the method comprises the steps: obtaining electrocardiogram data with cardiac beat labels in a source domain, inputting a pre-established source domain model for pre-training, and obtaining a pre-trained source domain model; acquiring unlabeled electrocardiogram data of a target domain, and screening and classifying the data of the target domain based on a pre-trained source domain model and a preset category threshold to obtain data with high false label confidence and data with low false label confidence; using the source domain model to initialize target domain model parameters, revising a strategy based on a pseudo label of local and global semantic perception, updating the pseudo label of the low-confidence data, and combining the high-confidence data to obtain updated pseudo-labeled target domain data; target domain data subjected to data augmentation pseudo labeling are input into a target domain model, a target domain model optimization total loss function is calculated, and cross-domain cardiac beat intelligent recognition irrelevant to a source domain is achieved.

The invention provides a fault diagnosis method for a conveying belt. The fault diagnosis method comprises the steps that vibration data, pressure data and temperature data in the belt running process are collected through a sensor module; carrying out denoising processing on the collected vibration data, pressure data and temperature data, and removing abnormal values; feature parameters are extracted from the data subjected to data preprocessing; the extracted feature parameters are uploaded to a private cloud server through an Internet of Things module, and a lightweight algorithm is adopted to analyze the feature parameters; according to the analysis result of the lightweight algorithm, a preset fault threshold value is matched, and the fault type of the belt is output. According to the method, multi-source data fusion and targeted feature extraction are adopted, the fault false alarm rate is reduced to 10% or below, industrial data leakage is avoided through private cloud deployment, and the deployment cost is reduced by 60% compared with public cloud; based on a sensor and an open source tool, deployment can be completed within 30 days without professional teams, and belt conveyors in different scenes such as mine underground and logistics sorting can be adapted.

The invention provides a large-model content security multi-level defense method. The method comprises the steps that an input-processing-output full-link multi-stage cooperative defense framework is constructed, and the full-link multi-stage cooperative defense framework receives to-be-detected user input data and transmits sensitive word detection passing content to an intention recognition module; the intention identification module judges the risk level of the user input data, and routes the user input data identified as medium and high risks to the risk label classification module; the risk label classification module performs risk category classification on the user input data, and transmits the user input data subjected to risk category classification to the security enhancement module; and the security enhancement module generates compliant system response content through a domain fine tuning and reinforcement learning strategy, and returns the system response content to the user after performing security interception processing on the system response content. Through hierarchical defense, dynamic routing and security enhancement module training, a large model security enhancement scheme covering the whole process of input, reasoning and output is constructed.

The invention relates to an on-satellite disaster risk area lightweight extraction method and system, and belongs to the cross technical field of satellite remote sensing data processing and emergency disaster response. The method comprises the following steps: receiving a satellite remote sensing image and performing on-satellite preprocessing, and then performing on-satellite extraction and geospatial data conversion on a disaster risk area; performing data lightweight: performing boundary simplification on the data subjected to format conversion by using a boundary simplification function, performing centroid extraction by using a centroid calculation function, and generating simplified vector data; and downloading data based on the Beidou short message, and receiving and visually displaying ground data. According to the invention, core processing can be completed on a satellite, lightweight data is adapted to Beidou, and minute-level downloading to the ground is realized, and the timeliness bottleneck of disaster emergency response is broken through.

The invention provides a multi-mode fusion laser directional energy deposition real-time monitoring method and system, and belongs to the technical field of additive manufacturing. Comprising the steps that real-time data collection is conducted on a molten pool area in the laser directional energy deposition process through synchronous triggering of a multi-source sensor; a distributed clock network is constructed, environment influence parameters are collected in real time in combination with a working condition sensing unit, dynamic modeling and correction are conducted on asymmetric link delay in the clock synchronization process, and space-time alignment of multi-source sensor data is achieved; after the multi-source sensor data subjected to space-time alignment is preprocessed, data-level fusion, feature-level fusion and decision-level fusion are performed in sequence. The state of the molten pool can be accurately monitored in real time, and the quality and stability of the laser directional energy deposition process are improved.

A rehabilitation evaluation system for a patient with central nervous system dysfunction comprises the following steps of S1, collecting videos, motion data and pressure data of a user through a data collection layer, S2, uploading the video data to an edge computing server, performing preliminary posture estimation, and S3, performing rehabilitation evaluation. The method comprises the following steps: S1, carrying out edge calculation on the data, S3, transmitting the data subjected to edge calculation to a cloud server for deep modeling analysis of gait and balance parameters, S4, carrying out abnormal mode identification and comparison after parameter analysis, S5, uploading the analyzed data after comparison to a decision application layer, S6, transmitting each parameter and a corresponding processing scheme to a doctor end for a doctor to check, and S5, carrying out data processing. The method has the advantages that through full-link innovation of multi-mode lightweight acquisition, edge cloud hierarchical processing, double-model intelligent diagnosis, reinforcement learning dynamic optimization and visual remote decision, central nervous rehabilitation evaluation extends to a daily life scene from a laboratory, and core breakthrough of precision improvement, efficiency multiplication and personalized enhancement is achieved.

The invention belongs to the technical field of nuclear power data processing, and particularly relates to a multi-level data cleaning method for the nuclear power industry. Comprising a first hierarchy data importing and preprocessing layer, and the first hierarchy is a basic unit for nuclear power multi-hierarchy data cleaning; the second level carries out local processing layer data processing, and the data subjected to secondary cleaning is recompressed, feature codes are extracted, and then the data are transmitted to the third level; or directly forwarding the unprocessed original compressed data; the third hierarchy carries out data processing of the platform layer in the data; and the fourth level performs data processing of the data application layer, and is responsible for performing persistent storage on the data according to a standardized format after the data is subjected to full-process cleaning, compression, feature extraction and desensitization processing. The method has the beneficial effects that the method is specially customized for the nuclear power industry: special requirements of high confidentiality, network isolation, large data volume and the like of data in the nuclear power industry are fully considered, and a special cleaning and desensitization process is designed.

The invention discloses a common data platform compliance early warning system based on legal risk indicators, which belongs to the technical field of compliance management, and specifically comprises the following steps: a request analysis module receives a data processing request containing a data subject identifier, a processing purpose and a timestamp; the feature extraction module obtains dynamic authorization features such as a historical authorization range sequence and recent change records; the compliance analysis module generates legal risk indexes based on the legal rule base; the decision fusion module generates a compliance early warning decision through multi-dimensional fusion calculation; the execution control module realizes complete release, condition limitation or complete blocking according to the decision; the traceability recording module constructs a complete decision traceability record; according to the invention, through dynamic authorization analysis, multi-level law matching and intelligent decision fusion, accurate early warning and effective management and control of the compliance risk of the public data platform are realized.

An illustrative data storage management system comprises a management database that stores administrative preferences and system configurations, as well as results and / or statistics of completed secondary storage operations, i.e., information needed by the system to protect customers' data and to track and recover the protected data, including secondary copies such as backup copies, archive copies, etc. The disclosed data storage management system is configured to protect its own system data subject to a very aggressive (short) Recovery Point Objective (RPO), by using an innovative infrastructure that enables the system's storage manager to fail over to any number of other failover destination storage managers, each one comprising a destination management database. An illustrative database granularly tracks whether each and every transaction log file has been successfully applied to each and every destination management database to synchronize with the source management database.

Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and / or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and / or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.

The invention discloses a building carbon emission credible authentication method and system fusing the Internet of Things and an alliance chain, and belongs to the technical field of block chains, the Internet of Things and carbon emission management, and the method comprises the following steps: S1, introducing a multi-channel mechanism, and constructing an alliance chain network; s2, building energy consumption original data and basic building information are collected to form uplink data, and signature authentication is carried out; s3, each energy supply enterprise submits the data subjected to signature authentication to an alliance chain network through an on-chain API interface for integrity verification, and after verification is passed, the data subjected to signature authentication form a solidified storage evidence through a multi-node consensus mechanism; s4, on the basis of an intelligent contract authentication rule, performing authentication on the solidified and stored uplink building data; and S5, the building carbon emission intensity of the energy supply enterprise is calculated, and a calculation result and corresponding original data are bound and written into the alliance chain. According to the invention, the whole-process credible acquisition, authentication and supervision of the building carbon emission data are realized, and the carbon management efficiency and the data transparency are improved.

The invention provides a multi-source data chained storage system for a temporary flood response scene of a river channel, and relates to the technical field of data storage, time sequence association is performed on multi-source heterogeneous data to obtain the multi-source heterogeneous data after time sequence association, the problem of data islands is avoided, and data integration is realized; the method comprises the following steps: firstly, performing distributed encryption processing on target data, then performing distributed storage on the target data subjected to the distributed encryption processing to form chained storage data, and meanwhile, allowing a user to access the chained storage data, so that not only is the credibility of the data improved, but also the fault tolerance and the loss resistance of the data are improved; and finally, network security protection is carried out in the process that the user uploads the multi-source heterogeneous data after time sequence association and accesses the chain storage data, so that illegal users are prevented from tampering or attacking the data, and the loss resistance of the data is further improved.

The invention relates to the technical field of industrial automation and control, and discloses a whole-plant auxiliary network monitoring access processing method and system for IP conflict sub-control equipment, and the method comprises the steps: accessing a data interface machine and a network switch into each independently operating electric precipitation upper computer of different sub-control systems; the monitoring data are read from the corresponding electric precipitation upper computer through the data interface machine; label name conversion is carried out on the monitoring data in the data interface machine, and the name duplication phenomenon of the monitoring data is eliminated; performing network isolation on conflicting IP addresses of the sub-control systems through a network switch to form an independent communication link; and uploading the monitoring data subjected to label conversion and network isolation to a database of the auxiliary network monitoring system to realize data integration of the sub-control system and the auxiliary network monitoring system. According to the method, adverse factors such as normal operation of the electric precipitation equipment in the implementation stage are overcome, and the purpose of accessing the electric precipitation monitoring data to the power plant auxiliary network monitoring system is safely achieved.

The present disclosure involves systems, software, and computer implemented methods for data privacy. One example method includes receiving a first request to correct personal data or restrict processing of personal data of a data subject. Response data is identified that is provided by applications in a multiple-application landscape in response to a second request for access to personal data processed by respective applications in a multiple-application landscape. Relevant applications for the first request are identified based on the response data. A data correction or data restriction work package is sent to each relevant application and data correction or data restriction work package responses are received from relevant applications. An overall data correction or data restriction result is determined based on the data correction or data restriction work package responses and is provided in response to the first request.

The invention relates to the technical field of user portrait optimization, in particular to a user portrait optimization method and system based on big data, and the method comprises the steps: obtaining the multi-dimensional portrait data of a user in a user portrait system, and extracting the data distribution features of feature dimensions; calculating a consistency index, a redundancy index and a timeliness index of the data in the feature dimension according to the data distribution features to obtain an entropy value of the feature dimension; calculating an initial total entropy value of the user portrait system according to the entropy values of all the feature dimensions; when the initial total entropy value exceeds a preset entropy threshold value, a chaos source causing entropy increase is identified, and negentropy flow mechanism processing is carried out; recalculating the multi-dimensional portrait data processed by the negentropy flow mechanism to obtain an optimized total entropy value, and when the optimized total entropy value is smaller than a preset entropy threshold value, outputting the optimized user portrait data subjected to ordering processing and then analyzing to obtain an optimized user portrait; the user portrait system can be continuously optimized, and the user portrait accuracy is improved.

The invention discloses a mine intelligent decision-making method based on big data analysis, and belongs to the technical field of mine intelligent operation and maintenance management. Real-time change data of a mine is acquired, and the real-time change data is provided by a multi-source heterogeneous sensor network deployed at a corresponding production environment and execution equipment of the mine; data normalization processing is conducted on the real-time change data, a preset mine digital twinborn body is updated according to the data subjected to data normalization processing, and the mine digital twinborn body is a virtual mine model constructed by data corresponding to mine geomechanical parameters, equipment mechanisms and production process nodes; and generating an optimal decision scheme for optimizing the current production process according to the updated mine digital twin, and issuing the optimal decision scheme to the execution equipment to control the execution equipment so as to realize the real-time accurate operation and maintenance effect of the mine.

The invention relates to the technical field of cooling liquid characteristic analysis, and discloses a cooling liquid characteristic analysis and evaluation method, system, equipment and medium, and the method comprises the steps: collecting the characteristic data of cooling liquid and the related operation data of the equipment in real time, and carrying out the first processing; performing multi-dimensional feature extraction on the data subjected to the first processing to obtain a cooling liquid comprehensive performance index and a material compatibility risk score; the cooling liquid is grouped through a first clustering algorithm, high-cost-performance candidate liquid is identified, and the long-term performance attenuation trend of the cooling liquid is predicted; and generating a cooling liquid performance evaluation report based on an analysis result, and providing real-time monitoring, early warning and optimization suggestions. According to the method, scientificity and reliability of cooling liquid type selection are remarkably improved, the problem of balance among heat dissipation efficiency, material compatibility and environmental protection of a traditional evaluation method is effectively solved, and a scientific basis is provided for data center liquid cooling system design, cooling liquid type selection and operation and maintenance standard formulation.

A telemetry processing system in a cluster network receives telemetry data from a plurality of telemetry producers and formats it into a structured format for storage in a datastore. A telemetry producer can identify consumers to receive a new metric dataset as part of the telemetry data. This data may include sensitive data subject to compliance regulations with respect to data security and privacy. A telemetry collector validates schema of the new metric dataset and a list of consumers through an acceptance or rejection decision. If accepted, the listed consumers can receive the new metric dataset, otherwise they are blocked. This prevents the exposure of sensitive data to unvetted consumers.

The invention relates to the technical field of intelligent traffic systems, in particular to a marine traffic data fusion method based on a big data platform, and the method comprises the steps: carrying out the type division of marine traffic data, obtaining metadata information, respectively inputting the metadata information to a batch synchronization module and a real-time flow collection module, and obtaining the information after batch synchronization and the information after collection; processing the offline data, wherein the processing comprises storage decision, analysis and format conversion; the information subjected to batch synchronization and the processed offline data enter a batch processing layer to obtain data subjected to batch processing, and the collected information is input to a stream processing layer to obtain data subjected to stream processing; storing the batch synchronized information after data cleaning, the processed off-line data, the batch processed data and the data after the stream processing layer into a data storage center; and on the basis of the data stored in the data storage center, constructing a spatio-temporal joint index model to retrieve the maritime traffic data. According to the invention, technical support is provided for ship trajectory prediction business.

The invention discloses an intelligent fusion processing method, system and device for coal mine multi-source heterogeneous data and a storage medium, and relates to the technical field of data processing and distributed calculation scheduling, and the method comprises the steps: collecting multi-source heterogeneous data, constructing a feature signature, and generating a quality score; and generating a fusion result according to the feature signature and the quality score, collecting a node state through the fusion result, calculating a reward and punishment factor, and executing dynamic scheduling by using the reward and punishment factor. According to the method, format recognition is performed on collected multi-source heterogeneous data, and the multi-source heterogeneous data subjected to format recognition is output, so that unified input of the multi-source heterogeneous data in the same processing link is realized; constructing a feature signature object and generating a quality score based on the multi-source heterogeneous data subjected to format recognition, so that quality information is transmitted along with the data in a computable form; and the fusion processing has clear input, calculation paths and output.

The invention discloses a data resource sharing method and system based on a cloud platform, and relates to the technical field of data sharing, and the system comprises a coordination maintenance end, an authority management end and an exception handling end; the coordination maintenance end is used for carrying out data resource division by setting different keywords according to different data resources, and judging whether the same data subjected to data updating maintenance is the same or not; the authority management end is used for setting an authority level and performing level authentication; and the exception handling end is used for automatically identifying an authority authentication password number through password grading when data resource sharing is abnormal, positioning an abnormal authority grade in real time, and determining a data resource sharing compensation scheme through responsibility. According to the data resource sharing method and system based on the cloud platform, different departments are prevented from recording different address information of the same client, the authority authentication password of each user can be clearly distinguished during authority authentication of the system, and the safety, reliability and stability in the data resource sharing process are improved.

The invention relates to the technical field of space application, and discloses a spatial load material scientific experiment data processing method and system and related equipment, and the method comprises the steps: collecting load working condition data, experiment image data and material temperature data of a spatial load material scientific experiment, and carrying out the time sequence processing; time points corresponding to scientific experiment load laser closing in the load working condition data, the experiment image data and the material temperature data are judged respectively; carrying out data alignment and acquisition frequency normalization on the load working condition data, the experimental image data and the material temperature data based on the time point; and carrying out conjoint analysis processing based on various types of data subjected to data alignment and acquisition frequency normalization. According to the method, the time points of the scientific experiment load laser closing of the various data are determined, and the various data are subjected to data alignment and acquisition frequency normalization according to the time points, so that accurate association of the various data is realized, and the efficiency and accuracy of scientific experiment data analysis are greatly improved.

The invention discloses a building corrosion condition prediction method based on a deep learning model. The method comprises the following steps: collecting environment data of a building from a plurality of sensors and weather historical records; performing data cleaning and data standardization on the environment data; converting the environment data subjected to data cleaning and data standardization into time series data; based on the time sequence data and the corrosion occurrence rule, time sequence dimensions T and the feature number F of each time sequence dimension are determined; arranging the time sequence data into a time sequence data set with a format of [T, F]; building a building corrosion condition prediction model which comprises a convolutional layer, a pooling layer, a bidirectional long-short-term memory network layer, an attention mechanism layer and an output layer; and inputting the time sequence data set into the building corrosion condition prediction model to obtain a prediction result of the building corrosion condition. Multi-source heterogeneous data can be effectively integrated and processed, and the accuracy and efficiency of building corrosion prediction are improved.

The present disclosure belongs to the technical field of security of the Internet of vehicles, and discloses an intrusion detection method and device for an in-vehicle controller area network. The method of the present disclosure includes: digitizing and normalizing collected original data, obtaining preprocessed data, and dividing the preprocessed data into a training set and a test set; conducting feature selection on the preprocessed data through a particle swarm optimization (PSO)-light gradient boosting machine (GBM) bidirectional feature selection method; and classifying test set data subjected to the feature selection with a stacking integrated model, and obtaining an intrusion detection result. The present disclosure is configured to efficiently and accurately detect intrusion information appearing in the in-vehicle controller area network, and prevent security incidents of the Internet of vehicles caused by intrusion into the in-vehicle controller area network.