304 results about "Data subject" patented technology

The invention discloses an intelligent access API generation method, system and device and a medium, and the method specifically comprises the steps: generating a dynamic dragging interface based on user role information, and obtaining function access parameters customized by a user through the dynamic dragging interface; according to semantic features and operation sequences in historical user interaction data, user intentions are analyzed, and target data topic logic is generated in combination with function access parameters; converting the subject logic of the target data into an executable query statement by utilizing an SQL splicing tool, and recommending an index optimization scheme to a database engine based on a high-frequency condition and field combination in a historical query mode; and distributing the optimized executable query statements to Serverless workflow nodes adapted to different query engines for execution, and packaging returned data into a standardized API (Application Program Interface) for output. According to the method and the device, full-process automation from parameter customization by a user to standardized API output is realized, and the efficiency and the flexibility of data acquisition are remarkably improved.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

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 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 discloses a workflow-based multi-modal water conservancy large model decision support method, which comprises the following steps of: S1, preprocessing multi-modal input data based on a local model, and converting a data format; s2, constructing a modular workflow supporting parallel processing through a specified DSL (Digital Subscriber Line) framework; s3, constructing a feature model suitable for the water conservancy field through the multi-modal data subjected to synchronous acquisition and processing, and performing field adaptation and fine adjustment on the feature model; s4, on the basis of the feature model subjected to domain adaptation and fine adjustment, a specified workflow is constructed, and multi-modal task resources are dynamically scheduled; and S5, combining a local knowledge base with networking data by using a dynamic fusion algorithm to realize dual-channel knowledge fusion for executing the multi-modal task in the S4 in parallel. According to the invention, real-time acquisition and parallel processing of multi-modal data can be realized, and the stability of a local knowledge base and the real-time performance of networking data are dynamically balanced through a dual-channel knowledge fusion mechanism.

The invention relates to the technical field of data processing, and discloses an online content recommendation method and system based on semantic discovery, and the method comprises the steps: obtaining literature data, and carrying out the data cleaning; performing natural language processing on the literature data after data cleaning, and performing vector conversion on the literature data after natural language processing; training and updating a vector semantic model in real time by using the literature data subjected to vector conversion, and generating a vector semantic recommendation list by using the trained vector semantic model; carrying out keyword frequency calculation on the basis of the literature data after data cleaning, obtaining a text similarity result based on a result of keyword frequency calculation in combination with a BM25 algorithm, and generating a text similarity recommendation list by using the text similarity result; and fusing the vector semantic recommendation list and the text similarity recommendation list to generate a fusion recommendation list. According to the method, the relevance between the literatures can be comprehensively captured from the two dimensions of semantics and text similarity.

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 an intelligent disk monitoring system based on multi-modal data fusion and a fault early warning method, and relates to the technical field of power plant management, and the method comprises the steps: collecting the real-time operation data of equipment; performing dimension reduction processing on the operation data by using a principal component analysis algorithm; inputting the data subjected to dimension reduction into a long short-term memory network model, modeling the time sequence dependence of the data by using the long short-term memory network model, and predicting the future operation state of the equipment; based on the prediction result of the operation state, performing feature extraction on the data through a convolutional neural network; according to an output result of the convolutional neural network, alarm signals of different levels are generated according to set threshold levels; and according to the grade of the alarm signal, adjusting operation parameters of the equipment through a feedback control mechanism. According to the intelligent disk monitoring system based on multi-modal data fusion, the accuracy, the real-time performance and the intelligent level of equipment monitoring are improved, and the fault early warning precision and the system response speed are remarkably improved.

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.

The invention belongs to the field of multi-modal RGB-T data fusion, and discloses a distributed streaming multi-modal fusion-oriented adaptive gradient compression optimization method and system, and the method comprises the steps: carrying out the personalized compression of data features of different modals through employing a modal-sensitive gradient compression strategy, and enabling the modals to comprise an RGB modal and an infrared modal; introducing physical constraint time sequence alignment loss, and performing time sequence alignment on the multi-modal data subjected to personalized compression by utilizing a physical model based on a thermal diffusion equation; the dynamic batch processing strategy is sensed through the video memory, the use condition of the video memory is monitored in real time, and the batch processing size is dynamically adjusted according to the residual capacity of the video memory. According to the method, a brand new solution is provided for efficient training of distributed streaming multi-modal data, and the precision of a multi-modal data fusion task and the resource utilization rate are improved.

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 discloses a relational data regression value prediction method based on composite ensemble learning, and belongs to the technical field of data analysis and machine learning. Firstly, principal component analysis and uniform manifold approximate projection are utilized to carry out dimensionality reduction and feature selection on data, main features of the data are extracted, dimensionality of the data is reduced, and main information in the data is reserved. Then, two stacked integrated learning models are adopted to predict the data subjected to dimension reduction, the two models are trained and predicted by adopting different base learners, and the advantages of the base learners are fully utilized; meanwhile, the output weight of the base learner is optimized by adopting a gradient descent method, so that the prediction accuracy is improved. And finally, integrating the output results of the first two models by using the third mulberry stack ensemble learning model, and optimizing the output weight of the model by using the gradient descent method. The multi-level integrated learning strategy can effectively improve the prediction stability and improve the generalization ability of the model. The method has universality and expansibility and is applied to various data intensive fields.

The invention discloses a communication method and system for interconnection and intercommunication between charging platforms, and the method comprises the steps: obtaining a platform standardized data package according to the original data of a charging pile; according to the platform standardized data packet, obtaining a data stream after peak clipping; according to the data stream after the peak clipping, obtaining a middle and electric connection 102-X protocol interaction message; obtaining a data frame which is converted into a target platform according to the message interacted by the medium and electric connection 102-X protocol; obtaining an encrypted data block and a signature abstract according to the data frame of the target platform; according to the encrypted data block and the signature abstract, obtaining data subjected to secure transmission and verification; and according to the data failed in verification or the abnormal data, obtaining the repaired data or alarm information. According to the invention, the efficiency and accuracy of data communication are improved.

The invention discloses a belt tearing detection model training method and detection method based on space-time sample enhancement, and the training method comprises the steps: collecting an original image sequence of a material conveying belt, and marking the original image sequence to obtain an original mask sequence; performing time sequence sample amplification of random cloning on the time dimension on each original image sequence and the original mask sequence, and further performing scene batch amplification for enhancing sample richness; and performing iterative optimization on pre-constructed belt tearing detection network parameters by using the data subjected to time and space enhancement: capturing time sequence dependence between images, complementing spatial features of key frames with time sequence dependence features, and gradually fusing complementary spatial and temporal feature information with multi-scale spatial features of the key frames to obtain a final feature map. And carrying out region segmentation and error loss calculation on the tearing target, gradually iteratively optimizing a target segmentation result, and finally obtaining a model with good training parameters.

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.

A pseudonymisation method is disclosed. The pseudonymisation method comprises receiving a data subject identifier from a first data controller, the first data controller having a first data controller identifier, computing a pseudonym seed based on the data subject identifier using a cryptographic mixing function based on a pseudonym seed generation key, and computing a global pseudonym as a first hash-based message authentication code on the pseudonym seed with a global pseudonym key.

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 provides a system and computer implemented method for managing personal data protection and mitigating risks through predefined classes of harmful activities. It establishes criteria for Privacy Impact Assessments (PIA), pseudonymization, and privacy-by-design and by-default measures, enabling precise risk evaluation and mitigation. The process operates in iterative phases addressing misuse, identification, and root risks, with decisions based on context and processing of personal data. The system includes modules for data processing, risk assessment, and action proposals to ensure effective implementation of protective measures. If standard methods are insufficient, consultation with data protection authorities is included. This innovation streamlines compliance with privacy regulations, offering a clear, scalable, and efficient approach to safeguard data subjects' rights and freedoms.

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 provides a one-stop multi-mode video generation system and method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining various types of data from a plurality of input nodes in response to a video generation task; carrying out preprocessing on the obtained various types of data; carrying out multi-modal data fusion processing on the preprocessed data of various types; and according to the data subjected to multi-modal data fusion processing, generating corresponding videos by using pre-constructed customized video generation models corresponding to different types of video generation tasks. The problem of single function in the video generation process in the prior art is solved, the quality of the generated video is improved, the user interactivity and personalization are enhanced, the automation and intelligence level is improved, and creative fusion and diversified output are realized.

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.

The invention discloses a large language model prediction and evaluation method for financial statement analysis. The method comprises the following steps: collecting and arranging original financial statement data; anonymization processing and standardization processing are sequentially carried out on the financial statement data; designing a structured thinking chain prompt based on the financial statement data after anonymization processing and standardization processing; inputting the financial statement data subjected to anonymization processing and standardization processing and the thinking chain prompt into a large language model, so that the large language model performs prediction according to the thinking chain prompt; the prediction result output by the large language model is evaluated from multiple dimensions, if the evaluation result of each dimension is within a set error range, an analysis report is generated according to the prediction result, and otherwise, the designed thinking chain prompt is optimized. According to the method, the data processing efficiency is improved, the analysis dimension and depth are improved, and the subjectivity and consistency in analysis are improved.

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.