438 results about "Data subject" patented technology

The embodiment of the invention provides a document generation method and device based on artificial intelligence, and the method comprises the steps: extracting multi-modal data features of a historical multi-modal file through natural language processing and a computer vision algorithm, and constructing a corresponding knowledge graph according to the multi-modal data features, converting the multi-modal data subjected to feature extraction into high-dimensional vectors by using a vectorization algorithm, and storing the high-dimensional vectors to form a vector library; splitting a current document generation task into a plurality of sub-tasks by adopting a task planning algorithm, and executing the sub-tasks at the same time to obtain a first document; performing iterative optimization of document content according to the document first draft, the cue word and the language model to obtain an optimized first draft, acquiring related information from the vector library by using an attention mechanism-based retrieval algorithm, and fusing the related information and the optimized first draft through a text generation algorithm to obtain a final document; according to the invention, the accuracy and intelligence in the document generation process can be effectively improved.

The invention discloses a photovoltaic field area unmanned aerial vehicle inspection system, which relates to the field of unmanned aerial vehicle inspection, and specifically comprises a data acquisition and preprocessing module, a data fusion module, a data processing module, a data storage module, a data processing module and a data processing module, and is characterized in that the data acquisition and preprocessing module is used for acquiring environmental parameter data and carrying out dynamic data cleaning and preprocessing to obtain preprocessed environmental parameter data; carrying out weight adjustment on the basis of the preprocessed environmental parameter data, and carrying out cross-modal attention mechanism processing and synchronous fusion processing on the basis of the environmental parameter data subjected to weight adjustment to obtain cross-modal fusion features and synchronous fusion features; by introducing adaptive adjustment of environmental parameter data, cross-modal data fusion, anomaly detection and a self-learning optimization module, the high-precision inspection capability in a complex environment is realized, a data acquisition strategy is dynamically adjusted based on different environmental states, weight distribution and time synchronization fusion are performed on video images, thermal imaging and audio data, and the accuracy of inspection is improved. And the fusion precision of the multi-modal data and the accuracy of anomaly detection are effectively improved.

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 provides a glutenite logging curve identification method and device based on deep learning, and the method comprises the steps: collecting logging curve data, marking the types, and obtaining an original training data set; performing sample generation based on a quantum state mixed generative adversarial network algorithm to realize data expansion; inputting the expanded data into a feature extraction model, and training the feature extraction model; inputting the data after feature extraction into a feature dimension reduction model to train the feature dimension reduction model; inputting the data subjected to dimension reduction into a classifier model to train a classifier; and carrying out glutenite logging curve identification by utilizing the trained model. According to the method, the processing efficiency and the recognition precision of the logging curve data are effectively improved, the diversity and the quality of data generation are enhanced, the optimization problem in a traditional deep learning method is solved, and the generalization ability and the classification accuracy of the model for unknown data are further improved.

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 power plant data management method and system based on an SIS system, and relates to the technical field of data management, and the method comprises the steps: obtaining power plant operation data, and carrying out the unified formatting processing of the power plant operation data; preprocessing the formatted data, and removing noise and invalid data; performing quality verification on the preprocessed data, and checking data integrity and consistency; classifying, marking and storing the data subjected to quality verification into a database; and extracting and analyzing data from the database according to operation requirements of the power plant, and generating a decision report. According to the power plant data management method based on the SIS system, through unified formatting and time alignment processing, data format difference and time disorder between different devices are eliminated, and consistency and accuracy of data are ensured. And noise elimination, interpolation processing and anomaly detection are adopted, so that the data quality is guaranteed, and the reliability of an analysis result is improved. And aiming at data with different access frequencies.

The invention relates to a variable sampling start-based satellite-borne sliding spotlight SAR data processing method, which comprises the following steps of: carrying out distance direction blocking on echo data of variable sampling start, carrying out Fourier transform on sub-block data along the distance direction, multiplying by a phase compensation function, and carrying out sampling start alignment; performing two-step azimuth frequency domain aliasing removal on the sub-block data after sampling initial alignment; performing two-dimensional frequency domain matched filtering on the sub-block data after the azimuth frequency domain aliasing is removed; carrying out resampling on the sub-block data subjected to two-dimensional frequency domain matched filtering; and splicing the resampled sub-block data to obtain an imaging result of the whole imaging scene. According to the method, sampling initial alignment is carried out through a distance frequency domain azimuth time domain phase compensation function, zero padding does not need to be carried out on echo data, and the data processing difficulty is reduced; the focusing quality is improved by eliminating azimuth frequency domain aliasing, two-dimensional frequency domain matched filtering and two-dimensional frequency domain resampling.

The invention discloses a hierarchical processing system for energy storage data, and the system comprises an optimization feature extraction module which collects initial energy storage data, builds a unified time sequence on the time dimension of the initial energy storage data through a specified time interval # imgabs0 #, carries out the alignment sampling, introduces the initial energy storage data after the alignment sampling into an auto-encoder, and carries out the noise reduction processing, performing feature extraction on the data subjected to noise reduction processing to obtain optimized energy storage feature data; the dynamic grading adjustment module is used for standardizing the optimized energy storage characteristic data, calculating the information entropy of the standardized characteristic to obtain an initial weight, introducing a Q reinforcement learning algorithm to carry out online optimization on the weight, designing a third-order grading model based on the optimized initial weight, and carrying out dynamic grading adjustment on the optimal energy storage characteristic data; and performing data classification on different optimization energy storage characteristic data combinations and dynamic weights according to a third-order classification model, dividing the data into an emergency level, an important level and a common level based on the third-order classification model, and providing targeted processing and management strategies for different levels of data.

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 voice early warning system and method for coping with campus bullying prevention, and relates to the technical field of information technology and artificial intelligence application, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module used for acquiring audio data and point cloud data in campus toilets; the data processing module is used for measuring and calculating a decibel value of the audio data and judging whether the decibel value is greater than a preset decibel threshold value or not; obtaining a three-dimensional point cloud model and compartment point cloud slices based on the point cloud data; the bullying analysis module is used for performing bullying analysis on the basis of the audio data subjected to noise reduction processing, the three-dimensional point cloud model and the compartment point cloud slices; the bullying analysis comprises the following steps: performing semantic recognition and segmentation based on the audio data to obtain audio semantics; carrying out human body posture estimation, behavior classification and recognition and abnormal behavior detection based on the three-dimensional point cloud model to obtain a behavior monitoring result; and carrying out area calculation based on the compartment point cloud slices to obtain the point cloud slice area.

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.

In order to apply a text-based label inference approach to infer a label to be assigned to target data in a common data form, an inference apparatus (1) includes: a data converting section (11) for converting target data subject to label assignment into text; and a label inferring section (12) for inferring a label to be assigned to the target data, in accordance with a label inference model for inferring a label to be assigned to text and the text obtained by the converting carried out by the data converting means.

The invention relates to an intelligent glasses control method and system based on multi-modal privacy protection, and the method comprises the steps: collecting multi-modal sensory data of a user, carrying out the preprocessing of the multi-modal sensory data, and generating sensory sharing data; analyzing the sensory shared data by using a multi-modal privacy feature extraction algorithm to generate a privacy feature vector; classifying and identifying the privacy feature vectors based on a multi-layer privacy judgment rule to obtain a judgment result; performing data processing based on processing logic in the multi-layer privacy decision rule to generate shared data after privacy protection processing; storing the shared data subjected to privacy protection processing to a cloud end, retrieving the corresponding shared data subjected to privacy protection processing from the cloud end according to the sharing demand information of the target equipment, and encrypting the shared data to generate encrypted shared data; and decrypting and reconstructing the encrypted shared data in the target equipment to generate shared display data. The method has the effect of improving the accuracy of identifying the privacy data of the intelligent glasses.

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 discloses a site pollution abatement scheme generation method and device, and the method comprises the steps: collecting the pollution data of a target region in multiple dimensions, and the pollution data at least comprise pollutant characteristic data, site characteristic data and environmental factor data; performing data processing on the pollution data of each dimension, and fusing the pollution data of each dimension after data processing to obtain fused multi-modal data; inputting the fused multi-modal data into a pre-constructed scheme generation model to obtain a pollution abatement scheme output by the scheme generation model; wherein the scheme generation model is constructed by utilizing the historical site pollution data subjected to data processing and a historical treatment scheme corresponding to the historical site pollution data. Through model construction and calling, the pollution control scheme for selection and reference is generated, and the technical problem that generation of the site pollution control scheme completely depends on artificial experience and site investigation is solved.

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 embodiment of the invention provides a differential privacy algorithm optimization method and device for enterprise credit data privacy protection, and the method comprises the steps: analyzing the query type of enterprise credit data, and adaptively adjusting a noise parameter; based on an improved noise mechanism, selectively increasing noise; combining the plurality of data queries into one data query; preprocessing the enterprise credit data subjected to noise processing so as to perform privacy protection on the enterprise credit data through a differential privacy algorithm introduced by noise; noise is flexibly adjusted according to specific query conditions, accuracy of results can be remarkably improved while privacy is protected, performance of data query and analysis is improved through selective noise addition, unnecessary noise interference is reduced, multiple queries are combined into one query, noise introduction frequency is reduced, and data query efficiency is improved. The comparability among different query results can be improved.

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 discloses a stain analysis and learning model combined static source code scanning method, and belongs to the technical field of source code scanning. Comprising the following steps: acquiring source codes in a project code library to establish a source code set, and analyzing the source codes in the source code set into an abstract syntax tree and code features in the abstract syntax tree; obtaining source codes in the source code set, public data of the vulnerability database and historical vulnerability code samples in the enterprise code library to build a data set, building a deep learning model, standardizing data in the data set to obtain marked data, forming a training set by the data subjected to optimization training of the deep learning model, and storing the training set in the enterprise code library; training data in the optimization model are collected to serve as a verification set to evaluate the optimization model, model analysis results are obtained, an analysis report is obtained for the two analysis results, specific vulnerability types are recognized, and repair suggestions and codes are provided. According to the method, potential security vulnerabilities can be identified, detailed repair suggestions can be provided, and developers are helped to quickly solve security problems.