395 results about "Data Property" patented technology

Various systems and methods are described implementing a multi-access edge computing (MEC) based system to realize MEC federation management and broker functions for MEC frameworks. In an example, performing edge federation management functions of edge computing systems, to establish a partnership among multiple edge federation managers as a federation, include: using system data attributes to establish the partnership; using authentication data attributes to enable the edge federation managers to securely authenticate; using authorization data attributes to enable the edge federation managers to perform authorization; using availability zone data attributes to define zones in the federation; and using management and settlement information data attributes to enable management of resources in the federation. Further operations include communicating the data attributes via respective connections with the edge federation managers, and the use of defined interfaces and operations.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a data grouping optimization processing method, device and equipment and a medium. The method comprises the steps that input data are collected, cleaning and format unification are completed, and cleaned data are generated; generating a grouping mark from a condition constraint library based on the data attribute field; dividing the data into a plurality of subsets according to a segmentation strategy and distributing the subsets to a plurality of processing nodes; generating a grouping key in each processing node according to the grouping mark and the data attribute field; grouping identifiers are distributed for the records based on the grouping keys, and a local grouping result table is formed; and finally, collecting all local results, verifying the consistency, and generating a global grouping result table. According to the method, sorting and key generation are performed in combination with the data attribute fields and the grouping marks, high-precision grouping identification is realized, and the accuracy of a global grouping result is guaranteed through an inter-node consistency verification mechanism.

The invention relates to the technical field of data cross-domain access control, in particular to a block chain-based trusted data space cross-domain access control method, system and device and a medium, comprising: receiving a data access request, and analyzing a target data identifier, a target heterogeneous system identifier and access scene information; obtaining the data attribute of the target data according to the target data identifier, generating a dynamic access strategy in combination with the access scene information, and deploying the dynamic access strategy into the block chain; mapping the dynamic access strategy into an equivalent access control rule, and converting the data access request into target compatible request data; verifying zero-knowledge proof provided by the user based on an equivalent access control rule and a user public key; after the verification is passed, routing the target compatible request data to the target heterogeneous system; and the target heterogeneous system generates original response data, converts the original response data into user-side compatible response data and returns the user-side compatible response data to the user. According to the invention, cross-system efficient cooperation is realized, and the real-time adaptability and privacy enhancement security of access control can be improved.

System and methods are provided for achieving data standardization and normalization through an Agnostic Data Format (ADF) architecture. ADFs systems and processes provide a transformative bridge, enabling disparate data sources to converge into a unified and standardized format within the Real-Time Data Mesh (RTDM) framework. This dynamic process utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms to interpret and align diverse data attributes. The ADF management system, integrated into a dynamic event-driven architecture, allows vendors to interact with RTDM by translating and standardizing their data. The synchronized data integrates canonically, incorporating real-time updates and collaborative decision-making across the distribution platform. This innovative approach enhances operational efficiency, enables data-driven decision-making, and provides users improved ability to use data within the distribution ecosystem.

The invention provides a multi-attribute score fusion recommendation method based on user preferences and attention, and the method comprises a data collection and preparation module which is used for obtaining and cleaning user, project and score data; the attribute coding module is used for carrying out numerical coding on user and project attributes; the multi-attribute feature fusion module is used for obtaining user-project comprehensive representation by splicing attribute vectors and utilizing a deep neural network to extract interaction features; the fusion attention mechanism module is used for dynamically distributing each attribute weight and realizing weighted fusion of multi-attribute features; the comprehensive evaluation score calculation module is used for predicting the score of the user on the project according to the fusion feature and the attention distribution; the global preference modeling module is used for fusing historical behaviors of the user and improving the modeling capability of the overall preference; the recommendation result output module is used for generating a personalized recommendation list; and the method evaluating and updating module is used for evaluating recommendation performance and dynamically optimizing the model.

The invention discloses a data processing method and device, a medium and a program product, and relates to the technical field of storage. Encoding the data to be stored according to the size of the data file; determining a target data hierarchy according to the data attribute of the to-be-stored data; determining a target data protection strategy corresponding to the target data hierarchy according to the data attribute of the target data hierarchy; according to the target data protection strategy, determining the number of data blocks and verification blocks needing to be written into the to-be-stored data; determining target storage nodes corresponding to the data blocks and the verification blocks according to the number of the data blocks and the verification blocks needing to be written in the to-be-stored data; and storing the coded data to be stored to the target storage node according to the target data hierarchy and the target data protection strategy. The utilization rate of the disk and the data read-write performance can be considered, the resource calculation overhead is reduced, and the file processing efficiency is improved.

The invention relates to a sensitive data identification and desensitization method and system based on an AI large model, and belongs to the technical field of data security processing and artificial intelligence. The method comprises the following steps: receiving a multi-source data processing request, establishing a sensitive data identification framework, analyzing elements of sensitive data, and extracting a sensitive data feature vector through a natural language processing algorithm; classifying the multi-source data, dynamically adjusting an identification threshold value of the sensitive data identification framework through a sensitive data feature vector, and generating a self-adaptive sensitive data identification strategy; establishing a terminal desensitization verification model, performing desensitization verification on the identified sensitive data, performing mask replacement or generalization processing according to privacy requirements, and outputting a desensitization retry processing scheme; the sensitive data is subjected to block desensitization, a large data set is divided into small data sets for independent desensitization, replaceable entities in information are removed according to data attributes, and the desensitized data is further subjected to secondary desensitization.

The invention provides a user electricity consumption anomaly detection method, system and device based on intelligent electric meter terminals and a medium, applied to a user electricity consumption anomaly detection system, the system comprises a plurality of intelligent electric meter terminals, a central processing unit and a plurality of cloud servers, and the method comprises the following steps: the intelligent electric meter terminals collect user electricity consumption, environment and scene data; and the power consumption data set is sent to the central processing unit. And the central processor determines an encryption strategy and a target server according to the data attribute, and transmits the encrypted data to the target server. And the target server decrypts the screened data to form a target data set, extracts features to generate a user power consumption portrait, screens a reference power consumption portrait for comparison, and judges whether the target user is abnormal in power consumption according to the reference power consumption portrait. By adopting the scheme, the accuracy, the safety and the efficiency of electricity consumption anomaly detection can be improved.

The invention discloses a vehicle data transmission method and device, electronic equipment and a storage medium. The method is applied to a data sending end, and comprises the following steps: in response to a received data transmission instruction, determining to-be-transmitted data corresponding to the data transmission instruction and a data receiving end; determining a target compression algorithm based on target compression configuration information and the multi-dimensional decision information, the target compression configuration information being used for representing compression configuration information jointly supported by the data sending end and the data receiving end, the multi-dimensional decision information at least comprises two of the following: a network resource parameter, a computing resource parameter and a data attribute parameter of the to-be-transmitted data; compressing the to-be-transmitted data by using the target compression algorithm to obtain processed data; and transmitting the processed data to a data receiving end. According to the invention, the technical problem of low transmission efficiency and adaptability of vehicle data transmission in the prior art is solved.

Apparatus for predicting an outcome of user engagement and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive user data that include a plurality of data attributes, populate a data retrieval module using the user data, the data retrieval module including assessment metrics, wherein populating the data retrieval module includes assigning a weight to each data attribute of the data attributes as a function of at least an assessment metric of the assessment metrics, rank the data attributes as a function of the weight, and generate an outcome prediction, as a function of the ranked data attributes.

A collaborative management authority rule optimization system based on community discovery comprises a feature extraction module, an organization role screening module and a data attribute screening and authority distribution control module. Generating a standardized user feature vector and a data vector through structured coding and semantic modeling; the organization role screening module constructs a user collaboration graph based on a community division algorithm, and reasones user optimal role mapping by using a community portrait model; the data attribute screening module is combined with a data classification tree and a structure dependency relationship extraction algorithm to recognize a semantic category and upstream and downstream dependency chains of target data; and the permission allocation control module performs reasoning in combination with a permission knowledge base according to roles, data classification and a dependency relationship, and finally generates a permission allocation table, so that dynamic permission configuration and strategy matching oriented to a collaborative scene are realized. Through automatic role mapping, data semantic association analysis and authority inheritance reasoning, accurate authority distribution and real-time strategy adjustment in a ship collaboration scene are realized, so that data security and collaboration efficiency are improved, and core support is provided for digital transformation of the ship industry.

The invention discloses a big data real-time calculation abnormal value filtering method and a terminal. The method comprises the following steps: acquiring scene basic data, establishing a relevance rule of master data and slave data in the scene basic data, determining upper and lower limit parameters of the master data, the slave data and the scene basic data according to data attributes, and calculating a basic data value of the scene basic data; determining a preset range of the scene basic data by using machine learning in combination with upper and lower limit parameters of the master and slave data and the scene basic data; if the basic data value of the scene basic data is within the preset range, calculating the fluctuation range and the fluctuation value, and if the fluctuation value exceeds the fluctuation range, determining that the scene is abnormal; if the fluctuation value does not exceed the limit, the deviation proportion is calculated, and if the deviation proportion exceeds a preset abnormity judgment coefficient, abnormity is judged; if the deviation proportion does not exceed the limit, finally judging the deviation proportion through an abnormal rule in a preset decision tree; abnormal data are eliminated, filtered data and basic data values thereof are obtained, and the adaptability and reliability of abnormal value detection are improved.

The invention discloses a sensitive information safety protection method and system, and relates to the technical field of intelligent preoperative evaluation and planning of thoracic surgery, and the method comprises the steps: S1, receiving an environment sensing signal which comprises an access subject attribute, a target data attribute and an operation environment parameter; s2, generating a dynamic strategy signal and a dynamic strategy signal based on the environment sensing signal; s3, sending the dynamic strategy signal to an execution engine, and triggering the engine to generate an execution signal containing a field-level mask instruction or a record-level permission instruction; s4, performing real-time processing on the target data according to the execution signal, and generating an output signal containing a part of visible fields or a permission limited operation interface; and S5, binding the environment sensing signal and the dynamic strategy signal as an auditing traceability signal and storing the auditing traceability signal. According to the sensitive information security protection method and system, the problem of excessive protection or insufficient defense of sensitive information in a dynamic use scene due to a static protection strategy can be solved.

The invention relates to a power operation data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring power data of each data source in a power system; analyzing data attributes of the power data, and converting the data attributes into a predetermined format to obtain target data; based on the target data and the corresponding data attributes, establishing a knowledge graph; wherein the knowledge graph comprises nodes and association relationships between the nodes; when it is monitored that the electric power data of any data source has a change condition, updating the knowledge graph based on the change condition; and screening out key nodes from the knowledge graph, and carrying out real-time monitoring on the key nodes according to an anomaly detection algorithm to obtain an anomaly detection result. Therefore, the knowledge graph is established based on the target data and the data attributes, the dispersed data sources and data entities in the power system are presented in a node form, and the association between the data sources and the data entities is reflected by using the association relationship, so that workers can intuitively understand the internal relationship among the elements of the power system.

The invention discloses a privacy protection data classification method and system based on a cloud platform, and relates to the field of data classification management, and the method comprises the steps: distributing sensitivity tags for different data attributes at a client according to a preset strategy, and carrying out the encryption through selecting a homomorphic encryption public key matched with the tag level, and forming a ciphertext beam containing the ciphertext and the label. And then, at the cloud, guiding a subsequent calculation process by using the tag vector in the ciphertext beam. Through a self-adaptive encryption model, different calculation paths can be dynamically selected to execute homomorphic feature engineering and classification calculation according to whether high-sensitivity data exists in a label or not. According to the method, refinement and isolation processing of data with different sensitivities is realized, an encryption strategy and calculation logic are deeply bound, and dynamic balance between calculation efficiency and security level is realized on the premise of ensuring data security.

The present application relates to the technical field of storage, and discloses a data attribute-aware storage system and data management method for a key-value database. The method comprises two parts, i.e., data hotness attribute-based writing and data density attribute-based merging. The data hotness attribute-based writing comprises classifying key-value pairs into cold key-value pairs and hot key-value pairs, to realize separate storage of cold data and hot data by means of a data hotness attribute-based writing strategy. The data density attribute-based merging comprises: when key-value pairs in an SSTable are extracted during a merging operation and form a sorted key-value pair sequence, extracting the key-value pairs one by one, and calculating a binary difference between every two adjacent key-value pairs; and if the difference is greater than a set threshold, stopping filling the current SSTable, and creating a new SSTable. In the present invention, by analyzing data hotness and data density, data having different attributes are classified to different data partitions, thereby reducing I / O amplification caused by repeated reading and writing in a compression process.

Embodiments of the present disclosure provide a data query method and apparatus, an electronic device, and a storage medium. A data query statement inputted by a user is displayed. The data query statement includes at least one expression field, and the expression field includes a first object field and a second object field that are arranged in sequence, the first object field representing a data object, and the second object field representing a data attribute of the data object. The expression field is used to define a data query dimension of a target business process, and the query statement is used to represent a target query dimension formed by at least one data query dimension. Target data is obtained by responding to the data query statement. The target data is a result of querying business node data of the target business process based on the target query dimension.

Recommending machine learning models is provided. The method comprises training machine learning models, wherein each machine learning model is trained with a unique respective dataset. Metadata associated with each machine learning model is extracted, wherein the metadata includes properties of the respective dataset used to train the machine learning model. The machine learning models and metadata are stored in a model catalog. Upon receiving a new dataset, similarity scores are calculated between the new dataset and the machine learning models in the model catalog according to the properties of the datasets in the metadata of the machine learning models. A closest match machine learning model is identified from the model catalog for the new dataset according to similarity score. Responsive to a determination that the closest match machine learning model exceeds a similarity threshold, predictions for the new dataset are generated with the closest match machine learning model.

The invention belongs to the technical field of data security, and particularly relates to a community data sharing and exchanging security control method based on a block chain, which realizes fine-grained and dynamic access control by performing refined permission division on user characteristics and data attributes and constructing a verifiable attribute permission mapping relationship in an intelligent contract. The slave logic layer prevents unauthorized access vulnerabilities; a mode of combining a fully homomorphic encryption algorithm and a lightweight homomorphic encryption algorithm is adopted, direct operation of data in an encryption state is realized, a permission verification mechanism without data content leakage is constructed in combination with zero-knowledge proof, and dual protection of'calculation without decryption and verification without leakage 'is formed. According to the method, the data leakage risk caused by smart contract code vulnerabilities is fundamentally immunized, an extensive permission allocation mode is broken through by an attribute-based permission management method, refined permission division can be performed according to actual attributes of users and data, permission abuse and unauthorized access are effectively avoided, and the normalization and security of data access are greatly improved.

The invention provides a multi-variety order dynamic scheduling management method and system for a liquid crystal display module production line, and relates to the technical field of industrial automation, and the method comprises the steps: collecting the size, resolution, interface type, batch and delivery time data of all orders, obtaining the process equipment, jig configuration, material inventory state and material supply plan data of the production line at the same time, obtaining an order data set and a resource data set; based on the order data set and the resource data set, an order and resource associated data point set is constructed, a three-dimensional virtual voxel space is defined in the virtual structure, and the order and resource associated data point set is mapped to corresponding voxels one by one; and recording the number of data points of each voxel falling into the three-dimensional virtual voxel space, the corresponding order type, the resource number and the material demand matching degree information to obtain a voxel sub-region with a data attribute mark. According to the invention, dynamic optimization of the scheduling scheme is realized.

The invention discloses an information-fused centrifugal pump cavitation fault diagnosis method, which comprises the following steps of: data acquisition: installing an acoustic sensor and an acceleration sensor at key parts of a centrifugal pump, and acquiring an acoustic signal and a vibration signal; data preprocessing: sequentially carrying out noise filtering and normalization processing on the collected sound signals and vibration signals to obtain preprocessed signals; enhancing fusion, and performing weighted fusion coefficient adaptive adjustment on the preprocessed signal; clustering fusion: the weighted fusion signals are clustered according to data attributes, fusion of different sensor data is completed in combination with data difference and complementarity, and fused information is obtained; performing MFDFA analysis, inputting the online preprocessed data into a digital-analog linkage prediction model, and outputting a predicted value of the residual life of the equipment; and fault judgment: inputting the online preprocessed data into the digital-analog linkage prediction model, and outputting a predicted value of the residual life of the equipment. According to the method, the cavitation fault of the centrifugal pump can be accurately recognized.

The invention relates to a data security management and control strategy optimization method based on multi-dimensional risk assessment. The method comprises the steps that in response to a data operation request for a power grid system, a risk factor tuple of the data operation request is acquired, and the risk factor tuple comprises at least one of data attribute information, operation behavior information and environment state information; obtaining a first risk score of the data operation request according to the risk factor tuple and the corresponding risk weight information; determining a target management and control strategy of the data operation request from a preset strategy template library according to the first risk score; and performing internal and external circulation verification according to the risk factor tuple, the first risk score and the target management and control strategy, obtaining a single strategy validity identifier, and updating the risk weight information. By adopting the method, the risk management and control effect can be improved.

The invention relates to the technical field of server data protection, and particularly provides a server data security reinforcement method and system for dynamic vulnerability monitoring, and the method comprises the steps: setting a data classification rule according to the attribute of data borne by a server, and synchronously obtaining multi-source security data and server real-time operation data based on a data synchronization rule; inputting the two types of data into a pre-training risk analysis model, adjusting a risk judgment weight in combination with a protection priority of a grading rule, and outputting a risk level and a development trend; based on the risk result and the protection priority, determining a reinforcement mode and an execution priority of the hierarchical data, and executing security reinforcement according to the priority; and verifying reinforcement validity, dynamically adjusting a grading rule, a synchronization rule or a model parameter based on the risk and reinforcement related data, and completing iterative optimization. The problems of many invalid early warning, passive response, insufficient reinforcement flexibility and the like in the prior art are solved by constructing closed-loop protection logic, the data security is balanced, and the protection precision and the operation and maintenance efficiency are improved.

Embodiments of the present disclosure provide a data processing method and cluster, a computing device, a computer readable storage medium, and a computer program product. The data processing method is applied to an application program interface service unit of the data processing cluster. The method comprises: receiving a data processing request sent by a client for a target storage unit, wherein the data processing request carries data to be processed and a unit path of the target storage unit; analyzing the data to be processed, and when it is determined on the basis of an analysis result that the data attribute of the data to be processed is a target data attribute, encrypting the data to be processed to obtain ciphertext data and an encryption key; and sending the ciphertext data and the encryption key to the target storage unit on the basis of the unit path, thereby minimizing an attack surface of a malicious user to a node level, and reducing the risk of sensitive data leakage.

The invention relates to the technical field of data security, and discloses an automatic data grading protection method and system based on multi-feature fusion. The method comprises the following steps: acquiring a multi-source heterogeneous data stream, and extracting metadata type features and content semantic features in the multi-source heterogeneous data stream; dividing data dimension levels according to metadata type features, and generating a multi-dimension feature set in each dimension level based on content semantic features; and performing density clustering analysis on the multi-dimensional feature set, generating a dynamic grade threshold according to a clustering boundary distance and feature distribution density, and completing initial data grading. By fusing metadata types and content semantic multi-class features and combining density clustering and dynamic threshold adjustment, the method can comprehensively capture data attributes and improve the accuracy and adaptability of grading, is suitable for automatic grading protection of multi-source heterogeneous data, and effectively deals with challenges brought by dynamic changes of data features.

A method uses natural language processing for visual analysis of a dataset by a computer. The computer displays a data visualization based on a dataset retrieved from a database. The computer computes an initial visualization state that includes elements corresponding to data attributes of the data visualization. The computer receives user input to specify a natural language command related to the displayed data visualization. The computer extracts cue phrases from the natural language command. The computer also determines a transitional goal, to transform the data visualization, based on the cue phrases. The computer derives an updated visualization state based on the transitional goal, by applying transitional rules to each element of the initial visualization state. The computer subsequently computes analytical functions associated with the visualization states, thereby creating functional phrases. The computer then updates the data visualization based on the functional phrases.

The invention relates to an electric power metering equipment supplier evaluation method and device. The method comprises the steps that when supplier data belongs to asset data, sorting is carried out based on data attributes to which the asset data belongs, scoring is carried out through a statistical method according to index scores of the asset data and the number of first scoring intervals in combination with a sorting result of the asset data, and a first scoring result is obtained, when the supplier data belongs to the business data, sorting based on the data attribute to which the business data belongs, and scoring by combining a ranking result in the sorting result of the business data through a statistical method according to the index score of the business data and the number of second scoring intervals to obtain a second scoring result, determining a comprehensive score of the electric power metering equipment supplier according to the first scoring result and the second scoring result; according to the method, based on a statistical method, a corresponding method is adopted for scoring for supplier data of different data types, and the evaluation reliability is improved.

The invention relates to the field of remote sensing image processing, and particularly discloses a natural resource normalized monitoring-oriented remote sensing image rapid selection method, which comprises the following steps of S1, acquiring multi-source remote sensing image data, and performing preprocessing to obtain preprocessed image data containing semantic annotation; s2, carrying out coordinate conversion on the preprocessed image data containing the semantic annotation, obtaining feature parameters of the preprocessed image data containing the semantic annotation by adopting a coordinate mapping model based on deep learning, inputting the feature parameters into the coordinate mapping model for targeted correction, inputting an image data path, and outputting the image data path; reading a field name of a data attribute table in the image data path; s3, for the image data after coordinate conversion, firstly inputting data paths of a front batch of falling images and a rear batch of falling images, and reading the front batch of falling image data and the rear batch of falling image data; by adopting the technical scheme of the invention, the image data quality can be improved, the multi-source image coordinate mapping error is reduced, and the result reliability is ensured.

The application discloses a method and equipment for automatically converting from ontology to data collection templates, comprising the following steps: S100, parsing an ontology file, and obtaining an ontology instance tree by parsing the ontology file; S200, obtaining collection instances, traversing the ontology instance tree, obtaining all instances as leaf nodes and storing them in an array, and obtaining a collection instance group of the ontology; S300, extracting collection entries, obtaining data attributes of each instance as collection entries of the ontology data after traversing the collection instance group of the ontology; and S400, template generation, reading a predefined equipment configuration table, obtaining a template format used by a collection equipment, and then generating a data collection template of a corresponding ontology for the equipment. The existing application scene ontology model can be automatically converted into a data collection template which can be processed by the collection equipment and has higher flexibility.

The present application relates to the technical field of federated learning, in particular to a federated learning-based information big data privacy protection collection method and system, comprising the following steps: calculating a privacy sensitivity coefficient according to data attributes, perturbing and recombining a privacy gradient according to the coefficient, calculating gradient bias variance to construct a node contribution table, performing secure weighted aggregation based on the contribution table and the privacy gradient, calculating an encryption parameter to generate a global ciphertext parameter set, updating a global model and constructing a privacy feedback table.In the present application, the sensitive coefficient is used to replace the original feature field operation by regulating the perturbation limit in the training stage, the underlying sample hidden distribution is maintained to completely block the security collapse caused by the aggregation of plaintext, the evaluation system is constructed by calculating the cross-period bias variance to perform weighted aggregation, the weight of the heterogeneous terminal is balanced to correct the evolution direction deviation, the dynamic encryption parameter is injected into the global sequence to establish a ciphertext lossless defense line to resist flow disassembly interception and monitoring.