574 results about "Data Property" patented technology

The invention provides a construction dynamic twinning method based on the Internet of Things and BIM, and relates to the technical field of digital twinning, and the method comprises the steps: obtaining heterogeneous data streams collected by a sensor, and carrying out the analysis and classification of the data streams, and generating a standardized data set; performing semantic mapping on the standardized data set according to association rules of BIM component identifiers and data attributes to generate a component association data set; decrypting and decompressing the encrypted data packet at the cloud, analyzing the construction progress time sequence data by adopting a distributed computing framework, and generating a deviation detection result; dynamically adjusting a routing rule of a data link according to a deviation detection result, and redistributing a processing priority to optimize link configuration; updating a BIM component state based on the optimized link configuration, and generating a real-time updated digital twin model by adopting cloud computing resources; three-dimensional visual data are extracted from the digital twin model and mapped to a BIM model interface by adopting a rendering technology, so that the monitoring precision and the management efficiency of the construction process are effectively improved.

The invention relates to the technical field of message queues, in particular to a message controllable optimization method and system based on kafka, and the method comprises the steps: obtaining a data attribute, and calculating a data priority value according to the data attribute; the method comprises the steps of establishing a position index cache mechanism based on priority data, persisting partition-offset data in a cache pool to an index table according to the position index cache mechanism, and performing priority consumption of high-priority messages based on persistent priority indexes, including setting a consumption control program architecture, performing priority consumption on the high-priority messages, and performing priority consumption on the high-priority messages based on the persistent priority indexes. Isolating the consumption queue and introducing a message de-duplication operation; according to the method, full-link performance adjustment and optimization are carried out on the premise of preferential consumption of high-priority messages, service scene extension is carried out based on the adjusted and optimized full links, the messages are pulled directionally according to the priorities through a consumption control program, the disorder of parallel consumption of Kafka partitions is broken through, the high-priority messages can directly jump to corresponding positions to be processed preferentially, and the service scene extension efficiency is improved. The consumption delay is obviously shortened.

The invention discloses a big data resource service method and system based on multi-modal data fusion, belongs to the technical field of big data processing, and solves the problems that a scheduling scheme is formulated only by calculating an energy consumption value and a resource utilization rate of a server in an existing method, the adaptability to dynamic changes is poor, and the overall performance and the resource utilization rate of the system are reduced. The method comprises the steps that a multi-modal data sequence is preprocessed, a shared database is created, multi-modal data are encrypted and distributed to a distributed hash table of distributed nodes, a resource retrieval instruction is recognized based on a resource scheduling model, and the resource scheduling model dynamically deploys a data transmission task based on target data attributes and real-time transmission path information; according to the method, the resource scheduling model considering the multi-modal data distribution and transmission characteristics in the distributed environment is constructed, collaborative learning and optimization among distributed nodes are realized, the transmission path is dynamically optimized, the delay and energy consumption of data transmission are reduced, and the intelligent level of scheduling is improved.

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 BIM-combined building facility digital operation and maintenance method and system, and relates to the technical field of building information models.The BIM-combined building facility digital operation and maintenance method comprises the steps that firstly, a BIM model data set of a target building facility is obtained, the BIM model data set comprises geometric feature data, attribute feature data and facility association relationship data, and preprocessing operation is conducted; state feature analysis processing is conducted on the preprocessed BIM model data set to obtain an operation state feature set of the building facilities, then strategy matching processing is conducted according to the operation state feature set and a preset operation and maintenance rule set, a facility operation and maintenance optimization strategy set is generated, and finally, a facility operation and maintenance optimization strategy set is generated. And updating the BIM model data set based on the facility operation and maintenance optimization strategy set to generate an updated BIM model data set, and feeding back the updated BIM model data set to the building facility operation and maintenance system to trigger a periodic updating process, thereby realizing digital and intelligent operation and maintenance of the building facility, and improving efficiency and accuracy of operation and maintenance management.

The present invention relates to a system and method for monetizing access to data using a blockchain-based framework with hardware secure enclaves (HSEs). The system enables data owners to securely store and control access to their data while ensuring payment for access through blockchain smart contracts. The system comprises a hardware secure enclave configured to lock data off-chain, store it securely, and provide cryptographic proof of data properties to authorized parties. Data access is monetized through the submission of funds to a smart contract, with payment verified on-chain and processed by the enclave. The system utilizes unique enclave signatures to manage notifications and triggers related to data access requests. Furthermore, the system employs blockchain relayers to ensure timely communication and transaction verification. A universal attestation process is used to ensure the integrity of the enclave's code. This invention provides a secure, efficient, and scalable solution for monetizing data access, leveraging secure enclave technology and blockchain for transparent and tamper-resistant transactions.

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 relates to the technical field of data processing, and discloses a data dynamic transaction value evaluation method, which is applied to a server, and comprises the following steps: S1, receiving a data set from a data source, the data set comprising data attributes, association relationships among data and historical operation records; s2, on the basis of the received data set, adopting a mixed model of a graph attention neural network and a long and short-term memory network to calculate the total transaction value # imgabs2 # of data # imgabs0 # under the operation of a user # imgabs1 # at the time t; and S3, outputting the total transaction value to a user terminal, carrying out real-time risk monitoring according to the dynamic risk, and sending early warning information to the user terminal when the risk exceeds a preset threshold value. By adopting the graph attention neural network and long short-term memory network hybrid model, the data intrinsic value, network value, time value and dynamic risk are comprehensively evaluated, and compared with an existing method based on static rules, the accuracy is higher.

Disclosed are methods, systems, and non-transitory computer readable memory for generating models and digital threads using graphs. For instance, a method may include: process engineering information to output node information which includes, for at least one data artifact, a set of entities, a set of data attributes, and / or a set of relationships; determine and add nodes and / or edges to a graph based on the node information and the graph; generate, using agents and an interface, at least one data structure based on at least the nodes and / or the edges of the graph, wherein the at least one data structure is compliant to a model language syntax; and generate at least one digital thread that connects the at least one data artifact and the at least one data structure.

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.

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: in response to a data acquisition request sent by a target application, determining to-be-processed data according to data acquisition information in the data acquisition request; acquiring data attribute information corresponding to the to-be-processed data and a network performance load corresponding to the target host; under the condition that the to-be-processed data is dynamic data, the to-be-processed data is divided into a plurality of data fragments according to the data attribute information, and the dynamic data is used for representing communication service data stored by the target host; determining the queue depth of a data cache queue according to the network performance load of the target host; and caching the plurality of data fragments into a data cache queue with queue depth. According to the scheme provided by the embodiment of the invention, the stability and reliability of data processing can be effectively improved.

The invention discloses a dynamic biomarker analysis method and system based on big data, and relates to the technical field of data analys.The dynamic biomarker analysis method comprises the steps of obtaining dynamic biomarker data, obtaining a batch effect removing method corresponding to each dynamic biomarker data according to the dynamic biomarker data, and obtaining a batch effect removing method corresponding to each dynamic biomarker data based on the batch effect removing method; and processing the dynamic biomarker data to obtain biomarker processing data. According to the method, the data comparability is ensured and the reliability of subsequent analysis is improved through a data attribute intelligent matching batch removal effect method, the batch removal effect is verified through the differential gene number and the overlapping rate, the stability of the data is ensured, the biomarker data types related to the target disease are selected through the biomarker feature set, and the accuracy of the biomarker data types related to the target disease is improved. According to the method, the data calculation amount is reduced, the data analysis efficiency is improved, and a more comprehensive risk assessment basis is provided for clinic by constructing the regulation and control network containing the molecular interaction relationship.

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 embodiment of the invention discloses a data storage method and related equipment. The related equipment can comprise a data storage device, electronic equipment, a computer program product and a computer readable storage medium. According to the embodiment of the invention, after at least one piece of to-be-stored data and the data attribute information of the to-be-stored data are obtained, the data capacity of the to-be-stored data is identified in the data attribute information; the to-be-stored data is transferred to a second storage space of a second storage medium through a first storage space of the first storage medium, the writing speed of the first storage medium is larger than that of the second storage medium, and when the data capacity exceeds a preset data capacity threshold value, the to-be-stored data is written into the second storage space of the second storage medium; according to the scheme, the storage efficiency of data storage can be improved. The embodiment of the invention can be applied to various scenes such as cloud technology, artificial intelligence, intelligent traffic, auxiliary driving and the like.

The invention provides a block chain-based dynamic encryption evidence storage data property right registration method and system, and relates to the technical field of block chains, and the method comprises the steps: carrying out the trusted measurement of to-be-registered data through constructing a trust transfer network; establishing a dynamic verification mechanism and selecting a verification path; constructing a data block association graph based on verification node importance to realize distributed storage; and finally, submitting the storage index and the verification summary information to a block chain network to complete evidence storage and generate a registration certificate. According to the invention, the credibility and verification efficiency of data property right registration are improved, and the security and integrity of evidence storage data are enhanced.

The embodiment of the invention provides a data aggregation method and device, computer equipment, a storage medium and a computer program product, and relates to the technical field of data processing. The method comprises the following steps: acquiring to-be-aggregated information from a plurality of data acquisition nodes of a data aggregation system; the plurality of data acquisition nodes are used for acquiring data from a plurality of data sources; if the to-be-aggregated information meets the preset contract rule, executing a smart contract corresponding to the preset contract rule, and obtaining aggregated data corresponding to the to-be-aggregated information according to the to-be-aggregated information; according to the data attribute of the aggregated data, storing the aggregated data to a corresponding data storage area in a data aggregation system to obtain storage data corresponding to the aggregated data; the data attribute is determined according to the sensitive degree of the aggregated data; and acquiring an aggregation processing record corresponding to the storage data, and storing the storage data and the aggregation processing record to a block chain module of the data aggregation system. According to the method, the efficiency and the security of data aggregation can be 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.

Method and system for securely communicating data, the method comprising the steps of: initiating a secure communication between a mobile device and a requester using a cryptographic key to verify the user of the mobile device. Receiving, using the secure communication, at a mobile device a request from the requester, the request including an indication of one or more requested data attributes associated with a user of the mobile device. At the mobile device, querying a data store for the requested one or more data attributes and a verification attribute. In response to the request, providing the requester, using the secure communication, with the requested one or more data attributes and the verification attribute from the data store, wherein the verification attribute is generated from one or more data items of a physical token issued to the user, the one or more data items including information characterising a biological feature of the user, the verification attribute also indicating that sensor data of a biological feature of the user detected by the mobile device has been matched to the information characterising the biological feature of the user. At the requester, receiving the verification attribute over the secure communication.

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 invention relates to the technical field of power system automation, in particular to a transformer substation SCD model consistency matching method and system based on a scanning algorithm, and the method comprises the steps: analyzing an SCD file, and generating an LN feature vector based on a logic node LN, a data object DO and a data attribute DA; performing clustering analysis on the LN feature vector based on an SCAN algorithm to generate an LN cluster; generating an SCD file matching model based on the LN clusters; performing consistency matching on the first substation SCD file matching model and the second substation SCD file matching model, including traversing all LN clusters of the two substation SCD file matching models, and judging whether each corresponding LN cluster is matched or not; and summarizing matching results of all the LN clusters of the two substation SCD file matching models, and generating a substation SCD model consistency verification report. According to the SCD model consistency verification method, efficient and accurate SCD model consistency verification is realized by introducing the SCAN algorithm.

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.