700 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.

Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.

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 discloses a memory compression method and device, equipment, a storage medium and a product. Firstly, memory use information and data attributes of a data block are acquired; wherein the data attribute comprises at least one of a data type, a current access frequency and an importance index; then, selecting a target compression strategy for the data block according to the memory use information and the data attribute; and finally, the target compression strategy is utilized to perform targeted compression on the data block, so that the memory compression effect is improved, and memory resources can be effectively utilized.

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 embodiment of the invention provides a data receiving method and device, a program product, a storage medium and electronic device.The method comprises the steps that when first data sent by a target data source is received, first data information of the first data is determined, and the first data information comprises transmission parameters and data attributes of the first data; comparing the first data information with historical data information to obtain a comparison result; generating a first transmission instruction according to the comparison result; and sending the first transmission instruction to the target data source, and receiving second data transmitted by the target data source in response to the first transmission instruction. Through the data transmission method and device, the problem that the accuracy of data transmission cannot be ensured due to the fact that the data transmission mode cannot be adjusted in real time in the data transmission process in the prior art is solved, and the effect of improving the data transmission quality is achieved.

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 request processing method and system, and relates to the field of data storage, and the method comprises the steps: in response to a data operation request, determining a target physical memory address applied by target data needing to be accessed by the data operation request in a graphics processor; establishing a transmission path between the distributed file system and the graphics processor based on the target physical memory address; segmenting the target data into a plurality of data segments with different data attributes; selecting a corresponding target transmission protocol for the plurality of data segments, and distributing the plurality of data segments to different transmission sub-paths; determining a target position of the target data in the distributed file system according to the data operation request; and transmitting the plurality of data segments from a target position in the distributed file system to a target physical memory address of the graphics processor in parallel. According to the customized transmission mode, low efficiency and delay of a one-cutting mode are avoided, and the transmission efficiency is improved.

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 embodiment of the invention relates to the technical field of computers, and provides a data reporting method and device, equipment, a storage medium and a product, and the method comprises the steps: obtaining application data and data attributes collected by an application program, and storing the application data to a local database of a mobile terminal; matching a reporting strategy of the application program from a pre-constructed strategy library according to the data attribute; acquiring a network state of the mobile terminal; determining a reporting mode corresponding to the network state of the mobile terminal in a reporting strategy of the application program; and reporting the application data in the local database to a cloud server according to the determined reporting mode. According to the embodiment of the invention, different reporting strategies can be issued for different APPs, the reporting mode in the reporting strategies is selected according to the network state of the mobile terminal, and the problem that the APP function is affected by frequent data reporting when network resources are insufficient is avoided.

The invention belongs to the field of data processing, and particularly relates to an intelligent service analysis optimization method and system based on a multi-dimensional data warehouse, and the method comprises the steps: obtaining the data attribute information of a to-be-recognized object for the to-be-processed object loaded in the data warehouse; inputting the data attribute information into a dynamic data analysis model, and performing data identity recognition on the to-be-recognized object to obtain target identity information of the to-be-recognized object; based on the target identity information, performing identity tracing on the to-be-identified object by adopting a dynamic tracing model to obtain target tracing information of the to-be-identified object; and based on the target identity information and the target traceability information, triggering a target data service for the to-be-identified object to complete intelligent service analysis of the to-be-processed object. According to the method, the data identity recognition and traceability capability can be improved, powerful support is provided for intelligent service analysis, and the method is dedicated to realizing data-driven decision making.

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.

The invention discloses an intelligent salary management method and system for the human resource industry, and relates to the technical field of data processing, and the method comprises the steps: carrying out the abnormal screening of to-be-counted salary data; performing performance evaluation based on the to-be-counted salary data; based on the to-be-counted salary data and the performance evaluation result, performing salary accounting by using fluctuation anomaly analysis to obtain target salary data; on the basis of the target salary data, performance target correction data and work target correction data are obtained through salary influence analysis; acquiring data attributes of to-be-desensitized data in the target salary data, the performance target correction data and the work target correction data to determine a desensitization mode, and desensitizing the to-be-desensitized data based on the desensitization mode; and storing the target salary data, the performance target correction data and the work target correction data in a storage node. The intelligent degree of salary management is greatly improved, and the salary management achieves a more ideal effect.

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 discloses a network traffic data processing method, a computer device and a storage medium. The processing method comprises the following steps: obtaining to-be-processed network traffic data; determining data attribute information of the network flow data, and determining the category of the network flow data according to the data attribute information; determining a target processing strategy matched with the category from a plurality of processing strategies in a target database; and processing the network traffic data according to the target processing strategy to obtain target network traffic data. Therefore, different processing strategies are applied to different types of traffic, corresponding protection can be provided according to the sensitivity and risk level of the data, the fine-grained encryption mode is more targeted than traditional global encryption, and sensitive data can be more effectively protected while strategy management flexibility is improved.