907 results about "Data Property" patented technology

The invention discloses a self-adaptive SQL differential privacy noise adding method, and relates to the technical field of data protection. In order to accurately calculate and add noise of a proper magnitude in a complex SQL statement, the adopted scheme comprises the following steps: regularly collecting and updating metadata of a database, analyzing attribute features of a data table, and forming a data attribute feature library and a corresponding data sensitivity library; the SQL query request is rewritten into an abstract syntax tree and traversed, and an aggregation function, multi-table connection and a sub-query structure are recorded; key semantic information contained in the request is bound with the data sensitivity library, the sensitivity of the request is calculated, and a privacy budget is distributed in a self-adaptive mode; the privacy budget allowance is checked, when the allowance is sufficient, a data disturbance mechanism meeting-differential privacy is used for disturbing the query result, a disturbed result is obtained, and otherwise, the request is rejected. The method is used for accurately calculating the dynamic allocation privacy budget according to the characteristics of the complex SQL query.

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 relates to the technical field of privacy protection, and discloses a privacy data anonymization protection method, which comprises the following steps: carrying out data desensitization processing on original privacy data to obtain desensitized privacy data, carrying out differential privacy noise addition processing on the desensitized privacy data to obtain noise-added privacy data, and carrying out differential privacy noise addition processing on the noise-added privacy data to obtain the privacy data. Adding dynamic noise to the desensitized privacy data by adopting a Laplace mechanism to obtain privacy data with a differential privacy protection characteristic; carrying out confusion on the privacy data attributes after noise addition to obtain confused privacy data; and performing data fuzzy processing on the confused privacy data obtained by querying the query statement, limiting the accuracy of the query result, performing pseudo data generation on the privacy data of which the privacy data items of the query result are less than the specified threshold, and returning the query result and the generated pseudo data as the query result together, thereby avoiding the leakage of the differential privacy data.

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.

Methods and systems are provided for facilitating efficient intrusion detection via hierarchical signatures, in accordance with embodiments described herein. In particular, embodiments described herein include obtaining an intrusion signature that includes an intrusion attribute value(s) indicating a malicious attack on a computer network or system. Based on the intrusion signature, a hierarchical signature is generated by including the intrusion attribute value(s) of the intrusion signature in one of a corresponding data structure of hierarchical data structures. Thereafter, a network packet, having a data attribute(s) is obtained. The network packet is determined as suspect of being malicious based on the data attribute(s) of the network packet matching the at intrusion attribute value(s) of the hierarchical signature. The indication of the suspect network packet can be provided, for example for notification of such a suspect network packet.

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 high-speed train bogie fault detection, and discloses a bogie unknown fault diagnosis method based on a generative model zero sample learning strategy, and the method comprises the steps: carrying out a bogie mechanical fault simulation experiment on an electric locomotive through employing multi-body dynamics analysis software SIMPACK, and collecting normal and fault data; establishing a fault attribute semantic matrix for the train operation influence factors; performing feature extraction according to the data attributes and the fault data; building an unknown fault data feature sample generation model based on a diffusion model, and performing unknown fault data feature generation by using random noise and unknown fault attribute combination; and a fault diagnosis classification model is constructed, and mapping from data features to fault attributes and then to fault categories is realized. According to the method, diagnosis of known faults and unknown faults of the high-speed train bogie is completed, the accuracy and generalization of fault diagnosis are improved, and comprehensive guarantee is provided for safe use of the high-speed train bogie.

The invention provides a multi-application environment encryption communication method and system for user privacy protection, and the method comprises the steps: recognizing risk factors and types in a current application environment, and evaluating the risk coefficient of each risk factor based on a communication knowledge system; receiving to-be-communicated private data, analyzing the to-be-communicated private data, and determining data attributes of each unit data in the to-be-communicated private data and a topological relation among all the unit data; determining the multi-level attack intensity of the current application environment to each unit data based on the type of the risk factor in the current application environment, the risk coefficient and the data attribute and topological relation of each unit data; and determining an encryption communication mode of the to-be-communicated private data based on the multi-level attack intensity, and performing encryption communication on the to-be-communicated private data based on the encryption communication mode. According to the invention, the security of user privacy data communication is improved, and meanwhile, the flexibility of encrypting communication data in different application environments is improved.

The invention relates to the technical field of power grid operation control, in particular to a distribution network dispatching operation intelligent identification control method based on a cloud platform, which comprises the following steps: calculating a power consumption representative value of a power consumption area in each time interval according to power consumption data stored by the cloud platform, determining an interval characteristic value of each time interval according to the power consumption representative value, the method comprises the following steps: determining data properties of electricity representative values based on interval characteristic values, determining a parallel region according to the data properties, calculating combined path values in the parallel region to obtain an optimal combined region, and automatically adjusting route configuration by a power distribution network according to the optimal combined region so as to realize uniform distribution of electricity loads and avoid local overload or light load. And meanwhile, the reliability and economy of the power grid are enhanced.

The invention discloses a data sharing privacy protection enhancement method and system in a power grid cloud environment. The method comprises the steps that a trusted access control center generates a system public key, a system key, a user public and private key pair, an attribute public and private key, an attribute request private key and a re-encryption key; the data owner divides data attributes into a sensitive part and a non-sensitive part, noise meeting differential privacy protection is added to a sensitive field, the sensitive part and the non-sensitive part after noise adding are combined, and a privacy data set is obtained; the data owner uploads the encrypted privacy data set to a cloud server; the cloud service provider converts the ciphertext into the ciphertext of the data user through a re-encryption algorithm; and the data user performs analysis processing by using the decrypted privacy data set. According to the method, the time for the data owner and the data user to access the resources on the cloud is saved, the privacy protection intensity and the data availability are optimized and balanced, and the privacy and the security of the power data in the transmission, storage and application processes are improved.

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.

The invention relates to a method for storing data, a computing device, a storage medium and a program product. The method comprises the following steps: generating a data acquisition instruction in response to a predetermined condition; the method further comprises the steps that in response to the data collection instruction, data collection is carried out so as to obtain target data. And determining a storage strategy corresponding to the target data based on the data attribute of the target data and the storage state corresponding to the memory. In addition, the method further comprises the step of storing the target data based on the storage strategy. Through the mode, the optimal storage strategy can be selected for the to-be-stored data according to the real-time data characteristics and the state of the memory, so that the performance and the capacity of the memory can be fully utilized, and the storage efficiency is 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 discloses a multi-scene coverage engineering data calculation method and equipment, and relates to the technical field related to data processing, and the method comprises the steps: responding to an engineering data calculation request input by a user side; matching based on a formula database of a memory of the intelligent calculator to obtain a first calculation formula; obtaining a second calculation formula; constructing a collaborative computing tree until the Nth input data attribute of the Nth computing formula is the same as the computing input data attribute; and obtaining a calculation target data characteristic value and feeding back to a user side display interface. The technical problems that in existing engineering data calculation, calculation formulas are mutually isolated, it is difficult to cooperatively match and schedule multiple formulas for calculation according to data attributes, and then the engineering data calculation accuracy and calculation efficiency are insufficient are solved, efficient engineering data processing is achieved, and the engineering data calculation efficiency is improved. The technical effect of improving data calculation efficiency and calculation accuracy is achieved.

The invention discloses a storage and calculation integrated architecture data layering method and system oriented to a calculation-intensive system, and the method comprises the steps: setting a control module, and carrying out the redirection of a part of file data corresponding to a concurrent read-write task based on the control module; a file pre-retrieval module is set, data attributes of the file data are distinguished according to file types on the basis of the file pre-retrieval module, and the data attributes comprise cold data and hot data; and migrating and storing the file data based on the determined data attributes. According to the method, the overall performance of the mixed medium storage system under the compute-intensive task can be optimized, cold and hot data can be dynamically identified, and the performance overhead caused by data migration is reduced.

The present subject matter discloses a system and a method for providing insights of an information technology ecosystem. The system comprises an ingestion module for ingesting at-least one data attribute from at least one data source into a data lake and a data analysis module for generating a dataset using data validation rules, wherein the data validation rules converts the at-least one data attribute to a predetermined format, and for generating at least one service request. Furthermore, the system comprises an automation module for determining a class of the at least one service request, wherein the class of at least one service request is determined based upon the dataset, wherein the class of at least one service request identifies a service provider from a plurality of service providers. Moreover, the automation module executes a pre-determined action based upon the class of the at least one service request and the identified service provider, wherein the pre-determined action provides a predictive insight in real-time.

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 examples of a system that publishes metadata to a blockchain network. The metadata can contain properties of data generated by a system. The metadata can allow for other entities to validate or trust the data generated by the system. The metadata can include data attributes that profile the data.

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.

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.