232 results about "Data Property" patented technology

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.

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 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.

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

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

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

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

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

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

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

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

The application discloses a system construction method for realizing CAD model security and data property right identification management, and realizes automatic identification management of UG / NX model security and data property right classification based on a PLM system.The technical scheme comprises the following steps: step 1: security classification label, that is, determining the security label of the model through a security review process node; step 2: intellectual property right classification label, that is, determining the intellectual property right label of the model through an intellectual property right review process node; step 3: specifying the corresponding label based on an improved document review process of product life cycle management of an enterprise; step 4: remotely transmitting the label information embedding program to a task queue server, that is, triggering the embedding program to remotely transmit the label information and related model information to the task queue server after the review is completed; and step 5: automatically writing the label into the server based on UG / NX secondary development technology, so as to write the label information into the UG / NX model.

The present application relates to the field of computer data security and encryption technology, and particularly relates to a cloud government affair sensitive data encryption protection method.A cloud government affair sensitive data encryption protection method comprises the following steps: S1: obtaining a full-amount data asset composition original data set in a target cloud government affair environment, and determining a data attribute combination set according to the original data set; S2: constructing a data set for statistical analysis based on the original data set, and performing sensitivity calculation on the data attribute combination set according to the data set to obtain a sensitivity calculation result.The present application realizes accurate classification and encryption of government affair data by dynamic sensitivity evaluation and file classification, and constructs a joint sensitive probability model; and the present application combines a basic protection and a strategy inheritance double architecture, dynamically adjusts a key, and effectively improves the protection capability of temporary and time-effective files and cloud self-adaptability.

The objective is to obtain an information processing method that can appropriately remove the influence of singular parts contained in the data to be normalized, thereby improving the estimation accuracy in user profile estimation using multiple data sets with different data attributes. [Solution] The information processing device acquires first data to be normalized, generates frequency distribution information for the first data, determines singular portions included in the frequency distribution information, generates second data by deleting singular data included in the singular portions of the first data, generates third data by normalizing the second data, and outputs the third data.

The invention relates to the technical field of natural language query, and provides a data query method and device, electronic equipment and a storage medium, and the method comprises the steps: extracting a plurality of data attributes of a target data object, and constructing an attribute association graph containing the plurality of data attributes and a logic dependency relationship among the plurality of data attributes; screening out a candidate attribute set according to the semantic similarity between the natural language query text and the semantic description information of each data attribute; traversing and recalling the associated attributes in the attribute association graph based on the operation intention of the natural language query text; and generating query logic according to the candidate attribute set and the associated attributes, and querying the target data object by using the query logic to obtain a query result. Due to the fact that the complete and simple query logic containing the candidate attributes and the associated attributes is constructed, the problem of context overflow caused by full-amount attribute input in an ultra-wide table scene is solved, and query generation errors or execution failures caused by missing of key dependent fields are effectively avoided.

The invention provides a memory management method and a memory controller, and aims to solve the problem of storage mismatch caused by dynamic change of a data access mode. The method comprises the following steps: acquiring target data stored in a target entity page in a plurality of entity pages; determining a data attribute of the target data and an offset attribute of the target entity page; and if the data attribute of the target data is not matched with the offset attribute of the target entity page, selecting a matched entity page of which the offset attribute is matched with the data attribute from the plurality of entity pages, and moving the target data from the target entity page to the matched entity page. Through the technical scheme of the invention, the memory controller can actively and periodically identify and correct the attribute mismatch between the stored data and the physical position thereof, and the continuous dynamic optimization of the data layout is realized, so that the data reliability and the overall performance of the storage device in the long-term use process are improved.

The application discloses a kind of electrical model light weight translation software methods based on IFC data format, it is related to model data processing technical field, including the following steps: extracting industrial foundation IFC data format electrical model geometry data, attribute data and topological relation data, based on the electrical equipment shell of the electrical model, electrical circuit and electrical component, the electrical model is divided into geometry layer, attribute layer and topological layer, and the geometry layer, attribute layer and topological layer are removed to redundant information, obtain the simplified simplified electrical model.This is through the geometry, attribute and topological data of electrical model and implements hierarchical light weight processing, rather than using general simplification strategy, effectively removes the redundant information of each level, achieves the effect of accurately reducing model data amount, realizes the efficient transmission and quick loading of translated model in different hardware terminal, improves the basic application ability of model cross-device platform.

The application discloses a kind of based on multi-source data's single building attribute automatic updating method, including single building height attribute updating method and single building other attribute updating method;In height attribute updating process, projection conversion is carried out to single and target layer, format standardization is carried out and is superimposed, and overlapping information is recorded;With single building ID as index, eliminate non-overlapping map spot, find the target map spot of the largest overlapping proportion, according to independent, add calculation space in the case of containing body or body;Height calculation is carried out to the map spot in calculation space, according to type selection method, finally select the highest value as result;In other attribute updating process, according to map spot quantity and type, according to different assignment strategy calculation.The application adopts the above steps a kind of based on multi-source data's single building attribute automatic updating method, and multi-source, multi-period data attribute is fused and updated to single building data, to enhance the comprehensiveness and accuracy of data, improve the efficiency of data updating.

Data characterizing a request to access a database record, a data attribute of the database record, and an authorization token are received. Based on the received data, permission to access the database record is determined by at least validating the authorization token using the data attribute. The authorization token can be generated by producing hash values for the database record and generating a bit array for the hash values. Related apparatus, systems, techniques, and articles are also described.

The application relates to the technical field of medical data privacy protection, and discloses a surgical operation data privacy protection system based on a block chain. After a surgical operation data classification module obtains surgical operation data, the data content is analyzed, and data partitions are divided based on data attributes; a privacy policy mapping module obtains the data partitions, associates privacy policies stored in a block chain, and establishes a mapping relationship between the data partitions and the privacy policies; a key combination module extracts key partitions based on the mapping relationship, combines encryption keys, and calculates key matching degrees; a policy updating module updates the privacy policies and adjusts the mapping relationship based on the key combination and access records; and an access management module processes access requests and generates an access control library based on the updated mapping relationship. Through the collaborative operation of the multiple modules, the adaptability and reliability of surgical operation data privacy protection are improved.

The application discloses a data transmission method and device, electronic equipment and storage medium, and can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving. The method comprises the following steps: receiving data attribute information corresponding to target transmission data sent by a transmission server, performing data detection on the data attribute information to obtain a transmission detection result corresponding to the target transmission data, and sending the transmission detection result to a client, so that the client performs a transmission operation of the target transmission data based on the transmission detection result. The method can screen the target transmission data based on the data attribute information, directly determine the target transmission data that does not need to be detected as passing the detection, and only detect the target transmission data that needs to be detected, so as to improve the data detection efficiency while ensuring the safety of data transmission.

The invention discloses a data storage method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining data features and data attributes of to-be-stored data when the to-be-stored data is received; obtaining node information of all nodes in the resource center, and screening out candidate nodes from all the nodes according to the node information; according to the data features and the node information of the candidate nodes, target nodes matched with the to-be-stored data are screened out; determining a target storage hierarchy corresponding to the target node according to the data attribute; and storing the to-be-stored data to the target storage hierarchy. By adopting the embodiment of the invention, the dynamic scheduling of data storage can be realized, the problem of non-uniform node load of the traditional centralized architecture is effectively solved, and the resource utilization rate of the node is improved.

The invention discloses a multi-source heterogeneous data fusion method based on dynamic loading. The multi-source heterogeneous data fusion method is applied to the field of data fusion integration of different data sources and different structures. The method comprises the following steps: (1) constructing a reliable data acquisition layer comprising a main data platform, a unified data integration interface service realized based on message middleware, and an integrated log management module; (2) in a data loading and data processing stage, cleaning and integrating data by adopting dynamic fragmentation and streaming processing, and constructing a multi-level authority control function based on role and data attribute matching and a full-process data transmission and storage encryption and decryption function based on an annotation technology aiming at a data security problem; and (3) constructing a user-defined data display layer. The method mainly solves the problems that data transmission is unreliable, authority control is dispersed and granularity is inconsistent in data processing, and dynamic data cannot be displayed in a user-defined mode, and an effective method is provided for full-process data fusion of a discrete manufacturing enterprise information system.

This invention discloses a method and system for verifying the integrity of approval data in a trusted data space, relating to the field of data security technology. The method includes: assessing and determining an approval risk coefficient and an approval urgency index based on the data attribute information and approval-related characteristics of the data to be approved; calculating and obtaining an appropriate approval time limit based on the approval risk coefficient and approval urgency index; using a time limit shorter than the appropriate approval time limit as an efficiency constraint to optimize the initial approval strategy, selecting the combination of approval nodes and the combination of approval node verification depths corresponding to the minimum predicted approval error probability as the appropriate approval scheme; and performing integrity verification on the data to be approved within the trusted data space according to the appropriate approval scheme. This invention effectively improves the integrity, reliability, and processing efficiency of the entire approval data chain in a trusted data space.

The invention discloses an enterprise financial counterfeiting behavior detection method and device and a program product, and is applied to the field of artificial intelligence and financial science and technology, and the method comprises the steps: collecting financial statement data issued by a target enterprise in a target year; performing preprocessing operation on the financial statement data based on the data attributes of the financial statement data to obtain processed financial statement data; the enterprise information of the target enterprise, the processed financial statement data and the index rule set are input into a target detection model, a detection result output by the target detection model is obtained, and the detection result is used for indicating whether the financial statement data have financial counterfeiting behaviors or not. The index rule set is constructed according to financial index data of the historical financial statement data. Through the method and the device, the problem that potential financial counterfeiting behaviors in enterprise financial statement data are difficult to accurately detect by adopting a traditional single-dimensional financial index analysis or manual auditing method in related technologies is solved.

Embodiments can relate to techniques for comparing and evaluating data from disparate data sources and / or data streams. The technique can involve receiving data from at least two disparate data sources and / or data streams (the data including object movement information). The technique can involve abstracting data and generating data sets representative of an attribute of an object, generating normalized data sets, aggregating normalized data sets based at least in part on time windows spanning between attributes of abstracted data, which can be stored. The method can involve retrieving data from the aggregated data sets and comparing the retrieved data to benchmark data representative of at least one or more of a standard, a pattern, or a statistic of an attribute of at least one or more objects. An output representative of the comparison can be generated.

The application discloses a differential privacy synthetic data publishing method based on maximum weight matching, which comprises the following steps: firstly, pre-processing user data collected by a reliable third-party server; then, using a graph model method to perform information representation on the processed data attributes, so as to obtain an attribute association graph; then, selecting a suitable low-dimensional marginal set according to a maximum weight matching algorithm; subsequently, adding noise to the low-dimensional marginal set, wherein the noise satisfies a differential privacy definition; then, performing post-processing on the low-dimensional marginal set with noise, so as to obtain a standardized low-dimensional marginal set; performing data synthesis according to the low-dimensional marginal set, so that the synthetic data set is as similar as possible to the original data set in statistical information; and finally, publishing the synthetic data set. By using the technical method, the computational complexity can be reduced while ensuring the utility of the synthetic data set, and the method has better utility for high-dimensional data.

The invention relates to a method for transmitting a data stream of vehicle data attributes between a vehicle and a plurality of vehicle-external data receivers, the method comprising: providing the vehicle data attributes by the vehicle; receiving, by the vehicle from a first vehicle external data receiver, a first subscription message for reading vehicle data attributes; receiving, by the vehicle from a second vehicle external data receiver, a second subscription message for reading vehicle data attributes; providing, by the vehicle, a data stream of vehicle data attributes according to the first subscription message and the second subscription message; transmitting a data stream of the provided vehicle data attributes from the vehicle to a server external to the vehicle according to the first subscription message and the second subscription message; transmitting a first subset of the data stream of the vehicle data attributes from the vehicle-external server to the first vehicle-external data receiver according to the first subscription message; and transmitting a second subset of the data stream of vehicle data attributes from the vehicle-external server to a second vehicle-external data receiver in accordance with the second subscription message.

The embodiment of the present application provides a kind of data cleaning method and device, the method includes determining data attribute field from each business field of business scene, by data attribute field, determine the search field set from each database table, and determine the database table set matched with search field set, the source data associated with business scene is analyzed, determine the business data value of the business field to be cleaned, according to search field set and database table set, the structured query language sql data cleaning script corresponding to the business data value of the business field to be cleaned is constructed, and the relevant business data in the database table set associated with the business data value of the business field to be cleaned is cleaned by sql data cleaning script.It is thus, the scheme can realize the accurate cleaning of dirty data automatically by the constructed sql data cleaning script, so as to effectively reduce the time consumed by test personnel for cleaning dirty data.

The application provides a processing method of unstructured data, comprising the following steps: performing word segmentation on unstructured data to obtain a word segmentation result, determining the weight of a sensitive word in the word segmentation result, and determining the weight of a non-sensitive word according to the similarity between the non-sensitive word and a privacy data attribute in the word segmentation result, and determining the privacy degree of the unstructured data through the weight of the sensitive word and the weight of the non-sensitive word. Since the non-sensitive word with a context relationship is considered, the method has high accuracy for privacy degree classification, and privacy protection processing based on the same has good privacy protection effect.