71 results about "Heterogeneous source" patented technology

The invention provides an integrated sharing method and platform for health data of old people, and relates to the technical field of data processing.The method comprises the steps that multi-source heterogeneous health data of the old people are accessed, a rule template in a historical grading preprocessing rule base is matched based on data source capacity and data features, and data cleaning and standardization are completed; further identifying a sensitive field based on a medical data standard and an industry privacy specification, generating a sensitive field vector, carrying out sensitive level scoring, and calling a data desensitization strategy library to execute desensitization operations such as de-identification and masking; an access control function is constructed based on user attributes, access context and field sensitivity levels, authorization levels are determined, differential encryption is performed, and authorization views and data tokens are generated for secure access. Through the method, the problems of heterogeneous sources, complex processing, insufficient privacy protection and lack of flexibility of a sharing mechanism of old-age health data in the prior art can be solved.

The present disclosure describes systems and methods for detecting and preventing data misconfigurations within a security-focused data fabric platform. The system integrates an advanced script migration engine designed to streamline the translation of security rules and scripts across different scripting languages while ensuring alignment with the fabric's unified schema. The method involves receiving inputs from data sources, mapping these inputs to entities of a target schema, monitoring real-time data changes, and simulating impacts on operational dependencies to detect misconfigurations proactively. Leveraging AI-driven mechanisms, including Large Language Models (LLMs), the system dynamically identifies breaking changes in third-party data streams, issues alerts, and provides suggested fixes. The script migration engine further enhances the platform's functionality by automating cross-platform script translations and enabling faster onboarding of security tools. Together, these innovations ensure scalable, accurate, and resilient integration and management of security data across heterogeneous sources, strengthening operational integrity and minimizing security risks.

The embodiment of the invention discloses a dynamic metadata sensing and self-adaptive mapping method and system, and the method comprises the following steps: deploying a lightweight probe to be agented to a plurality of heterogeneous source database systems, and building a trusted communication channel between the probe and a coordination center through equipment fingerprint generation and security authentication; a database log event is captured in real time, log analysis is carried out through an FPGA module, the change of a field structure or a semantic level is identified, and structured event data is generated; performing context embedding representation on the captured event based on a semantic understanding model, performing strategy reasoning in combination with a historical strategy and a knowledge graph, and generating a matched mapping strategy; loading and executing the mapping strategy in a trusted execution environment (TEE) to realize data conversion, cleaning and complementation, and performing desensitization processing on sensitive data through a differential privacy mechanism; and collecting performance indexes and abnormal logs of a strategy execution result, continuously training the model through a closed-loop optimization mechanism, and correcting an unreasonable strategy or updating a rule base.

The invention relates to an electricity market information extracting and pushing method and system based on multi-modal semantic fusion. The method comprises the steps that an electricity market field pre-training corpus is constructed; adopting a staged pre-training strategy; performing multi-modal coding on the input power market information data; a text confidence score and an image confidence score are generated, a cross-modal attention incidence matrix is established, and image-text fine-grained interaction features are captured and fused to generate unified semantic representation; a user model containing long-term stable preferences and short-term dynamic interests is established based on unified semantic representation generated through fusion, a sequence decreasing exploration strategy is adopted, high-correlation intelligence is pushed through known preferences, new domain content is randomly inserted to mine potential interests, and the weight of pushed content is adjusted in real time according to user feedback. The problems of heterogeneous sources, diversified formats, different standards, asymmetric information and the like of power market information data can be effectively solved, and accurate and real-time power market information pushing service is provided for users.

The invention relates to the technical field of information retrieval, in particular to a vertical website deep crawling method based on intention recognition, which comprises the following steps: crawling data from a plurality of heterogeneous information sources, and constructing a dynamic knowledge graph; identifying a weak signal association path formed by connecting a plurality of relation edges in the dynamic knowledge graph, and converting the weak signal association path into a to-be-verified risk hypothesis; generating a data acquisition intention, and based on the to-be-verified risk hypothesis, generating a rejection query for searching reverse evidence as a first data acquisition intention; receiving an input query instruction, and performing semantic analysis on the query instruction to generate a second data acquisition intention; a multi-source crawling strategy is generated based on the type of the data collection intention, and the multi-source crawling strategy comprises a plurality of vertical website sources and crawling priorities of the vertical website sources; executing a deep crawling task according to the multi-source crawling strategy to obtain target data; and processing the target data, verifying the to-be-verified risk hypothesis, and judging whether the reverse evidence exists or not and judging the intensity of the reverse evidence.

The invention discloses a unified cross-source context enhancement method for multi-source false news detection, and relates to the field of computer vision, natural language processing and multi-modal information fusion. According to the scheme, rich global context information can be effectively learned and integrated from a plurality of data sources through a unified model, so that the problems that different models need to be trained and the models are poor in performance when the traditional technology faces complex and multi-source data are solved, and the accuracy and reliability of false news detection are remarkably improved. The context enhancement can effectively capture the long-distance interaction among different sources, and can fuse the characteristics of heterogeneous sources, thereby reducing the deviation caused by the difference of data sources. The model can more intelligently align and integrate data from different sources, and the overall performance of false news identification is improved. The local-global double-layer structure improves the ability of the model to process the unseen data source, and improves the stability and detection effect of the system.

The invention discloses a data deduplication method and system based on incremental calculation and feature clustering and electronic equipment, and the method comprises the steps: receiving multi-source heterogeneous text, image, audio and video data streams in real time, adding a timestamp to each data unit, and generating an input data set with a time attribute; performing multi-modal feature extraction on the input data set, and dynamically adjusting feature weights of the extracted multi-modal features through a sliding window model and a time decay factor to obtain an increment feature vector set with the weights; executing two-stage clustering based on the feature vector set to generate a plurality of fine-grained data clusters; and comparing the intra-cluster data of each fine-grained data cluster in pairs by adopting a composite similarity model to determine intra-cluster repeated data, and performing data deduplication on the input data set according to the intra-cluster repeated data to obtain a deduplicated input data set. According to the method, the processing efficiency of the real-time data is improved, and the storage and calculation cost is remarkably reduced.

The invention relates to the technical field of electric digital data processing, and discloses an intelligent data processing method for clinical research. The method comprises the following steps: constructing a basic clinical knowledge graph fused with an international medical ontology; the method comprises the following steps: performing deep learning driven entity recognition and knowledge graph linking on original data from heterogeneous sources such as an electronic medical record and an inspection system; executing cross-source entity alignment and knowledge fusion based on the graph attention network; performing automatic data quality verification and restoration according to clinical logic rules embedded in the atlas; and finally, extracting and generating a standardized analysis ready data set from the enhanced atlas according to research requirements. According to the technical scheme, high-quality, automatic and semantic integration and flexible delivery of clinical research data are realized, and the data processing efficiency and research reliability are improved.

The invention provides a configuration-based multi-level index calculation system and method, and relates to the technical field of data processing, and the system comprises an acquisition module, a rule configuration module and an index calculation module. The acquisition module is configured with a metadata model; the acquisition module is used for acquiring a plurality of heterogeneous source data and extracting field metadata by utilizing a metadata model; the rule configuration module is used for configuring a calculation rule for the target index based on the index type of the target index; the index calculation module is used for matching the field metadata of the plurality of heterogeneous source data according to each calculation rule, and determining the heterogeneous source data of which the field metadata is successfully matched as index calculation data required for calculating a target index; and according to a sequence of the calculation priorities from high to low, sequentially executing statistical calculation methods corresponding to the calculation rules on the index calculation data to obtain index result data of the target index. And full-process dynamic generation from original data to multi-level indexes is realized.

The invention belongs to the technical field of material management, and discloses a management method and device based on material product matching and automatic generation, equipment and a storage medium. The method comprises the following steps: carrying out AI semantic analysis on a material product description of a heterogeneous source, and extracting key parameters of a product; constructing a dynamic product feature matrix based on the key parameters of the product, and performing multi-level matching by using the dynamic product feature matrix and a supply database to obtain a plurality of candidate product matching schemes; determining a target product matching scheme from the plurality of candidate product matching schemes according to a semantic similarity threshold; performing parameter specification alignment processing on the target product matching scheme through a parameter mapping engine; and based on the processed target product matching scheme, establishing a target mapping relationship between the materials and the products, and managing the materials and the products by using the mapping relationship. By means of the method, the mapping relation can be dynamically updated in real time, and meanwhile the accuracy can be improved by means of the constructed mapping relation.

The invention provides a heterogeneous data mapping optimization method, and relates to the technical field of software development. A JSON Schema model library containing business and voucher objects is constructed by obtaining heterogeneous source data, the construction process comprises data format analysis and feature extraction, and construction and classified storage are completed by means of DSL rules and visual editing; based on the model library, generating a mapping relation description document by utilizing a preset abstract mapping rule template and an expanded JSON Schema grammar; a mapping engine analyzes the document to construct a field-level dependency graph, data conversion is executed according to the field-level dependency graph, and an inerting execution and caching mechanism is adopted in the conversion process; and verifying the converted data and the mapping process by adopting a mode of combining static pre-verification and incremental verification, dynamically adjusting a mapping rule according to a verification result or model change, and performing version management. Through the steps, the data mapping efficiency, flexibility and accuracy are improved, and the system performance is optimized.

The model generation platform enables generation of machine learning models based on modular storage of user-specified training data. The platform can obtain a dataset from a different heterogenous source (e.g., a structured database, an unstructured database, a semi-structured file system, manual upload of a comma-separated value file, a spreadsheet, and / or big data) through an associated application programming interface and store this data in a first storage medium. The model generation platform can obtain an indication of a portion of the dataset from a user via a user interface and determine a second storage medium for this portion of the dataset based on an associated estimated performance metric. In response to a request for generation of a machine learning model, the model generation platform can generate a machine learning model using training data comprising a subset of the portion of the dataset.

A database comparison tool is provided that compares a source database to a target database. The database comparison tool performs the comparison in two steps: an initial comparison step and a confirmation step. In the initial comparison step, a server retrieves rows of a table from source and target database tables with a query using an agent. The server compares rows by using a hash value for all non-key columns. The resulting rows are flagged as potentially being out-of-sync. In the confirmation step, the server fetches the potentially out-of-sync rows from the source and target databases and performs a literal value-by-value comparison. Each agent receives a batch of rows, inserts the batch of rows into a temporary table, and performs a join operation between the temporary table and the actual source or target table. The agent returns the rows resulting from the join operation to the server.

The application provides a multi-level index calculation system and method based on configuration, and relates to the technical field of data processing, and comprises an acquisition module, a rule configuration module and an index calculation module; the acquisition module is configured with a metadata model; the acquisition module is used for acquiring a plurality of heterogeneous source data and extracting field metadata by using the metadata model; the rule configuration module is used for configuring a calculation rule for a target index based on the index type of the target index; the index calculation module is used for matching the field metadata of the plurality of heterogeneous source data according to each calculation rule, and determining the heterogeneous source data with matched field metadata as index calculation data required for calculating the target index; and in the order from high to low of the calculation priority, the statistical calculation method of the corresponding calculation rule is sequentially executed on the index calculation data to obtain index result data of the target index. The whole process dynamic generation from original data to multi-level indexes is realized.

The invention discloses a method for integrating and analyzing heterogeneous data (3) from a heterogeneous source application (2). The method is used for solving the inherent non-standardization problem of a data model used by a Web-based database platform. All platforms adopt different data configurations, so that a unified and standard data presentation mode cannot be followed, and structural aggregation of information is not facilitated. In order to achieve the purpose, the computer implementation method comprises the steps that a calculation data collection program (1) is executed to access and load heterogeneous source data (3); providing a heterogeneous data integration module (4) for converting the heterogeneous source data (3) into target data (5); performing a data analysis process (6) based at least on the target data (5) to generate output data (7); and writing the output data (7) to the normalized database (8).

A real-time cloud pipeline system (100) for ingesting, transforming, and indexing real estate data from multiple heterogeneous sources, comprising: (a) an ingestion module configured to receive data from structured and unstructured real estate sources; b) a transformation module configured to normalise, enrich and map data to a unified schema; (c) an indexing engine configured to extract metadata and structure content for searchable storage; and d) a cloud-based orchestration module that manages data flow, processing, and system scaling.

This invention relates to a fault early warning system for hydro-generator units based on CEGD-FNN. The system includes: a feature extraction module for early fault signals of the hydro-generator unit, a fault diagnosis module for the hydro-generator unit, a status assessment module for the hydro-generator unit, a fault prediction module for the hydro-generator unit, and a human-machine interface module. These modules are sequentially connected. This invention employs a unified data platform integrating heterogeneous source information and a unified platform for open knowledge resource sharing. It can promptly obtain heterogeneous data from distributed systems and knowledge resources from different users, thereby achieving intelligent collaborative diagnosis of equipment faults and reducing losses caused by insufficient timely handling of emergency faults.

The application provides a connection code generation method and device, electronic equipment, computer readable storage medium and computer program product; the method comprises the following steps: determining a plurality of uniform interface models in the memory for a plurality of source heterogeneous source files corresponding to a plurality of hardware modules; based on each uniform interface model, a hierarchical signal tree is constructed for each hardware module; based on a plurality of signal trees, determine the signal pair to be matched, and determine the bit width topology characteristics of the interface signal in the signal pair to be matched; calculate the similarity between the names of the interface signals in the signal pair to be matched, and the compatibility score of the direction; based on the bit width topology characteristics, the similarity and the compatibility score, determine the consistency score of the signal pair to be matched, and when the consistency score is greater than the score threshold, it is determined that the signal pair to be matched has a connection matching relationship; based on the connection matching relationship, a first connection code is generated. Through the application, the generation accuracy of the connection code of the interface signal can be improved.

The application provides a data management method, comprising: acquiring heterogeneous source data; the heterogeneous source data comprises basic information and power consumption data of an enterprise; performing data adaptation on the heterogeneous source data to obtain first data; performing standardization on the first data to obtain second data; performing quality evaluation on the second data; and generating a quality report according to the result of the quality evaluation. By adapting and standardizing the data, the type of data that can be analyzed is increased, the adaptability of analysis is expanded, the heterogeneous source data is comprehensively analyzed, and the efficiency of data processing is improved.

A system for determining a degree of dependence on screens of a user, includingat least one software module embedded in a set of information processing apparatuses, adapted for collecting first measurements about the behavior of the applications installed on said information processing apparatuses and second measurements about the behavior of said information processing apparatuses;a database for storing said measurements;a server for periodically determining an aggregation of said measurements and a degree of dependence on screens according to said aggregation.

This application provides a method, apparatus, device, and medium for heterogeneous code translation. The method includes: acquiring heterogeneous source code running on a source heterogeneous hardware system; compiling the heterogeneous source code to generate an intermediate representation of the source heterogeneous hardware system; mapping the intermediate representation of the source heterogeneous hardware system to obtain an LLVM intermediate representation; and mapping the LLVM intermediate representation to obtain RISC-V extended code, which is used to run on a target hardware system. This application improves the compatibility between code and target hardware systems.

The invention discloses a mammary gland endoscope preoperative anatomical variation identification method and system, and relates to the technical field of computer data processing. The method comprises the following steps: S1, acquiring heterogeneous source data, and performing spatial registration and data standardization fusion to generate a multi-dimensional fusion data set; s2, performing basic topological feature extraction based on the multi-dimensional fusion data set, and calculating edge weight information to construct a target topological graph; s3, matching and comparing the target topological graph with a preset standard topological database, and calculating topological compliance parameters based on comparison differences; s4, identifying a parameter abnormal region according to the spatial distribution characteristics of the topological compliance parameters, and generating a spatial security constraint model in combination with local spatial medium attributes; and S5, based on the spatial security constraint model, outputting structure abnormal information and corresponding optimized path planning parameters. And through multi-channel tensor quantization variation characteristics, safety constraints are constructed by using an anisotropic bounding box, and a path is optimized in combination with a potential field algorithm, so that high-precision automatic obstacle avoidance is realized.

Systems and methods for generating deployment databases based on preloaded data from heterogeneous sources are disclosed herein. The system may receive first structured data. The system may extract first data of a first format and second data of a second format. The system may determine that the first data has a first update rate. The system may determine that the second data has a second update rate. The system may preload the first data by retrieving a first identifier and by storing a first preloaded representation of a first parent dataset for the first data. The system may receive a first request for a deployment database. The system, based on the first request, may retrieve the first preloaded representation. The system may generate the deployment database.

Systems and methods for generating deployment databases based on preloaded data from heterogeneous sources are disclosed herein. The system may receive first structured data. The system may extract first data of a first format and second data of a second format. The system may determine that the first data has a first update rate. The system may determine that the second data has a second update rate. The system may preload the first data by retrieving a first identifier and by storing a first preloaded representation of a first parent dataset for the first data. The system may receive a first request for a deployment database. The system, based on the first request, may retrieve the first preloaded representation. The system may generate the deployment database.

This invention provides a method, device, and storage medium for generating and utilizing a multimodal discourse tree (MMDT). [Solution] The method generates an Extended Discourse Tree (EDT) from a text corpus (e.g., from a Discourse Tree (DT) or Communication DT (CDT)), links data records (e.g., records containing numerical data) to the Extended Discourse Tree, and generates a Multimedia Discourse Tree (MMDT). The MMDT links any suitable text / records from heterogeneous sources. For example, entities identified from the basic discourse units of the EDT are matched with entities in the data records. The method also identifies causal links between EDTs and / or between data records, identifies rhetorical relationships for each entity match / causal link match, and merges the data records with the EDT to generate the MMDT.

A system for training a model from a subset of data representing decentrally stored source databases. A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the training data from a source interface, compresses the training data, and synchronizes the training data with the meta-data using a meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines training variables indicative of the meta-data, generates variable probability distributions, produces training variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface. The key interface allows for searching the meta-database for training variables, variable probability distributions, and / or variable associations. A model of the system may be trained in less time with a subset of data associated with the training variable.

The invention relates to the field of intelligent doorplate management, and particularly discloses an intelligent doorplate dynamic management system and method based on an LORA technology, which are characterized in that external heterogeneous source data such as OA, educational administration and the like are grabbed, cleaned and packaged in a standardized manner at a front end through protocol adaptation and data engine technologies, and information islands are broken to realize automatic content production; the transmission layer reconstructs network data into high-penetrability LORA radio-frequency signals for wide-area broadcasting by utilizing a cloud compiling and gateway protocol conversion mechanism; and the terminal side adopts a periodic wake-up strategy based on a CAD mechanism, and drives the electronic ink screen to refresh and immediately and automatically cut off the power supply only after the verification of the effective signaling is passed. Therefore, real-time synchronization of the physical identification and the digital service is realized without manual intervention through full-link data closed-loop circulation, and meanwhile, extremely-low-power-consumption long-term operation of the system is ensured by utilizing a bistable display characteristic.

This invention provides a three-dimensional registration method for neutron / X-ray heterogeneous source images of cylindrical multilayer structures. The method includes edge extraction operators and PCA principal component analysis for vertical adjustment of image pairs; an edge contour extraction module to determine the height mapping of heterogeneous source tomographic images; a circle extraction algorithm and calculation of the circle center positioning translation; an image super-resolution reconstruction algorithm to achieve image scaling; and image rotation matching based on feature tomography and phase cross-correlation methods. This method effectively corrects geometric and modal differences between heterogeneous source images, achieving accurate alignment of metallic and non-metallic regions. It has significant advantages in registration accuracy, edge preservation, and modal feature alignment. It exhibits high adaptability and stability for complex structures and multimodal characteristic characterization, and is suitable for three-dimensional registration of neutron / X-ray heterogeneous source images of cylindrical multilayer structures such as cylindrical lithium batteries.

This invention proposes a heterogeneous data mapping optimization method, relating to the field of software development technology. It constructs a JSON Schema model library containing business and credential objects by acquiring heterogeneous source data. The construction process includes parsing the data format, extracting features, and completing the construction and classification storage using DSL rules and visual editing. Based on this model library, a mapping relationship description document is generated using a preset abstract mapping rule template and extended JSON Schema syntax. The mapping engine parses the document to construct a field-level dependency graph, and performs data transformation accordingly. The transformation process employs lazy execution and caching mechanisms. The transformed data and mapping process are verified using a combination of static pre-verification and incremental verification. Furthermore, the mapping rules are dynamically adjusted and version managed based on the verification results or model changes. This invention improves the efficiency, flexibility, and accuracy of data mapping and optimizes system performance through the above steps.

The invention provides a logistics path control system based on machine learning. Relates to the technical field of logistics technology and intelligent control, and comprises a data acquisition module used for acquiring logistics related data in real time and outputting original multi-source heterogeneous data, a feature processing module used for performing cleaning, self-supervised space-time pre-training and feature construction on the source heterogeneous data, generating a high-quality comprehensive feature vector used for path optimization, and outputting the high-quality comprehensive feature vector. And the feature processing module comprises a data cleaning unit. According to the logistics path control system based on machine learning, multi-target logistics path optimization is realized through a deep reinforcement learning algorithm, and the driving distance, the distribution time efficiency and the operation cost of a distribution path are effectively balanced. By dynamically adjusting the target weight, the system can automatically adjust the optimization strategy according to the real-time traffic condition, weather change and other multi-dimensional factors, so that the distribution path can respond to the change in real time, and the distribution efficiency is improved while the total distribution cost is reduced.