27 results about "Data harmonization" patented technology

The invention relates to the technical field of industrial data storage, in particular to an industrial data storage method and system based on a knowledge graph, and the method comprises the following steps: obtaining cooling tower operation data and binding identification information, constructing a time sequence node and a graph path, extracting a relationship between an attribute tag and a behavior type labeling direction, classification fields belong to path channels in sequence, and structural area additional labels are filled in to write into node relations. According to the method, multi-field synchronous collection is completed through a binding mode of operation data and identification content, an information flow path is constructed according to a time sequence, and paths consistent in sequence are screened according to a field change rule in combination with a transmission direction between attribute labels and behavior type labeling nodes to perform channel division and attribution arrangement. A structural region is filled through unified arrangement of paths and node contents, and label information is added, so that organization association, process continuity and structural logic among data are enhanced, and path stability, node orderliness and data coordination in map construction are improved.

Various systems and methods are disclosed relating to protecting cross-system exchanges. A data processing system includes one or more processing circuits configured to identify a plurality of data elements and generate a first plurality of cryptographic objects. The one or more processing circuits are further configured to record the first plurality of cryptographic objects on a distributed ledger and generate metadata corresponding to at least one of the plurality of data elements, the metadata including temporal data. The one or more processing circuits are further configured to generate a bi-temporal record including a plurality of links to the plurality of data elements and storing the corresponding metadata. The one or more processing circuits are further configured to provide an interface including a query element corresponding to the bi-temporal record.

The invention belongs to the field of continuous variable quantum key distribution, and particularly relates to a spline coordination method suitable for discrete modulation continuous variable quantum key distribution, which can effectively reduce the data coordination difficulty and improve the operation speed of data coordination by using constellation points to replace a quantization mode of mapping a binary sequence by a single orthogonal component. The method is an effective scheme easy to realize high-speed quantum secret communication. And the high-performance error correction code with a fixed code rate is obtained by using the characteristics of the RL-LDPC code, so that the difficulty in obtaining the error correction code is reduced. The method mainly comprises the steps of continuous variable quantization and multi-level coding and multi-level decoding (MLC-MSD). In the continuous variable quantization process, the continuous variables are mapped into the multi-level binary sequence through the constellation point division interval, the number of coding and decoding levels in spline coordination is reduced, and then the complexity of a data coordination model is reduced. In the MLC-MSD, the characteristic that the code rate is moderately modified while the high error correction performance of the RL-LDPC code is maintained is utilized, and the error correction code with the required code rate is obtained.

A system and method for reconciling data from a local database streamed to a Cloud storage service is disclosed. A data transaction is received from a structured query language (SQL) database service operated by a server. The transaction includes log sequence numbers and corresponding data records. The log sequence numbers are stored in a change data capture (CDC) table. The sent data transaction is streamed via a data integration service. A storage object service stores the sent data into an object storage service bucket. A data reconciliation service deploys a cloud trail target to generate a log file of log sequence numbers from the sent data stored in the object storage service bucket. The log file of log sequence numbers is compared with the log sequence numbers in the CDC table. A missing data record is flagged to an exception table based on the comparison. An alert is issued for the missing data record.

A distributed system for collaboration of the operations of a single or multiple healthcare provider entities, and more particularly, a distributed healthcare system and method that operates to provide unification of communications, siloed and fragmented data harmonization, remoteness and decentralization exacerbating the need for a unique solution to address new age healthcare needs, authentication for security and privacy and real-time data sharing.

Systems and methods for metadata driven data reconciliation are disclosed. A method may include: (1) identifying origin dataset metadata for an origin dataset; (2) identifying new dataset metadata for a new dataset; (3) comparing the origin dataset metadata to the new dataset metadata; (4) identifying a sample size for the origin dataset, wherein the sample size comprises a number of cells; (5) extracting an origin dataset sample of the sample size of random cells from the origin dataset; (6) generating origin dataset features for the origin dataset sample; (7) generating new dataset features for the new dataset; (8) searching for the origin dataset features in the new dataset features; and (9) returning a matching result in response to the origin dataset features being found in the new dataset features.

A machine learning based (ML-based) method and system for automatic data reconciliation across multiple datasets, is disclosed. The process begins by obtaining inputs related to first data in a natural language, which are pre-processed to generate refined first data. An ML model is employed to generate first rules for a rule model based on this pre-processed first data, facilitating the matching of at least two datasets to identify second data comprising matched records. During subsequent matchings, the ML model analyses third data, consisting of unmatched records, to produce matching suggestions, confidence scores, and rationales. As the system evolves, it generates second rules informed by user feedback and ongoing matchings. The rule model is iteratively updated to enhance accuracy by examining correlations between first and second rules. Ultimately, this system provides users with efficient, automated, data reconciliation outputs, enabling improved integration and accuracy between at least two datasets.

An integrated system manages real-time healthcare data with geolocation capabilities. A controller operates interfaces to ingest geolocation and to receive patient data from EHRs, remote monitoring, providers, devices, third parties, and applications. A data harmonization module standardizes multimodal inputs; an anonymization / deanonymization service governs personally identifiable information. Secure storage preserves integrity and audited access. A Continuous Analytics Processing System provides file repositories, transforms, working databases, analyzers, and results. A job management subsystem maintains partner, client, event, and log data. A clinical decision support platform generates evidence-based insights, personalized recommendations, and real-time alerts contextually adapted to patient location. An analytics engine performs descriptive, geospatial, network, temporal, predictive, classification, clustering, anomaly detection, NLP, regression, and longitudinal analyses. Deployment spans secure cloud and edge environments with a smart device application. The system produces operational outputs and geolocation-aware interventions to enhance care delivery and infection prevention.

The invention relates to the crossing field of artificial intelligence and medical information technologies, and discloses a medical agent, method and equipment based on large language model driving. The system receives a user request through a multi-modal input interface, and a tool calling decision module performs semantic understanding and automatically decides an execution path: 1, a medical service tool set is called to complete operations such as appointment registration and the like, and a database is directly inquired through a service data coordination module to perform parameter verification and consistency maintenance in the process; an anti-illusion business operation core is formed; and 2, starting a hybrid retrieval enhancement generation framework, cooperating with a vector knowledge retrieval module and a real-time information acquisition module, and providing professional and real-time knowledge support for a large model. According to the invention, in combination with the dialogue memory management module, integrated intelligent processing from natural language medical consultation to accurate and reliable business operation is realized, and the problems of information illusion, insufficient multi-modal ability and knowledge update lag in the prior art are effectively solved.

The invention discloses a transformer substation three-dimensional layout intelligent optimization design method based on deep learning, and aims at transformer substation equipment space layout optimization, complex constraints are adaptively processed through a deep learning model, and a three-dimensional layout scheme considering space utilization rate, electrical safety distance and maintenance accessibility is generated. The method comprises the following steps: acquiring spatial characteristics of equipment layout through a three-dimensional layout generation module, and representing geometric distribution, electrical safety distance and topographic constraints; a data coordination module is used for coordinating a dynamic mapping relation among electrical parameters, heat distribution and layout constraints of the equipment to generate data interaction characteristics; inputting the spatial features and the data interaction features into a deep learning optimization module, and generating an optimized three-dimensional layout sequence through self-adaptive deep neural network fusion and optimization; and distributing an optimization time window according to the complexity of the layout sequence, generating a dynamic optimization adjustment strategy, and iteratively generating a final three-dimensional layout path. Intelligent optimization of the substation layout is realized through deep learning, and the space utilization rate and the operation efficiency are improved.

The invention relates to the technical field of geological exploration data processing, and discloses a geological multi-modal data AI fusion identification analysis method and system. According to the method, multi-modal data of a physical field, chemical elements, a geological structure, a drilling core and the like are subjected to convergence and structured processing; constructing and iteratively optimizing a knowledge topology network based on geological semantics, and expressing semantic association and spatial constraints among data entities; actively detecting a high-value information link and a contradiction point in the knowledge network; generating a plurality of sets of parallel fusion decision plans, and defining a data coordination rule and a feature enhancement direction; introducing an external intervention instruction to screen the reconstruction plan, and forming a target fusion operation sequence; executing the sequence and synchronizing the evolved knowledge network. According to the method, the transformation from static data association to dynamic knowledge guidance and from a fixed algorithm to man-machine collaborative active decision is realized, and the reliability and intelligent analysis level of geological target recognition are improved.

In some implementations, a device may obtain, via a source platform, source data that is to be provided to a target platform to be stored in one or more databases included in the target platform, wherein the one or more databases are accessible via a data integration pipeline. The device may provide the source data via the data integration pipeline to cause input data to be provided to the target platform. The device may obtain sets of intermediate data associated with respective channels of multiple channels included in the data integration pipeline. The device may determine, based on a comparison of the source data, the sets of intermediate data, and the input data, data reconciliation information indicating one or more data discrepancy events and channels associated with respective data discrepancy events of the one or more data discrepancy events. The device may perform, based on the data reconciliation information, an action.

In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.

The invention relates to the technical field of data communication, and discloses a network flow deep packet inspection multi-task classification method and system, and the method comprises the steps: firstly obtaining a target network flow data flow, recognizing the corresponding protocol and load content of the target network flow data flow, and generating a flow topological graph related to the target network flow data flow and the load content; collecting a topological data sequence in the atlas, analyzing a corresponding data transmission type, analyzing a type guiding mode, and fitting a flow transmission curve; identifying an abnormal traffic peak value and a core transmission hotspot in the curve, calculating a traffic throughput threshold and analyzing a network cache state; then, a deep packet detection framework is constructed based on the cache state, a processing delay value and a classification misjudgment rate of a real-time classification path in the framework are collected, and a traffic coordination index is calculated; and finally, reconstructing a data coordination task according to the index, identifying a task classification protocol, and generating a multi-task-oriented classification result of the flow data flow. According to the invention, the detection and classification efficiency of multi-task traffic in a complex network environment can be improved.

An ASIC for an artificial neural network includes an array of neurons each including a register, processing element, and input; and synaptic circuits each including a memory for storing a synaptic weight. Each neuron is connected to another neuron via a synaptic circuit. The processing elements receive data sets from different providers, the data sets including data features representative of locations used in connection with flying aircraft. The processing elements examine the data features using the synaptic weights and the synaptic circuits to identify duplicative data features, select one or more of the duplicative data features for elimination based on prior selections; and send remaining data features to data consumers for use in flying or managing flight of one or more aircraft.

An autonomous operational health intelligence system for mission-critical SAP cloud workloads and method thereof is disclosed. The invention relates to a device-based structure configured to continuously monitor, analyze, predict, and regulate operational conditions of enterprise workloads deployed across distributed cloud infrastructure environments. The system comprises at least one processing unit coupled with a non-transitory memory unit, a telemetry acquisition unit for receiving real-time operational data from application execution systems, database systems, and infrastructure resources, a data harmonization processor for transforming heterogeneous telemetry signals into structured operational datasets, an analytical processing unit for deriving workload health indicators, a predictive assessment processor for generating forward-looking stability indicators based on historical and real-time execution patterns, a control determination unit for identifying deviations and producing corrective directives, and a response execution unit configured to transmit operational control signals to remote computing elements.

A system for high-speed data compression using context-adaptive transformation logic, consisting of: a processing unit comprising one or more processors operationally connected to non-volatile memory, wherein the processing unit is configured to receive an input data stream from an external data source via a data input interface; a context evaluation unit, executed by one or more processors and configured to continuously analyze the input data stream during reception in order to derive context features such as symbol frequency distribution, local entropy variation, repetition locality, temporal continuity and structural regularity across successive data segments; a transformation selection unit that is operationally coupled with the context evaluation unit and is configured to dynamically determine, based on the derived context features, a set of reversible data transformation operations and corresponding transformation parameters applicable to the input data stream; a transformation execution unit that is operationally coupled with the transformation selection unit and is configured to apply the specified data transformation operations to the input data stream in a streaming manner to produce a transformed data stream with modified statistical properties that is suitable for compression; an encoding unit that is operationally coupled to the transformation execution unit and is configured to perform entropy-based encoding of the transformed data stream to produce compressed output data; and A coordination control unit, operationally connected to the context evaluation unit, the transformation selection unit, the transformation execution unit, and the encoding unit, is configured to synchronize the execution times, the memory access sequence, and the data flow continuity between the aforementioned units, so that compression is performed at high speed and without interrupting the input data stream.

The invention relates to a power grid project digital design system and method based on an image engine basic function, and the system comprises a project management module which is used for defining the basic information of a project, carrying out the level-to-level management of component types, carrying out the management of a standard set, a project sample and a line style, and setting a line type, a character style, a filling style and a material style; the user-defined family library classification management module is used for performing classification management on the power grid engineering according to project types and professions; the professional modeling module is used for creating components and setting linkage of a two-dimensional drawing and a three-dimensional model; the import and export module is used for importing and exporting external data; the collaborative management module is used for linking local files for data coordination and mutual reference; and the digital drawing output module is used for outputting final power grid engineering drawing information. Compared with the prior art, the method has the advantages that the whole process of power grid project setting is covered, and the design efficiency and the standardization level are improved.

The invention discloses a powder particle distribution prediction method and device based on machine learning and a medium, and relates to the technical field of industrial control, and the method comprises the steps: collecting multi-source time sequence data related to particle distribution, learning a causal dependency relationship among parameters in the multi-source time sequence data through a causal discovery algorithm, and generating a causal graph; taking a causal graph as a guidance basis of data coordination, and adopting a game theory coordination mechanism to execute dynamic weight distribution for the multi-source time sequence data to obtain a coordination data vector; constructing a powder particle distribution prediction model, inputting the coordination data vector into the powder particle distribution prediction model, and outputting a powder particle distribution prediction value at a future moment; and calculating the difference quantity between the intervened particle distribution simulation result and the powder particle distribution predicted value, and generating a process parameter adjustment suggestion. According to the method, through forward propagation calculation of the causal diagram, accurate quantitative simulation of a process intervention effect is realized, and interpretability and reliability of a prediction result are improved.

The present application relates to the technical field of power distribution network optimization operation, in particular to a multi-objective collaborative configuration method based on regional power distribution network resources, comprising: collecting real-time operation data of the power distribution network, and establishing a multi-time scale data coordination mechanism of minute, hour and day levels to form a hierarchical data set. Based on the set, a decision space is constructed with capacitor banks, on-load tap-changing transformers and distributed energy storage systems as dispatchable resources, and a multi-objective optimization problem is generated considering network loss, voltage deviation and power supply reliability. An adaptive weight adjustment strategy is adopted to dynamically adjust the weight coefficients of each objective according to the current operating state to solve the optimization problem, and the optimal scheme is issued for execution. Through monitoring the execution deviation and feedback adjustment strategy parameters, a closed-loop control is formed. The method improves the adaptability of the optimization strategy to different operating scenarios and the overall decision effect through multi-time scale data fusion and dynamic weight adaptation.

A method includes receiving a data reconciliation request requesting data reconciliation for a first dataset and a second dataset. The method also includes obtaining the first dataset including one or more dimensions each having a plurality of dimension members and obtaining the second dataset including one or more dimensions each having a plurality of dimension members. For each dimension of the first dataset, the method includes obtaining a bridge member that associates the respective dimension of the first dataset with a dimension of the second dataset. The method includes generating a first and second set of combination dimension members and refreshing the first and second set of combination dimension members using an execution delimiter value. The method includes generating, using the refreshed first and second set of combination dimension members, a third set of combination dimension members to generate a data reconciliation report.

A system and method are provided for reconciling data used by a data management system. In some examples, the data management system provides a workspace for machine learning. The method includes obtaining an input dataset, the input dataset being replicated from a baseline dataset to enable the data management system to operate on the input dataset. The method also includes comparing the input dataset to the baseline data set to determine discrepancies between the input and baseline datasets, by, for each of a plurality of database pools, process data assigned to that pool by concurrently checking for the discrepancies and executing statements without waiting for all pools to have finished processing. The method also includes creating a delta table for each pool to identify extracted data associated with the discrepancies; and combining delta pools from the plurality of database pools and process columns in the delta table.

The application discloses an adapter life prediction system and a dynamic early warning method based on digital twinning, and the system comprises a multi-source data acquisition module, a digital twinning model construction module, a data coordination module, a life prediction module and a calibration module; by constructing an adapter full life cycle digital twinning, the application breaks through the limitations of one-way static analysis of traditional life prediction technology, and realizes dynamic health assessment under multi-dimensional data driving; the application innovatively proposes cross-dimension feature fusion and closed-loop calibration mechanism, effectively solves the industry problems of weak correlation of multi-source asynchronous data and lag response of sudden abnormality; through the synergistic effect of the generative adversarial network and the attention model, the stability and reliability of the prediction result are significantly improved under complex working conditions; the technology can adapt to the harsh use environment of industrial adapters, and provides quantifiable and traceable decision support for intelligent operation and maintenance of power electronic equipment.

The invention discloses an automobile part order management method which specifically comprises the following steps: S1, an order receiving and confirming stage: an EDI system obtains a purchase order from a client server and immediately feeds back order receiving confirmation to a client; according to the invention, the JIS system directly processes order sorting and is in butt joint with the MES, the resource planning bottleneck of the ERP system is bypassed, order changes can be quickly responded and emergency order insertion can be executed, the JIS system is used as a unique data coordination center, the accuracy and consistency of circulation of order, material and production state information among the EDI, the ERP and the MES are ensured, information islands are eliminated, and the quality of the EDI, the ERP and the MES is improved. According to the invention, a data basis is provided for accurate decision making, business logic is concentrated in a JIS system, the direct coupling degree among ERP, MES and EDI systems is reduced, the loose coupling architecture enables the system to be easier to maintain, upgrade and expand, a new system such as a WMS warehouse management system can be accessed more conveniently in the future, the system architecture is optimized, and the expandability is enhanced.

The present disclosure provides a unified clinical trial data management system comprising a centralized database configured to store data related to multiple aspects of clinical trials and a plurality of interconnected modules corresponding to specific clinical trial functions. The system includes a data linking mechanism that creates and maintains associations between data entries across different modules based on predefined rules and enforces data dependencies between modules to ensure data integrity and consistency. The system automatically creates corresponding entries or placeholders in linked modules upon data entry in a primary module, prevents certain actions in one module until prerequisite conditions are met in another module, and enables real-time data reconciliation between linked modules without manual intervention or data transfer between separate applications.

This invention relates to the field of geological exploration data processing technology, and discloses a method and system for AI fusion, identification, and analysis of multimodal geological data. The method aggregates and structures multimodal data such as physical fields, chemical elements, geological structures, and drilling core samples; constructs and iteratively optimizes a knowledge topology network based on geological semantics to express semantic relationships and spatial constraints between data entities; actively detects high-value information links and contradictions within the knowledge network; generates multiple parallel fusion decision-making plans accordingly, clarifying data coordination rules and feature enhancement directions; introduces external intervention commands to screen and reconstruct the plans, forming a target fusion operation sequence; executes the sequence and synchronously evolves the knowledge network. This method realizes a transformation from static data association to dynamic knowledge guidance, and from fixed algorithms to human-machine collaborative proactive decision-making, improving the reliability of geological target identification and the level of intelligent analysis.

The invention belongs to the field of computers, and particularly relates to a multifunctional information interaction system and method and storage device.The multifunctional information interaction system comprises a man-machine interaction system, a data management platform and a data visualization platform, and the man-machine interaction system obtains user information by detecting dynamic changes in a user environment, converts the user information into an operation command and sends the operation command to the data management platform; classifying and judging according to different types of data, inputting the data into a corresponding processing module, and adjusting an algorithm and outputting corresponding information by the processing module according to the data types; the data management platform receives data from a user environment and system operation, and processes the user data to coordinate the operation condition of the whole system on the basis of a hardware system; and the data visualization platform receives data from a user environment and system operation, and carries out comprehensive visualization operation on the data. Human-computer interaction of embedded equipment can be realized, and the embedded comprehensive system has a higher data utilization rate.