341 results about "Data warehouse" patented technology

The invention relates to the technical field of lithium battery safety monitoring, and discloses a lithium battery pack thermal runaway early warning system based on multi-mode sensing. The system comprises multi-source sensing data acquisition, thermal field feature tensor construction, thermal field reconstruction and thermal coupling association network generation. The multi-source sensing data acquisition module acquires multi-dimensional heterogeneous data from a temperature sensor, a voltage and current monitoring unit, a gas component detector and an acoustic emission sensor, and generates a standardized data bin through timestamp alignment and missing value compensation; the thermal field feature tensor construction module extracts features such as temperature gradient and electrochemical response from the data bin in a multi-scale manner, and constructs a tensor in combination with time continuity; the thermal field reconstruction module generates association diagrams according to the feature space-time distribution and fuses the association diagrams into a lithium battery pack three-dimensional thermal field reconstruction map; and the thermal coupling association network generation module extracts a feature vector cluster, calculates an entropy weight value, and generates a network according to a high-entropy node space adjacency relationship. According to the system, multi-dimensional data fusion is realized, and the thermal runaway evolution law can be comprehensively described.

The invention discloses a cable fault accurate positioning system based on an intelligent sensor system, and relates to the technical field of power fault positioning, and the cable fault accurate positioning system comprises a cloud monitoring center which is in communication connection with the following modules: a signal acquisition and processing module which is used for deploying the intelligent sensor system along a cable, operation environment data and traveling wave signals in the cable operation process are obtained, and the signals are preprocessed and stored in a cloud data warehouse. According to the invention, the front band-pass filter and the self-adaptive trap filter are combined to filter out high-frequency harmonic waves and specific-frequency interference signals, and the differential sampling technology is adopted to counteract common-mode interference, so that the signal-to-noise ratio of the signals is remarkably improved, a system can accurately capture and process weak traveling wave signals caused by a high-resistance grounding fault in a complex environment, and the fault detection accuracy is improved. Therefore, the fault positioning precision is greatly improved, the positioning error caused by signal interference is reduced, and the determination of the fault position is more reliable and accurate.

The invention discloses a water conservancy data analysis method and device based on a self-attention mechanism and a medium, and relates to the technical field of water conservancy. The method comprises the following steps: constructing a water conservancy time sequence data warehouse, and establishing a water conservancy source data access mechanism to access to-be-processed water conservancy time sequence data; performing standardized preprocessing on the to-be-processed water conservancy time sequence data to obtain standard time sequence data; inputting the standard time sequence data into a pre-trained water conservancy data analysis model, and calculating attention weight distribution between different time steps and different variables through a self-attention mechanism unit in the water conservancy data analysis model; according to the attention weight distribution, performing weighted summation on the standard time sequence data, generating a spatial-temporal feature representation vector, inputting the spatial-temporal feature representation vector into a gating circulation unit in the water conservancy data analysis model, and executing spatial-temporal information fusion by updating a gate and resetting a gate; and performing water conservancy data trend prediction based on the fused features, and outputting flood prevention scheduling decision support data.

The invention relates to the technical field of data analysis, and discloses a business analysis and decision support method and system, and the method comprises the steps: collecting purchase-related business data from a plurality of data sources, and carrying out the preprocessing; storing the preprocessed data in a centralized data warehouse, and performing management based on a preset data governance strategy; performing multi-dimensional analysis on the structured data based on a statistical analysis model and a machine learning algorithm, and generating a key performance indicator KP I and a trend prediction result; providing a customized visual display interface according to a user role, and presenting the KP I and a trend prediction result through an interactive instrument panel; and generating a purchase optimization suggestion based on the analysis result, evaluating the influence of different purchase strategies through analogue simulation, and outputting corresponding decision support information. The system effectively solves the problems of weak analysis capability, insufficient decision support and the like in an existing purchase decision system, and is suitable for multiple application scenes such as enterprise purchase management and the like.

The invention relates to the technical field of big data, and provides a data verification method and device in big data cluster migration, equipment and a medium, and the method comprises the steps: obtaining a source data cluster and a target data cluster; dividing the source data cluster and the target data cluster into a plurality of data layers according to a standard data warehouse layering strategy, and binding different data comparison strategies for each data layer; establishing a fingerprint baseline table and constructing a sampling model feature library; performing data fragmentation processing on each data layer, and controlling each data layer to execute a data comparison task in parallel; and if any data layer of the target data cluster and any data layer of the source data cluster are detected to have difference, recording difference data and performing data restoration. According to the method, the source data cluster and the target data cluster are subjected to hierarchical parallel fragmentation comparison, a plurality of fragments of each layer are subjected to data comparison tasks in parallel at the same time, different comparison strategies are bound to the data layers, the comparison process can be achieved, and the overall data processing efficiency is improved.

The invention discloses a fire-fighting spatial-temporal trajectory data processing and off-line data warehouse construction method based on a stream batch fusion architecture, and relates to the field of digital fire fighting, and the core steps of the method are as follows: after multi-source data are collected by an edge gateway, high throughput distribution and storage are realized by Kafka; real-time optimization such as track cross repair and offset correction is completed through Flink streaming calculation; performing offline batch processing on the historical data by the Spark to generate an analysis result; generating an optimal rescue path through a distributed Dijkstra algorithm in combination with road topology, real-time traffic and fire fighting truck parameters; the method comprises the following steps: realizing mixed storage by adopting ElasticSearch secondary index and HBase total storage; based on hot and cold data hierarchical management, intelligent applications such as fire-fighting commanding and dispatching, battle comment and review and the like are supported. Practice verifies that the method can shorten the fire control time of the main urban area by 60%, significantly improves the fire emergency response capability, and is suitable for large-scale fire track data scenes.

The invention relates to the technical field of nuclear power data asset automatic management, in particular to a nuclear power digital twinning data warehouse structure system, which comprises a data processing layer, and is characterized in that the data processing layer reads original data of a nuclear power industrial internet platform, performs data standardization processing on the original data to obtain standard data, and sends the standard data to the nuclear power industrial internet platform; constructing a nuclear power industry theme library according to the standard data; the data integration layer performs data coding mapping, data classification and data fusion on the data of the nuclear power industry theme library to form a thematic library; the semantic enhancement layer marks the data of the nuclear power industry theme library and the data in the thematic library formed by the data integration layer, and constructs a structured feature tag library for the data associated with feature tags; the analysis application layer reads data of the feature tag library, and constructs a digital twinborn algorithm library and a digital twinborn sample library through algorithm analysis and sample training. The system supports equipment operation and maintenance from regular maintenance to predictive maintenance based on bottom support and four-layer progressive architecture of a nuclear power industrial internet platform.

The invention provides a control method for layer-by-layer carrying in a segmented data warehouse, which belongs to the technical field of layer-by-layer carrying, and comprises the following steps: step 1, carrying out segmented division on the segmented data warehouse; step 2, performing independent manufacturing on non-adjustment segments in the data warehouse for segment manufacturing; step 3, carrying out non-adjustment segmented welding and precision measurement on the data bins manufactured in a segmented manner; step 4, performing adjustment segmentation allowance compensation on the data bins manufactured in a segmentation manner; step 5, carrying out layer-by-layer carrying operation on the data bins which are manufactured in a segmented manner; 6, front equipment and piping system installation is carried out on the data bins manufactured in a segmented mode; and 7, carrying the segmented data bins in a segmented manner, and integrally folding the segmented data bins. Through scientific segmentation division, accurate precision compensation, a reasonable carrying sequence and preposed equipment installation procedures, effective control over the overall size precision of the data bin is achieved, and the construction quality and efficiency are improved.

The invention relates to the technical field of electric power intelligent interaction, and discloses an AI large language model intelligent interaction system oriented to the electric power industry. The system comprises a multi-source data acquisition module for acquiring power industry multi-dimensional data from a plurality of heterogeneous data sources and integrating the power industry multi-dimensional data into a data warehouse; the semantic feature extraction module performs semantic analysis and feature extraction on the integrated data warehouse to obtain a semantic feature matrix; the industry knowledge graph construction module is used for constructing a power industry knowledge graph based on the semantic feature matrix; the user query analysis module is used for analyzing user query and extracting query feature vectors; the core semantic screening module is used for screening a core semantic sequence based on the semantic feature matrix and the query feature vector; and the intelligent response generation module generates an intelligent response based on the core semantic sequence and the power industry knowledge graph. The system can deeply understand user demands, generates professional and accurate responses, and is suitable for diversified intelligent interaction scenes in the power industry.

Embodiments described herein comprise an advanced Software as a Service (SaaS) platform addressing challenges including trust, governance, quality, data accuracy, and transparency, ensuring full visibility of enterprise data warehouse data pipelines. These embodiments seamlessly integrate with cloud-based and legacy data platforms, enhancing visibility through monitoring data quality, tracking metadata changes, and generating exceptions for review. Data quality management allows for custom script creation to validate pipelines and business rules. Cloud-native connectors facilitate integration with one or more cloud-based services streamlining data delivery processes. Personalized notifications aid administrators in responsive action, while data profiling capabilities identify quality issues and provide distribution insights. Actionable data intelligence for privacy, security, and governance is enabled through data classification. Automation features encompass data quality checks, metadata monitoring, profiling, and classification, orchestrated by a flexible scheduler. The architecture leverages a scalable serverless design with independent microservices, dynamically scaling based on demand.

The invention discloses a data asset management system and method, and relates to the field of data management, and the system comprises a data collection layer which is used for collecting multi-source heterogeneous data; the data integration layer is used for integrating the multi-source heterogeneous data and constructing a data warehouse; the data storage layer is used for carrying out structured storage on the data in the data warehouse; the data governance layer is used for performing standardization processing and quality management on the data stored in the data storage layer to obtain target data; and the data application layer is used for performing dynamic tracking inventory and classification level-to-level management on the data assets based on the target data. Through cooperative work of five layers of architectures, full-process closed-loop management of the multi-source heterogeneous data from collection to application is realized, and the problems of difficulty in integration of the multi-source data, low management efficiency, uncontrollable quality and the like are effectively solved by the overall scheme.

The invention provides a sonar intelligent interpretation method and system, and the method comprises the steps: integrating end-side equipment (disposed on a ship-borne platform) and side-side equipment (disposed in a shore-based machine room) to form a sonar intelligent interpretation system; through the intelligent sonar interpretation system, full-process automatic processing of sonar interpretation from data acquisition of sonar images to interpretation is realized, the interpretation efficiency of the sonar images is effectively improved, and in addition, the intelligent sonar interpretation system also can be used for realizing automatic interpretation of the sonar images on the basis of mass sonar image data (namely sonar images stored in a data warehouse) obtained through actual measurement. Model training of the sonar interpretation model and training examination of interpretation personnel are synchronously achieved, a sustainable closed-loop optimization system is formed, and the problems that in traditional sonar interpretation, the interpretation accuracy is low, the data reuse rate is poor, and personnel training examination is difficult are solved.

A method (400) and system (100) of generating knowledge graph of the data repository is disclosed. The method (400) includes receiving input data (302) and access of data repository (304). The method (400) may include generating semantic (310) representation of data repository (304) schema based on input data (302) and data repository (304) using language model. The method (400) may further include validating semantic representation (310) syntactically and with respect to input data (302). The method (400) may further include generating mapping (320) file of data repository (304) schema based on semantic representation (310) and data repository (304) using language model. The mapping file (320) may include mapping of plurality of elements of semantic representation (310) to corresponding elements in input data (302). Further, the method (400) includes validating mapping file (320) syntactically and semantically based on semantic representation (310), data repository (304) and input data (302).

The invention discloses an automatic test method and test platform based on cloud computing, and belongs to the technical field of software testing, and the method comprises the steps: firstly constructing a dynamic elastic resource pool containing a virtualized test node cluster, a tool chain and a distributed data warehouse; after obtaining a performance test request, analyzing data to screen an optimal node combination and tool configuration, and generating a test case set covering a boundary scene and an abnormal path in combination with historical data and reinforcement learning; using an improved genetic algorithm and a load prediction model to disassemble the subtasks and intelligently distributing the subtasks; the progress is tracked in real time through a digital twinborn model, abnormity is recognized, and a multi-stage processing mechanism is activated; and after the test is finished, normalizing the processed data, and generating a panoramic report by means of the analysis model. According to the method, the intelligent level, the execution efficiency, the resource elasticity and the exception handling capacity of automatic testing are improved, and the high-performance testing requirement in a complex scene can be met.

In accordance with an embodiment, described herein is a system and method for providing query acceleration with a computing environment such as, for example, a business intelligence environment, database, data warehouse, or other type of environment that supports data analytics. A middle layer is provided as a long-term table data storage format; and one more acceleration formats, or acceleration tables, can be periodically regenerated from the middle layer, wherein a determination can be made as to whether an accelerated table exists for a dataset table, and if so, then the accelerated table is used to process the query.

The invention provides a metadata-driven integrated data warehouse and governance platform construction method and system, and the method comprises the steps: collecting full-quantity metadata in a data source through a metadata automatic collection adapter, extracting a full-link blood relationship map and an enterprise data map, and constructing a target data set; executing a data table one-key synchronization operation by adopting a control table and parameterized pipeline mode, and generating an ODS target table and an ETL synchronization task; constructing a DDL table establishment statement according to the ODS target table and the ETL synchronization task; on the basis of DDL table establishment statements, dividing models according to business subject domains, and combining data magnitude prediction to automatically recommend a bucket dividing strategy to generate a data warehouse model; a table structure of a data warehouse model, a full-link blood relationship graph and a partitioning strategy are adopted to construct a one-stop data governance platform. A data warehouse table model can be quickly constructed, data can be automatically synchronized, and full-link data development, treatment and operation capabilities are realized.

The invention relates to the technical field of business system monitoring, and provides a business system health state intelligent analysis method and system, and the method comprises the steps: obtaining business node data, application performance data and log data, and marking an associated business serial number; associating the service node data, the application performance data and the log data into an event record in the time window by taking the service serial number as an association key; the event records are periodically synchronized into the data warehouse, the incidence relation between the service entities and the technical components is recognized for data in the data warehouse, a relation chain is obtained, and the graph database is updated; and based on the business node data, the application performance data and the log data, performing anomaly detection through an intelligent analysis model to obtain business problems and technical faults, and combining a graph database and utilizing a causal inference algorithm to obtain a causal relationship between the business problems and the technical faults. And causal relationship positioning of business problems and technical faults is realized.

The invention discloses a system and a method for evaluating a delivery and education fusion index, and the system comprises a data collection unit which collects related data of delivery and education fusion index evaluation; the data pre-processing unit is used for pre-processing the acquired production and education fusion index evaluation related data; the big data storage and analysis unit is used for building a Hive data warehouse on a Hadoop cluster, creating table structures of an ODS layer, a DWD layer, a DWS layer and an ADS layer, loading the preprocessed data into corresponding layers of the data warehouse by adopting SparkSQL, and carrying out data analysis and calculation; the production and education fusion evaluation model construction unit is used for designing the weight of each third-level index of production and education fusion index evaluation related data by adopting an analytic hierarchy process, carrying out weight design on a second-level index by adopting an entropy weight method, obtaining the weight of each evaluation index by combining the two methods, and establishing a production and education fusion evaluation model; and the model comprehensive calculation unit is used for substituting the production and education fusion index evaluation related data into the production and education fusion evaluation model, and calculating to obtain a comprehensive score.

An exemplary virtual IMU extraction system and method are disclosed for human activity recognition (HAR) or classifier system that can estimate inertial measurement units (IMU) of a person in video data extracted from public repositories of video data having weakly labeled video content. The exemplary virtual IMU extraction system and method of the human activity recognition (HAR) or classifier system employ an automated processing pipeline (also referred to herein as “IMUTube”) that integrates computer vision and signal processing operations to convert video data of human activity into virtual streams of IMU data that represents accelerometer, gyroscope, or other inertial measurement unit estimation that can measure acceleration, inertia, motion, orientation, force, velocity, etc. at a different location on the body. In other embodiments, the automated processing pipeline can be used to generate high-quality virtual accelerometer data from a camera sensor.

The invention, which relates to the technical field of intelligent production scheduling, discloses an intelligent production scheduling management system for a cable production processing workshop, comprising a prior data acquisition module, a preliminary production scheduling module, an optimized production scheduling module, a Web set display terminal and a data warehouse. According to the method, the equipment sharing correlation degree among various production cables is fused, preliminary production scheduling is carried out on the production cables, the preliminary production scheduling table of the production cables is obtained, the equipment sharing performance of a cable production and processing workshop is improved, and the equipment vacancy rate of the cable production and processing workshop is reduced; according to the invention, the preliminary production scheduling table of the production cable is optimized in the optimized production scheduling module according to the associated production equipment corresponding to the plurality of demand detection items of various cables, and the optimized production scheduling table of the production cable is obtained, so that the defects in the prior art are overcome, the detection efficiency is improved, the quality control difficulty is reduced, and the consumption of detection resources is reduced.

The application discloses a kind of civil aircraft aerodynamic theme data management methods, belong to aerodynamics test data management technical field, the core of management method is using aerodynamic wind tunnel test train table to realize the management of test data for the basic model of civil aircraft and its research category;Managed data includes four levels of research category, research object, project and result, each level contains relevant data resources;The application includes civil aircraft aerodynamic theme data warehousing method based on train table, civil aircraft aerodynamic theme data organization management, civil aircraft aerodynamic theme data retrieval and display, civil aircraft aerodynamic theme data maintenance and civil aircraft aerodynamic theme data call.The application replaces the mode of original data organization with test condition and test state, and operation is more simple, intuitive and convenient and fast.

The application discloses a data bloodline collection method and device based on a Gbase stored procedure, and the method comprises the following steps: removing fixed keywords of a stored procedure definition process body; capturing a stored procedure variable through a regular expression and converting the stored procedure variable into a Key-Value form; replacing a variable name with a variable value according to the Key; processing a loop structure and a branch structure, and processing a self-defined label; and performing syntax compatibility and replacing or removing SQL statements according to some keywords. The application processes some general keywords of the stored procedure, removes redundant information irrelevant to the data bloodline, processes some syntaxes specific to the Gbase in a compatible manner, so that the syntaxes can be parsed by open-source SQL tools, and the application is adapted to the stored procedure of the Gbase, so that the application can help some manufacturers using the Gbase stored procedure to process data in an automatic form to the data bloodline, and the application is convenient for data warehouse construction.

The invention relates to the technical field of load identification, in particular to a non-intrusive load identification method based on high-frequency power data. The method comprises the following steps: acquiring high-frequency power signal data of voltage and current of a user terminal, and constructing a layered data warehouse according to a high-frequency power signal data processing requirement; performing signal analysis on the collected high-frequency electric power signals, extracting transient and steady-state features, and constructing a multi-view feature matrix based on the transient and steady-state features; based on the multi-view feature matrix, a multi-branch structured learning model is adopted to carry out load prediction, meanwhile, a deep correlation analysis model is constructed to process the uncertainty problem in high-frequency power signal data in the prediction process, and a time domain correction function is constructed by using the inherent periodic characteristics of the load. According to the method, high-precision and structured recognition of complex composite loads and dynamic similar equipment is realized by fusing high-frequency transient characteristics and a time domain dynamics decoupling mechanism.

The application discloses an intelligent accounting method for industrial water consumption, and relates to the technical field of industrial water consumption monitoring and intelligent accounting, and comprises the following steps: step one: receiving and analyzing water consumption accounting requirements, simultaneously acquiring historical data and real-time data from a historical data warehouse and a real-time data acquisition system, respectively, pre-processing, and generating a pre-processed data set. The intelligent accounting method for industrial water consumption realizes real-time self-adaptive adjustment of time-varying factors in industrial water consumption accounting by adopting multi-level data fusion, an improved deep learning network, data sliding window online learning, feedback closed-loop control and a hierarchical incremental updating strategy, significantly improves the prediction accuracy and system response speed of nonlinear water consumption behavior, and thus effectively overcomes the deficiency of static offline training of an accounting model in the prior art.

The invention belongs to the technical field of nuclear fuel assembly detection, and particularly relates to a fuel assembly operation robot positioning method and system based on adaptive contour feature matching. Comprising the steps that a global surface optimization tool converts acquired contour data into optimized ideal contour surface information, a fitting contour surface evaluation optimization tool feeds back a fitting contour surface overall deviation evaluation result, and a task data integration tool is an algorithm tool for relevant data configuration in the operation execution process of an execution assembly. And the task data integration tool constructs a data warehouse, and obtains trajectory data corresponding to the contour from the contour surface optimization module. The method has the advantages that one-key self-adaptive execution of assembly operation work can be achieved, accurate operation positioning is achieved through high-precision contour modeling, the method adapts to various motion control cards through a configuration type mode, dependence on the operation technology of operators is reduced, and the operation safety of equipment is improved.

The present application relates to a data storage method and apparatus, a computer device, and a computer readable storage medium. The method comprises: a data middle platform first acquiring current application environment information, user requirement information, and data information to be stored, and generating corresponding configuration information on the basis of the current application environment information and the user requirement information; constructing a corresponding storage directory tree on the basis of the configuration information; and finally storing said data information into a corresponding data storage node on the basis of data warehouse elements and data indicators of said data information.

This application discloses a resource scheduling method, apparatus, device, and medium based on a real-time data warehouse, relating to the fields of financial technology and data processing technology. It can ensure real-time service while significantly improving the release efficiency of idle resources during periods of low traffic, thereby greatly improving the overall resource utilization of the cluster while ensuring system stability. The method includes: predicting the data traffic of computing nodes in the real-time data warehouse using a time-sharing traffic prediction model; monitoring the load status of computing nodes in the real-time data warehouse using a load-aware model; determining the initial resource configuration of computing nodes based on the data traffic predicted by the time-sharing traffic prediction model before the start of the real-time data warehouse scheduling cycle; dynamically adjusting the initial resource configuration of computing nodes based on the load status monitored by the load-aware model during the real-time data warehouse scheduling cycle, generating resource scheduling instructions; and allocating resources to computing nodes in the real-time data warehouse according to the resource scheduling instructions.

The invention discloses an intelligent project monitoring method, system and device based on big data analysis, and the method comprises the steps: integrating business data received from a business terminal when a preset data integration time point is reached, obtaining corresponding project data information, and storing the corresponding project data information in a preset data warehouse; monitoring project data information in a data warehouse according to a problem monitoring rule to obtain problem monitoring information, and classifying the project data information to obtain problem monitoring data corresponding to each problem mark; adding a corresponding optimization label to the problem monitoring data according to the historical optimization project information to obtain to-be-optimized data information; and performing optimization adjustment on the project configuration information of the to-be-optimized data information according to the received optimization configuration information, and updating the corresponding project data information in the data warehouse. According to the intelligent monitoring method for the project, the business data can be integrated and intelligently monitored, so that problems can be quickly positioned and labels can be added, and efficient monitoring and problem identification can be realized.

The present application belongs to the technical field of data management, and particularly relates to a data management system for a twisted wire automatic detection platform, to solve the technical problem that the prior art cannot identify periodic defects and the traceability analysis process cannot correlate defects. The management system comprises: a data acquisition and synchronization unit for forming a longitudinal data profile; a main data modeling unit for constructing a pedigree main data model containing twisted wire product, single wire raw material and production process data; a data warehouse hierarchical processing unit for establishing a hierarchical data warehouse; an identification unit for constructing a data theme layer above the data detail layer; and an application service unit for constructing a data application layer above the data theme layer. The present application realizes traceability from quality problems to production, and provides data decision basis for process improvement and quality control.

A seabed data center structure based on a large-diameter steel pipe pile belongs to the technical field of seabed data centers and comprises a tension band, a rotating structure, five jacking structures and five data bin structures and is mounted on the large-diameter steel pipe pile. Through a rotating structure, when a data cabin in the large-diameter steel pipe pile is subjected to daily maintenance and overhaul, a worker logs in through a working platform, at the moment, through driving of a first driving motor, an output shaft of the worker can drive a rotating disc to rotate, and when the rotating disc rotates in a manner of being attached to the concave surface of a flower gear, an extension rod drives a shifting rod to penetrate into a vertical groove; the spline gear is shifted to drive the rotating shaft to rotate, so that the data cabin body needing to be overhauled in the rotating shaft can be rotated to be close to the working platform, workers can conveniently go down to overhaul and maintain, and compared with a traditional data cabin overhaul mode, diving operation is avoided, a large crane ship is not needed, the operation and maintenance cost and risk are remarkably reduced, and the maintenance efficiency is improved. The operation safety is improved, and the problems of high operation and maintenance difficulty and high cost in the prior art are solved.