231 results about "Data criteria" patented technology

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

The invention provides a standard adaptive execution method and equipment based on data standard management, and belongs to the technical field of data management. The method comprises the following steps: in response to a standardization request for a service system from a user, generating a corresponding mapping relation library based on a preset root library and a source system field; wherein the mapping relation library at least comprises a mapping relation, established on the basis of a preset semantic matching model, between the source system field and the standard field. And converting each source data value of the business system according to the mapping relation library, and executing real-time verification based on a preset multi-level value domain enumeration constraint before the source data values are written into the target library, so as to intercept conversion abnormal data according to a verification result and generate an abnormal snapshot. And when it is determined that the data standard attribute change event exists or the abnormal snapshots meeting the preset conditions exist, determining a corresponding update strategy type based on the change content of the data standard version change or the root cause classification result of each abnormal snapshot, so as to perform standardized execution according to a corresponding update strategy.

The invention provides an abnormal value screening method based on particulate matter component reconstruction, and relates to the technical field of environment monitoring. The method comprises the following steps: obtaining standard ion component data, standard carbon component data and standard inorganic element component data; obtaining a plurality of reconstruction component data; target PM2.5 data are determined; determining a doubt reconstruction component data judgment result of the plurality of reconstruction component data at each moment; determining a univariate time sequence abnormal point judgment result of each kind of reconstruction component data at each moment; determining a multivariable time sequence abnormal point judgment result of the multiple reconstruction component data at each moment; and determining whether the reconstruction component data at each moment is abnormal data. According to the method, the component reconstruction characteristics under different pollution levels can be evaluated, and the understanding of the model on the relationship among the particulate matter components is enhanced, so that the stability and accuracy of identifying whether the reconstructed component data is abnormal data are improved, the calculation efficiency is optimized, and environment monitoring auditing personnel are assisted to make decisions.

The invention relates to the technical field of data governance, and provides a data platform metadata annotation completion method, system and equipment, and the method comprises the steps: collecting metadata basic annotations in a multi-source heterogeneous database through a standard application program interface; collecting a logic model definition document and a data standard specification document, and constructing a local knowledge base through vectorization processing; when a user triggers annotation completion, calling a local knowledge base through a large language model, and generating metadata intelligent annotations; and returning the collected basic annotation of the metadata and the generated intelligent annotation of the metadata to the user as annotation completion results. According to the data platform metadata annotation completion method, system and equipment, metadata collection normalization, multi-source knowledge fusion, knowledge base construction and intelligent annotation completion can be achieved, cost reduction and efficiency improvement are achieved, the metadata annotation quality is remarkably improved, and the use threshold is lowered.

The invention relates to the technical field of video recognition and analysis, and discloses a video recognition and analysis method based on physical characteristics. Video frame pixels are mapped to a two-dimensional coordinate system with the upper left corner as an original point, and mirror image expansion and median filtering are carried out on a gray level image; constructing a binary image based on a gray threshold value, and analyzing and extracting a target region by using a four-neighborhood connected domain; using neighborhood search and polar angle sorting to close the tracking contour, and generating equidistant re-sampling points based on Euclidean distance and an interpolation method; the curvature of the re-sampling points is estimated through a three-point difference algorithm, and zero denominator is avoided through numerical protection; performing discrete Fourier transform on the curvature sequence to extract a frequency spectrum, and normalizing an amplitude to form a standardized feature vector; and finally, inter-frame similarity is calculated based on the feature vector, and the most similar frame is automatically retrieved. By processing unified data standards in stages, edge noise is suppressed, sampling uniformity is ensured, and feature stability and cross-frame comparability are improved.

The invention relates to the technical field of logistics monitoring, in particular to a cold-chain logistics monitoring method and system. The method comprises the following steps: calculating a data integrity index value at each moment in a historical time period, namely an accumulated value of a product of an effective data proportion and an importance weight of each sensor; obtaining threshold exceeding degree index values of all moments, and preprocessing data and corresponding set numerical value intervals based on the sensor; obtaining a multi-source consistency index value at each moment according to a redundant sensor data standard deviation, a semantic tag and a data variable quantity at adjacent moments; carrying out weighted summation on the three indexes to obtain a confidence coefficient weight at each moment; constructing an enhanced input vector according to the confidence coefficient weight, the semantic tag and the data of each sensor; based on the enhanced input vector at each moment in the historical time period, the data and the semantic tag preprocessed by each sensor in the future preset time step length are predicted, the cold-chain logistics can be accurately monitored, and the monitoring reliability is improved.

The invention discloses a simulation data management method based on a servitization integrated architecture, and relates to the technical field of combat simulation and deduction, and the method comprises the steps: building a servitization integrated data management architecture according to a data collection layer, a data storage layer, a data management layer, a data management layer and a data service layer; based on a service integrated data management architecture, data standardization is carried out, a data model is designed, data fusion preprocessing, data integration and data analysis are carried out on collected multi-source heterogeneous data according to a data standard and the data model, data fusion and warehousing management are completed, and meanwhile balance and unification of data security and sharing are guaranteed. According to the invention, a one-stop and all-around data management solution can be provided for simulation application.

The invention relates to the technical field of data management and information, and particularly discloses a coal market data management method and system. According to the method, a coal industry global data standard model is constructed, a multi-source data interface layer is deployed to collect heterogeneous data, an intelligent analysis module is utilized to identify field semantics and extract key information, then data is mapped into a standard format through a standard conversion engine, unit unified conversion and code conversion are executed, and finally standardized data assets are formed. According to the system, highly automatic and precise processing of coal market data is realized, the data integration efficiency and quality are improved, and the dependence on manpower is reduced.

The invention relates to the technical field of power grid data fusion analysis, and discloses a power grid multi-dimensional spatial data fusion analysis method and system, and the method comprises the steps: collecting power grid multi-dimensional spatio-temporal data, and forming a spatio-temporal data standard set; constructing a dynamic weight fusion mechanism according to the spatio-temporal data standard set, obtaining a data quality factor, an aging factor, an importance factor and a correlation factor, and obtaining a fusion weight factor set; constructing a three-dimensional space-time coordinate frame, and mapping each dimension of data in the space-time data standard set to the three-dimensional space-time coordinate frame; fusing the data of each dimension in the three-dimensional space-time coordinate framework based on the fusion weight factor set to obtain a comprehensive association degree value of each space-time coordinate point as a data fusion analysis result; the data fusion analysis result is used for formulating a power grid operation strategy and judging power grid operation abnormity. The method has the outstanding advantages of being high in adaptive capacity, capable of accurately quantifying the comprehensive correlation degree of each coordinate point, capable of being applied to power grids of different scales and different operation scenes and the like.

The invention discloses an intelligent management system and method for a food detection sample, and relates to the technical field of food management, and the method achieves the real-time collection and dynamic perception of multi-source smell diffusion data such as smell diffusion data through setting a plurality of data collection points in a target area of a food detection storage environment. And after the sample management center platform receives the data transmitted by each data acquisition point, high-precision alignment and dimensionless integration of the data are realized through preprocessing. The processing flow effectively eliminates the influence of sensing error, time drift and unit dimension difference, and ensures the comparability and consistency of smell data between samples. Therefore, compared with a traditional monitoring mode only depending on a single-point gas sensor, the storage smell dynamic monitoring system has the advantages that the'smell basic data set 'of the sample can be constructed in a software algorithm mode, and the storage smell dynamic monitoring system with low hardware dependence, uniform data standard and higher calculation precision is realized.

The invention discloses a data modeling method and device, computer equipment and a storage medium, relates to the technical field of artificial intelligence, and is applied to data modeling of a financial big data platform. The original data elements are processed in a standardized mode, and the type labels are automatically marked according to the preset classification rules; in combination with a feature coding technology, converting multi-dimensional data elements into uniform characterization representation; calculating by using a data field standard map and semantic similarity, automatically identifying an abnormal mode, and performing quantitative scoring through a pre-training review model; and finally, performing iterative optimization according to the review index score, and generating a detailed feedback report. According to the method, the modeling efficiency is greatly improved, automation and intelligentization of the field creation, type judgment and standard binding processes are realized, the data standard coverage rate and the modeling quality are effectively improved, the risk of manual review and script deployment errors is reduced, and the modeling efficiency is improved. And the modeling reliability and the operation and maintenance efficiency of the financial big data platform in a complex business scene are enhanced.

The invention discloses a building construction quality optimization method and system based on a large-span transfer beam, and relates to the technical field of building structure intelligent construction quality control, and the method comprises the steps: collecting multi-source engineering data, constructing a unified data standard, and carrying out the multi-working-condition disturbance modeling through combining a simulation platform; importing a finite element system for dynamic structure response analysis and outputting a response risk map; mapping the response variable to a multi-factor coupled bearing capacity function, and outputting a load limit which can be actually and stably borne by the template system; and constructing an on-site real-time monitoring network, performing linkage comparison on monitoring data and a multi-working-condition disturbance modeling result, and driving response verification, maintenance strategy adjustment and demolding node management and control. According to the method, a construction quality optimization mechanism with disturbance modeling, bearing capacity evaluation and real-time monitoring as the core is constructed, and risk prediction, response regulation and control and quality closed-loop control of the whole construction process of the large-span transfer beam are achieved.

The application discloses a data model and data standard mapping method, device, equipment and medium, wherein the data model and data standard mapping method comprises: obtaining data standard information of metadata corresponding to each data type through a standard source end; based on a data standard template corresponding to the data type, using the data standard information to define governance attributes of the metadata corresponding to the data type, so as to generate preliminary data standards corresponding to all the metadata; a review end returns public data standards corresponding to the metadata based on the preliminary standards; any data model is obtained, the semantic distance between the data model and the public data standards is calculated, and based on the semantic distance, the mapping relationship between the data model and the public data standards is confirmed. The method can improve the efficiency of extracting general standards for different data models offline in advance in the process of formulating data standards, and intelligently establishes the associated mapping relationship between the data model and the data standards.

The invention discloses a budget management system, and the system comprises a budget basic configuration module which is used for defining a budget data standard, and carrying out the configuration of the dimensions and attributes of a budget pool and budget items; the business data acquisition module is used for being connected with a data system of a business department of a preset type and acquiring business budget data of each business department; the budget execution control module is used for monitoring and controlling application, use and change processes of the budget according to the budget management target; the budget flow tracing module is used for managing budget data in the whole budget execution process; according to the method, the problem that budget management and control are not centralized and unified in the prior art is effectively solved, the efficiency of budget management is improved through standardized data and a centralized management mode, the accuracy and reliability of budget data are ensured, powerful data support is provided for various decisions of enterprises, and the advantages of the enterprises in market competition are enhanced.

The invention relates to the technical field of complex equipment data management, in particular to a model-driven complex equipment data relation automatic construction method and system. The method comprises the following steps: constructing a business entity meta-model standard framework comprising a base layer and a domain extension layer through a meta-model technology, and establishing a dual-check system; a hierarchical modeling method is adopted to construct a data model system, and model management is performed through a multi-source heterogeneous data reference mechanism and semantic version control; constructing a data association relationship by adopting a multi-modal fusion method and integrating structural analysis and semantic reasoning; and finally, four-dimensional quality evaluation of integrity, consistency, timeliness and accuracy is carried out on the data association relationship. According to the method, the problems of non-uniform data standards, low association efficiency and incomplete change transmission in complex equipment development are solved, and an automatic closed loop from data association construction to quality control is realized.

The invention belongs to the technical field of pumped storage power station intelligent data processing, and discloses a pumped storage power station data processing method and system based on a space-time knowledge graph. The 5G Internet of Things dynamic data, the BIM + GIS static data and the construction log semi-static data are deeply associated with a millisecond-level timestamp based on a national 2000 geodetic coordinate system; in an underground cavern excavation scene, real-time displacement monitoring data can be synchronously matched with BIM component deformation simulation, the problem of monitoring-simulation asynchronization is solved, obvious lag does not exist in progress deviation early warning any more, the management and control delay risk caused by data splitting is effectively avoided, and the dynamic safety of underground cavern group construction is guaranteed; in a data acquisition stage, a data standard is directly embedded into a source through a standard semantic pre-labeling module, and unified semantic tags such as unique component identifiers and process codes are preset for various types of data.

The invention discloses a data governance method, device and equipment, and relates to the technical field of data information management, and the method comprises the steps: carrying out the resource checking operation of data based on a data governance overall plan, and determining a business attribute set and a corresponding technical metadata set; performing ownership confirmation operation based on the business attribute set and the technical metadata set, and determining a management responsibility subject; based on the management responsibility subject, performing standard formulation operation on the service attribute set, and establishing a data standard system; on the basis of the data standard system, performing lake entering operation on the data, and loading the data into a data storage system; based on a preset data quality rule, performing quality verification operation on the data, and outputting a data quality evaluation result; based on a data quality evaluation result, performing capitalization release operation on the data to generate a data asset directory; and based on the data asset catalog, performing service packaging operation on the data assets, and generating a standard data service interface for each business unit to call.

The invention relates to the technical field of production intelligent management and control of energy enterprises such as thermal power enterprises and nuclear power enterprises, and discloses a power plant material full-life-cycle intelligent management method and related equipment.The method comprises the steps that a power plant material full-life-cycle big data center is established, and the power plant material full-life-cycle big data center integrates multi-source data through a distributed architecture; unified data standard and interface specification are carried out on the distributed architecture integrated multi-source data; respectively carrying out life management, material management and equipment maintenance management on the unified data standard and interface specification to obtain dynamic multi-service data; dynamic multi-service data are butted and fused for data association analysis, material full-process penetration management, material full-life-cycle tracing, intelligent purchase decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and full-process visual monitoring. According to the invention, power plant material management is converted from passive response to active prediction and from segmented control to full-process collaboration, and the management efficiency is significantly improved.

The invention relates to a BIM + AI-based steel bridge full-life-cycle design method and system and a medium, and the method comprises the following steps: constructing a steel bridge BIM parameterized component library, the component library comprises standardized parameter components of beam segments, nodes, supports and connecting pieces, and each component has multi-dimensional data of geometric information, material attributes, mechanical parameters and cost information; establishing a data standard based on an IFC extension standard, and newly adding design parameters, material attributes, a construction process, a monitoring sensor and a steel bridge specific attribute set of maintenance records on the basis of an IFC framework; and deploying an AI engine, wherein the AI engine comprises a structure type selection recommendation model based on the historical project database. Through AI-driven intelligent design assistance, the structure type selection recommendation model can quickly give technical feasibility scores, economic scores and comprehensive recommendation indexes based on not less than 10,000 groups of historical project data, and a traditional trial and error process depending on experience is avoided.

The embodiment of the invention relates to the technical field of operation and maintenance, and discloses an intelligent fault self-healing and performance optimization method and device based on K8s and MCP, and the method comprises the steps: obtaining standard multi-dimensional data which is a comprehensive data set which accords with the MCP protocol specification and reflects the overall operation condition and performance of K8s; performing detection processing on the standard multi-dimensional data based on a fault detection model, identifying fault risk information, and positioning to a target fault based on the fault risk information and a preset knowledge graph; analyzing the standard multi-dimensional data by combining time sequence analysis with statistical analysis, predicting a prediction performance problem after the current moment, and identifying performance bottleneck information; generating a fault solving instruction based on the fault risk information and the target fault, and executing the fault solving instruction to realize fault self-healing; and generating a performance optimization instruction based on the performance bottleneck information and the prediction performance problem, and executing the performance optimization instruction to perform performance optimization. And the data interoperability is improved based on the MCP protocol specification.

The invention relates to an enterprise data analysis method and device, and relates to the technical field of data governance, and the method comprises the steps: carrying out the Text2SQL task fine tuning of a large language model through a low-rank adaptation technology, constructing a structured knowledge base containing an enterprise data dictionary, a field calling relation and a national standard, converting the unstructured industry specification into a structured data standard knowledge base, and realizing knowledge vectorization storage by using an M3E-base vector model; sampling representative samples based on to-be-sorted data fields and retrieving related rules, integrating the representative samples into thinking chain cues, inputting the thinking chain cues into a fine tuning model to generate a labeling classification result, and outputting a data sorting result after multi-model evaluation and verification; generating a regular expression through a retrieval data standard to screen non-standard data, sampling to generate an SQL conversion code, iteratively cleaning until the standard is reached, and outputting standardized data; and finally, executing index optimization and anomaly detection based on the fine tuning model, and receiving natural language query to generate an SQL and a visual report.

The invention provides a data standard element intelligent extraction method and system based on a large model, and the method comprises the steps: obtaining multi-modal heterogeneous data, carrying out the preprocessing and alignment, and mapping the data to a unified semantic space, so as to form an initial modal; respectively inputting the initial modality into a shared encoder and a private encoder of a large model to carry out domain adversarial transfer learning to obtain domain invariant features and domain specific features, and synthesizing the domain invariant features and the domain specific features into first features; executing cross-modal dynamic fusion on the first feature in a unified semantic space to obtain a fused feature; data standard elements and confidence coefficients thereof are obtained based on fusion feature decoding; comparing the confidence with a preset confidence threshold; when the confidence is not less than the confidence threshold, directly outputting corresponding data standard elements; when the confidence coefficient is smaller than the confidence coefficient threshold value, the dynamic rule engine is triggered to carry out post-processing, and the automatic verification layer executes consistency verification according to the domain knowledge graph to obtain element output after verification. And the intelligent extraction capability of the data standard elements based on the large language model is improved.

The invention relates to a financial regularization conversion system based on a rule chain engine. The system comprises a data acquisition module used for acquiring multi-source business data from a data lake; the standard unification module is used for presetting a unified data standard rule and an articulation rule, obtaining a data circulation map based on the multi-source business data, and storing an articulation library of the articulation rule and the standardized multi-source business data; the data flow link building module is used for building a data flow link for the standardized multi-source business data, distributing a unique traceability ID for each piece of business data and generating a full-link data traceability log; and the rule chain engine processing module is used for identifying a business scene based on the standardized multi-source business data, carrying out data circulation in the business scene according to a data circulation link, calculating various financial elements according to a set rule chain, and finally forming a financial voucher. Compared with the prior art, the method has the advantages of full-link tracing, reliable result and the like.

The invention discloses an urban rail cross-professional multi-mode production and maintenance plan and scheduling method, which comprises the following steps: a cross-professional production plan dynamic generation step: establishing a unified data interface and a data standard, converging and integrating data in different professional systems, analyzing the production and maintenance requirements of each professional system, and combining the existing plan and real-time construction information to generate a cross-professional production plan; generating a trans-professional preliminary production plan; a cross-professional production scheduling step: based on the preliminary production plan, performing preliminary scheduling on three resources of personnel, time and tools, and generating a globally optimized production plan and resource scheduling scheme based on an improved genetic algorithm; and a multi-mode production plan execution and tracking step: according to the work order type, performing execution management by respectively adopting a precise mode of a curing period and a rough mode of a non-curing period, and establishing a real-time monitoring and dynamic adjustment mechanism. The method is used for solving the problems that professional system data standards are different, sharing is difficult, and cross-professional conjoint analysis and collaborative planning cannot be supported.

The application discloses a kind of based on knowledge base data standard domain automatic generation method and system, it is related to data standard domain generation technical field, method includes: obtaining data dictionary field, retrieve matching candidate standard item from knowledge base and build standard item evidence package containing semantic matching score and evidence source type;Candidate standard item is mapped to standard domain, domain support is calculated and is eliminated by evidence unit exclusion allocation to eliminate shared evidence, and the initial domain distribution of revised domain support is obtained, according to which the initial mode of single domain or multi-domain mode is judged;Calibration is carried out based on domain constraint image, domain change cost coefficient and uncertainty penalty coefficient are generated adaptively according to determination score and conflict score, score function is constructed, refined candidate domain set is filtered out according to initial mode and distribution is reorganized, post-calibration and backflow determination are carried out, and finally single-domain or multi-domain standard domain result is output;The application improves the accuracy and automation degree of data standard domain generation.

The present application belongs to the technical field of industrial data processing application, and particularly relates to a data management method based on industrial big data, comprising the following steps: S1, obtaining multi-source heterogeneous industrial data; S2, cleaning and converting the data obtained in S1; S3, auditing and improving the quality of the data according to the published data standard; S4, externally opening the audited and improved data in the form of API sharing and providing data application services. Using the method, the effectiveness, format consistency and applicability of the data can be ensured, and the audited and improved data can be fully utilized, thereby solving the problems of low data quality and difficult data utilization in the industrial field. In addition, the method can reduce the human waste caused by manual data quality checking.

The present application relates to the technical field of data management, and particularly relates to a centralized management method, system and device of a data center cluster and a storage medium. The method comprises the following steps: obtaining environmental control data of at least one data center in the data center cluster; converting the obtained environmental control data into standardized environmental control data according to a preset environmental control data standard; performing dynamic scoring on the standardized environmental control data based on a real-time scoring model to obtain the importance of the standardized environmental control data, wherein the real-time scoring model performs multi-dimensional weighted calculation according to real-time attributes, historical behavior patterns and environmental perception characteristics of data; and classifying and storing the environmental control data according to the importance. Through the dynamic scoring mechanism, the system can preferentially process data with high importance, optimize storage resource allocation and reduce the processing overhead of data with low importance, so as to realize more efficient data management in a complex operation and maintenance environment.

The present application relates to the technical field of enterprise management, in particular to an enterprise qualification evaluation system and method. The enterprise qualification evaluation system comprises: a file interpretation module for extracting relevant files according to the evaluation type and interpreting the relevant files to obtain file information related to the evaluation of enterprise qualification. An enterprise information acquisition module is used to collect relevant information of the enterprise and obtain enterprise qualification information. An enterprise information comparison module is used to determine whether the enterprise qualification information item in the enterprise qualification information exists. The enterprise qualification information item is compared with the data standard, and it is determined whether the enterprise qualification information item meets the data standard. An evaluation analysis module is used to calculate the evaluation value of each project directory and the total evaluation value of the evaluation type. The present application can automatically extract relevant files and relevant information of the enterprise, and then compare them. It can quickly and automatically locate the uncollected and unqualified enterprise qualification information items.

This invention discloses a testing method, apparatus, electronic device, and storage medium for analog-to-digital converter (ADC) circuits. The testing method includes: acquiring the output dataset and data standard range set of each transmission channel of the ADC circuit; wherein the data standard range set includes the normal range of each output data in the output dataset; determining whether abnormal output data exists in the output dataset of each transmission channel based on the output dataset and data standard range set of each transmission channel; if at least one transmission channel has abnormal output data, determining the type of faulty component in the corresponding transmission channel based on the abnormal output data; determining the faulty component in the transmission channel corresponding to the abnormal output data based on the faulty component type; and ending the test if the output datasets of all transmission channels are normal. The ADC circuit testing method provided by this invention improves the testing and maintenance efficiency of ADC circuits.

The invention discloses a human resource integrated management platform system which comprises a basic data standardization module which is used for establishing a data standard system in the whole field of human resource management. Standardized definition, format specification and verification rule setting are carried out on employee basic information, organization structure information, post information, salary and welfare information, attendance information and performance information, and unified collection, storage and sharing of data are achieved. The method has the beneficial effects that based on a basic data standardization module, all-field data standards are unified and automatically checked, data redundancy and errors are eliminated, cross-module data integration is smoother, and the efficiency of basic transactions such as salary accounting and attendance statistics is improved; the multi-dimensional intelligent analysis module integrates data and applies big data and a machine learning algorithm, not only generates a basic analysis report, but also can accurately predict employee demission risks, outputs decision suggestions such as retention, training optimization and the like, and assists enterprise data-driven management.