103 results about "Data assessment" patented technology

The invention provides a multi-dimensional quality evaluation method, device and equipment for carbon footprint data and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data of a product, the multi-source heterogeneous data comprising basic information of the product and carbon footprint related data of the whole life cycle of the product; generating carbon footprint data of the product based on the multi-source heterogeneous data; and performing multi-dimensional quality evaluation on the carbon footprint data based on a pre-constructed carbon footprint knowledge graph, a time dimension detection model, a space dimension detection model and a cross-dimension detection rule. According to the invention, the carbon footprint knowledge graph, the time dimension detection model, the space dimension detection model and the cross-dimension detection rule are introduced to realize semantic, time, space and cross-dimension multi-dimensional quality evaluation of the carbon footprint data, and the detection dimension can cover the multi-dimensional features required by the carbon footprint calculation. Therefore, it is ensured that the carbon footprint data quality evaluation result can reflect the influence on the carbon footprint calculation reliability more accurately.

The invention discloses an intelligent processing system for enhanced training of legal text small samples, and relates to the field of intelligent processing of legal texts. The system comprises a knowledge base module, a data enhancement module and a data quality evaluation module. The basic synonym library ensures standardization and authority of term replacement through an authoritative dictionary, judgment document mining and co-occurrence frequency calculation, scene accurate matching of term replacement is achieved, the legal relation qualitative change problem is reduced, scene classification is achieved through the context adaptation library based on an LSTM-CRF model, scene key terms are extracted in combination with an attention mechanism, and the scene key terms are extracted through the context adaptation library based on the LSTM-CRF model. The term replacement is more fit with the field context of the legal text, the expansion explanation library maintains the hyponymy of the legal concept through the knowledge graph, it is ensured that the replaced terms do not change the abstract-concrete level of the legal concept, and the preciseness of the legal logic is maintained.

The invention provides a hollow-core optical fiber cable fiber breakage detection method based on a reference optical fiber and an optical cable, and belongs to the technical field of optical fiber communication. The method comprises the following steps: acquiring monitoring data of a reference optical fiber in a hollow-core optical fiber cable, and performing quality evaluation on the data to generate a signal quality comprehensive score; when the score is higher than a preset threshold value, feature extraction and dynamic weighted fusion are carried out to generate a comprehensive feature vector; outputting a fiber breakage probability by using the fiber breakage state recognition model, calculating a fiber breakage risk value through a prediction model in combination with time sequence data, and obtaining a fiber breakage risk index through a risk fusion algorithm; and finally, matching and executing a hierarchical response strategy according to the risk index. According to the invention, multi-modal data fusion, intelligent state identification and active early warning are realized, and the reliability, precision and operation and maintenance efficiency of broken fiber detection are significantly improved.

The invention provides an air quality data quality control method based on time series data, and relates to the technical field of air detection, and the method comprises the steps: obtaining multi-source atmosphere monitoring station data, constructing a spatial topological relation network of monitoring stations, and defining a dynamic hierarchical structure of atmosphere pollutant data under different spatial and temporal scales and a cross-hierarchical association rule of the dynamic hierarchical structure; carrying out anomaly detection and traceability analysis on the obtained monitoring data, and carrying out data correction and filling; performing credibility evaluation and grading on the corrected monitoring data by applying a space-time clustering algorithm and combining a site topological relation and a data association rule, and generating a data credibility grading result of a space-time dimension; when the data credibility is detected to be abnormal or the potential failure risk is detected, a graph neural network model integrated with atmospheric diffusion physical constraints is adopted; and generating a visual atmosphere data quality evaluation and control decision report, and automatically triggering an instrument calibration, maintenance or data review instruction for a specific site or region according to the risk level.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The invention discloses an aerospace equipment performance evaluation method based on digital test data fusion, and belongs to the field of electronic engineering and computer science, and the method comprises the steps: designing a physical test and a digital test, selecting related indexes of aerospace equipment, and carrying out corresponding physical and digital tests around the related indexes of the aerospace equipment; performing preprocessing and multi-source data integration on the physical test data; performing data quality evaluation and virtual data division on the digital test data; and performing double fusion on the result through an adaptive fusion algorithm to obtain a performance evaluation result. The advantages of the digital test and the physical test can be fully exerted, and comprehensive and accurate evaluation of the aerospace equipment performance is realized through deep fusion of the digital test data and the physical test data.

The invention relates to the technical field of water quality monitoring, and discloses a sewage quality on-line monitoring method and system.The sewage quality on-line monitoring method comprises the steps that time alignment processing and data quality evaluation are conducted on original water quality sequences collected at all monitoring point positions, and aligned data and data quality weights are obtained; constructing a time delay space diagram; performing joint estimation on the global water quality state to obtain global state estimation and uncertainty; performing anomaly detection and identifying an abnormal propagation path and a suspected source to obtain an anomaly score and a source confidence sorting list; formulating a self-adaptive sampling strategy, and determining the sampling frequency of each monitoring point position; on the basis of the time-delay observation equation and weighted optimization, online joint estimation of the global water quality hidden state is achieved, reverse traceability is carried out by combining the residual standardization anomaly score and the time-delay space diagram, the anomaly can be found, the possible source and propagation direction of the anomaly can be located, and the pertinence and interpretability of disposal are improved.

The invention relates to the technical field of data quality evaluation, in particular to an automatic quality evaluation method for a high-quality data set in the cultural field, which comprises the following steps: extracting semantic fragments and vector information, performing clustering analysis on a coverage structure, screening redundant samples, labeling image-text differences, and outputting a consistency evaluation result. According to the method, high-precision recognition of semantic coverage and structure density can be realized through structured extraction of semantic information and vector distribution measurement, and the discriminability of redundancy detection is enhanced through linkage screening of vector coincidence frequency and similarity threshold. The systematicness of image-text consistency marking is improved through an image-text position difference and semantic span combined marking mechanism, and it is ensured that redundant and deviated intersection samples can be accurately recognized through a synchronous contrast mechanism between text screening content and image-text difference marking results. The intensive clustering of the cultural semantic content in the vector space and the quantitative analysis capability of the structure deviation point are synchronously improved, and the comprehensive dimension and logic closure of the evaluation result are optimized.

The invention relates to the technical field of AI reasoning optimization, and discloses a user AI reasoning service method based on data quality intelligent evaluation, which comprises the following steps: a target AI model receives reasoning request data input by a user and executes data format and integrity verification, and the data comprises text, image or sensor time sequence data; performing multi-dimensional quality evaluation on the inference request data passing the verification, and aggregating and generating a data quality score for judging data reliability by extracting quality indexes and analyzing data features; the reasoning precision of a target AI model is dynamically regulated and controlled in real time based on the data quality score, so that prediction deviation caused by input data noise is inhibited; and executing an inference task by using the regulated and controlled target AI model, generating a prediction result, adding quality evaluation metadata, and returning the prediction result to the user terminal. According to the method, the reasoning precision of the AI model is regulated and controlled in real time through the data quality score, the problem of prediction deviation caused by input data noise is solved, and the robustness of the model under different quality data is improved.

The invention discloses a hydrogel phase state identification method and system based on dual-polarization radar networking fusion, and relates to the technical field of hydrogel phase state identification. The method comprises the following specific steps: acquiring dual-polarization radar observation data from a plurality of radar stations; performing data quality evaluation on the dual-polarization radar observation data to obtain qualified data; qualified data are interpolated into a unified grid, networking fusion is performed through an inverse distance weight algorithm, a data set is constructed, and the data set is divided into a training set, a test set and a verification set; constructing a convolutional neural network, training the convolutional neural network by using the training set, and extracting hydrogel particle features in the training set; and after the training set finishes training, verifying the convolutional neural network by using the verification set, carrying out cycle training for multiple times to obtain a phase state identification model, and carrying out hydrogel phase state identification through the phase state identification model. According to the method, a hydrogel classification result with high robustness and accuracy can be obtained, the method does not depend on manually set weight coefficients, and the phase state identification process is objective.

The invention discloses a carbon market data quality evaluation method based on a Bayesian updating mechanism, and the method comprises the following steps: 1, recognizing input parameters in a carbon emission accounting model, employing a sensitivity analysis method to evaluate the influence degree of each parameter on the total carbon emission, and determining main parameters; step 2, performing grade evaluation on the data quality of the main parameters from four dimensions of numerical value accuracy, source reliability, evidence storage normalization and evidence storage digitization degree to form an MRV data quality index, and converting a grading result into a parameter uncertainty U value according to a preset uncertainty mapping table; according to the carbon market data quality evaluation method, a data quality evaluation system combining multi-dimensional subjective scoring and objective numerical modeling is constructed, empirical judgment and statistical expression are considered, and conversion of carbon emission data quality from'non-quantifiable 'to'measurable and traceable' is realized.

The invention discloses a data quality evaluation and restoration method and system based on machine learning. The method comprises the following steps: firstly, preprocessing data, and constructing a structured generator network containing a parameterized adjacent matrix; in the training stage, the network outputs preliminary reconstruction data, and manifold constraint projection is utilized to find a logic correction target matrix meeting domain constraints; and constructing a potential consistency loop through gradient blocking, and calculating a consistency loss to drive the network to approach a compliance manifold. Meanwhile, an overall target integrating reconstruction errors, logic consistency and acyclic constraints is constructed, and an augmented Lagrangian method and a double-layer circulation strategy are adopted to jointly optimize parameters. And after the double convergence judgment is met, final inference is executed based on mask synthesis. According to the method, the logic rule is internalized into the generation capability, and the sparse causal structure is mined synchronously, so that high-quality data recovery with logic compliance, distribution consistency and interpretability is realized.

This invention discloses a method for assessing the quality of carbon emission data, relating to the field of carbon emission data quality assessment. The method employs sensitivity analysis to determine the main parameters affecting total carbon emissions; it scores the quality of these main parameters across four dimensions: numerical accuracy, source reliability, evidence standardization, and the degree of digitization of evidence, converting the scores into parameter uncertainty U-values; it performs two rounds of Monte Carlo simulations based on the parameter uncertainty U-values ​​to construct the likelihood function corresponding to the current observation; it calculates historical uncertainty U-values ​​based on multi-period historical carbon emission data and fits the prior probability distribution of the uncertainty; and it applies a Bayesian update formula to fuse the likelihood function corresponding to the current observation with the prior probability distribution, deriving the posterior distribution of the current carbon emission data uncertainty to determine whether the current carbon emission data meets quality requirements. This method improves the scientific rigor, transparency, and operability of carbon emission data quality management.

This specification discloses an adaptive adjustment method, apparatus, device, and medium for a data quality assessment model, comprising: determining data quality assessment indicators corresponding to a data table to be assessed in multiple dimensions; obtaining metadata tables related to the data table to be assessed; analyzing the features of the data table to be assessed and the metadata tables to obtain feature analysis results; determining the weight values ​​of each data quality assessment indicator based on the feature analysis results; and adaptively adjusting a pre-generated data quality assessment model based on the weight values ​​to obtain a data quality assessment model that meets the requirements.

The invention provides a health degree assessment method and device for data quality and electronic equipment, and belongs to the technical field of big data. The method comprises the steps of obtaining to-be-evaluated data, and obtaining a plurality of evaluation score results of the to-be-evaluated data under a data quality evaluation index; based on the plurality of evaluation score results, constructing a relationship between the possibility distribution of the to-be-evaluated data in the data quality evaluation index and the possibility distribution of the to-be-evaluated data in the preset data quality grade; based on the to-be-evaluated data, determining a health value corresponding to the to-be-evaluated data in the data quality evaluation index; and determining health degree assessment information of the to-be-assessed data based on the relationship and a health value corresponding to the to-be-assessed data in the data quality assessment index.

This invention provides a method for processing real-device data of an intelligent agent and related equipment. The method includes: acquiring a target action acquisition task and a target action type; in response to the target action type being a long-term task, acquiring key continuous operation segment data in the long-term task; acquiring failure scenario data and corresponding recovery operation data during the execution of the target action acquisition task; identifying and deleting invalid static frames from the key continuous operation segment data and recovery operation data using a pre-trained invalid frame recognition model to obtain cleaned data; evaluating the quality of the cleaned data based on a preset data quality assessment model to obtain a quality assessment score; and training the target intelligent agent using the cleaned data in response to the quality assessment score meeting a preset quality score threshold. This application can effectively reduce the redundancy of real-device data for intelligent agents and enrich data diversity, thereby improving the generalization ability of visual language action models.

The invention relates to the technical field of medical care informatics, and discloses a hemodialysis patient dry body weight auxiliary regulation system which comprises the steps that hemodynamic stability feature vectors are extracted from continuous physiological data flow in real time and used for updating an individualized safe ultrafiltration boundary prediction model, and the cross-cycle model correction engine generates a stability index according to the long-term treatment data, and then adjusts diagnosis parameters of the data quality evaluation module. Dialysis regulation is converted into active management of evaluation based on a hemodynamics continuous process trend from hysteresis response depending on a discrete threshold value; the core of the method is that a feedback path is established, so that the instantaneous risk monitoring level of the system can be dynamically matched with slow change information representing the long-term state of a patient, and the predictability and safety of adjustment are improved.

The invention discloses a data quality evaluation method, device and equipment, and relates to the technical field of data processing, and a preset rule generation model can generate different data quality rules according to the difference of input data tables, so that adaptation to different business scenes is realized. A preset rule generation model is constructed based on a Reflection Agent framework, the preset rule generation model is iteratively optimized based on an reflection signal, so that a better data quality rule is generated, the data quality rule is dynamically generated based on structural metadata of different data tables, an index calculation task is dynamically generated according to the target data quality rule, and the index calculation task is executed. The index calculation result of the to-be-evaluated data table is obtained in a targeted manner; and according to a target data quality rule, evaluating the index calculation result to obtain a more accurate data quality evaluation result of the to-be-evaluated data table. Therefore, the data quality evaluation accuracy is improved.

The invention discloses a multi-source sounding data fusion processing method and a related device. The method comprises the steps of filtering a plurality of pieces of sounding second data received in real time based on a detection range and a geographic position range of sounding related equipment, then calculating a data quality score of each piece of sounding second data by using an established multi-dimensional sounding second data quality evaluation model, and calculating a data quality score of each piece of sounding second data according to the data quality score. And in the sounding second data from the same sonde, the sounding second data with the highest quality score in each second is spliced and fused into the complete sounding data of the sonde, and then the complete sounding data of each sonde is obtained through statistical arrangement. According to the data fusion method and device, it can be guaranteed that the data quality of each piece of sounding second data is high, then the data quality, integrity and reliability of the sounding second data obtained through splicing fusion can be guaranteed, and the data fusion mode is more efficient and reliable.

This invention belongs to the interdisciplinary field of artificial intelligence and geological resource assessment, specifically relating to a method and system for predicting the energy storage potential of salt caverns based on multi-source geological information. It aims to address the problems of low prediction accuracy and difficulty in balancing breadth and precision in existing technologies due to single-source data, static models, and lack of adaptive capabilities. The method includes: acquiring multi-source data such as seismic, well logging, core, and geostress data; constructing a geological feature fusion vector containing seven core parameters; establishing a high-dimensional nonlinear mapping model driven by a deep neural network; dynamically adjusting the prediction range based on data coverage density and confidence level; and achieving continuous model iteration through online incremental learning. The system integrates data quality assessment, anomaly handling, and distributed computing modules. By adopting the above technical solutions, this application can achieve a significant improvement in prediction accuracy and adaptive intelligent adjustment of the prediction range.

This invention discloses an intelligent management and control method and system for online monitoring data of multi-source sewage discharge in industrial parks using edge computing. The method includes the following steps: S1: Unified edge-side access and semantic modeling of multi-source sewage discharge monitoring data. This invention introduces edge computing into the sewage discharge monitoring system of industrial parks to achieve unified access, semantic modeling, data quality assessment, and adaptive cleaning of multi-source heterogeneous sewage discharge monitoring data, effectively eliminating the interference of noisy, abnormal, and low-reliability data on cloud analysis. Simultaneously, by performing multi-source data fusion, sewage discharge status feature construction, anomaly identification, and trend prediction at the edge, real-time detection, trend prediction, and tiered early warning of sewage discharge anomalies can be achieved. Combining edge-driven local linkage management and cloud-edge collaborative sewage discharge situation analysis and model self-updating significantly improves the real-time performance, accuracy, and intelligence level of sewage discharge supervision in industrial parks.

The invention discloses a K-band inter-satellite range finder data quality assessment method and system, and relates to the technical field of data quality assessment, and the method comprises the steps: obtaining a K-band radio frequency phase noise sequence and multispectral visual data of an inter-satellite range finder; generating a position coding feature vector and a direction coding feature vector according to the radio frequency phase noise sequence and the multispectral visual data, and fusing the position coding feature vector and the direction coding feature vector to generate an interference dynamic distribution diagram; constructing a three-dimensional convolution kernel, inputting the interference dynamic distribution map into the three-dimensional convolution kernel, and generating a K-band interference intensity prediction sequence by constraining the three-dimensional convolution kernel; calculating a data reliability evaluation index according to the K-band interference intensity prediction sequence, and marking a reliability label; and generating a control instruction based on the reliability label, executing the control instruction and recording an evaluation report. According to the method, the position coding feature vector and the direction coding feature vector are generated through coding, and the problem of multi-source data collaborative correction missing is solved.

A data asset lifecycle management system for SMEs and its implementation method are disclosed, comprising a data access and governance module, a data asset modeling and evaluation module, a data ownership and compliance module, a data circulation and authorization module, a data usage monitoring and revenue module, a data archiving and destruction module, and a unified control and scheduling platform. By constructing a lifecycle state model centered on data asset objects, the system provides unified management of the data collection, governance, ownership confirmation, valuation, authorized use, revenue calculation, and archiving and destruction processes. Data quality assessment results, risk levels, and value assessment results are embedded into the access control and authorization decision-making mechanism, achieving synergistic optimization of data asset availability, security, and economic value. This addresses the problems of unclear data ownership, fragmented governance, restricted circulation, and high compliance risks faced by SMEs, enabling controllable operation and value transformation of data assets throughout the entire process.

This invention relates to the field of AI-based data quality assessment technology, specifically to an AI-based method and system for assessing data quality throughout its entire lifecycle. The method includes: acquiring multi-stage data samples throughout the data lifecycle; decoupling data content and metadata structure; separating the core content field from the process trajectory field; automatically constructing a multi-dimensional quality feature space in the core content field; extracting native feature points and mapping them into the space; tracking data transfer events in the process trajectory field to generate dynamic transfer trajectories; injecting the process information carried by the trajectory back into the feature space; applying process constraint offsets to the native feature points to form quality state points; and finally generating a data quality assessment value. This solution achieves dynamic quality representation throughout the data lifecycle through field decoupling and process constraint perturbation, making the assessment results more closely reflect the actual data flow state.

This application discloses a method, apparatus, device, and medium for monitoring the quality of electricity consumption data, relating to the field of power information system technology. The method includes: acquiring real-time electricity consumption data; performing anomaly assessment on the real-time electricity consumption data based on a self-attention time series analysis module and a multimodal data fusion module in a pre-constructed data quality assessment model to obtain a corresponding anomaly score; determining the relationship between the anomaly score and various preset thresholds, and determining whether the real-time electricity consumption data is abnormal and the corresponding warning signal when the real-time electricity consumption data is abnormal based on the corresponding judgment results; wherein different preset thresholds correspond to different anomaly levels; determining the anomaly type of the real-time electricity consumption data based on the warning signal, and determining corresponding processing operations based on the anomaly type. Therefore, this application improves the timeliness and accuracy of data anomaly detection.

The application relates to an input data quality evaluation method for photolithography process window analysis, and the method comprises the following steps: S1, obtaining standardized data; S2, obtaining a robust regression residual analysis confidence degree by using a RANSAC regression algorithm; S3, obtaining an isolation tree confidence degree based on an isolation tree anomaly score; S4, obtaining a local outlier factor confidence degree by using a local outlier factor method; S5, comprehensively obtaining an unsupervised confidence degree based on the isolation tree confidence degree and the local outlier factor confidence degree; S6, obtaining a Gaussian confidence degree; S7, comprehensively obtaining a comprehensive confidence degree based on the robust regression residual analysis confidence degree, the unsupervised confidence degree and the Gaussian confidence degree, and obtaining an input data quality evaluation result for photolithography process window analysis. Compared with the prior art, the application has the advantages of improving the input data quality of photolithography process window analysis.

The application relates to the technical field of data processing, in particular to a data quality evaluation method and device, electronic equipment and medium, which are used to solve the problem that the data quality in a database is affected by unreasonable data blood relationship in the prior art. The method comprises the following steps: acquiring a plurality of data tables included in a database; determining a data dependency relationship corresponding to each data table according to the table name of each data table, and obtaining a plurality of data dependency relationships; the data dependency relationship corresponding to any data table comprises an upstream data layer of any data table and a current data layer of any data table; the upstream data layer of any data table is used for indicating the source of any data table; determining the data dependency quality category of each data dependency relationship according to a mapping relationship between a pre-stored data dependency quality category and a data dependency relationship; and determining the data dependency quality of the database according to the data dependency quality category of each data dependency relationship and the weight of each data dependency quality category.

The invention relates to the technical field of data management, and discloses a management method and system for data asset entry, and the method comprises the steps: collecting project approval data, evaluating the technical feasibility and market profit, verifying four essential components of capitalization, and forming a project approval voucher package and an approval record. Establishing a data resource catalog and classification, registering information elements and measuring integrity, checking consistency and issuing changes, and forming an information ledger and version; and enterprise and project double-view compliance diagnosis and closed loop rectification are carried out, ownership certification is collected and examined, and a compliance and ownership certificate package and risk measurement are output. According to the method, a standardized table entry system is established, then a standardized table entry process is realized, table entry steps are clarified, detailed guidance is provided for each table entry step through the system, clear, sufficient, complete and visual evidence chains are guided and collected, product content gathers knowledge and rich experience of domestic table entry experts, responsibilities of all departments are clarified, and internal cooperation efficiency is improved.

A method and system for assessing the quality of multimodal embodied data are disclosed. The method includes: preprocessing the multimodal embodied data, which includes video data, semantic text data, and motion pose data; encoding the preprocessed video data, semantic text data, and motion pose data frame by frame; concatenating the resulting video feature codes, text feature codes, and pose feature codes into a feature vector; combining the feature vectors from multiple frames into a feature tensor; inputting the feature tensor into a pre-trained language model to obtain the embodied data quality assessment result; displaying the data quality assessment result; and constructing a loss function based on the quality assessment result and ground truth annotations to train the pre-trained language model using a backpropagation algorithm. This invention achieves automated data quality assessment based on deep learning, and the output quality assessment results have significant practical value.

This invention provides a calibration method for metrological data, relating to the field of metrological calibration technology, comprising the following steps: S1: Constructing a multi-dimensional data acquisition system to obtain multi-dimensional data information; and preprocessing the acquired multi-dimensional data; S2: Performing quality assessment and reliability analysis on the preprocessed data, and calculating adaptive weights for each dimension; S3: Using a weighted fusion algorithm, combining the calibration results of each dimension to perform multi-dimensional fusion calibration; S4: Constructing a machine learning-based prediction model to perform predictive calibration on the data after multi-dimensional fusion calibration; S5: Verifying the calibration results through mutual measurement using a quantum benchmark, and optimizing the prediction model parameters by combining historical calibration data; This invention improves data quality, calibration accuracy, and reliability through the multi-dimensional data quality assessment and adaptive weight calculation in step S2.