62 results about "Data assessment" patented technology

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 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 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.

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 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.

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 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.

This invention discloses a method for evaluating the quality of marine controlled-source electromagnetic data, belonging to the field of data quality evaluation technology. The method includes: projecting the acquired marine controlled-source electromagnetic data onto a two-dimensional plane, whereby the real and imaginary parts of the data signal are projected onto the horizontal and vertical coordinates to obtain the data distribution; performing cluster statistics on the data distribution using the K-medoids clustering method to obtain cluster centers; and using the cohesion (CP) calculation formula to perform aggregation degree analysis on the data distributed according to the cluster centers to obtain the data quality assessment result. This invention can directly quantify and evaluate data based on the cohesion of the electromagnetic data distribution characteristics, without defining empirical functions, requiring no specific data volume, and is unaffected by fly-spot data, thus improving the applicability and accuracy of the evaluation method.

This invention relates to the field of big data processing and intelligent recommendation technology, and discloses a method for intelligent analysis and recommendation of science and technology innovation policies based on big data. The method includes: format conversion and word segmentation of the original policy documents; inputting a structured policy dataset into a policy domain ontology model; inputting multi-source enterprise data into a multi-dimensional feature system and performing data preprocessing; performing semantic matching calculations on the policy knowledge graph and multi-dimensional enterprise profiles; analyzing using an enterprise policy preference model; adjusting the recommendation model parameters through a feedback data quality assessment model and online learning; analyzing using a cross-departmental policy association identification algorithm; and obtaining policy collaborative analysis results through policy association network graph construction and policy combination optimization algorithms. This invention, by constructing a multi-dimensional policy knowledge graph and a deep semantic understanding mechanism, can accurately analyze the complex logical relationships and fine-grained semantic elements in policy provisions.

This application relates to the field of urban intelligent transportation technology, and in particular to a method for quality assessment and alarm of multi-source traffic data based on a time-series large-scale model. The method includes: acquiring historical multi-source traffic flow data for a target road, associating and filtering these data, using a dynamic time warping algorithm to obtain the confidence level of each pair of associated devices, and selectively retaining data based on the confidence level to obtain a historical reliable traffic flow dataset; training a time-series large-scale model based on the historical reliable traffic flow dataset, and inputting the multi-source traffic flow data from the previous time period into the trained time-series large-scale model to obtain the predicted multi-source traffic flow data and the predicted difference value between the two sources at the current time; acquiring the actual multi-source traffic flow data and the difference value between the two sources at the current time, comparing them with the predicted values ​​at the current time, and generating a multi-dimensional anomaly score; and issuing a data anomaly alarm based on the multi-dimensional anomaly score. This application significantly improves the accuracy and real-time response capability of anomaly detection.

The invention provides a method and a system for evaluating the importance degree of appearance flaws of a product, and belongs to the fields of industrial quality inspection, data quality evaluation and the like. The method comprises the steps of obtaining a score marking result through double marking, generating a defect target and evaluating the importance degree, and predicting the importance degree of the product appearance defect through a trained importance degree regression model based on marking score data. According to the method, a relatively consistent continuous result can be given for the severity of the appearance defects of the product, and when subjective changes occur in the standard, the scheme can adapt to the subjective changes only by adjusting the threshold value, so that high workload and time cost caused by re-labeling data are avoided. The problems of subjectivity and reliability in product appearance defect quality evaluation are solved, and the method can be popularized and applied in the field of industrial quality inspection or visual inspection.

This application discloses a data quality assessment method, device, and medium. The method includes: establishing a data quality assessment index system based on multiple preset assessment dimensions; obtaining the data table and metadata table corresponding to the data to be assessed, and determining the measured values ​​of each assessment index for the data to be assessed under the data quality assessment index system; determining the assessment index weight matrix and assessment index selection matrix corresponding to the data to be assessed; and constructing a data quality assessment model based on the assessment index weight matrix, assessment index selection matrix, and measured values, so as to conduct data quality assessment on the data to be assessed according to the data quality assessment model. By establishing a comprehensive multi-dimensional, multi-indicator quality assessment index system and constructing a reasonable and effective quality assessment analysis model, quantitative assessment and analysis of data quality can be achieved, providing targeted guidance for data development and use, and laying the foundation for improving data quality.

This invention discloses a multimodal data quality assessment method based on a surrogate model mapping relationship. By pre-establishing an actual mapping relationship model from the performance gain of the surrogate model to the performance gain of the target model, this invention, when actually assessing the quality of a specific multimodal dataset, uses a lightweight surrogate model to replace the target model to obtain the predicted performance gain value of the target model. Based on the actual mapping relationship model, the predicted performance gain value of the target model is converted into an equivalent predicted performance gain value of the target model. This significantly reduces the computational power and time costs of the assessment. Furthermore, through the calibration of the actual mapping relationship model, the output equivalent predicted performance gain value of the target model can accurately approximate the true target model gain. Thus, while ensuring the accuracy of the assessment conclusions, this method achieves efficient and scalable operation of certification assessment services, and is applicable to business scenarios involving standardized quality evaluation of various artificial intelligence training datasets.

This invention relates to the field of data asset management technology, and in particular to a method for asset valuation and operational analysis of spatial whole-domain data. The method involves collecting spatial whole-domain data and ownership-related data; integrating and governing multi-source heterogeneous data through data quality assessment; constructing a spatiotemporal hybrid index tree and performing online performance evaluation and structural adjustment; extracting the multi-dimensional features of the spatiotemporal hybrid index tree to obtain a whole-domain data asset value index; generating data ownership certificates through blockchain anchoring; and using a data operational analysis decision engine to perform supply and demand matching modeling and operational analysis decision output, thereby improving the accuracy of data asset valuation.

This invention provides a method for medical device location tracking and idle status identification that integrates spatiotemporal information. This method acquires real-time positioning and status data of medical devices through a multi-source sensor network, performs data quality assessment, cleaning, and fusion, and constructs a spatiotemporal trajectory sequence. A trajectory prediction model is built based on a long short-term memory network and attention mechanism, and trajectory prediction is performed in conjunction with a hospital semantic map. Furthermore, clinical scheduling data is integrated, and a multi-layer status identification model is used to achieve refined identification and confidence assessment of the idle status of medical devices. This invention can effectively improve the positioning accuracy and status judgment reliability of medical devices, providing intelligent support for hospital equipment scheduling and management.

This invention discloses a data quality detection method, apparatus, device, and storage medium. This invention relates to the field of big data processing technology. The method includes: acquiring data to be detected; determining the information class to which the data belongs; determining the data fields contained in the data based on the information class; selecting fields to be detected from the data fields contained in the data according to preset data quality assessment criteria; for any field to be detected, determining the data quality assessment rules and influence level of the field from the data quality assessment criteria; and judging whether the quality of the data to be detected is qualified based on the data quality assessment rules and influence level corresponding to each field. The technical solution of this invention achieves rapid and accurate detection of the quality of large batches of data, with a high degree of automation, requiring no manual intervention, and effectively improving the efficiency and accuracy of data quality detection.

The invention relates to the field of finance and the technical field of computers, and discloses a data quality evaluation method and device, a medium and equipment, and the method comprises the steps: determining a basic quality score of target business data based on each quality evaluation index of the to-be-evaluated target business data; based on the target business data and a target model corresponding to the target business data, determining a training value score when the target model is trained by the target business data; performing an online comparison test based on the target business data to obtain an actual contribution score of the target business data; and based on the basic quality score, the training value score and the actual contribution score, determining a target score of the target business data to obtain a data quality evaluation result. According to the invention, multi-dimensional evaluation of the data value is realized, and the accuracy of a quality evaluation result is improved.

A data processing device is provided, which includes a storage device and a data quality assessment measurement calculation module. The storage device is configured to obtain change data of data tables from a data center. The data quality assessment measurement calculation module is configured to calculate an updated data quality assessment measurement value and an updated data quality characteristic data according to the change data and a data quality assessment measurement reference value and perform a notification function according to the updated data quality assessment measurement value and a data quality threshold value. The data quality assessment measurement calculation module is configured to compare the updated data quality assessment measurement value with the data quality threshold value to generate a comparison result. The data quality assessment measurement calculation module is configured to generate and send a notification signal to perform a notification function according to the comparison result.

The application provides a kind of data governance system based on data quality evaluation, it is related to data governance technical field.The system data monitoring module intercepts data packet and transmits after cutting according to time window, evaluation rule generation module calculates instantaneous deviation percentage and is mapped as trusted mark, obtains stability index by statistical turnover frequency, generates executable vector.Real-time evaluation module compares data packet field value with vector boundary, and outputs report if it is out of range.Data governance module covers abnormal field with boundary value and marks, and sends back downstream link.Feedback optimization module adjusts stability index to optimize evaluation rule.The application improves the timeliness of capturing abnormal data, accurately determines the eligibility of data through three progressive determinations, improves the fault tolerance rate of quality evaluation, realizes self-optimization iteration of evaluation rule using feedback mechanism, and enhances the long-term operation adaptability of the system.

The invention provides an intelligent data quality evaluation system and method based on a large model, and relates to the technical field of data processing.The method comprises the steps that data fields of basic data are divided into a plurality of hierarchies, a hierarchy description vector is constructed for each data field, the number of co-occurrence times of value sequences of all the data fields in the same hierarchy is counted, and the number of co-occurrence times of value sequences of all the data fields in the same hierarchy is calculated; performing multilayer back propagation updating on semantics of each data field through a large model, adjusting a field embedding weight, calculating a coordination degree of each data field in the same hierarchy to obtain a hierarchy harmonic factor, calculating a stability degree of a semantic relationship of each data field in the hierarchy under a disturbance condition to obtain a semantic drift factor, and constructing a fusion matrix; and carrying out weight distribution and principal component space extraction on each level of data field in the fusion matrix, and calculating the deviation rate of each data field in the fusion matrix under the principal component space to obtain a data quality evaluation result. According to the invention, accurate evaluation of data quality is realized through a large model.

This application relates to the field of data processing, specifically disclosing a mountain environment perception system and method based on multi-sensor fusion. It utilizes a real-time data quality assessment module to not only independently analyze the internal data characteristics of each sensor to evaluate their inherent quality, but also introduces a cross-modal inconsistency measure. By comparing the geometric structure understanding of the same scene from different sensors, it determines whether there are conflicts or contradictions between the data. Based on the dynamic confidence score obtained from the assessment, a confidence-modulated adaptive feature fusion mechanism is further constructed. This mechanism uses confidence as a key modulation factor to dynamically adjust the contribution weight of each sensor feature during the fusion process. Therefore, when the information provided by any sensor is unreliable, it actively suppresses its influence and prioritizes the adoption of information sources with high credibility. This ensures that the system can still output stable and reliable perception results when facing extreme challenges such as glare and dense fog.