Data quality detection method and system using deep learning

Through deep learning methods, knowledge vector mining and network optimization of marine environmental monitoring data is solved, and the problems of inefficiency and subjective influence of traditional data quality detection methods are achieved, and efficient and reliable data quality detection is achieved.

CN119128609BActive Publication Date: 2025-03-28平潭综合实验区行政服务中心(平潭综合实验区公证处平潭综合实验区社会治安综合治理中心) +1
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Patent Information

Application Number
CN202411597042.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-11-11
Publication Date
2025-03-28
Estimated Expiration
2044-11-11

AI Technical Summary

Technical Problem

Traditional data quality detection methods are inefficient and are susceptible to subjective factors, making it difficult to deal with the processing needs of large-scale marine environmental monitoring data.

Method used

Deep learning method is adopted to mine the marine environmental sensing monitoring sample data and augmented data through the monitoring data analysis network, determine the target network debugging error, and improve network configuration variables based on this to obtain the data quality detection network.

Benefits of technology

It improves data processing efficiency, enhances the comprehensiveness and reliability of data analysis, can quickly identify and eliminate low-quality data, and improves the credibility and effectiveness of marine environmental monitoring data.

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Abstract

The embodiment of the present application relates to the field of data processing technology, and specifically to a data quality detection method and system using deep learning. Through automated data processing and analysis processes, large-scale marine environment monitoring data can be efficiently processed, and quality problems in the data can be accurately detected. By constructing a special monitoring data analysis network, the original marine environment sensor monitoring sample data and augmented data are subjected to in-depth knowledge vector mining, thereby extracting key features reflecting environmental quality, which not only improves the efficiency of data processing, but also enhances the comprehensiveness and reliability of data analysis. And by accurately determining the target network debugging error of the monitoring data analysis network, a solid foundation is provided for subsequent network optimization. By improving the network configuration variables, the data quality detection network can quickly identify and eliminate low-quality data in practical applications, thereby greatly improving the credibility and effectiveness of marine environment monitoring data.
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