Carbon footprint factor quality detection system fused with artificial intelligence

By integrating artificial intelligence into the carbon footprint factor quality detection system, the problem of inconsistent data quality in carbon footprint accounting has been solved, enabling efficient and interpretable multi-dimensional quality assessment and risk management, and optimizing the accuracy and reliability of carbon management.

CN122432845APending Publication Date: 2026-07-21CHINA ELECTRONICS STANDARDIZATION INST
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA ELECTRONICS STANDARDIZATION INST
Filing Date
2026-04-28
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

The quality of basic data in carbon footprint accounting varies, quality assessment relies on expert experience, which is inefficient and highly subjective. Data quality detection and risk warning are disconnected, failing to form a technical closed loop, which affects the accuracy and reliability of carbon management.

Method used

The carbon footprint factor quality detection system adopts artificial intelligence and includes modules for data collection, quality feature extraction, artificial intelligence quality detection, and blockchain notarization and incentive. It utilizes uncertainty quantification, anomaly detection, and adaptive learning to incentivize the supply of high-quality data through smart contracts, collaborative verification, and supply chain impact simulation to achieve multi-dimensional quality assessment and risk management.

Benefits of technology

It improves the accuracy and interpretability of carbon footprint factor quality assessment, optimizes the data ecosystem, quantifies data quality risks in the supply chain, and provides precise decision support for carbon management.

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Abstract

The application discloses a carbon footprint factor quality detection system integrating artificial intelligence, relates to the technical field of data quality management, and comprises a data acquisition module, a quality feature extraction module and an artificial intelligence quality detection module.The data acquisition module is used for acquiring original data of carbon footprint factors from multiple data sources.The quality feature extraction module is in communication connection with the data acquisition module, is used for cleaning and feature extraction of the original data, and generates quality feature data.The application utilizes a hybrid artificial intelligence framework of expert rule guidance initialization-uncertainty quantification-human-machine collaborative calibration, improves the accuracy, interpretability and self-adaptive evolution capability of carbon footprint factor quality evaluation, and through an incentive mechanism based on a smart contract and dynamically bound with quality scores and risk levels, high-quality data is continuously supplied, and a data ecology is optimized.
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