一种基于碳氮协同的农业农村环境减排分析方法、系统、电子设备及存储介质
By acquiring and fusing dynamic data of carbon-nitrogen coupling, a hybrid neural network model was constructed, which solved the problem of accurately capturing the dynamic changes of carbon-nitrogen coupling in the analysis of emission reduction in agricultural and rural environments. This enabled the quantitative analysis of carbon-nitrogen coupling effects and the generation of emission reduction strategies, thereby improving the sustainability of agricultural production.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INSTITUTE OF ENVIRONMENT AND SUSTAINABLE DEVELOPMENT IN AGRICULTURE CAAS
- Filing Date
- 2025-05-22
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for analyzing emissions reduction in agricultural and rural environments are insufficient to accurately capture the dynamic changes in carbon-nitrogen coupling within complex production and living ecosystems. This results in a lack of systematicity and precision in the quantitative analysis of carbon-nitrogen synergistic effects, and also leads to high uncertainty in obtaining mathematical model parameters.
Carbon-nitrogen coupling dynamic data are acquired through IoT sensors, remote sensing monitoring, and field sampling. The data is preprocessed and fused to construct a hybrid neural network model with spatiotemporal dual channels. The model is then trained to predict the dynamic changes of carbon-nitrogen coupling, calculate carbon-nitrogen synergistic emission reduction indicators, and generate emission reduction strategies.
It enables real-time prediction and precise quantification of the dynamic changes in carbon and nitrogen coupling in agricultural and rural ecosystems, provides emission reduction strategies applicable to different scenarios, and improves the sustainability and environmental friendliness of agricultural production.
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Figure CN120494291B_ABST