Composite Heat-Drought Monitoring With Multi-Source Data Coupling
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Solution Overview
Problem
Existing high temperature and drought disaster monitoring systems face inaccuracies due to errors in data acquisition and processing, leading to inadequate disaster early warning, particularly in regions lacking sufficient meteorological stations.
Innovation Solution
A method and system utilizing multi-source remote sensing and meteorological data to acquire, preprocess, and analyze soil moisture and ground observation data, establishing a coupled model for precise identification of time-space features, and implementing real-time monitoring and early warning through deep learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-source remote sensing and meteorological data are used for data acquisition, then measurement precision and reliability are improved, but device complexity and data processing difficulty increase
Solution Approach 1:
The system segments the complex monitoring task into distinct functional modules: data acquisition module for collecting multi-source data, data extraction module for processing and cleaning data, model establishment module for building prediction models, and early warning module for generating alerts. This segmentation reduces system complexity by making each module independent and manageable while maintaining overall measurement precision.
Solution Approach 2:
The patent introduces a data extraction module as an intermediary between data acquisition and model establishment. This intermediary layer performs data cleaning, preprocessing, and feature extraction, transforming raw multi-source data into standardized formats suitable for modeling. This mediator reduces the complexity burden on both data acquisition and analysis components.
2Measurement precision
If coupled multi-source data modeling is implemented, then event identification accuracy and space resolution are improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary data extraction and preprocessing before model establishment. The data extraction module pre-processes multi-source data by cleaning, standardizing formats, and extracting key features in advance. This preliminary action reduces the computational burden during real-time prediction, decreasing data processing time while maintaining high identification accuracy.
Solution Approach 2:
The patent transforms raw meteorological and remote sensing data into standardized parameters and indices suitable for the prediction model. By changing data parameters into standardized formats with defined relationships, the system enables efficient computational processing while preserving the accuracy needed for precise event identification.
3Reliability
If real-time monitoring and prediction are performed, then reliability of early warning is improved, but use of energy and computational resources increase
Solution Approach 1:
The system implements continuous real-time monitoring through an automated early warning module that continuously receives data from acquisition modules, processes them through extraction and modeling, and generates predictions without interruption. This continuous operation improves early warning reliability by ensuring no critical events are missed, while the automated workflow optimizes energy usage by eliminating manual intervention gaps.
Solution Approach 2:
The prediction model operates autonomously once established, automatically processing incoming data and generating early warnings without requiring continuous external computational intervention. The system serves itself by maintaining the prediction functionality through automated data flow, reducing the need for additional computational resources while sustaining high reliability.
Data Source
AI summary
The present invention relates to a high temperature and drought composite disaster monitoring and early-warning method and system, belonging to the technical fields of disaster risk assessment and early warning. Internal correlation features and abnormality information of high temperature and drought events are input into a model, multi-time-space scale features of the high temperature and drought events can be identified accurately, high event identification accuracy and space resolution are achieved, the progress can be predicted progressively, the drought and high temperature threshold change can be monitored closely, and fine forecasting and early warning can be performed in different periods, regions and intensities, thereby ensuring that indicators are in the same time scale, avoiding the complication of the high temperature and drought process caused by frequent time and space discontinuities of the indicators in a single point or small region, and ensuring the suitability for any periods of the process.