A forest fire danger grade prediction method based on multi-source data

By using a multi-source data network and dynamic weighting coefficients to calculate the forest fire risk index, the uncertainty and data accuracy issues in existing fire risk assessment technologies have been resolved, resulting in more accurate and reliable fire risk warnings and improving the practicality and reliability of forest fire prevention.

CN122116539APending Publication Date: 2026-05-29HUIZHOU JINGWEI FORESTRY DEV CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
HUIZHOU JINGWEI FORESTRY DEV CO LTD
Filing Date
2026-02-10
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing methods for assessing forest fire risk levels suffer from uncertainty, insufficient accuracy and reliability, and difficulty in comprehensively reflecting fire dynamics. Furthermore, they suffer from uneven data sources and insufficient analytical precision.

Method used

Multi-source data networks are used to acquire forest microenvironment, meteorological and static topographic data. The dynamic comprehensive fire risk index (FFI) is calculated by sub-index calculation and dynamic weight coefficient fusion. Data reliability is assessed, and fire risk level and early warning information are output.

Benefits of technology

It improves the spatiotemporal accuracy and sensitivity of fire risk warnings, automatically identifies data anomalies, outputs credibility information, enhances the practicality and reliability of forest fire prevention, and avoids blind responses and resource waste.

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Abstract

The present application belongs to the technical field of fire prevention monitoring, and specifically relates to a forest fire risk grade prediction method based on multi-source data, which acquires multi-source data of the micro environment in the forest, external meteorology, terrain and vegetation conditions through a gridded sensor network, satellite radar data and static geographic information, calculates a meteorological fire risk index, a ground condition index and a terrain fire risk index respectively after preprocessing, and introduces weight coefficients alpha, beta and gamma which dynamically adjust with the season, real-time weather, combustible material dryness and terrain, obtains a dynamic comprehensive fire risk index through weighted fusion, and improves the adaptability and accuracy of the method to different environmental conditions. While outputting the fire risk grade and early warning, data reliability evaluation is implemented to avoid misjudgment caused by data abnormalities or model limitations and to enhance the reliability and practical value of the method.
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