Plant maintenance method, system, device and storage medium based on traffic pollution
By combining data and a long short-term memory network prediction model, traffic pollution is predicted and the plant maintenance system is controlled, which solves the problem of oxidative damage to plants caused by traffic pollution and improves the stress resistance and ecological restoration efficiency of plants.
CN120875261BActive Publication Date: 2026-03-17INST OF GEOGRAPHY HENAN ACAD OF SCI
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Patent Information
- Application Number
- CN202511012204.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-22
- Publication Date
- 2026-03-17
- Estimated Expiration
- 2045-07-22
AI Technical Summary
Technical Problem
Existing plant care methods have failed to effectively address oxidative damage caused by traffic pollution, affecting plant stress resistance and ecological restoration efficiency.
Method used
By combining traffic congestion data, meteorological data, soil physicochemical data, and plant physiological data, a pollution prediction model is constructed using a long short-term memory network to predict the spread of traffic pollution and control plant maintenance systems, including measures such as irrigation and salicylic acid spraying.
Benefits of technology
It improved the plants' tolerance to urban traffic pollution, alleviated oxidative damage, and enhanced the plants' stress resistance.
✦ Generated by Eureka AI based on patent content.
Abstract
The application discloses a plant maintenance method, system and device based on traffic pollution and a storage medium, and the technical scheme points of the application are as follows: traffic congestion data, meteorological data, air pollution data, soil physicochemical data and plant physiological data of target plants in a target area are acquired; a pollution diffusion coefficient is calculated according to the meteorological data and the air pollution data; a first composite feature is constructed according to the pollution diffusion coefficient, the meteorological data and the air pollution data, a second composite feature is constructed according to the pollution diffusion coefficient, the traffic congestion data and the meteorological data, and a third composite feature is constructed according to the soil physicochemical data and the plant physiological data; the first composite feature, the second composite feature and the third composite feature are input into a pre-trained pollution prediction model to obtain a pollution prediction result; and a plant maintenance system corresponding to the target plants is controlled according to the pollution prediction result. The application helps to improve the ability of the target plants to resist urban traffic pollution and relieve the degree of oxidative damage.
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