6 results about "Green vegetation" patented technology

The present application belongs to the technical field of waste treatment, and relates to a compost control method for producing and preparing fertilizer based on degradation of organic garbage, which comprises the following steps: mixing the organic garbage with wood chips and carrying out fermentation; the fermentation reaction comprises two same stages, fresh air is first introduced into a closed tunnel workshop, and the oxygen content of the tunnel workshop is controlled to be not less than 5%; then the temperature is increased, the temperature in the tunnel workshop is controlled to be increased to 70-80 DEG C, and pasteurization is carried out for three days; after the pasteurization is completed, the temperature is started to be decreased at a rate of 2-4 DEG C / h, until the temperature is decreased to 45-54 DEG C, and then the temperature is kept for a period of time, and finally the temperature is decreased to the ambient temperature; the fermented fertilizer is exposed to the sun until it is dried; and finally, the fertilizer product is obtained through fine treatment and screening. The compost control method of the present application produces fertilizer by using organic garbage, realizes waste-to-resource, and is helpful for large-scale planting of green vegetation in desert areas (such as Saudi Arabia), and effectively reduces greenhouse gas emissions.

This invention relates to a method, system, and product for extracting cyanobacteria regions from optical remote sensing images. The method includes: acquiring optical remote sensing images; constructing different types of background noise extraction index algorithms; the types of background noise include terrestrial green vegetation, water bodies, exposed land from roads and buildings, and remaining vegetation in water other than cyanobacteria; normalizing the background noise extraction index algorithms to generate normalized background noise extraction index algorithms; constructing a noise suppression and separation function based on the normalized background noise extraction index algorithms, and suppressing the background noise according to the noise suppression and separation function to separate cyanobacteria and the background noise, generating suppression and separation results; and extracting cyanobacteria regions from the optical remote sensing images based on the suppression and separation results. This invention can improve the accuracy of cyanobacteria region identification.

This invention discloses a comprehensive ecological revetment system for river channels, relating to the field of ecological revetments. It includes natural earthen slopes along both sides of a river channel, with slope one and slope two constructed on these slopes. A horizontal lower walkway is provided between slope one and slope two, and an upper walkway is provided on the upper side of slope one. An erosion-resistant bank is constructed on the slope two near the river channel. This invention reinforces and protects the riverbank near the water's edge using an erosion-resistant filler composed of revetment bricks, mixed soil, a middle layer of backfill soil, and a top layer of planting soil. Simultaneously, the root system of the planted vegetation extends to protect soil and water, effectively preventing soil erosion. Furthermore, the erosion-resistant bank formed by reinforced steel grids as foundation piles further strengthens the revetment, maximizing protection against erosion. The floating green vegetation layer effectively conceals the clear boundary between the river surface and the riverbank edge.

The application relates to a highway environment-friendly structure and relates to the technical field of road environment protection. The highway environment-friendly structure comprises a plurality of channel bases connected in sequence, the channel bases are used for planting green plants, water flow holes are formed in the bottoms of the channel bases, the water flow holes of adjacent two channel bases are communicated, the water flow hole of the channel base at the head end is communicated with a water pump, the water pump is communicated with a water tank, the channel base at the tail end is communicated with the water tank through a pipeline, the water tank is used for storing water, a collecting hole for collecting rainwater is formed between the top end of the channel base and the water flow hole, the water flow hole and the channel base are internally provided with water supply holes for water supply, and the channel base is provided with an adjusting assembly for adjusting the closing or opening of the water supply holes and the water flow holes; when the channel base lacks water, the adjusting assembly makes the water supply holes open and the water flow holes close; and when the channel base stores water, the adjusting assembly makes the water supply holes close and the water flow holes open. The application has the effect of stably supplying water to the highway green vegetation layer for a long time.

This invention relates to the field of scientific afforestation and discloses a method for recommending vegetation species based on random forest machine learning. The method includes the following steps: acquiring field survey data of afforestation plots; parameterizing the survey data in the plot attribute database based on plant ecology principles; constructing a vegetation species recommendation model using a random forest algorithm; performing multi-scale spatial independence verification and interpretability analysis on the vegetation species recommendation model; optimizing the vegetation species recommendation model based on the analysis results; and outputting recommended vegetation species and corresponding decision-making criteria. By parameterizing the field survey data of afforestation plots to construct a feature set, constructing a vegetation species recommendation model using a random forest algorithm, and performing multi-scale spatial independence verification and interpretability analysis on the model, the recommendation model is optimized based on the analysis results. This achieves accurate learning of the complex nonlinear relationship between vegetation species and the site environment.