3 results about "Industrial land" patented technology

This utility model discloses a sampling device for industrial land soil testing. The key technical features include a column with a hollow cylindrical structure and an embedded support rod; both the column and the support rod have through-grooves; the support rod has a hollow main column structure and an embedded lifting assembly; and a drone is also included, which is connected to the upper end of a drone lifting ring and used for lifting and flying the lifting assembly. The soil sampling instrument mounted on the lifting assembly can be adjusted by the lifting assembly to move up and down. The user can set the sampling depth as needed to ensure accurate sampling layers and representative data. Whether on flat ground, slopes, or uneven areas, the combination of the drone's flight and the lifting assembly's adjustment of the soil sampling instrument allows the sampling device to quickly complete the sampling task and improve the overall testing progress.

This invention provides a method and system for analyzing the efficiency of industrial land use based on multi-source spatiotemporal data. By collecting and preprocessing multi-source spatiotemporal data, and using intelligent recognition for data spatial fusion and information extraction, it identifies and classifies multi-dimensional inefficient land use, analyzes the causes of inefficient industrial land use, predicts future efficiency trends of inefficient industrial land use, and finally generates an intelligent report. This achieves a comprehensive, dynamic, and causal assessment and prediction of construction land efficiency. This invention responds to the core requirement of new urbanization and urban renewal policies to improve the level of economical and intensive land use.

This invention discloses an industrial carbon emission scenario simulation system and method based on multi-source data fusion, comprising: constructing urban development factors based on multi-dimensional nighttime light data, introducing them together with the quadratic term of GDP into the STIRPAT model to form the ISTIRPAT model for directly calculating municipal-level industrial carbon emissions; using the random forest algorithm to identify and calculate the density of industrial land to obtain industrial land density distribution data; decomposing the aforementioned municipal-level carbon emissions into spatial grid cells through a gridded allocation mechanism based on the industrial land density distribution data to generate gridded high-resolution industrial carbon emission distribution data; establishing different development paths based on historical and predicted municipal-level carbon emissions, gridded distribution data, and preset growth rates of driving factors through scenario analysis, constructing an early warning probability model, and solving the model using Monte Carlo simulation to quantify the predicted values ​​of future municipal-level carbon emissions under multiple paths and their probability of exceeding preset peak values, thereby achieving risk early warning.