9 results about "Industrial land" patented technology

The utility model provides a soil sampling device for industrial land, which relates to the technical field of sampling devices, and comprises a branch pipe, the bottom of the branch pipe is fixedly connected with a branch shell, the inner wall of the branch shell is fixedly connected with two limiting frames, a sliding block is connected between the inner walls of the two limiting frames in a sliding manner, the inner walls of the limiting frames are provided with sliding chutes, and the sliding chutes are communicated with the branch pipe. A sliding plate is slidably connected to the inner wall of the sliding groove, and a shaft hole is formed in one side of the sliding plate. The digging spoon is driven to rotate by pulling the pull ring, so that the digging spoon can dig soil, the supporting rod is matched with the fixing ring, the sliding stability of the supporting rod in the supporting pipe can be improved, the stability of the supporting rod in the supporting pipe can be secondarily improved through the limiting ring, and the supporting rod can serve as a linkage structure. The center of the pull rod and the center of the connecting rod are located on the same plane all the time, so that the connecting rod is driven to move when the pull rod is pulled, a stress point is provided when the pull ring is pulled through the supporting plate, and the convenience of equipment is improved.

The application relates to the technical field of data processing, and provides a method for identifying contiguous industrial land based on deep learning and multi-source data fusion, which comprises the following steps: converting text information of each interest point in a region to be identified into interest point features; constructing a to-be-identified semantic feature map based on the interest point features and each segmented land plot in a remote sensing image of the region to be identified; inputting the remote sensing image of the region to be identified and the to-be-identified semantic feature map into a contiguous industrial land identification model to obtain an initial contiguous industrial land identification map; the model is obtained by training a U-Net model based on remote sensing images, semantic feature maps and grid labels of sample contiguous industrial land; each sample contiguous industrial land contains remote sensing images of different seasons; and post-processing such as kernel density statistics, semantic similarity verification and geometric simplification is performed on the initial contiguous industrial land identification map to obtain a target contiguous industrial land identification map. The application significantly improves the efficiency of contiguous industrial land identification.

The invention discloses a low-efficiency industrial land data identification method, and relates to the technical field of land resource management, and the method comprises the steps: collecting the business income, tax, power consumption, employees, land area, building area and other basic information of an industrial land through on-site treading, and calculating the land average evaluation index of each land block. A natural discontinuity point grading method is adopted to carry out grading assignment on each index data, and an analytic hierarchy process is adopted to determine the evaluation weight of each index. And calculating the efficiency comprehensive score of each land parcel in combination with the grading assignment and the evaluation weight, and judging whether the land parcel is a low-efficiency industrial land or not through a preset score threshold value. And finally, performing coupling verification on a model judgment result and a preliminary result of field exploration of a professional, and establishing a final low-efficiency industrial land database after taking a union set. According to the method, the quantitative model and field verification are integrated, and effective support is provided for fine management and efficiency improvement of land resources.

The invention discloses an industrial land management and control index and investment attraction admission optimization method based on machine learning, and the method comprises the steps: collecting the historical data of an established industrial land, including land parcel features, location features, enterprise development attributes and the output performance over the years; and constructing an industrial land output performance prediction model by using the data and a machine learning algorithm. The planning data of the industrial land to be transferred is obtained and then input into the model, and the output performance of the industrial land in the target year can be predicted. Furthermore, a plurality of different investment attracting condition combinations (involving different industrial development attributes or space management and control indexes) are set for the same plot, and performance prediction is performed respectively, so that quantitative simulation and comparison of different investment attracting schemes are realized. Finally, an optimal investment attracting scheme can be selected based on comparative evaluation of prediction results. According to the invention, the scientificity and accuracy of industrial land performance evaluation and investment attraction decision making are effectively improved.

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.

The invention discloses a prediction method for an industrial land output performance standard, and the method comprises the steps: integrating the industrial land of a target city, the built environment over the years, and enterprise economic data, and constructing a basic spatial database; further extracting land parcel features (such as area, plot ratio, shape regularity and the like) of each land parcel, and analyzing time evolution and location association features of the land parcel by combining space-time superposition; meanwhile, land dominant industries are matched according to enterprise economic data, and historical output performance is calculated. On the basis, plot features, space-time indexes, industry classification and performance data are fused, a'feature-performance 'associated data set is constructed, and a prediction model is established by taking the'feature-performance' associated data set as a training sample and adopting a machine learning algorithm. And aiming at the industrial land to be given, calculating each characteristic index and inputting the characteristic indexes into the model, so that a predicted value of the land output performance standard can be output. According to the method, quantitative mapping from multi-dimensional features to output performance is realized, and data-driven decision support is provided for industrial land planning and management.

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.

The invention relates to an industrial land modulus interval calculation method based on space matching. The method comprises the following steps: acquiring industrial land data; the industrial land data comprises multiple pieces of enterprise data; each piece of enterprise data comprises an ellipsoid floor area and various operation data; according to the ellipsoid occupied area of each enterprise, obtaining screened industrial land data; constructing a multiple linear regression equation according to the screened industrial land data; calculating to obtain an optimal coefficient combination according to a multiple linear regression equation and the screened industrial land data; according to the optimal coefficient combination, the multiple linear regression equation and the screened industrial land data, a modulus interval is obtained through calculation. The method has the characteristics of comprehensive evaluation and high accuracy.

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.