Urban and rural garbage yield prediction method based on spatial scale extension and data fusion

A technology of spatial scale and data fusion, applied in the environmental field, can solve the problems of prediction, unclear garbage distribution characteristics, difficult management of basic management data, etc., and achieve the effect of broad application prospects.

Pending Publication Date: 2022-01-21
哈尔滨工业大学人工智能研究院有限公司
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Problems solved by technology

The invention provides an urban and rural garbage output prediction method based on spatial scale extension and data fusion, which has the characteristics of solving the pro

Method used

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  • Urban and rural garbage yield prediction method based on spatial scale extension and data fusion
  • Urban and rural garbage yield prediction method based on spatial scale extension and data fusion
  • Urban and rural garbage yield prediction method based on spatial scale extension and data fusion

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specific Embodiment

[0051] refer to Figures 2 to 8 This embodiment is specifically described. This embodiment takes the spatio-temporal distribution of household garbage in the Hong Kong Special Administrative Region and its surrounding townships in March 2021 as an example, taking Hong Kong Island, Kowloon, New Territories, and outlying islands as four major areas and 18 small areas. The statistical unit, in order to find out the demand of local garbage production distribution in time and space, carried out the inversion research of garbage classification and generation, obtained the inversion of the time and space distribution of domestic garbage in 2020, and provided decision-making support for the layout of the future domestic garbage collection, storage and transportation system , the specific implementation process is:

[0052] Research area map: Obtain maps of 18 small areas such as Yuen Long and Tuen Mun in Hong Kong, integrate their scattered area elements, obtain complete area elements...

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Abstract

The invention discloses an urban and rural garbage yield prediction method based on spatial scale extension and data fusion, and belongs to the technical field of environment. The method comprises the steps of S1, carrying out the fusion of scattered surface elements in the shp data of a map file of a detected region; and S2, setting the garbage yield data of all the districts and towns in the detected region to be in a csv format, and associating the garbage yield data with the administrative districts of the corresponding districts and towns. According to the invention, machine learning regression prediction is carried out on garbage yield change in a long-time range by utilizing fusion of space remote sensing data for the first time, application effects of different machine learning algorithms are compared, analysis on different types of garbage yield change characteristics in time and space is realized, the garbage generation condition of remote areas where data statistics is difficult and the change condition of urban and rural garbage can be obtained, and the method is particularly applied to rural areas which are difficult to manage.

Description

technical field [0001] The invention belongs to the field of environmental technology, and in particular relates to a method for predicting urban and rural garbage output based on spatial scale extension and data fusion. Background technique [0002] The macro planning and management of garbage has developed into an important emerging industry at home and abroad, which has attracted widespread attention. In recent years, the amount of waste generated in rural areas in my country has reached 200 million tons per year. In order to meet the demand for fine-grained management and construction of the sanitation environment in villages and towns, it is necessary to conduct a detailed analysis of the magnitude of waste-related data on time and space scales. [0003] In the process of spatial analysis of social statistics, due to the small amount, scattered, and complex components of rural garbage, the characteristics of garbage spatial distribution are not clear, the basic managem...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06K9/62
CPCG06Q10/04G06Q50/26G06F18/241G06F18/25G06F18/214
Inventor 田禹赵天瑞李俐频
Owner 哈尔滨工业大学人工智能研究院有限公司
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