Street household garbage collection and transportation quantity prediction method coupled with multi-source big data

Abstract

The invention discloses a street household garbage collection and transportation quantity prediction method coupled with multi-source big data, which comprises the steps of connecting a street map ina region with a garbage transfer station geographic coordinate map, matching street information to which each garbage transfer station belongs for each garbage transfer station, and performing overlayanalysis on base area data of various land buildings in the region and a street boundary map, and calculating the building density of each street; obtaining the population density of each street according to the population density data in the region, the house price data and the social consumer goods retail total amount of each street; normalizing the building area, the height data, the buildingdensity and the population density of each street; and inputting the normalized data into a BP neural network which is constructed and trained in advance. According to the invention accurate prediction of the garbage collection and transportation amount in communities and streets is achieved.

Description

technical field [0001] The present invention relates to the technical field of garbage collection and transportation volume prediction, in particular to a method for predicting street domestic garbage collection and transportation volume coupled with multi-source big data. Background technique [0002] The main methods used in domestic and foreign research in domestic waste prediction can be roughly divided into two categories: the first method mainly uses economic and cultural factors as indicators as independent variables, such as per capita income level, gross regional product, house price, Educational level, etc., the analysis models used mainly include multiple regression analysis model, per capita waste generation forecasting method, artificial neural network model, system dynamics model, etc.; the second type of method is to obtain the historical data of the amount of domestic waste , by using mathematical methods to compare and analyze historical data to infer future...

AI Technical Summary

Problems solved by technology

The above-mentioned current waste treatment measures put forward higher requirements for the prediction accuracy of the amount of waste in the small-scale research area, but the existing methods for predicting the amount of domestic waste in the small-scale area are relatively lacking, and it is difficult to design communities, The capacity and layout of garbage sorting and treatment facilities in the street and the formulation of relevant garbage treatment policies

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