Facility site selection method based on graph convolutional neural network

A technology of convolutional neural network and facilities, applied in the field of data processing, to improve the quality of decision-making and solve the effect of insufficient computing power

Pending Publication Date: 2020-04-10
朱递 +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a facility site selection method based on gr

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  • Facility site selection method based on graph convolutional neural network
  • Facility site selection method based on graph convolutional neural network

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Embodiment

[0039] In this embodiment, the target setting category for the location to be selected is set as a coffee shop, which is located in the area of ​​Zhongguancun, Haidian District, Beijing. First, all idle shops in the area of ​​Zhongguancun, Haidian District, Beijing are extracted, and the collection is collected in Haidian District, Beijing. Existing facilities within the scope of Zhongguancun, as well as street view information and the number of social media check-ins for various active facilities in the past year; during the site selection process, it is necessary to comprehensively consider the type and activity of existing facilities within the scope of the candidate location Impact, evaluate the potential benefits of each candidate location to determine the best candidate location.

[0040] Among them, the target facility category t to be selected is: coffee shop;

[0041] The research area polygon A is: the administrative area polygon of Zhongguancun;

[0042] The candidate geo...

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Abstract

The invention discloses a facility site selection method based on a graph convolutional neural network. The method comprises the steps: carrying out the prediction and evaluation of all candidate position points through an inputted to-be-selected facility type, the spatial layout of built-up facilities in a current target range, and observable human activity intensity information, and selecting anoptimal position as a site selection result for output. Technically, modeling is carried out on the position relationship and the environmental characteristics of urban facilities through a model with strong spatial relationship depicting capability, namely a graph convolutional neural network, and objective and accurate urban facility candidate position prediction and evaluation are realized ina data driving manner by taking observed urban human activity data as a training basis of the model. The defects that target constraints are too subjective and the computing power is insufficient in the urban facility site selection problem are overcome, and the decision quality is improved. And an intelligent and objective facility site selection solution can be provided for a high-density and multi-attribute-dimension urban space.

Description

Technical field [0001] The present invention relates to the field of data processing, and in particular to a facility location method based on graph convolutional neural network. Background technique [0002] The rapid development of information and communication technology and the explosive emergence of geographic information containing location marks and attribute data provide a basis for people to understand the socio-economic characteristics of public facilities in cities. [0003] Current urban facility location selection is mostly based on macro-planning decision-making or space optimization algorithms. The basic idea is to use the location relationship of the established facilities and the corresponding functional conditions to define constraints and objective functions, and solve the planning equations to determine the space of the proposed facility position. For example, to build a supermarket in a certain area to serve all users in that area, it is necessary to consider ...

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Application Information

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IPC IPC(8): G06Q10/06G06Q30/02G06N3/04G06N3/08
CPCG06Q10/0637G06Q30/0202G06N3/08G06N3/045
Inventor 朱递刘瑜
Owner 朱递
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