Life convenience analysis method based on digital twinborn technology

Abstract

The invention discloses a life convenience analysis method based on a digital twinborn technology, and the method comprises the steps: obtaining POI point of interest data of public service facilities in a regional range, constructing a life convenience evaluation index system, analyzing the distribution density of different types of public service facilities in different ranges, and carrying out the calculation of a life convenience index. The diversity of public service facilities in a certain range of the living space is analyzed, and the living convenience of the area is identified by comparing and analyzing the calculation result, so that reference is provided for optimized layout of urban service facilities, and guidance is provided for resident life.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a method for analyzing life convenience based on digital twin technology. Background Art

[0002] A digital twin is a digital representation of a specific physical entity or process with data connectivity that ensures consistent convergence between physical and virtual states, provides an integrated view of the lifecycle of the physical entity or process, and helps optimize overall performance.

[0003] Through digital twin technology, the physical and virtual worlds are combined, and research findings from the virtual world can be applied to the physical world. In recent years, with the in-depth mining of open internet data, the application of internet big data and open data in urban planning has continued to emerge. This new data research is making urban planning and urban research more rational and scientific. This invention utilizes point-of-interest (POI) data to obtain data on the current status of public service facilities within the study area, studying the density of public service facilities and the urban living convenience index, and evaluating the living convenience of residents in the study area. POI data has the advantages of a wide range, multiple access methods, a wide range of applications, a comprehensive range of types, and low processing difficulty, attracting the attention and research of many scholars.

[0004] The present invention obtains public service facility data within the study area, reclassifies the study area data according to the degree of correlation between each type of data and life convenience, and assigns weights, thereby constructing a life convenience index evaluation system. The life convenience of different administrative units in the study area is calculated separately, and the POI data of residential communities is added. By counting the diversity of public service facilities within a 500m buffer zone centered on the residential community, the life convenience of residents in each district is measured, in order to provide reference and technical support for the further optimization of public service facilities in the study area. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: in order to overcome the shortcomings of the existing technology, a life convenience analysis method based on digital twin technology is provided, aiming to provide a reference for the optimized layout of urban service facilities and provide guidance for residents' lives.

[0006] To achieve the above objectives, the present invention provides a method for analyzing the convenience of life based on digital twin technology. The method for analyzing the convenience of life based on digital twin technology comprises the following steps: Obtain the scope and administrative divisions of the study area, and generate the scope boundary of the study area based on the administrative boundary; Acquire data on points of interest of life service facilities in the study area and perform data cleaning to form data that meets the needs of the present invention; Based on the classification of the acquired data, a life convenience evaluation index system is constructed; Calculation of the living convenience index within different spatial ranges; Statistics on the diversity and density of public service facilities within a certain range of residential space; By comparing the calculation results, we can identify the living convenience in different areas.

[0007] Among them, the choice of administrative divisions should choose appropriate administrative scopes, and should avoid administrative scopes that are too large or too small.

[0008] Among them, the data of points of interest of life service facilities are of various types and huge in quantity, so data cleaning is required. The steps of data cleaning include: Consistency checking is to check whether the data meets the requirements based on the reasonable value range and mutual relationship of each variable, and to detect data that exceeds the normal range, is logically unreasonable or contradictory.

[0009] Invalid and missing values ​​may be present in the data due to survey, coding, and data entry errors. These values ​​need to be properly handled. Common methods include imputation, case deletion, variable deletion, and pairwise deletion.

[0010] After data cleaning is completed, the data needs to be reclassified to form data resources that meet the needs of the present invention.

[0011] Based on the above reclassification results, a life convenience evaluation index system is constructed.

[0012] Among them, the determination of different spatial ranges is based on the residents' daily travel habits. The residents' daily travel range is divided into basic living circle (500m), basic living circle (1000m) and urban living circle.

[0013] In this paper, two spatial ranges of 500 m and 1000 m were selected to construct a 1 km grid to calculate the average living convenience index of each district, and a 500 m buffer zone was constructed with the residential area as the center to analyze the living convenience of the residential space.

[0014] The calculation steps of the living convenience index are as follows: Calculate the distribution density of service facilities; Normalization of score intervals; Calculate the convenience of living index.

[0015] In the invention, the diversity of service facilities is analyzed with the residential area as the center, and the diversity of service facilities and the density of service facilities within a 500m buffer zone are analyzed.

Claims

1. A method for analyzing the convenience of life based on digital twin technology, characterized in that: The method for analyzing the convenience of life based on digital twin technology includes the following steps: Obtain the scope and administrative divisions of the study area and select appropriate administrative boundaries to divide the study area to facilitate analysis and comparison of calculation results; Obtain data on points of interest of living service facilities in the study area, and perform data cleaning and reclassification; According to the classification of the acquired data, the data required by the present invention is formed, and the evaluation index system of the convenience of life is constructed using the hierarchical analysis method; Calculation of the living convenience index within the spatial scope; Statistics on the diversity and density of public service facilities within a certain range of residential space; By comparing the calculation results, we can identify the living convenience in different areas.

2. The method for analyzing the convenience of life based on digital twin technology according to claim 1, characterized in that: The data obtained is classified and a life convenience evaluation index system is constructed using the hierarchical analysis method based on the specific data.

3. The method for analyzing the convenience of life based on digital twin technology according to claim 1, characterized in that: Steps for calculating the living convenience index within the spatial scope: Calculate the distribution density of service facilities, and calculate the distribution density of different types of living service facilities within the spatial range; Normalization of score intervals: To facilitate comparison and subsequent calculations, score intervals are normalized to simplify complex values; Calculate the convenience of life index by assigning weights to various public service facilities based on the degree of their impact on residents' basic lives, and then calculate the convenience of life index, so as to quantitatively analyze and process complex data indicators.

4. The method for analyzing the convenience of life based on digital twin technology according to claim 1, characterized in that: Statistics on the diversity and density of public service facilities within a certain range of residential space. The total number of public service facilities within a spatial range cannot reflect the convenience of public facilities for residents within that range. An analysis of residents' accessibility to these facilities is also needed to more realistically analyze whether the layout of public service facilities is convenient.