CN-122022205-A - High-density residential area suitability evaluation method, system and storage medium based on multi-source data fusion

CN122022205ACN 122022205 ACN122022205 ACN 122022205ACN-122022205-A

Abstract

The invention relates to the technical field of livability evaluation, in particular to a high-density residential area livability evaluation method, a system and a storage medium based on multi-source data fusion. According to the method, firstly, the macro-scale habitability score of each high-density habitation area is calculated, then each high-density habitation area is screened based on the macro-scale habitation score to obtain a representative high-density habitation area, then the micro-scale habitation score of the representative high-density habitation area is obtained according to the image data of the representative high-density habitation area, finally, the habitation distribution map of a target city where the high-density habitation area is located is drawn based on the micro-scale habitation score and the macro-scale habitation score, the habitation degree of each high-density habitation area in the target city is evaluated on the macro-scale and the micro-scale habitation distribution map, and accordingly the habitation evaluation accuracy is improved.

Inventors

CAO RUI
ZHOU ZHIJIE
SONG CHUN

Assignees

香港科技大学(广州)

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (10)

1. The method for evaluating the suitability of the high-density residential area based on the multi-source data fusion is characterized by comprising the following steps of: Acquiring respective multi-source geographic data of each high-density residential area in a target city, and obtaining a macroscopic suitability score of each high-density residential area based on the multi-source geographic data; Screening each high-density residential area based on the macro-suitability score of each high-density residential area to obtain a representative high-density residential area; acquiring image data of the representative high-density living areas, and obtaining microscopic suitability scores of the representative high-density living areas based on the image data of the representative high-density living areas; And drawing a livability distribution map of the target city based on the macroscopic livability score of each high-density living area and the microscopic livability score of each representative high-density living area.
2. The method for evaluating the suitability of a high-density residential area based on multi-source data fusion according to claim 1, wherein obtaining a macro-suitability score of each high-density residential area based on the multi-source geographic data comprises: determining the positions of the healthy interest points, the positions of the safe interest points and road network data in the multi-source geographic data and building contour data; Determining a security index score based on the security interest point location and the road network data and the building profile data, the security index score being used to characterize reachability from a building along a road network to a security interest point; Determining a health index score based on the health point of interest location and the road network data and the building contour data, the health index score being used to characterize air quality and distance from a building along a road network to a health point of interest and distance between the building and a arterial road in the road network; determining a traffic convenience index score and a comfort index score of each high-density living area; And obtaining a macro-suitability score of each high-density living area based on the safety index score, the health index score, the traffic convenience index score and the comfort index score of each high-density living area.
3. The method for evaluating the suitability of a high-density residential area based on multi-source data fusion as claimed in claim 1, wherein the step of screening each of the high-density residential areas based on the macro suitability score of each of the high-density residential areas to obtain a representative high-density residential area comprises the steps of: Dividing each of the high-density populated areas into a number of levels based on a macroscopic suitability score for each of the high-density populated areas; and screening the high-density residential areas contained in each grade to obtain the representative high-density residential areas contained in each grade.
4. The method for evaluating the suitability of a high-density living area based on multi-source data fusion according to claim 1, wherein acquiring the image data of the representative high-density living area comprises: acquiring the length of an internal road of the representative high-density residential area; sampling the internal road based on the length of the internal road to obtain sampling points; based on the distance between the sampling points, carrying out cluster division on the sampling points to obtain sampling clusters; screening from the sampling points of the sampling cluster based on the priority of the sampling points in the sampling cluster to obtain representative points of the sampling cluster; and acquiring image data of the representative high-density living area at a representative point of each sampling cluster.
5. The method for evaluating the suitability of a high-density residential area based on multi-source data fusion as claimed in claim 4, wherein the priority of the sampling points is the priority formed by three sampling points, namely a road intersection, a road endpoint and a road midpoint, and the priority is the road intersection, the road endpoint and the road midpoint in sequence from high to low.
6. The method of evaluating the suitability of a high-density living area based on multi-source data fusion according to claim 1, wherein obtaining a microscopic suitability score of each of the representative high-density living areas based on the image data of each of the representative high-density living areas comprises: And applying a visual language model to the image data of each representative high-density living area to obtain a microscopic index score, wherein the microscopic index score at least comprises one of lighting, openness, greening rate, walking performance, maintenance and cleanliness, and the microscopic index score is used as a microscopic suitability score.
7. The multi-source data fusion-based high-density residential area suitability assessment method of claim 1, wherein the macro-suitability score consists of individual macro-index scores, the micro-suitability score comprising individual micro-index scores, the method further comprising: determining the correlation coefficient of any two macro index scores, and constructing a macro scale internal correlation matrix based on the correlation coefficient of any two macro index scores; determining the correlation coefficient of any two micro index scores, and constructing a micro-scale internal correlation matrix based on the correlation coefficient of any two micro index scores; Determining the correlation coefficient of the macro index score and the micro livability score, and constructing a macro-micro coupling correlation matrix based on the correlation coefficient of the macro index score and the micro livability score; And constructing a double-scale correlation analysis thermodynamic diagram consisting of a macro scale and a micro scale based on the macro scale internal correlation matrix, the micro scale internal correlation matrix and the macro-micro coupling correlation matrix.
8. The method for evaluating the suitability of a high-density residential area based on multi-source data fusion as claimed in any one of claims 1 to 7, wherein the high-density residential area is a village in city.
9. A multi-source data fusion-based high-density residential area suitability evaluation system, which is characterized by comprising the following components: The macro score calculation module is used for acquiring the multi-source geographic data of each high-density residential area in the target city and obtaining the macro suitability score of each high-density residential area based on the multi-source geographic data; The screening module is used for screening each high-density residential area based on the macro-suitability score of each high-density residential area to obtain a representative high-density residential area; The microcosmic score calculating module is used for collecting the image data of the representative high-density living areas and obtaining microcosmic suitability scores of the representative high-density living areas based on the image data of the representative high-density living areas; And the distribution map making module is used for drawing the livability distribution map of the target city based on the macroscopic livability score of each high-density living area and the microscopic livability score of each representative high-density living area.
10. A computer-readable storage medium, wherein a high-density residential area suitability evaluation program based on multi-source data fusion is stored on the computer-readable storage medium, and when the high-density residential area suitability evaluation program based on multi-source data fusion is executed by a processor, the steps of the high-density residential area suitability evaluation method based on multi-source data fusion according to any one of claims 1 to 8 are implemented.

Description

High-density residential area suitability evaluation method, system and storage medium based on multi-source data fusion Technical Field The invention relates to the technical field of livability evaluation, in particular to a high-density residential area livability evaluation method, a system and a storage medium based on multi-source data fusion. Background High-density populated areas (e.g., villages in cities) have complex morphological and non-formal features, and high-density populated areas often face problems with poor populated environments and need to improve populated environments. In the prior art, the suitability of a high-density living area is evaluated through collected remote sensing data, and corresponding measures are implemented according to evaluation results so as to improve living environment. However, the remote sensing data is macroscopic data, which can only represent the configuration of resources outside the residential area, but cannot reflect microscopic data such as infrastructure data inside the residential area, and the microscopic data also affects the suitability, and the prior art does not consider the microscopic data. In summary, the accuracy of the evaluation result of the livability is reduced in the prior art. Accordingly, there is a need for improvement and advancement in the art. Disclosure of Invention In order to solve the technical problems, the invention provides a high-density residential area suitability evaluation method, a system and a storage medium based on multi-source data fusion, which solve the problem that the accuracy of a suitability evaluation result is reduced in the prior art. In order to achieve the above purpose, the present invention adopts the following technical scheme: In a first aspect, the present invention provides a method for evaluating the suitability of a high-density residential area based on multi-source data fusion, wherein the method comprises the following steps: Acquiring respective multi-source geographic data of each high-density residential area in a target city, and obtaining a macroscopic suitability score of each high-density residential area based on the multi-source geographic data; Screening each high-density residential area based on the macro-suitability score of each high-density residential area to obtain a representative high-density residential area; acquiring image data of the representative high-density living areas, and obtaining microscopic suitability scores of the representative high-density living areas based on the image data of the representative high-density living areas; And drawing a livability distribution map of the target city based on the macroscopic livability score of each high-density living area and the microscopic livability score of each representative high-density living area. In one implementation, obtaining a macro-habitability score for each of the high-density populated areas based on the multi-source geographic data includes: determining the positions of the healthy interest points, the positions of the safe interest points and road network data in the multi-source geographic data and building contour data; Determining a security index score based on the security interest point location and the road network data and the building profile data, the security index score being used to characterize reachability from a building along a road network to a security interest point; Determining a health index score based on the health point of interest location and the road network data and the building contour data, the health index score being used to characterize air quality and distance from a building along a road network to a health point of interest and distance between the building and a arterial road in the road network; determining a traffic convenience index score and a comfort index score of each high-density living area; And obtaining a macro-suitability score of each high-density living area based on the safety index score, the health index score, the traffic convenience index score and the comfort index score of each high-density living area. In one implementation, screening each of the high-density populated areas based on a macroscopic suitability score of each of the high-density populated areas to obtain a representative high-density populated area includes: Dividing each of the high-density populated areas into a number of levels based on a macroscopic suitability score for each of the high-density populated areas; and screening the high-density residential areas contained in each grade to obtain the representative high-density residential areas contained in each grade. In one implementation, capturing image data of the representative high-density populated area includes: acquiring the length of an internal road of the representative high-density residential area; sampling the internal road based on the length of the internal road to obtain sampling points; based on the dist