CN116485143A - Spatial planning processing method based on population density big data - Google Patents

Spatial planning processing method based on population density big data Download PDF

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CN116485143A
CN116485143A CN202310474540.2A CN202310474540A CN116485143A CN 116485143 A CN116485143 A CN 116485143A CN 202310474540 A CN202310474540 A CN 202310474540A CN 116485143 A CN116485143 A CN 116485143A
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张宸铭
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Abstract

The embodiment of the application provides a space planning processing method based on population density big data. The method belongs to the technical field of big data and urban planning. The method comprises the following steps: carrying out group identification and attribute classification and group aggregation on people in a preset area, obtaining a crowd attribute distribution density image, carrying out crowd subarea grid division according to crowd distribution characteristic information to obtain a corresponding cognitive map, then processing to obtain crowd arrangement coefficients, obtaining arrangement priority factors of subarea arrangement facility units, obtaining crowd facility arrangement stages by combining the crowd distribution characteristic data and the crowd arrangement coefficients, and planning and arranging facility crowds according to the stage priority; therefore, the regional population is identified and classified based on the big data, the population and facilities are planned and placed by combining the placement and arrangement facilities to obtain the priority of the placement and arrangement of the population facilities, and the technology of planning, evaluating and arranging the population distribution and placement facilities according to the big data is realized.

Description

基于人口密度大数据的空间规划处理方法Spatial planning processing method based on population density big data

技术领域technical field

本申请涉及大数据及城市规划技术领域,具体而言,涉及基于人口密度大数据的空间规划处理方法。This application relates to the technical field of big data and urban planning, specifically, to a spatial planning processing method based on population density big data.

背景技术Background technique

现代城市进程加速导致人口多样化和城市空间急剧拥挤,由于城市空间规划和区域建筑设施的资源利用普遍存在缺乏科学性统筹、合理性规划,导致城市功能区域或设施对于人口容积、规划和安置缺少精准、有机、可续的布局规划手段,现阶段如何根据地区或区域的人群特点以及人口需求构建科学、合理、优化的规划布局,以适应人口发展的需要和城市功能多样性以及人口城市的有机扩张,是传统手段无法实施的短板,因此目前缺乏可对地区、区域、城市的人口类别和资源设施进行科学处理识别和匹配规划布局的数字化、智慧化技术手段。The acceleration of the modern urban process has led to population diversification and rapid urban space congestion. Due to the lack of scientific overall planning and rational planning in urban space planning and resource utilization of regional building facilities, urban functional areas or facilities lack precise, organic, and sustainable layout planning methods for population volume, planning, and resettlement. At this stage, how to construct a scientific, reasonable, and optimized planning layout according to the population characteristics and population needs of a region or region to meet the needs of population development and urban functional diversity. It is a digital and intelligent technical means to scientifically process, identify and match the planned layout of the city's population categories and resource facilities.

针对上述问题,目前亟待有效的技术解决方案。For the above problems, effective technical solutions are urgently needed at present.

发明内容Contents of the invention

本申请实施例的目的在于提供基于人口密度大数据的空间规划处理方法,可以通过大数据对区域人群进行识别分类获得分布情况的相关画像、图谱和数据,并结合安置布列设施的优先级进行处理获得人群设施安置布设的优先级数对人群和相应设施进行规划安置,实现根据大数据对人口分布和安置设施进行规划评估排布的技术。The purpose of the embodiment of this application is to provide a spatial planning processing method based on population density big data, which can identify and classify regional populations through big data to obtain relevant portraits, maps, and data of distribution, and process them in combination with the priority of resettlement and arrangement facilities to obtain the priority number of resettlement and arrangement of crowd facilities, plan and arrange crowds and corresponding facilities, and realize the technology of planning, evaluating and arranging population distribution and resettlement facilities based on big data.

本申请实施例还提供了基于人口密度大数据的空间规划处理方法,包括以下步骤:The embodiment of the present application also provides a spatial planning processing method based on population density big data, including the following steps:

获取预设区域内人口的人员标识信息,根据人员标识信息进行人员群类识别和属性分类,获得人员属性特征信息;Obtain the personnel identification information of the population in the preset area, perform personnel group identification and attribute classification according to the personnel identification information, and obtain personnel attribute characteristic information;

根据所述人员属性特征信息进行群类聚合,并通过预设人群分布密度热力模型进行处理,获得预设区域内各类人群的人群属性分布密度画像;performing group aggregation according to the characteristic information of the personnel attributes, and processing through a preset crowd distribution density thermal model to obtain crowd attribute distribution density portraits of various crowds in the preset area;

根据所述人群属性分布密度画像提取人群分布特征信息,包括人群群体分布信息、人群热力密度信息、人群常驻度信息以及人群空间需求属性信息;Extracting crowd distribution feature information according to the crowd attribute distribution density portrait, including crowd crowd distribution information, crowd thermal density information, crowd resident degree information, and crowd space demand attribute information;

根据所述人群分布特征信息输入预设人群区域划分模型中进行人群子区域栅格划分,获得子区域人群分布特征认知图谱;Inputting the crowd distribution feature information into a preset crowd area division model to perform grid division of crowd sub-regions, and obtain a cognitive map of population distribution characteristics in sub-regions;

根据所述子区域人群分布特征认知图谱提取各对应子区域内的人群分布特征数据,并根据人群分布特征数据处理获得人群安置排布系数;Extracting the crowd distribution feature data in each corresponding sub-area according to the cognition map of the crowd distribution feature in the sub-areas, and processing the crowd distribution feature data to obtain the crowd placement arrangement coefficient;

获取所述子区域内的各布列设施单元的设施单元布设信息,并根据设施单元布设信息处理获得各布列设施单元的布设优先级因子;Acquire the layout information of each facility unit in the sub-area, and process and obtain the layout priority factor of each facility unit according to the layout information of the facility unit;

根据所述各布列设施单元对应人群的所述人群分布特征数据结合所述人群安置排布系数以及所述布设优先级因子进行处理,获得人群设施布设级数;Processing according to the crowd distribution characteristic data of the crowd corresponding to each arrangement facility unit combined with the crowd placement arrangement coefficient and the layout priority factor, to obtain the crowd facility layout series;

根据所述人群设施布设级数在所述子区域内对设施及对应人群进行规划安置。Plan and place the facilities and corresponding groups of people in the sub-area according to the arrangement levels of the facilities for the group of people.

可选地,在本申请实施例所述的基于人口密度大数据的空间规划处理方法中,所述获取预设区域内人口的人员标识信息,根据人员标识信息进行人员群类识别和属性分类,获得人员属性特征信息,包括:Optionally, in the spatial planning processing method based on population density big data described in the embodiment of the present application, the acquisition of personnel identification information of the population in the preset area, performing personnel group identification and attribute classification according to the personnel identification information, and obtaining personnel attribute feature information include:

获取预设区域内人口的人员标识信息,包括年龄身份信息、常驻地信息、职业信息、注册地信息以及轨迹点信息;Obtain the personnel identification information of the population in the preset area, including age identity information, resident information, occupation information, registration information and track point information;

根据所述人员标识信息按照预设人群停驻识别分类模型对预设区域内全部人口进行人员群类识别和分类;Carry out personnel group identification and classification for all populations in the preset area according to the personnel identification information according to the preset group parking identification classification model;

根据人员群类识别和分类结果获得各类人群的人员属性特征信息,包括工作学习类别信息、居住地类别信息、活动轨迹域度信息以及停驻时长频次信息。According to the identification and classification results of the personnel group, the personnel attribute characteristic information of various groups of people is obtained, including work and study category information, residence category information, activity track domain degree information, and parking duration and frequency information.

可选地,在本申请实施例所述的基于人口密度大数据的空间规划处理方法中,所述根据所述人员属性特征信息进行群类聚合,并通过预设人群分布密度热力模型进行处理,获得预设区域内各类人群的人群属性分布密度画像,包括:Optionally, in the spatial planning processing method based on population density big data described in the embodiment of the present application, the group aggregation is performed according to the characteristic information of the personnel attributes, and processing is performed through a preset thermal model of population distribution density to obtain the distribution density portraits of the crowd attributes of various groups of people in the preset area, including:

对所述预设区域内人口根据所述人员属性特征信息进行群类聚合获得各类人群的人群属性特征信息;Perform group aggregation on the population in the preset area according to the personnel attribute feature information to obtain the crowd attribute feature information of various groups of people;

通过预设人群分布密度热力模型对各类人群的所述人群属性特征信息进行处理,获得对应人群分布密度热力图信息;Processing the crowd attribute feature information of various crowds through a preset crowd distribution density thermal model to obtain corresponding crowd distribution density heat map information;

根据各类人群的所述人群分布密度热力图信息合成所述预设区域内各类人群的人群属性分布密度画像。Synthesizing crowd attribute distribution density portraits of various crowds in the preset area according to the crowd distribution density heat map information of various crowds.

可选地,在本申请实施例所述的基于人口密度大数据的空间规划处理方法中,所述根据所述人群分布特征信息输入预设人群区域划分模型中进行人群子区域栅格划分,获得子区域人群分布特征认知图谱,包括:Optionally, in the spatial planning processing method based on population density big data described in the embodiment of the present application, inputting the population distribution feature information into the preset crowd area division model to perform grid division of crowd sub-regions to obtain a cognitive map of sub-region population distribution characteristics includes:

根据所述人群群体分布信息、人群热力密度信息、人群常驻度信息以及人群空间需求属性信息输入预设人群区域划分模型中进行处理,获得人群子区域栅格分布数据,包括子区域栅格容量数据、人群子区域栅格密度数据、人群子区域分布热力数据以及人群子区域空间容量数据;According to the crowd group distribution information, crowd thermal density information, crowd resident degree information and crowd space demand attribute information input into the preset crowd area division model for processing, obtain crowd sub-area grid distribution data, including sub-area grid capacity data, crowd sub-area grid density data, crowd sub-area distribution thermal data and crowd sub-area spatial capacity data;

根据所述子区域栅格容量数据、人群子区域栅格密度数据、人群子区域分布热力数据以及人群子区域空间容量数据输入预设人群分布特征认知模型中进行处理,获得子区域人群分布特征认知图谱。According to the grid capacity data of the sub-region, the grid density data of the crowd sub-region, the thermal data of the distribution of the crowd sub-region and the spatial capacity data of the crowd sub-region are input into the preset cognitive model of crowd distribution characteristics for processing, and the cognitive map of the population distribution characteristics of the sub-region is obtained.

可选地,在本申请实施例所述的基于人口密度大数据的空间规划处理方法中,所述根据所述子区域人群分布特征认知图谱提取各对应子区域内的人群分布特征数据,并根据人群分布特征数据处理获得人群安置排布系数,包括:Optionally, in the spatial planning processing method based on population density big data described in the embodiment of the present application, the crowd distribution feature data in each corresponding sub-area is extracted according to the cognitive map of the crowd distribution feature of the sub-area, and the crowd placement arrangement coefficient is obtained according to the crowd distribution feature data processing, including:

根据所述子区域人群分布特征认知图谱提取各对应子区域内的人群分布特征数据,包括人群总量数据、人群空间需求数据以及人群排布要素数据;extracting crowd distribution feature data in each corresponding sub-area according to the cognition map of crowd distribution features in the sub-areas, including total crowd data, crowd space demand data, and crowd arrangement element data;

根据所述人群总量数据、人群空间需求数据以及人群排布要素数据结合所述子区域栅格容量数据和人群子区域空间容量数据进行处理计算,获得对应人群的人群安置排布系数。According to the total crowd data, the crowd space demand data and the crowd arrangement element data combined with the sub-area grid capacity data and the crowd sub-area space capacity data, the crowd arrangement coefficient of the corresponding crowd is obtained.

可选地,在本申请实施例所述的基于人口密度大数据的空间规划处理方法中,所述获取所述子区域内的各布列设施单元的设施单元布设信息,并根据设施单元布设信息处理获得各布列设施单元的布设优先级因子,包括:Optionally, in the spatial planning processing method based on population density big data described in the embodiment of the present application, the acquisition of the layout information of each facility unit in the sub-area, and processing and obtaining the layout priority factor of each facility unit according to the layout information of the facility unit includes:

获取所述子区域内的各布列设施单元的设施单元布设信息,包括设施单位容量信息、设施功用信息、设施属性要素信息以及设施用户属性信息;Obtain the facility unit layout information of each arrangement facility unit in the sub-area, including facility unit capacity information, facility function information, facility attribute element information, and facility user attribute information;

根据所述设施单位容量信息、设施功用信息、设施属性要素信息以及设施用户属性信息通过预设设施布列要素处理模型进行处理,获得各布列设施单元的布设优先级因子。According to the facility unit capacity information, facility function information, facility attribute element information and facility user attribute information, the arrangement priority factor of each arrangement facility unit is obtained by processing through a preset facility arrangement element processing model.

可选地,在本申请实施例所述的基于人口密度大数据的空间规划处理方法中,所述根据所述各布列设施单元对应人群的所述人群分布特征数据结合所述人群安置排布系数以及所述布设优先级因子进行处理,获得人群设施布设级数,包括:Optionally, in the spatial planning processing method based on population density big data described in the embodiment of the present application, according to the crowd distribution characteristic data of the crowd corresponding to each arrangement facility unit combined with the crowd arrangement coefficient and the layout priority factor, the crowd facility layout series is obtained, including:

根据所述子区域内各布列设施单元的所述设施用户属性信息通过所述人群属性特征信息进行人群属性匹配,获得对应一类或多类匹配人群用户;performing crowd attribute matching according to the facility user attribute information of each facility unit arranged in the sub-area through the crowd attribute feature information, and obtaining corresponding one or more types of matching crowd users;

根据所述各布列设施单元的布设优先级因子结合对应所述一类或多类匹配人群用户的对应人群分布特征数据以及所述人群安置排布系数输入预设人群设施布设优先度模型中进行处理,获得人群设施布设级数;According to the layout priority factors of each arrangement facility unit, combined with the corresponding crowd distribution characteristic data corresponding to the one or more types of matching crowd users and the crowd placement arrangement coefficient, input it into the preset crowd facility layout priority model for processing, and obtain the crowd facility layout series;

所述人群设施布设级数的计算公式为:The formula for calculating the number of levels of facilities for the crowd is as follows:

其中,RP为人群设施布设级数,mri、kli、wαi分别为第i类匹配人群用户的人群总量数据、人群空间需求数据、人群排布要素数据,Tzi为第i类匹配人群用户的人群安置排布系数,dx为布设优先级因子,n为匹配人群用户的数量,n为大于等于1的自然数,β、μ为预设特征系数。Among them, R P is the arrangement level of crowd facilities, m ri , k li , and w αi are the total crowd data, crowd space demand data, and crowd arrangement element data of the i-th matching crowd users respectively ; μ is the preset characteristic coefficient.

可选地,在本申请实施例所述的基于人口密度大数据的空间规划处理方法中,所述根据所述人群设施布设级数在所述子区域内对设施及对应人群进行规划安置,包括:Optionally, in the spatial planning processing method based on population density big data described in the embodiment of the present application, the planning and placement of facilities and corresponding groups of people in the sub-area according to the number of groups of facilities layout includes:

根据所述子区域内各布列设施单元对应所述人群设施布设级数进行优先级排序;performing priority sorting according to the arrangement levels of facilities for the crowd corresponding to each arrangement unit in the sub-area;

根据优先级排序结果对所述各布列设施单元以及对应匹配人群用户进行按序规划安置。According to the priority sorting results, the arranged facility units and corresponding matching group users are planned and arranged in order.

由上可知,本申请实施例提供的基于人口密度大数据的空间规划处理方法,通过对预设区域内人口的人员标识信息进行人员群类识别和属性分类,根据人员属性特征信息进行群类聚合,并通过预设人群分布密度热力模型进行处理获得人群属性分布密度画像,并提取人群分布特征信息输入预设人群区域划分模型中进行人群子区域栅格划分获得子区域人群分布特征认知图谱,再提取人群分布特征数据并处理获得人群安置排布系数,获取子区域各布列设施单元的设施单元布设信息并处理获得布列设施单元的布设优先级因子,根据人群分布特征数据结合人群安置排布系数以及布设优先级因子处理获得人群设施布设级数,最后根据级数优先性对设施人群进行规划安置;从而基于大数据对区域人群进行识别分类获得分布情况的相关画像、图谱和数据,并结合安置布列设施的优先级进行处理获得人群设施安置布设的优先级数对人群和相应设施进行规划安置,实现根据大数据对人口分布和安置设施进行规划评估排布的技术。It can be seen from the above that the spatial planning processing method based on population density big data provided by the embodiment of the present application performs personnel group identification and attribute classification on the personnel identification information of the population in the preset area, performs group aggregation according to the personnel attribute characteristic information, and processes through the preset crowd distribution density thermal model to obtain the crowd attribute distribution density portrait, and extracts the crowd distribution feature information and inputs it into the preset crowd area division model to perform grid division of the crowd sub-area to obtain the cognitive map of the sub-area crowd distribution feature, and then extract the crowd distribution feature data and process it to obtain the crowd placement and arrangement coefficient, and obtain the arrangement of the sub-area The facility unit layout information of the facility unit is processed to obtain the layout priority factor of the facility unit. According to the crowd distribution characteristic data combined with the crowd placement arrangement coefficient and the layout priority factor processing, the crowd facility layout level is obtained, and finally the facility crowd is planned and resettled according to the level priority; thus, based on the big data, the regional population is identified and classified to obtain the relevant portraits, maps and data of the distribution, and combined with the priority of the resettlement and arrangement facilities to obtain the priority number of the crowd facility placement layout. Plan and place the crowd and corresponding facilities, and realize the population distribution and placement based on big data Techniques for planning and assessing arrangements for facilities.

本申请的其他特征和优点将在随后的说明书阐述,并且,部分地从说明书中变得显而易见,或者通过实施本申请实施例了解。本申请的目的和其他优点可通过在所写的说明书、权利要求书、以及附图中所特别指出的结构来实现和获得。Other features and advantages of the present application will be set forth in the ensuing description and, in part, will be apparent from the description, or can be learned by practicing the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

附图说明Description of drawings

为了更清楚地说明本申请实施例的技术方案,下面将对本申请实施例中所需要使用的附图作简单地介绍,应当理解,以下附图仅示出了本申请的某些实施例,因此不应被看作是对范围的限定,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他相关的附图。In order to more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be considered as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained according to these drawings without creative work.

图1为本申请实施例提供的基于人口密度大数据的空间规划处理方法的一种流程图;Fig. 1 is a kind of flowchart of the spatial planning processing method based on population density big data provided by the embodiment of the present application;

图2为本申请实施例提供的基于人口密度大数据的空间规划处理方法的获得人员属性特征信息的一种流程图;Fig. 2 is a flow chart of obtaining personnel attribute feature information of the spatial planning processing method based on population density big data provided by the embodiment of the present application;

图3为本申请实施例提供的基于人口密度大数据的空间规划处理方法的获得人群属性分布密度画像的一种流程图。Fig. 3 is a flow chart of obtaining crowd attribute distribution density portraits of the spatial planning processing method based on population density big data provided by the embodiment of the present application.

具体实施方式Detailed ways

下面将结合本申请实施例中附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本申请一部分实施例,而不是全部的实施例。通常在此处附图中描述和示出的本申请实施例的组件可以以各种不同的配置来布置和设计。因此,以下对在附图中提供的本申请的实施例的详细描述并非旨在限制要求保护的本申请的范围,而是仅仅表示本申请的选定实施例。基于本申请的实施例,本领域技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例,都属于本申请保护的范围。The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all of them. The components of the embodiments of the application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

应注意到,相似的标号和字母在下面的附图中表示类似项,因此,一旦某一项在一个附图中被定义,则在随后的附图中不需要对其进行进一步定义和解释。同时,在本申请的描述中,术语“第一”、“第二”等仅用于区分描述,而不能理解为指示或暗示相对重要性。It should be noted that like numerals and letters denote similar items in the following figures, therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second" and the like are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

请参照图1,图1是本申请一些实施例中的基于人口密度大数据的空间规划处理方法的一种流程图。该基于人口密度大数据的空间规划处理方法用于终端设备中,例如电脑、手机终端等。该基于人口密度大数据的空间规划处理方法,包括以下步骤:Please refer to FIG. 1 . FIG. 1 is a flowchart of a spatial planning processing method based on population density big data in some embodiments of the present application. The spatial planning processing method based on population density big data is used in terminal equipment, such as computers, mobile terminals, and the like. The spatial planning processing method based on population density big data includes the following steps:

S101、获取预设区域内人口的人员标识信息,根据人员标识信息进行人员群类识别和属性分类,获得人员属性特征信息;S101. Obtain the personnel identification information of the population in the preset area, perform personnel group identification and attribute classification according to the personnel identification information, and obtain personnel attribute feature information;

S102、根据所述人员属性特征信息进行群类聚合,并通过预设人群分布密度热力模型进行处理,获得预设区域内各类人群的人群属性分布密度画像;S102. Perform group aggregation according to the characteristic information of the personnel attributes, and process through a preset thermal model of crowd distribution density to obtain crowd attribute distribution density portraits of various crowds in the preset area;

S103、根据所述人群属性分布密度画像提取人群分布特征信息,包括人群群体分布信息、人群热力密度信息、人群常驻度信息以及人群空间需求属性信息;S103. Extract crowd distribution feature information according to the crowd attribute distribution density portrait, including crowd distribution information, crowd thermal density information, crowd resident degree information, and crowd space demand attribute information;

S104、根据所述人群分布特征信息输入预设人群区域划分模型中进行人群子区域栅格划分,获得子区域人群分布特征认知图谱;S104. Input the crowd distribution feature information into the preset crowd area division model to perform grid division of crowd sub-regions, and obtain a cognitive map of population distribution characteristics in sub-regions;

S105、根据所述子区域人群分布特征认知图谱提取各对应子区域内的人群分布特征数据,并根据人群分布特征数据处理获得人群安置排布系数;S105. Extract the crowd distribution feature data in each corresponding sub-area according to the cognitive map of the crowd distribution features in the sub-area, and process the crowd distribution feature data to obtain a crowd placement arrangement coefficient;

S106、获取所述子区域内的各布列设施单元的设施单元布设信息,并根据设施单元布设信息处理获得各布列设施单元的布设优先级因子;S106. Obtain the layout information of each facility unit in the sub-area, and process and obtain the layout priority factor of each facility unit according to the layout information of the facility unit;

S107、根据所述各布列设施单元对应人群的所述人群分布特征数据结合所述人群安置排布系数以及所述布设优先级因子进行处理,获得人群设施布设级数;S107. Process according to the crowd distribution characteristic data of the crowds corresponding to each arrangement unit in combination with the crowd arrangement coefficient and the arrangement priority factor, to obtain the rank of arrangement of crowd facilities;

S108、根据所述人群设施布设级数在所述子区域内对设施及对应人群进行规划安置。S108. Plan and arrange the facilities and corresponding groups of people in the sub-area according to the arrangement levels of the facilities for the group of people.

需要说明的是,为实现针对地区或区域的人口功能需求和资源设施布局的有效匹配和合理规划,使区域人口按照其功能需求如上学、工作、生活、商务、养老等,与相匹配的设施建筑单元如学校、工厂、写字楼、住宅、酒店、养老院等设施单元的布列规划做到精准匹配和有序规划,使区域内各类人群的功能安置需求与对应布列设施单元能够适配,并实现数字化分类规划和安置布局,根据识别人群的属性特征以及分布情况与相适配的设施单元进行匹配和处理,使区域内各类人群与对应安置设施进行信息化、数据化处理和评估,根据处理获得的结果数据进行人群设施的规划安置,实现根据大数据对人口分布和安置设施进行规划评估排布的技术,具体通过获取预设区域内人口的人员标识信息并进行人员群类识别和属性分类,获得人员属性特征信息,再根据人员属性特征信息进行群类聚合并通过预设人群分布密度热力模型进行处理获得预设区域内各类人群的人群属性分布密度画像,根据该画像提取人群分布特征信息,包括反映人群群体分布、人群热力分布的密度、人群生活居住等常驻情况程度、以及人群对工作学习生活的设施需求属性的相关信息,再根据人群分布特征信息输入预设人群区域划分模型中进行人群子区域栅格划分,以对预设区域进行细分规划,并获得子区域人群分布特征认知图谱,再提取各对应子区域内的人群分布特征数据并处理获得人群安置排布系数,再通过处理子区域内的各布列设施单元的设施单元布设信息获得各布列设施单元的布设优先级因子,以及结合根据各布列设施单元对应功能适配人群的人群分布特征数据进行处理获得人群设施布设级数,该级数反映各类人群与适配安置的布列设施单元在子区域内的规划布局先后级数,再根据人群设施布设级数的优先性在子区域内对设施人群进行规划安置,实现通过大数据对人口群类分布和适配安置设施进行规划排布的技术。It should be noted that in order to achieve effective matching and reasonable planning for the functional needs of the population and the layout of resource facilities in a region or region, the regional population can be accurately matched and ordered according to its functional needs, such as schooling, work, life, business, elderly care, etc. The matched facility units are matched and processed, so that various groups of people in the area can be informatized, data-based, processed, and evaluated. According to the result data obtained from the processing, the population facilities are planned and resettled, and the technology of planning, evaluating and arranging population distribution and resettlement facilities is realized based on big data. Specifically, by obtaining the population identification information of the population in the preset area and performing personnel group identification and attribute classification, personnel attribute characteristic information is obtained, and then group aggregation is performed according to the personnel attribute characteristic information and processed through the preset population distribution density thermal model to obtain the population attribute distribution density portrait of various groups of people in the preset area. According to the portrait, crowd distribution characteristic information is extracted, including information reflecting the crowd distribution, density of crowd heat distribution, degree of permanent residence of the crowd, and crowd’s demand for facilities for work, study and life. Then, according to the crowd distribution feature information, it is input into the preset crowd area division model for grid division of crowd sub-regions, so as to carry out subdivision planning of the preset area, and obtain a cognitive map of population distribution characteristics in sub-regions, and then extract the population distribution characteristic data in each corresponding sub-region and process them to obtain the crowd placement arrangement coefficient. According to the layout information, the layout priority factors of each arrangement facility unit are obtained, and the distribution characteristics of the crowd according to the corresponding functions of each arrangement facility unit are processed to obtain the crowd facility layout series, which reflects the planning and layout levels of various groups of people and the suitable arrangement facility units in the sub-area, and then according to the priority of the crowd facility layout series, the facility crowd is planned and resettled in the sub-area, realizing the technology of planning and arranging the distribution of population groups and adaptive resettlement facilities through big data.

请参照图2,图2是本申请一些实施例中的基于人口密度大数据的空间规划处理方法的获得人员属性特征信息的一种流程图。根据本发明实施例,所述获取预设区域内人口的人员标识信息,根据人员标识信息进行人员群类识别和属性分类,获得人员属性特征信息,具体为:Please refer to FIG. 2 . FIG. 2 is a flow chart of obtaining personnel attribute feature information in a spatial planning processing method based on population density big data in some embodiments of the present application. According to the embodiment of the present invention, the acquisition of the personnel identification information of the population in the preset area, the identification of personnel groups and attribute classification according to the personnel identification information, and the acquisition of personnel attribute feature information are specifically:

S201、获取预设区域内人口的人员标识信息,包括年龄身份信息、常驻地信息、职业信息、注册地信息以及轨迹点信息;S201. Obtain the personnel identification information of the population in the preset area, including age identity information, permanent residence information, occupation information, registration place information, and track point information;

S202、根据所述人员标识信息按照预设人群停驻识别分类模型对预设区域内全部人口进行人员群类识别和分类;S202. Perform personnel group identification and classification on all populations in the preset area according to the personnel identification information according to the preset crowd parking identification classification model;

S203、根据人员群类识别和分类结果获得各类人群的人员属性特征信息,包括工作学习类别信息、居住地类别信息、活动轨迹域度信息以及停驻时长频次信息。S203. Obtain personnel attribute feature information of various groups of people according to the results of personnel group identification and classification, including work and study category information, residence category information, activity trajectory domain degree information, and parking duration and frequency information.

需要说明的是,为获得针对地区或区域的人口功能需求的匹配资源设施从而进行科学规划,首先需明确区域内人口的结构、分类、分布等属性特征,通过获取预设区域内人口的人员标识信息,包括年龄、身份、常驻地、职业、户口或商业注册地以及行径轨迹点的信息,将上述信息通过预设的人群停驻识别分类模型进行处理从而对区域内人口进行群类的识别和分类,即根据上述人口的信息通过第三方平台获取的人群停驻识别分类模型进行人口属性的识别和分类,通过对人员群类的识别分类,获得各类人群的人员属性特征信息,即通过识别分类将区域内人口属性如学生、居家老人、外来商旅人员、公司职员等进行有效识别和分类,获得的人员属性特征信息包括工作学习类别如工作地职员或学校学生、居住地类别如暂住或常驻居民、活动轨迹域度如日常活动地点轨迹、以及停驻时长频次如常驻点的出现停留频率次数的特征信息。It should be noted that in order to obtain matching resource facilities for the population function needs of a region or region for scientific planning, it is first necessary to clarify the attribute characteristics of the population in the region, such as structure, classification, and distribution. By obtaining the personnel identification information of the population in the preset area, including age, identity, permanent residence, occupation, household registration or business registration, and track point information, the above information is processed through the preset crowd identification classification model to identify and classify the population in the area. The identification and classification of population attributes, through the identification and classification of personnel groups, obtains the personnel attribute characteristic information of various groups of people, that is, effectively identifies and classifies the population attributes in the area such as students, home-based elderly, foreign business travelers, and company employees through identification and classification.

请参照图3,图3是本申请一些实施例中的基于人口密度大数据的空间规划处理方法的获得人群属性分布密度画像的一种流程图。根据本发明实施例,所述根据所述人员属性特征信息进行群类聚合,并通过预设人群分布密度热力模型进行处理,获得预设区域内各类人群的人群属性分布密度画像,具体为:Please refer to FIG. 3 . FIG. 3 is a flow chart of obtaining crowd attribute distribution density portraits of the spatial planning processing method based on population density big data in some embodiments of the present application. According to the embodiment of the present invention, the group aggregation is performed according to the characteristic information of the personnel attributes, and the preset crowd distribution density thermal model is used for processing to obtain the crowd attribute distribution density portraits of various crowds in the preset area, specifically:

S301、对所述预设区域内人口根据所述人员属性特征信息进行群类聚合获得各类人群的人群属性特征信息;S301. Perform group aggregation on the population in the preset area according to the personnel attribute feature information to obtain crowd attribute feature information of various groups of people;

S302、通过预设人群分布密度热力模型对各类人群的所述人群属性特征信息进行处理,获得对应人群分布密度热力图信息;S302. Process the crowd attribute characteristic information of various crowds through a preset crowd distribution density thermal model, and obtain corresponding crowd distribution density heat map information;

S303、根据各类人群的所述人群分布密度热力图信息合成所述预设区域内各类人群的人群属性分布密度画像。S303. Synthesize the distribution density portraits of crowd attributes of various crowds in the preset area according to the crowd distribution density heat map information of various crowds.

需要说明的是,在对区域内人口进行人群属性特征的识别分类后,需进一步描绘出区域内各类人群的分布情况,以便根据人群分布情况进行下一步规划,将区域内人群的各人员属性特征信息进行群体的聚合,获得各类人群的人群属性特征信息,该人群属性特征信息反映人群群体的属性特征,再通过第三方平台的预设人群分布密度热力模型对各类人群的人群属性特征信息进行处理,获得各对应人群的人群分布密度热力图信息,该人群分布密度热力图信息是对各类人群的分布密度进行热力图映射的信息,将各类人群的人群分布密度热力图信息进行合成,即得到区域内各类人群的人群属性分布密度画像,该分布密度画像是反映各类人群在区域内的分布密度情况的信息画像,通过该画像可数据化描绘出区域内各类人群的分布情况。It should be noted that after identifying and classifying the population attribute characteristics of the population in the region, it is necessary to further describe the distribution of various groups of people in the region, so as to carry out the next step of planning according to the distribution of the population. The attribute information of the people in the region is aggregated to obtain the attribute characteristics of various groups of people. The attribute characteristics of the population reflect the attribute characteristics of the population group. The distribution density of various groups of people is mapped to the information of the heat map, and the information of the heat map of the distribution density of the various groups of people is synthesized to obtain the distribution density portrait of the crowd attributes of the various groups of people in the area.

根据本发明实施例,所述根据所述人群分布特征信息输入预设人群区域划分模型中进行人群子区域栅格划分,获得子区域人群分布特征认知图谱,具体为:According to an embodiment of the present invention, according to the input of the crowd distribution feature information into the preset crowd area division model, the grid division of the crowd sub-area is carried out, and the cognition map of the crowd distribution feature of the sub-area is obtained, specifically:

根据所述人群群体分布信息、人群热力密度信息、人群常驻度信息以及人群空间需求属性信息输入预设人群区域划分模型中进行处理,获得人群子区域栅格分布数据,包括子区域栅格容量数据、人群子区域栅格密度数据、人群子区域分布热力数据以及人群子区域空间容量数据;According to the crowd group distribution information, crowd thermal density information, crowd resident degree information and crowd space demand attribute information input into the preset crowd area division model for processing, obtain crowd sub-area grid distribution data, including sub-area grid capacity data, crowd sub-area grid density data, crowd sub-area distribution thermal data and crowd sub-area spatial capacity data;

根据所述子区域栅格容量数据、人群子区域栅格密度数据、人群子区域分布热力数据以及人群子区域空间容量数据输入预设人群分布特征认知模型中进行处理,获得子区域人群分布特征认知图谱。According to the grid capacity data of the sub-region, the grid density data of the crowd sub-region, the thermal data of the distribution of the crowd sub-region and the spatial capacity data of the crowd sub-region are input into the preset cognitive model of crowd distribution characteristics for processing, and the cognitive map of the population distribution characteristics of the sub-region is obtained.

需要说明的是,为进一步更精准衡量出区域内各类人群分布情况,详细描述出各类人群的分布具体特征数据,以便更精准对区域内各类人群的分布情况获取对应匹配的设施单元,从而进行精准化匹配安置和规划,将区域按照人群分布特征情况进行栅格划分为多个子区域,再对各子区域的人群分布情况进行分析处理,以实现通过小区域的规划获得对整个区域的精准规划的实施路线,为获得人群子区域的有效划分和数据获取,将人群分布特征信息输入至预设的人群区域划分模型中进行处理,获得人群子区域栅格分布数据,即通过运用预设模型对区域的人群分布特征信息进行栅格化拆分和分布处理,获得栅格分布数据,也就是将区域根据人群的分布情况进行栅格化分解为多个子区域,每个子区域中包含一个或多个栅格,如预设区域内某商务区的商务差旅人群分布高度集中,则通过该模型将该商务区进行栅格化并划分为多个子区域,获得的人群子区域栅格分布数据包括反映子区域栅格总容量即子区域内栅格个数、子区域各栅格人群的分布密度、子区域人群分布热力状况、子区域的空间容量如高层建筑的子区域的容积挑高等空间容量的数据,再将人群子区域栅格分布数据输入第三方平台的预设人群分布特征认知模型中进行处理,获得子区域人群分布特征认知图谱,即通过认知模型对数据进行识别、认知、融合和链接,从而获得反映子区域人群分布特征的认知图谱。It should be noted that in order to further more accurately measure the distribution of various groups of people in the region, describe the specific characteristic data of the distribution of various groups of people in detail, so as to obtain the corresponding matching facility units for the distribution of various groups of people in the region more accurately, so as to carry out precise matching and planning. To process in the preset crowd area division model to obtain the grid distribution data of the crowd sub-area, that is, to use the preset model to rasterize, split and distribute the crowd distribution feature information in the area to obtain the raster distribution data, that is, to decompose the area into multiple sub-areas according to the distribution of the crowd, and each sub-area contains one or more grids. The total capacity of the regional grid is the number of grids in the sub-region, the distribution density of the population in each grid of the sub-region, the thermal status of the population distribution in the sub-region, and the spatial capacity of the sub-region, such as the volume height of the sub-region of a high-rise building.

根据本发明实施例,所述根据所述子区域人群分布特征认知图谱提取各对应子区域内的人群分布特征数据,并根据人群分布特征数据处理获得人群安置排布系数,具体为:According to an embodiment of the present invention, the crowd distribution feature data in each corresponding sub-area is extracted according to the cognitive map of the crowd distribution feature in the sub-area, and the crowd placement arrangement coefficient is obtained according to the crowd distribution feature data processing, specifically:

根据所述子区域人群分布特征认知图谱提取各对应子区域内的人群分布特征数据,包括人群总量数据、人群空间需求数据以及人群排布要素数据;extracting crowd distribution feature data in each corresponding sub-area according to the cognition map of crowd distribution features in the sub-areas, including total crowd data, crowd space demand data, and crowd arrangement element data;

根据所述人群总量数据、人群空间需求数据以及人群排布要素数据结合所述子区域栅格容量数据和人群子区域空间容量数据进行处理计算,获得对应人群的人群安置排布系数。According to the total crowd data, the crowd space demand data and the crowd arrangement element data combined with the sub-area grid capacity data and the crowd sub-area space capacity data, the crowd arrangement coefficient of the corresponding crowd is obtained.

需要说明的是,在获得了各子区域的人群分布特征的认知图谱后,为评估各人群对于子区域内安置排布规划的度量值,即衡量人群在子区域获得安置规划排布的映射系数,以便与人群匹配的设施单元进行规划布局的综合评估,根据子区域人群分布特征认知图谱提取各对应子区域内的人群分布特征数据,其中包括人群总量、人群空间需求以及人群排布要素即人群排布的难度、重要度、倾斜度等安置行为要素的数据,再将数据结合子区域栅格容量数据和人群子区域空间容量数据进行处理计算,获得对应人群的人群安置排布系数;其中,所述人群安置排布系数的计算公式为:It should be noted that after obtaining the cognitive map of the population distribution characteristics of each sub-region, in order to evaluate the measurement value of each group for the resettlement and arrangement planning in the sub-region, that is, to measure the mapping coefficient of the resettlement planning and arrangement of the population in the sub-region, so as to comprehensively evaluate the planning and layout of the facility units that match the population, the population distribution characteristic data in each corresponding sub-region is extracted according to the cognitive map of the population distribution characteristics of the sub-region. The data is processed and calculated in combination with the grid capacity data of the sub-area and the spatial capacity data of the crowd sub-area to obtain the crowd placement and arrangement coefficient of the corresponding crowd; wherein, the calculation formula of the crowd placement and arrangement coefficient is:

其中,Tz为人群安置排布系数,mr、kl、wα分别为人群总量数据、人群空间需求数据、人群排布要素数据,Cg、Ls分别为子区域栅格容量数据、人群子区域空间容量数据,qh为预设人群安置容量参数,β、μ、κ、ρ为预设特征系数(特征系数通过第三方平台数据库进行查询获得)。Among them, T z is the population arrangement coefficient, m r , k l , and w α are the total population data, crowd space demand data, and crowd arrangement element data, respectively; C g , L s are the sub-regional grid capacity data and crowd sub-regional space capacity data respectively; q h is the preset crowd resettlement capacity parameter; β, μ, κ, and ρ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the third-party platform database).

根据本发明实施例,所述获取所述子区域内的各布列设施单元的设施单元布设信息,并根据设施单元布设信息处理获得各布列设施单元的布设优先级因子,具体为:According to an embodiment of the present invention, the acquisition of the layout information of each facility unit in the sub-area, and processing and obtaining the layout priority factor of each facility unit according to the layout information of the facility unit, specifically:

获取所述子区域内的各布列设施单元的设施单元布设信息,包括设施单位容量信息、设施功用信息、设施属性要素信息以及设施用户属性信息;Obtain the facility unit layout information of each arrangement facility unit in the sub-area, including facility unit capacity information, facility function information, facility attribute element information, and facility user attribute information;

根据所述设施单位容量信息、设施功用信息、设施属性要素信息以及设施用户属性信息通过预设设施布列要素处理模型进行处理,获得各布列设施单元的布设优先级因子。According to the facility unit capacity information, facility function information, facility attribute element information and facility user attribute information, the arrangement priority factor of each arrangement facility unit is obtained by processing through a preset facility arrangement element processing model.

需要说明的是,在明确了子区域内各类人群的安置排布情况及相关数据参数后,需对子区域内各布列设施单元的规划情况进行处理,以便进一步评估出子区域内各布列设施单元以及匹配安置的人群的排布规划情况,首先需获取子区域内拟布列规划的设施单元相关信息,布列设施单元即为各类规划的设施、建筑、场馆、楼体等主体设施单元,包括学校、住宅、养老院、商务写字楼、工厂等整体建筑设施,获取设施单元布设信息,其中包括反映设施的单位容量、设施的功能用途、设施属性要素如规划倾向、扶持力、需求度、重要优先级等、以及设施匹配适用的人群用户属性的相关信息,再将上述各信息通过第三方平台的预设的设施布列要素处理模型进行计算处理,获得各布列设施单元对应的布设优先级因子,即反映各设施单元的布设优先状态量;其中,所述布设优先级因子的计算公式为:It should be noted that, after clarifying the resettlement and arrangement of various groups of people in the sub-area and related data parameters, it is necessary to process the planning of each arrangement of facility units in the sub-area in order to further evaluate the arrangement and planning of each arrangement of facility units in the sub-area and the arrangement and planning of the matched population. Obtain the layout information of facility units, including information reflecting the unit capacity of the facility, the functional use of the facility, the attribute elements of the facility such as planning tendency, support force, demand degree, important priority, etc., and the user attributes of the people to whom the facility is matched, and then calculate and process the above information through the processing model of the preset facility layout elements of the third-party platform to obtain the layout priority factors corresponding to each layout facility unit, which reflects the layout priority status of each facility unit; wherein, the calculation formula of the layout priority factor is:

dx=(εFc+γGe+υBq)/φAsd x = (εF c +γG e +υB q )/φA s ;

其中,dx为布设优先级因子,Fc、Ge、Bq、As分别为设施单位容量信息、设施功用信息、设施属性要素信息、设施用户属性信息,ε、γ、υ、φ为预设特征系数(特征系数通过第三方平台数据库进行查询获得)。Among them, d x is the layout priority factor, F c , G e , B q , and A s are facility unit capacity information, facility function information, facility attribute element information, and facility user attribute information, respectively, and ε, γ, υ, φ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the third-party platform database).

根据本发明实施例,所述根据所述各布列设施单元对应人群的所述人群分布特征数据结合所述人群安置排布系数以及所述布设优先级因子进行处理,获得人群设施布设级数,具体为:According to an embodiment of the present invention, the crowd distribution characteristic data of the crowds corresponding to each arrangement facility unit is processed in combination with the crowd placement arrangement coefficient and the layout priority factor to obtain the crowd facility layout series, specifically:

根据所述子区域内各布列设施单元的所述设施用户属性信息通过所述人群属性特征信息进行人群属性匹配,获得对应一类或多类匹配人群用户;performing crowd attribute matching according to the facility user attribute information of each facility unit arranged in the sub-area through the crowd attribute feature information, and obtaining corresponding one or more types of matching crowd users;

根据所述各布列设施单元的布设优先级因子结合对应所述一类或多类匹配人群用户的对应人群分布特征数据以及所述人群安置排布系数输入预设人群设施布设优先度模型中进行处理,获得人群设施布设级数;According to the layout priority factors of each arrangement facility unit, combined with the corresponding crowd distribution characteristic data corresponding to the one or more types of matching crowd users and the crowd placement arrangement coefficient, input it into the preset crowd facility layout priority model for processing, and obtain the crowd facility layout series;

所述人群设施布设级数的计算公式为:The formula for calculating the number of levels of facilities for the crowd is as follows:

其中,RP为人群设施布设级数,mri、kli、wαi分别为第i类匹配人群用户的人群总量数据、人群空间需求数据、人群排布要素数据,Tzi为第i类匹配人群用户的人群安置排布系数,dx为布设优先级因子,n为匹配人群用户的数量,n为大于等于1的自然数,β、μ为预设特征系数(特征系数通过第三方平台数据库进行查询获得)。Among them, R P is the arrangement level of crowd facilities, m ri , k li , and w αi are the total crowd data, crowd space demand data, and crowd arrangement element data of the i-th matching crowd users respectively ; μ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the third-party platform database).

需要说明的是,为评估子区域内各布列设施单元以及对应安置人群用户的排布规划优先度情况,需首先匹配与各布列设施单元的设施用户属性相适配的一类或多类人群,即通过设施用户属性也就是布列设施单元对应用户如工厂对应企业职员、综合性学校对应中小学生等,通过布列设施单元的设施用户属性信息与人群属性特征信息进行人群属性的匹配,获得与设施相适配的目标人群,由于可能一种布列设施单元匹配的人群用户不止一类人群,因此存在大于等于1的多个人群用户,根据各布列设施单元的布设优先级因子结合对应适配的一类或多类匹配人群用户的人群分布特征数据以及人群安置排布系数,通过预设人群设施布设优先度模型进行处理,获得人群设施布设级数,即将上述数据通过预设的人群设施布设优先度模型进行计算处理获得结果级数,获得的级数是该布列设施单元与适配人群规划布局优先度的映射数据。It should be noted that, in order to evaluate the arrangement and planning priority of each facility unit and the corresponding resettlement users in the sub-region, it is necessary to first match one or more types of people that are compatible with the facility user attributes of each facility unit, that is, through the facility user attributes, that is, the facility user attributes corresponding to users such as factory employees corresponding to enterprises, comprehensive schools corresponding primary and secondary school students, etc., through the facility user attribute information of the facility unit and the crowd attribute feature information to match the crowd attributes to obtain the target group that is suitable for the facility. A group of people, so there are multiple group users greater than or equal to 1. According to the layout priority factors of each arrangement facility unit combined with the corresponding adapted one or more types of crowd distribution characteristic data and crowd placement arrangement coefficients of matching group users, the preset crowd facility layout priority model is used to obtain the crowd facility layout level. The above data is calculated and processed through the preset crowd facility layout priority model to obtain the result series.

根据本发明实施例,所述根据所述人群设施布设级数在所述子区域内对设施及对应人群进行规划安置,具体为:According to the embodiment of the present invention, the planning and placement of the facilities and corresponding groups of people in the sub-area according to the arrangement of the facilities for the group of people is as follows:

根据所述子区域内各布列设施单元对应所述人群设施布设级数进行优先级排序;performing priority sorting according to the arrangement levels of facilities for the crowd corresponding to each arrangement unit in the sub-area;

根据优先级排序结果对所述各布列设施单元以及对应匹配人群用户进行按序规划安置。According to the priority sorting results, the arranged facility units and corresponding matching group users are planned and arranged in sequence.

需要说明的是,在获得了子区域内布列设施单元对应的人群设施布设级数后,根据级数的优先级排序对各个布列设施单元以及匹配的人群用户按级数优先级排序进行规划布局,使各布列设施单元以及对应匹配安置的人群用户得到有序和规划布局和安置,进而通过子区域的规划,实现对整个预设区域内的布列设施单元以及对应匹配的人群用户进行合理、科学、有序地规划布局和安置的实施目的。It should be noted that, after obtaining the arrangement levels of crowd facilities corresponding to the facility units in the sub-area, each arrangement facility unit and the matching crowd users are planned and arranged according to the priority order of the levels, so that each arrangement facility unit and the corresponding matched crowd users are arranged and arranged in an orderly manner, and then through the sub-area planning, the implementation purpose of rational, scientific, and orderly planning, layout and placement of the arrangement facility units and the corresponding matching crowd users in the entire preset area is realized.

根据本发明实施例,还包括:According to an embodiment of the present invention, it also includes:

根据所述子区域内各类人群的人群群体分布信息和人群热力密度信息在预设人群区域分布模型进行处理,获得子区域内的人群分布密度重心数据;According to the crowd group distribution information and crowd thermal density information of various groups of people in the sub-area, process it in the preset crowd area distribution model to obtain the center of gravity data of the crowd distribution density in the sub-area;

根据所人群分布密度重心数据结合所述人群总量数据、人群排布要素数据以及对应安置的布列设施单元的所述设施单位容量信息输入预设设施单元排布模型中进行处理,获得所述布列设施单元的规划布局参数;According to the center of gravity data of the crowd distribution density combined with the total crowd data, the crowd arrangement element data and the facility unit capacity information of the corresponding arranged arrangement facility unit, input it into the preset facility unit arrangement model for processing, and obtain the planned layout parameters of the arrangement facility unit;

根据所述规划布局参数对所述布列设施单元进行规划布局。The arrangement facility unit is planned and laid out according to the planned layout parameters.

需要说明的是,为实现对子区域内某类人群匹配的布列设施单元的规划布局,使设施的规划更适配于人群的分布需求,根据子区域内各类人群的人群群体分布信息和人群热力密度信息在预设人群区域分布模型进行处理,获得子区域内的人群分布密度重心数据,即通过该预设人群区域分布模型对人群分布和人群热力密度的信息进行处理,获得人群对应的分布密度重心,即人群分布的平均中心点位置,再将人群分布密度重心数据结合人群总量数据、人群排布要素数据,以及对应匹配的布列设施单元的设施单位容量信息输入到设施单元排布模型中进行处理,获得布列设施单元的规划布局参数,即通过设施单元排布模型对上述数据进行处理,获得设施规划布局的相关参数,该设施单元排布模型是通过大量已知的样本数据的人群分布密度重心数据、人群总量数据、人群排布要素数据、设施单位容量信息以及规划布局参数进行输入并训练获得的处理模型,通过大量样本数据信息的训练使该模型的输出准确率精准,因而通过该模型可获得精准的规划布局参数,该规划布局参数是对设施单元按照人群排布状况进行规划布局的规划方法数据,如设施单元占地、设施单元定位、设施单元建筑面积、设施单元空间结构等规划布局的设计参数,最后根据规划布局参数对布列设施单元进行规划布局。It should be noted that, in order to realize the planning and layout of the arrangement of facility units matching a certain type of crowd in the sub-area and make the facility planning more suitable for the distribution needs of the crowd, according to the crowd distribution information and crowd thermal density information of various groups of people in the sub-area, the preset crowd distribution model is processed to obtain the center of gravity data of the crowd distribution density in the sub-area. The data is combined with the total crowd data, the crowd arrangement element data, and the facility unit capacity information of the corresponding matching facility units to be input into the facility unit layout model for processing to obtain the planned layout parameters of the facility units. The output accuracy of the model is accurate, so the precise planning and layout parameters can be obtained through the model. The planning and layout parameters are the planning method data for planning and layout of the facility units according to the arrangement of the crowd, such as the design parameters of the planning and layout of the facility unit occupation, facility unit positioning, facility unit building area, and facility unit spatial structure. Finally, the layout of the facility units is planned and laid out according to the planning layout parameters.

根据本发明实施例,还包括:According to an embodiment of the present invention, it also includes:

若子区域内布列设施单元不满足待规划安置人群的容量需求,则获取预设区域内与子区域邻近的多个邻近子区域的设施单元资源信息;If the arrangement of facility units in the sub-area does not meet the capacity requirements of the population to be resettled, obtain the facility unit resource information of multiple adjacent sub-areas adjacent to the sub-area in the preset area;

所述设施单元资源信息包括所述待规划安置人群匹配的布列设施单元的设施空置度数据、设施距离数据以及设施交通顺畅度系数;The facility unit resource information includes facility vacancy data, facility distance data, and facility traffic smoothness coefficient of the arrangement facility unit matching the population to be resettled;

获取所述待规划安置人群的外置意向度系数;Obtain the external intention coefficient of the population to be resettled;

根据所述外置意向度系数结合所述设施空置度数据、设施距离数据以及设施交通顺畅度系数输入预设人群迁置匹配度模型中进行处理,获得各邻近子区域对应的人群迁置匹配度指数;According to the external intention degree coefficient combined with the facility vacancy data, facility distance data and facility traffic smoothness coefficient input into the preset crowd relocation matching degree model for processing, to obtain the crowd relocation matching degree index corresponding to each adjacent sub-area;

对所述各邻近子区域的所述人群迁置匹配度指数进行排序,将人群迁置匹配度指数排名最高的对应邻近子区域的对应设施单元作为所述待规划安置人群的目标安置设施。The population relocation matching index of each adjacent sub-area is sorted, and the corresponding facility unit corresponding to the adjacent sub-area with the highest population relocation matching index is used as the target resettlement facility for the population to be resettled.

需要说明的是,若区域内的某子区域内某类人群无法获得布列设施单元的安置,即该子区域内的布列设施单元无法满足某类人群的功能安置需求或出现安置容量缺口,则需对子区域相邻的其他子区域进行勘察和识别,以获得能满足该子区域内未获得安置规划的人群的功需,如某区域内的工厂设施无法满足本区域内的职工人群的工作需求,则需通过考察和识别邻近区域的匹配工厂设施单元,以寻求能满足待规划安置人群需求的目标安置设施,通过获取邻近的多个邻近子区域的设施单元资源信息,包括与待规划安置人群匹配的布列设施单元的设施空置度数据、设施距离数据以及设施交通顺畅度系数,即获得邻近子区域内能与待规划安置人群匹配的设施单元的空置情况、距离以及交通状况的相关数据,同时还需获取待规划安置人群的外置意向度系数,即人群外置外出的意向度,再根据外置意向度系数结合设施空置度数据、设施距离数据以及设施交通顺畅度系数输入预设人群迁置匹配度模型中进行处理,获得各邻近子区域对应的人群迁置匹配度指数,即通过预设模型对上述数据参数进行计算处理,获得各邻近子区域的该人群的迁置匹配度情况,后对各邻近子区域的人群迁置匹配度指数进行排序,将人群迁置匹配度指数排名最高的对应邻近子区域的对应设施单元作为待规划安置人群的目标安置设施,即获得最佳匹配的该人群的安置设施;其中,所述人群迁置匹配度指数的计算公式为:It should be noted that if a certain group of people in a certain sub-area cannot be resettled by the arrangement of facility units, that is, the arrangement of facility units in this sub-area cannot meet the functional resettlement needs of a certain group of people or there is a gap in resettlement capacity, then it is necessary to survey and identify other sub-areas adjacent to the sub-area to meet the functional needs of the people who have not received resettlement planning in this sub-area. For the target resettlement facilities required by the planned resettlement population, obtain the facility unit resource information in multiple adjacent adjacent sub-areas, including the facility vacancy data, facility distance data, and facility traffic smoothness coefficient of the facility units that match the planned resettlement population, that is, obtain the relevant data on the vacancy, distance, and traffic conditions of the facility units in the adjacent sub-area that can match the planned resettlement population. The data, facility distance data, and facility traffic smoothness coefficient are input into the preset population relocation matching degree model for processing, and the population relocation matching degree corresponding to each adjacent sub-region is obtained, that is, the above-mentioned data parameters are calculated and processed through the preset model, and the relocation matching degree of the population in each adjacent sub-region is obtained, and then the population relocation matching degree index of each adjacent sub-region is sorted, and the corresponding facility unit corresponding to the adjacent sub-region with the highest population relocation matching degree index is used as the target resettlement facility for the population to be planned, that is, the best matching resettlement facility for the population is obtained; The calculation formula of population relocation matching degree index is:

其中,sc为人群迁置匹配度指数,yf、fn、cv分别为设施空置度数据、设施距离数据、设施交通顺畅度系数,ey为外置意向度系数,σ、η、δ为预设特征系数。Among them, s c is the population relocation matching degree index, y f , f n , and c v are the facility vacancy data, facility distance data, and facility traffic smoothness coefficients, respectively, e y is the external intention coefficient, and σ, η, δ are preset characteristic coefficients.

本发明公开的基于人口密度大数据的空间规划处理方法,通过对预设区域内人口的人员标识信息进行人员群类识别和属性分类,根据人员属性特征信息进行群类聚合,并通过预设人群分布密度热力模型进行处理获得人群属性分布密度画像,并提取人群分布特征信息输入预设人群区域划分模型中进行人群子区域栅格划分获得子区域人群分布特征认知图谱,再提取人群分布特征数据并处理获得人群安置排布系数,获取子区域各布列设施单元的设施单元布设信息并处理获得布列设施单元的布设优先级因子,根据人群分布特征数据结合人群安置排布系数以及布设优先级因子处理获得人群设施布设级数,最后根据级数优先性对设施人群进行规划安置;从而基于大数据对区域人群进行识别分类获得分布情况的相关画像、图谱和数据,并结合安置布列设施的优先级进行处理获得人群设施安置布设的优先级数对人群和相应设施进行规划安置,实现根据大数据对人口分布和安置设施进行规划排布的技术。The space planning and processing method of the public density of the population density is publicized by the personnel group recognition and attribute classification of the personnel logo information of the population in the preset area. The sub -area grid division obtains the characteristic cognitive diagram of the distribution of the sub -regional population, and then extracts the distribution characteristics of the crowd and processes the facility unit of the facility unit of the facility unit of the sub -area area and processes the priority factors of the setting facility unit. Obtain the number of crowd facilities, and finally plan the facility population according to the priority of the level; thereby identifying the regional population to identify and classify the distribution of the distribution based on the big data, and combine the priority of the placement facility to deal with the priority of crowd facilities and corresponding facilities to achieve the distribution and resettlement of population distribution and resettlement according to the big data. Applying the technology of planning.

在本申请所提供的几个实施例中,应该理解到,本发明所揭露的方法可以通过其它的方式实现。以上所描述的实施例仅仅是示意性的。In the several embodiments provided in the present application, it should be understood that the method disclosed in the present invention can be implemented in other ways. The embodiments described above are illustrative only.

本领域普通技术人员可以理解:实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成,前述的程序可以存储于可读取存储介质中,该程序在执行时,执行包括上述方法实施例的步骤;而前述的存储介质包括:移动存储设备、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,RandomAccess Memory)、磁碟或者光盘等各种可以存储程序代码的介质。Those of ordinary skill in the art can understand that all or part of the steps for realizing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed.

或者,本发明上述集成的单元如果以软件功能模块的形式实现并作为独立的产品销售或使用时,也可以存储在一个可读取存储介质中。基于这样的理解,本发明实施例的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机、服务器、或者网络设备等)执行本发明各个实施例所述方法的全部或部分。而前述的存储介质包括:移动存储设备、ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。Alternatively, if the above-mentioned integrated units of the present invention are realized in the form of software function modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on such an understanding, the technical solutions of the embodiments of the present invention can essentially be embodied in the form of a software product, or the part that contributes to the prior art. The software product is stored in a storage medium and includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media capable of storing program codes such as removable storage devices, ROM, RAM, magnetic disks or optical disks.

Claims (8)

1. The space planning processing method based on population density big data is characterized by comprising the following steps:
acquiring personnel identification information of population in a preset area, and carrying out personnel group identification and attribute classification according to the personnel identification information to acquire personnel attribute characteristic information;
group aggregation is carried out according to the personnel attribute characteristic information, and a preset crowd distribution density thermal model is used for processing, so that crowd attribute distribution density images of various crowds in a preset area are obtained;
extracting crowd distribution characteristic information according to the crowd attribute distribution density portrait, wherein the crowd distribution characteristic information comprises crowd distribution information, crowd thermal density information, crowd resident degree information and crowd space requirement attribute information;
inputting the crowd distribution characteristic information into a preset crowd area division model to divide the crowd subareas according to the crowd distribution characteristic information, and obtaining a subarea crowd distribution characteristic cognitive map;
extracting crowd distribution characteristic data in each corresponding subarea according to the subarea crowd distribution characteristic cognitive map, and processing according to the crowd distribution characteristic data to obtain crowd arrangement coefficients;
acquiring facility unit layout information of each arrangement facility unit in the subarea, and processing according to the facility unit layout information to acquire layout priority factors of each arrangement facility unit;
Processing according to the crowd distribution characteristic data of the crowd corresponding to each arrangement facility unit and combining the crowd arrangement and distribution coefficient and the arrangement priority factor to obtain crowd facility arrangement stages;
and planning and arranging facilities and corresponding crowds in the subareas according to the crowd facility layout progression.
2. The space planning processing method based on population density big data according to claim 1, wherein the step of obtaining personnel identification information of population in a preset area, performing personnel group identification and attribute classification according to the personnel identification information, and obtaining personnel attribute feature information comprises the steps of:
acquiring personnel identification information of population in a preset area, wherein the personnel identification information comprises age identification information, resident information, occupation information, registered place information and track point information;
according to the personnel identification information, carrying out personnel group identification and classification on all population in a preset area according to a preset population parking identification classification model;
and acquiring personnel attribute characteristic information of various crowds according to personnel group identification and classification results, wherein the personnel attribute characteristic information comprises work and study category information, residence category information, activity track domain degree information and residence time duration information.
3. The space planning processing method based on population density big data according to claim 2, wherein the group aggregation is performed according to the personnel attribute feature information, and the processing is performed through a preset population distribution density thermal model, so as to obtain population attribute distribution density portraits of various populations in a preset area, and the method comprises the following steps:
group aggregation is carried out on population in the preset area according to the personnel attribute characteristic information to obtain the crowd attribute characteristic information of various crowds;
processing the crowd attribute characteristic information of various crowds through a preset crowd distribution density thermodynamic model to obtain corresponding crowd distribution density thermodynamic diagram information;
and synthesizing crowd attribute distribution density portraits of all people in the preset area according to the crowd distribution density thermodynamic diagram information of all people.
4. The spatial planning processing method based on population density big data according to claim 3, wherein the inputting the population distribution feature information into a preset population region division model to perform population sub-region grid division to obtain a sub-region population distribution feature cognitive map comprises:
inputting the crowd distribution information, crowd thermodynamic density information, crowd resident information and crowd space requirement attribute information into a preset crowd area division model for processing to obtain crowd sub-area grid distribution data, wherein the crowd sub-area grid distribution data comprises sub-area grid capacity data, crowd sub-area grid density data, crowd sub-area distribution thermodynamic data and crowd sub-area space capacity data;
And inputting the data of the subarea grid capacity, the data of the crowd subarea grid density, the data of the crowd subarea distribution heat and the data of the crowd subarea space capacity into a preset crowd distribution characteristic cognitive model for processing, and obtaining a subarea crowd distribution characteristic cognitive map.
5. The method for space planning and processing based on population density big data according to claim 4, wherein the extracting the population distribution feature data in each corresponding sub-region according to the sub-region population distribution feature cognitive map and obtaining the population placement and arrangement coefficients according to the population distribution feature data processing comprises:
extracting crowd distribution characteristic data in each corresponding subarea according to the subarea crowd distribution characteristic cognitive map, wherein the crowd distribution characteristic data comprises crowd total amount data, crowd space demand data and crowd arrangement element data;
and processing and calculating according to the crowd total amount data, the crowd space demand data and the crowd arrangement element data in combination with the sub-region grid capacity data and the crowd sub-region space capacity data to obtain crowd arrangement coefficients of corresponding crowds.
6. The method for space planning and processing based on population density big data according to claim 5, wherein the steps of obtaining the facility unit layout information of each of the arrangement facility units in the sub-area, and obtaining the layout priority factor of each of the arrangement facility units according to the facility unit layout information processing include:
Acquiring facility unit layout information of each arranged facility unit in the subarea, wherein the facility unit layout information comprises facility unit capacity information, facility function information, facility attribute element information and facility user attribute information;
and processing the facility unit capacity information, the facility utility information, the facility attribute element information and the facility user attribute information through a preset facility arrangement element processing model to obtain arrangement priority factors of each arrangement facility unit.
7. The method for spatial planning based on population density big data according to claim 6, wherein the processing according to the population distribution characteristic data of the population corresponding to each of the arrangement facility units in combination with the population placement arrangement coefficient and the placement priority factor to obtain a population facility placement progression includes:
carrying out crowd attribute matching according to the facility user attribute information of each arranged facility unit in the subarea through the crowd attribute feature information to obtain corresponding one or more types of matched crowd users;
inputting the corresponding crowd distribution characteristic data of the users corresponding to the one or more types of matched crowd and the crowd arrangement and distribution coefficients into a preset crowd facility arrangement priority model according to the arrangement priority factors of the arrangement facility units for processing, and obtaining crowd facility arrangement stages;
The calculation formula of the crowd facility layout stage number is as follows:
wherein R is P Arranging stages for people group facilities, m ri 、k li 、w αi Respectively is crowd total data, crowd space demand data and crowd arrangement element data of i-th matched crowd users, T zi Arranging arrangement coefficients for the group of users of the i-th matched group, d x For arranging priority factors, n is the number of users of the matched crowd, n is a natural number greater than or equal to 1, beta,Mu is a preset characteristic coefficient.
8. The method of claim 7, wherein the planning and positioning facilities and corresponding people in the sub-area according to the population facility layout level comprises:
priority ordering is carried out according to the crowd facility arrangement series corresponding to each arrangement facility unit in the subarea;
and planning and arranging the arrangement facility units and the users of the corresponding matched groups in sequence according to the priority ordering result.
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