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

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

Spatial planning processing method based on population density big data

technical field

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.

Background technique

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.

For the above problems, effective technical solutions are urgently needed at present.

Contents of the invention

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.

The embodiment of the present application also provides a spatial planning processing method based on population density big data, including the following steps:

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;

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;

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;

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;

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;

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;

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;

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.

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:

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;

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;

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.

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:

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;

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;

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.

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:

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;

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.

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:

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;

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.

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:

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;

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.

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:

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;

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;

The formula for calculating the number of levels of facilities for the crowd is as follows:

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.

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:

performing priority sorting according to the arrangement levels of facilities for the crowd corresponding to each arrangement unit in the sub-area;

According to the priority sorting results, the arranged facility units and corresponding matching group users are planned and arranged in order.

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.

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.

Description of drawings

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.

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;

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;

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.

Detailed ways

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.

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.

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. 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. 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. 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. 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. 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. 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. 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. 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.

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.

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. 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. 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. 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.

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.

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. 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. 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. 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.

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.

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:

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;

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.

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.

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:

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;

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.

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:

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).

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:

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;

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.

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:

d _x = (εF _c +γG _e +υB _q )/φA _s ;

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).

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:

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;

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;

The formula for calculating the number of levels of facilities for the crowd is as follows:

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).

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.

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:

performing priority sorting according to the arrangement levels of facilities for the crowd corresponding to each arrangement unit in the sub-area;

According to the priority sorting results, the arranged facility units and corresponding matching group users are planned and arranged in sequence.

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.

According to an embodiment of the present invention, it also includes:

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;

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;

The arrangement facility unit is planned and laid out according to the planned layout parameters.

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.

According to an embodiment of the present invention, it also includes:

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;

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;

Obtain the external intention coefficient of the population to be resettled;

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;

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.

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:

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.

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.

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.

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.

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.