CN116485143A - Space planning processing method based on population density big data - Google Patents
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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
Technical Field
The application relates to the technical field of big data and city planning, in particular to a space planning processing method based on population density big data.
Background
The modern urban process acceleration leads to population diversification and urban space rapid crowding, and as urban space planning and resource utilization of regional building facilities generally lack scientific overall planning and rationality planning, urban functional regions or facilities lack accurate, organic and continuous layout planning means for population volume, planning and arrangement, how to construct scientific, reasonable and optimized planning layout according to the population characteristics of regions or areas and population demands at present so as to adapt to the needs of population development and urban functional diversity and organic expansion of population cities, is a short board which cannot be implemented by traditional means, and therefore, the current lack of digital and intelligent technical means for scientific processing identification and matching planning layout for population categories and resource facilities of regions, areas and cities.
In view of the above problems, an effective technical solution is currently needed.
Disclosure of Invention
The embodiment of the application aims to provide a space planning processing method based on population density big data, which can be used for identifying and classifying regional population by big data to obtain related images, maps and data of distribution conditions, processing by combining the priorities of arrangement facilities to obtain the priorities of arrangement facilities of the population, planning and arranging the population and corresponding facilities, and realizing the technology of planning, evaluating and arranging the population distribution and arrangement facilities according to the big data.
The embodiment of the application also provides a space planning processing method based on population density big data, which comprises 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.
Optionally, in the method for space planning processing based on population density big data according to the embodiment of the present application, the acquiring personnel identification information of population in the preset area, performing personnel group identification and attribute classification according to the personnel identification information, and obtaining personnel attribute feature information includes:
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.
Optionally, in the spatial planning processing method based on population density big data according to the embodiment of the present application, the group aggregation is performed according to the personnel attribute feature information, and 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, including:
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.
Optionally, in the spatial planning processing method based on population density big data according to the embodiment of the present application, the inputting the crowd distribution feature information into a preset crowd area division model to perform crowd subarea grid division, to obtain a subarea crowd distribution feature cognitive map includes:
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.
Optionally, in the method for space planning processing based on population density big data according to the embodiment of the present application, extracting population distribution feature data in each corresponding sub-region according to the sub-region population distribution feature cognitive map, and obtaining population placement and arrangement coefficients according to population distribution feature data processing includes:
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.
Optionally, in the space planning processing method based on population density big data according to the embodiment of the present application, the obtaining facility unit layout information of each listed facility unit in the sub-area, and obtaining the layout priority factor of each listed facility unit according to the facility unit layout information processing includes:
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.
Optionally, in the spatial planning processing method based on population density big data according to the embodiment of the present application, the processing according to the population distribution feature data of the population corresponding to each of the arrangement facility units in combination with the population placement and arrangement coefficient and the arrangement priority factor, to obtain a population facility arrangement stage number 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.
Optionally, in the method for space planning processing based on population density big data according to the embodiment of the present application, the planning and positioning the facilities and the corresponding population in the sub-area according to the population facility layout level includes:
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.
As can be seen from the above, according to the space planning processing method based on population density big data provided by the embodiment of the application, by performing personnel group identification and attribute classification on personnel identification information of population in a preset area, performing group aggregation according to personnel attribute feature information, processing through a preset population distribution density thermal model to obtain a population attribute distribution density image, extracting population distribution feature information, inputting the population sub-area grid division into a preset population area division model to obtain a sub-area population distribution feature cognition map, extracting population distribution feature data, processing to obtain population arrangement coefficients, obtaining facility unit arrangement information of each arrangement facility unit of the sub-area, processing to obtain arrangement priority factors of the arrangement facility units of the arrangement sub-area, processing to obtain population facility arrangement stages according to the population distribution feature data in combination with the population arrangement coefficients and the arrangement priority factors, and finally planning and arranging the facility population according to the stage priorities; based on the big data, the regional population is identified and classified to obtain related images, maps and data of distribution conditions, the priority of the arrangement facilities is combined to process the regional population to obtain the priority of the arrangement of the population facilities, the population and the corresponding facilities are planned and arranged, and the technology of planning, evaluating and arranging the population distribution and the arrangement facilities according to the big data is realized.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a spatial planning processing method based on population density big data according to an embodiment of the present application;
fig. 2 is a flowchart of a method for obtaining personnel attribute feature information according to a spatial planning processing method based on population density big data according to an embodiment of the present application;
fig. 3 is a flowchart of a method for obtaining a crowd attribute distribution density portrait according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of a spatial planning method based on population density data according to some embodiments of the present application. The space planning processing method based on population density big data is used in terminal equipment, such as computers, mobile phone terminals and the like. The space planning processing method based on population density big data comprises the following steps:
s101, 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;
s102, group aggregation is carried out according to the personnel attribute characteristic information, and the group aggregation is processed through a preset crowd distribution density thermal model to obtain crowd attribute distribution density images of various crowds in a preset area;
s103, extracting crowd distribution characteristic information according to the crowd attribute distribution density portrait, wherein the crowd distribution characteristic information comprises crowd group distribution information, crowd thermal density information, crowd resident degree information and crowd space requirement attribute information;
s104, 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;
S105, 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;
s106, 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;
s107, processing according to the crowd distribution characteristic data of the crowd corresponding to each arrangement facility unit and combining the crowd arrangement and arrangement coefficients and the arrangement priority factors to obtain crowd facility arrangement stages;
s108, planning and arranging facilities and corresponding crowds in the subareas according to the crowd facility layout progression.
It should be noted that, in order to achieve effective matching and reasonable planning of population functional requirements and resource facility layout for regions or areas, the population of the areas is made to match and orderly plan with the layout planning of facility building units such as schools, factories, office buildings, houses, hotels, rest homes and the like which are matched according to the functional requirements of the areas, such as learning, work, life, business, rest and the like, the functional arrangement requirements of various populations in the areas can be matched with the corresponding layout facility units, digital classification planning and arrangement layout are achieved, the various populations in the areas are subjected to informatization, data processing and evaluation according to the attribute characteristics and distribution conditions of the identified populations and the matched facility units, the population distribution of the areas is subjected to planning arrangement of the population facilities according to the result data obtained by the processing, the population distribution and arrangement of the arrangement facilities are achieved according to the large data, the personnel identification information in the preset areas is acquired, the personnel attribute characteristic information is obtained by the personnel group identification and attribute classification, the personnel characteristic information is aggregated according to the personnel characteristic information, the preset image characteristic information is acquired by the preset image distribution thermal distribution, the population distribution is divided into the population distribution pattern and the population distribution pattern is divided according to the preset image distribution density, the population distribution pattern is acquired, the population distribution is subjected to the population distribution pattern is divided according to the distribution pattern, and the population distribution is subjected to the distribution information is subjected to the distribution and the distribution arrangement according to the distribution, and the distribution of the population is subjected to the distribution and the distribution is according to the distribution and the human in the condition, obtaining a sub-region crowd distribution feature cognitive map, extracting crowd distribution feature data in each corresponding sub-region, processing to obtain crowd arrangement coefficients, processing facility unit arrangement information of each arrangement facility unit in the sub-region to obtain arrangement priority factors of each arrangement facility unit, processing to obtain crowd facility arrangement levels in combination with crowd distribution feature data of the matched crowd according to the corresponding function of each arrangement facility unit, reflecting planning and layout levels of various crowds and the matched arrangement facility units in the sub-region, planning and arranging facility crowds in the sub-region according to the priority of the crowd facility arrangement levels, and realizing the planning and arrangement technology of population distribution and matched arrangement facilities through big data.
Referring to fig. 2, fig. 2 is a flowchart of a method for obtaining personnel attribute feature information in a spatial planning processing method based on population density data according to some embodiments of the present application. According to the embodiment of the invention, the personnel identification information of the population in the preset area is obtained, and personnel group identification and attribute classification are carried out according to the personnel identification information, so as to obtain personnel attribute characteristic information, which is specifically as follows:
s201, 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;
s202, identifying and classifying all population in a preset area according to the personnel identification information and a preset population parking identification classification model;
s203, personnel attribute characteristic information of various groups of people is obtained 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.
It should be noted that, in order to obtain the matched resource facilities aiming at the population functional requirements of the region or the area to perform scientific planning, firstly, the attribute characteristics of the population structure, classification, distribution and the like in the region are required to be clarified, the personnel identification information of the population in the preset region including the information of age, identity, residence, occupation, residence or business registration place and travel track point is obtained, the information is processed through the preset population residence identification classification model to perform group identification and classification on the population in the region, namely, the population attribute is identified and classified through the population residence identification classification model obtained through the third-party platform according to the information of the population, the personnel attribute characteristic information of various populations is obtained through the identification classification, namely, the personnel attribute characteristic information of the population in the region such as students, resident old people, foreign business people, company staff and the like is effectively identified and classified through the identification classification, and the obtained personnel attribute characteristic information includes the work learning category such as the work site staff or school students, the residence category such as temporary residence or residence time, the activity track degree such as the residence time and residence time.
Referring to fig. 3, fig. 3 is a flowchart of a method for obtaining a crowd attribute distribution density representation according to some embodiments of the application. According to the embodiment of the invention, the group aggregation is performed according to the personnel attribute characteristic information, and the group aggregation is processed through a preset crowd distribution density thermal model to obtain crowd attribute distribution density portraits of various crowds in a preset area, specifically:
s301, 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;
s302, 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;
s303, 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.
After the population attribute characteristics of the population in the area are identified and classified, the distribution situation of various populations in the area needs to be further depicted so as to conduct next planning according to the distribution situation of the populations, the population attribute characteristic information of the various populations in the area is aggregated to obtain the population attribute characteristic information of the various populations, the population attribute characteristic information reflects the attribute characteristics of the population, the population attribute characteristic information of the various populations is processed through a preset population distribution density thermodynamic model of a third party platform to obtain the population distribution density thermodynamic diagram information of each corresponding population, the population distribution density thermodynamic diagram information is the information of carrying out thermodynamic diagram mapping on the distribution density of the various populations, the population distribution density thermodynamic diagram information of the various populations in the area is synthesized, and the attribute distribution density image of the various populations in the area is obtained, and can be used for data-depicted through the image.
According to the embodiment of the invention, the crowd sub-region grid division is performed by inputting the crowd distribution characteristic information into a preset crowd region division model, and a sub-region crowd distribution characteristic cognitive map is obtained, specifically:
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.
It should be noted that, to measure the distribution situation of various people in the area more accurately, the distribution specific feature data of various people are described in detail, so as to obtain the corresponding matched facility units for the distribution situation of various people in the area more accurately, thus performing accurate matching placement and planning, dividing the area into a plurality of subareas according to the distribution feature situation of the people, then analyzing the distribution situation of each subarea, so as to obtain the implementation route of accurate planning of the whole area through planning of small areas, to obtain the effective division and data acquisition of the subareas of the people, to input the distribution feature information of the people into a preset crowd area division model for processing, to obtain the subarea grid distribution data, namely, to apply the preset model to perform grid splitting and distribution processing on the distribution feature information of the people in the area, to obtain the grid distribution data, namely, to perform grid splitting into a plurality of subareas according to the distribution situation of the people, and each subarea comprises one or more grids, if the business subareas in the preset area are distributed at a high-level business subarea, then the business subarea is distributed in a high-volume area, namely, the first subarea is processed by the grid distribution data of the preset subarea, and the first subarea is obtained by the grid distribution data of the high-volume area, and the first subarea is obtained, and identifying, cognizing, fusing and linking the data through the cognition model, so as to obtain a cognition map reflecting the crowd distribution characteristics of the subareas.
According to the embodiment of the invention, the crowd distribution feature data in each corresponding subarea is extracted according to the subarea crowd distribution feature cognitive map, and crowd placement and arrangement coefficients are obtained according to the crowd distribution feature data processing, specifically:
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.
After the cognitive map of the crowd distribution characteristics of each subarea is obtained, in order to evaluate the measurement value of the arrangement plan of each crowd in the subarea, namely, the mapping coefficient of the arrangement plan arrangement is obtained by measuring the crowd in the subarea, so that the comprehensive evaluation of the arrangement plan is carried out on the facility units matched with the crowd, the crowd distribution characteristic data in each corresponding subarea is extracted according to the subarea crowd distribution characteristic cognitive map, wherein the data comprises the crowd total amount, the crowd space requirement, the crowd arrangement elements, namely, the data of arrangement behavior elements such as the difficulty, the importance, the gradient and the like of the crowd arrangement, and the data is processed and calculated by combining the subarea grid capacity data and the crowd subarea space capacity data, so that the crowd arrangement coefficient of the corresponding crowd is obtained; the calculation formula of the crowd arrangement coefficient is as follows:
Wherein T is z Arranging arrangement coefficients for a group of people, m r 、k l 、w α C is crowd total data, crowd space demand data and crowd arrangement element data respectively g 、L s Respectively, sub-region grid capacity data and crowd sub-region space capacity data, q h The capacity parameter is arranged for the preset crowd, beta,μ, κ, ρ are preset feature coefficients (feature coefficients are obtained by querying a third party platform database).
According to the embodiment of the invention, the facility unit layout information of each listed facility unit in the subarea is obtained, and the layout priority factor of each listed facility unit is obtained according to the facility unit layout information, specifically:
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.
After the arrangement situation and related data parameters of various groups of people in the subarea are defined, the arrangement situation of various arrangement facility units in the subarea is required to be processed so as to further evaluate the arrangement planning situation of various arrangement facility units in the subarea and the matched arrangement situation of the groups of people in the subarea, firstly, related information of the planned arrangement facility units in the subarea is required to be acquired, the arrangement facility units are main facility units of various planning facilities, buildings, venues, building bodies and the like, including schools, residences, nursing homes, business office buildings, factories and the like, the arrangement information of the facility units is acquired, wherein the arrangement information comprises related information reflecting the unit capacity of the facilities, the functional use of the facilities, facility attribute elements such as planning tendency, supporting force, demand level, important priority and the like, and the user attribute of the applicable group of the facilities, and the arrangement priority factors corresponding to the arrangement facility units are obtained through calculation processing of a preset facility arrangement element processing model of a third-party platform, namely the arrangement priority factors reflecting the arrangement priority factors of the facility units; the calculation formula of the layout priority factor is as follows:
d x ＝(εF c +γG e +υB q )/φA s ；
Wherein d x To arrange priority factors F c 、G e 、B q 、A s Respectively, facility unit capacity information, facility utility information, facility attribute element information and facility user attribute information, wherein epsilon, gamma, upsilon and phi are preset characteristic coefficients (the characteristic coefficients are obtained by inquiring a third party platform database).
According to the embodiment of the invention, the crowd distribution characteristic data of the crowd corresponding to each arrangement facility unit is combined with the crowd arrangement and arrangement coefficient and the arrangement priority factor to be processed, so as to obtain crowd facility arrangement stages, which specifically are:
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,μ is a preset feature coefficient (the feature coefficient is obtained by querying a third party platform database).
It should be noted that, in order to evaluate the arrangement planning priority situation of each of the arrangement facility units and the corresponding user of the placement crowd in the sub-area, one or more types of people matched with the facility user attribute of each of the arrangement facility units need to be matched first, that is, the arrangement priority model is used to process one or more types of people matched with the facility user attribute of each of the arrangement facility units, that is, the corresponding user of each of the arrangement facility units is, for example, the corresponding enterprise staff of a factory, the corresponding pupil of a comprehensive school, and the like, the matching of the crowd attribute information of each of the arrangement facility units and the crowd attribute feature information is used to obtain the target crowd matched with the facility, and because more than one type of crowd matched with one of the arrangement facility units are likely, a plurality of crowd users exist, and according to the arrangement priority factor of each of the arrangement facility units, the crowd distribution feature data and the crowd arrangement coefficient of the corresponding matched one or more types of the crowd are combined, the crowd arrangement priority model is used to process the crowd arrangement priority model to obtain the crowd arrangement stage number, that is obtained by calculating the result through the preset facility arrangement priority model.
According to the embodiment of the invention, the planning and arrangement of facilities and corresponding crowds in the subarea are carried out according to the crowd facility layout stage number, specifically:
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.
After the number of the arrangement stages of the crowd facilities corresponding to the arrangement facility units in the subarea is obtained, planning and layout are carried out on each arrangement facility unit and the matched crowd users according to the priority order of the number of stages, so that each arrangement facility unit and the matched crowd users are orderly and planned and laid out, and further the implementation purposes of reasonably, scientifically and orderly planning and laying out the arrangement facility units and the matched crowd users in the whole preset area are realized through planning of the subarea.
According to an embodiment of the present invention, further comprising:
processing in a preset crowd area distribution model according to crowd distribution information and crowd thermal density information of various crowds in the subarea to obtain crowd distribution density gravity center data in the subarea;
Inputting the crowd total amount data, crowd arrangement element data and the facility unit capacity information of the corresponding arranged arrangement facility units into a preset facility unit arrangement model according to the crowd distribution density gravity center data, and processing to obtain planning layout parameters of the arrangement facility units;
and planning and laying out the arrangement facility units according to the planning and laying out parameters.
It should be noted that, in order to implement the planning layout of the arrangement facility units matching with a certain type of crowd in the subregion, the layout of the facility is more adapted to the distribution requirement of the crowd, the crowd distribution information and the crowd thermal density information of various types of crowd in the subregion are processed in a preset crowd region distribution model, so as to obtain crowd distribution density barycenter data in the subregion, that is, the information of crowd distribution and crowd thermal density is processed by the preset crowd region distribution model, so as to obtain the corresponding distribution density barycenter of the crowd, that is, the average central point position of the crowd distribution, then the crowd distribution density barycenter data is combined with crowd total amount data and crowd arrangement element data, and the facility unit capacity information of the corresponding matched arrangement facility units is input into a facility unit layout model for processing, so as to obtain the planning layout parameters of the arrangement facility units, that is, the data is processed by the facility unit layout model, so as to obtain the relevant parameters of the facility planning layout, the facility through the crowd distribution density barycenter data, total amount data, facility element data, facility unit capacity information and layout parameters are input and training parameters, so that the layout parameters can be accurately obtained through the layout of the facility unit, the layout of the layout is accurate layout of the facility unit, the layout is designed according to the method, and finally, planning and laying out the listing facility units according to the planning and laying out parameters.
According to an embodiment of the present invention, further comprising:
if the arrangement facility units in the subareas do not meet the capacity requirement of the crowd to be planned and placed, acquiring facility unit resource information of a plurality of adjacent subareas adjacent to the subareas in the preset area;
the facility unit resource information comprises facility vacancy degree data, facility distance data and facility traffic smoothness coefficients of the arrangement facility units matched with the crowd to be planned and placed;
acquiring an external intention coefficient of the crowd to be planned and placed;
inputting the external intention degree coefficient into a preset crowd relocation matching degree model according to the facility vacancy degree data, the facility distance data and the facility traffic smoothness coefficient to process, and obtaining crowd relocation matching degree indexes corresponding to adjacent subareas;
and sequencing the crowd migration matching degree indexes of the adjacent subareas, and taking the corresponding facility units of the corresponding adjacent subareas with the highest ranking of the crowd migration matching degree indexes as target arrangement facilities of the crowd to be planned and arranged.
It should be noted that, if a certain group of people in a certain sub-area in the area cannot obtain the arrangement of arrangement facility units, that is, the arrangement facility units in the sub-area cannot meet the functional arrangement requirement of the certain group of people or an arrangement capacity gap occurs, investigation and identification are required to be performed on other sub-areas adjacent to the sub-area to obtain the work requirement of the group of people who cannot obtain arrangement planning in the sub-area, if the factory facilities in the certain area cannot meet the work requirement of the employee group in the area, the matching factory facility units of the adjacent area are required to be inspected and identified to seek to meet the target arrangement facility required by the group of people to be planned, the arrangement facility unit resource information of a plurality of adjacent sub-areas is acquired, including the arrangement facility empty position data and the arrangement facility distance data of the arrangement facility units matched with the group of people to be planned, that is, the empty position condition, distance and traffic smoothness coefficient of the arrangement facility traffic of the adjacent sub-areas can be obtained, that is, the external intention coefficient of people to be planned is also required to be obtained, that is, the external intention coefficient of people to be planned is calculated according to the external intention coefficient of people is calculated, the outside intention coefficient is calculated according to the external intention coefficient, the preset people is matched with the preset position coefficient, and the preset position coefficient is calculated by the match with the preset position coefficient of the adjacent people, and the empty position coefficient of the adjacent people is calculated, and the adjacent people is smooth coefficient, taking a corresponding facility unit of a corresponding adjacent subarea with the highest crowd migration matching degree index ranking as a target arrangement facility of the crowd to be planned to be arranged, and obtaining the arrangement facility of the crowd with the best matching; the calculation formula of the crowd migration matching degree index is as follows:
Wherein s is c Setting matching degree index, y for crowd migration f 、f n 、c v E is respectively facility vacancy degree data, facility distance data and facility traffic smoothness coefficient y The external intention coefficient is the preset characteristic coefficient of sigma, eta and delta.
The invention discloses a space planning processing method based on population density big data, which comprises the steps of carrying out personnel group identification and attribute classification on personnel identification information of population in a preset area, carrying out group aggregation according to personnel attribute characteristic information, processing through a preset population distribution density thermal model to obtain a population attribute distribution density image, extracting population distribution characteristic information, inputting the population distribution characteristic information into a preset population area division model, carrying out population subarea grid division to obtain a subarea population distribution characteristic cognition map, extracting population distribution characteristic data, processing to obtain population arrangement coefficients, obtaining facility unit arrangement information of each arrangement facility unit of the subarea, processing to obtain arrangement priority factors of the arrangement facility units, combining the population arrangement coefficients and the arrangement priority factors according to the population distribution characteristic data to obtain population facility arrangement stages, and finally planning and arranging facility populations according to the stage priorities; the regional population is identified and classified based on the big data to obtain related images, maps and data of distribution conditions, the priority of the arrangement facilities is combined to process the regional population to obtain the priority of the arrangement of the population facilities, the population and the corresponding facilities are planned and arranged, and the technology of planning and arranging the population distribution and the arrangement facilities according to the big data is realized.
In the several embodiments provided in this application, it should be understood that the method disclosed in the present invention may be implemented in other manners. The embodiments described above are merely illustrative.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, randomAccess Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
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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