CN117056722A - Prediction method and system for population quantity of planned land parcel - Google Patents
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Abstract
The invention provides a prediction method and a prediction system for population quantity of a planned land parcel, wherein the method comprises the following steps: acquiring urban land data, planning land parcel data and population distribution data, and constructing a corresponding geographic space database; extracting land parcel parameters corresponding to the target planning land parcel according to the geographic space database, and generating corresponding learning samples by combining the land parcel parameters and population distribution data; constructing a loss function and a precision evaluation index corresponding to the target planning land parcel, and performing sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and a learning sample to generate a population prediction model; and predicting the number of planning population corresponding to the target planning land parcel by using a population prediction model, and comparing the number of planning population with the calculated theoretical population number to obtain the land parcel population number. The method and the system can rapidly and accurately predict the population number of the planned land parcel, and improve the working efficiency.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a prediction method and a prediction system for population quantity of a planned land parcel.
Background
With the continuous development of cities, population is a core element in urban detailed planning, and the population number of the prediction planning land is an important precondition of urban detailed planning, and is also an important basis for determining the configuration of various facilities.
In the actual city detailed planning process, the planned population is determined by comprehensively calculating theoretical calculation values through planning indexes such as building area, volume rate and population density and principles, and the theoretical calculation values can primarily reflect the population number of the planned land parcel.
However, the calculation method in the prior art is easily affected by factors such as the socioeconomic level of development, population migration, traffic conditions and the like, so that the theoretical calculation value calculated based on the planning principle is greatly different from the population quantity actually carried finally, the matched land planning efficiency is difficult to exert maximally, and the urban development efficiency is correspondingly reduced.
Disclosure of Invention
Based on the above, the invention aims to provide a prediction method and a prediction system for population quantity of a planned land parcel, so as to solve the problem that the calculation method in the prior art is easily influenced by external factors, so that the calculated theoretical calculation value has a larger difference from the population quantity actually born finally.
The first aspect of the embodiment of the invention provides:
a method of predicting a population of a planned plot, the method comprising:
acquiring urban land data, planning land parcel data and population distribution data, and constructing a corresponding geospatial database according to the urban land data, the planning land parcel data and the population distribution data;
extracting land parcel parameters corresponding to a target planning land parcel according to the geographic space database, and generating corresponding learning samples by combining the land parcel parameters and the population distribution data;
constructing a loss function and a precision evaluation index corresponding to the target planning land parcel, and performing sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and the learning sample to generate a corresponding population prediction model;
and predicting the planned population quantity corresponding to the target planned land parcels through the population prediction model, and comparing the planned population quantity with the theoretical population quantity calculated through a preset algorithm to determine the final land parcel population quantity according to the comparison result.
The beneficial effects of the invention are as follows: the needed geographic space database is generated through the urban land data, the planned land data and the population distribution data which are acquired in real time, meanwhile, land parameters and learning samples corresponding to the determined target planned land are extracted, further, sample training and effect evaluation are carried out on a preset machine learning model according to the acquired learning samples, therefore, a needed population prediction model can be generated on the premise that factors such as population migration and traffic conditions are not needed to be considered, the number of planning population corresponding to the current target planned land can be effectively predicted through the population prediction model, meanwhile, the number of needed land population can be simply and rapidly determined correspondingly by comparing with the theoretical population number calculated in real time, and the working efficiency and the use experience of workers are correspondingly improved.
Preferably, the step of constructing a corresponding geospatial database from the urban land data, the planned land parcel data, and the demographic data includes:
extracting urban ground-based elements, population surface-based elements and planning ground-based elements respectively contained in the urban ground data, planning ground-based data and population distribution data, wherein the surface-based elements comprise points, lines and planes;
Unifying coordinate systems of the urban ground-based element, the population ground-based element and the planning ground-based element, and performing association processing on attribute fields in the urban ground-based element, the population ground-based element and the planning ground-based element to generate the geospatial database.
Preferably, the step of associating attribute fields in the urban floor-like element, the population floor-like element, and the planning floor-like element includes:
generating a corresponding first basic attribute according to the ground-based elements for the city, and constructing a first attribute field between a planning land parcel and a city center based on the first basic attribute;
converting the population facial elements into corresponding population punctual elements according to the central point positions of the coordinate system, and transferring population attributes in the population facial elements into the population punctual elements so as to generate corresponding second attribute fields;
generating a corresponding second basic attribute according to the planar elements of the planning land parcel, and constructing a third attribute field corresponding to the planning land parcel based on the second basic attribute;
And simultaneously importing the first attribute field, the second attribute field and the third attribute field into a new database, and performing association processing on the first attribute field, the second attribute field and the third attribute field in the new database to generate the geospatial database.
Preferably, the step of generating the corresponding learning samples by combining the plot parameters and the demographic data includes:
correspondingly filling the land parcel parameters and the population distribution data into a preset sample data template, and identifying a plurality of attribute values contained in the preset sample data template;
performing Box-Cox conversion processing on a plurality of attribute values, and generating the learning sample according to the attribute values subjected to the Box-Cox conversion processing, wherein the expression of the Box-Cox conversion processing is as follows:
where Y represents the original continuous dependent variable and λ represents the attribute value.
Preferably, the step of performing sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and the learning sample to generate a corresponding population prediction model includes:
Performing data segmentation processing on the learning sample to generate a corresponding training set and a corresponding testing set, and inputting the training set into the preset machine learning model to perform corresponding model fitting and optimization processing;
and inputting the test set into a trained preset machine learning model to test a corresponding initial prediction model, and evaluating the effect of the initial prediction model through the loss function and the precision evaluation index to finally generate the population prediction model.
Preferably, the expression of the loss function is:
wherein J is MSE Represents root mean square error, N represents the number of samples, y i Andrepresenting the true value and the predicted value of the i-th sample, respectively.
Preferably, the step of comparing the planned population number with the calculated theoretical population number to determine the final land parcel population number according to the comparison result includes:
obtaining land area and volume rate corresponding to the target planning land, and calculating the total building area corresponding to the target planning land according to the land area and the volume rate;
acquiring a preset population density corresponding to the target planning land parcel, and calculating the theoretical population quantity according to the total building area and the population density;
Judging whether the difference value between the planned population quantity and the theoretical population quantity is within a preset threshold value or not in real time;
and if the difference value between the planned population quantity and the theoretical population quantity is judged to be within the preset threshold in real time, judging that the planned population quantity is effective, and setting the planned population quantity as the land parcel population quantity of the target planned land parcel.
A second aspect of an embodiment of the present invention proposes:
a planned land parcel population prediction system, wherein the system comprises:
the acquisition module is used for acquiring urban land data, planning land parcel data and population distribution data and constructing a corresponding geospatial database according to the urban land data, the planning land parcel data and the population distribution data;
the extraction module is used for extracting land parcel parameters corresponding to a target planning land parcel according to the geographic space database, and generating a corresponding learning sample by combining the land parcel parameters and the population distribution data;
the training module is used for constructing a loss function and a precision evaluation index corresponding to the target planning land parcels, and carrying out sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and the learning sample so as to generate a corresponding population prediction model;
And the prediction module is used for predicting the planned population quantity corresponding to the target planned land parcel through the population prediction model, and comparing the planned population quantity with the theoretical population quantity calculated through a preset algorithm so as to determine the final land parcel population quantity according to the comparison result.
In the above prediction system for population quantity of planned land parcels, the obtaining module is specifically configured to:
extracting urban ground-based elements, population surface-based elements and planning ground-based elements respectively contained in the urban ground data, planning ground-based data and population distribution data, wherein the surface-based elements comprise points, lines and planes;
unifying coordinate systems of the urban ground-based element, the population ground-based element and the planning ground-based element, and performing association processing on attribute fields in the urban ground-based element, the population ground-based element and the planning ground-based element to generate the geospatial database.
In the above prediction system for population quantity of planned land parcels, the obtaining module is further specifically configured to:
generating a corresponding first basic attribute according to the ground-based elements for the city, and constructing a first attribute field between a planning land parcel and a city center based on the first basic attribute;
Converting the population facial elements into corresponding population punctual elements according to the central point positions of the coordinate system, and transferring population attributes in the population facial elements into the population punctual elements so as to generate corresponding second attribute fields;
generating a corresponding second basic attribute according to the planar elements of the planning land parcel, and constructing a third attribute field corresponding to the planning land parcel based on the second basic attribute;
and simultaneously importing the first attribute field, the second attribute field and the third attribute field into a new database, and performing association processing on the first attribute field, the second attribute field and the third attribute field in the new database to generate the geospatial database.
In the above prediction system for population quantity of planned land parcels, the extraction module is specifically configured to:
correspondingly filling the land parcel parameters and the population distribution data into a preset sample data template, and identifying a plurality of attribute values contained in the preset sample data template;
performing Box-Cox conversion processing on a plurality of attribute values, and generating the learning sample according to the attribute values subjected to the Box-Cox conversion processing, wherein the expression of the Box-Cox conversion processing is as follows:
Where Y represents the original continuous dependent variable and λ represents the attribute value.
In the system for predicting population numbers of planned plots, the training module is specifically configured to:
performing data segmentation processing on the learning sample to generate a corresponding training set and a corresponding testing set, and inputting the training set into the preset machine learning model to perform corresponding model fitting and optimization processing;
and inputting the test set into a trained preset machine learning model to test a corresponding initial prediction model, and evaluating the effect of the initial prediction model through the loss function and the precision evaluation index to finally generate the population prediction model.
In the system for predicting the population quantity of the planned land parcel, the expression of the loss function is as follows:
wherein J is MSE Represents root mean square error, N represents the number of samples, y i Andrespectively representing the sum of the true values of the ith samplePredicted values.
In the above prediction system for population quantity of planned land parcels, the prediction module is specifically configured to:
obtaining land area and volume rate corresponding to the target planning land, and calculating the total building area corresponding to the target planning land according to the land area and the volume rate;
Acquiring a preset population density corresponding to the target planning land parcel, and calculating the theoretical population quantity according to the total building area and the population density;
judging whether the difference value between the planned population quantity and the theoretical population quantity is within a preset threshold value or not in real time;
and if the difference value between the planned population quantity and the theoretical population quantity is judged to be within the preset threshold in real time, judging that the planned population quantity is effective, and setting the planned population quantity as the land parcel population quantity of the target planned land parcel.
A third aspect of an embodiment of the present invention proposes:
a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the planned land parcel population prediction method as described above when executing the computer program.
A fourth aspect of the embodiment of the present invention proposes:
a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of planning a parcel population prediction as described above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flowchart of a method for predicting population of a planned land parcel according to a first embodiment of the present invention;
fig. 2 is a block diagram of a system for predicting population numbers of a planned land parcel according to a sixth embodiment of the invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a method for predicting population of a planned land parcel according to a first embodiment of the present invention is shown, and the method for predicting population of a planned land parcel according to the present embodiment can simply and rapidly determine population of a required land parcel, which correspondingly improves working efficiency and use experience of staff.
Specifically, the method for predicting the population quantity of the planned land parcel provided by the embodiment specifically comprises the following steps:
step S10, urban land data, planning land parcel data and population distribution data are obtained, and a corresponding geospatial database is constructed according to the urban land data, the planning land parcel data and the population distribution data;
specifically, in the present embodiment, it should be first noted that, the method for predicting population numbers of planned plots provided in the present embodiment is specifically applied to a land that has been planned but has not yet been developed, so as to predict population numbers that can be accommodated in a currently planned land, so as to complete corresponding development.
In this step, it should be noted that, first, urban land data, planned land parcel data, and population distribution data corresponding to the current city need to be acquired, where the urban land data is regional land data of the current city, that is, includes a plurality of already-divided administrative regions. Further, the planned land data is the area data which is planned to be developed in the current city, and the population distribution data is the population distribution state data of all areas in the current city. Based on the above, a needed geospatial database can be correspondingly constructed according to the current urban land use data, planning land parcel data and population distribution data.
Step S20, extracting land parcel parameters corresponding to a target planning land parcel according to the geographic space database, and generating corresponding learning samples by combining the land parcel parameters and the population distribution data;
further, in this step, it should be noted that, after the required geospatial database is obtained through the above steps, a required target planning land block, that is, a region to be developed, needs to be determined in the current city according to the actual situation.
Based on the above, after the target planning land parcels are determined, the land parcels parameters corresponding to the current target planning land parcels can be extracted from the geospatial database correspondingly, and meanwhile, the needed learning samples can be generated by combining the current land parcels parameters and the population distribution data.
Step S30, constructing a loss function and a precision evaluation index corresponding to the target planning land parcel, and performing sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and the learning sample to generate a corresponding population prediction model;
furthermore, in this step, it should be noted that, in order to enable the preset machine learning model to accurately predict the population number that can be accommodated by the current target planning plot, a loss function and an accuracy evaluation index adapted to the current target planning plot need to be constructed.
Based on the above, the effect evaluation can be performed on the preset machine learning model trained by the learning sample through the current loss function and the precision evaluation index, that is, whether the predicted accuracy meets the requirement is judged, and after the predicted accuracy meets the requirement, the corresponding population prediction model can be generated.
And S40, predicting the planned population quantity corresponding to the target planned land parcels through the population prediction model, and comparing the planned population quantity with the theoretical population quantity calculated through a preset algorithm to determine the final land parcels population quantity according to the comparison result.
Finally, in this step, after the required population prediction model is obtained through the above steps, the number of planning population corresponding to the current target planning land parcel can be predicted correspondingly through the population prediction model, and a specific numerical value can be predicted specifically.
Based on the method, the current planning population quantity is compared with the theoretical population quantity calculated by the prior art, namely, which number is more accurate at present is judged, and then the required land parcel population quantity can be accurately determined.
Second embodiment
Specifically, in this embodiment, it should be noted that the step of constructing the corresponding geospatial database according to the urban land data, the planned land parcel data, and the population distribution data includes:
extracting urban ground-based elements, population surface-based elements and planning ground-based elements respectively contained in the urban ground data, planning ground-based data and population distribution data, wherein the surface-based elements comprise points, lines and planes;
Unifying coordinate systems of the urban ground-based element, the population ground-based element and the planning ground-based element, and performing association processing on attribute fields in the urban ground-based element, the population ground-based element and the planning ground-based element to generate the geospatial database.
Specifically, in this embodiment, in order to accurately construct a required geospatial database, urban ground elements, population ground elements, and planning land block plane elements that are respectively included in the current urban ground data, planning ground data, and population distribution data are further extracted, and specifically, it is noted that the plane elements are two-dimensional distribution graphs, and points, lines, and surfaces corresponding to the buildings and streets of the current city are disposed in the plane elements, where the lines represent various streets, and the points and surfaces may represent various buildings and terrains, so that the regional distribution, the building distribution, and the terrains of the current city can be clearly reflected by the three plane elements.
Further, the geographic space database can be generated by setting the current urban ground-based element, population ground-based element and planning ground-based element in the same two-dimensional coordinate system at the same time, and performing association processing on attribute fields respectively contained in the three elements, namely combining the three elements into a whole.
After the geospatial database is generated, when a planned plot is randomly selected in the city, the data and parameters corresponding to the selected planned plot can be found out through the geospatial database so as to facilitate subsequent processing.
Specifically, in this embodiment, the step of associating attribute fields in the urban floor-like element, the population floor-like element, and the planning floor-like element includes:
generating a corresponding first basic attribute according to the ground-based elements for the city, and constructing a first attribute field between a planning land parcel and a city center based on the first basic attribute;
converting the population facial elements into corresponding population punctual elements according to the central point positions of the coordinate system, and transferring population attributes in the population facial elements into the population punctual elements so as to generate corresponding second attribute fields;
generating a corresponding second basic attribute according to the planar elements of the planning land parcel, and constructing a third attribute field corresponding to the planning land parcel based on the second basic attribute;
and simultaneously importing the first attribute field, the second attribute field and the third attribute field into a new database, and performing association processing on the first attribute field, the second attribute field and the third attribute field in the new database to generate the geospatial database.
Specifically, in this embodiment, it should also be noted that, first, a required first basic attribute is generated according to the above-mentioned ground-like elements for the city, where the first basic attribute includes basic attributes such as a land perimeter, a land area, and a land type of an area covered by the current city, and meanwhile, a first attribute field between the current planned land and the city center is constructed, and specifically, the first attribute field includes attribute fields such as a distance between the current planned land and the city center, a distance between the current planned land and a subway station shop, and a population number of neighboring lands.
Further, a second basic attribute is generated according to the planar elements of the planned land parcel, and the second basic attribute also comprises basic attributes such as land parcel perimeter, land parcel area, land parcel type and the like corresponding to the current planned land parcel, and meanwhile, a third attribute field corresponding to the current planned land parcel is constructed, wherein the third attribute field comprises planning parameter fields such as volume rate, density and the like. Further, a corresponding second attribute field having a status attribute including population distribution is generated based on the population faceelements. On the basis, the three attribute fields are simultaneously imported into a newly built database, and corresponding association processing is carried out, namely, a one-to-one mapping relation is established among the three fields, so that the geospatial database can be generated, and subsequent processing is facilitated.
Specifically, the land parcel parameters of the planned land parcel may include parameters such as a land parcel volume rate and a land parcel building density, and the theoretical population number of the current planned land parcel can be preliminarily calculated by the following expression:
Num＝D*A
where Num represents the theoretical population number, D represents population density, and a represents land area.
Third embodiment
In this embodiment, the step of generating the corresponding learning sample by combining the land parcel parameter and the population distribution data includes:
correspondingly filling the land parcel parameters and the population distribution data into a preset sample data template, and identifying a plurality of attribute values contained in the preset sample data template;
performing Box-Cox conversion processing on a plurality of attribute values, and generating the learning sample according to the attribute values subjected to the Box-Cox conversion processing, wherein the expression of the Box-Cox conversion processing is as follows:
where Y represents the original continuous dependent variable and λ represents the attribute value.
In addition, in this embodiment, it should be noted that, in this embodiment, the perimeter and the area of the planned plots and the distances between the planned plots and the city center and the subway station can be calculated through the statistical analysis tool in the existing GIS software, and further, when two planned plots have an adjacent relationship, the population ratio of each plot can be correspondingly calculated through the following algorithm:
Wherein R is i Represents population ratio, num i Representing the population sum of demographics regular grid points covered by a plot, T i The general population of the region is represented, and in addition, the preset sample data templates provided in this embodiment are shown in table 1 below:
TABLE 1
Furthermore, the current attribute values are transformed by a Box-Cox algorithm to finally obtain the required learning sample, and in addition, in the actual transformation process, the maximum and minimum normalization methods or the median absolute deviation normalization method can be used for transformation.
Fourth embodiment
In addition, in this embodiment, the step of performing sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and the learning sample to generate a corresponding population prediction model further includes:
performing data segmentation processing on the learning sample to generate a corresponding training set and a corresponding testing set, and inputting the training set into the preset machine learning model to perform corresponding model fitting and optimization processing;
and inputting the test set into a trained preset machine learning model to test a corresponding initial prediction model, and evaluating the effect of the initial prediction model through the loss function and the precision evaluation index to finally generate the population prediction model.
In addition, in this embodiment, it should be further noted that, in order to effectively complete training of the population prediction model, the above-mentioned learning samples need to be subjected to data segmentation processing, that is, the current learning samples are divided into a training set and a test set according to a ratio of 8:2, further, the current training set is input into a preset machine learning model to perform fitting and optimization processing on the current preset machine learning model, that is, the processing parameters in the current preset machine learning model are correspondingly adjusted, preferably, an XGBoost model is selected to perform sample training so as to correspondingly improve the training efficiency.
After the required loss function and the preset machine learning model are obtained, corresponding training can be performed immediately.
In this embodiment, it should be noted that, the expression of the loss function is:
wherein J is MSE Represents root mean square error, N represents the number of samples, y i Andrepresenting the true value and the predicted value of the i-th sample, respectively.
In this embodiment, it should be noted that, in this embodiment, the XGBoost model provided by the present embodiment has the following expression:
Obj＝L+Ω
wherein,y i and->The method and the device respectively represent the true value and the predicted value of the ith sample, N represents the number of samples, lambda, T and j represent constants, L and omega represent prediction functions, and w represents the learning samples, so that a required population prediction model can be trained.
Fifth embodiment
In this embodiment, it should be noted that the step of comparing the planned population number with the calculated theoretical population number to determine the final land parcel population number according to the comparison result includes:
obtaining land area and volume rate corresponding to the target planning land, and calculating the total building area corresponding to the target planning land according to the land area and the volume rate;
acquiring a preset population density corresponding to the target planning land parcel, and calculating the theoretical population quantity according to the total building area and the population density;
judging whether the difference value between the planned population quantity and the theoretical population quantity is within a preset threshold value or not in real time;
and if the difference value between the planned population quantity and the theoretical population quantity is judged to be within the preset threshold in real time, judging that the planned population quantity is effective, and setting the planned population quantity as the land parcel population quantity of the target planned land parcel.
In this embodiment, it should be noted that, after the required population prediction model is obtained through the above steps, further, a land area and a volume rate corresponding to the current target planned land are obtained, the total building area of the current target planned land is calculated based on the above formula and the current land area and the volume rate, further, a preset population density corresponding to the current target planned land is obtained, and based on this, the theoretical population quantity corresponding to the current target planned land is further calculated according to the calculated total building area and the current preset population density.
Based on the difference, the difference between the current theoretical population number and the planned population number predicted by the population prediction model is calculated in real time, and whether the current difference is within a preset threshold value is correspondingly judged. Specifically, if so, the current deviation of the two is smaller, and the current planning population is taken as the current planning population of the land parcel, if not, the current planning population is larger, the population is predicted once again through the population prediction model, comparison is performed again, and the current target planning population can be accurately determined until the predicted planning population meets the requirement.
Referring to fig. 2, a sixth embodiment of the present invention provides:
a planned land parcel population prediction system, wherein the system comprises:
the acquisition module is used for acquiring urban land data, planning land parcel data and population distribution data and constructing a corresponding geospatial database according to the urban land data, the planning land parcel data and the population distribution data;
the extraction module is used for extracting land parcel parameters corresponding to a target planning land parcel according to the geographic space database, and generating a corresponding learning sample by combining the land parcel parameters and the population distribution data;
The training module is used for constructing a loss function and a precision evaluation index corresponding to the target planning land parcels, and carrying out sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and the learning sample so as to generate a corresponding population prediction model;
and the prediction module is used for predicting the planned population quantity corresponding to the target planned land parcel through the population prediction model, and comparing the planned population quantity with the theoretical population quantity calculated through a preset algorithm so as to determine the final land parcel population quantity according to the comparison result.
In the above prediction system for population quantity of planned land parcels, the obtaining module is specifically configured to:
extracting urban ground-based elements, population surface-based elements and planning ground-based elements respectively contained in the urban ground data, planning ground-based data and population distribution data, wherein the surface-based elements comprise points, lines and planes;
unifying coordinate systems of the urban ground-based element, the population ground-based element and the planning ground-based element, and performing association processing on attribute fields in the urban ground-based element, the population ground-based element and the planning ground-based element to generate the geospatial database.
In the above prediction system for population quantity of planned land parcels, the obtaining module is further specifically configured to:
generating a corresponding first basic attribute according to the ground-based elements for the city, and constructing a first attribute field between a planning land parcel and a city center based on the first basic attribute;
converting the population facial elements into corresponding population punctual elements according to the central point positions of the coordinate system, and transferring population attributes in the population facial elements into the population punctual elements so as to generate corresponding second attribute fields;
generating a corresponding second basic attribute according to the planar elements of the planning land parcel, and constructing a third attribute field corresponding to the planning land parcel based on the second basic attribute;
and simultaneously importing the first attribute field, the second attribute field and the third attribute field into a new database, and performing association processing on the first attribute field, the second attribute field and the third attribute field in the new database to generate the geospatial database.
In the above prediction system for population quantity of planned land parcels, the extraction module is specifically configured to:
Correspondingly filling the land parcel parameters and the population distribution data into a preset sample data template, and identifying a plurality of attribute values contained in the preset sample data template;
performing Box-Cox conversion processing on a plurality of attribute values, and generating the learning sample according to the attribute values subjected to the Box-Cox conversion processing, wherein the expression of the Box-Cox conversion processing is as follows:
where Y represents the original continuous dependent variable and λ represents the attribute value.
In the system for predicting population numbers of planned plots, the training module is specifically configured to:
performing data segmentation processing on the learning sample to generate a corresponding training set and a corresponding testing set, and inputting the training set into the preset machine learning model to perform corresponding model fitting and optimization processing;
and inputting the test set into a trained preset machine learning model to test a corresponding initial prediction model, and evaluating the effect of the initial prediction model through the loss function and the precision evaluation index to finally generate the population prediction model.
In the system for predicting the population quantity of the planned land parcel, the expression of the loss function is as follows:
Wherein J is MSE Represents root mean square error, N represents the number of samples, y i Andrepresenting the true value and the predicted value of the i-th sample, respectively.
In the above prediction system for population quantity of planned land parcels, the prediction module is specifically configured to:
obtaining land area and volume rate corresponding to the target planning land, and calculating the total building area corresponding to the target planning land according to the land area and the volume rate;
acquiring a preset population density corresponding to the target planning land parcel, and calculating the theoretical population quantity according to the total building area and the population density;
judging whether the difference value between the planned population quantity and the theoretical population quantity is within a preset threshold value or not in real time;
and if the difference value between the planned population quantity and the theoretical population quantity is judged to be within the preset threshold in real time, judging that the planned population quantity is effective, and setting the planned population quantity as the land parcel population quantity of the target planned land parcel.
A seventh embodiment of the invention provides a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of planning a population volume prediction as described above when executing the computer program.
An eighth embodiment of the present invention provides a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a method of planning a population volume prediction as described above.
In summary, the method and the system for predicting the population of the planned land parcel provided by the embodiment of the invention can simply and rapidly determine the population of the land parcel needed, and correspondingly improve the working efficiency and the use experience of staff.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A method of predicting a population of a planned plot, the method comprising:
Acquiring urban land data, planning land parcel data and population distribution data, and constructing a corresponding geospatial database according to the urban land data, the planning land parcel data and the population distribution data;
extracting land parcel parameters corresponding to a target planning land parcel according to the geographic space database, and generating corresponding learning samples by combining the land parcel parameters and the population distribution data;
constructing a loss function and a precision evaluation index corresponding to the target planning land parcel, and performing sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and the learning sample to generate a corresponding population prediction model;
and predicting the planned population quantity corresponding to the target planned land parcels through the population prediction model, and comparing the planned population quantity with the theoretical population quantity calculated through a preset algorithm to determine the final land parcel population quantity according to the comparison result.
2. The method of claim 1, wherein the step of constructing a corresponding geospatial database from the urban land data, the planned land data, and the demographic data comprises:
Extracting urban ground-based elements, population surface-based elements and planning ground-based elements respectively contained in the urban ground data, planning ground-based data and population distribution data, wherein the surface-based elements comprise points, lines and planes;
unifying coordinate systems of the urban ground-based element, the population ground-based element and the planning ground-based element, and performing association processing on attribute fields in the urban ground-based element, the population ground-based element and the planning ground-based element to generate the geospatial database.
3. The method of predicting population of a planned land parcel of claim 2, wherein the step of associating attribute fields in the urban floor-like element, the population-like element, and the planned land parcel-like element comprises:
generating a corresponding first basic attribute according to the ground-based elements for the city, and constructing a first attribute field between a planning land parcel and a city center based on the first basic attribute;
converting the population facial elements into corresponding population punctual elements according to the central point positions of the coordinate system, and transferring population attributes in the population facial elements into the population punctual elements so as to generate corresponding second attribute fields;
Generating a corresponding second basic attribute according to the planar elements of the planning land parcel, and constructing a third attribute field corresponding to the planning land parcel based on the second basic attribute;
and simultaneously importing the first attribute field, the second attribute field and the third attribute field into a new database, and performing association processing on the first attribute field, the second attribute field and the third attribute field in the new database to generate the geospatial database.
4. The method of planning a population volume of a plot of land according to claim 1, wherein the step of generating corresponding learning samples in combination with the plot parameters and the demographic data comprises:
correspondingly filling the land parcel parameters and the population distribution data into a preset sample data template, and identifying a plurality of attribute values contained in the preset sample data template;
performing Box-Cox conversion processing on a plurality of attribute values, and generating the learning sample according to the attribute values subjected to the Box-Cox conversion processing, wherein the expression of the Box-Cox conversion processing is as follows:
where Y represents the original continuous dependent variable and λ represents the attribute value.
5. The method of claim 1, wherein the step of performing sample training and effect evaluation on a predetermined machine learning model by the loss function, the precision evaluation index, and the learning samples to generate a corresponding population prediction model comprises:
performing data segmentation processing on the learning sample to generate a corresponding training set and a corresponding testing set, and inputting the training set into the preset machine learning model to perform corresponding model fitting and optimization processing;
and inputting the test set into a trained preset machine learning model to test a corresponding initial prediction model, and evaluating the effect of the initial prediction model through the loss function and the precision evaluation index to finally generate the population prediction model.
6. The method of predicting population in a planned plot of claim 5, wherein the expression of the loss function is:
wherein,J MSE represents root mean square error, N represents the number of samples, y i Andrepresenting the true value and the predicted value of the i-th sample, respectively.
7. The method of claim 1, wherein the step of comparing the number of planned population to the calculated theoretical population to determine the final number of land parcel population based on the comparison comprises:
Obtaining land area and volume rate corresponding to the target planning land, and calculating the total building area corresponding to the target planning land according to the land area and the volume rate;
acquiring a preset population density corresponding to the target planning land parcel, and calculating the theoretical population quantity according to the total building area and the population density;
judging whether the difference value between the planned population quantity and the theoretical population quantity is within a preset threshold value or not in real time;
and if the difference value between the planned population quantity and the theoretical population quantity is judged to be within the preset threshold in real time, judging that the planned population quantity is effective, and setting the planned population quantity as the land parcel population quantity of the target planned land parcel.
8. A system for predicting population of a planned plot, the system comprising:
the acquisition module is used for acquiring urban land data, planning land parcel data and population distribution data and constructing a corresponding geospatial database according to the urban land data, the planning land parcel data and the population distribution data;
the extraction module is used for extracting land parcel parameters corresponding to a target planning land parcel according to the geographic space database, and generating a corresponding learning sample by combining the land parcel parameters and the population distribution data;
The training module is used for constructing a loss function and a precision evaluation index corresponding to the target planning land parcels, and carrying out sample training and effect evaluation on a preset machine learning model through the loss function, the precision evaluation index and the learning sample so as to generate a corresponding population prediction model;
and the prediction module is used for predicting the planned population quantity corresponding to the target planned land parcel through the population prediction model, and comparing the planned population quantity with the theoretical population quantity calculated through a preset algorithm so as to determine the final land parcel population quantity according to the comparison result.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of planning a population volume prediction as claimed in any one of claims 1 to 7 when the computer program is executed.
10. A readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of predicting a population of a planned land parcel as claimed in any one of claims 1 to 7.
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