CN115409671B - Method, device, terminal and storage medium for synthesizing microscopic data of community resident population - Google Patents
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Abstract
The invention discloses a method, a device, a terminal and a storage medium for synthesizing community resident population microscopic data. The prediction of urban community scale population socioeconomic performance information is realized, data information support is provided for application scenes such as public facilities site selection, traffic simulation, disaster prevention resource allocation and the like, and the promotion of urban fine treatment is promoted.
Description
Technical Field
The invention relates to the technical field of social simulation, in particular to a community resident population microscopic data synthesis method, device, terminal and storage medium.
Background
With the continuous promotion of urban digitization and refined treatment, demands for population microscopic data with spatial information and resident attributes are increasing in various fields including public service facility demand prediction, pension education facility layout and site selection, disaster prevention and evacuation accurate simulation and traffic network design.
In the existing living area population information prediction method, geographic information data or space remote sensing information is mostly adopted to generate population space distribution information without resident attributes, or population micro data with resident attributes but without space information is generated by adopting population synthesis technology.
Thus, the prior art has not had a way to obtain microscopic population data with spatial information and resident attributes.
Disclosure of Invention
The invention mainly aims to provide a community resident population microscopic data synthesis method, device, intelligent terminal and storage medium, which can generate community resident population microscopic data with spatial information and resident attributes.
In order to achieve the above object, a first aspect of the present invention provides a community resident population microscopic data synthesis method, the method comprising:
acquiring a community population microscopic data set based on administrative region macroscopic population data and community microscopic population sampling data according to an iterative proportion updating algorithm, wherein the community population microscopic data set comprises household information and liveness information;
inputting the community population microscopic data set into a neural network model to obtain a prediction model for predicting the relationship between families and living things;
based on the prediction model and constraint conditions formed by the real capacities of various living things, distributing all family samples in the community population microscopic data set to the various living things by taking the minimum utility loss as a target, and obtaining community resident population microscopic data with space information and resident attributes.
Optionally, the input layer of the neural network model extracts family features and span features, where the family features include: the number of family population, the highest education level, the maximum age, and the interplanting structure; the living characteristics include: a living house, a living rent, and property rights.
Optionally, the obtaining the community population micro data set according to the iterative proportion updating algorithm based on the administrative region macro demographic data and the community micro population sampling data includes:
obtaining a frequency matrix according to the relation between family classification and population characteristics in microscopic population sampling data;
based on the frequency matrix, obtaining a joint distribution value corresponding to each human mouth characteristic according to an iterative proportion updating algorithm;
circularly iterating and calculating the fitting degree of each iteration according to the frequency matrix and the joint distribution value until the fitting degree is smaller than a set threshold value, so as to obtain probability distribution of each family classification;
based on the probability distribution, adopting a Monte Carlo method to randomly extract family samples to the community population microscopic data set.
Optionally, the expression for calculating the fitting degree is:
wherein sigma is the fitting degree, D ij For a frequency matrix, C j For the joint distribution value, m is the number of human mouth features, and W is a weight vector.
Optionally, the error function model of the neural network model is:
wherein t is n To be output, u n For actual output, N is the number of home samples.
Optionally, the distributing each family sample in the community population microscopic data set to each family based on the constraint condition formed by the prediction model and the real capacity of each family with minimum utility loss as a target to obtain community resident population microscopic data with space information and resident attributes includes:
based on constraint conditions of real capacities of various living things, constructing an objective function of a dynamic optimization model by taking utility loss minimization as a target, wherein the dynamic optimization model is used for optimizing and distributing each living things;
and obtaining the characteristics of various livestocks, inputting the characteristics and the prediction results of the prediction model into a dynamic optimization model, and obtaining microscopic data of the community resident population.
Optionally, the expression of the objective function is:
wherein A is ik Selecting a probability of k-class livers for the predicted i-th class of households, i=1, 2, 3..m, M being the number of family classes; b (B) ik K=1, 2,3,4 for the 4 attributes corresponding to class i family; d (D) ij The number of livers allocated to the j-th class for the i-th class family, i=1, 2, 3..m, j=1, 2, 3..n, N being the number of liver types; p (P) i =0 or 1 indicates whether the span is selected during the allocation process, i=1, 2,..n; w (W) k As the weight of the kth living attribute,is P i Is a mean value of (c).
The second aspect of the present invention provides a community resident population microscopic data synthesizing apparatus, wherein the apparatus comprises:
the community population microscopic data set acquisition module is used for acquiring a community population microscopic data set based on administrative region macroscopic demographic data and community microscopic population sampling data according to an iterative proportion updating algorithm, wherein the community population microscopic data set comprises household information and family information;
the prediction module is used for inputting the community population microscopic data set into a neural network model to obtain a prediction model for predicting the relationship between families and livers;
the distribution module is used for distributing each family sample in the community population microscopic data set to each type of the livers based on the prediction model and constraint conditions formed by the real capacities of each type of livers, and obtaining community resident population microscopic data with space information and resident attributes by taking the minimum utility loss as a target.
A third aspect of the present invention provides an intelligent terminal including a memory, a processor, and a community-resident-population microscopic-data synthesizing program stored in the memory and operable on the processor, the community-resident-population microscopic-data synthesizing program implementing any one of the community-resident-population microscopic-data synthesizing methods when executed by the processor.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a community-resident-population microscopic data synthesizing program which, when executed by a processor, implements the steps of any one of the community-resident-population microscopic data synthesizing methods.
From the above, the invention firstly obtains the community population microscopic data set through the iterative proportion updating algorithm on the basis of the administrative region macroscopic population data and the community microscopic population sampling data, then learns the relationship between the families and the living being according to the neural network model, and optimally distributes each family sample to each living being with the minimum utility loss as the target, thereby obtaining the community resident population microscopic data with space information and resident attributes.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a community resident population microscopic data synthesis method provided by the embodiment of the invention;
FIG. 2 is a schematic flowchart of step S300 in the embodiment of FIG. 1;
FIG. 3 is a schematic flowchart of step S100 in the embodiment of FIG. 1;
fig. 4 is a schematic structural diagram of a community resident population microscopic data synthesizing device provided by an embodiment of the invention;
fig. 5 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used 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. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted in context as "when …" or "upon" or "in response to a determination" or "in response to detection. Similarly, the phrase "if a condition or event described is determined" or "if a condition or event described is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a condition or event described" or "in response to detection of a condition or event described".
The following description of the embodiments of the present invention will be made more fully hereinafter with reference to the accompanying drawings, in which embodiments of the invention are shown, it being evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
In general, the acquisition of microscopic information of population is time-consuming and laborious, and involves the problem of resident privacy protection, most of related researches and practices only can summarize census data of population taking administrative areas as units, and the spatial resolution and the temporal resolution of the census data can not meet the actual requirements.
The existing living area population information prediction method mostly adopts geographic information data or space remote sensing information to generate population space distribution information without resident attribute information, or adopts population synthesis technology to generate population microscopic data with resident attribute but without space information. Thus, there is currently no method for generating microscopic population data with spatial information and resident attributes.
According to the invention, community resident population microscopic data synthesis and space information are combined, a neural network model and a dynamic programming algorithm are fused, microscopic household samples are matched with living space information under the consideration of living capacity limitation, and population microscopic data with space information and resident attributes is generated, so that the urban digitization and fine treatment are adapted.
Exemplary method
As shown in FIG. 1, the embodiment of the invention provides a community resident population microscopic data synthesis method which can be operated on various terminals, such as a computer terminal, a tablet computer, a smart phone and the like. Specifically, the method comprises the following steps:
step S100: acquiring a community population microscopic data set based on administrative region macroscopic population data and community microscopic population sampling data according to an iterative proportion updating algorithm, wherein the community population microscopic data set comprises household information and liveness information;
specifically, the construction of an artificial population generally refers to the reconstruction of individual social features, and an artificial population data set consistent with the actual social features is automatically generated according to the actual social features such as the age, sex, family, educational level, occupation, and other social and economic relationships of the individual. The population synthesis method is to carry out population synthesis by adopting methods such as Monte Carlo simulation on the basis of population characteristic joint probability distribution, and the generated data set contains information such as overall population scale, all possible attribute combinations and the like.
The specific process is as follows: and (3) adopting an iterative proportion updating algorithm to iterate macroscopic population data and microscopic population sampling data of the administrative region, and obtaining a community population microscopic data set after the iteration is completed. The iterative proportion updating algorithm takes the characteristic variables of families and population as a whole, takes macroscopic overall data of two layers as constraint conditions together, optimizes and generates a family entity set, and then generates members of the family entity set according to the characteristics of each family, so as to obtain population individuals.
The administrative region macroscopic demographic data can be obtained from public census data and economic census data of the national bureau of statistics and the bureau of statistics of each province and city. The micro population sample data of the community can provide multi-dimensional demographic attributes for restoring family composition in society based on small-scale high-precision individuals and family structure data.
Administrative region macro demographic data includes: age, sex, educational level, family population, type of household, etc.; the microscopic population sample data includes: age, gender, educational level, family population, household type, and name of residential district; the span information includes: house type, building year, listing selling price, property right type, etc.; the family information includes: number of family population, number of family generations, etc.
After the data are obtained, quantitative processing is firstly required to be carried out on the factor indexes such as age, gender, education received degree, family population, household registration type, listing selling price, property right type and the like. Such as: the sex quantification results were: the number of men is 1 and the number of women is 2; the age quantification results were: the ages of 0-15 are 1, 15-30 are 2, 30-45 are 3, 45-60 are 4, 60-75 are 5, and above 75 are 6; other element indexes are quantized similarly, and are not described in detail herein.
It should be noted that, the specific items and the number of the element indexes are not limited, and may be changed according to specific calculation requirements.
Step S200: inputting the community population microscopic data set into a neural network model to obtain a prediction model for predicting the relationship between families and living span;
specifically, the neural network model predicts the living selection probability of the microscopic family sample under each category based on the community population microscopic data set and obtains the weight corresponding to each category. And establishing more accurate relation between the microscopic family sample and the living space information through the neural network model.
After the community population microscopic data set is input into the neural network model, the input layer of the neural network model extracts family characteristics and span characteristics, wherein the family characteristics comprise: the number of family population, the highest education level, the maximum age, and the interplanting structure; the living characteristics include: a living house, a living rent, and property rights. And carrying out quantization processing on family characteristics in a neural network model, such as: number of family population: 1-3 is 1, 4-6 is 2, and more than 7 is 3; the substitution structure is as follows: 1 generation of user is 1,2 generation of user is 2,3 generation and above are 3; and so on, similar quantization processing is performed on other features in the family features and the household features.
The neural network model in this embodiment is: the input layer neuron number S is 4, namely: the number of family population, the maximum age, the highest education degree and the intercity structure are respectively set as W 1s W 2s W 3s W 4s The method comprises the steps of carrying out a first treatment on the surface of the The output layer neuron number O is 3, namely: a hanging selling price, a living house type and a living property type; the number of neurons in the hidden layer is 4, and the method is calculated by experienceObtained. The error function of the neural network model is +.>Wherein t is n To be output, u n For actual output, N is the number of home samples. And (3) carrying out feedback processing according to the output, adjusting the weights of neurons of an input layer, an output layer and an implicit layer, and repeating training until the prediction accuracy is greater than the set accuracy.
Step S300: based on a prediction model and constraint conditions formed by the real capacities of various livestocks, distributing all family samples in the community population microscopic data set to the various livestocks by taking the minimum utility loss as a target to obtain community resident population microscopic data with space information and resident attributes;
specifically, a dynamic planning algorithm is adopted, real capacities of various living buildings are considered, all family samples are distributed to the living buildings with the aim of minimum utility loss, and community resident population microscopic data with space information and resident attributes are generated.
In one embodiment, as shown in fig. 2, the method specifically includes the following steps:
step S310: based on constraint conditions of real capacities of various living things, constructing an objective function of a dynamic optimization model by taking utility loss minimization as a target, wherein the dynamic optimization model is used for optimizing and distributing each living things;
step S320: and obtaining the characteristics of various livestocks, inputting the characteristics and the prediction results of the prediction model into a dynamic optimization model, and obtaining microscopic data of the community resident population.
Specifically, firstly, an objective function is established, based on the living selection probability of each classified family sample in the prediction model, the objective function of population microscopic data distribution is established with the aim of minimizing utility loss in the distribution process, the living selection probability of each living characteristic, population microscopic sample and the prediction model is input into an optimization model formed by the objective function and constraint conditions together, and the model is operated.
Wherein the constraint conditionsIs thatNamely, the sum of the family numbers of a certain class contained in all living areas is less than or equal to the total number of the family numbers of the class; />I.e. the sum of the number of households allocated to each living area is less than or equal to the total capacity of the living area. The expression of the objective function is:
in the above formula, C i Represents the number of class i households, i=1, 2,3,..m, M represents a total of M class of households; a is that ik Selecting a probability of k-class livers for the predicted i-th class of households, i=1, 2, 3..m, M being the number of family classes; t (T) j The number of households capacity of the j-th class, j=1, 2,3,..n, N total classes of livers; b (B) ik K=1, 2,3,4 for the 4 attributes corresponding to class i family; d (D) ij The number of livers allocated to the j-th class for the i-th class family, i=1, 2, 3..m, j=1, 2, 3..n, N being the number of liver types; p (P) i =0 or 1 indicates whether the span is selected during the allocation process, i=1, 2,..n; w (W) k As the weight of the kth living attribute,is P i Is a mean value of (c).
By combining the community resident population microscopic data synthesis technology with the neural network model and the dynamic programming algorithm, the method provided by the invention realizes generation of the resident population microscopic data and prediction of each layer attribute of the resident population, obtains the community resident population microscopic data with space information and resident attributes, realizes prediction of the community population attribute information of the city, provides data information support for application scenes such as public facilities site selection, traffic simulation, disaster prevention resource allocation and the like, and promotes the promotion of city fine treatment.
In one embodiment, as shown in fig. 3, the step S100 of obtaining the community population microscopic dataset specifically includes the following steps:
step S110: obtaining a frequency matrix according to the relation between family classification and population characteristics in microscopic population sampling data;
specifically, the ratio of each family is calculated according to the micro population sampling data, and the ratio is defined as lambda as the marginal constraint of iterative fitting of family information, wherein lambda represents the ratio of the number of families containing the ith feature to the total number. Generating a frequency matrix D, wherein the dimension is Nxm, N is the number of all family samples, and m is the human mouth feature constraint (including family features and population features).
D ij Is the contribution value of the family sample of class i to the occurrence of the population of class j.
Step S120: based on the frequency matrix, acquiring a joint distribution value corresponding to each human mouth characteristic according to an iterative proportion updating algorithm;
step S130: circularly iterating and calculating the fitting degree of each iteration according to the frequency matrix and the joint distribution value until the fitting degree is smaller than a set threshold value, so as to obtain probability distribution of each family classification;
specifically, first, an iterative proportion updating algorithm is adopted to calculate the joint distribution value of each person mouth feature. Assume target tableWherein (1)>A joint distribution value representing a family sample i, +.>Representing the joint distribution value of individual j. Initially setting the tolerance threshold to 0.0001, +.>Is->Initialization->j=1, 2, … m; initializing a weight column vector W i =1, i=1, 2, … N, initializing the fitness scalar σ min =σ, initializing scalar k=1 as constraint counter.
Calculating a temporary error in each cycle:
continuing the loop iteration when the temporary error is greater than the tolerance threshold:
there is->
Obtaining a joint distribution value C corresponding to each human mouth characteristic j Wherein c i ＝b i ，i＝1,2,3,…m。
Calculating the fitting degree of each iteration according to the frequency matrix and the joint distribution value:wherein sigma is the fitting degree, D ij For a frequency matrix, C j For the joint distribution value, m is the number of human mouth features, and W is a weight vector.
Generating m column vectors S for record index j ＝index(D ij ≠0)，i＝1,2,…N，j＝1,2,…m，S j An index column vector representing non-zero elements in a j-th column of the frequency matrix, S qj An index representing the q-th non-zero element of the j-th column in the frequency matrix.
Calculating a fitness value:updating the weight vector W according to the fitness value, whereinAnd recalculate the fitness +.>
Calculating the improvement condition delta= |sigma-sigma of the fitting degree according to the fitting degree value of the current iteration and the fitting degree value of the last iteration prev I, if sigma<σ min ，σ min ＝σ，SW i ＝W i ，i＝1,2,3…N。
If delta > epsilon, namely the fitting degree is too large, recalculating the temporary error for loop iteration; and stopping running when the final sigma is smaller than the threshold value, obtaining the family weight of each family with the attribute of the residential type, and converting the weight value into probability distribution of each family classification.
Step S140: based on probability distribution, adopting a Monte Carlo method to randomly extract family samples to the community population microscopic data set.
Specifically, a Monte Carlo method is used for randomly extracting family samples according to the obtained probability distribution, and the family samples are selected into a final data set, so that a community population microscopic data set which has no significant difference from the editing distribution and has residence information is obtained.
In this embodiment, according to probability distribution of individual attributes such as age, gender, occupation type and the like in population microscopic data, corresponding individual attributes are allocated to individuals of each synthetic community population through Monte Carlo simulation. And then constructing a synthetic family according to the probability of family attributes such as family category, family scale, family age component and the like in the census data, and filling individuals of the synthetic community population into the synthetic family.
The artificial population has the advantage of simulating the travel and other social behaviors of people at low cost and checking the feasibility and effectiveness of policies such as traffic control, social management and the like. Meanwhile, the calculation model of the individual behaviors can simulate to obtain quantitative evaluation results, and a reference is provided for quantitative decisions of a management department. When the artificial population can accurately reflect the attribute, structure and distribution characteristics of the real population, the obtained simulation results such as trip simulation, economic activities, city evolution and the like have higher credibility.
Based on the population microscopic sampling data and the population census data, the iteration proportion updating algorithm is adopted to generate the community population microscopic data set.
Although community population data is taken as an example for explanation in this embodiment, the present invention can also construct a micro database of very large urban families and individuals.
Exemplary apparatus
As shown in fig. 4, corresponding to the method for synthesizing community-resident population microscopic data, the embodiment of the invention further provides a device for synthesizing community-resident population microscopic data, where the device for synthesizing community-resident population microscopic data includes:
the community population micro data set obtaining module 600 is configured to obtain a community population micro data set according to an iterative proportion updating algorithm based on administrative region macro demographic data and community micro population sampling data, where the community population micro data set includes family information and family information;
a prediction module 610, configured to input the community population microscopic dataset into a neural network model, and obtain a prediction model for predicting a relationship between a household and a living;
the allocation module 620 is configured to allocate each family sample in the community population microscopic data set to each family based on the prediction model and constraint conditions formed by real capacities of each family, and obtain community resident population microscopic data with spatial information and resident attributes, with the objective of minimizing utility loss.
Specifically, in this embodiment, specific functions of each module of the community-resident-population microscopic data synthesizing apparatus may refer to corresponding descriptions in the community-resident-population microscopic data synthesizing method, which are not described herein again.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a functional block diagram thereof may be shown in fig. 5. The intelligent terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. The processor of the intelligent terminal is used for providing computing and control capabilities. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The nonvolatile storage medium stores an operating system and a community resident population microscopic data synthesizing program. The internal memory provides an environment for the operation of an operating system and community resident population microscopic data synthesizing program in the non-volatile storage medium. The network interface of the intelligent terminal is used for communicating with an external terminal through network connection. The community resident population microscopic data synthesizing program, when executed by the processor, realizes the steps of any one of the community resident population microscopic data synthesizing methods. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.
It will be appreciated by those skilled in the art that the schematic block diagram shown in fig. 5 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the smart terminal to which the present inventive arrangements are applied, and that a particular smart terminal may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, there is provided a smart terminal including a memory, a processor, and a community resident population microscopic data synthesizing program stored on the memory and executable on the processor, the community resident population microscopic data synthesizing program when executed by the processor performing the following operation instructions:
acquiring a community population microscopic data set based on administrative region macroscopic population data and community microscopic population sampling data according to an iterative proportion updating algorithm, wherein the community population microscopic data set comprises household information and liveness information;
inputting the community population microscopic data set into a neural network model to obtain a prediction model for predicting the relationship between families and living things;
based on the prediction model and constraint conditions formed by the real capacities of various living things, distributing all family samples in the community population microscopic data set to the various living things by taking the minimum utility loss as a target, and obtaining community resident population microscopic data with space information and resident attributes.
Optionally, the input layer of the neural network model extracts family features and span features, where the family features include: the number of family population, the highest education level, the maximum age, and the interplanting structure; the living characteristics include: a living house, a living rent, and property rights.
Optionally, the obtaining the community population micro data set according to the iterative proportion updating algorithm based on the administrative region macro demographic data and the community micro population sampling data includes:
obtaining a frequency matrix according to the relation between family classification and population characteristics in microscopic population sampling data;
based on the frequency matrix, obtaining a joint distribution value corresponding to each human mouth characteristic according to an iterative proportion updating algorithm;
circularly iterating and calculating the fitting degree of each iteration according to the frequency matrix and the joint distribution value until the fitting degree is smaller than a set threshold value, so as to obtain probability distribution of each family classification;
based on the probability distribution, adopting a Monte Carlo method to randomly extract family samples to the community population microscopic data set.
Optionally, the expression for calculating the fitting degree is:
wherein sigma is the fitting degree, D ij For a frequency matrix, C j For the joint distribution value, m is the number of human mouth features, and W is a weight vector.
Optionally, the error function model of the neural network model is:
wherein t is n To be output, u n For actual output, N is the number of home samples.
Optionally, the distributing each family sample in the community population microscopic data set to each family based on the constraint condition formed by the prediction model and the real capacity of each family with minimum utility loss as a target to obtain community resident population microscopic data with space information and resident attributes includes:
based on constraint conditions of real capacities of various living things, constructing an objective function of a dynamic optimization model by taking utility loss minimization as a target, wherein the dynamic optimization model is used for optimizing and distributing each living things;
and obtaining the characteristics of various livestocks, inputting the characteristics and the prediction results of the prediction model into a dynamic optimization model, and obtaining microscopic data of the community resident population.
Optionally, the expression of the objective function is:
wherein A is ik Selecting a probability of k-class livers for the predicted i-th class of households, i=1, 2, 3..m, M being the number of family classes; b (B) ik K=1, 2,3,4 for the 4 attributes corresponding to class i family; d (D) ij The number of livers allocated to the j-th class for the i-th class family, i=1, 2, 3..m, j=1, 2, 3..n, N being the number of liver types; p (P) i =0 or 1 indicates whether the span is selected during the allocation process, i=1, 2,..n; w (W) k As the weight of the kth living attribute,is P i Is a mean value of (c).
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a community resident population microscopic data synthesis program, and the community resident population microscopic data synthesis program realizes the steps of any community resident population microscopic data synthesis method provided by the embodiment of the invention when being executed by a processor.
It should be understood that the sequence number of each step in the above embodiment does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not be construed as limiting the implementation process of the embodiment of the present invention.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units described above is merely a logical function division, and may be implemented in other manners, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed.
The integrated modules/units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of each method embodiment may be implemented. The computer program comprises computer program code, and the computer program code can be in a source code form, an object code form, an executable file or some intermediate form and the like. The computer readable medium may include: any entity or device capable of carrying the computer program code described above, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. The content of the computer readable storage medium can be appropriately increased or decreased according to the requirements of the legislation and the patent practice in the jurisdiction.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions are not intended to depart from the spirit and scope of the various embodiments of the invention, which are also within the spirit and scope of the invention.
Claims (8)
1. The community resident population microscopic data synthesis method is characterized by comprising the following steps of:
acquiring a community population microscopic data set based on administrative region macroscopic population data and community microscopic population sampling data according to an iterative proportion updating algorithm, wherein the community population microscopic data set comprises household information and liveness information;
inputting the community population microscopic data set into a neural network model to obtain a prediction model for predicting the relationship between families and living, wherein the prediction result of the prediction model is the living selection probability of each classified family sample;
based on the prediction model and constraint conditions formed by the real capacities of various living things, distributing all family samples in the community population microscopic data set to the various living things by taking the minimum utility loss as a target to obtain community resident population microscopic data with space information and resident attributes;
the constraint condition formed based on the prediction model and the real capacity of each type of residential span, which is to distribute each family sample in the community population microscopic data set to each type of residential span with the minimum utility loss as a target, obtains community resident population microscopic data with space information and resident attributes, and comprises the following steps:
based on constraint conditions of real capacities of various living things, constructing an objective function of a dynamic optimization model by taking utility loss minimization as a target, wherein the dynamic optimization model is used for optimizing and distributing each living things;
acquiring characteristics of various livestocks, inputting the characteristics and the prediction results of the prediction model into a dynamic optimization model, and acquiring microscopic data of population of the community residents;
the expression of the objective function is:
wherein A is ik Selecting a probability of k-class livers for the predicted i-th class of households, i=1, 2, 3..m, M being the number of family classes; b (B) ik K=1, 2,3,4 for the 4 attributes corresponding to class i family; d (D) ij The number of livers allocated to the j-th class for the i-th class family, i=1, 2, 3..m, j=1, 2, 3..n, N being the number of liver types; p (P) i =0 or 1 indicates whether the span is selected during the allocation process, i=1, 2,..n; w (W) k As the weight of the kth living attribute,is P i Is a mean value of (c).
2. The community-resident-population microscopic data synthesizing method of claim 1, wherein the input layer of the neural network model extracts family features and living features, the family features comprising: the number of family population, the highest education level, the maximum age, and the interplanting structure; the living characteristics include: a living house, a living rent, and property rights.
3. The method for synthesizing microscopic data of community resident population as in claim 1, wherein the obtaining the microscopic data set of community population according to the iterative scale updating algorithm based on the macroscopic demographic data of administrative area and the microscopic population sampling data of community comprises:
obtaining a frequency matrix according to the relation between family classification and population characteristics in microscopic population sampling data;
based on the frequency matrix, obtaining a joint distribution value corresponding to each human mouth characteristic according to an iterative proportion updating algorithm;
circularly iterating and calculating the fitting degree of each iteration according to the frequency matrix and the joint distribution value until the fitting degree is smaller than a set threshold value, so as to obtain probability distribution of each family classification;
based on the probability distribution, adopting a Monte Carlo method to randomly extract family samples to the community population microscopic data set.
4. The community-resident-population microscopic data synthesizing method of claim 3, wherein the expression for calculating the fitness is:
wherein sigma is the fitting degree, D ij For a frequency matrix, C j For the joint distribution value, m is the number of human mouth features, and W is a weight vector.
5. The community-resident-population microscopic data synthesizing method of claim 1, wherein the error function model of the neural network model is:
wherein t is n To be output, u n For actual output, N is the number of home samples.
6. A community resident population microscopic data synthesizing device, the device comprising:
the community population microscopic data set acquisition module is used for acquiring a community population microscopic data set based on administrative region macroscopic demographic data and community microscopic population sampling data according to an iterative proportion updating algorithm, wherein the community population microscopic data set comprises household information and family information;
the prediction module is used for inputting the community population microscopic data set into a neural network model to obtain a prediction model for predicting the relationship between families and livers;
the distribution module is used for distributing each family sample in the community population microscopic data set to each type of the livers based on the prediction model and constraint conditions formed by the real capacities of each type of livers, and obtaining community resident population microscopic data with space information and resident attributes by taking the minimum utility loss as a target;
the constraint condition formed based on the prediction model and the real capacity of each type of residential span, which is to distribute each family sample in the community population microscopic data set to each type of residential span with the minimum utility loss as a target, obtains community resident population microscopic data with space information and resident attributes, and comprises the following steps:
based on constraint conditions of real capacities of various living things, constructing an objective function of a dynamic optimization model by taking utility loss minimization as a target, wherein the dynamic optimization model is used for optimizing and distributing each living things;
acquiring characteristics of various livestocks, inputting the characteristics and the prediction results of the prediction model into a dynamic optimization model, and acquiring microscopic data of population of the community residents;
the expression of the objective function is:
wherein A is ik Selecting a probability of k-class livers for the predicted i-th class of households, i=1, 2, 3..m, M being the number of family classes; d (D) ik K=1, 2,3,4 for the 4 attributes corresponding to class i family; d (D) ij The number of livers allocated to the j-th class for the i-th class family, i=1, 2, 3..m, j=1, 2, 3..n, N being the number of liver types; p (P) i =0 or 1 indicates whether the span is selected during the allocation process, i=1, 2,..n; w (W) k As the weight of the kth living attribute,is P i Is a mean value of (c).
7. A smart terminal comprising a memory, a processor, and a community-resident-population microscopic-data synthesizing program stored on the memory and operable on the processor, the community-resident-population microscopic-data synthesizing program, when executed by the processor, implementing the steps of the community-resident-population microscopic-data synthesizing method of any one of claims 1-5.
8. A computer-readable storage medium, wherein a community-resident-population microscopic data synthesizing program is stored on the computer-readable storage medium, which when executed by a processor, implements the steps of the community-resident-population microscopic data synthesizing method according to any one of claims 1 to 5.
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