CN114328654A - Demographic method, system and readable storage medium based on big data - Google Patents
Demographic method, system and readable storage medium based on big data Download PDFInfo
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Abstract
The invention discloses a demographic method, a demographic system and a readable storage medium based on big data, which can be analyzed through dynamic and static demographic information data; the method and the device have the advantages that the predicted population information is obtained, the population information can be sent to a preset terminal to be displayed in a multi-dimensional mode, and using experience is improved. According to the invention, through the application of the neural network model, population information can be rapidly predicted, and the population prediction efficiency is improved. In addition, the invention can also carry out self-adaptive correction on the neural network model, thereby improving the accuracy of prediction.
Description
Technical Field
The invention relates to the field of analysis of big population data, in particular to a demographic method, a demographic system and a readable storage medium based on big data.
Background
Demographics is a method or study that studies demographics from the "volume" aspect. By demographics, the regularity of the demographic process and the nature of the demographic phenomenon can be revealed. In China, demographic statistics can be used for controlling population quantity and improving population quality, so that population development is adapted to economic and social development. The demand for population prediction is raised as socio-economic development progresses. The total population of a country directly influences the utilization of economic and social resources. Therefore, it is very important to be able to correctly predict the total statistical population of a country.
The traditional demographic means has a plurality of constraints of small sample size of data acquisition, lag of acquisition period, higher acquisition cost, overlong process time consumption, weak flexibility and the like. As the population flow increases, traditional demographic methods face significant challenges.
The prior art has defects and needs to be improved urgently.
Disclosure of Invention
The application aims to provide a demographic method, a demographic system and a readable storage medium based on big data, which are used as effective supplement of population work, combine with statistical principles, and conduct deep mining on population space-time trajectories, parking rules and the like by means of analysis of the big data, can dynamically monitor population change trends, and provide a big data plus decision basis for the population work.
The invention provides a big data-based demographic method in a first aspect, which comprises the following steps:
acquiring first category information and second category information of population;
big data analysis is carried out according to the first category information and the second category information to obtain population information;
and sending the population information to a preset terminal for multi-dimensional display.
In the scheme, the first type information is population dynamic information, and the second type information is population static information; the population dynamic information is one or more of dynamic survey information, recruitment information and dynamic position information; the population static information is one or more of static survey information, enterprise information and static location information.
In this scheme, the performing big data analysis according to the first type information and the second type information specifically includes:
inputting the first type information into a dynamic population neural network model to obtain first population information; and inputting the second type information into a static population neural network model to obtain second population information, and analyzing the first population information and the second population information to obtain predicted population information serving as population information.
In this scheme, still include:
acquiring population information data monitored by a current area or a preset rule in real time;
comparing the difference with the predicted population information to obtain a difference rate;
and if the difference rate is greater than a preset difference rate threshold value, correcting the dynamic population neural network model and the static population neural network model.
In this scheme, include:
acquiring input data of a dynamic population neural network model and a static population neural network model;
comparing the input data with corresponding data in the current region or population information data monitored by a preset rule to obtain difference information;
correcting a dynamic population neural network model and a static population neural network model according to the difference information;
acquiring error rates of a current dynamic population neural network model and a static population neural network model;
and if the error rate is less than a preset error rate threshold value, stopping correction to obtain a corrected dynamic population neural network model and a corrected static population neural network model.
In the scheme, the population information is sent to a preset terminal for multidimensional display, and the method specifically comprises the following steps: decomposing population information into a plurality of display information according to a preset dimension;
and displaying the plurality of display information according to a preset display rule.
A second aspect of the invention provides a big-data based demographic system, comprising: a memory including a program for big data based demographics, and a processor, wherein the program for big data based demographics, when executed by the processor, performs the steps of:
acquiring first category information and second category information of population;
big data analysis is carried out according to the first category information and the second category information to obtain population information;
and sending the population information to a preset terminal for multi-dimensional display.
In the scheme, the first type information is population dynamic information, and the second type information is population static information; the population dynamic information is one or more of dynamic survey information, recruitment information and dynamic position information; the population static information is one or more of static survey information, enterprise information and static location information.
In this scheme, the performing big data analysis according to the first type information and the second type information specifically includes:
inputting the first type information into a dynamic population neural network model to obtain first population information; and inputting the second type information into a static population neural network model to obtain second population information, and analyzing the first population information and the second population information to obtain predicted population information serving as population information.
In this scheme, still include:
acquiring population information data monitored by a current area or a preset rule in real time;
comparing the difference with the predicted population information to obtain a difference rate;
and if the difference rate is greater than a preset difference rate threshold value, correcting the dynamic population neural network model and the static population neural network model.
In this scheme, include:
acquiring input data of a dynamic population neural network model and a static population neural network model;
comparing the input data with corresponding data in the current region or population information data monitored by a preset rule to obtain difference information;
correcting a dynamic population neural network model and a static population neural network model according to the difference information;
acquiring error rates of a current dynamic population neural network model and a static population neural network model;
and if the error rate is less than a preset error rate threshold value, stopping correction to obtain a corrected dynamic population neural network model and a corrected static population neural network model.
In the scheme, the population information is sent to a preset terminal for multidimensional display, and the method specifically comprises the following steps: decomposing population information into a plurality of display information according to a preset dimension;
and displaying the plurality of display information according to a preset display rule.
A third aspect of the present invention provides a readable storage medium, wherein the readable storage medium includes a big data based demographic method program, and the big data based demographic method program, when executed by a processor, implements the steps of a big data based demographic method as described in any one of the above.
The invention discloses a demographic method, a demographic system and a readable storage medium based on big data, which can be analyzed through dynamic and static demographic information data; the method and the device have the advantages that the predicted population information is obtained, the population information can be sent to a preset terminal to be displayed in a multi-dimensional mode, and using experience is improved. According to the invention, through the application of the neural network model, population information can be rapidly predicted, and the population prediction efficiency is improved. In addition, the invention can also carry out self-adaptive correction on the neural network model, thereby improving the accuracy of prediction.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 illustrates a flow chart of a big data based demographics method of the present invention;
FIG. 2 illustrates a block diagram of a big data based demographics system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
FIG. 1 illustrates a flow chart of a big data based demographics method of the present invention.
As shown in FIG. 1, the invention discloses a big data-based demographic method, which comprises the following steps:
s102, acquiring first category information and second category information of a population;
s104, performing big data analysis according to the first type information and the second type information to obtain population information;
and S106, sending the population information to a preset terminal for multi-dimensional display.
The population information may be population inflow information, population outflow information, population living information, external population information, or the like. The acquired first category information and second category information of the population can be automatically captured through a third party platform or a government agency platform, and can be information of multiple dimensions. The preset terminal may be a government-related working organization or a manager terminal. The multi-dimensional display represents that the population data can be displayed in various fields, such as population mobility, population consumption index, tourist area population mobility and the like. The invention is not limited to the above dimensions and any solution relying on the method of the invention will fall within the scope of protection of the invention.
According to the embodiment of the invention, the first category information is population dynamic information, and the second category information is population static information; the population dynamic information is one or more of dynamic survey information, recruitment information and dynamic position information; the population static information is one or more of static survey information, enterprise information and static location information.
The population dynamic information is information reflecting dynamic changes of population, and may include population dynamic survey information, where the population dynamic survey information is dynamic change information obtained through surveys conducted by government agencies or third-party agencies, for example, information about the mobility and migration of population, and changes in home drop addresses. The recruitment information is also included, and the inflow of talents is reflected by the requirement of enterprises on the employment. The population dynamic information also includes dynamic location information, which can be determined by navigation software and communication base station parameters, for example, when a user drives from north to north, the inflow and outflow of the population can be obtained by the location information. The population static information includes static survey information, which is static variable information obtained through surveys conducted by government agencies or third-party agencies, such as information on the population of a certain area. The enterprise information specifically refers to the number of workers, employees and the like of the current enterprise, and can be acquired through a third party platform or a government agency platform, for example, the number of social security workers and the like can be acquired through the government agency. The population static information may also include static location information, which may be determined by navigation software and communication base station parameters, and may refer to movement within a predetermined area, for example, a predetermined area of a hai lake area, and the user may be considered to be static location information if the user does not go out of the hai lake area.
According to an embodiment of the present invention, the performing big data analysis according to the first kind of information and the second kind of information specifically includes:
inputting the first type information into a dynamic population neural network model to obtain first population information; and inputting the second type information into a static population neural network model to obtain second population information, and analyzing the first population information and the second population information to obtain predicted population information serving as population information.
It should be noted that the prediction and analysis of the population information in the invention are based on the artificial intelligence theory, and a dynamic population neural network model and a static population neural network model are trained in advance. Each neural network model can be provided with a plurality of sub-neural network models respectively, each sub-neural network model is independent, and finally, the result of each sub-neural network model can be input into the dynamic population neural network model or the static population neural network model to obtain corresponding population information. After the first population information and the second population information obtained by the neural network model are analyzed, population information is obtained through prediction, wherein the first population information and the second population information can contain various population characteristic information, and the predicted population information can also be information with various dimensions, such as population inflow, population outflow, population residence and the like. The first population information and the second population information are analyzed, and a weighted calculation mode can be adopted, specifically:
predicted population information is A first population information + B second population information
Wherein, a is the coefficient of the first population information, B is the coefficient of the second population information, and a and B are numbers greater than 0, which may be decimal numbers.
It should be noted that a and B may be dynamically changed, that is, may be changed according to actual situations, specifically:
acquiring population information actually monitored in a plurality of preset areas;
acquiring first population information and second population information of each preset area;
analyzing to obtain a coefficient of the first population information and a coefficient of the second population information of each preset area;
and analyzing the coefficient of the first population information and the coefficient of the second population information of each preset area to obtain a coefficient of recommending the first population information and a coefficient of recommending the second population information, wherein the coefficient of recommending the first population information is used as the coefficient of the first population information, and the coefficient of recommending the second population information is used as the coefficient of the second population information.
The present invention can also analyze real-time population data of different areas to obtain a recommended coefficient, which is used as a coefficient of population information of a local area. The plurality of regions may be preset by a person skilled in the art according to actual conditions, for example, the peripheral critical region of the local region is set as a plurality of preset regions, or a plurality of regions with similar characteristics are set as a plurality of preset regions. The corresponding coefficients may be extrapolated from the actual monitored demographic information and the corresponding first demographic information and second demographic information for each region. After the coefficients of the plurality of first population information and the coefficients of the plurality of second population information are obtained, the coefficients recommending the first population information and the coefficients recommending the second population information can be obtained through calculation by a weighted averaging method, the coefficients recommending the first population information are used as the coefficients of the first population information, and the coefficients recommending the second population information are used as the coefficients of the second population information.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring population information data monitored by a current area or a preset rule in real time;
comparing the difference with the predicted population information to obtain a difference rate;
and if the difference rate is greater than a preset difference rate threshold value, correcting the dynamic population neural network model and the static population neural network model.
It should be noted that, through the simulation analysis of the neural network model, some errors may exist, and if the existing errors are large, the neural network model needs to be adjusted to ensure the accuracy of prediction and analysis. Firstly, population information data monitored by a current area or a preset rule is obtained in real time, the monitoring is an accurate monitoring behavior after authentication, and real population information data can be reflected. The preset rules can be rules or dimensions set by those skilled in the art according to actual needs, such as population inflow information. And then comparing the population information with the predicted population information to obtain a difference rate, wherein the difference rate is as follows: (monitored demographic data-predicted demographic data)/monitored demographic data. The higher the difference rate is, the larger the difference between the real population information data and the predicted population information data is, and the dynamic population neural network model and the static population neural network model need to be corrected.
According to the embodiment of the invention, the method comprises the following steps:
acquiring input data of a dynamic population neural network model and a static population neural network model;
comparing the input data with corresponding data in the current region or population information data monitored by a preset rule to obtain difference information;
correcting a dynamic population neural network model and a static population neural network model according to the difference information;
acquiring error rates of a current dynamic population neural network model and a static population neural network model;
and if the error rate is less than a preset error rate threshold value, stopping correction to obtain a corrected dynamic population neural network model and a corrected static population neural network model.
It should be noted that, when the dynamic population neural network model and the static population neural network model are corrected, the input data of the dynamic population neural network model and the static population neural network model need to be acquired, the difference between the input data and the real data is compared to obtain difference information, and then the dynamic population neural network model and the static population neural network model are corrected according to the difference information, for example, the data of a communication base station in the dynamic population information is compared to obtain the difference information. And when the neural network model is corrected, detecting the error rate of the predicted population information output by the neural network model in real time, and stopping correction if the error rate is less than a preset error rate threshold value. The error rate threshold may be set at 5%.
According to the embodiment of the invention, the population information is sent to a preset terminal for multidimensional display, and the multidimensional display specifically comprises the following steps: decomposing population information into a plurality of display information according to a preset dimension;
and displaying the plurality of display information according to a preset display rule.
The population information may include information of multiple dimensions, and is decomposed into a plurality of display information according to preset dimensions, and then the display information is displayed separately, and a method such as a graph, a pie chart, a grid chart, or the like may be used.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring characteristic information of a plurality of preset areas;
comparing the feature information with the feature information of the current region to obtain feature similarity;
acquiring an area with the highest similarity to the current regional characteristic information as a first area;
and correcting the population information by taking the population information of the first area as reference information.
It should be noted that the present invention may also search for an area similar to the current analysis area, and perform population information correction by analyzing population information of the similar area. The characteristic information comprises various characteristics and can be parameters such as economy, geography, environment, population composition and the like, an area similar to the current area can be obtained through the characteristic information, then the population information of the area is used as reference information, and the population information is corrected through the reference information, so that the result is more accurate.
According to the embodiment of the invention, the method further comprises the following steps:
and acquiring the information of the first kind of information by adopting an x time interval, and acquiring the information of the second kind of information by adopting a y time interval. Wherein x time interval and y time interval can be determined by those skilled in the art according to the actual situation.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring enterprise information of a current area;
analyzing the enterprise information to obtain enterprise employment condition information;
and taking the enterprise employment condition information as a parameter to obtain population information.
It should be noted that when the population structure is considered, analysis needs to be performed in combination with the economic conditions of local enterprises, and the conditions of talent inflow and outflow can be judged by analyzing the current employment demand and the future employment demand of the enterprises, so as to assist in obtaining population information and increase the accuracy of the population information.
FIG. 2 illustrates a block diagram of a big data based demographics system of the present invention.
As shown in FIG. 2, the present invention discloses a big data based demographic system 2, comprising: a memory 21 and a processor 22, the memory including a program of big data based demographics methods, the program of big data based demographics methods when executed by the processor implementing the steps of:
acquiring first category information and second category information of population;
big data analysis is carried out according to the first category information and the second category information to obtain population information;
and sending the population information to a preset terminal for multi-dimensional display.
The population information may be population inflow information, population outflow information, population living information, external population information, or the like. The acquired first category information and second category information of the population can be automatically captured through a third party platform or a government agency platform, and can be information of multiple dimensions. The preset terminal may be a government-related working organization or a manager terminal. The multi-dimensional display represents that the population data can be displayed in various fields, such as population mobility, population consumption index, tourist area population mobility and the like. The invention is not limited to the above dimensions and any solution relying on the method of the invention will fall within the scope of protection of the invention.
According to the embodiment of the invention, the first category information is population dynamic information, and the second category information is population static information; the population dynamic information is one or more of dynamic survey information, recruitment information and dynamic position information; the population static information is one or more of static survey information, enterprise information and static location information.
The population dynamic information is information reflecting dynamic changes of population, and may include population dynamic survey information, where the population dynamic survey information is dynamic change information obtained through surveys conducted by government agencies or third-party agencies, for example, information about the mobility and migration of population, and changes in home drop addresses. The recruitment information is also included, and the inflow of talents is reflected by the requirement of enterprises on the employment. The population dynamic information also includes dynamic location information, which can be determined by navigation software and communication base station parameters, for example, when a user drives from north to north, the inflow and outflow of the population can be obtained by the location information. The population static information includes static survey information, which is static variable information obtained through surveys conducted by government agencies or third-party agencies, such as information on the population of a certain area. The enterprise information specifically refers to the number of workers, employees and the like of the current enterprise, and can be acquired through a third party platform or a government agency platform, for example, the number of social security workers and the like can be acquired through the government agency. The population static information may also include static location information, which may be determined by navigation software and communication base station parameters, and may refer to movement within a predetermined area, for example, a predetermined area of a hai lake area, and the user may be considered to be static location information if the user does not go out of the hai lake area.
According to an embodiment of the present invention, the performing big data analysis according to the first kind of information and the second kind of information specifically includes:
inputting the first type information into a dynamic population neural network model to obtain first population information; and inputting the second type information into a static population neural network model to obtain second population information, and analyzing the first population information and the second population information to obtain predicted population information serving as population information.
It should be noted that the prediction and analysis of the population information in the invention are based on the artificial intelligence theory, and a dynamic population neural network model and a static population neural network model are trained in advance. Each neural network model can be provided with a plurality of sub-neural network models respectively, each sub-neural network model is independent, and finally, the result of each sub-neural network model can be input into the dynamic population neural network model or the static population neural network model to obtain corresponding population information. After the first population information and the second population information obtained by the neural network model are analyzed, population information is obtained through prediction, wherein the first population information and the second population information can contain various population characteristic information, and the predicted population information can also be information with various dimensions, such as population inflow, population outflow, population residence and the like. The first population information and the second population information are analyzed, and a weighted calculation mode can be adopted, specifically:
predicted population information is A first population information + B second population information
Wherein, a is the coefficient of the first population information, B is the coefficient of the second population information, and a and B are numbers greater than 0, which may be decimal numbers.
It should be noted that a and B may be dynamically changed, that is, may be changed according to actual situations, specifically:
acquiring population information actually monitored in a plurality of preset areas;
acquiring first population information and second population information of each preset area;
analyzing to obtain a coefficient of the first population information and a coefficient of the second population information of each preset area;
and analyzing the coefficient of the first population information and the coefficient of the second population information of each preset area to obtain a coefficient of recommending the first population information and a coefficient of recommending the second population information, wherein the coefficient of recommending the first population information is used as the coefficient of the first population information, and the coefficient of recommending the second population information is used as the coefficient of the second population information.
The present invention can also analyze real-time population data of different areas to obtain a recommended coefficient, which is used as a coefficient of population information of a local area. The plurality of regions may be preset by a person skilled in the art according to actual conditions, for example, the peripheral critical region of the local region is set as a plurality of preset regions, or a plurality of regions with similar characteristics are set as a plurality of preset regions. The corresponding coefficients may be extrapolated from the actual monitored demographic information and the corresponding first demographic information and second demographic information for each region. After the coefficients of the plurality of first population information and the coefficients of the plurality of second population information are obtained, the coefficients recommending the first population information and the coefficients recommending the second population information can be obtained through calculation by a weighted averaging method, the coefficients recommending the first population information are used as the coefficients of the first population information, and the coefficients recommending the second population information are used as the coefficients of the second population information.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring population information data monitored by a current area or a preset rule in real time;
comparing the difference with the predicted population information to obtain a difference rate;
and if the difference rate is greater than a preset difference rate threshold value, correcting the dynamic population neural network model and the static population neural network model.
It should be noted that, through the simulation analysis of the neural network model, some errors may exist, and if the existing errors are large, the neural network model needs to be adjusted to ensure the accuracy of prediction and analysis. Firstly, population information data monitored by a current area or a preset rule is obtained in real time, the monitoring is an accurate monitoring behavior after authentication, and real population information data can be reflected. The preset rules can be rules or dimensions set by those skilled in the art according to actual needs, such as population inflow information. And then comparing the population information with the predicted population information to obtain a difference rate, wherein the difference rate is as follows: (monitored demographic data-predicted demographic data)/monitored demographic data. The higher the difference rate is, the larger the difference between the real population information data and the predicted population information data is, and the dynamic population neural network model and the static population neural network model need to be corrected.
According to the embodiment of the invention, the method comprises the following steps:
acquiring input data of a dynamic population neural network model and a static population neural network model;
comparing the input data with corresponding data in the current region or population information data monitored by a preset rule to obtain difference information;
correcting a dynamic population neural network model and a static population neural network model according to the difference information;
acquiring error rates of a current dynamic population neural network model and a static population neural network model;
and if the error rate is less than a preset error rate threshold value, stopping correction to obtain a corrected dynamic population neural network model and a corrected static population neural network model.
It should be noted that, when the dynamic population neural network model and the static population neural network model are corrected, the input data of the dynamic population neural network model and the static population neural network model need to be acquired, the difference between the input data and the real data is compared to obtain difference information, and then the dynamic population neural network model and the static population neural network model are corrected according to the difference information, for example, the data of a communication base station in the dynamic population information is compared to obtain the difference information. And when the neural network model is corrected, detecting the error rate of the predicted population information output by the neural network model in real time, and stopping correction if the error rate is less than a preset error rate threshold value. The error rate threshold may be set at 5%.
According to the embodiment of the invention, the population information is sent to a preset terminal for multidimensional display, and the multidimensional display specifically comprises the following steps: decomposing population information into a plurality of display information according to a preset dimension;
and displaying the plurality of display information according to a preset display rule.
The population information may include information of multiple dimensions, and is decomposed into a plurality of display information according to preset dimensions, and then the display information is displayed separately, and a method such as a graph, a pie chart, a grid chart, or the like may be used.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring characteristic information of a plurality of preset areas;
comparing the feature information with the feature information of the current region to obtain feature similarity;
acquiring an area with the highest similarity to the current regional characteristic information as a first area;
and correcting the population information by taking the population information of the first area as reference information.
It should be noted that the present invention may also search for an area similar to the current analysis area, and perform population information correction by analyzing population information of the similar area. The characteristic information comprises various characteristics and can be parameters such as economy, geography, environment, population composition and the like, an area similar to the current area can be obtained through the characteristic information, then the population information of the area is used as reference information, and the population information is corrected through the reference information, so that the result is more accurate.
According to the embodiment of the invention, the method further comprises the following steps:
and acquiring the information of the first kind of information by adopting an x time interval, and acquiring the information of the second kind of information by adopting a y time interval. Wherein x time interval and y time interval can be determined by those skilled in the art according to the actual situation.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring enterprise information of a current area;
analyzing the enterprise information to obtain enterprise employment condition information;
and taking the enterprise employment condition information as a parameter to obtain population information.
It should be noted that when the population structure is considered, analysis needs to be performed in combination with the economic conditions of local enterprises, and the conditions of talent inflow and outflow can be judged by analyzing the current employment demand and the future employment demand of the enterprises, so as to assist in obtaining population information and increase the accuracy of the population information.
A third aspect of the present invention provides a readable storage medium, wherein the readable storage medium includes a big data based demographic method program, and the big data based demographic method program, when executed by a processor, implements the steps of a big data based demographic method as described in any one of the above.
The invention discloses a demographic method, a demographic system and a readable storage medium based on big data, which can be analyzed through dynamic and static demographic information data; the method and the device have the advantages that the predicted population information is obtained, the population information can be sent to a preset terminal to be displayed in a multi-dimensional mode, and using experience is improved. According to the invention, through the application of the neural network model, population information can be rapidly predicted, and the population prediction efficiency is improved. In addition, the invention can also carry out self-adaptive correction on the neural network model, thereby improving the accuracy of prediction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A big-data based demographic method, comprising:
acquiring first category information and second category information of population;
big data analysis is carried out according to the first category information and the second category information to obtain population information;
sending the population information to a preset terminal for multi-dimensional display;
the first kind information is population dynamic information, and the second kind information is population static information; the population dynamic information is one or more of dynamic survey information, recruitment information and dynamic position information; the population static information is one or more of static survey information, enterprise information and static location information.
2. A big data based demographic method as claimed in claim 1, wherein the big data analysis is performed according to the first category information and the second category information, specifically:
inputting the first type information into a dynamic population neural network model to obtain first population information; and inputting the second type information into a static population neural network model to obtain second population information, and analyzing the first population information and the second population information to obtain predicted population information serving as population information.
3. The big-data based demographic method of claim 1, further comprising:
acquiring population information data monitored by a current area or a preset rule in real time;
comparing the difference with the predicted population information to obtain a difference rate;
and if the difference rate is greater than a preset difference rate threshold value, correcting the dynamic population neural network model and the static population neural network model.
4. A big data based demographic method as defined in claim 3, comprising:
acquiring input data of a dynamic population neural network model and a static population neural network model;
comparing the input data with corresponding data in the current region or population information data monitored by a preset rule to obtain difference information;
correcting a dynamic population neural network model and a static population neural network model according to the difference information;
acquiring error rates of a current dynamic population neural network model and a static population neural network model;
and if the error rate is less than a preset error rate threshold value, stopping correction to obtain a corrected dynamic population neural network model and a corrected static population neural network model.
5. The big data-based demographic method according to claim 1, wherein the demographic information is sent to a preset terminal for multidimensional display, specifically: decomposing population information into a plurality of display information according to a preset dimension;
and displaying the plurality of display information according to a preset display rule.
6. A big data based demographic system, the system comprising: a memory including a program for big data based demographics, and a processor, wherein the program for big data based demographics, when executed by the processor, performs the steps of:
acquiring first category information and second category information of population;
big data analysis is carried out according to the first category information and the second category information to obtain population information;
sending the population information to a preset terminal for multi-dimensional display;
the first kind information is population dynamic information, and the second kind information is population static information; the population dynamic information is one or more of dynamic survey information, recruitment information and dynamic position information; the population static information is one or more of static survey information, enterprise information and static location information.
7. A big data based demographic system as claimed in claim 6, wherein the big data analysis based on the first category information and the second category information is specifically:
inputting the first type information into a dynamic population neural network model to obtain first population information; and inputting the second type information into a static population neural network model to obtain second population information, and analyzing the first population information and the second population information to obtain predicted population information serving as population information.
8. The big-data based demographic system of claim 6, further comprising:
acquiring population information data monitored by a current area or a preset rule in real time;
comparing the difference with the predicted population information to obtain a difference rate;
and if the difference rate is greater than a preset difference rate threshold value, correcting the dynamic population neural network model and the static population neural network model.
9. A big-data based demographic system as recited in claim 8, comprising:
acquiring input data of a dynamic population neural network model and a static population neural network model;
comparing the input data with corresponding data in the current region or population information data monitored by a preset rule to obtain difference information;
correcting a dynamic population neural network model and a static population neural network model according to the difference information;
acquiring error rates of a current dynamic population neural network model and a static population neural network model;
and if the error rate is less than a preset error rate threshold value, stopping correction to obtain a corrected dynamic population neural network model and a corrected static population neural network model.
10. A readable storage medium, comprising a big data based demographics program which, when executed by a processor, performs the steps of a big data based demographics method as claimed in any one of claims 1 to 5.
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