CN116883219A - Digital rural population information management method and system based on big data - Google Patents
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Abstract
The application discloses a digital rural population information management method and system based on big data, and relates to the technical field of data processing. The method comprises the following steps: acquiring and importing historical rural population sample data into a preset feature extraction model to obtain population feature data; classifying the demographic data to obtain a plurality of categories of demographic data; constructing a data acquisition architecture; acquiring and constructing a data management model according to the real-time management requirements and the categories of population characteristic data, wherein the data management model comprises a plurality of management nodes; and acquiring target rural population data of the target area in real time based on the data acquisition framework, importing the target rural population data into a data management model, and storing and managing the population data based on the data management model. The application constructs an accurate data acquisition architecture and a data management architecture, comprehensively and accurately acquires rural population data from multiple aspects, and greatly improves management efficiency and effect.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a digital rural population information management method and system based on big data.
Background
The digital village is an application in the economic and social development of agricultural rural areas along with networking, informatization and digitalization, and an agricultural rural modern development and transformation process which is generated due to improvement of modern information skills of peasants, and is an important content for building digital China. The digital village is the development digitization of the village, and is the process of enabling and remodelling the operation and development of the village economy and society through the integration of modern information technology and means such as big data, the Internet, intelligentization, block chain and the like. The digital village is also a process of adaptation of the village to the digital revolution, and is a process of application and innovation of the village to digital technology and digital business.
In the digital rural construction process, one of the important points is to manage population information, but because of the complicated rural condition, comprehensive and accurate population data cannot be clearly and effectively acquired, so that population management is difficult, and management efficiency is low; therefore, how to accurately and comprehensively collect rural population information and accurately analyze data later so as to better effectively manage rural population and promote the construction of digital rural areas becomes a problem to be solved urgently.
Disclosure of Invention
In order to overcome the problems or at least partially solve the problems, the embodiment of the application provides a digital rural population information management method and system based on big data, which combines the characteristics of the rural area to construct an accurate data acquisition architecture and an accurate data management architecture, comprehensively and accurately acquire the rural population data from multiple aspects, and greatly improve the management efficiency and effect.
Embodiments of the present application are implemented as follows:
in a first aspect, an embodiment of the present application provides a digital rural population information management method based on big data, including the steps of:
acquiring and importing historical rural population sample data into a preset feature extraction model to obtain population feature data;
classifying the demographic data according to preset category indexes to obtain demographic data of a plurality of categories;
constructing a data acquisition framework according to different types of demographic data, wherein the data acquisition framework comprises an acquisition mode and corresponding acquisition equipment;
acquiring and constructing a data management model according to the real-time management requirements and the categories of population characteristic data, wherein the data management model comprises a plurality of management nodes;
and acquiring target rural population data of the target area in real time based on the data acquisition framework, importing the target rural population data into a data management model, and storing and managing the population data based on the data management model.
In order to solve the problems in the prior art, the method combines historical data to perform data analysis based on a preset feature extraction model, further analyzes to obtain population feature data, performs classification processing on the population feature data to obtain population feature data of multiple categories, further subsequently builds a targeted data acquisition architecture to provide accurate data reference, facilitates the subsequent rapid building of a reasonable data acquisition architecture according to the population feature categories, takes the categories as nodes, and configures a plurality of acquisition devices corresponding to the association under each node; in order to further improve the management effect of the data country population data, a targeted comprehensive data management model is built by combining real-time management requirements and population feature categories so as to facilitate efficient population data management subsequently; when population data is required to be managed, corresponding collection equipment is called based on a data collection framework to collect different types of population data in a targeted mode, then the collected data is imported into a data management model, and the population data is stored and managed in a targeted mode based on the data management model. The application combines the characteristics of the village to construct an accurate data acquisition architecture and a data management architecture, comprehensively and accurately acquire the population data of the village from multiple aspects, and greatly improves the management efficiency and effect.
Based on the first aspect, in some embodiments of the present application, the method for constructing a data collection architecture according to different categories of demographic data includes the steps of:
constructing initial category collection nodes according to different categories of demographic data;
matching corresponding acquisition schemes in a preset acquisition configuration database according to the categories of the population characteristic data;
and constructing a data acquisition architecture according to the corresponding acquisition scheme and the initial category acquisition node.
Based on the first aspect, in some embodiments of the present application, the method for constructing a data management model according to the real-time management needs and the category of the demographic data includes the following steps:
analyzing the real-time management requirements to obtain and construct an initial management framework according to the requirement characteristics;
constructing a category management node according to the category of the population characteristic data;
the category management nodes are imported into the initial management architecture to build a data management model.
Based on the first aspect, in some embodiments of the present application, the method for collecting target country population data of a target area in real time based on the data collection architecture includes the following steps:
and acquiring data of people with different data characteristics in the target area according to a corresponding acquisition mode based on acquisition equipment in the data acquisition architecture so as to obtain the population data of the target village.
Based on the first aspect, in some embodiments of the application, the big data based digital rural population information management method further comprises the steps of:
and encrypting the population data under each storage node based on the data management model calling encryption key.
Based on the first aspect, in some embodiments of the application, the big data based digital rural population information management method further comprises the steps of:
obtaining geographic condition information of a target area, and constructing a target three-dimensional map;
and analyzing the population data of the target village based on the data management model, carrying out position coding on each person, importing the corresponding person position coding into a target three-dimensional map, and generating and displaying the distribution condition information of the person.
In a second aspect, an embodiment of the present application provides a digital rural population information management system based on big data, including: the system comprises a feature analysis module, a classification processing module, an acquisition architecture construction module, a management model construction module and a data management module, wherein:
the characteristic analysis module is used for acquiring and importing historical rural population sample data into a preset characteristic extraction model to obtain population characteristic data;
the classification processing module is used for classifying the population characteristic data according to preset category indexes to obtain population characteristic data of a plurality of categories;
the acquisition framework construction module is used for constructing a data acquisition framework according to different types of population characteristic data, and the data acquisition framework comprises an acquisition mode and corresponding acquisition equipment;
the management model construction module is used for acquiring and constructing a data management model according to the real-time management requirements and the categories of the population characteristic data, and the data management model comprises a plurality of management nodes;
and the data management module is used for acquiring the target rural population data of the target area in real time based on the data acquisition framework, importing the target rural population data into the data management model, and storing and managing the population data based on the data management model.
In order to solve the problems in the prior art, the system performs data analysis by combining historical data based on a preset feature extraction model through the cooperation of a plurality of modules such as a feature analysis module, a classification processing module, an acquisition architecture construction module, a management model construction module and a data management module, further analyzes and obtains population feature data, and performs classification processing on the population feature data to obtain population feature data of a plurality of categories, further provides accurate data reference for the subsequent construction of a targeted data acquisition architecture, facilitates the subsequent rapid construction of a reasonable data acquisition architecture according to the population feature categories, takes the categories as nodes, and configures a plurality of acquisition devices corresponding in an associated mode under each node; in order to further improve the management effect of the data country population data, a targeted comprehensive data management model is built by combining real-time management requirements and population feature categories so as to facilitate efficient population data management subsequently; when population data is required to be managed, corresponding collection equipment is called based on a data collection framework to collect different types of population data in a targeted mode, then the collected data is imported into a data management model, and the population data is stored and managed in a targeted mode based on the data management model. The application combines the characteristics of the village to construct an accurate data acquisition architecture and a data management architecture, comprehensively and accurately acquire the population data of the village from multiple aspects, and greatly improves the management efficiency and effect.
Based on the second aspect, in some embodiments of the present application, the acquisition architecture building module includes a node building unit, a scheme matching unit, and an architecture building unit, where:
the node construction unit is used for constructing initial category collection nodes according to different categories of demographic characteristic data;
the scheme matching unit is used for matching corresponding acquisition schemes in a preset acquisition configuration database according to the categories of the population characteristic data;
the architecture construction unit is used for constructing a data acquisition architecture according to the corresponding acquisition scheme and the initial category acquisition node.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory for storing one or more programs; a processor. The method of any of the first aspects described above is implemented when one or more programs are executed by a processor.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as in any of the first aspects described above.
The embodiment of the application has at least the following advantages or beneficial effects:
the embodiment of the application provides a digital rural population information management method and system based on big data, which are used for carrying out data analysis by combining historical data based on a preset feature extraction model, further analyzing to obtain population feature data, carrying out classification processing on the population feature data to obtain population feature data of multiple categories, further subsequently constructing a targeted data acquisition architecture to provide accurate data reference, and facilitating the subsequent rapid construction of a reasonable data acquisition architecture according to the population feature categories; constructing a targeted comprehensive data management model by combining real-time management requirements and population characteristics so as to facilitate efficient population data management subsequently; and acquiring population data of different categories by corresponding acquisition equipment in a targeted manner based on a data acquisition architecture, then importing the acquired data into a data management model, and storing and managing the population data in a targeted manner based on the data management model. The application combines the characteristics of the village to construct an accurate data acquisition architecture and a data management architecture, comprehensively and accurately acquire the population data of the village from multiple aspects, and greatly improves the management efficiency and effect.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a digital rural population information management method based on big data according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for constructing a data acquisition architecture in a digital rural population information management method based on big data according to an embodiment of the present application;
FIG. 3 is a flow chart of a method for managing digital rural population information based on big data for collecting target rural population data according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of a digital rural population information management system based on big data according to an embodiment of the present application;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 100. a feature analysis module; 200. a classification processing module; 300. the acquisition architecture construction module; 400. a management model construction module; 500. a data management module; 101. a memory; 102. a processor; 103. a communication interface.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the present application, "plurality" means at least 2.
Examples:
as shown in fig. 1 to 3, in a first aspect, an embodiment of the present application provides a digital rural population information management method based on big data, including the steps of:
s1, acquiring and importing historical rural population sample data into a preset feature extraction model to obtain population feature data; the demographic data includes demographic age distribution characteristics, gender characteristics, demographic location distribution characteristics, and the like.
S2, classifying the population characteristic data according to preset category indexes to obtain population characteristic data of a plurality of categories;
further, as shown in fig. 2, includes:
s21, constructing initial category collection nodes according to different categories of demographic data;
s22, matching corresponding acquisition schemes in a preset acquisition configuration database according to the category of the population characteristic data;
s23, constructing a data acquisition framework according to the corresponding acquisition scheme and the initial category acquisition node.
In some embodiments of the present application, in order to ensure that accurate population data acquisition can be performed in a subsequent step, an initial category acquisition node is constructed in combination with population feature categories, the category is used as a node identifier, and then acquisition schemes suitable for different categories are matched in a preset acquisition configuration database in combination with historical population data, wherein the acquisition schemes comprise feature data and corresponding acquisition equipment, equipment proportion, acquisition modes and the like; then, an effective data acquisition architecture is constructed by combining the corresponding acquisition scheme and the initial category acquisition node.
S3, constructing a data acquisition framework according to different types of demographic data, wherein the data acquisition framework comprises an acquisition mode and corresponding acquisition equipment;
s4, acquiring and constructing a data management model according to the real-time management requirements and the categories of the population characteristic data, wherein the data management model comprises a plurality of management nodes;
further, as shown in fig. 3, includes:
s41, analyzing the real-time management requirements to obtain and construct an initial management framework according to the requirement characteristics;
s42, constructing a category management node according to the category of the population characteristic data;
s43, importing the category management nodes into an initial management framework to construct a data management model.
In some embodiments of the present application, to further improve the demographic data management effect and management efficiency, in combination with real-time management requirements, an initial management architecture is constructed, where the initial management architecture includes a plurality of requirement management nodes with different requirement characteristics as nodes; after an initial management framework is constructed based on requirements, a plurality of category management nodes taking categories as nodes are constructed by combining population feature categories, and then the category management nodes and the requirement management nodes are combined, so that a comprehensive data management model is constructed. The plurality of category management nodes can be respectively associated with each demand management node according to different demands, and the category management nodes and the demand management nodes can be combined in a parallel mode, so that a corresponding data management model is obtained.
S5, acquiring target rural population data of the target area in real time based on the data acquisition framework, importing the target rural population data into a data management model, and storing and managing the population data based on the data management model.
Further, the method comprises the steps of: and acquiring data of people with different data characteristics in the target area according to a corresponding acquisition mode based on acquisition equipment in the data acquisition architecture so as to obtain the population data of the target village.
In some embodiments of the application, when population data acquisition is performed, different acquisition devices are called based on a data acquisition architecture to perform targeted and efficient data acquisition on population data with different characteristics in a target area according to a corresponding acquisition mode, so that efficient and accurate population data acquisition of a target village is realized.
In order to solve the problems in the prior art, the method combines historical data to perform data analysis based on a preset feature extraction model, further analyzes to obtain population feature data, performs classification processing on the population feature data to obtain population feature data of multiple categories, further subsequently builds a targeted data acquisition architecture to provide accurate data reference, facilitates the subsequent rapid building of a reasonable data acquisition architecture according to the population feature categories, takes the categories as nodes, and configures a plurality of acquisition devices corresponding to the association under each node; in order to further improve the management effect of the data country population data, a targeted comprehensive data management model is built by combining real-time management requirements and population feature categories so as to facilitate efficient population data management subsequently; when population data is required to be managed, corresponding collection equipment is called based on a data collection framework to collect different types of population data in a targeted mode, then the collected data is imported into a data management model, and the population data is stored and managed in a targeted mode based on the data management model. The application combines the characteristics of the village to construct an accurate data acquisition architecture and a data management architecture, comprehensively and accurately acquire the population data of the village from multiple aspects, and greatly improves the management efficiency and effect.
Based on the first aspect, in some embodiments of the application, the big data based digital rural population information management method further comprises the steps of:
and encrypting the population data under each storage node based on the data management model calling encryption key.
In order to ensure the authenticity and safety of population data, an encryption key is called based on a data management model to accurately encrypt the population data under each storage node, so that the data storage safety is further ensured, the data leakage or the tampering is prevented, and the management effect of a digital village is further improved.
Based on the first aspect, in some embodiments of the application, the big data based digital rural population information management method further comprises the steps of:
obtaining geographic condition information of a target area, and constructing a target three-dimensional map;
and analyzing the population data of the target village based on the data management model, carrying out position coding on each person, importing the corresponding person position coding into a target three-dimensional map, and generating and displaying the distribution condition information of the person.
In the construction of digital villages, in order to be convenient for effectively managing population data, timely and accurately control population data, a three-dimensional clear target three-dimensional map is constructed by combining actual geographic conditions, then the positions of people are combined, the positions of the people are marked and displayed in the target three-dimensional map, and further a map of clear and complete personnel distribution conditions is obtained, and the personnel distribution conditions are clearly displayed.
As shown in fig. 4, in a second aspect, an embodiment of the present application provides a digital rural population information management system based on big data, including: the system comprises a feature analysis module 100, a classification processing module 200, an acquisition architecture construction module 300, a management model construction module 400 and a data management module 500, wherein:
the feature analysis module 100 is configured to acquire and import historical rural population sample data into a preset feature extraction model to obtain population feature data;
the classification processing module 200 is configured to perform classification processing on the demographic data according to a preset category index, so as to obtain demographic data of a plurality of categories;
the acquisition architecture construction module 300 is configured to construct a data acquisition architecture according to different types of demographic data, where the data acquisition architecture includes an acquisition mode and corresponding acquisition equipment;
the management model construction module 400 is configured to acquire and construct a data management model according to the real-time management requirements and categories of demographic data, where the data management model includes a plurality of management nodes;
the data management module 500 is configured to collect target rural population data of the target area in real time based on the data collection architecture, import the target rural population data into the data management model, and store and manage the population data based on the data management model.
In order to solve the problems in the prior art, the system performs data analysis by combining historical data based on a preset feature extraction model through the cooperation of a plurality of modules such as a feature analysis module 100, a classification processing module 200, an acquisition architecture construction module 300, a management model construction module 400 and a data management module 500, further analyzes to obtain population feature data, performs classification processing on the population feature data to obtain population feature data of a plurality of categories, further provides accurate data reference for the subsequent construction of a targeted data acquisition architecture, facilitates the subsequent rapid construction of a reasonable data acquisition architecture according to the population feature categories, takes the categories as nodes, and configures a plurality of acquisition devices corresponding in an associated mode under each node; in order to further improve the management effect of the data country population data, a targeted comprehensive data management model is built by combining real-time management requirements and population feature categories so as to facilitate efficient population data management subsequently; when population data is required to be managed, corresponding collection equipment is called based on a data collection framework to collect different types of population data in a targeted mode, then the collected data is imported into a data management model, and the population data is stored and managed in a targeted mode based on the data management model. The application combines the characteristics of the village to construct an accurate data acquisition architecture and a data management architecture, comprehensively and accurately acquire the population data of the village from multiple aspects, and greatly improves the management efficiency and effect.
Based on the second aspect, in some embodiments of the present application, the acquisition architecture building module 300 includes a node building unit, a scheme matching unit, and an architecture building unit, where:
the node construction unit is used for constructing initial category collection nodes according to different categories of demographic characteristic data;
the scheme matching unit is used for matching corresponding acquisition schemes in a preset acquisition configuration database according to the categories of the population characteristic data;
the architecture construction unit is used for constructing a data acquisition architecture according to the corresponding acquisition scheme and the initial category acquisition node.
In order to ensure that accurate population data acquisition can be performed in a follow-up mode, an initial category acquisition node is constructed by combining population characteristic categories through cooperation of a plurality of units such as a node construction unit, a scheme matching unit and a framework construction unit, the categories are used as node identifiers, and then acquisition schemes suitable for different categories are matched in a preset acquisition configuration database by combining historical population data, wherein the acquisition schemes comprise characteristic data, corresponding acquisition equipment, equipment proportion and the like; then, an effective data acquisition architecture is constructed by combining the corresponding acquisition scheme and the initial category acquisition node.
As shown in fig. 5, in a third aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The method of any of the first aspects described above is implemented when one or more programs are executed by the processor 102.
And a communication interface 103, where the memory 101, the processor 102 and the communication interface 103 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules that are stored within the memory 101 for execution by the processor 102 to perform various functional applications and data processing. The communication interface 103 may be used for communication of signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 102 may be an integrated circuit chip with signal processing capabilities. The processor 102 may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method, system and method may be implemented in other manners. The above-described method and system embodiments are merely illustrative, for example, flow charts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by the processor 102, implements a method as in any of the first aspects described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above is only a preferred embodiment of the present application, and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A digital rural population information management method based on big data, comprising the following steps:
acquiring and importing historical rural population sample data into a preset feature extraction model to obtain population feature data;
classifying the demographic data according to preset category indexes to obtain demographic data of a plurality of categories;
constructing a data acquisition framework according to different types of demographic data, wherein the data acquisition framework comprises an acquisition mode and corresponding acquisition equipment;
acquiring and constructing a data management model according to the real-time management requirements and the categories of population characteristic data, wherein the data management model comprises a plurality of management nodes;
and acquiring target rural population data of the target area in real time based on the data acquisition framework, importing the target rural population data into a data management model, and storing and managing the population data based on the data management model.
2. The method of claim 1, wherein the method of constructing a data collection architecture from different categories of demographic data comprises the steps of:
constructing initial category collection nodes according to different categories of demographic data;
matching corresponding acquisition schemes in a preset acquisition configuration database according to the categories of the population characteristic data;
and constructing a data acquisition architecture according to the corresponding acquisition scheme and the initial category acquisition node.
3. The digital rural demographic information management method based on big data as claimed in claim 1, wherein said method for constructing a data management model according to real-time management requirements and categories of demographic data comprises the steps of:
analyzing the real-time management requirements to obtain and construct an initial management framework according to the requirement characteristics;
constructing a category management node according to the category of the population characteristic data;
the category management nodes are imported into the initial management architecture to build a data management model.
4. The digital rural population information management method based on big data according to claim 1, wherein the method for acquiring the target rural population data of the target area in real time based on the data acquisition architecture comprises the following steps:
and acquiring data of people with different data characteristics in the target area according to a corresponding acquisition mode based on acquisition equipment in the data acquisition architecture so as to obtain the population data of the target village.
5. The digital rural demographic information management method based on big data as recited in claim 1, further comprising the steps of:
and encrypting the population data under each storage node based on the data management model calling encryption key.
6. The digital rural demographic information management method based on big data as recited in claim 1, further comprising the steps of:
obtaining geographic condition information of a target area, and constructing a target three-dimensional map;
and analyzing the population data of the target village based on the data management model, carrying out position coding on each person, importing the corresponding person position coding into a target three-dimensional map, and generating and displaying the distribution condition information of the person.
7. A digital rural demographic information management system based on big data, comprising: the system comprises a feature analysis module, a classification processing module, an acquisition architecture construction module, a management model construction module and a data management module, wherein:
the characteristic analysis module is used for acquiring and importing historical rural population sample data into a preset characteristic extraction model to obtain population characteristic data;
the classification processing module is used for classifying the population characteristic data according to preset category indexes to obtain population characteristic data of a plurality of categories;
the acquisition framework construction module is used for constructing a data acquisition framework according to different types of population characteristic data, and the data acquisition framework comprises an acquisition mode and corresponding acquisition equipment;
the management model construction module is used for acquiring and constructing a data management model according to the real-time management requirements and the categories of the population characteristic data, and the data management model comprises a plurality of management nodes;
and the data management module is used for acquiring the target rural population data of the target area in real time based on the data acquisition framework, importing the target rural population data into the data management model, and storing and managing the population data based on the data management model.
8. The big data based digital rural demographic information management system of claim 7, wherein the acquisition architecture construction module comprises a node construction unit, a scheme matching unit, and an architecture construction unit, wherein:
the node construction unit is used for constructing initial category collection nodes according to different categories of demographic characteristic data;
the scheme matching unit is used for matching corresponding acquisition schemes in a preset acquisition configuration database according to the categories of the population characteristic data;
the architecture construction unit is used for constructing a data acquisition architecture according to the corresponding acquisition scheme and the initial category acquisition node.
9. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the method of any of claims 1-6 is implemented when the one or more programs are executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-6.
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