An urban-rural spatial control method and system based on big data technology

Abstract

The present invention discloses an urban and rural spatial control method and system based on big data technology, which relates to the technical field of data processing. The method includes: constructing a spatial control simulation model based on the urban and rural spatial region resource data information, spatial region collaborative regulation criteria, and regional planning target information obtained by big data; performing simulation on the spatial control simulation model according to the location advantages and preset stage planning targets, and outputting a spatial region control plan; conducting a feasibility analysis on the spatial region control plan, and optimizing and adjusting the spatial region control plan based on the feasibility coefficient; implementing control on the urban and rural spatial region to be controlled based on the optimized and adjusted spatial region control plan, and performing intelligent supervision through the big data intelligent supervision module. The technical effect of improving the spatial control level and control efficiency by using big data technology to apply control to the urban and rural space, and further improving the effectiveness and rationality of the urban and rural spatial planning control quality is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to an urban and rural space control method and system based on big data technology. Background Art

[0002] Urban and rural space control takes the cross - administrative boundary spatial resources, environmental protection and development coordination as the main objects, conducts unified protection and rights and interests distribution of cross - administrative boundary spatial resources, coordinates the interests of multiple organizations, promotes the comprehensive integration of production factors such as capital, land, labor, technology, and information, so as to facilitate the implementation of important cross - regional projects and decisions. Therefore, clarifying the urban and rural space control plan has important practical significance for the reasonable construction and development of urban and rural areas.

[0003] However, the existing technology has technical problems such as many space control elements and long time consumption, which affect the quality and efficiency of urban and rural control. Summary of the Invention

[0004] This application provides an urban and rural space control method and system based on big data technology, solves the technical problems of many space control elements and long time consumption in the existing technology, which affect the quality and efficiency of urban and rural control, and achieves the technical effect of improving the space control level and efficiency through the application of big data technology to control urban and rural space, and further improving the effectiveness and rationality of the quality of urban and rural space planning control.

[0005] In view of the above problems, the present invention provides an urban and rural space control method and system based on big data technology.

[0006] In the first aspect, this application provides an urban and rural space control method based on big data technology. The method includes: collecting information on the urban and rural space area to be controlled through big data to obtain urban and rural space area resource data information; obtaining the spatial area collaborative regulation criteria and regional planning target information of the urban and rural space area to be controlled; constructing a space control simulation model based on the urban and rural space area resource data information, the spatial area collaborative regulation criteria, and the regional planning target information; obtaining the location advantages and preset stage planning targets of the urban and rural space area to be controlled; performing simulation on the space control simulation model according to the location advantages and the preset stage planning targets, and outputting a spatial area control plan; performing feasibility analysis on the spatial area control plan to obtain a feasibility coefficient, and optimizing and adjusting the spatial area control plan based on the feasibility coefficient; implementing control on the urban and rural space area to be controlled based on the optimized and adjusted spatial area control plan, and performing intelligent supervision on the control process through a big data intelligent supervision module.

[0007] On the other hand, the present application also provides an urban-rural space control system based on big data technology. The system includes: an information collection module for collecting information on the urban-rural space area to be controlled through big data to obtain urban-rural space area resource data information; a data acquisition module for acquiring the spatial area collaborative regulation criteria and regional planning target information of the urban-rural space area to be controlled; a model construction module for constructing a space control simulation model based on the urban-rural space area resource data information, the spatial area collaborative regulation criteria, and the regional planning target information; an information acquisition module for acquiring the location advantages and preset stage planning targets of the urban-rural space area to be controlled; a simulation module for performing simulation on the space control simulation model according to the location advantages and the preset stage planning targets, and outputting a spatial area control plan; an optimization and adjustment module for performing feasibility analysis on the spatial area control plan to obtain a feasibility coefficient, and optimizing and adjusting the spatial area control plan based on the feasibility coefficient; and an intelligent supervision module for implementing control on the urban-rural space area to be controlled based on the optimized and adjusted spatial area control plan, and intelligently supervising the control process through the big data intelligent supervision module.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] By adopting the technical solution of constructing a space control simulation model based on the urban-rural space area resource data information, the spatial area collaborative regulation criteria, and the regional planning target information collected by big data, then performing simulation on the space control simulation model according to the location advantages and the preset stage planning targets, outputting a spatial area control plan, performing feasibility analysis on the spatial area control plan to obtain a feasibility coefficient, and optimizing and adjusting the spatial area control plan based on the feasibility coefficient; implementing control on the urban-rural space area to be controlled based on the optimized and adjusted spatial area control plan, and intelligently supervising the control process through the big data intelligent supervision module. Furthermore, the technical effect of improving the spatial control level and control efficiency by using big data technology to apply control to the urban-rural space, and thus improving the effectiveness and rationality of the urban-rural space planning and control quality is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flowchart of an urban-rural space control method based on big data technology according to the present application;

[0011] Figure 2 It is a flowchart of constructing a space control simulation model in an urban-rural space control method based on big data technology according to the present application;

[0012] Figure 3Schematic flowchart of determining the spatial region coding system in a method for urban-rural spatial control based on big data technology in this application;

[0013] Figure 4 Schematic structural diagram of a system for urban-rural spatial control based on big data technology in this application;

[0014] Explanation of reference numerals: Information collection module 11, data acquisition module 12, model construction module 13, information acquisition module 14, simulation module 15, optimization and adjustment module 16, intelligent supervision module 17. Detailed implementation manners

[0015] This application provides a method and system for urban-rural spatial control based on big data technology, which solves the technical problems in the prior art that there are many spatial control elements and it takes a long time, resulting in affecting the quality and efficiency of urban-rural control, and achieves the technical effect of improving the spatial control level and efficiency through the application of big data technology to urban-rural space, and further improving the effectiveness and rationality of the quality of urban-rural spatial planning and control.

[0016] Embodiment 1

[0017] As Figure 1 shown, this application provides a method for urban-rural spatial control based on big data technology, and the method includes:

[0018] Step S100: Collect information on the urban-rural spatial region to be controlled through big data to obtain the resource data information of the urban-rural spatial region;

[0019] Specifically, urban-rural spatial control takes the spatial resources, environmental protection and development coordination across administrative boundaries as the main objects, conducts unified protection and rights and interests distribution of the spatial resources across administrative boundaries, coordinates the interests of multiple organizations, promotes the comprehensive integration of production factors such as capital, land, labor, technology, and information, so as to facilitate the implementation of important projects and decisions across regions. Therefore, clarifying the urban-rural spatial control plan has important practical significance for the reasonable construction and development of urban and rural areas.

[0020] Collect relevant information on the urban-rural spatial region to be controlled through big data technology, including forms such as images, texts, and data. The collected data is more comprehensive and accurate, and the resource data information of the urban-rural spatial region is obtained. The resource data information of the urban-rural spatial region includes relevant information such as the surrounding environment, regional population, economic development, natural resources, historical buildings, regional layout, traffic conditions, education, energy, communication, medical care, water conservancy, culture and sports, and greening.

[0021] Step S200: Obtain the spatial region collaborative regulation criteria and regional planning target information of the urban-rural spatial region to be controlled;

[0022] Specifically, the spatial regional collaborative regulation criteria for the urban-rural spatial region to be regulated are the overall coordinated development principles formulated according to the characteristics of the region to be regulated, and the sustainable development principles of reasonably utilizing land resources, ecological resources, and market resources. The regional planning target information of the urban-rural spatial region to be regulated is the regional development target formulated according to the regulation scope of the region to be regulated, including cultural development targets, economic development targets, etc.

[0023] Step S300: Based on the urban-rural spatial region resource data information, the spatial regional collaborative regulation criteria, and the regional planning target information, construct a spatial regulation simulation model;

[0024] As Figure 2 shown, further, for the step of constructing a spatial regulation simulation model based on the urban-rural spatial region resource data information, the spatial regional collaborative regulation criteria, and the regional planning target information, step S300 of this application further includes:

[0025] Step S310: Conduct data structured classification on the urban-rural spatial region resource data information to obtain a structured resource data set, an unstructured resource data set, and a semi-structured resource data set;

[0026] Step S320: Determine a spatial region coding system according to the regional planning target information;

[0027] Step S330: Encode the structured resource data set, the unstructured resource data set, and the semi-structured resource data set according to the spatial region coding system to obtain encoded urban-rural spatial region resource data information;

[0028] Step S340: Conduct element analysis on the encoded urban-rural spatial region resource data information based on the spatial regional collaborative regulation criteria to obtain urban-rural spatial region resource element information;

[0029] Step S350: Construct the spatial regulation simulation model based on the urban-rural spatial region resource element information, the spatial regional collaborative regulation criteria, and the regional planning target information.

[0030] As Figure 3 shown, further, for the step of determining a spatial region coding system according to the regional planning target information, step S320 of this application further includes:

[0031] Step S321: Determine regional planning regulation strategies according to the regional planning target information;

[0032] Step S322: Obtain urban-rural spatial region distribution information based on the urban-rural spatial region resource data information;

[0033] Step S323: Divide the urban-rural spatial region distribution information according to the regional planning control strategy to obtain the spatial region control levels.

[0034] Step S324: Code according to the spatial region control levels to determine the spatial region coding system.

[0035] Furthermore, for the element analysis of the coded urban-rural spatial region resource data information based on the spatial region collaborative regulation criteria to obtain the urban-rural spatial region resource element information, step S340 of this application further includes:

[0036] Step S341: Obtain the resource allocation criteria, coordinated development criteria, and market regulation criteria according to the spatial region collaborative regulation criteria.

[0037] Step S342: Conduct element analysis on the coded urban-rural spatial region resource data information respectively based on the resource allocation criteria, the coordinated development criteria, and the market regulation criteria to obtain resource allocation analysis elements, coordinated development analysis elements, and market regulation analysis elements.

[0038] Step S343: Conduct element fusion on the resource allocation analysis elements, the coordinated development analysis elements, and the market regulation analysis elements to obtain the urban-rural spatial region resource element information.

[0039] Specifically, based on the urban-rural spatial region resource data information, the spatial region collaborative regulation criteria, and the regional planning target information, a spatial control simulation model is constructed, and the spatial control simulation model is used to perform data simulation on the spatial region to be controlled. Since the data types obtained by big data are diverse, first, the urban-rural spatial region resource data information is classified by data structuring to obtain the classified structured resource data set, which refers to the data that can be represented and stored using a relational database and can be logically expressed and implemented using a two-dimensional table, such as two-dimensional table data, numbers, etc.; unstructured resource data set, which is data without a fixed structure, such as pictures, videos, locations, etc.; and semi-structured resource data set, which is a form of structured data that does not conform to the data model structure associated with a relational database or other data tables, such as emails, HTML, resource libraries, etc.

[0040] According to the regional planning target information, a spatial region coding system is determined, and the spatial region coding system is the benchmark for classifying and coding the regions to be controlled. Specifically, according to the regional planning target information, a regional planning control strategy is determined. The regional planning control strategy is the criterion for spatial management of the regions to be controlled. Exemplarily, sub-regional control and sub-level control are performed on the regions to be controlled. Based on the urban and rural spatial region resource data information, the urban and rural spatial region distribution information is obtained, that is, the geographical distribution information of various spatial resources such as land, ecology, and buildings.

[0041] The urban and rural spatial region distribution information is divided according to the regional planning control strategy. Different spatial regions correspond to different control levels, and the corresponding control levels of each spatial region are obtained. Exemplarily, the spatial region is divided into suitable construction areas, restricted construction areas, and prohibited construction areas, and the corresponding control levels are carried out according to the divided regions, mainly including regulatory control, control-type control, and guidance-type control. Hierarchical coding is performed on each region according to the spatial region control level, and the spatial region coding system is determined. For example, coding is performed in the form of level + region number, which is conducive to the processing and classification of the data of the controlled regions.

[0042] The structured resource data set, the unstructured resource data set, and the semi-structured resource data set are coded according to the spatial region coding system to obtain the coded urban and rural spatial region resource data information after region coding. Element analysis is performed on the coded urban and rural spatial region resource data information based on the spatial region collaborative regulation criterion. Specifically, according to the spatial region collaborative regulation criterion, resource allocation criteria are obtained, including ecological resource, water and soil resource, cultural resource, infrastructure resource allocation criteria; coordinated development criteria, that is, the sustainable development planning criterion of resources and the economy; market regulation criteria, that is, the coordination principle of market economy development and infrastructure construction.

[0043] Element analysis is respectively performed on the coded urban and rural spatial region resource data information based on the resource allocation criterion, the coordinated development criterion, and the market regulation criterion, that is, planning analysis is performed on each controlled region by combining the resource allocation, coordinated development, and market regulation criteria to judge whether it is suitable for the development of this element and the corresponding element level suitable for development, and the corresponding resource allocation analysis elements, coordinated development analysis elements, and market regulation analysis elements are respectively obtained. Element fusion is performed on the resource allocation analysis elements, the coordinated development analysis elements, and the market regulation analysis elements to obtain the urban and rural spatial region resource element information. The urban and rural spatial region resource element information is the suitable development element information corresponding to each spatial region, including resource development and economic development elements.

[0044] Based on the information of the urban-rural spatial region resource elements, the spatial region collaborative regulation criteria, and the regional planning target information, construct the spatial control simulation model, which is used to simulate the spatial resource data of the spatial region to be controlled. By constructing a simulation model for urban-rural spatial data simulation, it is intuitive and efficient, reduces the control cost, and thus improves the spatial control level and control efficiency.

[0045] Step S400: Obtain the location advantages and the preset stage planning targets of the urban-rural spatial region to be controlled;

[0046] Specifically, the location advantages of the urban-rural spatial region to be controlled are the comprehensive resource advantages of the location, that is, the objectively existing favorable conditions or superior positions of the urban-rural spatial region to be controlled in terms of economic development. Its constituent factors mainly include: natural resources, geographical location, as well as aspects such as society, economy, science and technology, management, politics, culture, education, tourism, etc., mainly including transportation advantages (such as coastal areas), environmental advantages (such as Silicon Valley), talent advantages (university towns), resource advantages (such as minerals), energy advantages (such as water, coal), market advantages, etc. The preset stage planning targets are the planned stage control planning targets of the urban-rural spatial region to be controlled, which are determined by the overall regional planning targets.

[0047] Step S500: Perform a simulation on the spatial control simulation model according to the location advantages and the preset stage planning targets, and output a spatial region control plan;

[0048] Specifically, input the location advantages and the preset stage planning targets into the spatial control simulation model for simulation, and output a spatial region control plan. The spatial region control plan is the control plan for the urban-rural spatial region to be controlled, including regional construction plans, resource development plans, economic development plans, facility planning plans, landscape architecture construction plans, transportation facility planning plans, etc.

[0049] Step S600: Conduct a feasibility analysis on the spatial region control plan to obtain a feasibility coefficient, and optimize and adjust the spatial region control plan based on the feasibility coefficient;

[0050] Furthermore, for the feasibility analysis of the spatial region control plan to obtain a feasibility coefficient, step S600 of this application further includes:

[0051] Step S610: The feasibility analysis includes operation feasibility, economic feasibility, and ecological feasibility;

[0052] Step S620: Conduct a feasibility analysis on the spatial region control plan based on the operation feasibility, the economic feasibility, and the ecological feasibility to obtain an operation feasibility degree, an economic feasibility degree, and an ecological feasibility degree;

[0053] Step S630: Obtain weighted attribute information, and perform weighted analysis on the job feasibility, the economic feasibility, and the ecological feasibility based on the weighted attribute information to obtain the feasibility coefficient.

[0054] Furthermore, for the step of obtaining the weighted attribute information, step S630 of the present application further includes:

[0055] Step S631: Obtain spatial region control evaluation attribute information;

[0056] Step S632: Perform principal component analysis on the spatial region control evaluation attribute information to obtain dimension-reduced spatial region control evaluation attribute information;

[0057] Step S633: Obtain the weighted attribute information based on factor analysis of the dimension-reduced spatial region control evaluation attribute information.

[0058] Specifically, perform feasibility analysis on the spatial region control plan. The feasibility analysis includes job feasibility, that is, the feasibility of construction operations in the urban and rural control regions; economic feasibility, that is, the economic feasibility of control costs; and ecological feasibility, that is, the feasibility of the ecological environment impact brought about by control development. Perform feasibility calculation and analysis on the spatial region control plan based on the job feasibility, the economic feasibility, and the ecological feasibility, and sequentially obtain the corresponding job feasibility, economic feasibility, and ecological feasibility.

[0059] The weighted attribute information is the result of weight allocation for the job feasibility, the economic feasibility, and the ecological feasibility. The specific obtaining process is to first clarify the spatial region control evaluation attribute information. The spatial region control evaluation attribute information is a number of evaluation variables proposed to ensure the feasibility of the control plan, such as control time evaluation, construction planning parameters, and economic cost parameters. Each variable reflects the implementation feasibility of the control plan to varying degrees. Perform principal component analysis on the spatial region control evaluation attribute information, that is, perform dimension reduction processing on the spatial region control evaluation attribute information. Dimension reduction processing can reduce the time complexity and space complexity, remove the noise mixed in the matrix dataset, and clearly display the important features in the data, so as to obtain the attribute information strongly related to the evaluation of the implementation feasibility of the control plan, that is, the dimension-reduced spatial region control evaluation attribute information. By using principal component analysis to perform dimension reduction on the spatial region control evaluation attribute information, the system calculation complexity is reduced, thereby improving the accuracy and reliability of the feasibility analysis of the control plan.

[0060] Based on factor analysis of the dimensionality reduction space region control evaluation attribute information, that is, extracting the common features in each attribute information, so as to classify the attribute information with the same essence into one attribute information. Among them, the attribute information with more common factors has a corresponding larger weight, and the attribute information with fewer common factors has a corresponding smaller weight, so as to obtain the weighted attribute information. Based on the weighted attribute information, weighted analysis and calculation are carried out on the operation feasibility, the economic feasibility, and the ecological feasibility to obtain the feasibility coefficient, which is used to indicate the feasibility of the space region control plan. The larger the coefficient, the higher the feasibility of the control plan.

[0061] Based on the feasibility coefficient, the space region control plan is optimized and adjusted. When the feasibility coefficient is too low, the construction planning parameters of the control plan can be adjusted or the economic cost can be optimized to ensure that the feasibility of the control plan meets the standard, thereby improving the effectiveness and rationality of the urban and rural space planning control quality.

[0062] Step S700: Implement control on the urban and rural space region to be controlled based on the optimized and adjusted space region control plan, and conduct intelligent supervision on the control process through the big data intelligent supervision module.

[0063] Furthermore, for the intelligent supervision of the control process through the big data intelligent supervision module, step S700 of this application further includes:

[0064] Step S710: Conduct intelligent supervision on the control process through the big data intelligent supervision module to obtain real-time control information of the space region;

[0065] Step S720: Based on the real-time control information of the space region and the preset stage planning objectives, obtain the completion degree of the space control implementation;

[0066] Step S730: Iteratively update and train the space control simulation model according to the completion degree of the space control implementation to obtain a space control simulation updated model.

[0067] Specifically, implement control on the urban and rural space region to be controlled based on the optimized and adjusted space region control plan. At the same time, to ensure precise control of the control implementation process, conduct intelligent supervision on the control process through the big data intelligent supervision module to obtain real-time control information of the space region, including control progress information, control cost information, etc.

[0068] Based on the real-time control information of the spatial region and the preset stage planning objectives, a comparative analysis is carried out to obtain the completion degree of the spatial control implementation, and it is judged whether the implementation completion degree reaches the preset planning objectives. According to the completion degree of the spatial control implementation, the spatial control simulation model is iteratively updated and trained to ensure the real-time accuracy of the model training simulation data, and an updated spatial control simulation updated model is obtained, improving the model simulation accuracy, the spatial control level and the control efficiency, and further improving the effectiveness and rationality of the urban and rural spatial planning control quality.

[0069] In summary, the urban and rural spatial control method and system based on big data technology provided by this application have the following technical effects:

[0070] Due to adopting the urban and rural spatial region resource data information, the spatial region collaborative regulation criteria and the regional planning objective information based on big data collection, a spatial control simulation model is constructed, and then the spatial control simulation model is simulated according to the location advantages and the preset stage planning objectives, and a spatial region control plan is output. A feasibility analysis is carried out on the spatial region control plan to obtain a feasibility coefficient, and the spatial region control plan is optimized and adjusted based on the feasibility coefficient; based on the optimized and adjusted spatial region control plan, the urban and rural spatial region to be controlled is controlled and implemented, and the control process is intelligently supervised through the big data intelligent supervision module. Further, the technical effect of improving the spatial control level and the control efficiency by using big data technology to apply control to the urban and rural space, and further improving the effectiveness and rationality of the urban and rural spatial planning control quality is achieved.

[0071] Embodiment 2

[0072] Based on the same inventive concept as the urban and rural spatial control method based on big data technology in the foregoing embodiment, the present invention also provides an urban and rural spatial control system based on big data technology, as Figure 4 shown, the system includes:

[0073] An information collection module 11, configured to collect information on the urban and rural spatial region to be controlled through big data to obtain urban and rural spatial region resource data information;

[0074] A data acquisition module 12, configured to acquire the spatial region collaborative regulation criteria and the regional planning objective information of the urban and rural spatial region to be controlled;

[0075] A model construction module 13, configured to construct a spatial control simulation model based on the urban and rural spatial region resource data information, the spatial region collaborative regulation criteria and the regional planning objective information;

[0076] An information acquisition module 14, configured to acquire the location advantages and the preset stage planning objectives of the urban and rural spatial region to be controlled;

[0077] The simulation module 15 is configured to perform a simulation on the spatial control simulation model according to the location advantages and the preset stage planning objectives, and output a spatial area control plan.

[0078] The optimization and adjustment module 16 is configured to perform a feasibility analysis on the spatial area control plan to obtain a feasibility coefficient, and optimize and adjust the spatial area control plan based on the feasibility coefficient.

[0079] The intelligent supervision module 17 is configured to implement control on the urban and rural spatial area to be controlled based on the optimized and adjusted spatial area control plan, and perform intelligent supervision on the control process through the big data intelligent supervision module.

[0080] Furthermore, the model construction module further includes:

[0081] The data classification unit is configured to perform data structured classification on the urban and rural spatial area resource data information to obtain a structured resource data set, an unstructured resource data set, and a semi-structured resource data set.

[0082] The coding system determination unit is configured to determine a spatial area coding system according to the regional planning target information.

[0083] The data coding unit is configured to code the structured resource data set, the unstructured resource data set, and the semi-structured resource data set according to the spatial area coding system to obtain coded urban and rural spatial area resource data information.

[0084] The element analysis unit is configured to perform element analysis on the coded urban and rural spatial area resource data information based on the spatial area collaborative regulation criterion to obtain urban and rural spatial area resource element information.

[0085] The model construction unit is configured to construct the spatial control simulation model based on the urban and rural spatial area resource element information, the spatial area collaborative regulation criterion, and the regional planning target information.

[0086] Furthermore, the coding system determination unit further includes:

[0087] The control strategy determination unit is configured to determine a regional planning control strategy according to the regional planning target information.

[0088] The regional distribution acquisition unit is configured to acquire urban and rural spatial area distribution information based on the urban and rural spatial area resource data information.

[0089] The control level acquisition unit is configured to divide the urban and rural spatial area distribution information according to the regional planning control strategy to obtain a spatial area control level.

[0090] An encoding determination unit, configured to perform encoding according to the spatial region control hierarchy and determine the spatial region encoding system.

[0091] Furthermore, the element analysis unit further includes:

[0092] A regulation criterion determination unit, configured to obtain a resource allocation criterion, a coordinated development criterion, and a market regulation criterion according to the spatial region coordinated regulation criterion;

[0093] An analysis element obtaining unit, configured to perform element analysis on the encoded urban and rural spatial region resource data information respectively based on the resource allocation criterion, the coordinated development criterion, and the market regulation criterion, and obtain a resource allocation analysis element, a coordinated development analysis element, and a market regulation analysis element;

[0094] An element fusion unit, configured to perform element fusion on the resource allocation analysis element, the coordinated development analysis element, and the market regulation analysis element to obtain the urban and rural spatial region resource element information.

[0095] Furthermore, the optimization and adjustment module further includes:

[0096] A feasibility constitution unit, where the feasibility analysis includes operation feasibility, economic feasibility, and ecological feasibility;

[0097] A feasibility analysis unit, configured to perform feasibility analysis on the spatial region control plan based on the operation feasibility, the economic feasibility, and the ecological feasibility, and obtain an operation feasibility degree, an economic feasibility degree, and an ecological feasibility degree;

[0098] A weighted analysis unit, configured to obtain weighted attribute information, and perform weighted analysis on the operation feasibility degree, the economic feasibility degree, and the ecological feasibility degree based on the weighted attribute information to obtain the feasibility coefficient.

[0099] Furthermore, the weighted analysis unit further includes:

[0100] An evaluation attribute obtaining unit, configured to obtain spatial region control evaluation attribute information;

[0101] A principal component analysis unit, configured to perform principal component analysis on the spatial region control evaluation attribute information to obtain dimension-reduced spatial region control evaluation attribute information;

[0102] A factor analysis unit, configured to perform factor analysis on the dimension-reduced spatial region control evaluation attribute information to obtain the weighted attribute information.

[0103] Furthermore, the intelligent supervision module further includes:

[0104] A real-time control unit, configured to intelligently monitor the control process through a big data intelligent supervision module to obtain real-time control information of a spatial area;

[0105] An implementation completion degree obtaining unit, configured to obtain the implementation completion degree of spatial control based on the real-time control information of the spatial area and the preset stage planning target;

[0106] A model updating unit, configured to iteratively update and train the spatial control simulation model according to the implementation completion degree of spatial control to obtain a spatial control simulation updated model.

[0107] The present application provides an urban and rural spatial control method based on big data technology. The method includes: collecting information on a to-be-controlled urban and rural spatial area through big data to obtain resource data information of the urban and rural spatial area; obtaining the spatial area collaborative regulation criterion and regional planning target information of the to-be-controlled urban and rural spatial area; constructing a spatial control simulation model based on the resource data information of the urban and rural spatial area, the spatial area collaborative regulation criterion, and the regional planning target information; obtaining the location advantages and preset stage planning target of the to-be-controlled urban and rural spatial area; performing a simulation on the spatial control simulation model according to the location advantages and the preset stage planning target, and outputting a spatial area control plan; performing a feasibility analysis on the spatial area control plan to obtain a feasibility coefficient, and optimizing and adjusting the spatial area control plan based on the feasibility coefficient; implementing control on the to-be-controlled urban and rural spatial area based on the optimized and adjusted spatial area control plan, and intelligently monitoring the control process through a big data intelligent supervision module. The technical problem that in the prior art, there are many spatial control elements and it takes a long time, resulting in the influence on the quality and efficiency of urban and rural control is solved. The technical effect of improving the spatial control level and efficiency by using big data technology to perform application control on urban and rural spaces, and further improving the effectiveness and rationality of the quality of urban and rural spatial planning control is achieved.

[0108] This specification and the drawings are only exemplary descriptions of the present application. If the modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. An urban-rural space control method based on big data technology, characterized in that, the method includes: Collecting information on the urban-rural space area to be controlled through big data to obtain urban-rural space area resource data information; Obtaining the spatial area collaborative regulation criteria and regional planning target information of the urban-rural space area to be controlled; Based on the urban-rural space area resource data information, the spatial area collaborative regulation criteria and the regional planning target information, constructing a spatial control simulation model; Obtaining the location advantages and preset stage planning targets of the urban-rural space area to be controlled; Performing simulation on the spatial control simulation model according to the location advantages and the preset stage planning targets, and outputting a spatial area control plan; Performing feasibility analysis on the spatial area control plan to obtain a feasibility coefficient, and optimizing and adjusting the spatial area control plan based on the feasibility coefficient; Implementing control on the urban-rural space area to be controlled based on the optimized and adjusted spatial area control plan, and intelligently supervising the control process through a big data intelligent supervision module; The constructing a spatial control simulation model based on the urban-rural space area resource data information, the spatial area collaborative regulation criteria and the regional planning target information includes: Performing data structured classification on the urban-rural space area resource data information to obtain a structured resource data set, an unstructured resource data set and a semi-structured resource data set; Determining a spatial area coding system according to the regional planning target information; Encoding the structured resource data set, the unstructured resource data set and the semi-structured resource data set according to the spatial area coding system to obtain encoded urban-rural space area resource data information; Performing element analysis on the encoded urban-rural space area resource data information based on the spatial area collaborative regulation criteria to obtain urban-rural space area resource element information; Constructing the spatial control simulation model based on the urban-rural space area resource element information, the spatial area collaborative regulation criteria and the regional planning target information; The determining a spatial area coding system according to the regional planning target information includes: Determining a regional planning control strategy according to the regional planning target information; Based on the urban-rural space area resource data information, obtaining urban-rural space area distribution information; Dividing the urban-rural space area distribution information according to the regional planning control strategy to obtain a spatial area control level; Encoding according to the spatial area control level to determine the spatial area coding system; The intelligently supervising the control process through a big data intelligent supervision module includes: Intelligently supervising the control process through a big data intelligent supervision module to obtain spatial area real-time control information; Based on the spatial area real-time control information and the preset stage planning target, obtaining the completion degree of spatial control implementation; Performing iterative update training on the spatial control simulation model according to the completion degree of spatial control implementation to obtain a spatial control simulation update model.

2. The urban-rural space control method based on big data technology according to claim 1, characterized in that, Performing element analysis on the encoded urban and rural spatial region resource data information based on the spatial region collaborative regulation criterion to obtain urban and rural spatial region resource element information, including: Obtaining a resource allocation criterion, a coordinated development criterion, and a market regulation criterion according to the spatial region collaborative regulation criterion; Performing element analysis on the encoded urban and rural spatial region resource data information respectively based on the resource allocation criterion, the coordinated development criterion, and the market regulation criterion to obtain a resource allocation analysis element, a coordinated development analysis element, and a market regulation analysis element; Performing element fusion on the resource allocation analysis element, the coordinated development analysis element, and the market regulation analysis element to obtain the urban and rural spatial region resource element information.

3. A method for urban and rural spatial management and control based on big data technology according to claim 2, wherein, Performing feasibility analysis on the spatial region management and control scheme to obtain a feasibility coefficient, including: The feasibility analysis includes operation feasibility, economic feasibility, and ecological feasibility; Performing feasibility analysis on the spatial region management and control scheme based on the operation feasibility, the economic feasibility, and the ecological feasibility to obtain an operation feasibility degree, an economic feasibility degree, and an ecological feasibility degree; Obtaining weighted attribute information, and performing weighted analysis on the operation feasibility degree, the economic feasibility degree, and the ecological feasibility degree based on the weighted attribute information to obtain the feasibility coefficient.

4. A method for urban and rural spatial management and control based on big data technology according to claim 3, wherein, The obtaining of the weighted attribute information includes: Obtaining spatial region management and control evaluation attribute information; Performing principal component analysis on the spatial region management and control evaluation attribute information to obtain dimension-reduced spatial region management and control evaluation attribute information; Performing factor analysis on the dimension-reduced spatial region management and control evaluation attribute information to obtain the weighted attribute information.

5. An urban and rural spatial management and control system based on big data technology for implementing the method for urban and rural spatial management and control based on big data technology according to any one of claims 1 to 4, wherein, The system includes: An information collection module for collecting information on the urban and rural spatial region to be managed and controlled through big data to obtain urban and rural spatial region resource data information; A data acquisition module for acquiring the spatial region collaborative regulation criterion and the regional planning target information of the urban and rural spatial region to be managed and controlled; A model construction module for constructing a spatial management and control simulation model based on the urban and rural spatial region resource data information, the spatial region collaborative regulation criterion, and the regional planning target information; An information acquisition module for acquiring the location advantages and the preset stage planning target of the urban and rural spatial region to be managed and controlled; A simulation module for performing simulation on the spatial management and control simulation model according to the location advantages and the preset stage planning target and outputting a spatial region management and control scheme; An optimization and adjustment module for performing feasibility analysis on the spatial region management and control scheme to obtain a feasibility coefficient, and optimizing and adjusting the spatial region management and control scheme based on the feasibility coefficient; An intelligent supervision module, which is used to implement the control of the urban and rural spatial areas to be controlled based on the optimized and adjusted spatial area control plan, and to intelligently supervise the control process through the big data intelligent supervision module.

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