Automatic territorial space planning scheme generation system based on artificial intelligence

Abstract

The invention discloses a territorial space planning scheme automatic generation system based on artificial intelligence, relates to the technical field of territorial space planning, and aims to solve the technical problems that a current system is difficult to construct a reasonable regional framework, land evaluation is not comprehensive enough, and reliable data support is difficult to provide for territorial space planning. Comprising a scheme generation model construction module. According to the invention, the target region is segmented into a plurality of regional regions through the segmentation and division unit, and a clear regional architecture is constructed; the feature extraction unit deeply excavates the land features of each region and comprehensively evaluates the land; element values of a land utilization intensity matrix are calculated through the matrix, so that the precise land utilization intensity matrix is generated, and data support is provided for territorial space planning. According to the method, a reasonable regional framework is established through map segmentation, the land value is comprehensively evaluated through feature extraction, meanwhile, the land utilization intensity matrix is determined, and reliable data support is provided for planning through the matrix.

Description

Technical Field

[0001] The present invention relates to the technical field of land planning, and more specifically, to an automatic generation system for land space planning schemes based on artificial intelligence. Background Art

[0002] The role of national land space planning includes optimizing spatial layout, rationally zoning the functions of national land space, clarifying the boundaries and layout of production, living and ecological space, improving the rationality and efficiency of spatial utilization, promoting intensive and economical use of land, and avoiding overdevelopment and waste; promoting regional coordinated development, comprehensively evaluating the resource endowment, development foundation and potential of different regions, formulating differentiated strategies and spatial guidelines, guiding industrial transfer and acceptance, rationally allocating infrastructure construction and public service resources, narrowing regional gaps, achieving complementary advantages and coordinated progress, promoting urban-rural integrated development, and promoting population, economy, resource and environmental spatial balance; protecting the ecological environment, identifying and delineating ecologically sensitive areas, important ecosystems and ecological corridors, etc., establishing strict ecological protection systems and control measures, restricting or prohibiting unreasonable development activities, maintaining biodiversity and ecosystem stability, ensuring the continuous development of ecosystem service functions, and achieving harmonious coexistence between man and nature; ensuring the rational use of resources, comprehensively investigating and evaluating natural resources such as land, minerals, water, and forests, making overall arrangements based on resource distribution, reserves and development suitability, and ensuring sustainable use of resources. The plan for national land space planning can be generated through the system.

[0003] However, the regional division in the existing system is not accurate enough. When dividing regional areas, traditional technology does not use scientific methods based on elevation data and low-value points, resulting in fuzzy regional division, making it difficult to build a clear and reasonable regional framework, affecting subsequent planning, and feature mining is not comprehensive. When extracting land features, the dimension may be relatively single, and the land use degree, economic output and social needs are not fully considered, making the land assessment not comprehensive enough and difficult to accurately grasp the land value. At the same time, the intensity calculation is not accurate. In the past, the calculation of land use intensity lacked a specific formula like the one in this invention, and could not accurately measure the intensity of different land use types. For example, for urban construction land, agricultural and industrial land, there is no comprehensive multi-factor quantification, resulting in the generated land use intensity matrix being not accurate enough, and it is difficult to provide reliable data support for national space planning. In view of this, we propose an automatic generation system for national space planning schemes based on artificial intelligence. Summary of the invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology, adapt to actual needs, and provide an automatic generation system for national land space planning schemes based on artificial intelligence to solve the technical problems that the current system is difficult to construct a reasonable regional structure, and the land assessment is not comprehensive enough, and it is difficult to provide reliable data support for national land space planning.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an automatic generation system of national land space planning scheme based on artificial intelligence, comprising:

[0006] A solution generation model building module is used to build a generation solution model;

[0007] Data collection module, used to collect data from the target area;

[0008] The preliminary plan generation module is used to make preliminary plans for the land space of the target area based on the collected data;

[0009] The scheme adjustment module is used to adjust the preliminary planned scheme;

[0010] The solution generation model construction module is connected to the solution preliminary generation module, and the solution preliminary generation module is connected to the solution adjustment module.

[0011] Preferably, the solution generation model building module includes a model building unit, a segmentation unit, and a feature extraction unit;

[0012] The model building unit is connected to the segmentation unit and is used to build a generation scheme model;

[0013] The segmentation and division unit is connected to the feature extraction unit and is used to perform map segmentation and division on the target area.

[0014] Preferably, the data acquisition module includes an information acquisition unit, a segmentation processing unit, and a storage unit;

[0015] The information collection unit is connected to the segmentation processing unit and is used to collect data information of the target area;

[0016] The segmentation processing unit is connected to the storage unit and is used to perform segmentation processing on the collected data according to requirements.

[0017] Preferably, the scheme preliminary generation module includes a regional planning unit, a feature information acquisition unit, an utilization intensity extraction unit, and a matrix establishment unit;

[0018] The regional planning unit is connected to the feature information acquisition unit and the feature extraction unit respectively, and is used to perform preliminary planning on the target area;

[0019] The feature extraction unit is connected to the intensity extraction unit and is used to extract the land features of the target area;

[0020] The utilization intensity extraction unit and the matrix establishment unit are used to extract the utilization intensity of each region;

[0021] The matrix establishing unit is used to establish a land use intensity matrix.

[0022] Preferably, the scheme adjustment module includes a simulation and deduction unit, an adjustment unit, and a new scheme generation unit;

[0023] The simulation and deduction unit is connected to the characteristic information acquisition unit and the adjustment unit respectively, and is used to simulate and deduct the preliminary planning scheme;

[0024] The adjustment unit is connected to the new scheme generation unit and is used to adjust and improve the abnormal area in the planning scheme;

[0025] The new solution generating unit is used to generate a new solution for planning.

[0026] Preferably, the model construction unit constructs a generation scheme model based on land use intensity data, climate data and regional environmental data, and the generation scheme model includes a land use intensity data input port, a climate data input port, a regional environmental data input port and a scheme output port.

[0027] Preferably, the segmentation and division unit divides the target area into several regional areas according to the elevation data, each region includes a low-value point, and the regional areas are divided into different types according to the terrain characteristics of the low-value points. The data area contains the longitude and latitude coordinates of each regional area. The low-value point is the point with the lowest altitude in each regional area. The low-value point divides the regional area into two parts, and the altitude of the highest point in the two parts is higher than that of the low-value point. There is only one low-value point in each region.

[0028] Preferably, the regional area types include plains, hills, mountains and canyons. There is no vegetation in the plain area, and there are towns or villages at the low-value point locations. There are forests, cultivated land, wasteland and grassland in the hilly area. There are forests, cultivated land and grassland in the mountainous area. There are forests in the canyon area. The plain area includes a town area, and the mountainous area includes one or two villages and towns.

[0029] Preferably, the land quality levels contained in the urban areas and plain areas are level 1 and level 2, the land quality level corresponding to the villages and towns in the mountainous areas is level 2 or level 3, the land quality level corresponding to the cultivated land, forest land and grassland is level 2, the land quality level corresponding to the wasteland is level 3, and the land quality is level 1-3.

[0030] Preferably, the element L in the land use intensity matrix (L) ij The calculation formula is as follows:

[0031] L ij=α×I ij +β×E ij +γ×S ij

[0032] Where i represents the number of the region, j represents the number of the land use type, α, β, γ are weight coefficients, and α+β+γ=1;

[0033] I ij It represents the land utilization index, which is measured by the land development ratio and building density. For urban construction land in a certain region, if the ratio of developed land area to the total area of ​​the region is p1 and the building density is p2, then I ij =k1×p1+k2×p2, where k1 and k2 are corresponding weight coefficients, determined based on historical data;

[0034] E ij It represents the economic output index, which is measured by the economic output value per unit land area. For a certain agricultural land, the value of crop output is converted into the output value per unit area v, then E ij =v; for industrial land, it is measured by industrial added value per unit area;

[0035] S ij It represents the social demand index, taking into account the population density and job density factors. For a region with a population density of d1 and a job density of d2, S ij =m1×d1+m2×d2, m1 and m2 are weight coefficients.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention divides the target area into multiple regional areas through the segmentation unit to build a clear regional framework; the feature extraction unit deeply explores the land characteristics of each region and comprehensively evaluates the land; the matrix is ​​used to calculate the element value of the land use intensity matrix, thereby generating an accurate land use intensity matrix to provide data support for national land space planning. The present invention establishes a reasonable regional framework through map segmentation, and comprehensively evaluates the land value through feature extraction, while determining the land use intensity matrix, and using the matrix to provide reliable data support for planning.

[0038] 2. The present invention constructs an automatic generation system for national land space planning schemes based on artificial intelligence, including scheme generation model construction, data collection, preliminary generation and adjustment modules; in the scheme generation model construction module, the model construction unit relies on advanced algorithms and technologies to integrate multi-source data on land use intensity, climate and regional environment, and carefully carves out a generation scheme model to build a solid framework for subsequent planning. The data acquisition module uses a variety of high-tech means, such as satellite remote sensing and drone surveys, to widely collect all-round data information in the target area, and classifies and stores it through the segmentation processing unit to provide detailed and accurate basic data for planning. The preliminary generation module is based on the collected data, and the regional planning unit coordinates the planning, combines the collaborative work of feature information acquisition, utilization intensity extraction and matrix establishment units, and preliminarily outlines the blueprint of national land space layout, so that the planning scheme is initially formed. The modules work closely together to inject intelligent vitality into national land space planning.

[0039] 3. The present invention simulates and deduces the initially generated planning scheme, marks and adjusts abnormal areas, and ensures that the scheme takes all factors into account. The simulation and deduction unit uses complex simulation algorithms and geographic information system (GIS) technology to place the preliminary planning scheme in a virtual environment for dynamic deduction, simulate various scenarios of population growth, economic development, and ecological evolution, and comprehensively predict the potential impact of the implementation of the scheme. In this process, abnormal areas such as traffic congestion, uneven resource distribution, and ecological balance destruction are accurately identified and marked in detail. For these abnormal areas, the adjustment unit combines their land quality, utilization intensity, and surrounding environmental factors to re-examine the planning layout, optimize functional zoning, improve the transportation network, supplement public service facilities, and pay attention to ecological restoration and protection to ensure that the final generated new land space planning scheme can cleverly balance the relationship between economic development, social needs, and ecological protection, and achieve sustainable development of the region. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0041] Embodiment 1, as Figure 1 As shown, the present invention relates to an automatic generation system of land space planning scheme based on artificial intelligence, comprising:

[0042] The solution generation model building module is used to build the generation solution model.

[0043] The solution generation model building module includes a model building unit, a segmentation unit, and a feature extraction unit;

[0044] The model building unit is connected to the segmentation unit and is used to build a generation scheme model; the model building unit builds the generation scheme model based on land use intensity data, climate data and regional environmental data, and the generation scheme model includes a land use intensity data input port, a climate data input port, a regional environmental data input port and a scheme output port.

[0045] The model building unit first preprocesses the collected land use intensity data in detail. For example, for land use intensity data from different sources and with different precision, data fusion technology is used to make them reach a unified standard in spatial resolution and numerical accuracy, so that they can be accurately included in the model analysis later. At the same time, for climate data, downscaling is performed through meteorological models to refine large-scale climate information to the local scope of the target area, more accurately reflecting the climate differences in the region and its potential impact on land use. For regional environmental data, methods such as geographically weighted regression are used to highlight the changing characteristics of environmental factors in different geographical locations, thereby improving the model's adaptability to the complexity of the regional environment.

[0046] The segmentation unit is connected to the feature extraction unit and is used to perform map segmentation on the target area. The segmentation unit divides the target area into several regional areas according to the elevation data. Each region includes a low-value point. The regional areas are divided into different types according to the terrain features of the low-value points. The data area contains the longitude and latitude coordinates of each regional area. The low-value point is the point with the lowest altitude in each regional area. The low-value point divides the regional area into two parts, and the altitude of the highest point in the two parts is higher than that of the low-value point. There is only one low-value point in each region.

[0047] The types of regional areas include plains, hills, mountains and canyons. There is no vegetation in the plain area, and there are towns or villages at the low value points. There are forests, cultivated land, wasteland and grassland in the hilly area. There are forests, cultivated land and grassland in the mountainous area. There are forests in the canyon area. The plain area includes a town area, and the mountainous area includes one or two villages and towns.

[0048] The land quality levels of urban areas and plain areas are 1 and 2, the land quality levels of villages and towns in mountainous areas are 2 or 3, the land quality level of cultivated land, forest land and grassland is 2, the land quality level of wasteland is 3, and the land quality is 1-3.

[0049] The data collection module is used to collect data from the target area.

[0050] The data acquisition module includes an information acquisition unit, a segmentation processing unit, and a storage unit;

[0051] The information collection unit is connected to the segmentation processing unit and is used to collect data information of the target area;

[0052] The segmentation processing unit is connected to the storage unit and is used to segment the collected data according to requirements.

[0053] In the data collection process, the information collection unit not only focuses on the collection of routine data, but also strengthens the collection of social and cultural data. For example, through web crawler technology, the information such as the living needs and environmental satisfaction reflected by local residents on social media can be obtained. These data can provide a unique perspective for the layout of public service facilities and the optimization of the ecological environment in national land space planning. At the same time, in cooperation with local culture, cultural resource data such as historical and cultural relics and traditional folk activity venues are collected to ensure that the regional cultural characteristics are fully protected and inherited in the planning, so that the national land space planning has a deeper humanistic background.

[0054] The preliminary plan generation module is used to make preliminary plans for the land space of the target area based on the collected data.

[0055] The preliminary scheme generation module includes a regional planning unit, a feature information acquisition unit, an utilization intensity extraction unit, and a matrix establishment unit;

[0056] The regional planning unit is connected to the feature information acquisition unit and the feature extraction unit respectively, and is used to make a preliminary plan for the target area;

[0057] The feature extraction unit is connected to the intensity extraction unit and is used to extract the land features of the target area;

[0058] The utilization intensity extraction unit and the matrix establishment unit are used to extract the utilization intensity of each region;

[0059] The matrix building unit is used to build the land use intensity matrix.

[0060] In the initial generation stage of the plan, the regional planning unit fully considers the value of ecosystem service functions when conducting preliminary planning. For example, the ecological economics evaluation method is used to quantify the service value of wetland, forest and other ecosystems in water conservation, air purification, carbon sequestration and other aspects, and incorporate it into the planning trade-off factors. For areas with important ecological functions, a protection priority and moderate development strategy is adopted. By delineating ecological protection red lines and ecological corridors, the integrity and connectivity of the ecosystem are guaranteed, thereby improving the stability and service functions of the ecosystem in the entire region.

[0061] The element L in the land use intensity matrix (L) ij The calculation formula is as follows:

[0062] L ij =α×I ij+β×E ij +γ×S ij

[0063] Where i represents the number of the region, j represents the number of the land use type, α, β, γ are weight coefficients, and α+β+γ=1;

[0064] I ij It represents the land utilization index, which is measured by the land development ratio and building density. For urban construction land in a certain region, if the ratio of developed land area to the total area of ​​the region is p1 and the building density is p2, then I ij =k1×p1+k2×p2, where k1 and k2 are corresponding weight coefficients, determined based on historical data;

[0065] E ij It represents the economic output index, which is measured by the economic output value per unit land area. For a certain agricultural land, the value of crop output is converted into the output value per unit area v, then E ij =v; for industrial land, it is measured by industrial added value per unit area;

[0066] S ij It represents the social demand index, taking into account the population density and job density factors. For a region with a population density of d1 and a job density of d2, S ij =m1×d1+m2×d2, m1 and m2 are weight coefficients.

[0067] The scheme adjustment module is used to adjust the preliminary planned scheme.

[0068] The scheme adjustment module includes a simulation and deduction unit, an adjustment unit, and a new scheme generation unit;

[0069] The simulation and deduction unit is connected to the characteristic information acquisition unit and the adjustment unit respectively, and is used to simulate and deduces the preliminary planning scheme;

[0070] The adjustment unit is connected to the new scheme generation unit and is used to adjust and improve the abnormal areas in the planning scheme;

[0071] The new scheme generating unit is used to generate new schemes for planning.

[0072] The solution generation model construction module is connected with the solution preliminary generation module, and the solution preliminary generation module is connected with the solution adjustment module.

[0073] In the scheme adjustment module, the simulation and deduction unit uses intelligent algorithms to add simulation analysis of sudden disaster scenarios. For example, when natural disasters such as floods, earthquakes, and fires occur, it simulates whether the traffic evacuation channels in the planning scheme are smooth and whether there are sufficient emergency shelters. Based on the simulation results, the adjustment unit conducts targeted optimization of areas with safety hazards, such as widening evacuation channels and adding supporting facilities to shelters, to ensure that the national land space planning scheme has stronger resilience and emergency response capabilities when facing sudden disasters.

[0074] The method for constructing the system comprises the following steps:

[0075] S1: Model construction: The generation scheme model is obtained by training land use intensity data, climate data and regional environment data. The input data is input into the generation scheme model through the data input port contained in the generation scheme model, and the scheme output port in the generation scheme model outputs the new scheme;

[0076] S2: Data storage: Collect data information of the target area and store it in a storage unit;

[0077] S3: Map segmentation: The target area is segmented into several regions based on elevation data. Each region includes a low-value point. The low-value point area and its surrounding regions are divided based on the terrain features of the low-value point.

[0078] S4: Feature extraction: extract the characteristics of each geographical area and determine the land quality;

[0079] S5: Preliminary planning: Obtain the characteristic information and land type of each region, determine the utilization intensity matrix based on the land utilization rate in different seasons, determine the land utilization intensity based on the land quality and land utilization matrix, and make preliminary plan according to the land utilization intensity;

[0080] S6: Scheme simulation: Obtain meteorological data from the storage unit, simulate and deduce the land in the target area, further determine the land use intensity matrix, make preliminary planning for the target area based on the land use intensity matrix, and obtain the land use intensity data included in the initial planning scheme;

[0081] S7: Adjust the plan: Confirm the land use type of the area that is consistent with the climate data of the target area in the initial planning plan obtained through simulation in step S6, mark the area that does not meet the climate data of the target area as an abnormal area, extract features from the abnormal area, and mark the corresponding land quality, extract the land use rate of each season and the average land use rate of each season in the abnormal area, and obtain the land use rate of each season; obtain an abnormal plan model through training with the obtained abnormal area and land use intensity data, the land use rate data included in the abnormal plan model is the land use rate of each season, import the corresponding land use intensity data in the initial planning plan into the abnormal plan model through the land use intensity data input port in the abnormal plan model, and output the new plan through the plan output port of the abnormal plan model.

[0082] The embodiments of the present invention disclose preferred embodiments, but are not limited thereto. A person skilled in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not deviate from the spirit of the present invention, they are all within the protection scope of the present invention.

Claims

1. An automatic generation system of national land space planning scheme based on artificial intelligence, characterized in that: include: A solution generation model building module is used to build a generation solution model; Data collection module, used to collect data from the target area; The preliminary plan generation module is used to make preliminary plans for the land space of the target area based on the collected data; The scheme adjustment module is used to adjust the preliminary planned scheme; The solution generation model construction module is connected to the solution preliminary generation module, and the solution preliminary generation module is connected to the solution adjustment module.

2. According to the artificial intelligence-based automatic generation system of national land space planning schemes in claim 1, it is characterized in that: The solution generation model building module includes a model building unit, a segmentation unit, and a feature extraction unit; The model building unit is connected to the segmentation unit and is used to build a generation scheme model; The segmentation and division unit is connected to the feature extraction unit and is used to perform map segmentation and division on the target area.

3. According to claim 2, the system for automatically generating land space planning schemes based on artificial intelligence is characterized in that: The data acquisition module includes an information acquisition unit, a segmentation processing unit, and a storage unit; The information collection unit is connected to the segmentation processing unit and is used to collect data information of the target area; The segmentation processing unit is connected to the storage unit and is used to perform segmentation processing on the collected data according to requirements.

4. According to claim 3, the system for automatically generating land space planning schemes based on artificial intelligence is characterized in that: The scheme preliminary generation module includes a regional planning unit, a feature information acquisition unit, an utilization intensity extraction unit, and a matrix establishment unit; The regional planning unit is connected to the feature information acquisition unit and the feature extraction unit respectively, and is used to perform preliminary planning on the target area; The feature extraction unit is connected to the intensity extraction unit and is used to extract the land features of the target area; The utilization intensity extraction unit and the matrix establishment unit are used to extract the utilization intensity of each region; The matrix establishing unit is used to establish a land use intensity matrix.

5. According to claim 4, the system for automatically generating land space planning schemes based on artificial intelligence is characterized in that: The scheme adjustment module includes a simulation and deduction unit, an adjustment unit, and a new scheme generation unit; The simulation and deduction unit is connected to the characteristic information acquisition unit and the adjustment unit respectively, and is used to simulate and deduct the preliminary planning scheme; The adjustment unit is connected to the new scheme generation unit and is used to adjust and improve the abnormal area in the planning scheme; The new solution generating unit is used to generate a new solution for planning.

6. The system for automatically generating land space planning schemes based on artificial intelligence according to claim 2 is characterized in that: The model building unit builds a generation scheme model based on land use intensity data, climate data and regional environment data. The generation scheme model includes a land use intensity data input port, a climate data input port, a regional environment data input port and a scheme output port.

7. The system for automatically generating land space planning schemes based on artificial intelligence according to claim 2 is characterized in that: The segmentation and division unit divides the target area into several regional areas according to the elevation data, each region includes a low-value point, and the regional areas are divided into different types according to the terrain characteristics of the low-value points. The data area contains the longitude and latitude coordinates of each regional area. The low-value point is the point with the lowest altitude in each regional area. The low-value point divides the regional area into two parts, and the altitude of the highest point in the two parts is higher than that of the low-value point. There is only one low-value point in each region.

8. The system for automatically generating land space planning schemes based on artificial intelligence according to claim 7 is characterized in that: The regional area types include plains, hills, mountains and canyons. There is no vegetation in the plain area, and there are towns or villages at the low value points. There are forests, cultivated land, wasteland and grassland in the hilly area. There are forests, cultivated land and grassland in the mountainous area. There are forests in the canyon area. The plain area includes a town area, and the mountainous area includes one or two villages and towns.

9. The system for automatically generating land space planning schemes based on artificial intelligence according to claim 8 is characterized in that: The land quality levels contained in the urban areas and plain areas are level 1 and level 2, the land quality levels corresponding to the villages and towns in the mountainous areas are level 2 or level 3, the land quality levels corresponding to the cultivated land, forest land and grassland are level 2, and the land quality level corresponding to the wasteland is level 3. The land quality is level 1-3.

10. The system for automatically generating land space planning schemes based on artificial intelligence according to claim 8 is characterized in that: The element L in the land use intensity matrix (L) is ij The calculation formula is as follows: L ij =α×I ij +β×E ij +γ×S ij Where i represents the number of the region, j represents the number of the land use type, α, β, γ are weight coefficients, and α+β+γ=1; I ij It represents the land utilization index, which is measured by the land development ratio and building density. For urban construction land in a certain region, if the ratio of developed land area to the total area of ​​the region is p1 and the building density is p2, then I ij =k1×p1+k2×p2, where k1 and k2 are corresponding weight coefficients, determined based on historical data; E ij It represents the economic output index, which is measured by the economic output value per unit land area. For a certain agricultural land, the value of crop output is converted into the output value per unit area v, then E ij =v; for industrial land, it is measured by industrial added value per unit area; S ij It represents the social demand index, taking into account the population density and job density factors. For a region with a population density of d1 and a job density of d2, S ij =m1×d1+m2×d2, m1 and m2 are weight coefficients.

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