Territorial space land partition planning system based on big data

Abstract

The invention discloses a territorial space land zoning planning system based on big data, which belongs to the technical field of territorial space land planning and comprises a data acquisition module, a classification filing module, a zoning planning module and an idle planning module. The data acquisition module is used for collecting historical county and district land planning information, collecting population data, landform information and GDP data of counties and districts at the same time, and transmitting the data to a subsequent module; and the classification filing module is based on the collected county and district population data, landform information and GDP data. According to the territorial space land zoning planning system based on big data, by integrating different types of regional historical territorial planning data and combining the specific type of the current region, decision support is provided for territorial zoning planning of the current region, and the three-region planning proportion of the current region is flexibly adjusted in combination with the local policy, so that the territorial space land zoning planning efficiency is improved. And the types of the idle areas are adjusted according to policy changes, so that the practicability and functionality of the system are enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of national land space planning, and specifically is a national land space zoning planning system based on big data. Background Art

[0002] National land use planning refers to a comprehensive plan for the rational allocation and efficient use of land resources in a country or region. It mainly includes the systematic design and arrangement of the types, functions, layouts, and intensities of land use to achieve the coordinated development of economy, society, and ecology. The existing national land use zoning plan often plans relevant areas based on the "three zones", including urban space, ecological space, and agricultural space.

[0003] The existing land space zoning planning system often conducts mechanized planning for relevant areas based on fixed ratios. Due to the vast territory of my country, the development conditions of various regions are different, and the land occupation ratios of towns, water bodies, and mountainous areas are different. The existing land space zoning planning system cannot adapt well to the specific planning conditions of different counties and districts, and has the problem of low practicality. In addition, the development policies of different provinces and cities are different. The existing land space zoning planning system cannot adjust the division basis according to the specific regional conditions, and has the problem of low functionality. It is also difficult to flexibly adjust the idle areas in the planned areas according to policy changes;

[0004] In response to the above, this case proposes a national land use zoning planning system based on big data to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a national land space zoning planning system based on big data, which solves the above technical problems by improving the detection method and processing method.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A national land space zoning planning system based on big data, including a data collection module, a classification and filing module, a zoning planning module, and an idle planning module;

[0008] The data collection module is used to collect historical county and district land use planning information, as well as population data, geomorphic information, and GDP data of counties and districts, and transmit the data to subsequent modules;

[0009] The classification and archiving module, based on the collected county and district population data, geomorphic information, and GDP data, establishes characteristic planning archives through similarity matching, processes the land planning strategies within the archives, including the division ratios of urban space, agricultural space, and ecological space, and establishes division matching intervals for different archives;

[0010] The zoning planning module matches the characteristic planning archives based on the current county and district population data, landform information, and GDP data, and performs land pre-planning for the current county and district through the division and matching template of the characteristic planning archives. Based on the current provincial and municipal development policies, the pre-planning is feature-checked to obtain the land revision planning ratio;

[0011] The idle land planning module collects statistics on idle land in planned county and district urban spaces and agricultural spaces, analyzes the surrounding environment of the idle land, and conducts secondary planning of the idle land according to the development laws of the surrounding environment.

[0012] Furthermore, the classification and archiving module, based on the collected county and district population data, landform information, and GDP data, establishes characteristic planning archives through similarity matching, processes the land planning strategies within the archives, including the division ratios of urban space, agricultural space, and ecological space, and establishes the division matching intervals of different archives, including the following steps:

[0013] Based on the historical county and district population data, geomorphic information, and GDP data collected by the data collection module, all counties and districts are classified based on geomorphic types, and similarity matching is performed on the counties and districts under the geomorphic classification based on population data and GDP data to establish feature planning archives;

[0014] Based on historical planning proportion data, including the division ratios of urban space, agricultural space, and ecological space, matching intervals for the three-zone divisions of different archives are established.

[0015] Furthermore, according to the historical county and district population data, landform information, and GDP data collected by the data collection module, all counties and districts are classified based on landform types, and similarity matching is performed on the counties and districts under the landform classification based on population data and GDP data to establish a characteristic planning file. The specific steps are:

[0016] Collect the percentage of mountainous areas under the jurisdiction of all counties and districts Urban area share Water area ratio Z represents the total area of ​​the county or district, where:

[0017] Z = M + C + W;

[0018]

[0019] In the above formula, I represents the county or district number. Different mountain area ratio intervals, urban area ratio intervals, and water area ratio intervals are set. The I that meets the same mountain area ratio interval, urban area ratio interval, and water area ratio interval are classified into the same classification file. The specific steps are as follows:

[0020] The mountain area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively;

[0021] The urban area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively;

[0022] The water area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively;

[0023] Divide I into proportional intervals, where I i =(A,B,C), where I i is the proportion interval number of the county or district numbered I, A, B, and C represent the proportion interval of mountain area, urban area, and water area, respectively. i The counties and districts are divided into the same landform classification file;

[0024] According to the population data and GDP data of counties and districts under the geomorphic classification, the population density and GDP density are calculated. The algorithm formula is:

[0025]

[0026] The population density and GDP density are divided into three levels: high, medium and low by the quantile method. For the counties and districts in the same landform classification file, the county and district level grades P are obtained according to the population density and GDP density. i =(population density level, GDP density level), the same P i The counties and districts are divided into the same characteristic planning file.

[0027] Furthermore, the three-district division matching intervals of different archives are established based on the historical planning ratio data, including the division ratios of urban space, agricultural space, and ecological space. The specific steps are as follows:

[0028] Collect the division ratios of urban space, agricultural space, and ecological space in different counties and districts in the same landform classification archive, and arrange the division ratios of urban space, agricultural space, and ecological space in descending order;

[0029] By removing the extreme value method, we remove one minimum value and one maximum value from each group of proportion data, and calculate the mean and standard deviation of the remaining proportion data. Based on the mean and standard deviation, we obtain the division proportion intervals of urban space, agricultural space, and ecological space. The algorithm formula is as follows:

[0030] Urban space division matching interval = [A1-K·σ1, A1+K·σ1];

[0031] Agricultural space division matching interval = [A2-K·σ2, A2+K·σ2];

[0032] Ecological space division matching interval = [A3-K·σ3, A3+K·σ3];

[0033] In the above formula, A1, A2, and A3 represent the mean proportions of urban space, agricultural space, and ecological space, respectively; σ1, σ2, and σ3 represent the standard deviations of the proportions of urban space, agricultural space, and ecological space, respectively; and K represents the coefficient.

[0034] Furthermore, the zoning planning module matches the characteristic planning archives based on the current county and district population data, landform information, and GDP data, and performs land pre-planning for the current county and district through the division and matching template of the characteristic planning archives. Based on the current provincial and municipal development policies, the pre-planning is feature-checked to obtain the land correction planning ratio. The specific steps are as follows:

[0035] Collect the current county and district geomorphic information, calculate the proportion of mountain area, urban area, and water area under the jurisdiction of the current county and district, obtain the proportion interval number of the current county and district, and match it to the geomorphic classification file in the classification filing module according to the proportion interval number;

[0036] Further calculate the population density and GDP density of the current county and district, match the current county and district into the characteristic planning archive in the landform classification archive, and generate the current county and district urban space, agricultural space, and ecological space pre-planning ratios through simple random sampling according to the urban space division matching interval, agricultural space division matching interval, and ecological space division matching interval. Pre-plan the current county and district, where a, b, and c represent the pre-planning ratios of urban space, agricultural space, and ecological space, respectively:

[0037] a∈Current characteristic planning archive urban space division matching interval;

[0038] b∈Current characteristic planning archive agricultural space division matching interval;

[0039] c∈Current characteristic planning archive ecological space division matching interval;

[0040] a+b+c=1;

[0041] In combination with the current provincial and municipal development policies, the preliminary plan is feature checked to obtain the proportion of the revised land plan.

[0042] Furthermore, the above-mentioned steps of combining the current provincial and municipal development policies, performing feature verification on the preliminary planning, and obtaining the land revision planning ratio are as follows:

[0043] By collecting policy documents and special plans within 1 to 5 years, the current provincial and municipal development strategies are determined, including economic priority and agricultural priority, and the adjustment coefficients β1 and β2 are set respectively to adjust the urban space and agricultural space pre-planned by the current counties and districts. The steps are as follows:

[0044] Urban space correction and proofreading planning ratio = a × β1;

[0045] Agricultural space correction and proofreading planning ratio = b × β2;

[0046]

[0047] Based on the revised and proofread planning ratio of urban space and the revised and proofread planning ratio of agricultural space, combined with the pre-planning ratio of ecological space, the current county and district land planning ratio is generated.

[0048] Furthermore, the idle land planning module collects statistics on the idle land in the planned county and district urban space and agricultural space, analyzes the surrounding environment of the idle land, and conducts secondary planning for the idle land according to the development law of the surrounding environment. The specific steps are as follows:

[0049] For urban and agricultural spaces in planned counties and districts, collect information on idle land, including regional type, geographical location, and regional area. At the same time, collect information on the surrounding conditions of idle land, including regional type and development type. Development types include commercial areas, urban residential areas, agricultural areas, and rural residential areas.

[0050] Based on the changes in the local 1-5 year development strategy and the development patterns of the surrounding environment of the idle land, secondary planning adjustments will be made to the idle land.

[0051] Furthermore, for the urban space and agricultural space in the planned counties and districts, the idle land information is collected, including the regional type, geographical location, and regional area, and the surrounding information of the idle land is collected, including the regional type and development type, wherein the development type includes commercial areas, urban residential areas, agricultural areas, and rural residential areas. The specific steps are:

[0052] Obtain urban and agricultural spaces in planned counties and districts through GIS, collect idle land information, collect land use information within X square kilometers around idle land, and determine the proportion of commercial areas O1, urban residential areas O2, agricultural areas O3, and rural residential areas O4 within X kilometers around;

[0053] Based on the proportion of commercial area O1, urban residential area O2, agricultural area O3, and rural residential area O4 within the surrounding X kilometers, the areas are arranged in descending order, and the scores y1, y2, y3, and y4 are set in descending order to calculate the urban zone value and agricultural zone value respectively:

[0054] T1=O1+O2;

[0055] T2=O3+O4;

[0056] In the above formula, O1, O2, O3, and O4 represent the values ​​of commercial areas, urban residential areas, agricultural areas, and rural residential areas in descending order, respectively; T1 and T2 represent the urban zoning value and agricultural zoning value, respectively.

[0057] Furthermore, according to the changes in the local development strategy within 1-5 years and the development law of the surrounding environment of the idle land, the idle land is adjusted for secondary planning. The specific steps are:

[0058] Based on the local 1-5 year development strategy, including economic priority and agricultural priority, secondary planning adjustments are made to idle areas of urban space and agricultural space, including:

[0059] For idle areas in urban space, when the development strategy is agricultural priority, when the idle area T2>T1, the current idle area will be divided into agricultural space, and when T2<T1, no adjustment will be made;

[0060] For idle areas of agricultural space, when the development strategy is economic priority, when the idle area T2>T1, no adjustment will be made. When T2<T1, the current idle area will be divided into urban space.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] 1. In the present invention, by setting up a classification and archiving module, based on the urban area, water area and mountain area of ​​different counties and districts, the landform classification statistics are carried out for the historical urban space, ecological space and agricultural space division ratio of different counties and districts, and the same type of counties and districts are classified into the same characteristic planning file in combination with the population density and GDP density of the counties and districts contained in the landform classification file, so as to ensure that the subsequent planning scheme is more in line with the local reality and reduce the situation of "one size fits all";

[0063] 2. In the present invention, by setting up a zoning planning module in conjunction with a classification and archiving module, the urban space, ecological space, and agricultural space division ratios in the same characteristic planning file are arranged and processed, thereby obtaining the pre-planning intervals of different characteristic planning files, and combining the current district and county development policies to perform feature proofreading on the pre-planning, obtain the land correction planning ratio, ensure that the land space zoning planning scheme of the current region is consistent with the policy orientation, and improve the adaptability and flexibility of the land zoning planning;

[0064] 3. In the present invention, by setting up characteristic planning archives, the closest existing division ratio basis to the current national land space division of counties and districts can be obtained, which can provide scientific data support for the current county and district planning, ensure the rationality and effectiveness of the planning scheme, ensure that the planning is more in line with local actual needs and development strategies, and ensure that the planning scheme meets national and local policy requirements through policy proofreading, thereby promoting the effective implementation of the policy;

[0065] 4. In the present invention, by setting an idle planning module to cooperate with the zoning planning module, the idle areas in the divided areas can be adjusted according to the policy changes of the planned areas in combination with the surrounding conditions and policy changes to avoid waste of land resources. Based on recent policy changes, the idle areas are adjusted to ensure that the current county and district planning schemes are consistent with the current regional adjustment policies, thereby enhancing the practicality and functionality of the system;

[0066] The entire national land use zoning planning system based on big data integrates historical land planning data of different types of regions and combines the specific types of the current region to provide decision-making support for the land zoning planning of the current region. It also combines local policies to flexibly adjust the three-zone planning ratio of the current region and adjust the types of idle areas according to policy changes, thereby enhancing the practicality and functionality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a block diagram of a national land use zoning planning system based on big data according to the present invention. DETAILED DESCRIPTION

[0068] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Embodiment 1:

[0070] like Figure 1 As shown, a national land space zoning planning system based on big data includes a data collection module, a classification and filing module, a zoning planning module, and an idle planning module;

[0071] The data collection module is used to collect historical county and district land use planning information, as well as population data, geomorphic information, and GDP data of counties and districts, and transmit the data to subsequent modules;

[0072] The classification and archiving module, based on the collected county and district population data, geomorphic information, and GDP data, establishes characteristic planning archives through similarity matching, processes the land planning strategies within the archives, including the division ratios of urban space, agricultural space, and ecological space, and establishes the division matching intervals of different archives, including the following steps:

[0073] According to the historical county and district population data, landform information, and GDP data collected by the data collection module, all counties and districts are classified based on landform types, and similarity matching is performed on the counties and districts under the landform classification based on population data and GDP data to establish feature planning files. The specific steps are as follows:

[0074] Collect the percentage of mountainous areas under the jurisdiction of all counties and districts Urban area share Water area ratio Z represents the total area of ​​the county or district, where:

[0075] Z = M + C + W;

[0076]

[0077] In the above formula, I represents the county or district number. Different mountain area ratio intervals, urban area ratio intervals, and water area ratio intervals are set. The I that meets the same mountain area ratio interval, urban area ratio interval, and water area ratio interval are classified into the same classification file. The specific steps are as follows:

[0078] The mountain area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively;

[0079] The urban area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively;

[0080] The water area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively;

[0081] Divide I into proportional intervals, where I i =(A,B,C), where I i is the proportion interval number of the county or district numbered I, A, B, and C represent the proportion interval of mountain area, urban area, and water area, respectively. i The counties and districts are divided into the same landform classification file;

[0082] It should be noted that by calculating the proportion of mountainous area, urban area and water area in different counties and districts and classifying them according to the ratio of the three, the characteristics of regional types can be fully considered to provide a basis for the establishment of subsequent characteristic planning files, see Table 1:

[0083] Table 1 Different counties and districts I i Partition table

[0084] name Mountain area ratio Urban area ratio Water area ratio <![CDATA[I i ]]> County A 42％ 38％ 20％ (5,4,2) County B 41％ 35％ 24％ (5,4,3) County C 30％ 50％ 20％ (3,5,2)

[0085] According to the population data and GDP data of counties and districts under the geomorphic classification, the population density and GDP density are calculated. The algorithm formula is:

[0086]

[0087] The population density and GDP density are divided into three levels: high, medium and low by the quantile method. For the counties and districts in the same landform classification file, the county and district level level P is obtained according to the population density and GDP density. i =(population density level, GDP density level), the same P i The county and district divisions are entered into the same characteristic planning file;

[0088] Based on the historical planning proportion data, including the division ratio of urban space, agricultural space, and ecological space, the three-zone division matching intervals of different archives are established. The specific steps are as follows:

[0089] Collect the division ratios of urban space, agricultural space, and ecological space in different counties and districts in the same landform classification archive, and arrange the division ratios of urban space, agricultural space, and ecological space in descending order;

[0090] By removing the extreme value method, we remove one minimum value and one maximum value from each group of proportion data, and calculate the mean and standard deviation of the remaining proportion data. Based on the mean and standard deviation, we obtain the division proportion intervals of urban space, agricultural space, and ecological space. The algorithm formula is as follows:

[0091] Urban space division matching interval = [A1-K·σ1, A1+K·σ1];

[0092] Agricultural spatial division matching interval = [A2-K·σ2, A2+K·σ2];

[0093] Ecological space division matching interval = [A3-K·σ3, A3+K·σ3];

[0094] In the above formula, A1, A2, and A3 represent the mean proportions of urban space, agricultural space, and ecological space, respectively; σ1, σ2, and σ3 represent the standard deviations of the proportions of urban space, agricultural space, and ecological space, respectively; and K represents the coefficient.

[0095] It should be noted that by removing a minimum value and a maximum value, the impact of extreme values ​​on the results can be reduced, making the proportional interval division more robust. K represents the coefficient, which is usually set to 1. You can also select 2 or 3 as the coefficient according to actual needs. Selecting different coefficients can obtain proportional intervals of different accuracies.

[0096] Embodiment 2:

[0097] The zoning planning module matches the characteristic planning archives based on the current county and district population data, landform information, and GDP data. Through the division and matching template of the characteristic planning archives, the land pre-planning is carried out for the current county and district. Based on the current provincial and municipal development policies, the pre-planning is feature-checked to obtain the land correction planning ratio. The specific steps are as follows:

[0098] Collect the current county and district geomorphic information, calculate the proportion of mountain area, urban area, and water area under the jurisdiction of the current county and district, obtain the proportion interval number of the current county and district, and match it to the geomorphic classification file in the classification filing module according to the proportion interval number;

[0099] Further calculate the population density and GDP density of the current county and district, match the current county and district into the characteristic planning archive in the landform classification archive, and generate the current county and district urban space, agricultural space, and ecological space pre-planning ratios through simple random sampling according to the urban space division matching interval, agricultural space division matching interval, and ecological space division matching interval. Pre-plan the current county and district, where a, b, and c represent the pre-planning ratios of urban space, agricultural space, and ecological space, respectively:

[0100] a∈Current characteristic planning archive urban space division matching interval;

[0101] b∈Current characteristic planning archive agricultural space division matching interval;

[0102] c∈Current characteristic planning archive ecological space division matching interval;

[0103] a+b+c=1;

[0104] Combined with the current provincial and municipal development policies, the preliminary plan is feature checked to obtain the land amendment planning ratio. The specific steps are as follows:

[0105] By collecting policy documents and special plans within 1 to 5 years, the current provincial and municipal development strategies are determined, including economic priority and agricultural priority, and the adjustment coefficients β1 and β2 are set respectively to adjust the urban space and agricultural space pre-planned by the current counties and districts. The steps are as follows:

[0106] Urban space correction and proofreading planning ratio = a × β1;

[0107] Agricultural space correction and proofreading planning ratio = b × β2;

[0108]

[0109] Based on the revised and proofread planning ratio of urban space and the revised and proofread planning ratio of agricultural space, combined with the pre-planning ratio of ecological space, the current county and district land planning ratio is generated.

[0110] It should be noted that policy documents and special plans collected within 1 to 5 years include news, media reports, bulletins and notices issued by local governments. In the case of economic priority, the proportional coefficients β1 and β2 are usually set to 1.25 and 0.75. In the case of agricultural priority, the proportional coefficients β1 and β2 are usually set to 0.75 and 1.25. The proportional coefficients can be flexibly adjusted by consulting experts in related fields to meet the needs of different situations.

[0111] Embodiment 3:

[0112] The idle land planning module counts the idle land in the planned county and district urban space and agricultural space, analyzes the surrounding environment of the idle land, and conducts secondary planning for the idle land according to the development law of the surrounding environment. The specific steps are as follows:

[0113] For the urban and agricultural spaces in the planned counties and districts, collect information on idle land, including regional type, geographical location, and regional area. At the same time, collect information on the surrounding conditions of idle land, including regional type and development type. The development type includes commercial areas, urban residential areas, agricultural areas, and rural residential areas. The specific steps are as follows:

[0114] Obtain urban and agricultural spaces in planned counties and districts through GIS, collect idle land information, collect land use information within X square kilometers around idle land, and determine the proportion of commercial areas O1, urban residential areas O2, agricultural areas O3, and rural residential areas O4 within X kilometers around;

[0115] Based on the proportion of commercial area O1, urban residential area O2, agricultural area O3, and rural residential area O4 within the surrounding X kilometers, the areas are arranged in descending order, and the scores y1, y2, y3, and y4 are set in descending order to calculate the urban zone value and agricultural zone value respectively:

[0116] T1=O1+O2;

[0117] T2=O3+O4;

[0118] In the above formula, O1, O2, O3, and O4 represent the values ​​of commercial areas, urban residential areas, agricultural areas, and rural residential areas in descending order, respectively; T1 and T2 represent the urban zoning value and agricultural zoning value, respectively.

[0119] It should be noted that by arranging the proportions of commercial areas, urban residential areas, agricultural areas, and rural residential areas within X kilometers of the idle area in descending order, the surrounding environment of the idle area can be obtained. The value of X can be set by consulting experts in related fields, and is usually set to 6.5. By arranging the commercial areas, urban residential areas, agricultural areas, and rural residential areas in descending order and setting scores, it can help determine the subsequent development of the relevant idle areas. Among them, y1, y2, y3, and y4 are set to 8.5, 7, 4.5, and 2.5 respectively, and can also be adjusted by consulting experts in related fields.

[0120] According to the changes in the local development strategy within 1-5 years and the development law of the surrounding environment of the idle land, the idle land is adjusted for secondary planning. The specific steps are as follows:

[0121] Based on the local 1-5 year development strategy, including economic priority and agricultural priority, secondary planning adjustments are made to idle areas of urban space and agricultural space, including:

[0122] For idle areas in urban space, when the development strategy is agricultural priority, when the idle area T2>T1, the current idle area will be divided into agricultural space, and when T2<T1, no adjustment will be made;

[0123] For idle areas of agricultural space, when the development strategy is economic priority, when the idle area T2>T1, no adjustment will be made. When T2<T1, the current idle area will be divided into urban space.

[0124] It should be noted that by comparing the urban zoning value and the agricultural zoning value of the surrounding environment of idle areas, the use of idle areas can be flexibly adjusted according to different development strategies, so that land resources can be used more efficiently. Regardless of whether economic priority or agricultural priority, idle areas can be planned in a targeted manner to meet the development needs of a specific period.

[0125] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0126] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A national land use zoning planning system based on big data, characterized by: It includes data collection module, classification and archiving module, zoning planning module and idle planning module; The data collection module is used to collect historical county and district land use planning information, as well as population data, geomorphic information, and GDP data of counties and districts, and transmit the data to subsequent modules; The classification and archiving module, based on the collected county and district population data, geomorphic information, and GDP data, establishes characteristic planning archives through similarity matching, processes the land planning strategies within the archives, including the division ratios of urban space, agricultural space, and ecological space, and establishes division matching intervals for different archives; The zoning planning module matches the characteristic planning archives based on the current county and district population data, landform information, and GDP data, and performs land pre-planning for the current county and district through the division and matching template of the characteristic planning archives. Based on the current provincial and municipal development policies, the pre-planning is feature-checked to obtain the land revision planning ratio; The idle land planning module collects statistics on idle land in planned county and district urban spaces and agricultural spaces, analyzes the surrounding environment of the idle land, and conducts secondary planning of the idle land according to the development laws of the surrounding environment.

2. According to the big data-based national land use zoning planning system of claim 1, it is characterized by: The classification and archiving module, based on the collected county and district population data, landform information, and GDP data, establishes characteristic planning archives through similarity matching, processes the land planning strategies within the archives, including the division ratios of urban space, agricultural space, and ecological space, and establishes the division matching intervals of different archives, including the following steps: Based on the historical county and district population data, geomorphic information, and GDP data collected by the data collection module, all counties and districts are classified based on geomorphic types, and similarity matching is performed on the counties and districts under the geomorphic classification based on population data and GDP data to establish feature planning archives; Based on historical planning proportion data, including the division ratios of urban space, agricultural space, and ecological space, matching intervals for the three-zone divisions of different archives are established.

3. The national land use zoning planning system based on big data according to claim 2 is characterized by: The historical county and district population data, landform information, and GDP data collected by the data collection module are classified based on landform types, and similarity matching is performed on the counties and districts under the landform classification based on population data and GDP data to establish a feature planning file. The specific steps are: Collect the percentage of mountainous areas under the jurisdiction of all counties and districts Urban area share Water area ratio Z represents the total area of ​​the county or district, where: Z = M + C + W; In the above formula, I represents the county or district number. Different mountain area ratio intervals, urban area ratio intervals, and water area ratio intervals are set. The I that meets the same mountain area ratio interval, urban area ratio interval, and water area ratio interval are classified into the same classification file. The specific steps are as follows: The mountain area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively; The urban area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively; The water area ratio is divided into the following intervals: (0%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], (50%, 60%], (60%, 70%], (70%, 80%], (80%, 90%], (90%, 100%], representing intervals 1 to 10 respectively; Divide I into proportional intervals, where I i =(A,B,C), where I i is the proportion interval number of the county or district numbered I, A, B, and C represent the proportion interval of mountain area, urban area, and water area, respectively. i The counties and districts are divided into the same landform classification file; According to the population data and GDP data of counties and districts under the geomorphic classification, the population density and GDP density are calculated. The algorithm formula is: The population density and GDP density are divided into three levels: high, medium and low by the quantile method. For the counties and districts in the same landform classification file, the county and district level grades P are obtained according to the population density and GDP density. i =(population density level, GDP density level), the same P i The counties and districts are divided into the same characteristic planning file.

4. The national land space zoning planning system based on big data according to claim 2 is characterized by: The above-mentioned historical planning proportion data, including the division proportions of urban space, agricultural space, and ecological space, establishes the three-district division matching intervals of different archives, and the specific steps are as follows: Collect the division ratios of urban space, agricultural space, and ecological space in different counties and districts in the same landform classification archive, and arrange the division ratios of urban space, agricultural space, and ecological space in descending order; By removing the extreme value method, we remove one minimum value and one maximum value from each group of proportion data, and calculate the mean and standard deviation of the remaining proportion data. Based on the mean and standard deviation, we obtain the division proportion intervals of urban space, agricultural space, and ecological space. The algorithm formula is as follows: Urban space division matching interval = [A1-K·σ1, A1+K·σ1]; Agricultural space division matching interval = [A2-K·σ2, A2+K·σ2]; Ecological space division matching interval = [A3-K·σ3, A3+K·σ3]; In the above formula, A1, A2, and A3 represent the mean proportions of urban space, agricultural space, and ecological space, respectively; σ1, σ2, and σ3 represent the standard deviations of the proportions of urban space, agricultural space, and ecological space, respectively; and K represents the coefficient.

5. The national land use zoning planning system based on big data according to claim 1 is characterized by: The zoning planning module matches the characteristic planning archives based on the current county and district population data, landform information, and GDP data. Through the division and matching template of the characteristic planning archives, the land pre-planning is carried out for the current county and district. Based on the current provincial and municipal development policies, the pre-planning is feature-checked to obtain the land correction planning ratio. The specific steps are as follows: Collect the current county and district geomorphic information, calculate the proportion of mountain area, urban area, and water area under the jurisdiction of the current county and district, obtain the proportion interval number of the current county and district, and match it to the geomorphic classification file in the classification filing module according to the proportion interval number; Further calculate the population density and GDP density of the current county and district, match the current county and district into the characteristic planning archive in the landform classification archive, and generate the current county and district urban space, agricultural space, and ecological space pre-planning ratios through simple random sampling according to the urban space division matching interval, agricultural space division matching interval, and ecological space division matching interval. Pre-plan the current county and district, where a, b, and c represent the pre-planning ratios of urban space, agricultural space, and ecological space, respectively: a∈Current characteristic planning archive urban space division matching interval; b∈Current characteristic planning archive agricultural space division matching interval; c∈Current characteristic planning archive ecological space division matching interval; a+b+c=1; In combination with the current provincial and municipal development policies, the preliminary plan is feature checked to obtain the proportion of the revised land plan.

6. The national land use zoning planning system based on big data according to claim 5 is characterized by: The above-mentioned steps of combining the current provincial and municipal development policies to perform feature verification on the preliminary plan and obtain the land amendment planning ratio are as follows: By collecting policy documents and special plans within 1 to 5 years, the current provincial and municipal development strategies are determined, including economic priority and agricultural priority, and the adjustment coefficients β1 and β2 are set respectively to adjust the urban space and agricultural space pre-planned by the current counties and districts. The steps are as follows: Urban space correction and proofreading planning ratio = a × β1; Agricultural space correction and proofreading planning ratio = b × β2; Based on the revised and proofread planning ratio of urban space and the revised and proofread planning ratio of agricultural space, combined with the pre-planning ratio of ecological space, the current county and district land planning ratio is generated.

7. The national land use zoning planning system based on big data according to claim 1 is characterized by: The idle land planning module collects statistics on idle land in planned county and district urban spaces and agricultural spaces, analyzes the surrounding environment of the idle land, and conducts secondary planning for the idle land according to the development law of the surrounding environment. The specific steps are as follows: For urban and agricultural spaces in planned counties and districts, collect information on idle land, including regional type, geographical location, and regional area. At the same time, collect information on the surrounding conditions of idle land, including regional type and development type. Development types include commercial areas, urban residential areas, agricultural areas, and rural residential areas. Based on the changes in the local 1-5 year development strategy and the development patterns of the surrounding environment of the idle land, secondary planning adjustments will be made to the idle land.

8. The national land use zoning planning system based on big data according to claim 7 is characterized by: For the urban and agricultural spaces in the planned counties and districts, the idle land information is collected, including the regional type, geographical location, and regional area. At the same time, the surrounding information of the idle land is collected, including the regional type and development type. The development type includes commercial areas, urban residential areas, agricultural areas, and rural residential areas. The specific steps are as follows: Obtain urban and agricultural spaces in planned counties and districts through GIS, collect idle land information, collect land use information within X square kilometers around idle land, and determine the proportion of commercial areas O1, urban residential areas O2, agricultural areas O3, and rural residential areas O4 within X kilometers around; Based on the proportion of commercial area O1, urban residential area O2, agricultural area O3, and rural residential area O4 within the surrounding X kilometers, the areas are arranged in descending order, and the scores y1, y2, y3, and y4 are set in descending order to calculate the urban zone value and agricultural zone value respectively: T1=O1+O2; T2=O3+O4; In the above formula, O1, O2, O3, and O4 represent the values ​​of commercial areas, urban residential areas, agricultural areas, and rural residential areas in descending order, respectively; T1 and T2 represent the urban zoning value and agricultural zoning value, respectively.

9. The national land use zoning planning system based on big data according to claim 8 is characterized by: According to the changes in the local development strategy within 1-5 years and the development law of the surrounding environment of the idle land, the idle land is adjusted for secondary planning. The specific steps are as follows: Based on the local 1-5 year development strategy, including economic priority and agricultural priority, secondary planning adjustments are made to idle areas of urban space and agricultural space, including: For idle areas in urban space, when the development strategy is agricultural priority, when the idle area T2>T1, the current idle area will be divided into agricultural space, and when T2<T1, no adjustment will be made; For idle areas of agricultural space, when the development strategy is economic priority, when the idle area T2>T1, no adjustment will be made. When T2<T1, the current idle area will be divided into urban space.

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