Land space planning intelligent index accurate calculation algorithm system and method thereof

Abstract

The embodiment of the invention relates to the technical field of territorial space planning, and particularly discloses a territorial space planning intelligent index accurate calculation algorithm system and a method thereof. According to the embodiment of the invention, multi-source spatial data of a target planning area are collected; dividing the target planning area into a plurality of measurement and calculation units; carrying out normalization processing, and calculating the calculation scores of the plurality of calculation units; and constructing a multi-dimensional spatial distribution diagram of the target planning area, and dividing the target planning area into a plurality of spatial functional areas. The method can collect multi-source spatial data of a target planning area, divide the target planning area into a plurality of measurement and calculation units, perform data extraction and normalization processing on a plurality of measurement and calculation indexes, calculate measurement and calculation scores of the plurality of measurement and calculation units, construct a multi-dimensional spatial distribution map, and divide a plurality of spatial functional areas, and has consideration of dynamic adjustment and regional difference. Automatic index measurement and calculation of land space planning can be realized, and the accuracy and the stability of a measurement and calculation result are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of national land space planning, and in particular relates to a national land space planning intelligent indicator precise measurement algorithm system and method. Background Art

[0002] National land space planning is a comprehensive plan that coordinates and scientifically allocates multiple factors, including natural resources, population distribution, economic development, and the ecological environment, within a specific region. Its core purpose is to achieve efficient utilization, coordinated development, and sustainable management of national land space, ensuring an organic balance between economic and social development and ecological and environmental protection. It covers not only urban and rural construction, industrial layout, and infrastructure development, but also various control factors such as land use, ecological red lines, permanent basic farmland, and urban development boundaries. Strategic, comprehensive, and binding, it is a unified planning system formed by integrating existing urban and rural planning, land use planning, and major functional area planning within the context of multi-plan integration.

[0003] In the existing technology, the measurement of indicators for national land space planning usually relies on subjective manual setting of indicator systems and weights, through simple empirical scoring and weighted summation. The correlation between various indicators is poor, and the selection of indicators is highly arbitrary. There is a lack of dynamic adjustment and consideration of regional differences, and the accuracy and stability of the indicator measurement results are insufficient. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an algorithm system and method for accurately measuring intelligent indicators of national land space planning, aiming to solve the problems raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A precise measurement algorithm system for intelligent indicators of national land space planning, comprising a data acquisition and processing unit, an indicator data extraction unit, a measurement score calculation unit, and a spatial multi-dimensional division unit, wherein:

[0007] A data acquisition and processing unit is used to determine a target planning area, collect multi-source spatial data of the target planning area, and perform unified processing and missing supplementation on the multi-source spatial data to obtain complete spatial data;

[0008] An indicator data extraction unit is used to divide the target planning area into a plurality of measurement units, determine a plurality of measurement indicators, and extract indicator feature data corresponding to the plurality of measurement units from the complete spatial data;

[0009] A measurement score calculation unit, configured to normalize the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data, generate multiple indicator normalized values, and calculate the measurement scores of the multiple measurement units;

[0010] The spatial multi-dimensional division unit is used to construct a multi-dimensional spatial distribution map of the target planning area according to the multiple measurement scores, and to perform spatial clustering planning to divide the target planning area into multiple spatial functional areas.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the data acquisition and processing unit specifically includes:

[0012] A target area determination module is used to determine the target planning area;

[0013] A multi-source acquisition module for collecting remote sensing image data, GIS spatial data, planning data and social statistical data of the target planning area;

[0014] A comprehensive collating module is used to comprehensively collate the remote sensing image data, the GIS spatial data, the planning data and the social statistical data to generate multi-source spatial data;

[0015] A unified processing module, configured to perform coordinate unification and resolution normalization on the multi-source spatial data to generate multi-source unified data;

[0016] The missing complement module is used to complement the missing parts of the multi-source unified data to generate complete spatial data.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, the indicator data extraction unit specifically includes:

[0018] Unit parameter acquisition module, used to obtain unit grid parameters;

[0019] A regional unit division module is used to perform grid processing on the target planning area according to the unit grid parameters, and divide the target planning area into multiple measurement units;

[0020] A measurement indicator determination module, used to determine multiple measurement indicators;

[0021] The indicator feature data extraction module is used to extract the indicator feature data of the multiple measurement indicators corresponding to the multiple measurement units from the complete spatial data.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, the measurement score calculation unit specifically includes:

[0023] A normalization processing module, configured to perform normalization processing on a plurality of measurement indicators corresponding to a plurality of measurement units according to a plurality of the indicator characteristic data, and calculate a plurality of normalized values ​​of the indicators;

[0024] An indicator proportion determination module, configured to determine the indicator proportions of a plurality of the measurement indicators of a plurality of the measurement units;

[0025] An indicator entropy value determination module, configured to determine the indicator entropy values ​​of a plurality of measurement indicators according to the proportions of the plurality of indicators;

[0026] An indicator weight calculation module, used to calculate the indicator weights of multiple measurement indicators based on the multiple indicator entropy values;

[0027] The measurement score calculation module is used to calculate the measurement scores of multiple measurement units based on multiple indicator weights and multiple indicator normalization values.

[0028] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula for the normalized values ​​of the multiple indicators is:

[0029]

[0030] Among them, i represents the i-th measurement unit, j represents the j-th measurement indicator, Q′ ij is the normalized value of the jth measurement indicator of the i-th measurement unit, Q ij is the initial characteristic value of the jth measurement indicator of the i-th measurement unit, min(Q j ) is the maximum value of the jth measurement indicator, min(Q j ) is the minimum value of the jth measurement indicator;

[0031] The calculation formula for the weights of the multiple indicators is:

[0032]

[0033] Among them, w j is the indicator weight of the jth measurement indicator, E j is the index entropy value of the jth measurement index;

[0034] The calculation formula for the multiple measurement scores is:

[0035]

[0036] Among them, N i is the measurement score of the i-th measurement unit, and there are m measurement indicators in total.

[0037] As a further limitation of the technical solution of the embodiment of the present invention, the multi-dimensional spatial division unit specifically includes:

[0038] A measurement score distribution map construction module is used to directly construct a measurement score distribution map of the target planning area based on the plurality of measurement scores;

[0039] A suitability grade distribution map construction module is used to perform grade classification according to the plurality of measurement scores, determine a plurality of suitability grades, and construct a suitability grade distribution map of the target planning area;

[0040] A multidimensional space distribution map generating module, configured to generate a multidimensional space distribution map of a target planning area by combining the calculated score distribution map and the suitability level distribution map;

[0041] The similarity clustering division module is used to perform similarity clustering planning on the target planning area and divide the target planning area into multiple spatial functional areas.

[0042] A method for accurately calculating intelligent indicators of national land space planning includes the following steps:

[0043] Determine the target planning area, collect multi-source spatial data of the target planning area, and perform unified processing and missing supplement on the multi-source spatial data to obtain complete spatial data;

[0044] Dividing the target planning area into a plurality of measurement units, determining a plurality of measurement indicators, and extracting indicator feature data corresponding to the plurality of measurement units from the complete spatial data;

[0045] Normalizing the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data to generate multiple indicator normalized values, and calculating the measurement scores of the multiple measurement units;

[0046] Based on the multiple measurement scores, a multidimensional spatial distribution map of the target planning area is constructed, and spatial clustering planning is performed to divide the target planning area into multiple spatial functional areas.

[0047] As a further limitation of the technical solution of the embodiment of the present invention, the determining of the target planning area, collecting multi-source spatial data of the target planning area, and performing unified processing and missing supplementation on the multi-source spatial data to obtain complete spatial data specifically include the following steps:

[0048] Determine the target planning area;

[0049] Collect remote sensing image data, GIS spatial data, planning data and social statistical data of the target planning area;

[0050] Comprehensively collating the remote sensing image data, the GIS spatial data, the planning data, and the social statistical data to generate multi-source spatial data;

[0051] performing coordinate unification and resolution normalization on the multi-source spatial data to generate multi-source unified data;

[0052] The multi-source unified data is supplemented for missing parts to generate complete spatial data.

[0053] As a further limitation of the technical solution of the embodiment of the present invention, dividing the target planning area into a plurality of measurement units, determining a plurality of measurement indicators, and extracting the indicator feature data corresponding to the plurality of measurement units from the complete spatial data specifically comprises the following steps:

[0054] Get the unit grid parameters;

[0055] Performing gridding processing on the target planning area according to the unit grid parameters, and dividing the target planning area into a plurality of measurement units;

[0056] Determine multiple measurement indicators;

[0057] The indicator characteristic data of the multiple measurement indicators corresponding to the multiple measurement units are extracted from the complete spatial data.

[0058] As a further limitation of the technical solution of the embodiment of the present invention, the normalizing of the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data to generate multiple indicator normalized values, and calculating the measurement scores of the multiple measurement units specifically includes the following steps:

[0059] According to the plurality of indicator characteristic data, normalizing the plurality of measurement indicators corresponding to the plurality of measurement units, and calculating the normalized values ​​of the plurality of indicators;

[0060] determining the indicator proportions of the plurality of measurement indicators of the plurality of measurement units;

[0061] Determining the index entropy values ​​of the multiple measurement indicators according to the proportions of the multiple indicators;

[0062] Calculating the indicator weights of the multiple measurement indicators based on the multiple indicator entropy values;

[0063] The measurement scores of the multiple measurement units are calculated based on the multiple indicator weights and the multiple indicator normalized values.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] The embodiment of the present invention collects multi-source spatial data of the target planning area; divides the target planning area into multiple measurement units; performs normalization processing, calculates the measurement scores of the multiple measurement units; constructs a multi-dimensional spatial distribution map of the target planning area, and divides the target planning area into multiple spatial functional areas. The embodiment of the present invention can collect multi-source spatial data of the target planning area, divide the target planning area into multiple measurement units, extract and normalize data on multiple measurement indicators, calculate the measurement scores of multiple measurement units, construct a multi-dimensional spatial distribution map, and divide multiple spatial functional areas. It has dynamic adjustment and regional differences. It can realize automatic indicator measurement of national land space planning and effectively improve the accuracy and stability of standard measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0067] Figure 1 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0068] Figure 2 The structure block diagram of the data acquisition and processing unit in the system provided by the embodiment of the present invention is shown.

[0069] Figure 3 The figure shows a structural block diagram of an indicator data extraction unit in a system provided by an embodiment of the present invention.

[0070] Figure 4 The figure shows a structural block diagram of a measurement score calculation unit in a system provided by an embodiment of the present invention.

[0071] Figure 5 The structure block diagram of the spatial multi-dimensional division unit in the system provided by the embodiment of the present invention is shown.

[0072] Figure 6 A schematic flow chart of the method provided by an embodiment of the present invention is shown.

[0073] Figure 7 The flowchart of multi-source spatial data acquisition and processing in the method provided by the embodiment of the present invention is shown.

[0074] Figure 8 A flow chart of extracting indicator feature data in the method provided by an embodiment of the present invention is shown.

[0075] Figure 9 A flow chart of calculating the measurement scores of multiple measurement units in the method provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0077] It is understandable that in the existing technology, the measurement of indicators for national land space planning usually relies on subjective manual setting of indicator systems and weights, through simple empirical scoring and weighted summation. The correlation between various indicators is poor, and the selection of indicators is highly arbitrary. There is a lack of dynamic adjustment and consideration of regional differences, and the accuracy and stability of the indicator measurement results are insufficient.

[0078] To solve the above problems, the embodiment of the present invention determines the target planning area, collects multi-source spatial data of the target planning area, and performs unified processing and missing supplementation on the multi-source spatial data to obtain complete spatial data; divides the target planning area into multiple measurement units, determines multiple measurement indicators, and extracts the indicator feature data corresponding to the multiple measurement units from the complete spatial data; normalizes the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data, generates multiple indicator normalized values, and calculates the measurement scores of the multiple measurement units; constructs a multidimensional spatial distribution map of the target planning area based on the multiple measurement scores, and performs spatial clustering planning to divide the target planning area into multiple spatial functional areas. The multi-source spatial data of the target planning area can be collected, the target planning area can be divided into multiple measurement units, data extraction and normalization processing can be performed on multiple measurement indicators, measurement scores of multiple measurement units can be calculated, a multidimensional spatial distribution map can be constructed, and multiple spatial functional areas can be divided. It has dynamic adjustment and regional differences. It can realize automatic indicator measurement of national land space planning and effectively improve the accuracy and stability of standard measurement results.

[0079] Figure 1 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0080] Specifically, a precise calculation algorithm system for intelligent indicators of national land space planning includes:

[0081] The data acquisition and processing unit 101 is used to determine a target planning area, acquire multi-source spatial data of the target planning area, and perform unified processing and missing supplementation on the multi-source spatial data to obtain complete spatial data.

[0082] In an embodiment of the present invention, the data acquisition and processing unit 101 determines the target planning area, collects remote sensing image data (including land use, vegetation index, surface temperature, etc.), GIS spatial data (including DEM, slope and aspect, water system, transportation network, administrative division, etc.), planning data (including ecological red line, permanent basic farmland, urban development boundary, etc.) and social statistical data (including population density, GDP, construction land intensity, etc.) of the target planning area, and then comprehensively organizes the remote sensing image data, GIS spatial data, planning data and social statistical data to obtain multi-source spatial data of the target planning area. Thereafter, the multi-source spatial data is coordinate unified and resolution normalized to generate multi-source unified data, and the multi-source unified data is supplemented for missing data to generate complete spatial data.

[0083] Specifically, Figure 2 FIG. 1 shows a structural block diagram of the data acquisition and processing unit 101 in the system provided by an embodiment of the present invention.

[0084] In a preferred embodiment of the present invention, the data acquisition and processing unit 101 specifically includes:

[0085] The target area determination module 1011 is used to determine the target planning area;

[0086] A multi-source acquisition module 1012 is used to acquire remote sensing image data, GIS spatial data, planning data and social statistical data of the target planning area;

[0087] A comprehensive arrangement module 1013 is used to comprehensively arrange the remote sensing image data, the GIS spatial data, the planning data and the social statistical data to generate multi-source spatial data;

[0088] A unified processing module 1014 is configured to perform coordinate unification and resolution normalization on the multi-source spatial data to generate multi-source unified data;

[0089] The missing complement module 1015 is used to complement the missing parts of the multi-source unified data to generate complete spatial data.

[0090] Furthermore, the national land space planning intelligent indicator precise calculation algorithm system also includes:

[0091] The indicator data extraction unit 102 is used to divide the target planning area into multiple measurement units, determine multiple measurement indicators, and extract indicator feature data corresponding to the multiple measurement units from the complete spatial data.

[0092] In an embodiment of the present invention, the indicator data extraction unit 102 obtains unit grid parameters (such as a unit grid size of 1km×1km), grids the target planning area according to the unit grid parameters, divides the target planning area into multiple measurement units, and then determines multiple measurement indicators (including the proportion of cultivated land, surface water resource density, construction land carrying capacity, slope, traffic accessibility, population density, ecological red line ratio, biodiversity index, water source protection area ratio, etc.), and then extracts the indicator characteristic data of multiple measurement indicators corresponding to multiple measurement units from the complete spatial data.

[0093] Specifically, Figure 3 It shows a structural block diagram of the indicator data extraction unit 102 in the system provided by an embodiment of the present invention.

[0094] In a preferred embodiment of the present invention, the indicator data extraction unit 102 specifically includes:

[0095] The unit parameter acquisition module 1021 is used to obtain unit grid parameters;

[0096] The regional unit division module 1022 is used to perform gridding processing on the target planning area according to the unit grid parameters, and divide the target planning area into multiple measurement units;

[0097] The measurement index determination module 1023 is used to determine multiple measurement indicators;

[0098] The indicator feature data extraction module 1024 is used to extract the indicator feature data of the multiple measurement indicators corresponding to the multiple measurement units from the complete spatial data.

[0099] Furthermore, the national land space planning intelligent indicator precise calculation algorithm system also includes:

[0100] The measurement score calculation unit 103 is used to normalize the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data, generate multiple indicator normalized values, and calculate the measurement scores of the multiple measurement units.

[0101] In an embodiment of the present invention, the measurement score calculation unit 103 normalizes the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data, calculates the multiple indicator normalized values, and determines the indicator proportions of the multiple measurement indicators of the multiple measurement units. According to the multiple indicator proportions, the indicator entropy values ​​of the multiple measurement indicators are determined, and then the indicator weights of the multiple measurement indicators are calculated according to the multiple indicator entropy values. Then, according to the multiple indicator weights and the multiple indicator normalized values, the measurement scores of the multiple measurement units are calculated. Specifically, the calculation formula of the multiple indicator normalized values ​​is:

[0102]

[0103] Among them, i represents the i-th measurement unit, j represents the j-th measurement indicator, Q′ ij is the normalized value of the jth measurement indicator of the i-th measurement unit, Q ij is the initial characteristic value of the jth measurement indicator of the i-th measurement unit, min(Q j ) is the maximum value of the jth measurement indicator, min(Q j ) is the minimum value of the jth measurement indicator;

[0104] The calculation formula for multiple indicator weights is:

[0105]

[0106] Among them, w j is the indicator weight of the jth measurement indicator, E j is the index entropy value of the jth measurement index;

[0107] The calculation formula for multiple measurement scores is:

[0108]

[0109] Among them, N i is the measurement score of the i-th measurement unit, and there are m measurement indicators in total.

[0110] Specifically, Figure 4 FIG. 1 shows a structural block diagram of the measurement score calculation unit 103 in the system provided by an embodiment of the present invention.

[0111] In a preferred embodiment of the present invention, the measurement score calculation unit 103 specifically includes:

[0112] A normalization processing module 1031 is configured to perform normalization processing on a plurality of measurement indicators corresponding to a plurality of measurement units according to the plurality of indicator characteristic data, and calculate normalized values ​​of the plurality of indicators;

[0113] An indicator proportion determination module 1032 is used to determine the indicator proportions of the plurality of measurement indicators of the plurality of measurement units;

[0114] An indicator entropy value determination module 1033 is configured to determine the indicator entropy values ​​of a plurality of measurement indicators according to the proportions of the plurality of indicators;

[0115] An indicator weight calculation module 1034 is used to calculate the indicator weights of multiple measurement indicators based on the multiple indicator entropy values;

[0116] The measurement score calculation module 1035 is used to calculate the measurement scores of the multiple measurement units based on the multiple indicator weights and the multiple indicator normalized values.

[0117] Furthermore, the national land space planning intelligent indicator precise calculation algorithm system also includes:

[0118] The spatial multi-dimensional division unit 104 is used to construct a multi-dimensional spatial distribution map of the target planning area according to the multiple measurement scores, and perform spatial clustering planning to divide the target planning area into multiple spatial functional areas.

[0119] In an embodiment of the present invention, the spatial multidimensional division unit 104 directly constructs a measurement score distribution map of the target planning area based on multiple measurement scores, and performs level division according to the multiple measurement scores to determine multiple suitability levels (including low suitability, medium suitability and high suitability), and constructs a suitability level distribution map of the target planning area. The measurement score distribution map and the suitability level distribution map are then combined to generate a multidimensional spatial distribution map of the target planning area, and similarity clustering planning is performed on the target planning area to divide the target planning area into multiple spatial functional areas (including ecologically sensitive protection areas, key development and construction areas, and control construction buffer zones, etc.).

[0120] Specifically, Figure 5 FIG. 1 shows a structural block diagram of the spatial multi-dimensional division unit 104 in the system provided by an embodiment of the present invention.

[0121] In a preferred embodiment of the present invention, the multi-dimensional spatial division unit 104 specifically includes:

[0122] A calculation score distribution map construction module 1041 is used to directly construct a calculation score distribution map of the target planning area based on the plurality of the calculation scores;

[0123] A suitability level distribution map construction module 1042 is configured to perform level classification according to the plurality of measured scores, determine a plurality of suitability levels, and construct a suitability level distribution map for the target planning area;

[0124] A multi-dimensional space distribution map generating module 1043 is configured to generate a multi-dimensional space distribution map of a target planning area by combining the calculated score distribution map and the suitability level distribution map;

[0125] The similarity clustering division module 1044 is used to perform similarity clustering planning on the target planning area and divide the target planning area into multiple spatial functional areas.

[0126] Further, Figure 6 A schematic flow chart of the method provided by an embodiment of the present invention is shown.

[0127] Specifically, in another preferred embodiment of the present invention, a precise calculation algorithm method for intelligent indicators of national land space planning is provided, and the method specifically comprises the following steps:

[0128] Step S101 : determining a target planning area, collecting multi-source spatial data of the target planning area, and performing unified processing and missing supplementation on the multi-source spatial data to obtain complete spatial data.

[0129] In an embodiment of the present invention, by determining the target planning area, remote sensing image data (including land use, vegetation index, surface temperature, etc.), GIS spatial data (including DEM, slope and aspect, water system, transportation network, administrative division, etc.), planning data (including ecological red line, permanent basic farmland, urban development boundary, etc.) and social statistical data (including population density, GDP, construction land intensity, etc.) of the target planning area are collected, and then the remote sensing image data, GIS spatial data, planning data and social statistical data are comprehensively sorted to obtain multi-source spatial data of the target planning area. After that, the multi-source spatial data is coordinate unified and the resolution normalized to generate multi-source unified data, and the missing parts of the multi-source unified data are supplemented to generate complete spatial data.

[0130] Specifically, Figure 7 The flowchart of multi-source spatial data acquisition and processing in the method provided by the embodiment of the present invention is shown.

[0131] In a preferred embodiment of the present invention, determining the target planning area, collecting multi-source spatial data of the target planning area, and performing unified processing and missing supplementation on the multi-source spatial data to obtain complete spatial data specifically include the following steps:

[0132] Step S1011, determining the target planning area;

[0133] Step S1012, collecting remote sensing image data, GIS spatial data, planning data and social statistical data of the target planning area;

[0134] Step S1013, comprehensively arranging the remote sensing image data, the GIS spatial data, the planning data, and the social statistical data to generate multi-source spatial data;

[0135] Step S1014, performing coordinate unification and resolution normalization on the multi-source spatial data to generate multi-source unified data;

[0136] Step S1015 : supplement the missing parts of the multi-source unified data to generate complete spatial data.

[0137] Furthermore, the land space planning intelligent indicator precise calculation algorithm method further includes the following steps:

[0138] Step S102: Divide the target planning area into a plurality of measurement units, determine a plurality of measurement indicators, and extract indicator feature data corresponding to the plurality of measurement units from the complete spatial data.

[0139] In an embodiment of the present invention, unit grid parameters are obtained (such as a unit grid size of 1km×1km), and the target planning area is gridded according to the unit grid parameters. The target planning area is divided into multiple measurement units, and then multiple measurement indicators are determined (including the proportion of cultivated land, surface water resource density, construction land carrying capacity, slope, traffic accessibility, population density, ecological red line proportion, biodiversity index, water source protection area proportion, etc.), and then the indicator characteristic data of multiple measurement indicators corresponding to the multiple measurement units are extracted from the complete spatial data.

[0140] Specifically, Figure 8 A flow chart of extracting indicator feature data in the method provided by an embodiment of the present invention is shown.

[0141] In a preferred embodiment of the present invention, dividing the target planning area into a plurality of measurement units, determining a plurality of measurement indicators, and extracting the indicator feature data corresponding to the plurality of measurement units from the complete spatial data specifically comprises the following steps:

[0142] Step S1021, obtaining unit grid parameters;

[0143] Step S1022: performing gridding processing on the target planning area according to the unit grid parameters, and dividing the target planning area into a plurality of measurement units;

[0144] Step S1023, determining multiple measurement indicators;

[0145] Step S1024: extracting, from the complete spatial data, the indicator characteristic data of the multiple measurement indicators corresponding to the multiple measurement units.

[0146] Furthermore, the land space planning intelligent indicator precise calculation algorithm method further includes the following steps:

[0147] Step S103 , normalizing the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data, generating multiple indicator normalized values, and calculating the measurement scores of the multiple measurement units.

[0148] In an embodiment of the present invention, according to multiple indicator feature data, multiple measurement indicators corresponding to multiple measurement units are normalized, multiple indicator normalized values ​​are calculated, and indicator proportions of multiple measurement indicators of multiple measurement units are determined. According to the multiple indicator proportions, the indicator entropy values ​​of the multiple measurement indicators are determined. Then, according to the multiple indicator entropy values, the indicator weights of the multiple measurement indicators are calculated. Then, according to the multiple indicator weights and the multiple indicator normalized values, the measurement scores of the multiple measurement units are calculated. Specifically, the calculation formula of the multiple indicator normalized values ​​is:

[0149]

[0150] Among them, i represents the i-th measurement unit, j represents the j-th measurement indicator, Q′ ij is the normalized value of the jth measurement indicator of the i-th measurement unit, Q ij is the initial characteristic value of the jth measurement indicator of the i-th measurement unit, min(Q j ) is the maximum value of the jth measurement indicator, min(Q j ) is the minimum value of the jth measurement indicator;

[0151] The calculation formula for multiple indicator weights is:

[0152]

[0153] Among them, w j is the indicator weight of the jth measurement indicator, E j is the index entropy value of the jth measurement index;

[0154] The calculation formula for multiple measurement scores is:

[0155]

[0156] Among them, N i is the measurement score of the i-th measurement unit, and there are m measurement indicators in total.

[0157] Step S104: constructing a multidimensional spatial distribution map of the target planning area based on the multiple measured scores, and performing spatial cluster planning to divide the target planning area into multiple spatial functional areas.

[0158] In an embodiment of the present invention, a measurement score distribution map of the target planning area is directly constructed based on multiple measurement scores, and a grade division is performed according to the multiple measurement scores to determine multiple suitability grades (including low suitability, medium suitability and high suitability), and a suitability grade distribution map of the target planning area is constructed. The measurement score distribution map and the suitability grade distribution map are then combined to generate a multidimensional spatial distribution map of the target planning area, and similarity clustering planning is performed on the target planning area to divide the target planning area into multiple spatial functional areas (including ecologically sensitive protection areas, key development and construction areas, and controlled construction buffer zones, etc.).

[0159] Specifically, Figure 9 A flow chart of calculating the measurement scores of multiple measurement units in the method provided by an embodiment of the present invention is shown.

[0160] Among them, in the preferred embodiment provided by the present invention, the normalization processing of the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data, generating multiple indicator normalization values, and calculating the measurement scores of the multiple measurement units specifically includes the following steps:

[0161] Step S1041, performing normalization processing on a plurality of measurement indicators corresponding to a plurality of measurement units according to the plurality of indicator characteristic data, and calculating a plurality of normalized indicator values;

[0162] Step S1042, determining the index proportions of the plurality of measurement indicators of the plurality of measurement units;

[0163] Step S1043, determining the index entropy values ​​of the multiple measurement indicators according to the proportions of the multiple indicators;

[0164] Step S1044, calculating the indicator weights of the multiple measurement indicators according to the multiple indicator entropy values;

[0165] Step S1045: Calculate the measurement scores of the multiple measurement units according to the multiple indicator weights and the multiple indicator normalized values.

[0166] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0167] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0168] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0169] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0170] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A precise calculation algorithm system for intelligent indicators of national land space planning, characterized by: The system includes a data acquisition and processing unit, an indicator data extraction unit, a measurement score calculation unit, and a spatial multi-dimensional division unit, wherein: A data acquisition and processing unit is used to determine a target planning area, collect multi-source spatial data of the target planning area, and perform unified processing and missing supplementation on the multi-source spatial data to obtain complete spatial data; An indicator data extraction unit is used to divide the target planning area into a plurality of measurement units, determine a plurality of measurement indicators, and extract indicator feature data corresponding to the plurality of measurement units from the complete spatial data; A measurement score calculation unit, configured to normalize the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data, generate multiple indicator normalized values, and calculate the measurement scores of the multiple measurement units; The spatial multi-dimensional division unit is used to construct a multi-dimensional spatial distribution map of the target planning area according to the multiple measurement scores, and to perform spatial clustering planning to divide the target planning area into multiple spatial functional areas.

2. The national land space planning intelligent indicator precise calculation algorithm system according to claim 1 is characterized in that: The data acquisition and processing unit specifically includes: A target area determination module is used to determine the target planning area; A multi-source acquisition module for collecting remote sensing image data, GIS spatial data, planning data and social statistical data of the target planning area; A comprehensive collating module is used to comprehensively collate the remote sensing image data, the GIS spatial data, the planning data and the social statistical data to generate multi-source spatial data; A unified processing module, configured to perform coordinate unification and resolution normalization on the multi-source spatial data to generate multi-source unified data; The missing complement module is used to complement the missing parts of the multi-source unified data to generate complete spatial data.

3. The precise calculation algorithm system for intelligent indicators of national land space planning according to claim 1 is characterized in that: The indicator data extraction unit specifically includes: Unit parameter acquisition module, used to obtain unit grid parameters; A regional unit division module is used to perform grid processing on the target planning area according to the unit grid parameters, and divide the target planning area into multiple measurement units; A measurement indicator determination module, used to determine multiple measurement indicators; The indicator feature data extraction module is used to extract the indicator feature data of the multiple measurement indicators corresponding to the multiple measurement units from the complete spatial data.

4. The precise calculation algorithm system for intelligent indicators of national land space planning according to claim 1 is characterized in that: The measurement score calculation unit specifically includes: A normalization processing module, configured to perform normalization processing on a plurality of measurement indicators corresponding to a plurality of measurement units according to a plurality of the indicator characteristic data, and calculate a plurality of normalized values ​​of the indicators; An indicator proportion determination module, configured to determine the indicator proportions of a plurality of the measurement indicators of a plurality of the measurement units; An indicator entropy value determination module, configured to determine the indicator entropy values ​​of a plurality of measurement indicators according to the proportions of the plurality of indicators; An indicator weight calculation module, used to calculate the indicator weights of multiple measurement indicators based on the multiple indicator entropy values; The measurement score calculation module is used to calculate the measurement scores of multiple measurement units based on multiple indicator weights and multiple indicator normalization values.

5. The national land space planning intelligent indicator precise calculation algorithm system according to claim 4 is characterized in that: The calculation formula for the normalized values ​​of the multiple indicators is: Among them, i represents the i-th measurement unit, j represents the j-th measurement indicator, Q′ ij is the normalized value of the jth measurement indicator of the i-th measurement unit, Q ij is the initial characteristic value of the jth measurement indicator of the i-th measurement unit, min(Q j ) is the maximum value of the jth measurement indicator, min(Q j ) is the minimum value of the jth measurement indicator; The calculation formula for the weights of the multiple indicators is: Among them, w j is the indicator weight of the jth measurement indicator, E j is the index entropy value of the jth measurement index; The calculation formula for the multiple measurement scores is: Among them, M i is the measurement score of the i-th measurement unit, and there are m measurement indicators in total.

6. The precise calculation algorithm system for intelligent indicators of national land space planning according to claim 1 is characterized in that: The spatial multi-dimensional division unit specifically includes: A measurement score distribution map construction module is used to directly construct a measurement score distribution map of the target planning area based on the plurality of measurement scores; A suitability grade distribution map construction module is used to perform grade classification according to the plurality of measurement scores, determine a plurality of suitability grades, and construct a suitability grade distribution map of the target planning area; A multidimensional space distribution map generating module, configured to generate a multidimensional space distribution map of a target planning area by combining the calculated score distribution map and the suitability level distribution map; The similarity clustering division module is used to perform similarity clustering planning on the target planning area and divide the target planning area into multiple spatial functional areas.

7. A precise calculation algorithm for intelligent indicators of national land space planning, characterized by: The method specifically comprises the following steps: Determine the target planning area, collect multi-source spatial data of the target planning area, and perform unified processing and missing supplement on the multi-source spatial data to obtain complete spatial data; Dividing the target planning area into a plurality of measurement units, determining a plurality of measurement indicators, and extracting indicator feature data corresponding to the plurality of measurement units from the complete spatial data; Normalizing the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data to generate multiple indicator normalized values, and calculating the measurement scores of the multiple measurement units; Based on the multiple measurement scores, a multidimensional spatial distribution map of the target planning area is constructed, and spatial clustering planning is performed to divide the target planning area into multiple spatial functional areas.

8. The method for accurately calculating intelligent indicators of national land space planning according to claim 7 is characterized in that: The determining of the target planning area, collecting multi-source spatial data of the target planning area, and performing unified processing and missing supplementation on the multi-source spatial data to obtain complete spatial data specifically includes the following steps: Determine the target planning area; Collect remote sensing image data, GIS spatial data, planning data and social statistical data of the target planning area; Comprehensively collating the remote sensing image data, the GIS spatial data, the planning data, and the social statistical data to generate multi-source spatial data; performing coordinate unification and resolution normalization on the multi-source spatial data to generate multi-source unified data; The multi-source unified data is supplemented for missing parts to generate complete spatial data.

9. The method for accurately calculating intelligent indicators of national land space planning according to claim 7 is characterized in that: The step of dividing the target planning area into a plurality of measurement units, determining a plurality of measurement indicators, and extracting the indicator feature data corresponding to the plurality of measurement units from the complete spatial data specifically comprises the following steps: Get the unit grid parameters; Performing gridding processing on the target planning area according to the unit grid parameters, and dividing the target planning area into a plurality of measurement units; Determine multiple measurement indicators; The indicator characteristic data of the multiple measurement indicators corresponding to the multiple measurement units are extracted from the complete spatial data.

10. The method for accurately calculating intelligent indicators of national land space planning according to claim 7 is characterized in that: Normalizing the multiple measurement indicators corresponding to the multiple measurement units according to the multiple indicator feature data to generate multiple indicator normalized values ​​and calculating the measurement scores of the multiple measurement units specifically includes the following steps: According to the plurality of indicator characteristic data, normalizing the plurality of measurement indicators corresponding to the plurality of measurement units, and calculating the normalized values ​​of the plurality of indicators; determining the indicator proportions of the plurality of measurement indicators of the plurality of measurement units; Determining the index entropy values ​​of the multiple measurement indicators according to the proportions of the multiple indicators; Calculating the indicator weights of the multiple measurement indicators based on the multiple indicator entropy values; The measurement scores of the multiple measurement units are calculated based on the multiple indicator weights and the multiple indicator normalized values.

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