Construction method of main body function area evaluation index system based on GIS (Geographic Information System)

Abstract

The invention discloses a GIS-based main body function area evaluation index system construction method, and relates to the technical field of land resource management, and the method comprises the steps: obtaining multi-source geographic data of a target main body function area, and carrying out the preprocessing to form a unified geocoding evaluation database; generating an evaluation index system of the main body function area by adopting a composite weighting algorithm according to the evaluation database and the main body function area type division result; inputting the multi-dimensional raster data of the evaluation index system into the spatial decision tree classification model to obtain a classification result; performing overlay analysis on the multi-dimensional raster data to obtain comprehensive evaluation raster data; according to the classification result and the comprehensive evaluation raster data, obtaining a function goodness of fit thermodynamic diagram of the main body function area; and according to the function goodness of fit thermodynamic diagram, performing dynamic updating based on the time sequence, and when the change rate of the monitoring data exceeds a preset threshold, triggering an iterative optimization process. According to the invention, land space planning and main body function area optimization adjustment are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of land resource management, and particularly relates to a method for constructing an evaluation index system of main functional areas based on GIS. Background Art

[0002] With the deepening reform of the land space planning system, the main functional area strategy is the core means to optimize the spatial development and protection pattern. The main functional area is one of the important contents of spatial planning. Implementing the main functional area strategy and building a regional development pattern with complementary regional economic advantages, clear main functional positioning, efficient spatial utilization, and harmonious coexistence between humans and nature is an important support for optimizing the land space development pattern and ecological civilization construction.

[0003] Traditional evaluation methods for main functional areas mainly rely on the weighted integration of statistical indicators and have significant limitations: First, the index systems are homogenized and fail to reflect the differential causes of regional functions (for example, ecological functional areas focus on natural backgrounds, and urban functional areas focus on economic agglomeration); second, the spatial analysis ability is weak, it is difficult to reveal the spatial associations of functional areas, and it is easy to ignore the continuity of natural geographical boundaries, resulting in the disconnection between the division of functional areas and the integrity of the ecosystem.

[0004] In view of this, there is a need to provide a method for constructing an evaluation index system of main functional areas based on GIS. Summary of the Invention

[0005] Aiming at the limitation problem that traditional evaluation methods for main functional areas in the prior art mainly rely on the weighted integration of statistical indicators, the present invention provides a method for constructing an evaluation index system of main functional areas based on GIS, which can combine GIS with the construction of the evaluation index system, improve the scientificity of the evaluation, and also realize the optimization and adjustment of land space planning and main functional areas. The specific technical solutions are as follows:

[0006] A method for constructing an evaluation index system of main functional areas based on GIS, comprising:

[0007] S1. Obtain multi-source geographical data of the target main functional area, and form an evaluation database with unified geographical coding through spatial registration and standardization preprocessing;

[0008] S2. According to the evaluation database and based on the division results of main functional area types, adopt a composite weighting algorithm that couples the analytic hierarchy process and the entropy weight method to generate an evaluation index system for main functional areas;

[0009] S3. Input the multi-dimensional raster data of the evaluation index system into a trained spatial decision tree classification model to obtain a classification result;

[0010] S4. Use the raster calculation engine to perform overlay analysis on the multi-dimensional raster data of the evaluation index system to obtain comprehensive evaluation raster data;

[0011] S5. Obtain the function compliance heat map of the main functional area according to the classification result and the comprehensive evaluation raster data;

[0012] S6. According to the function compliance heat map and based on time series for dynamic update, when the change rate of the monitoring data exceeds the preset threshold, trigger the iterative optimization process, and repeat steps S1 - S6.

[0013] Preferably, the multi-source geographic data includes remote sensing image data, socio-economic statistical data, ecological environment monitoring data, and infrastructure vector data.

[0014] Preferably, the generation of the evaluation index system of the main functional area by using the analytic hierarchy process and entropy weight method coupling composite weighting algorithm according to the evaluation database and based on the classification result of the main functional area type includes:

[0015] Determine the main functional area type of the target area, and construct a hierarchical structure model including the target layer, criterion layer, and index layer;

[0016] Use the entropy weight method to weight the index layer indicators corresponding to different criterion layer indicators to obtain the objective weight result;

[0017] Use the analytic hierarchy process to weight the value layer indicators to obtain the subjective weight result;

[0018] Calculate the comprehensive weight of each indicator according to the objective weight result, subjective weight result, and preset weight coefficient;

[0019] Obtain the evaluation index system of the main functional area according to the comprehensive weight of each indicator.

[0020] Preferably, the training process of the spatial decision tree classification model includes:

[0021] Extract the multi-dimensional index data of the evaluation database and convert it into raster data format;

[0022] Select sample data according to the classification result of the main functional area type;

[0023] Input the sample data into the decision tree classification model for training to obtain a trained spatial decision tree classification model capable of outputting the classification result of the main functional area type.

[0024] Preferably, the use of the raster calculation engine to perform overlay analysis on the multi-dimensional raster data of the evaluation index system to obtain comprehensive evaluation raster data includes:

[0025] The raster data of each evaluation index is standardized using a raster calculation engine to obtain the standardized raster data;

[0026] According to the preset index composite weight, the raster calculation engine is used to perform weighted superposition calculation on the multi-dimensional standardized raster data to obtain the comprehensive evaluation raster data.

[0027] Preferably, the obtaining of the function matching degree heat map of the main functional area according to the classification result and the comprehensive evaluation raster data includes:

[0028] According to the classification result and the comprehensive evaluation raster data, calculate the function matching degree score of each raster unit;

[0029] Perform grading processing on the function matching degree according to the function matching degree score;

[0030] Assign different colors to each level after grading processing to establish a color mapping table;

[0031] Generate the function matching degree heat map according to the color mapping table.

[0032] Preferably, the dynamic update based on the time series according to the function matching degree heat map, and when the change rate of the monitoring data exceeds the preset threshold, triggering the iterative optimization process includes:

[0033] According to the time series data of each function in the function matching degree heat map, calculate the change rate within the corresponding preset time interval;

[0034] When the change rate of a certain function exceeds the preset threshold, trigger the iterative optimization process and repeat steps S1 - S6.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] A method for constructing an evaluation index system of main functional areas based on GIS in the present invention forms an evaluation database with unified geographic coding by acquiring multi-source geographic data and performing spatial registration and standardization preprocessing, providing a data basis; adopts a composite weighting algorithm that couples the analytic hierarchy process and the entropy weight method, taking into account the importance of each evaluation index and the objective information of the data, avoiding the subjectivity and one-sidedness of a single method, and making the generated evaluation index system more reasonable and reliable; inputs the multi-dimensional raster data of the evaluation index system into a trained spatial decision tree classification model to achieve intelligent classification of the main functional areas, improving the accuracy and efficiency of classification; uses a raster calculation engine for overlay analysis to obtain comprehensive evaluation raster data, and further generates a heat map of the functional compliance degree of the main functional areas, intuitively showing the matching degree and spatial distribution characteristics of each functional area with the planning objectives; dynamically updates the heat map of the functional compliance degree based on time series. When the change rate of the monitoring data exceeds a preset threshold, it can trigger an iterative optimization process in a timely manner to ensure that the evaluation index system and the division of functional areas always adapt to the latest changes in regional development, realizing continuous monitoring, evaluation, and optimization of the main functional areas. The present invention combines GIS technology with the construction of the evaluation index system, not only improving the scientificity and accuracy of the evaluation, but also realizing the intelligence of spatial decision-making and the visualization of evaluation results, optimizing land space planning and the adjustment of main functional areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally denoted by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0038] Figure 1 It is a flowchart of a method for constructing an evaluation index system of main functional areas based on GIS in the present invention.

[0039] Figure 2 It is a flowchart of an embodiment of a method for constructing an evaluation index system of main functional areas based on GIS in the present invention.

[0040] Figure 3 It is a comprehensive evaluation map of the urban functional area of the target main functional area in an embodiment of the present invention.

[0041] Figure 4 It is a comprehensive evaluation map of the ecological functional area of the target main functional area in an embodiment of the present invention.

[0042] Figure 5 It is a comprehensive evaluation map of the agricultural functional area of the target main functional area in an embodiment of the present invention.

[0043] Figure 6 This is the performance evaluation diagram of the main functional area of the target main body functional area in the embodiment of the present invention. Specific implementation manners

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0046] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0047] It should be further understood that the term " / and" as used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0048] In the following embodiments, according to the resource and environment carrying capacity, existing development density, and development potential, the main functional areas are spatially divided into main functional areas such as urban functional areas, ecological functional areas, and agricultural functional areas.

[0049] Please refer to the following embodiments Figures 1 to 6 .

[0050] The embodiment of the present application provides a method for constructing an evaluation index system for main functional areas based on GIS, including:

[0051] S1. Obtain multi-source geographical data of the target main functional area, and form an evaluation database with unified geographical coding through spatial registration and standardization preprocessing;

[0052] Specifically, the multi-source geographical data includes remote sensing image data, social and economic statistical data, ecological environment monitoring data, and infrastructure vector data.

[0053] GIS can collect geographic data from multiple channels, including satellite imagery, aerial photography, field measurements, sensor data, etc. Remote sensing image data extracts multi-spectral images, high-resolution images, etc. of the target area at different times through the GIS platform; social and economic statistical data collects data such as GDP, population, and industrial structure of the target area through statistical yearbooks and economic census data released by statistical departments; ecological environment monitoring data obtains data such as air quality, water quality, soil quality, and vegetation coverage from environmental protection departments and meteorological departments; infrastructure vector data obtains vector data of infrastructure such as roads, bridges, and hydropower facilities from the GIS platform. According to research requirements and accuracy requirements, appropriate data is selected and preliminarily sorted, such as removing duplicate data and correcting incorrect data.

[0054] According to the geographical location of the target area, a selected projection coordinate system is used as the unified coordinate system.

[0055] For remote sensing image data, the method of control point matching is adopted. Obvious ground feature features are selected as control points, and image registration algorithms are used to register different images so that they are spatially aligned.

[0056] For vector data, coordinate transformation is performed according to the unified coordinate system to ensure that all vector data is under the same spatial reference.

[0057] Convert data in different formats into a unified data format. At the same time, for numerical data, normalization methods are used for processing.

[0058] By designing the structure of the evaluation database, including the definition of data tables, field types, data relationships, etc., and using spatial databases (such as ArcSDE, PostGIS) to store data.

[0059] Geographic data from different sources, coordinate systems, and resolutions are unified into the same geographic spatial reference system through methods such as coordinate transformation and resampling to ensure the spatial consistency of each data layer. Standardize the data to eliminate the influence of dimensions and dimensional differences on subsequent analysis. Integrate the data that has undergone spatial registration and standardization preprocessing together, organize and store it according to a unified geographic coding, and form a structured evaluation database to provide a data basis for subsequent evaluation and analysis.

[0060] S2. According to the evaluation database and based on the results of the division of the main functional area types, a composite weighting algorithm that couples the analytic hierarchy process and the entropy weight method is used to generate an evaluation index system for the main functional areas;

[0061] By dividing the main functional area types into urban functional areas, agricultural functional areas, ecological functional areas, and superimposed functional areas, a differentiated index set is constructed.

[0062] Specifically, according to the evaluation database and based on the results of the classification of the main functional area types, a combined weighting algorithm that couples the analytic hierarchy process and the entropy weight method is used to generate an evaluation index system for the main functional areas, including:

[0063] S21. Determine the type of the main functional area of the target area and construct a hierarchical structure model including the target layer, criterion layer, and index layer;

[0064] The specific construction results are as follows:

[0065] In this embodiment, the evaluation index system for the implementation of the agricultural functional area plan includes:

[0066] At the first level is the target layer, that is, the evaluation of the implementation of the agricultural product functional area;

[0067] At the second level is the criterion layer, including 3 indicators such as cultivated land protection, grain production capacity, and supply of high-quality agricultural products;

[0068] At the third level is the specific index layer. Among them, the specific indicators for cultivated land protection are 3 indicators such as the cultivated land protection rate, the proportion of the area of permanent basic farmland in the national land area, and the per capita cultivated land area; the specific indicators for grain production capacity are 3 indicators such as the ratio of grain to cash crops, the proportion of the primary industry, and the grain yield per unit area; the specific indicators for the supply of high-quality agricultural products are 2 indicators such as the proportion of the area of land suitable for the production of selenium-rich agricultural products in protection and the proportion of the area of high-standard farmland. The specific indicators are shown in Table 1.

[0069] Table 1 Evaluation Index System for the Implementation of the Agricultural Functional Area Plan

[0070]

[0071] In this embodiment, the evaluation index system for the implementation of the ecological functional area plan includes:

[0072] At the first level is the target layer, that is, the evaluation of the implementation of the ecological functional area;

[0073] At the second level is the criterion layer, including 3 indicators such as ecological security, ecological protection, and ecological environment;

[0074] At the third level is the specific index layer. Among them, the specific indicators for ecological security are 2 indicators such as the proportion of the land area of the ecological protection red line in the land area of the country and the proportion of the land area of nature reserves in the land area of the country; the specific indicators for ecological protection are 2 indicators such as the forest stock volume and the forest coverage rate; the specific indicator for the ecological environment is the number of days with good air quality. The specific indicators are shown in Table 2.

[0075] Table 2 Evaluation Index System for the Implementation of the Ecological Functional Area Plan

[0076]

[0077] In this embodiment, the evaluation index system for the implementation of the urban functional area planning includes:

[0078] The first level is the target layer, that is, the evaluation of the implementation of the urban functional area;

[0079] The second level is the criterion layer, including 5 indicators such as economic development goals, population absorption, land output efficiency, contribution rate of scientific and technological progress, and mitigation rate of resource and environmental overloading;

[0080] The third level is the specific index layer. Among them, the specific index of economic development goal is the growth rate of regional gross domestic product; the specific index of population absorption is the urbanization rate of the permanent population; the specific index of land output efficiency is 2 indicators such as per capita urban construction land and land consumption per unit of industrial added value; the specific index of contribution rate of scientific and technological progress is the number of invention patents per 10,000 people; the specific index of mitigation rate of resource and environmental overloading is 2 indicators such as water consumption per 10,000 yuan of GDP and land consumption per 10,000 yuan of GDP. The specific indexes are shown in Table 3.

[0081] Table 3 Evaluation Index System for the Implementation of Urban Functional Area Planning

[0082]

[0083] S22. Use the entropy weight method to assign weights to the index layer indicators corresponding to different criterion layer indicators to obtain the objective weight results;

[0084] 1) Construct a dimensionless standard value matrix of m index layer indicators for n criterion layer indicators in a certain functional area:

[0085]

[0086] Among them, r ij is the dimensionless value of the jth index layer indicator among the i criterion layer indicators.

[0087] 2) Calculate the entropy value e j of the jth index:

[0088]

[0089] Among them, k = 1 / lnm, after correction,

[0090]

[0091] 3) Calculate the entropy value α j of the jth index:

[0092]

[0093] Thus, the objective weight set α = {α1, α2,..., α n}

[0094] S23. Use the analytic hierarchy process to assign weights to the value layer indicators to obtain the subjective weight results;

[0095] (1) Construct a judgment matrix

[0096] Determine the decision hierarchy. Then, compare each pair of elements in each hierarchy and use a 9-point scale for scoring, where 1 indicates that two elements have the same importance, 3 indicates that one element is slightly more important than the other, 5 indicates that one element is significantly more important, 7 indicates that one element is strongly more important, and 9 indicates that one element is very strongly more important. Next, construct a judgment matrix, where each element represents the relative importance between one element and other elements. When constructing the judgment matrix, the elements on the diagonal should be set to 1 because the relative importance of each element to itself is 1. Calculate the weight vector of each column by adding the numbers in each column and normalizing it to 1. Calculate the weight vector of each row by multiplying the numbers in each row by the corresponding column weight vector and adding them up. Check for consistency. Calculate the eigenvalue and consistency index of the judgment matrix and conduct a consistency test. If the consistency ratio is less than 0.1, the judgment matrix is considered consistent. If the consistency ratio is greater than 0.1, the matrix needs to be corrected until the consistency ratio is less than 0.1.

[0097] Table 4 Judgment matrix corresponding to the analytic hierarchy process

[0098]

[0099] The above introduced the placement position of the elements of the judgment matrix corresponding to the analytic hierarchy process, but did not explain how to determine the element sizes in the judgment matrix. Generally speaking, by consulting multiple experts in related fields and based on their subjective judgments on the evaluation objectives and objective reality, that is, comparing any two evaluation indicators or criteria in the judgment matrix.

[0100] Table 5 Table of significance scale meanings corresponding to the analytic hierarchy process

[0101]

[0102] The bij in the judgment matrix is the relative importance scale of two criteria or indicators. It should be noted that for any judgment matrix, bii = 1 (the element value on the diagonal is 1), and bji = 1 / bij (the two elements corresponding to the row and column interchange positions are reciprocals of each other).

[0103] (2) Calculate the subjective weights of the evaluation indicators

[0104] In step 1) above, the judgment matrix has been determined and a consistency test needs to be conducted. The formula is shown as follows:

[0105] CI = (λmax - n) / (n - 1), CR = CI / RI

[0106] Among them, RI is the random consistency ratio; λmax is the maximum eigenvalue; n is the number of criteria or indicators.

[0107] S24. Calculate the comprehensive weight of each indicator according to the objective weight result, subjective weight result and the preset weight coefficient.

[0108] According to the entropy weight method, the objective weight set α = {α1, α2, …, α 18} of the evaluation index system is obtained. According to the analytic hierarchy process, the subjective weight set β = {β1, β2, …, β 18} of the evaluation index system is obtained. To achieve the unity of subjectivity and objectivity and overcome the drawbacks of single weighting of subjectivity and objectivity, the comprehensive weight ω = {ω1, ω2, …, ω 18} of the evaluation index system is established.

[0109] The established comprehensive weight ω = {ω1, ω2, …, ω 18} should satisfy the following least squares model.

[0110]

[0111] Among them, m is the number of criterion layer indicators, m = 15; n is the number of evaluation indicators, n = 18.

[0112] To satisfy the above least squares model, the partial derivative of H(ω) with respect to each comprehensive weight ω j (j = 1, 2, ···, 18) must be equal to zero, that is, as shown below:

[0113]

[0114] The above formula can be sorted out as:

[0115]

[0116] It is the sum of the elements of the j-th column of the dimensionless standard value matrix R = (r ij ) m×n of the n indicators of the m criterion layer indicators. Since 0 ≤ r ij ≤ 1, the above formula can be equivalent to:

[0117]

[0118] From the above formula, the comprehensive weight ω j (j = 1, 2, ···, 18) can be obtained as the objective weight set α j and the subjective weight βj Arithmetic mean

[0119] S25. Obtain the evaluation index system of the main functional area according to the comprehensive weights of each index.

[0120] In this embodiment, the evaluation index system of the main functional area obtained according to the comprehensive weights of each index is shown in Table 6:

[0121] Table 6 Index Weights of the Implementation Evaluation System for the Main Functional Area

[0122]

[0123]

[0124] Finally, sort the comprehensive performance scores. The larger the value, the better the implementation performance of the corresponding plan for each functional area. The performance index is divided into 4 grade standards, as shown in Table 7.

[0125] Table 7 Judgment Criteria for the Implementation Performance of the Main Functional Area Plan

[0126]

[0127]

[0128] According to the above analysis, the implementation performance evaluation map of the main functional area of the target area can be calculated. In this embodiment, the above method is used to calculate the implementation performance evaluation result of the main functional area of a certain province, and the comprehensive evaluation map of the urban functional area corresponding to a certain provincial area (such as Figure 3 ), the comprehensive evaluation map of the ecological functional area (such as Figure 4 ), the comprehensive evaluation map of the agricultural functional area (such as Figure 5 ) and the implementation performance evaluation map of the main functional area (such as Figure 6 ) are obtained.

[0129] Integrate the subjective weight obtained by the analytic hierarchy process and the objective weight obtained by the entropy weight method, and use methods such as weighted average to obtain the final comprehensive weight, so as to generate a scientific and reasonable evaluation index system. It not only considers subjective judgment but also makes full use of the objective information of the data, improving the scientificity of weight determination.

[0130] S3. Input the multi-dimensional grid data of the evaluation index system into the trained spatial decision tree classification model to obtain the classification result;

[0131] In this embodiment, the classification result includes urban functional area, agricultural functional area or ecological functional area, etc.

[0132] Specifically, the training process of the spatial decision tree classification model includes:

[0133] Extract the multi-dimensional index data of the evaluation database and convert it into a raster data format;

[0134] Select sample data according to the classification results of the main functional area types;

[0135] Train by inputting the sample data into a decision tree classification model to obtain a trained spatial decision tree classification model capable of outputting the classification results of the main functional area types.

[0136] Convert the index data in the evaluation index system into a raster data format. Each raster cell corresponds to a specific geographical spatial location. The index raster data of each dimension has the same resolution and geographical range, forming a multi-dimensional raster data set.

[0137] Use the known sample data of the main functional area types to train the spatial decision tree classification model. During the training process, the model learns the relationship between the index features in the sample data and the main functional area types, and establishes classification rules and decision tree structures.

[0138] Input the multi-dimensional raster data into the trained spatial decision tree classification model. The model makes judgments and classifications according to the index features of each raster cell according to the classification rules of the decision tree, and finally obtains the classification results of the main functional areas. The results are presented in raster form, and each raster cell is assigned a main functional area type.

[0139] S4. Use a raster calculation engine to perform overlay analysis on the multi-dimensional raster data of the evaluation index system to obtain comprehensive evaluation raster data;

[0140] Specifically, the use of a raster calculation engine to perform overlay analysis on the multi-dimensional raster data of the evaluation index system to obtain comprehensive evaluation raster data includes:

[0141] Use a raster calculation engine to perform standardization processing on the raster data of each evaluation index to obtain standardized raster data;

[0142] According to the preset index composite weights, use a raster calculation engine to perform weighted overlay calculation on the standardized multi-dimensional raster data to obtain comprehensive evaluation raster data.

[0143] By selecting a suitable raster calculation engine, such as Spatial Analyst of ArcGIS, and performing corresponding parameter configuration on it to support the efficient calculation and processing of large-scale raster data. The multi-dimensional raster data in the evaluation index system is subjected to overlay analysis according to certain rules and weights. Through operations such as weighted summation of the raster data of each index, the information of multiple dimensions is integrated into a raster data layer to obtain the comprehensive evaluation raster data. The value of each raster cell represents the score or grade of this geographical location under the comprehensive evaluation, reflecting its comprehensive characteristics in the evaluation of the main functional area.

[0144] S5. According to the classification result and the comprehensive evaluation raster data, obtain the heat map of the functional compliance degree of the main functional area;

[0145] Specifically, the obtaining of the heat map of the functional compliance degree of the main functional area according to the classification result and the comprehensive evaluation raster data includes:

[0146] 1) According to the classification result and the comprehensive evaluation raster data, calculate the functional compliance score of each raster cell;

[0147] The functional compliance degree reflects the matching degree between the actual function of this raster cell and the planned function of the main functional area.

[0148] 2) Perform grading processing on the functional compliance degree according to the functional compliance score;

[0149] For the grading processing of the functional compliance degree data, in this embodiment, the equal interval method is used to divide the data into different grades.

[0150] 3) Assign different colors to each grade after the grading processing to establish a color mapping table

[0151] 4) Generate the heat map of the functional compliance degree according to the color mapping table.

[0152] By using geographic information system software (such as ArcGIS, QGIS) to draw the heat map of the functional compliance degree according to the color mapping table, visually display the functional compliance situation of the main functional area in the target area.

[0153] Combine the classification result with the comprehensive evaluation raster data to calculate the functional compliance degree of each raster cell. It can be determined by comparing the differences between the classification result and the comprehensive evaluation score, etc.

[0154] According to the functional compliance degree data, use the visualization function of GIS to generate the heat map of the functional compliance degree of the main functional area. The heat map represents the high and low of the functional compliance degree by different shades of colors, visually showing the distribution of the functional compliance degree of the main functional area in the geographical space, and helping to identify the areas with good and poor functional matching.

[0155] S6. Based on the heat map of functional compliance and updated dynamically based on the time series, when the change rate of the monitoring data exceeds the preset threshold, trigger the iterative optimization process, and repeat steps S1 - S6.

[0156] Specifically, the process of updating dynamically based on the time series according to the heat map of functional compliance and triggering the iterative optimization process when the change rate of the monitoring data exceeds the preset threshold includes:

[0157] Calculate the change rate within the corresponding preset time interval according to the time series data of each function in the heat map of functional compliance;

[0158] When the change rate of a certain function exceeds the preset threshold, trigger the iterative optimization process, and repeat steps S1 - S6.

[0159] In this embodiment, multi-source geographical data of the target area are collected at regular time intervals (such as quarterly, annually), including remote sensing image data, socio-economic statistical data, ecological environment monitoring data, etc. The newly collected data are preprocessed and updated into the evaluation database. And recalculate the functional compliance. Using the updated evaluation database and the spatial decision tree classification model, recalculate the functional compliance of each grid cell. According to the recalculated functional compliance data, update the heat map of functional compliance to reflect the dynamic changes of the main functional areas in the target area. And calculate the data change rates of each monitoring index at different time points, such as GDP growth rate, population growth rate, vegetation coverage change rate, etc. Compare the calculated change rates with the preset thresholds. If the change rates of one or more indicators exceed the thresholds, it is considered that the monitoring data has changed significantly, that is, the status of the main functional area has changed significantly, and it is necessary to re-evaluate and optimize the evaluation index system. At this time, trigger the iterative optimization process and re-execute steps S1 to S6 to adapt to the dynamic changes of the main functional area.

[0160] A construction method of the evaluation index system for the main functional areas based on GIS provides a data foundation by obtaining multi-source geographic data and performing spatial registration and standardization preprocessing to form an evaluation database with unified geographic coding; adopts a composite weighting algorithm that couples the analytic hierarchy process and the entropy weight method, taking into account the importance of each evaluation index and the objective information of the data, avoiding the subjectivity and one-sidedness of a single method, and making the generated evaluation index system more reasonable and reliable; inputs the multi-dimensional raster data of the evaluation index system into a trained spatial decision tree classification model to achieve intelligent classification of the main functional areas, improving the accuracy and efficiency of classification; uses a raster calculation engine for overlay analysis to obtain comprehensive evaluation raster data, and further generates a heat map of the functional compliance degree of the main functional areas, intuitively showing the matching degree and spatial distribution characteristics of each functional area with the planning objectives; dynamically updates the heat map of the functional compliance degree based on time series, and when the change rate of the monitoring data exceeds a preset threshold, it can timely trigger an iterative optimization process to ensure that the evaluation index system and the functional area division always adapt to the latest changes in regional development, realizing continuous monitoring, evaluation, and optimization of the main functional areas. The present invention combines GIS technology with the construction of the evaluation index system, not only improving the scientificity and accuracy of the evaluation, but also realizing the intelligence of spatial decision-making and the visualization of evaluation results, providing a decision-making basis for land spatial planning and the optimization and adjustment of the main functional areas.

[0161] Those of ordinary skill in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components of each example have been generally described according to their functions in the above description. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0162] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0163] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0164] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0165] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. A construction method for the evaluation index system of the main functional areas based on GIS, characterized in that, Including: S1. Obtain multi-source geographical data of the target subject's functional area, and form an evaluation database with unified geographical coding through spatial registration and standardized preprocessing; S2. According to the evaluation database and based on the division results of the main functional area types, adopt a composite weighting algorithm that couples the analytic hierarchy process and the entropy weight method to generate an evaluation index system for the main functional areas; S3. Input the multi-dimensional raster data of the evaluation index system into a trained spatial decision tree classification model to obtain a classification result; S4. Use a raster calculation engine to perform overlay analysis on the multi-dimensional raster data of the evaluation index system to obtain comprehensive evaluation raster data; S5. According to the classification result and the comprehensive evaluation raster data, obtain a heat map of the functional compliance of the main functional area; S6. According to the heat map of the functional compliance and perform dynamic updates based on the time series. When the change rate of the monitoring data exceeds the preset threshold, trigger an iterative optimization process, and repeat steps S1 - S6.

2. The construction method of a GIS-based evaluation index system for main functional areas according to claim 1, characterized in that, The multi-source geographical data includes remote sensing image data, social and economic statistical data, ecological environment monitoring data, and infrastructure vector data.

3. The construction method of a GIS-based evaluation index system for main functional areas according to claim 1, characterized in that, The process of generating an evaluation index system for the main functional areas by adopting a composite weighting algorithm that couples the analytic hierarchy process and the entropy weight method according to the evaluation database and based on the division results of the main functional area types includes: Determine the main functional area type of the target area, and construct a hierarchical structure model including an objective layer, a criterion layer, and an index layer; Use the entropy weight method to weight the index layer indicators corresponding to the different criterion layer indicators to obtain an objective weight result; Use the analytic hierarchy process to weight the value layer indicators to obtain a subjective weight result; According to the objective weight result, the subjective weight result, and a preset weight coefficient, calculate the comprehensive weight of each indicator; Obtain the evaluation index system for the main functional areas according to the comprehensive weight of each indicator.

4. The construction method of a GIS-based evaluation index system for main functional areas according to claim 1, characterized in that, The training process of the spatial decision tree classification model includes: Extract the multi-dimensional index data of the evaluation database and convert it into a raster data format; Select sample data according to the division results of the main functional area types; Input the sample data into the decision tree classification model for training to obtain a trained spatial decision tree classification model capable of outputting the classification result of the main functional area type.

5. The construction method of a GIS-based evaluation index system for main functional areas according to claim 1, characterized in that, The process of using a raster calculation engine to perform overlay analysis on the multi-dimensional raster data of the evaluation index system to obtain comprehensive evaluation raster data includes: Use the raster calculation engine to perform standardization processing on the raster data of each evaluation indicator to obtain standardized raster data; According to the preset index composite weight, use the raster calculation engine to perform weighted overlay calculation on the standardized multi-dimensional raster data to obtain comprehensive evaluation raster data.

6. The construction method of a GIS-based evaluation index system for main functional areas according to claim 1, characterized in that, The process of obtaining a heat map of the functional compliance of the main functional area according to the classification result and the comprehensive evaluation raster data includes: Calculate the functional compliance score of each raster cell according to the classification result and the comprehensive evaluation raster data; Perform grading processing on the functional compliance according to the functional compliance score; Assign different colors to each level after the grading processing to establish a color mapping table; Generate a heat map of the functional compliance according to the color mapping table.

7. The construction method of a GIS-based evaluation index system for main functional areas according to claim 1, characterized in that, The heat map of functional compliance is dynamically updated based on the time series. When the change rate of the monitoring data exceeds the preset threshold, the iterative optimization process is triggered, including: Calculate the change rate within the corresponding preset time interval according to the time series data of each function in the heat map of functional compliance; When the change rate of a certain function exceeds the preset threshold, trigger the iterative optimization process and repeat steps S1 - S6.

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