Method and system for analyzing intensive land-saving development potential of large infrastructure

Abstract

The invention discloses a large-scale infrastructure intensive land-saving development potential analysis method and system, and the method comprises the following steps: 1) constructing a large-scale infrastructure stock land geospatial database based on Arcgis, and collecting the related element data of the large-scale infrastructure stock land; 2) extracting land suitable for optimizing stock of large infrastructures, and screening and extracting municipal public facility land and road and traffic facility land which are larger than 5 hectare in land scale and are not dangerous; 3) constructing an AHP-entropy weight method hybrid model, optimizing the weight by means of a genetic algorithm to establish a site selection evaluation system, and comprehensively evaluating the alternative land; 4) constructing a land development population service coverage model to obtain the number of each development land service population; and 5) guiding a land redevelopment time sequence in combination with the comprehensive score of the alternative land and the number of serviceable population. The method provides a path for improving the utilization efficiency of land resources, eliminating the neighborhood effect of large infrastructures and realizing high-quality development.

Description

Technical Field

[0001] The invention belongs to the technical field of urban planning, and in particular relates to a method and system for analyzing the potential for intensive and economical land development of large-scale infrastructure. Background Art

[0002] Under the guidance of high-quality development, the reform of the land resource management system has shifted its focus from incremental expansion to stock conservation and potential tapping, structural optimization and improvement of utilization efficiency. Large-scale infrastructure land, consisting of urban municipal public utility land, road and transportation facility land, has become an important part of stock renewal in highly urbanized areas, especially in megacities with important political and economic status and abundant municipal public utility land, road and transportation facility land. It is urgent to fully tap the potential of inefficient land and realize efficient utilization of land resources. On the one hand, the land supply mode for municipal public facilities, roads and transportation facilities is mainly allocated. Due to the low cost of allocated land and the high professionalism of municipal public facilities, roads and transportation facilities, the scale of land use is extensive and the intensive land use is insufficient. On the other hand, the existing urban infrastructure, especially the large-scale infrastructure with NIMBY effect, while providing services for the normal operation of the city, is inevitably isolated due to the particularity of its function. Municipal public facilities represented by sewage treatment plants and waste incineration plants will produce odor, noise, and radiation due to special requirements of equipment, functions, and processes. Roads and transportation facilities represented by transportation hubs will produce noise, partitions, and negative externalities.

[0003] Summarizing the existing technical research, there are several shortcomings:

[0004] 1. At present, there is no effective evaluation method and system for the research on intensive and economical use of land. There is a lack of integration and summary of reference cases of urban infrastructure land use, a lack of overall consideration of various internal and external indicators, and a lack of a standardized evaluation indicator system. It is difficult to provide targeted guidance for the intensive and economical development and construction of large-scale infrastructure land.

[0005] 2. With the integration of multiple plans in the planning field and the implementation of national land space planning, many factors affecting the intensive utilization potential of construction land identified by existing research need to be changed with the times to meet the development requirements of national land space planning in the new era.

[0006] In summary, the present invention is mainly used for the analysis of the intensive and economical development potential of large-scale infrastructure in highly urbanized areas facing the problems of shortage of land resources and prominent NIMBY effect under the guidance of high-quality development. By comprehensively considering the land use of urban infrastructure series, combining the characteristics of the infrastructure itself and the upper construction cover, with the help of the AHP-entropy weight method hybrid model and genetic algorithm optimization, the subjective deviation correction of the entropy weight method is used on the basis of authoritative expert opinions, so that the scientificity and efficiency of weight distribution are significantly improved. At the same time, combined with the service population and social benefits, a standardized framework analysis method is established to support high-precision and dynamic infrastructure land potential assessment and guide the timing of land redevelopment. Summary of the invention

[0007] In the stock renewal of highly urbanized areas, in view of the extensive use of urban infrastructure land and the prominent problems of NIMBY effect, a method and system for analyzing the potential for intensive and economical land development of large-scale infrastructure is proposed to improve the efficiency of land resource utilization, eliminate the NIMBY effect of large-scale infrastructure, and achieve high-quality development. To achieve the above purpose, the technical solution of the present invention is as follows:

[0008] A method for analyzing the potential of intensive and economical land development of large-scale infrastructure, characterized by the following steps:

[0009] 1) Build a geospatial database of large-scale infrastructure stock land based on ArcGIS and collect relevant element data of large-scale infrastructure stock land, including land for municipal public facilities and land for roads and transportation facilities;

[0010] 2) Extract suitable and optimized large-scale infrastructure stock land, and select and extract municipal public facilities land, road and transportation facilities land with a land area of ​​more than 5 hectares and no danger;

[0011] 3) Construct an AHP-entropy weight method hybrid model, use genetic algorithm to optimize weights to establish a site selection evaluation system, and conduct a comprehensive evaluation of alternative sites;

[0012] 4) Construct a population service coverage model for land development to obtain the number of people served by each development land;

[0013] The step 4) land development population service coverage model is used to determine the social benefits of the redeveloped land, that is, based on the current population distribution, the number of people that can be served within the service scope of the redeveloped land is determined according to the national standard "Urban Public Service Facilities Planning Standard GB50442" as follows:

[0014] 4.1) Obtain street-level data from the seventh census through the National Bureau of Statistics of China, combine it with the administrative division street data, and obtain street surface data with population data fields through field connection;

[0015] 4.2) Use ArcGIS to divide the street surface data with population number segments into 100m*100m population number raster data;

[0016] 4.3) According to the national standard "Urban Public Service Facilities Planning Standard GB50442", the service radius of the redevelopment land set M = {1, 2, ... m} is determined, and the population of the grid within the service range is accumulated to obtain the service population set D = {D1, D2, ... D m};

[0017] 5) Guide the timing of land development based on the comprehensive scores of alternative sites and the number of people they can serve.

[0018] 5.1) Based on the comprehensive scores of the alternative land and the number of people it can serve, it is divided into four types of redevelopment land to guide land planning and development timing, namely: short-term high-efficiency redevelopment land with high comprehensive scores and a large number of people to serve, long-term high-efficiency redevelopment land with high comprehensive scores and a small number of people to serve, short-term high-investment redevelopment land with low comprehensive scores and a large number of people to serve, and long-term high-investment redevelopment land with low comprehensive scores and a small number of people to serve;

[0019] 5.2) Planning decision makers shall carry out the redevelopment of four types of land in an orderly manner based on the short-term construction and development planning, financial funds and socio-economic development.

[0020] Preferably, the step 3) constructs an AHP-entropy weight method hybrid model, objectively corrects the subjective weight deviation of AHP by the entropy weight method, and optimizes the weight distribution efficiency with the help of a genetic algorithm to establish a site selection evaluation system as follows:

[0021] 3.1) Based on the AHP analytic hierarchy process, select evaluation index factors, divide the evaluation index system and construct the judgment matrix;

[0022] 3.2) Use the sum-product method to calculate the weights initially and perform consistency check;

[0023] 3.3) Based on the entropy weight method, the data is normalized to the extreme value, the entropy value of each indicator is calculated, and the objective weight is calculated by the entropy value;

[0024] 3.4) Construct a weight fusion model of AHP-entropy weight method and optimize the hybrid weight based on genetic algorithm;

[0025] 3.5) Use mixed weights to calculate the scores of alternative sites and verify them with the help of historical data. If the error rate is large, the correction mechanism is triggered to obtain the final comprehensive score of the alternative sites.

[0026] Preferably, the steps of selecting evaluation index factors based on AHP, dividing the evaluation index system and constructing the judgment matrix in step 3.1) are as follows:

[0027] 3.1.1) Selecting evaluation index factors

[0028] By collecting and integrating a large number of successful practices and cases of complex utilization of infrastructure land, analyzing various internal and external indicators, and constructing an indicator system for evaluating land development potential.

[0029] 3.1.2) Divide the evaluation index system into target layer, criterion layer and index layer

[0030] The criteria layer includes four aspects: favorable planning factors a1, location and transportation factors a2, supporting facilities factors a3, and public service facilities factors a4;

[0031] The indicator layer includes 11 evaluation indicators, including land and sea use a11, detailed planning of land and space a12, "three zones and three lines" of land and space a13, distance from subway entrances and exits a21, external transportation stations a22, commercial facilities a31, financial service institutions a32, medical and health facilities a41, parks and green spaces and squares a42, cultural and sports facilities a43, and educational facilities a44. ;

[0032] 3.1.3) Constructing the judgment matrix

[0033] The importance of the elements in the criterion layer and the indicator layer is compared pairwise by the expert scoring method. According to the number of elements t, a judgment matrix in the form of a t-order square matrix is ​​constructed; xy Indicates the importance of factor number x over factor number y, using b xy =1-5 scale method to indicate importance.

[0034] Preferably, the step 3.2) uses the sum-product method to perform the initial weight calculation and performs the consistency check as follows:

[0035] 3.2.1) The numerical matrix B = (b xy ) t×t Each column vector value is normalized to obtain C = (c xy ) t×t ;

[0036]

[0037] Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), b xy A quantitative value indicating the importance of factor number x over factor number y, cxy Indicates the same column of the numerical matrix b xy The value of numerical normalization, B is b xy A t-row × t-column numerical matrix of values, C is c xy a t-row-by-t-column numeric matrix of values;

[0038] 3.2.2) Normalize the matrix C = (c xy ) t×t Add the row vectors of

[0039]

[0040] Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The value to be added, C is c xy A t-row × t-column numeric matrix of values, M is M x a t-row numeric matrix of values;

[0041] 3.2.3) The vector Normalize to get the feature weight vector ω x ′;

[0042]

[0043] Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The added value, ω x is the normalized value of the same column of matrix M, c xy A t-row × t-column numeric matrix of values, M is M x t-row numerical matrix of values, ω x ′ is the weight vector;

[0044] The consistency test of the weights is the maximum characteristic root λ of each judgment matrix. max and the corresponding eigenvector, and do a consistency test; if the test passes, the normalized eigenvector is the weight vector ω x ′; if it fails, the judgment matrix needs to be reconstructed for b xy To adjust; the steps are as follows:

[0045] 3.2.4) Calculate the maximum eigenvalue λ max :

[0046]

[0047] Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The added value, ω x is the normalized value of the same column of matrix M, c xy A t-row × t-column numeric matrix of values, M is M x t-row numerical matrix of values, ω x ′ is the weight vector;

[0048] 3.2.5) Calculate the consistency index CI and ratio CR:

[0049] CI=(λ max -t) / (t-1)

[0050] Among them, t is the order (the number of influencing factors), CI is the consistency index; λ max is the largest characteristic root;

[0051] CR=CI / RI

[0052] Among them, CR is the consistency ratio. If CR<0.1, the judgment matrix is ​​considered to have satisfactory consistency, otherwise it is returned for correction; CI is the consistency index; RI is the random consistency index, which is a constant and can be referred to the average random consistency index comparison table corresponding to the order;

[0053] 3.2.6) Determine the subjective weight of each indicator, repeat the above calculation for 11 indicators, and obtain the subjective weight vector set ω AHP ;

[0054] ω AHP={ω1′,ω2′,…,ω 11 ′}

[0055] Preferably, the step 3.3) performs range normalization processing on the data based on the entropy weight method, calculates the entropy value of each indicator, and calculates the objective weight through the entropy value as follows:

[0056] 3.3.1) Collect the original data of 11 evaluation indicators (a11-a44) (such as subway distance, commercial facility density, etc.) of the alternative site set M = {1, 2, ...m} and perform range standardization to eliminate dimensional differences:

[0057]

[0058] Among them, I ij is the index value of the jth item of the ith plot, I′ ij For I ij The index value after the range is standardized;

[0059] 3.3.2) Calculate the information entropy Ej of each indicator:

[0060]

[0061] Among them, p ij is the proportion of the total value of each range normalized in all plots, E j is the information entropy of each indicator;

[0062]

[0063] Among them, ω x ″The objective weight of the jth indicator, E j is the information entropy of the j-th indicator;

[0064] 3.3.3) Determine the objective weight of each indicator, repeat the above 3.3.1) and 3.3.2) calculations for the 11 indicators, and obtain the objective weight vector set ω Entropy :

[0065] ω Entropy ={ω1″,ω2″,…,ω 11 ″}

[0066] Preferably, the step 3.4) uses the weight fusion model of the AHP-entropy weight method to optimize the hybrid weight based on the genetic algorithm as follows:

[0067] 3.4.1) Construct a linear combination model:

[0068] ω Hybrid =α·ω AHp +β·ω Entropy

[0069] Among them, α and β are fusion coefficients, α+β=1;

[0070] 3.4.2) Genetic algorithm parameter setting:

[0071] Initialize the population size (50-100 individuals), the encoding method is real number encoding (α range [0,1]), and the fitness function is defined as the sum of squared errors (SSE) of the evaluation model, the formula is:

[0072]

[0073] 3.4.3) Iterative Optimization:

[0074] Update the population through selection, crossover, and mutation until the termination condition is met (maximum number of iterations 500 or fitness change rate <1e-5), and output the optimal fusion coefficient α opt With β opt , generating the final mixing weight ω Hybrid

[0075] Preferably, the step 3.5) uses a mixed weight to calculate the score of the candidate site, and verifies it with the help of historical data. If the error rate is large, the triggering correction mechanism is as follows:

[0076] 3.5.1) Input the hybrid weights into the ArcGIS platform to calculate the development potential of the alternative sites, and verify the prediction accuracy of the model through historical data;

[0077] 3.5.2) If the error rate is >10%, the weight correction mechanism is triggered to readjust the AHP judgment matrix or update the entropy weight method data source; if the error rate is <10%, the development potential value of the alternative site is output.

[0078] Based on the calculation model, the comprehensive score of the suitable optimized stock land in the set M = {1, 2, ...m} is calculated. The formula for calculating the comprehensive score is as follows:

[0079] P (ai) =∑(P (aii) ω Hybrid(aii) ), where aii={a11, a12...a44}

[0080] P=∑(P (ai) ω Hybrid(ai) ), where ai={a1, a2, a3, a4}

[0081] Where: P (ai) ——the score of a factor ai suitable for optimizing stock land use; P (aii) ——The score of a certain suitable optimized stock land according to the factor aii of the scoring criteria; ω Hybrid(aii)——The weight of factor aii calculated by the weight fusion model of AHP-entropy weight method; ω Hybrid(ai) ——The weight of factor ai calculated by the weight fusion model of AHP-entropy weight method; P——the final score of a suitable optimized stock land;

[0082] A large-scale infrastructure intensive land conservation development potential analysis system is used to implement the above-mentioned large-scale infrastructure intensive land conservation development potential analysis method, characterized by comprising the following modules:

[0083] Geospatial database building module: used to implement the above step 1);

[0084] Land screening and extraction module: used to implement the above step 2);

[0085] Indicator weight determination module: used to implement the above step 3);

[0086] The spatial overlay analysis module is used to implement the above step 4).

[0087] A large-scale infrastructure intensive and economical land development potential analysis system terminal includes a memory, a processor, and at least one instruction or at least one computer program stored in the memory and loadable and run on the processor, characterized in that the processor loads and runs at least one instruction or at least one computer program to implement the large-scale infrastructure intensive and economical land development potential analysis method.

[0088] A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, a method for analyzing the potential for intensive and economical land development of a large-scale infrastructure is implemented.

[0089] Compared with the prior art, the method and system for analyzing the potential of intensive and economical land development of large-scale infrastructure provided by the present invention have the following beneficial effects:

[0090] (1) Comprehensively consider the potential for intensive utilization of urban infrastructure land and establish a standardized framework for analysis. Urban infrastructure land includes: municipal public facilities land (water supply land, power supply land, communication facilities land, drainage facilities land, sanitation facilities land, and other public facilities land) and road and transportation facilities land (transportation hub land, transportation station land).

[0091] (2) Expand new paths for the complex development and construction of land and space for urban infrastructure. In order to address the problems of “NIMBY” infrastructure isolation, low land use efficiency and single function, by building covers on the upper floors of the facilities to create artificial plots for the introduction of development projects, the land value can be greatly increased while making intensive use of the land.

[0092] (3) Based on the development requirements of national land space planning in the new era, the characteristics of the infrastructure itself and the upper construction cover, important parameters for scientific evaluation are proposed. With the help of the hybrid model of AHP-entropy weight method and genetic algorithm optimization, the subjective bias correction of entropy weight method is used on the basis of authoritative expert opinions, so that the scientificity and efficiency of weight allocation are significantly improved.

[0093] (4) Based on the population served and social benefits, a standardized framework analysis method is established to support high-precision and dynamic assessment of infrastructure land potential and guide the timing of land redevelopment. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. The features and advantages of the present invention will be more clearly understood by referring to the drawings. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0095] Figure 1 A flow chart of a method for analyzing the potential of intensive and land-saving development of large-scale infrastructure;

[0096] Figure 2 To evaluate the favorable factors for planning;

[0097] Figure 3 It is the location and traffic evaluation map;

[0098] Figure 4 Evaluation diagram for public service facilities;

[0099] Figure 5 It is the supporting facilities evaluation map;

[0100] Figure 6 To provide a comprehensive development potential analysis diagram;

[0101] Figure 7 Flowchart for comprehensive development potential analysis. DETAILED DESCRIPTION

[0102] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0103] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0104] The specific implementation methods of the technical solution of the present invention are further clearly and completely described below in conjunction with the embodiments and drawings. The specific implementation methods of the present invention are exemplary and are not intended to limit the present invention.

[0105] The following describes a method for analyzing the potential of large-scale infrastructure intensive land conservation development. The specific flow chart is as follows: Figure 1 As shown, the steps are as follows:

[0106] 1) Build a geospatial database of existing land for large-scale infrastructure

[0107] Collect data on relevant elements of land use for existing large-scale infrastructure, including land for municipal public facilities (land for water supply, land for power supply, land for communication facilities, land for drainage facilities, land for sanitation facilities, and land for other public facilities) and land for roads and transportation facilities (land for transportation hubs and land for transportation stations), and build a geospatial database based on ArcGIS.

[0108] 2) Extract suitable and optimized land for large-scale infrastructure stock

[0109] According to the requirements for intensive and economical land use of infrastructure, the suitable optimization stock land set M = {1, 2, ...m} is determined; the suitable optimization requirements include: the land scale is greater than 5 hectares and complies with the provisions of the urban comprehensive disaster prevention planning standard GB / T51327-2018, such as: avoiding high-risk disaster areas and the impact range of major hazardous sources.

[0110] 3) Construct an AHP-entropy weight method hybrid model, use genetic algorithm to optimize weights to establish a site selection evaluation system, and conduct a comprehensive evaluation of alternative sites;

[0111] 3.1) Based on the AHP analytic hierarchy process, select evaluation index factors, divide the evaluation index system and construct the judgment matrix;

[0112] 3.1.1) Selecting evaluation index factors

[0113] By collecting and integrating a large number of successful practices and cases of complex utilization of infrastructure plots, analyzing various internal and external indicators, and determining the evaluation indicators for existing land use, including four aspects: planning favorable factors, location and transportation, supporting facilities, and public service facilities, a land development potential evaluation indicator system is constructed.

[0114] 3.1.2) Divide the evaluation index system into target layer, criterion layer and index layer

[0115] Among them, the criteria layer includes four aspects: favorable planning factors a1, location and transportation factors a2, supporting facilities factors a3, and public service facilities factors a4;

[0116] The indicator layer includes 11 evaluation indicators, namely, land and sea use a11, detailed land and space planning a12, "three zones and three lines" of land and space a13, distance from subway entrances and exits a21, external transportation stations a22, commercial facilities a31, financial service institutions a32, medical and health facilities a41, parks, green spaces and squares a42, cultural and sports facilities a43, and educational facilities a44 (as shown in Table 1).

[0117] Table 1 Evaluation index factors for the potential of intensive and economical development of large-scale infrastructure

[0118]

[0119]

[0120] 3.1.3) Constructing the judgment matrix

[0121] The importance of the elements in the criterion layer and the indicator layer are compared pairwise by the expert scoring method. According to the number t of the factors affecting the intensive and economical development of large-scale infrastructure, a t-order square judgment matrix is ​​constructed. x represents the number of the row vector elements of the matrix, and y represents the number of the column vector elements of the matrix. By numbering the influencing factors of each layer and comparing them pairwise, a relative importance score is given. The b xy Indicates the importance of factor number x over factor number y, to quantify the value b xy =1-5 scale method to determine, for example: b 12 =1 means that factor No. 1 is as important as factor No. 2. Table 2 below is a judgment matrix scale table.

[0122] Table 2 Judgment matrix scale table

[0123]

[0124] 3.2) Use the sum-product method to calculate the weights initially and perform consistency check;

[0125] 3.2.1) The numerical matrix B = (b xy ) t×t Each column vector value is normalized to obtain C = (c xy ) t×t ;

[0126]

[0127] Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), b xy A quantitative value indicating the importance of factor number x over factor number y, cxy Indicates the same column of the numerical matrix b xy The value of numerical normalization, B is b xy A t-row × t-column numerical matrix of values, C is c xy A t-by-t matrix of numbers.

[0128] When the number of influencing factors is 3 (i.e. t=3), the row vector element number x=(1,2,3) and the column vector element number y=(1,2,3); assume that element 3 is 2 times more important than element 1 (i.e. b13=2, b31=1 / 2), and element 3 is 4 times more important than element 2 (i.e. b23=4, b32=1 / 4).

[0129] The numerical matrix B is as follows:

[0130]

[0131]

[0132]

[0133] When the number of influencing factors is 3 (i.e. t = 3), x = (1, 2, 3), y∈{1, 2, 3}; element 3 is 2 times more important than element 1 (i.e. b13 = 2, b31 = 1 / 2), and element 3 is 4 times more important than element 2 (i.e. b23 = 4, b32 = 1 / 4):

[0134]

[0135] 3.2.2) Normalize the matrix C = (c xy ) t×t Add the row vectors of

[0136]

[0137] Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The value to be added, C is c xy A t-row × t-column numeric matrix of values, M is M x A t-row numeric matrix of values.

[0138] When the number of influencing factors is 3 (i.e. t = 3), the row vector element number x = (1, 2, 3), the column vector element number y = (1, 2, 3); element 3 is 2 times more important than element 1 (i.e. b13 = 2, b31 = 1 / 2), and element 3 is 4 times more important than element 2 (i.e. b23 = 4, b32 = 1 / 4)

[0139] M1=(c 11 +c 12 +c 13 )=1.537

[0140] …

[0141] M3=(c 31 +c 32 +c 33 )=0.475

[0142]

[0143] 3.2.3) The vector Normalize to get the feature weight vector ω x ′;

[0144]

[0145] Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The added value, ω x is the normalized value of the same column of matrix M, c xy A t-row × t-column numeric matrix of values, M is M x t-row numerical matrix of values, ω x ′ is the weight vector;

[0146] When the number of influencing factors is 3 (i.e., t=3), the row vector element number x=(1,2,3); element 3 is 2 times more important than element 1 (i.e., b13=2, b31=1 / 2), and element 3 is 4 times more important than element 2 (i.e., b23=4, b32=1 / 4):

[0147]

[0148]

[0149] 3.2.4) Calculate the maximum eigenvalue λ max :

[0150]

[0151] Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The added value, ω x is the normalized value of the same column of matrix M, c xy A t-row × t-column numeric matrix of values, M is M x t-row numerical matrix of values, ω x ′ is the weight vector;

[0152] When the number of influencing factors is 3 (i.e., t=3), the row vector element number x=(1,2,3); element 3 is 2 times more important than element 1 (i.e., b13=2, b31=1 / 2), and element 3 is 4 times more important than element 2 (i.e., b23=4, b32=1 / 4):

[0153]

[0154] 3.2.5) Calculate the consistency index CI and ratio CR:

[0155] CI=(λ max -t) / (t-1)

[0156] Among them, t is the order (the number of influencing factors), CI is the consistency index; λ max is the largest characteristic root;

[0157] CR=CI / RI

[0158] Among them, CR is the consistency ratio. If CR<0.1, the judgment matrix is ​​considered to have satisfactory consistency, otherwise it is returned for correction; CI is the consistency index; RI is the random consistency index, which is a constant and can be referred to the average random consistency index comparison table corresponding to the order;

[0159] Comparison table of average random consistency indicators corresponding to reference order

[0160] n 1 2 3 4 5 6 7 8 9 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46

[0161] If CR<0.1, the judgment matrix is ​​considered to have satisfactory consistency, otherwise it is returned for correction.

[0162] When the number of influencing factors is 3 (i.e., t=3), the row vector element number x=(1,2,3); element 3 is 2 times more important than element 1 (i.e., b13=2, b31=1 / 2), and element 3 is 4 times more important than element 2 (i.e., b23=4, b32=1 / 4):

[0163] CI=(λ max -t) / (t-1)=(3.086-3)-(3-1)=0.043

[0164] CR=CI / RI=0.043 / 0.52≈0.083<1

[0165] In summary, the judgment matrix has satisfactory consistency.

[0166] 3.2.6) Determine the subjective weight of each indicator, repeat the above calculation for 11 indicators, and obtain the subjective weight vector set ω AHP ;

[0167] ω AHP ={ω1′,ω2′,…,ω 11 ′}

[0168] 3.3) Based on the entropy weight method, the data is normalized to the extreme value, the entropy value of each indicator is calculated, and the objective weight is calculated by the entropy value;

[0169] 3.3.1) Collect the original data of 11 evaluation indicators (a11-a44) (such as subway distance, commercial facility density, etc.) of the alternative site set M = {1, 2, ...m} and perform range standardization to eliminate dimensional differences:

[0170]

[0171] Among them, I ij is the index value of the jth item of the ith plot, I′ ij For I ij The index value after the range is standardized;

[0172] 3.3.2) Calculate the information entropy Ej of each indicator:

[0173]

[0174]

[0175] Among them, p ij is the proportion of the total value of each range normalized in all plots, E j is the information entropy of each indicator;

[0176]

[0177] Among them, ω x ″The objective weight of the jth indicator, E j is the information entropy of the j-th indicator;

[0178] 3.3.3) Determine the objective weight of each indicator, repeat the above 3.3.1) and 3.3.2) calculations for the 11 indicators, and obtain the objective weight vector set ω Entropy :

[0179] ω Entropy ={ω1″,ω2″,…,ω 11 ″}

[0180] 3.4) Construct a weight fusion model of AHP-entropy weight method and optimize the hybrid weight based on genetic algorithm;

[0181] 3.4.1) Construct a linear combination model:

[0182] ω Hybrid =α·ω AHP +β·ω Entropy

[0183] Among them, α and β are fusion coefficients, α+β=1;

[0184] 3.4.2) Genetic algorithm parameter setting:

[0185] Initialize the population size (50-100 individuals), the encoding method is real number encoding (α range [0,1]), and the fitness function is defined as the sum of squared errors (SSE) of the evaluation model, the formula is:

[0186]

[0187] 3.4.3) Iterative Optimization:

[0188] Update the population through selection, crossover, and mutation until the termination condition is met (maximum number of iterations 500 or fitness change rate <1e-5), and output the optimal fusion coefficient α opt With β opt , generating the final mixing weight ω Hybrid .

[0189] 3.5) Use mixed weights to calculate the scores of alternative sites and verify them with the help of historical data. If the error rate is large, the correction mechanism is triggered to obtain the final comprehensive score of the alternative sites.

[0190] 3.5.1) Input the hybrid weights into the ArcGIS platform to calculate the development potential of the alternative sites, and verify the prediction accuracy of the model through historical data;

[0191] 3.5.2) If the error rate is >10%, the weight correction mechanism is triggered, and the AHP judgment matrix is ​​readjusted or the entropy weight method data source is updated. The AHP weight correction is performed by inviting experts to readjust the judgment matrix; the entropy weight method data is updated by supplementing the latest remote sensing and sensor data and recalculating the objective weights; if the error rate is <10%, the development potential value of the alternative site is output.

[0192] Based on the calculation model, the comprehensive score of the suitable optimized stock land in the set M = {1, 2, ...m} is calculated. The formula for calculating the comprehensive score is as follows:

[0193] P (ai) =∑(P (aii) ω Hybrid(aii) ), where aii={a11, a12...a44}

[0194] P=∑(P (ai) ω Hybrid(ai) ), where ai={a1, a2, a3, a4}

[0195] Where: P (ai) ——the score of a factor ai suitable for optimizing stock land use; P (aii) ——The score of a certain suitable optimized stock land according to the factor aii of the scoring criteria; ω Hybrid(aii) ——The weight of factor aii calculated by the weight fusion model of AHP-entropy weight method; ω Hybrid(ai) ——The weight of factor ai calculated by the weight fusion model of AHP-entropy weight method; P——the final score of a suitable optimized stock land.

[0196] 4) Construct a population service coverage model for land development to obtain the number of people served by each development land;

[0197] 4.1) Obtain street-level data from the seventh census through the National Bureau of Statistics of China, combine it with the administrative division street data, and obtain street surface data with population data fields through field connection;

[0198] 4.2) Use ArcGIS to divide the street surface data with population number segments into 100m*100m population number raster data;

[0199] 4.3) According to the national standard "Urban Public Service Facilities Planning Standard GB50442", the service radius of the redevelopment land set M = {1, 2, ... m} is determined, and the population of the grid within the service range is accumulated to obtain the service population set D = {D1, D2, ... D m};

[0200] 5) Guide the timing of land development based on the comprehensive scores of alternative sites and the number of people they can serve.

[0201] 5.1) Based on the comprehensive scores of the alternative land and the number of people it can serve, it is divided into four types of redevelopment land to guide land planning and development timing, namely: short-term high-efficiency redevelopment land with high comprehensive scores and a large number of people to serve, long-term high-efficiency redevelopment land with high comprehensive scores and a small number of people to serve, short-term high-investment redevelopment land with low comprehensive scores and a large number of people to serve, and long-term high-investment redevelopment land with low comprehensive scores and a small number of people to serve;

[0202] 5.2) Planning decision makers shall carry out the redevelopment of four types of land in an orderly manner based on the short-term construction and development planning, financial funds and socio-economic development.

[0203] The above description is only a preferred embodiment of the present invention. For example, there may be multiple combinations of different configurations and characteristic parameter settings. The embodiments of the present invention only show example parameters and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for analyzing the potential of intensive and economical land development of large-scale infrastructure, characterized in that: Here are the steps: 1) Based on ArcGIS, a geospatial database of large-scale infrastructure stock land was constructed to collect data on relevant elements of large-scale infrastructure stock land, including land for municipal public facilities and land for roads and transportation facilities; 2) Extract suitable and optimized large-scale infrastructure stock land, and select and extract municipal public facilities land, road and transportation facilities land with a land area of ​​more than 5 hectares and no danger; 3) Construct an AHP-entropy weight method hybrid model, use genetic algorithm to optimize weights to establish a site selection evaluation system, and conduct a comprehensive evaluation of alternative sites; 4) Construct a population service coverage model for land development to obtain the number of people served by each development land; The step 4) land development population service coverage model is used to determine the social benefits of the redeveloped land, that is, based on the current population distribution, the number of people that can be served within the service scope of the redeveloped land is determined according to the national standard "Urban Public Service Facilities Planning Standard GB50442" as follows: 4.1) Obtain street-level data from the seventh census through the National Bureau of Statistics of China, combine it with the administrative division street data, and obtain street surface data with population data fields through field connection; 4.2) Use ArcGIS to divide the street surface data with population number segments into 100m*100m population number raster data; 4.3) According to the national standard "Urban Public Service Facilities Planning Standard GB50442", the service radius of the redevelopment land set M = {1, 2, ... m} is determined, and the population of the grid within the service range is accumulated to obtain the service population set D = {D1, D2, ... D m }; 5) Guide the timing of land redevelopment based on the comprehensive scores of the alternative sites and the number of people they can serve; 5.1) Based on the comprehensive scores of the alternative land and the number of people it can serve, it is divided into four types of redevelopment land to guide land planning and development timing, namely: short-term high-efficiency redevelopment land with high comprehensive scores and a large number of people to serve, long-term high-efficiency redevelopment land with high comprehensive scores and a small number of people to serve, short-term high-investment redevelopment land with low comprehensive scores and a large number of people to serve, and long-term high-investment redevelopment land with low comprehensive scores and a small number of people to serve; 5.2) Planning decision makers shall carry out the redevelopment of four types of land in an orderly manner based on the short-term construction and development planning, financial funds and socio-economic development.

2. A method for analyzing the potential for intensive and economical development of large-scale infrastructure according to claim 1, characterized in that: The step 3) constructs an AHP-entropy weight method hybrid model, objectively corrects the subjective weight deviation of AHP through the entropy weight method, and uses the genetic algorithm to optimize the weight distribution efficiency to establish a site selection evaluation system as follows: 3.1) Based on the AHP analytic hierarchy process, select evaluation index factors, divide the evaluation index system and construct the judgment matrix; 3.2) Use the sum-product method to calculate the weights initially and perform consistency check; 3.3) Based on the entropy weight method, the data is normalized to the extreme value, the entropy value of each indicator is calculated, and the objective weight is calculated by the entropy value; 3.4) Construct a weight fusion model of AHP-entropy weight method and optimize the hybrid weight based on genetic algorithm; 3.5) Use mixed weights to calculate the scores of alternative sites and verify them with the help of historical data. If the error rate is large, the correction mechanism is triggered to obtain the final comprehensive score of the alternative sites.

3. A method for analyzing the potential for intensive and economical development of large-scale infrastructure according to claim 2, characterized in that: The steps of selecting evaluation index factors based on the AHP hierarchical analysis method, dividing the evaluation index system and constructing the judgment matrix are as follows: 3.1.1) Selecting evaluation index factors By collecting and integrating a large number of successful practices and cases of infrastructure land complex utilization, analyzing various internal and external indicators, and building a land development potential assessment indicator system; 3.1.2) Divide the evaluation index system into target layer, criterion layer and index layer The criteria layer includes four aspects: favorable planning factors a1, location and transportation factors a2, supporting facilities factors a3, and public service facilities factors a4; The indicator layer includes 11 evaluation indicators, namely, land and sea use a11, detailed planning of land and space a12, "three zones and three lines" of land and space a13, distance from subway entrances and exits a21, external transportation stations a22, commercial facilities a31, financial service institutions a32, medical and health facilities a41, parks, green spaces and squares a42, cultural and sports facilities a43, and educational facilities a44. 3.1.3) Constructing the judgment matrix The importance of the elements in the criterion layer and the indicator layer is compared pairwise by the expert scoring method. According to the number of elements t, a judgment matrix in the form of a t-order square matrix is ​​constructed; xy Indicates the importance of factor number x over factor number y, using b xy =1-5 scale method to indicate importance.

4. A method for analyzing the potential for intensive and economical land development of large-scale infrastructure according to claim 3, characterized in that: The step 3.2) uses the sum-product method to perform the initial weight calculation and the consistency check is as follows: 3.2.1) The numerical matrix B = (b xy ) t×t Each column vector value is normalized to obtain C = (c xy ) t×t ; Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), b xy A quantitative value indicating the importance of factor number x over factor number y, c xy Indicates the same column of the numerical matrix b xy The value of numerical normalization, B is b xy A t-row × t-column numerical matrix of values, C is c xy a t-row-by-t-column numeric matrix of values; 3.2.2) Normalize the matrix C = (c xy ) t×y Add the row vectors of Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The value to be added, C is c xy A t-row × t-column numerical matrix of values, M is M x a t-row numeric matrix of values; 3.2.3) The vector Normalize to get the feature weight vector ω x ′; Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The added value, ω x is the normalized value of the same column of matrix M, c xy A t-row × t-column numerical matrix of values, M is M x t-row numerical matrix of values, ω x ′ is the weight vector; The consistency test of the weights is the maximum characteristic root λ of each judgment matrix. max and the corresponding eigenvector, and do a consistency test; if the test passes, the normalized eigenvector is the weight vector ω x ′; if it fails, the judgment matrix needs to be reconstructed for b xy To adjust; the steps are as follows: 3.2.4) Calculate the maximum eigenvalue λ max : Where t is the order (the number of influencing factors), x represents the row vector element number of the matrix (x = (1, 2...t)), y represents the column vector element number of the matrix (y = (1, 2...t)), c xy Indicates the same column of the numerical matrix b xy The value of the numerical normalization, b xy A quantitative value indicating the importance of factor number x over factor number y, M x Represents the same row c in matrix C xy The added value, ω x is the normalized value of the same column of matrix M, c xy A t-row × t-column numerical matrix of values, M is M x t-row numerical matrix of values, ω x ′ is the weight vector; 3.2.5) Calculate the consistency index CI and ratio CR: CI=(λ max -t) / (t-1) Among them, t is the order (the number of influencing factors), CI is the consistency index; λ max is the largest characteristic root; CR=CI / RI Among them, CR is the consistency ratio. If CR<0.1, the judgment matrix is ​​considered to have satisfactory consistency, otherwise it is returned for correction; CI is the consistency index; RI is the random consistency index, which is a constant and can be referred to the average random consistency index comparison table corresponding to the order; 3.2.6) Determine the subjective weight of each indicator, repeat the above calculation for 11 indicators, and obtain the subjective weight vector set ω AHP ; oh AHP ={ω1′,ω2′,…,ω 11 ′}。 5. A method for analyzing the potential for intensive and economical development of large-scale infrastructure according to claim 3, characterized in that: The step 3.3) performs range normalization processing on the data based on the entropy weight method, calculates the entropy value of each indicator, and calculates the objective weight through the entropy value as follows: 3.3.1) Collect the original data of 11 evaluation indicators (a11-a44) (such as subway distance, commercial facility density, etc.) of the alternative site set M = {1, 2, ...m} and perform range standardization to eliminate dimensional differences: Among them, I ij is the index value of the jth item of the ith plot, I′ ij For I ij The index value after the range is standardized; 3.3.2) Calculate the information entropy Ej of each indicator: Among them, p ij is the proportion of the total value of each range normalized index in all plots, E j is the information entropy of each indicator; Among them, ω x ″The objective weight of the jth indicator, E j is the information entropy of the j-th indicator; 3.3.3) Determine the objective weight of each indicator, repeat the above 3.3.1) and 3.3.2) calculations for the 11 indicators, and obtain the objective weight vector set ω Entropy : oh Entropy ={ω1″,ω2″,…,ω 11 "}。 6. A method for analyzing the potential for intensive and economical development of large-scale infrastructure according to claim 3, characterized in that: The step 3.4) uses the weight fusion model of the AHP-entropy weight method to optimize the hybrid weight based on the genetic algorithm as follows: 3.4.1) Construct a linear combination model: oh Hybrid =a·oh AHP +b·h Entropy Among them, α and β are fusion coefficients, α+β=1; 3.4.2) Genetic algorithm parameter setting: Initialize the population size (50-100 individuals), the encoding method is real number encoding (α range [0,1]), and the fitness function is defined as the sum of squared errors (SSE) of the evaluation model, the formula is: 3.4.3) Iterative Optimization: Update the population through selection, crossover, and mutation until the termination condition is met (maximum number of iterations 500 or fitness change rate <1e-5), and output the optimal fusion coefficient α opt With β opt , generating the final mixing weight ω Hybrid .

7. A method for analyzing the potential for intensive and economical land development of large-scale infrastructure according to claim 3, characterized in that: The step 3.5) uses the mixed weight to calculate the score of the candidate site, and verifies it with the help of historical data. If the error rate is large, the trigger correction mechanism is as follows: 3.5.1) Input the hybrid weights into the ArcGIS platform to calculate the development potential of the alternative sites, and verify the prediction accuracy of the model through historical data; 3.5.2) If the error rate is >10%, the weight correction mechanism is triggered to readjust the AHP judgment matrix or update the entropy weight method data source; if the error rate is <10%, the development potential value of the alternative site is output; Based on the calculation model, the comprehensive score of the suitable optimized stock land in the set M = {1, 2, ...m} is calculated. The formula for calculating the comprehensive score is as follows: P (ai) =∑(P (aii) ω Hybrid(aii) ), where aii={a11, a12...a44} P=∑(P (ai) ω Hybrid(ai) ), where ai={a1, a2, a3, a4} Where: P (ai) ——the score of a factor ai suitable for optimizing stock land use; P (aii) ——The score of a certain suitable optimized stock land according to the factor aii of the scoring criteria; ω Hybrid(aii) ——The weight of factor aii calculated by the weight fusion model of AHP-entropy weight method; ω Hybrid(ai) ——The weight of factor ai calculated by the weight fusion model of AHP-entropy weight method; P——the final score of a suitable optimized stock land.

8. A system for analyzing the potential for intensive and economical land use of large infrastructure, used to implement the method for analyzing the potential for intensive and economical land use of large infrastructure as described in any one of claims 1 to 7, characterized in that: Includes the following modules: Geospatial database building module: used to implement the above step 1); Land screening and extraction module: used to implement the above step 2); Indicator weight determination module: used to implement the above step 3); The spatial overlay analysis module is used to implement the above step 4).

9. A large-scale infrastructure intensive land conservation development potential analysis system terminal, comprising a memory, a processor, and at least one instruction or at least one computer program stored in the memory and loadable and run on the processor, characterized in that: The processor loads and runs at least one instruction or at least one computer program to implement a method for analyzing the potential for intensive and economical land development of large-scale infrastructure as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for analyzing the potential for intensive and economical land development of large-scale infrastructure as described in any one of claims 1 to 10 is implemented.

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