Urban function complex and coordination method based on urban urbanization features

Abstract

This invention provides a method for the integration and coordination of urban functions based on urbanization characteristics, comprising the following steps: S1: Constructing a classification dataset of urbanization characteristics; S2: Generating a quantitative analysis report of urbanization characteristics; S3: Constructing an urban function classification system; S4: Creating a target system document for urban function integration; S5: Designing an urban function integration layout scheme; S6: Constructing an urban function coordination mechanism. This invention, through the integration of quantitative algorithms and multi-dimensional coordination mechanisms, breaks through the limitations of traditional planning, ensuring that urban functions are spatially integrated, coordinated in operation, and adaptable in development, providing systematic technical support for improving the quality of urbanization.

Description

Technical Field

[0001] This invention relates to the fields of urban planning and design, and the integration and coordination of urban functions, and in particular to methods for the integration and coordination of urban functions based on the characteristics of urbanization. Background Technology

[0002] Currently, the global and domestic urbanization process has moved from the "scale expansion" stage to the "quality improvement" stage. However, traditional urban development models still suffer from several core problems, becoming key bottlenecks restricting efficient urban operation and residents' quality of life:

[0003] Fragmented functional layout: Traditional planning often adopts a "single-function zoning" model (such as pure residential areas or pure industrial areas), which leads to the separation of residential, employment, commercial and public service spaces, resulting in a "job-housing imbalance" phenomenon, increasing residents' commuting costs and urban traffic pressure;

[0004] Insufficient functional synergy: There is a lack of spatial connection design between different functions (such as the isolation between green space and residential space, and weak connection between transportation hubs and commercial facilities), which prevents the functions from forming a complementary effect and reduces the efficiency of urban space use;

[0005] Lack of coordination mechanisms: The lack of unified spatial coordination standards, facility linkage rules and management loops for urban functions leads to problems such as "frequent spatial conflicts" and "mismatch between facility supply and demand".

[0006] The disconnect between planning and monitoring: Traditional planning is mostly "static blueprint" design, lacking quantitative verification and dynamic adjustment mechanisms based on real-time data. After the planning scheme is implemented, it is difficult to adapt to the dynamic changes in urbanization characteristics (such as population growth and industrial transformation).

[0007] The core issue of the above problems lies in the lack of systematic quantitative analysis of the characteristics of urban urbanization (population, economy, land, transportation, and public services), as well as the lack of a technical system covering the entire process of "layout optimization - coordination and guarantee - dynamic monitoring," which makes it impossible for urban functions to achieve "spatial integration, coordinated operation, and adaptability in development." Summary of the Invention

[0008] This invention provides a method for the integration and coordination of urban functions based on the characteristics of urban urbanization. It constructs a closed-loop technical system covering the entire process of "data collection, feature analysis, target setting, layout design, coordination and guarantee, simulation verification and dynamic monitoring". By integrating quantitative algorithms with multi-dimensional coordination mechanisms, it breaks through the limitations of traditional planning and ensures that urban functions are spatially integrated, coordinated in operation, and adapted in development, providing systematic technical support for improving the quality of urbanization.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A method for urban functional integration and coordination based on the characteristics of urban urbanization includes the following steps:

[0011] S1: Collect spatial basic data, social population data, economic and industrial data, and ecological environment data, and form a feature classification dataset after preprocessing;

[0012] S2: Based on the feature classification dataset, analyze the characteristics of urban urbanization through a quantitative indicator system, and output a quantitative analysis report on the characteristics of urban urbanization.

[0013] S3: Based on the quantitative analysis report and national standards, construct a six-category urban function classification system including residential, commercial services, industrial production, public management and public services, transportation and municipal facilities, and green space and squares, and output an explanation of the urban function classification system.

[0014] S4: Based on the quantitative analysis report and functional classification system description, set the functional proportions, efficiency, ecology and coordination targets for four types of zones: core urban area, emerging urban area, industrial park and ecological protection zone, and output the urban functional composite target system document;

[0015] S5: Based on the urban functional composite target system document, a multi-objective weighted optimization algorithm is adopted. Through weighted calculation of functional matching degree, land use efficiency, and ecological constraint satisfaction, the optimal land use ratio of the six functional types is selected under constraints such as total land use, and the functional land use ratio of each zone is determined. Then, the spatial intersection index and average distance of different functional combinations are calculated through the functional spatial coupling degree measurement algorithm. The spatial correlation and functional layout synergy are verified by comparing with the preset coupling degree threshold. The output is an urban functional composite layout scheme atlas containing land use ratio and coupling degree verification results.

[0016] S6: Based on the urban functional complex layout scheme atlas, the supply capacity and total demand of schools, hospitals, commercial centers, and bus stations are calculated using a facility supply and demand matching algorithm. The supply and demand relationship is assessed through the supply and demand matching degree, and facility configuration is monitored. The comprehensive coordination effect is calculated by weighting the spatial coordination score, facility coordination score, and management coordination score through a coordination effect evaluation algorithm, and the effectiveness of the mechanism is evaluated. The output is an implementation manual of the urban functional coordination mechanism that includes facility adjustment suggestions and coordination effect scores.

[0017] In this manual, the method for urban function integration and coordination based on the characteristics of urban urbanization also includes S7: Based on the implementation manual of urban function coordination mechanism, the effectiveness of the scheme is verified through multi-scenario simulation, and a simulation verification report of urban function integration and coordination scheme is output.

[0018] In this specification, the method for urban function integration and coordination based on the characteristics of urban urbanization also includes S8: Based on the simulation verification report of urban function integration and coordination scheme, a dynamic monitoring platform is constructed to monitor and dynamically adjust the functional layout and coordination mechanism in real time, and output a dynamic monitoring and adjustment report of urban functions.

[0019] In this manual, the interaction process between the multi-objective weighted optimization algorithm and the functional space coupling degree calculation algorithm in S5 includes: after the multi-objective weighted optimization algorithm outputs the functional land use ratio of each zone, it transmits the functional land use area parameter to the functional space coupling degree calculation algorithm to calculate the functional space coupling degree; if the functional space coupling degree does not reach the preset threshold, the functional space coupling degree calculation algorithm generates adjustment suggestions and feeds them back to the multi-objective weighted optimization algorithm to re-optimize the land use layout until the coupling degree meets the standard.

[0020] In this specification, the interaction process between the facility supply and demand matching algorithm and the coordination effect evaluation algorithm in S6 includes: after the facility supply and demand matching algorithm calculates the supply and demand matching degree, it passes the matching degree parameter to the coordination effect evaluation algorithm to calculate the facility coordination score; if the facility coordination score is lower than the preset value, the coordination effect evaluation algorithm generates a correction coefficient and feeds it back to the facility supply and demand matching algorithm to adjust the total demand calculation and improve the redundancy of the facility supply recommendations.

[0021] In this specification, the overall association process between the S5 and S6 algorithms includes: the functional land use ratio output by the multi-objective weighted optimization algorithm in S5 provides a spatial basis for the facility supply and demand matching algorithm in S6, constraining the upper limit of facility supply capacity; the functional coupling degree calculated by the functional spatial coupling degree measurement algorithm in S5 is used as an input parameter for the coordination effect evaluation algorithm in S6, which is used to calculate the spatial coordination score and affects the comprehensive coordination effect evaluation result.

[0022] In this specification, the implementation details of the multi-objective weighted optimization algorithm in S5 include: the weight of the functional matching degree is set differently according to the functional priority of the four types of partitions; the calculation of land use efficiency combines the current GDP and industrial entropy value of the partition; and the calculation of ecological constraint satisfaction is related to the actual planned area and target area of ​​green space and ecological isolation zone.

[0023] In this manual, the implementation details of the functional space coupling degree measurement algorithm in S5 include: the spatial intersection index is calculated through GIS spatial overlay analysis, and the average distance is calculated through GIS network analysis; the preset functional coupling degree threshold is set differently for 15 core functional combinations; if the coupling degree of a certain combination does not meet the standard, the adjustment suggestion generated by the algorithm must specify the number of facilities to be adjusted and the minimum value that the adjacent area should reach after the adjustment.

[0024] In this manual, the triggering conditions for dynamic adjustment in S8 include: when the multi-objective optimization score is below 80 points for two consecutive quarters, the functional coupling compliance rate is below 85%, the facility supply and demand balance rate is below 80%, or the comprehensive coordination effect score is below 70 points, the adjustment process will be automatically triggered, and the adjustment content includes re-optimizing the land use layout or adding new facilities.

[0025] In this manual, the closed-loop optimization mechanism formed by S1 to S8 ends when: the multi-objective optimization score is ≥85 points for three consecutive quarters, the functional coupling compliance rate is ≥90%, the facility supply and demand balance rate is ≥85%, and the comprehensive coordination effect score is ≥80 points. At this time, active adjustment will stop and only dynamic monitoring will be maintained.

[0026] In summary, the present invention has at least the following beneficial effects:

[0027] 1. Optimize the spatial structure of urban functions: Through quantitative analysis and multi-objective optimization algorithms based on urbanization characteristics, break through the limitations of traditional single-function zoning, realize the "spatial coupling" of functions such as residence, commerce, public services, transportation, and ecology, and form a functional layout of "work-life balance, industry-city integration, and ecological livability", thereby reducing the waste of resources and efficiency loss caused by spatial fragmentation;

[0028] 2. Improve the efficiency of urban operation coordination: Based on algorithms such as functional space coupling degree calculation and facility supply and demand matching, a three-dimensional coordination mechanism of "space-facilities-management" is constructed to solve the problems of management barriers and insufficient functional coordination, realize the dynamic adaptation of facility support to demand, early warning of spatial conflicts, and efficient execution of coordination decisions, thereby reducing the cost of urban operation and management.

[0029] 3. Improve residents' quality of life: Through functional integration layout (such as supporting commercial, educational and medical facilities around residences) and coordination mechanisms, shorten the distance residents can reach for daily services, reduce commuting time, improve the level of equalization of public services, and strengthen the connection between green spaces and residential and commercial spaces to improve the urban ecology and living environment;

[0030] 4. Enhance the adaptability of planning schemes: With the help of simulation verification and dynamic monitoring platforms, the planning schemes can be transformed from "static design" to "dynamic optimization". They can respond in real time to changes in urbanization characteristics (such as population growth and industrial upgrading), adjust functional layout and coordination strategies in a timely manner, and ensure that the planning schemes adapt to the needs of urban development in the long term.

[0031] 5. Promote sustainable urban development: Integrate ecological constraints into functional layout, balance urban development and ecological protection through the functional integration design of green spaces and ecological protection zones, and achieve synergy between intensive land use and ecological benefits by optimizing land use efficiency (such as increasing the output per unit area of ​​industrial parks and the functional mixing of core urban areas). Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating the method for the integration and coordination of urban functions based on the characteristics of urbanization involved in this invention.

[0034] Figure 2 This is a schematic diagram illustrating the process of designing a complex urban functional layout scheme involved in this invention.

[0035] Figure 3 This is a schematic diagram of the process for constructing a city function coordination mechanism involved in this invention.

[0036] Figure 4 This is a schematic diagram illustrating the simulation and verification process of the urban functional integration and coordination scheme involved in this invention. Detailed Implementation

[0037] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0038] like Figure 1 As shown, this embodiment provides a method for the integration and coordination of urban functions based on the characteristics of urban urbanization, including the following steps:

[0039] S1: Collect spatial basic data, social population data, economic and industrial data, and ecological environment data, and form a feature classification dataset after preprocessing;

[0040] S2: Based on the feature classification dataset, analyze the characteristics of urban urbanization through a quantitative indicator system, and output a quantitative analysis report on the characteristics of urban urbanization.

[0041] S3: Based on the quantitative analysis report and national standards, construct a six-category urban function classification system including residential, commercial services, industrial production, public management and public services, transportation and municipal facilities, and green space and squares, and output an explanation of the urban function classification system.

[0042] S4: Based on the quantitative analysis report and functional classification system description, set the functional proportions, efficiency, ecology and coordination targets for four types of zones: core urban area, emerging urban area, industrial park and ecological protection zone, and output the urban functional composite target system document;

[0043] S5: Based on the urban functional composite target system document, a multi-objective weighted optimization algorithm is adopted. Through weighted calculation of functional matching degree, land use efficiency, and ecological constraint satisfaction, the optimal land use ratio of the six functional types is selected under constraints such as total land use, and the functional land use ratio of each zone is determined. Then, the spatial intersection index and average distance of different functional combinations are calculated through the functional spatial coupling degree measurement algorithm. The spatial correlation and functional layout synergy are verified by comparing with the preset coupling degree threshold. The output is an urban functional composite layout scheme atlas containing land use ratio and coupling degree verification results.

[0044] S6: Based on the urban functional complex layout scheme atlas, the supply capacity and total demand of schools, hospitals, commercial centers, and bus stations are calculated using a facility supply and demand matching algorithm. The supply and demand relationship is assessed through the supply and demand matching degree, and facility configuration is monitored. The comprehensive coordination effect is calculated by weighting the spatial coordination score, facility coordination score, and management coordination score through a coordination effect evaluation algorithm, and the effectiveness of the mechanism is evaluated. The output is an implementation manual of the urban functional coordination mechanism that includes facility adjustment suggestions and coordination effect scores.

[0045] In some embodiments, the urban function integration and coordination method based on urban urbanization characteristics further includes S7: based on the implementation manual of urban function coordination mechanism, verifying the effectiveness of the scheme through multi-scenario simulation, and outputting a simulation verification report of urban function integration and coordination scheme.

[0046] In some embodiments, the method for urban function integration and coordination based on urban urbanization characteristics further includes S8: based on the simulation verification report of urban function integration and coordination scheme, construct a dynamic monitoring platform, monitor and dynamically adjust the functional layout and coordination mechanism in real time, and output an urban function dynamic monitoring and adjustment report.

[0047] In some embodiments, the interaction process between the multi-objective weighted optimization algorithm and the functional space coupling degree calculation algorithm in S5 includes: after the multi-objective weighted optimization algorithm outputs the functional land use ratio of each zone, it transmits the functional land use area parameter to the functional space coupling degree calculation algorithm to calculate the functional space coupling degree; if the functional space coupling degree does not reach the preset threshold, the functional space coupling degree calculation algorithm generates adjustment suggestions and feeds them back to the multi-objective weighted optimization algorithm to re-optimize the land use layout until the coupling degree meets the standard.

[0048] In some embodiments, the interaction process between the facility supply and demand matching algorithm and the coordination effect evaluation algorithm in S6 includes: after the facility supply and demand matching algorithm calculates the supply and demand matching degree, it passes the matching degree parameter to the coordination effect evaluation algorithm to calculate the facility coordination score; if the facility coordination score is lower than a preset value, the coordination effect evaluation algorithm generates a correction coefficient and feeds it back to the facility supply and demand matching algorithm to adjust the total demand calculation and improve the redundancy of the facility supply recommendations.

[0049] In some embodiments, the overall association process of algorithms S5 and S6 includes: the functional land use ratio output by the multi-objective weighted optimization algorithm in S5 provides a spatial basis for the facility supply and demand matching algorithm in S6, and constrains the upper limit of facility supply capacity; the functional coupling degree calculated by the functional spatial coupling degree measurement algorithm in S5 is used as an input parameter of the coordination effect evaluation algorithm in S6 to calculate the spatial coordination score and affect the comprehensive coordination effect evaluation result.

[0050] In some embodiments, the implementation details of the multi-objective weighted optimization algorithm in S5 include: the weight of the functional matching degree is set differently according to the functional priority of the four types of partitions; the calculation of land use efficiency is combined with the current GDP and industrial entropy value of the partition; and the calculation of ecological constraint satisfaction is related to the actual planned area and target area of ​​green space and ecological isolation zone.

[0051] In some embodiments, the implementation details of the functional space coupling degree calculation algorithm in S5 include: the spatial intersection index is calculated through GIS spatial overlay analysis, and the average distance is calculated through GIS network analysis; the preset functional coupling degree threshold is set differently for 15 core functional combinations; if the coupling degree of a certain combination does not meet the standard, the adjustment suggestion generated by the algorithm needs to specify the number of facilities to be adjusted and the minimum value that the adjacent area should reach after adjustment.

[0052] In some embodiments, the triggering conditions for dynamic adjustment in S8 include: when the multi-objective optimization score is below 80 points for two consecutive quarters, the functional coupling compliance rate is below 85%, the facility supply and demand balance rate is below 80%, or the comprehensive coordination effect score is below 70 points, the adjustment process is automatically triggered, and the adjustment content includes re-optimizing the land use layout or adding new facilities.

[0053] In some embodiments, the closed-loop optimization mechanism formed by S1 to S8 terminates when: the multi-objective optimization score is ≥85 points for three consecutive quarters, the functional coupling compliance rate is ≥90%, the facility supply and demand balance rate is ≥85%, and the comprehensive coordination effect score is ≥80 points. At this point, active adjustment is stopped, and only dynamic monitoring is maintained.

[0054] The technical concept of this invention is as follows:

[0055] A method for urban functional integration and coordination based on the characteristics of urban urbanization includes the following:

[0056] S1: Construction of Urbanization Feature Classification Dataset

[0057] 1.1 Data Collection Scope and Type

[0058] This step aims to collect core characteristic data on the urbanization process, providing foundational support for subsequent functional layout and coordination mechanisms. Data collection covers the entire target city area and is categorized according to four dimensions: spatial, social, economic, and ecological. Specifically, it includes:

[0059] Spatial baseline data (accuracy: 1:5000 topographic map)

[0060] Current land use status: Vector boundaries and areas of 6 types of functional land use (residential land, commercial service land, etc., corresponding to f=1 to f=6 in S5).

[0061] Traffic network data: routes of main and secondary roads, locations of bus stops, road network density (unit: km / km) 2 );

[0062] Topographic data: elevation, slope, and water distribution.

[0063] Social Demographic Data

[0064] Population size: total population, resident population, school-age population (6-15 years old), and employed population;

[0065] Population distribution: Population density of each street (township) (unit: people / km) 2 );

[0066] Public service needs: Residents' satisfaction ratings for education, healthcare, and commercial facilities (0-10 points).

[0067] Economic and industrial data

[0068] Total economic output: Regional GDP, GDP per capita (unit: 10,000 yuan / person);

[0069] Industrial structure: the proportion of secondary and tertiary industries, and the output value of sub-sectors (such as manufacturing and finance).

[0070] Land use benefits: industrial land value per unit (ten thousand yuan / hectare), commercial land turnover (ten thousand yuan / hectare).

[0071] Ecological and environmental data

[0072] Green space resources: Area and distribution of park green space and protective green space;

[0073] Environmental quality: PM2.5 concentration, noise level (decibels);

[0074] Ecological constraints: ecological protection red line and basic farmland boundary.

[0075] 1.2 Data Preprocessing and Standardization

[0076] To ensure the data can be used for subsequent algorithm calculations, the following processing is required:

[0077] Missing value handling: Use "spatiotemporal interpolation" to supplement missing data (e.g., if monthly population data for a certain street is missing, interpolate using the average of the same period of the three adjacent streets and the historical trend of that street).

[0078] Unit standardization: unify data units (e.g., area units are converted to "hectares", population units are converted to "persons");

[0079] Order-of-magnitude normalization: Converting non-proportional data into a 0-1 range (e.g., converting PM2.5 concentration (0-150 μg / m³)). 3 ) converted to This facilitates subsequent weighted calculations.

[0080] 1.3 Output Data

[0081] Output a dataset of urbanization feature classification, including:

[0082] 1. Data list of four dimensions (including field name, unit, source, and collection time);

[0083] 2. Vector data layers (land use, transportation networks, etc., format: Shapefile);

[0084] 3. Attribute data table (population, GDP, etc., format: Excel / CSV);

[0085] 4. Data preprocessing instructions (methods for handling missing values, normalization formulas).

[0086] S2: Generate a quantitative analysis report on urban urbanization characteristics.

[0087] 2.1 Quantitative Index System for Core Features

[0088] Based on the S1 dataset, a four-dimensional quantitative index of "strength-structure-efficiency-quality" is constructed to provide parameters for the S4 target system and the S5 algorithm.

[0089] Urbanization intensity index

[0090] Urbanization rate: ;

[0091] Speed ​​of expansion of construction land: (Unit: hectares / year, S is the area of ​​construction land).

[0092] Spatial structure indicators

[0093] Functional hybridity: ( The land area for the f-th functional category; The total construction land area of ​​the city; (Values ​​range from 0 to 1, with higher values ​​indicating better mixing).

[0094] Job-housing ratio: (Ideal value = 1.0).

[0095] Economic efficiency indicators

[0096] GDP per unit area: (Unit: RMB 10,000 / hectare);

[0097] Industry Diversification Index (Entropy Method):

[0098] ,in Let n be the proportion of the output value of the i-th industry, where n=3 (primary / secondary / tertiary industries). The larger the industry, the more diversified it is.

[0099] Ecological quality indicators

[0100] Green space ratio: ;

[0101] Ecological constraint compliance rate: .

[0102] 2.2 Quantitative Analysis Methods and Processes

[0103] Trend Analysis: Linear regression was used to calculate the trend of indicator changes from 2018 to 2023 (e.g., Average annual growth rate), identify the stage of urbanization (e.g., core urban areas are in a stable period, while emerging urban areas are in a rapid expansion period).

[0104] Zonal Difference Analysis: Comparing indicator values ​​among four types of zones, including core urban areas, emerging urban areas, etc. (e.g., industrial parks) 10,000 yuan / hectare, ecological protection area (10,000 yuan / hectare)

[0105] Correlation analysis: Calculate the relationship between indicators using the Pearson correlation coefficient (e.g., The correlation coefficient r = 0.72 between functional mixing and resident satisfaction indicates a strong correlation between functional mixing and satisfaction.

[0106] 2.3 Output Data

[0107] Output a quantitative analysis report on urban urbanization characteristics, including:

[0108] 1. Calculation results table of four major indicators (by year and region);

[0109] 2. Trend analysis charts (such as construction land expansion curves);

[0110] 3. Radar chart of zoning characteristics (visually displays the differences in indicators among the four types of zoning);

[0111] 4. Core conclusions (e.g., "The job-housing ratio in emerging urban areas is 0.6, indicating a job-housing imbalance," "Industrial parks") (Insufficient industrial diversification).

[0112] S3: Constructing a Classification System for Urban Functions

[0113] 3.1 Functional Classification Standards and Connotations

[0114] Based on the National Standard for Urban Land Use Classification and Planning Construction Land (GB50137-2011), and combined with the quantitative analysis results of S2, urban functions are divided into 6 categories (corresponding to f=1 to f=6 in S5):

[0115] 1. Residential function (f=1)

[0116] Definition: Spaces that meet the daily living needs of residents, including residences and community service facilities (such as neighborhood committees and elderly care service stations);

[0117] Classification criteria: Primarily residential land, with supporting facilities accounting for ≤15%, and a plot ratio of ≤3.5 in the core urban area and ≤2.5 in the emerging urban area.

[0118] 2. Commercial service functions (f=2)

[0119] Definition: Spaces that provide services such as shopping, dining, and entertainment, including shopping malls, supermarkets, restaurants, and cinemas;

[0120] Classification criteria: Centralized commercial areas with a building area of ​​≥5000㎡, or commercial streets with a continuous length of ≥300 meters of street-front commercial facilities.

[0121] 3. Industrial production function (f=3)

[0122] Definition: Spaces engaged in material production and product processing, including factories, warehousing and logistics facilities, and R&D pilot production bases;

[0123] Classification criteria: Land within industrial parks, with a land value per unit area ≥ 5 million yuan / hectare (land use is prohibited in ecological protection zones).

[0124] 4. Public management and public service functions (f=4)

[0125] Meaning: Spaces that provide public services and social management, including schools (s=1), hospitals (s=2), administrative offices, and cultural venues;

[0126] Allocation criteria: Schools are allocated based on "400 student places per 10,000 people," and hospitals are allocated based on "6 beds per 1,000 people" (similar to S6 Algorithm 3). echo).

[0127] 5. Transportation and municipal infrastructure functions (f=5)

[0128] Content: The transportation and infrastructure space that ensures the operation of the city, including bus stops (s=4), parking lots, water plants, and substations;

[0129] Classification criteria: Bus stop service radius ≤ 500 meters, main road spacing ≤ 800 meters (same as S5 algorithm 2) (Calculation related).

[0130] 6. Functions of green spaces and plazas (f=6)

[0131] Meaning: Providing open spaces for ecological regulation and public activities, including parks, protective green spaces, and civic squares;

[0132] Division criteria: Park service radius ≤ 1000 meters, protective green space width ≥ 20 meters (same as S5 algorithm 1). (Related).

[0133] 3.2 Functional Classification Verification and Adjustment

[0134] Verification method: Randomly select 50 plots of land and have 3 planning experts classify them independently according to the above standards. If the consistency rate is ≥90%, the classification system is valid.

[0135] Adjustment Case: A mixed-use plot of land with "R&D + office" functions, which accounted for 60% of the R&D and 40% of the office functions, was ultimately classified as industrial production function (f=3) and marked as "mixed office" in the attribute.

[0136] 3.3 Output Data

[0137] Output a description of the city functional classification system, including:

[0138] 1.6 Definition, classification criteria, and included subtypes of functional categories (e.g., residential functions include "affordable housing" and "commercial housing");

[0139] 2. Functional classification flowchart (to guide actual land parcel classification operations);

[0140] 3. Classification result vector image (overlaid on the land use layer of S1).

[0141] S4: Create a comprehensive urban functional target system document

[0142] 4.1 Principles for Constructing the Target System

[0143] Based on the quantitative analysis conclusions of S2 (such as "insufficient green space ratio" and "job-housing imbalance"), and following the principles of "problem-oriented - vision-oriented - quantifiable", four types of functional composite objectives for zoning are set as inputs to S5 Algorithm 1 (such as... ) and the benchmarks for S6 evaluation (such as ).

[0144] 4.2 Setting of Targets for Different Zones

[0145] 4.2.1 Core Urban Area Objectives (Example)

[0146] Functional proportion target (corresponding to Algorithm 1) ):

[0147] Residential functions (f=1) 32%, commercial services (f=2) 28%, public services (f=4) 12%, transportation facilities (f=5) 22%, green space (f=6) 6%, industry (f=3) 0%;

[0148] Efficiency target: GDP per unit area ≥ 15 million yuan / hectare (affects the E calculation of Algorithm 1).

[0149] Ecological goals: Green space ratio ≥ 6%, ecological constraint satisfaction C ≥ 90 points (C threshold of Algorithm 1);

[0150] Coordination Objective: Overall Coordination Score (S6 Algorithm 4 High Standard)

[0151] 4.2.2 Targets for Emerging Urban Areas / Industrial Parks / Ecological Protection Zones

[0152] (All are set according to the above structure; only the differences are listed here.)

[0153] Emerging urban areas: Job-housing ratio "Residential-Public Services" Coupling (Algorithm 2 threshold);

[0154] Industrial Park: Industrial land accounts for 40%, "industry-transportation" coupling degree ;

[0155] Ecological protection zones: green space ratio ≥ 60%, commercial land area ≤ 5%, "green space-residential" coupling degree .

[0156] 4.3 Target Value Calculation Method

[0157] Demand forecasting method: such as the target number of school places ( The projection for the school-age population in 2035 uses a redundancy coefficient of 1.05, consistent with Algorithm 3. Consistent);

[0158] Benchmarking method: The proportion of commercial land in the core urban area should be referenced to similar cities;

[0159] Constraint derivation method: Green area of ​​ecological protection zone = Total land area × 60% (based on ecological red line requirements).

[0160] 4.4 Output Data

[0161] Output the urban functional composite target system document, including:

[0162] 1. A table of sub-targets for the four types of zones (including functional proportions, efficiency, ecological, and coordination objectives);

[0163] 2. Target value calculation instructions (including formulas, etc.) );

[0164] 3. Timeline for achieving the goals (divided into three phases: 2025 / 2030 / 2035).

[0165] S5: Design a mixed-use urban functional layout scheme (specific process as follows) Figure 2 (As shown)

[0166] 5.1 Urban Functional Zoning

[0167] Based on the S2 Urbanization Characteristics Quantitative Analysis Report (Characteristic Area Distribution) and the S3 Urban Functional Status Assessment Report (Problem Area Distribution), four types of functional composite zones are divided, as enumerated below:

[0168] Core Urban Area: Characterized by high population density, concentrated commercial services, and a highly networked transportation system; problems include "insufficient integration of commercial and residential functions and overcrowded public service facilities."

[0169] Emerging Urban Area: Characterized by rapid population growth and large land use potential, but with problems of "oversupply of residential functions and weak commercial, public service, and transportation functions";

[0170] Industrial Park Area: Characterized by concentrated industrial production and significant economic output, but its problems include "insufficient separation between industrial and residential functions and a lack of public service facilities."

[0171] Ecological Protection Area: Characterized by abundant green space resources and high ecological value, but the problem is "insufficient integration of green space with recreational functions and a lack of service facilities".

[0172] 5.2 Functional Layout Design of Zones

[0173] 5.2.1 Algorithm 1: Multi-objective weighted optimization algorithm (determining the functional land use ratio of each zone)

[0174] 1. Model building process

[0175] The core objective of this algorithm is to determine the optimal proportion of six functional land uses in each zone through quantitative calculation, under the premise of meeting the characteristics of urbanization (such as population density and industrial structure) and ecological constraints, so as to achieve a balance between the three major goals of "functional matching, efficient land use, and ecological compliance".

[0176] Detailed definition of the objective function

[0177] Overall optimization score formula:

[0178] The detailed descriptions of each parameter are as follows:

[0179] F: Total score for multi-objective optimization (0-100 points), the higher the score, the better the layout scheme.

[0180] Weighting coefficient , , : These represent the importance weights of functional matching degree, land use efficiency, and ecological constraint satisfaction, respectively, with a sum of 1. The weight values ​​vary according to the differences in the core characteristics of different zones (see Table 1 for specific enumeration).

[0181] Table 1: Weight Coefficient Values ​​for Each Partition

[0182]

[0183] Functional matching degree M (0-100 points): Measures the degree of matching between the area of ​​each functional land use and the target demand. Calculation formula: ;

[0184] in:

[0185] f=1 to 6 correspond to 6 different functional categories;

[0186] : The matching weight of the f-th function (summing up to 1) reflects the priority of the function within the partition (see Table 2 for specific enumerations).

[0187] : The actual planned land area (in hectares) for the f-th function of this zone;

[0188] : The target land area for the f-th function in this zone in the S4 target system (unit: hectares, from the urban functional composite target system document).

[0189] Table 2: Functional Matching Weights for Each Partition Value table

[0190]

[0191] Land use efficiency E (0-100 points): Measures the economic output capacity of a unit of construction land. Calculation formula:

[0192] ;

[0193] in:

[0194] Potential GDP at the end of the zoning planning period (unit: 100 million yuan), calculated based on current GDP and industrial diversification level: ( For the current GDP, The industry entropy value calculated for S2; the higher the entropy value, the more diversified the industries, and the greater the potential GDP growth potential.

[0195] Total construction land area of ​​each zone (unit: hectares);

[0196] k: Efficiency coefficient (0.8 for core urban areas, 0.6 for emerging urban areas, 0.9 for industrial parks, and 0.5 for ecological protection zones), reflecting the priority of economic output in different zones.

[0197] Ecological constraint satisfaction C (0-100 points): Measures the degree to which green spaces and ecological buffer zones meet ecological protection requirements. Calculation formula:

[0198] ;

[0199] in:

[0200] Actual planned green space area (unit: hectares);

[0201] Green space target area in the S4 target system (unit: hectares);

[0202] Actual planned ecological buffer zone area (unit: hectares);

[0203] Target area of ​​the isolation zone in the S4 target system (unit: hectares).

[0204] Detailed explanation of constraints

[0205] To ensure that the layout plan complies with the basic requirements of urban planning, three types of rigid constraints are set:

[0206] Total land use constraints: (The sum of the areas of all types of functional land use equals the total construction land area of ​​the zone);

[0207] Single-function upper limit constraint: (such as industrial land in the core urban area) To avoid pollution; commercial land in ecological protection zones (To avoid over-development)

[0208] Ecological lower limit constraints: (Green space area shall not be less than 90% of the target value to ensure basic ecological functions).

[0209] 2. Model Training Process

[0210] The purpose of training is to calibrate the weight coefficients using historical data. Matching weight = To ensure that the model calculation results fit the actual high-quality layout scheme with a degree of ≥85%.

[0211] Training data source

[0212] Historical data from 2018 to 2023 for 12 typical zones (3 zones per city: core urban area, emerging urban area, industrial park, and ecological protection zone) of 3 cities of similar size (e.g., cities with similar population size and GDP to the target city) were selected, including:

[0213] Functional land use data: annual land area for 6 functional categories;

[0214] Economic data: Regional annual GDP, industry entropy values;

[0215] Ecological data: annual green space area and buffer zone area;

[0216] Performance data: Annual functional operation evaluation scores for each district (such as job-housing balance rate and resident satisfaction).

[0217] Training steps

[0218] ① Data preprocessing: Standardize the historical data of the 12 zones (e.g., convert land area to hectares and GDP to 100 million yuan) and select "high-quality solutions" (30 sets of annual data with an operation evaluation score ≥ 85 points).

[0219] ② Initial weight calculation: The initial weights are calculated using the Analytic Hierarchy Process (AHP). and (Five planning experts were invited to score the importance of the objectives.)

[0220] ③ Iterative optimization: Substitute the data of high-quality solutions into the objective function F, and adjust it using the particle swarm optimization (PSO) algorithm. and The goal is to achieve an F-value of ≥90 for a high-quality solution (a high-quality solution should correspond to a high optimization score).

[0221] ④ Validation: Select two partitions (10 groups in total) that were not used in training for validation. If the F-value has a good fit of ≥85% to the actual running score (i.e., ...), the validation will be successful. If the training is successful, then the training is complete; otherwise, repeat step ③ until the target is met.

[0222] 3. Model Application Process

[0223] It is applied to the specific calculation of the functional land use ratio of each zone, and outputs a land area scheme that can be directly used for planning.

[0224] Input data

[0225] S2 City Urbanization Characteristics Quantitative Analysis Report: Current GDP of the Region ( ), industry entropy ( );

[0226] S4 Urban Functional Composite Target System Document: Land Area of ​​6 Functional Targets ( ), target area of ​​green space ( ), target area of ​​the isolation zone ( );

[0227] S5.1 Zoning Results: Total Construction Land Area of ​​Zoning ( ).

[0228] Application steps

[0229] ① Data Import: Input data is entered into the algorithm system, and corresponding data is matched according to the zone type (core urban area / emerging urban area, etc.). , and k (from training results);

[0230] ② Scheme traversal: Within the constraints, traverse the possible combinations of land area for the 6 functional land use categories (generating a total of 1000 candidate schemes).

[0231] ③ Score Calculation: Calculate the F-value (a weighted sum of functional matching degree M, land use efficiency E, and ecological constraint satisfaction degree C) for each group of candidate schemes;

[0232] ④ Optimal Scheme Selection: Select the scheme with the highest F-score (≥85 points) that satisfies all constraints as the final land use ratio. For example, the final scheme for the core urban area might be:

[0233] hectares (residential, 32%) hectares (commercial, 28%) hectares (public services, 12%) hectares (transportation, 22%) hectares (green space, 6%). (Industrial, 0%)

[0234] 5.2.2 Algorithm 2: Functional Space Coupling Degree Calculation Algorithm (Verifying the Spatial Relationship of Functional Layout)

[0235] 1. Model building process

[0236] This algorithm is used to quantify the degree of spatial correlation between different functions, ensuring that the functional layout not only meets the area ratio requirements, but also forms effective synergy in space (such as the adjacent layout of residential and commercial areas to facilitate residents' lives).

[0237] Core Definitions and Calculation Formulas

[0238] Functional space coupling ( : This refers to the spatial correlation between function f1 and function f2 (f1, f2 ∈ {1, 2, 3, 4, 5, 6}, f1 ≠ f2) (values ​​range from 0 to 1, with higher values ​​indicating stronger correlation). Calculation formula:

[0239] ;

[0240] The detailed descriptions of each parameter are as follows:

[0241] Spatial intersection index (0-1) reflects the degree of adjacency between two types of functional land use, calculated through GIS spatial overlay analysis:

[0242] ;

[0243] In the formula, for and "Adjacent overlapping area" of land use categories (unit: hectares, defined as the overlapping part with a boundary distance ≤ 100 meters); This is the smaller of the two types of land use areas (to avoid deviations caused by excessive area differences).

[0244] : respectively The proportion of functional land use in the total land area of ​​the zone (from the output of Algorithm 1) ).

[0245] Average distance (km), reflecting the spatial distance between the two types of functional land use, calculated through GIS network analysis:

[0246] ;

[0247] In the formula, n1 is the number of land parcels for functional use of type f1 (e.g., residential function may be divided into 10 residential community parcels); n2 is the number of land parcels for functional use of type f2. This represents the shortest path distance between the i-th plot of land f1 and the j-th plot of land f2 (calculated based on the urban road network).

[0248] Coupling threshold setting ( )

[0249] Based on the functional integration requirements in the S4 target system, different functional combinations need to achieve different coupling thresholds (enumerating 15 core combinations, see Table 3):

[0250] Table 3: Coupling Degree Thresholds for Major Functional Combinations

[0251]

[0252] 2. Model Training Process

[0253] The purpose of the training is to calibrate the coupling degree threshold using historical data from domestic functional composite demonstration cities. Ensure that the threshold matches the actual functional operation effect (e.g., the resident satisfaction rate in areas where the coupling degree meets the standard is ≥85%).

[0254] Training data source

[0255] Data from 50 sub-regions (10 sub-regions per city) of 5 national functional demonstration cities were selected, including:

[0256] Spatial data: Land parcel vector data with 6 functions (location, boundary, area);

[0257] Coupling data: Historical coupling degree of 15 functional combinations calculated by GIS (2019-2023, once a year).

[0258] Performance data: Functional performance of the corresponding zones (e.g., resident shopping satisfaction survey results for areas with high residential-commercial coupling).

[0259] Training steps

[0260] ① Data matching: Match the historical coupling data of 50 zones with the corresponding operational performance data (e.g., when the coupling degree of "residential-commercial" is 0.72, the residents' shopping satisfaction rate is 88%).

[0261] ② Initial threshold setting: For each functional combination, the minimum coupling degree of "operational effect meets the standard" (e.g., satisfaction ≥ 85%) is calculated (e.g., in the "residential-commercial" combination, the minimum coupling degree of the standard-compliant area is 0.68), and this is used as the initial threshold;

[0262] ③ Threshold optimization: Substitute the initial threshold into the model and verify the proportion of regions with "coupling degree ≥ threshold" that meet the operational requirements (must be ≥ 80%). For example, when the initial threshold for "residential-commercial" is 0.68, the compliance rate is 78% (not meeting the requirements). The threshold needs to be increased to 0.7, at which point the compliance rate increases to 82% (meets the requirements).

[0263] ④ Confirm the threshold: Optimize each of the 15 function combinations one by one, and finally determine the threshold in Table 3. Training is then complete.

[0264] 3. Model Application Process

[0265] It is used to verify the spatial synergy of the land use layout scheme output by Algorithm 1, and if it does not meet the standard, it will provide feedback for adjustment.

[0266] Input data

[0267] Algorithm 1 output: Land area for each of the 6 functional categories in each zone ( ), total land area ( );

[0268] S1 Urbanization Feature Classification Dataset: Land Use Vector Data (Plot Location, Boundaries, for GIS Analysis);

[0269] Training results: Coupling thresholds for 15 functional combinations ( ).

[0270] Application steps

[0271] ①GIS Spatial Analysis: Load the land parcel vector data into the GIS software and calculate 15 functional combinations for each zone. (Adjacent overlapping area) and (Distance between plots);

[0272] ② Coupling degree calculation: Substitute into the formula to calculate. , and ;

[0273] ③ Threshold verification: comparison and To determine whether the standard is met (e.g., if the coupling degree of the "commercial-transportation" combination is 0.82 ≥ 0.8, it meets the standard; if the coupling degree of the "residential-public service" combination is 0.7 < 0.75, it does not meet the standard).

[0274] ④ Feedback and Adjustment: If there are substandard combinations, generate spatial coupling adjustment suggestions (e.g., "Insufficient coupling between residential and public services; three community health service centers need to be relocated to within 500 meters of the residential area to increase the adjacent area"). The algorithm is pushed to Algorithm 1 to re-optimize the land use layout.

[0275] 5.2.3 The interaction process between Algorithm 1 and Algorithm 2 (pairwise association)

[0276] Algorithm 1 (land use ratio optimization) and Algorithm 2 (spatial coupling verification) form a closed loop through "parameter transfer-result feedback" to ensure that the layout scheme simultaneously meets the requirements of area ratio and spatial correlation.

[0277] 1. Parameter passing from Algorithm 1 to Algorithm 2

[0278] Algorithm 1 output (Functional land area) is the core basis for Algorithm 2 calculation:

[0279] Land use ratio parameters: and The results directly from Algorithm 1 show that if Algorithm 1 adjusts the area of ​​a certain functional land use (e.g., increasing commercial land in the core urban area from 280 hectares to 300 hectares), then these two proportional parameters will change synchronously (the proportion of commercial land will increase from 28% to 30%), leading to... Recalculate.

[0280] 2. Feedback of results from Algorithm 2 to Algorithm 1

[0281] If Algorithm 2 calculates If the threshold is not reached (e.g., the "transportation-commerce" coupling degree is 0.75 < 0.8), then Algorithm 1 needs to add a "spatial coupling constraint":

[0282] Adjust the objective function: ;

[0283] In the formula, This is the coupling degree influence coefficient. When... When the condition is positive, F' increases; otherwise, it is negative and F' decreases. The guiding algorithm 1 prioritizes the scheme with the required coupling degree.

[0284] Example: If the "transportation-commerce" coupling degree is insufficient, Algorithm 1 will prioritize combinations where "transportation land and commercial land have larger adjacent areas" when traversing solutions (increasing...). (This continues until Algorithm 2 is verified to meet the criteria).

[0285] 5.3 Output Data

[0286] Output a city functional composite layout scheme atlas (including algorithm verification version), containing:

[0287] 1. Zoning Functional Land Use Proportion Table (Output of Algorithm 1, including...) (and percentage);

[0288] 2.15 Functional Combination Coupling Degree Calculation Table (Algorithm 2 Output, including...) and contrast);

[0289] 3. Algorithm parameter details ( , The value of k is... Threshold);

[0290] 4. Algorithm interaction adjustment records (e.g., "Due to the failure of the residential-public service coupling degree to meet the standard, the standard was met after the third iteration of Algorithm 1").

[0291] 5. Spatial layout map (marking the location and boundaries of various functional land uses, highlighting the core nodes that meet the coupling standards).

[0292] S6: Establish a city function coordination mechanism (specific process as follows) Figure 3 (As shown)

[0293] 6.1 Classification and Implementation Details of Coordination Mechanisms

[0294] 6.1.1 Algorithm 3: Facility Supply and Demand Matching Algorithm

[0295] 1. Model building process

[0296] This algorithm is used to monitor the supply and demand relationship of four types of core public service facilities (schools, hospitals, commercial centers, and bus stations) in real time, identify areas of supply and demand imbalance, and provide quantitative basis for the timing and scale of facility construction.

[0297] Core Definitions and Calculation Formulas

[0298] Facility supply and demand matching degree ( ): The degree of matching between the supply capacity and total demand of facility type s in month t (monthly monitoring) (s=1 to 4, corresponding to schools, hospitals, commercial centers, and bus stops, respectively). Calculation formula:

[0299] ;

[0300] The detailed descriptions of each parameter are as follows:

[0301] Matching degree (≥1 indicates supply and demand balance, <1 indicates insufficient supply).

[0302] Supply capacity Actual service capacity of facility type s in month t (calculated differentiated by facility type):

[0303] School (s=1): ( For the number of schools, (The number of available places at school i in month t, from the IoT degree monitoring system).

[0304] Hospital (s=2): ( For the number of hospitals, (This refers to the number of available beds in hospital j in month t, from the IoT bed monitoring system).

[0305] Business Center (s=3): ( For the number of business centers, Let represent the available retail space (㎡) of the k-th commercial center in month t. (10㎡ service for 1 person)

[0306] Bus stop (s=4): ( This refers to the number of bus stops. For the first The average departure interval (in minutes) of a bus stop in month t, where 60 / V is the number of departures per hour. (Each departure serves 50 people).

[0307] Total demand Total demand for Class s facilities in month t (calculated based on population and transportation data):

[0308] School (s=1): ( The population of school-age children (6-15 years old) in month t is from the monthly statistics update. (including 5% redundancy requirement);

[0309] Hospital (s=2): The total population of the region in month t; That is, 6 beds per 1,000 people).

[0310] Business Center (s=3): ( The population residing in month t; That is, each person needs 10 square meters of commercial services, corresponding to );

[0311] Bus stop (s=4): ( Traffic flow during peak hours in month t; =0.3, meaning 30% of the traffic requires public transportation services.

[0312] Facility type correction factor : Reflects facility priority (schools) ,Hospital Business Center Bus stop Healthcare and education are of higher priority and require more stringent supply-demand matching.

[0313] 2. Model Training Process

[0314] The purpose of the training is to calibrate the demand coefficient ( (etc.) and correction factor ,make sure The degree of matching with the actual facility operation results (such as the shortfall rate) is ≥90%.

[0315] Training data source

[0316] Monthly data from the target city over the past five years (2019-2023) were selected, totaling 60 samples, including:

[0317] Supply data: school vacancies, hospital vacancies, commercial vacancy rates, and bus departure intervals;

[0318] Demand data: school-age population, total population, resident population, traffic flow;

[0319] Results data: Facility operation assessment (such as school place shortage rate, hospital bed occupancy rate).

[0320] Training steps

[0321] ① Data cleaning: Remove outliers and standardize units (e.g., population is standardized to "person" and area to "㎡");

[0322] ② Initial calculation: Substitute the original coefficients to calculate Compare the actual effects (such as) At that time, the degree shortage rate was 8%).

[0323] ③ Coefficient calibration: Adjust the coefficients through linear regression to make... It showed the highest good fit to the outcome data. For example, the initial hospital... At the initial value of 1.2, the fit was 82%; after adjusting to 1.2, the fit improved to 93%.

[0324] ④ Determine the equilibrium interval: Based on the fitting results, define the "supply and demand equilibrium" range. The training was completed in the following areas (schools 1.1–1.3, hospitals 1.2–1.4, commercial centers 0.9–1.1, bus stops 1.0–1.2).

[0325] 3. Model Application Process

[0326] It is used for monthly facility supply and demand monitoring, and outputs specific adjustment suggestions for areas with imbalances.

[0327] Input data

[0328] S1 Urbanization Feature Classification Dataset: Monthly Population Data Update ( );

[0329] S2 City Urbanization Characteristics Quantitative Analysis Report: Monthly Traffic Flow Update ( );

[0330] IoT data from the S6 infrastructure coordination mechanism: (Real-time monitoring data);

[0331] Training results: Demand coefficient ( (etc.), correction factor .

[0332] Application steps

[0333] ① Data collection: The above data will be automatically collected on the 5th of each month and summarized by facility type;

[0334] ② Supply and demand calculation: calculate separately (Supply capacity) and (Total demand);

[0335] ③ Matching degree calculation: Substituting into the formula yields Determine whether it is within the equilibrium range (e.g., in a hospital). (determined to be a supply shortage).

[0336] ④ Generate suggestions: For areas with imbalances, output a list of facilities to be adjusted (e.g., "A hospital in the east is under-supplied and needs to add a community health service center (50 beds), which will start construction in Q3 2024") and push it to the corresponding management terminal.

[0337] 6.1.2 Algorithm 4: Coordination Effectiveness Evaluation Algorithm (Quantifying the Effectiveness of Coordination Mechanisms)

[0338] 1. Model building process

[0339] This algorithm comprehensively evaluates the implementation effect of the S6 coordination mechanism from three dimensions: "spatial coordination, facility coordination, and management coordination," providing a basis for dynamic adjustment.

[0340] Core Definitions and Calculation Formulas

[0341] Overall coordination effect score ( ): Total score of coordination effectiveness in quarter q (quarterly evaluation) (0-100 points). Calculation formula:

[0342] ;

[0343] The detailed descriptions of each parameter are as follows:

[0344] A score of ≥80 indicates "effective coordination", 60-79 indicates "basically effective", and <60 indicates "insufficient coordination".

[0345] Dimension weights =0.35 (spatial coordination) =0.4 (Facility Coordination) =0.25 (Management Coordination) (The sum is 1, determined by the AHP method).

[0346] Spatial coordination score (0-100 points): Calculation of functional coupling degree based on Algorithm 2:

[0347] ;

[0348] In the formula, The functional coupling degree for the qth quarter (algorithm updated in the 2nd quarter); The coupling threshold is used; the min function ensures that when the coupling exceeds the threshold, it is calculated as full score (avoiding the bias of over-correlation).

[0349] Facilities Coordination Score (0-100 points): Calculation of supply and demand matching degree based on Algorithm 3:

[0350] ;

[0351] In the formula, The supply and demand matching degree of facilities in the qth quarter (summary of the algorithm in the 3rd quarter); the min function ensures that when the matching degree exceeds 1.2, it is calculated as full score (to avoid the deviation of oversupply).

[0352] Management and coordination score (0-100 points): Calculated based on the implementation of the management mechanism:

[0353] ;

[0354] In the formula, Number of times the implementation of the coordination meeting resolutions in quarter q was delayed (from meeting minutes); Number of unresolved spatial conflicts / infrastructure imbalances (from the early warning log).

[0355] 2. Model Training Process

[0356] The purpose of training is to calibrate the dimensional weights. , , ,make sure The fit with residents' actual satisfaction level is ≥80%.

[0357] Training data source

[0358] Quarterly data from the past three years (2021-2023) were selected from three cities that have implemented coordination mechanisms, totaling 36 samples, including:

[0359] Three-dimensional score data: );

[0360] Resident satisfaction data: Results of quarterly urban function satisfaction surveys.

[0361] Training steps

[0362] ① Data Association: The three-dimensional scores of 36 samples were correlated with residents' satisfaction (e.g., (Time-based satisfaction rate = 88%)

[0363] ② Initial weight determination: The initial weights are calculated using the AHP method. (Expert scoring determines the importance of each dimension);

[0364] ③ Regression optimization: Adjusting through multiple linear regression ,make Fit with satisfaction

[0365] (such as the initial R) 2 =0.75, adjust After increasing from 0.35 to 0.4, R 2 =0.82);

[0366] ⑤ Validation: Validate using 4 samples that were not used in training. If the error is ≤5% ( If the training is complete, then the training is finished.

[0367] 3. Model Application Process

[0368] It is used for quarterly coordination effect evaluation, outputting overall results and directions for improvement.

[0369] Input data

[0370] Algorithm Q2 data: 15 groups of functional coupling ;

[0371] Algorithm Q3 data: Supply and demand matching degree of four types of facilities ;

[0372] S6 Management and Coordination Mechanism Data: (Number of delayed executions) (Number of unresolved issues);

[0373] Training results: Dimension weights , , .

[0374] Application steps

[0375] ① Quarterly Data Summary: Input data is collected at the end of each quarter (March / June / September / December), and calculations are performed. ;

[0376] ②Calculation of overall score: Substituting into the formula yields Compare with the target value (≥80 points);

[0377] ③Results analysis: If Score (basically effective), analyze low-scoring dimensions (such as...) (Due to insufficient hospital supply).

[0378] ④ Improvement push: Generate suggestions for improving the coordination mechanism (such as "accelerate the construction of a hospital in the east and improve the facility coordination score") and push them to the corresponding management terminal.

[0379] 6.1.3 The interaction process between Algorithm 3 and Algorithm 4 (pairwise association)

[0380] Algorithm 3 (facility supply and demand matching) and Algorithm 4 (coordination effect evaluation) form a closed loop through "real-time data transmission - effect feedback adjustment" to ensure continuous optimization of facility configuration.

[0381] 1. Parameter passing from Algorithm 3 to Algorithm 4

[0382] Algorithm 3 quarterly summary (Facility supply and demand matching degree) is calculated by Algorithm 4. The sole basis:

[0383] For example, in the second quarter, hospitals (If it is below the equilibrium range of 1.2 to 1.4), then The breakdown of the scores for the Traditional Chinese Medicine Hospital is as follows: , lower The overall score (e.g., from 85 points to 78 points) thus makes The score will decrease by approximately 2.8 points (0.4 × (78-85) = -2.8).

[0384] 2. Feedback on the results of Algorithm 4 → Algorithm 3

[0385] If Algorithm 4 calculates (If the facility coordination score is low), then Algorithm 3 needs to adjust the total demand calculation and increase the redundancy of the supply recommendations:

[0386] Introducing a coordination effect correction coefficient (like ,but );

[0387] Adjust the demand formula: (Total demand increased by 5%)

[0388] Adjusted matching formula: The algorithm 3 guides the algorithm to make more proactive supply recommendations (e.g., the original recommendation was to add 50 beds, but the revised recommendation is to add 53 beds).

[0389] 6.1.4 Overall Correlation of the Four Types of Algorithms (S5 and S6 Algorithms)

[0390] The four types of algorithms form a complete "layout-coordination" closed loop through parameter nesting, and the specific relationships are as follows:

[0391] 1. The foundation for Algorithm 1 to Algorithm 3

[0392] The functional land use ratio determined by Algorithm 1 (e.g., 15% for public service land in emerging urban areas) determines the upper limit of facility supply: insufficient public service land area will lead to insufficient space for schools and hospitals. Difficult to improve (no land available for development). If Algorithm 1 increases the proportion of residential land (e.g., from 35% to 40%), the residential population will... Increase, through (Commercial demand) is transmitted to Algorithm 3, which in turn forces an increase in the supply of commercial facilities.

[0393] 2. Space impact of Algorithm 2 to Algorithm 4

[0394] Coupling degree of Algorithm 2 Directly affecting Algorithm 4 Score (weight 35%). For example, if the "transportation-commerce" coupling degree in the core urban area increases from 0.75 (not up to standard) to 0.85 (up to standard), then... The component of this combination increased from 0.9375 (0.75 / 0.8) to 1, driving... From 82 points to 88 points, thus making Improve by 2.1 points (0.35 × 6 = 2.1).

[0395] 3. Overall Interaction Formula

[0396] The parameters of the four types of algorithms are nested using the following formulas:

[0397]

[0398] ;

[0399] 6.2 Output Data

[0400] Output the implementation manual for the urban function coordination mechanism, including:

[0401] 1. Algorithm 3 Parameter Table ( Demand coefficient wait);

[0402] 2. Algorithm 4 Parameter Table ( (Scoring rules)

[0403] 3. Four types of algorithm interaction logic diagrams (with parameter passing paths marked, such as "Algorithm 1 → Algorithm 2 → Algorithm 4");

[0404] 4. Monthly facility supply and demand matching report (including) (Calculation process)

[0405] 5. Quarterly coordination effectiveness evaluation report (including) (Score and suggestions for improvement)

[0406] 6. Responsibility assignment table (e.g., Big Data Algorithm Team: responsible for monthly updates of algorithm parameters).

[0407] S7: Simulation and Verification of Urban Functional Integration and Coordination Schemes (specific procedures are as follows) Figure 4 (As shown)

[0408] 7.1 Simulation Verification Index System

[0409] Based on the S4 target system and the output of the S5-S6 algorithms, a three-dimensional verification index of "layout rationality - coordination effectiveness - implementation feasibility" is constructed:

[0410] Layout rationality indicators

[0411] Multi-objective optimization score F (Result of Algorithm 1, must be ≥85 points);

[0412] Functional coupling compliance rate: (Requires ≥90%)

[0413] Coordination effectiveness indicators

[0414] Overall Coordination Score (The result of Algorithm 4 must be ≥80 points);

[0415] Facility supply and demand balance rate: (Requires ≥85%)

[0416] Feasibility indicators

[0417] Demolition costs: (Unit: 100 million yuan, which must be ≤ 30% of the fiscal budget);

[0418] Construction period: The average construction time for each functional plot (must be ≤5 years).

[0419] 7.2 Simulation Scenario Design and Comparison

[0420] Three scenarios were designed for simulation (using urban planning simulation software such as ArcGIS Urban):

[0421] Baseline scenario: Continue with the existing plan, without adopting the S5-S6 algorithm optimization scheme;

[0422] Solution scenario: Using S5 algorithm 1 for land use ratio + algorithm 2 for spatial coupling optimization + S6 for coordination mechanism;

[0423] Enhanced Scenarios: Based on the proposed solutions, increase the weight of ecological constraints. (For example, the core urban area increased from 0.3 to 0.4).

[0424] 7.3 Simulation Verification Process

[0425] 1. Input data: S5 layout scheme, S6 coordination mechanism parameters, and S1 current status data;

[0426] 2. Simulation Operation: Simulates the urban development process from 2025 to 2035 (one iteration per year);

[0427] 3. Results Output: Validation index values ​​for the three scenarios (e.g., F=89 points for the solution scenario). point);

[0428] 4. Difference Analysis: The improvement of the solution scenario compared to the baseline scenario (e.g., (Increase by 18 points), confirming the effectiveness of the plan.

[0429] 7.4 Output Data

[0430] Output a simulation verification report of the urban functional integration and coordination scheme, including:

[0431] 1. Comparison table of indicators for three types of scenarios;

[0432] 2. Dynamic simulation process diagram (showing the evolution of functional layout);

[0433] 3. Suggestions for optimizing the solution (e.g., "Strengthening the ecological benefits of the scenario is better, and it is recommended to adopt this suggestion").

[0434] S8: Urban Function Dynamic Monitoring and Adjustment Mechanism

[0435] 8.1 Construction of Dynamic Monitoring Platform

[0436] Integrating Internet of Things (IoT), big data, and GIS technologies, an integrated "air-ground-space" monitoring platform was built to collect four types of data in real time:

[0437] Spatial layout data: Quarterly updated functional land boundaries (via satellite remote sensing + drone aerial photography, used for Algorithm 2) calculate);

[0438] Facility operation data: Monthly updates on school places, hospital beds, etc. (IoT sensors, used for Algorithm 3) );

[0439] Coordination and execution data: Real-time recording of the implementation status of coordination meeting resolutions (interfacing with government systems, used for Algorithm 4). );

[0440] Resident feedback data: Real-time capture of residents' suggestions regarding functional layout (such as "There are no supermarkets around a certain community", used to verify the degree of coupling).

[0441] 8.2 Dynamically Adjust Trigger Conditions

[0442] The adjustment process is automatically triggered when the monitoring data meets one of the following conditions:

[0443] 1. The F-score of Algorithm 1 is less than 80 points for two consecutive quarters;

[0444] 2. The functional coupling compliance rate of Algorithm 2 is <85%;

[0445] 3. The facility supply-demand balance rate of Algorithm 3 is <80%;

[0446] 4. Algorithm 4 <70 points.

[0447] 8.3 Dynamically Adjust Implementation Process

[0448] 1. Problem Diagnosis: Locating the root cause of non-compliance indicators (e.g., (Due to insufficient hospital supply).

[0449] 2. Plan Adjustment:

[0450] If spatial coupling is not up to standard: call Algorithm 1 to re-optimize the land use layout (increase...) );

[0451] If there is an imbalance between supply and demand for facilities: add new facilities according to the suggestions of Algorithm 3 (e.g., "add 1 hospital in the east");

[0452] 3. Effect tracking: Monitor the changes in indicators for three consecutive months after adjustment until the target is met.

[0453] 8.4 Output Data

[0454] Output an annual report on dynamic monitoring and adjustment of urban functions, including:

[0455] 1. Summary table of annual monitoring indicators (including the core results of algorithms 1-4);

[0456] 2. Adjust the record (triggering conditions, adjustment measures, effects);

[0457] 3. Key monitoring areas for the next year (e.g., “focusing on monitoring the industrial-transportation coupling in emerging urban areas”).

[0458] Full Steps Data Association Explanation

[0459] 1. Data flow: S1→S2→S3→S4→S5→S6→S7→S8, where data from preceding steps provides input for subsequent steps (e.g., data from S4). →S5 Algorithm 1);

[0460] 2. Algorithm Connection: The results of S5 Algorithm 1-2 are used as inputs for S6 Algorithm 3-4 (e.g., → → );

[0461] 3. Closed-loop logic: The monitoring results of S8 are fed back to S5-S6 to achieve continuous optimization of "layout-coordination-monitoring-adjustment" (e.g., Low → Adjust Algorithm 3 ).

Claims

1. A method for the integration and coordination of urban functions based on the characteristics of urbanization, characterized in that: Includes the following steps: S1: Collect spatial basic data, social population data, economic and industrial data, and ecological environment data, and form a feature classification dataset after preprocessing; S2: Based on a feature classification dataset, analyze urban urbanization characteristics using a quantitative indicator system, and output a quantitative analysis report on urban urbanization characteristics; wherein, the quantitative indicator system includes the following indicators: urbanization intensity indicator, spatial structure indicator, economic efficiency indicator, and ecological quality indicator: The urbanization intensity indicators include the urbanization rate of the population and the rate of expansion of construction land; The spatial structure indicators include functional mixing degree and job-housing ratio, and the formula for calculating functional mixing degree is: ,in: For the land area of ​​the f-th function, The total construction land area of ​​the city. The value ranges from 0 to 1, with higher values ​​indicating better job-housing mix. The formula for calculating the job-housing ratio is: The ideal job-housing ratio is 1.

0. The economic efficiency indicators include GDP per unit area and an industry diversification index, which is calculated using the entropy method. ,in Let n = 3, where n represents the proportion of output value of industry i, corresponding to the primary, secondary, and tertiary industries, respectively. The larger the scale, the more diversified the industries; The ecological quality indicators include green space ratio and ecological constraint compliance rate. Green space ratio: The formula for calculating the ecological constraint compliance rate is: ; The method for analyzing urban urbanization characteristics using a quantitative indicator system is as follows: linear regression is used to calculate the trend of indicator changes and identify the urbanization stage; the indicator values ​​of the four types of zones are compared to conduct a zone difference analysis; and the relationship between indicators is calculated using the Pearson correlation coefficient to conduct a correlation analysis. The output of the quantitative analysis report on urban urbanization characteristics includes a table of calculation results for four dimensions of indicators, a trend analysis chart, a radar chart of zoning characteristics, and core conclusions. The radar chart of zoning characteristics is used to visually display the differences in indicators among the four types of zoning. S3: Based on the quantitative analysis report of urban urbanization characteristics and national standards, construct an urban functional classification system and output a description of the urban functional classification system; specifically including: 3.1 Functional Classification Standards and Connotations Based on the national urban land use classification and planning and construction land standards, and combined with the S2 quantitative analysis results, urban functions are divided into 6 categories: Residential function f=1: Space that meets the daily living needs of residents, including residences and community service facilities; Classification criteria: mainly residential land, with supporting facilities accounting for ≤15%, and plot ratio ≤3.5 in core urban areas and ≤2.5 in emerging urban areas; Commercial service function f=2: Provides space for commodity trading, catering, and entertainment services, including shopping malls, supermarkets, restaurants, and cinemas; Classification criteria: centralized commercial areas with a building area of ​​≥5000㎡, or commercial streets with a continuous length of ≥300 meters of street-front commercial facilities; Industrial production function f=3: Space engaged in material production and product processing, including factories, warehousing and logistics, and R&D pilot production bases; Classification standard: Land within industrial parks, with a land value per unit area ≥ 5 million yuan / hectare; Public management and public service functions f=4: providing space for public services and social management, including schools s=1, hospitals s=2, administrative offices, and cultural venues; classification criteria: schools are configured with "400 places per 10,000 people", and hospitals are configured with "6 beds per 1,000 people"; Transportation and municipal facilities function f=5: Transportation and infrastructure space to ensure urban operation, including bus stops s=4, parking lots, water plants, and substations; Classification criteria: service radius of bus stops ≤ 500 meters, and distance between main roads ≤ 800 meters; Green space and plaza function f=6: Provide open spaces for ecological regulation and public activities, including parks, protective green spaces, and civic plazas; Classification criteria: park service radius ≤1000 meters, protective green space width ≥20 meters; 3.2 Functional Classification Verification and Adjustment Verification method: Randomly select 50 plots of land and have 3 planning experts classify them independently according to the above standards. If the consistency rate is ≥90%, the classification system is valid. 3.3 Output Data Output a description of the city functional classification system, including: The definition, classification criteria, and included subtypes of the six functional categories; Functional classification flowchart to guide actual land parcel classification operations; Vector diagram of classification results; S4: Based on the quantitative analysis report of urban urbanization characteristics and the urban function classification system description, set functional proportions, efficiency, ecological and coordination targets for four types of zones: core urban area, emerging urban area, industrial park, and ecological protection zone, and output a composite urban function target system document; specifically including: 4.1 Principles for Constructing the Target System Based on the quantitative analysis conclusions of S2, following the principles of "problem-oriented, vision-oriented, and quantifiable", four types of functional composite objectives for partitions are set as inputs to the S5 multi-objective weighted optimization algorithm and benchmarks for S6 evaluation. 4.2 Setting of Targets for Different Zones 4.2.1 Core Urban Area Objectives Functional ratio targets: residential function f=1, commercial service function f=2, public management and public service function f=4, transportation and municipal facilities function f=5, green space and square function f=6, industrial production function f=3; Efficiency target: GDP per unit area; Ecological goals: green space ratio, ecological constraint satisfaction; Coordination objective: A comprehensive coordination score, based on the functional space coupling degree. The calculation yielded: In the formula, For the functional coupling degree in the qth quarter, The min function sets a coupling threshold, ensuring that when the coupling exceeds the threshold, it is calculated as a full score. The overall coordination score ranges from 0 to 100 points. 4.2.2 Targets for Emerging Urban Areas / Industrial Parks / Ecological Protection Zones Emerging urban areas: job-housing ratio, "residence-public service" coupling degree; Industrial parks: Proportion of industrial land use, degree of "industry-transportation" coupling; Ecological protection zones: green space ratio, commercial land ratio, and "green space-residential" coupling degree; 4.3 Target Value Calculation Method Demand forecasting; Benchmarking method: The proportion of commercial land in the core urban area should be referenced to similar cities; Constraint derivation method: Green area of ​​ecological protection zone = Total land area × 60%; 4.4 Output Data Output a comprehensive urban functional target system document, which includes sub-target tables for four types of zones, target value calculation instructions, and target achievement timelines. The sub-target tables include functional proportion, efficiency, ecological, and coordination targets. S5: Based on the urban functional composite target system document, a multi-objective weighted optimization algorithm is adopted. Through weighted calculation of functional matching degree, land use efficiency, and ecological constraint satisfaction, the optimal functional land use ratio is selected, and the functional land use ratio of each zone is determined. The functional matching degree is used to measure the degree of matching between the area of ​​each functional land use and the target requirements. The calculation formula is as follows: ; Among them, f=1 to 6 correspond to 6 types of functions respectively. The matching weight of function type f reflects the priority of that function within the partition. The actual planned land area for function category f in this zone. The target land area for the f-th function in this zone within the S4 target system; Then, the spatial intersection index and average distance of different functional combinations are calculated by the functional space coupling degree calculation algorithm. The spatial correlation and functional layout synergy are verified by comparing with the preset coupling degree threshold, and the urban functional composite layout scheme atlas is output. The functional space coupling degree measurement algorithm includes: (1). Model building process This algorithm is used to quantify the spatial correlation between different functions, ensuring that the functional layout not only meets the area ratio requirements, but also forms effective synergy in space; Core definitions and calculation formulas: Functional space coupling This refers to the spatial correlation between functions of type f1 and type f2, where f1 and f2 ∈ {1,2,3,4,5,6}, and f1 ≠ f2. The value ranges from 0 to 1, with higher values ​​indicating a stronger correlation. The calculation formula is as follows: ； The detailed descriptions of each parameter are as follows: The spatial intersection index, ranging from 0 to 1, reflects the degree of adjacency between two types of functional land use. It is calculated through GIS spatial overlay analysis. ； In the formula, for and The "adjacent overlapping area" of land use for different functions, in hectares, is defined as the overlapping portion with a boundary distance of ≤100 meters. This is the smaller of the two types of functional land use areas; : respectively The proportion of land designated for specific functions within the total land area of ​​each zone; Average distance, reflecting the spatial distance between the two types of functional land use, is calculated through GIS network analysis. ； In the formula, n1 represents the number of land parcels for functional use of type f1; n2 represents the number of land parcels for functional use of type f2. Let f1 be the shortest path distance between the i-th plot of land f1 and the j-th plot of land f2. Coupling threshold setting According to the functional integration requirements in the S4 target system, different functional combinations need to achieve different coupling thresholds. (2). Model training process The purpose of the training is to calibrate the coupling degree threshold using historical data from domestic functional composite demonstration cities. To ensure that the threshold matches the actual functional performance; (3) Training steps ① Data Matching: Match the historical coupling data of 50 partitions with the corresponding operational performance data. The historical coupling data includes: Spatial data: Vector data of land parcels with 6 functional categories; Coupling data: Historical coupling degrees calculated using GIS; Performance data: The functional performance of the corresponding partition; ② Initial threshold setting: The minimum coupling degree that achieves the target operating effect is statistically determined, and this is used as the initial threshold; ③ Threshold optimization: Substitute the initial threshold into the model and verify the proportion of regions where "coupling degree ≥ threshold" meets the performance target; ④ Confirm the threshold: Determine the threshold, and training is complete; S6: Based on the urban functional complex layout scheme atlas, the supply capacity and total demand of schools, hospitals, commercial centers, and bus stations are calculated using a facility supply and demand matching algorithm. The supply and demand relationship is assessed through the supply and demand matching degree, and the facility configuration is monitored. The comprehensive coordination effect is calculated by weighting the spatial coordination score, facility coordination score, and management coordination score through a coordination effect evaluation algorithm. The effectiveness of the mechanism is evaluated, and an implementation manual of the urban functional coordination mechanism, which includes facility adjustment suggestions and coordination effect scores, is output.

2. The method for urban functional integration and coordination based on urban urbanization characteristics according to claim 1, characterized in that, It also includes S7: Based on the implementation manual of the urban function coordination mechanism, the effectiveness of the scheme is verified through multi-scenario simulation, and a simulation verification report of the urban function integration and coordination scheme is output.

3. The method for urban functional integration and coordination based on urbanization characteristics according to claim 2, characterized in that, It also includes S8: Based on the simulation verification report of the urban function integration and coordination scheme, a dynamic monitoring platform is built to monitor and dynamically adjust the functional layout and coordination mechanism in real time, and output the urban function dynamic monitoring and adjustment report.

4. The method for urban functional integration and coordination based on urbanization characteristics according to claim 1, characterized in that, The interaction process between the multi-objective weighted optimization algorithm and the functional space coupling degree calculation algorithm in S5 includes: after the multi-objective weighted optimization algorithm outputs the functional land use ratio of each zone, it passes the functional land use area parameter to the functional space coupling degree calculation algorithm to calculate the functional space coupling degree; if the functional space coupling degree does not reach the preset threshold, the functional space coupling degree calculation algorithm generates adjustment suggestions and feeds them back to the multi-objective weighted optimization algorithm to re-optimize the land use layout until the coupling degree reaches the standard.

5. The method for urban functional integration and coordination based on urban urbanization characteristics according to claim 1, characterized in that, The interaction process between the facility supply and demand matching algorithm and the coordination effect evaluation algorithm in S6 includes: after the facility supply and demand matching algorithm calculates the supply and demand matching degree, it passes the matching degree parameter to the coordination effect evaluation algorithm to calculate the facility coordination score; if the facility coordination score is lower than the preset value, the coordination effect evaluation algorithm generates a correction coefficient and feeds it back to the facility supply and demand matching algorithm to adjust the total demand calculation and improve the redundancy of the facility supply recommendations.

6. The method for urban functional integration and coordination based on urban urbanization characteristics according to claim 1, characterized in that, The overall association process between the S5 and S6 algorithms includes: the functional land use ratio output by the multi-objective weighted optimization algorithm in S5 provides a spatial basis for the facility supply and demand matching algorithm in S6, constraining the upper limit of facility supply capacity; the functional coupling degree calculated by the functional spatial coupling degree measurement algorithm in S5 serves as an input parameter for the coordination effect evaluation algorithm in S6, used to calculate the spatial coordination score, and affects the comprehensive coordination effect evaluation result.

7. The method for urban functional integration and coordination based on urban urbanization characteristics according to claim 1, characterized in that, The implementation details of the multi-objective weighted optimization algorithm in S5 include: the weight of functional matching degree is set differently according to the functional priority of the four types of partitions; the calculation of land use efficiency is combined with the current GDP and industrial entropy value of the partition; and the calculation of ecological constraint satisfaction is related to the actual planned area and target area of ​​green space and ecological isolation zone.

8. The method for urban functional integration and coordination based on urban urbanization characteristics according to claim 1, characterized in that, The implementation details of the functional space coupling degree measurement algorithm in S5 include: the spatial intersection index is calculated through GIS spatial overlay analysis, and the average distance is calculated through GIS network analysis; the preset functional coupling degree threshold is set for the combination differentiation. If the coupling degree of a combination does not meet the standard, the adjustment suggestions generated by the algorithm must specify the number of facilities to be adjusted and the minimum value that the adjacent area should reach after the adjustment.

9. The method for urban functional integration and coordination based on urbanization characteristics according to claim 3, characterized in that, The triggering conditions for dynamic adjustment in S8 include: when the multi-objective optimization score is below 80 points for two consecutive quarters, the functional coupling compliance rate is below 85%, the facility supply and demand balance rate is below 80%, or the comprehensive coordination effect score is below 70 points, the adjustment process will be automatically triggered. The adjustment content includes re-optimizing the land use layout or adding new facilities.

10. The method for urban functional integration and coordination based on urbanization characteristics according to claim 3, characterized in that, The closed-loop optimization mechanism formed from S1 to S8 ends when: the multi-objective optimization score is ≥85 points for three consecutive quarters, the functional coupling compliance rate is ≥90%, the facility supply and demand balance rate is ≥85%, and the comprehensive coordination effect score is ≥80 points. At this time, active adjustment will stop and only dynamic monitoring will be maintained.

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