A Big Data-Based Urban Planning and Construction Land Zoning Control System

Abstract

This invention relates to the field of big data technology and provides a big data-based urban planning and construction land zoning control system. The system includes: a data acquisition module for real-time collection of multi-dimensional data on urban planning and construction land; a feature extraction module for extracting key features from the multi-dimensional data; a dynamic zoning module for constructing a dynamic land use zoning model based on the key features and determining a dynamic zoning index for each region based on the dynamic zoning model; a conflict potential module for determining a conflict potential index between two regions based on the dynamic zoning index corresponding to each pair of regions with different land use types; a land use optimization module for obtaining optimized urban land use; and a zoning control module for re-zoning and controlling the urban land use according to the new uses corresponding to the optimized urban land use. This invention can improve the accuracy, adaptability, and sustainability of urban planning and construction land zoning control.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and in particular to a big data-based urban planning and construction land zoning control system and electronic equipment. Background Technology

[0002] Currently, urban planning and construction land zoning control mainly relies on traditional spatial planning methods, such as using Geographic Information Systems (GIS) to classify land use, and combining expert experience and planning policies to formulate detailed zoning control plans. This approach typically divides urban land into different functional zones, such as residential areas, commercial areas, and industrial areas, based on historical data and planning objectives, supplemented by strict land use control and management policies to ensure the rational allocation and use of land resources.

[0003] However, traditional zoning management methods rely primarily on historical data and expert judgment, lacking effective integration of real-time data and failing to reflect the dynamic changes in urban development in a timely manner. Furthermore, traditional land use zoning methods struggle to accurately identify potential land use conflicts and future development trends, failing to provide decision-makers with comprehensive, data-driven intelligent support, thus affecting the efficient use and sustainable development of urban land resources. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a big data-based urban planning and construction land zoning control system and electronic equipment that can improve the accuracy, adaptability, and sustainability of urban planning and construction land zoning control.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] This invention provides a big data-based urban planning and construction land zoning control system, the system comprising:

[0007] Furthermore, the dynamic partitioning module is also specifically used for:

[0008] Obtain the climate change index for the region;

[0009] Obtain multiple influencing factors that affect the regional dynamic zoning index, and the average value of the multiple influencing factors;

[0010] The dynamic zoning index of the region is determined based on the climate change index of the region, the multidimensional data, the multiple influencing factors, and the average value of the multiple influencing factors.

[0011] Furthermore, the conflict potential module is specifically used for:

[0012] Obtain the incompatibility, importance, and distance between the two regions;

[0013] Obtain the environmental sensitivity and policy friendliness of the two regions;

[0014] The conflict potential index between the two regions is determined based on their incompatibility, importance, and distance, as well as their environmental sensitivity, policy friendliness, and dynamic zoning index.

[0015] Furthermore, the usage optimization module is specifically used for:

[0016] Obtain the area and value coefficient of each land use type;

[0017] Obtain the scores, indicator weights, and public participation of each assessment indicator for the region being evaluated;

[0018] Obtain the city's sustainable development index, biodiversity index, target biodiversity index, and green infrastructure index;

[0019] Obtain the importance, current consumption, and maximum sustainable consumption of each resource type in the city;

[0020] Based on the area and value coefficient of each land use type, the scores, weights, and public participation of each assessment indicator, the city's sustainable development index, biodiversity index, target biodiversity index, and green infrastructure index, the importance, current consumption, and maximum sustainable consumption of each resource type in the city, an optimization objective function is constructed. Based on the optimization objective function, land use is optimized and adjusted for each land use type to obtain the optimized urban land use.

[0021] Furthermore, the partition control module is also specifically used for:

[0022] Continuously evaluate the effectiveness of the control measures implemented by rezoning the urban land use;

[0023] The control effect is sent to the dynamic land use zoning model for iterative optimization.

[0024] Furthermore, the data acquisition module is also specifically used to: clean, denoise, and standardize the multidimensional data to enhance the quality and consistency of the multidimensional data.

[0025] Furthermore, the multidimensional data is collected from at least two data sources, including satellite remote sensing, IoT sensors, mobile devices, and social media.

[0026] Furthermore, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the functions of a big data-based urban planning and construction land zoning control system as described above.

[0027] The beneficial effects of this invention are:

[0028] (1) This invention acquires multi-dimensional data on urban land use, population flow, and economic activities in real time through multiple data acquisition channels, enabling planners to dynamically and in real time understand urban changes and make more accurate land use planning decisions. Compared with traditional planning methods that rely on historical data, this invention significantly improves the timeliness of data and narrows the gap between the actual urban development situation and the planning.

[0029] (2) This invention can extract key features such as land use intensity, population density, and economic activity index from massive amounts of data, and capture complex multidimensional relationships. This not only improves the efficiency of data analysis, but also helps planners identify future trends and predict land use changes. Based on data-driven dynamic prediction capabilities, it significantly enhances the forward-looking nature of urban planning.

[0030] (3) The conflict identification algorithm of this invention can analyze and evaluate potential conflicts between different land use types through the Dynamic Zoning Index (DZI), thereby enabling the early detection of incompatibility issues between land use types and avoiding potential problems in land use, such as ecological damage or resource waste. In addition, based on the conflict identification results, the optimization and adjustment of land use using heuristic algorithms can ensure that while maximizing land use efficiency, conflicts and contradictions between regions are minimized.

[0031] In summary, this invention utilizes big data and machine learning technologies to achieve intelligent, dynamic, and efficient urban land use zoning. Through real-time data collection, multi-dimensional feature extraction, conflict identification, and optimization adjustments, it significantly improves the accuracy, adaptability, and sustainability of urban land use, meeting the demands for dynamic planning and intelligent decision-making in the context of rapid urban development. Attached Figure Description

[0032] Figure 1 A scenario diagram of a big data-based urban planning and construction land zoning control system provided by this invention;

[0033] Figure 2 A schematic diagram of a big data-based urban planning and construction land zoning control system provided by the present invention;

[0034] Figure 3 This is a schematic diagram of the hardware structure of a possible electronic device provided by the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Please see Figure 1 , Figure 1 This is a scenario diagram of a big data-based urban planning and construction land zoning control system provided by the present invention. (Example) Figure 1 As shown, the terminal and server are connected via a network, such as a wired or wireless network. The terminal can include, but is not limited to, portable devices such as mobile phones and tablets with various network platform applications installed, as well as fixed terminals such as computers, kiosks, and advertising machines. The server provides users with various business services, including service push servers and user recommendation servers.

[0037] It should be noted that, Figure 1 The scenario diagram of a big data-based urban planning and construction land zoning control system shown is merely an example. The terminals, servers, and application scenarios described in this embodiment of the invention are for the purpose of more clearly illustrating the technical solutions of this embodiment of the invention and do not constitute a limitation on the technical solutions provided by this embodiment of the invention. As those skilled in the art will know, with the evolution of the system and the emergence of new business scenarios, the technical solutions provided by this embodiment of the invention are also applicable to similar technical problems.

[0038] The terminal can be used for:

[0039] Real-time collection of multi-dimensional data on urban planning and construction land; the multi-dimensional data includes at least land use, population flow, and economic activities in each area of ​​the city.

[0040] Key features are extracted from the multidimensional data; the key features include at least the city's land use intensity, population density, and economic activity index.

[0041] A dynamic land use zoning model is constructed based on the key features, and a dynamic zoning index for each region is determined based on the dynamic land use zoning model; the dynamic zoning index is used to predict the future land use trend of the corresponding region.

[0042] Based on the dynamic zoning index corresponding to each pair of areas of different land use types, determine the conflict potential index between each pair of areas;

[0043] By optimizing the objective function and combining it with the conflict potential index, the land use of each land use type is optimized and adjusted to obtain the optimized urban land use.

[0044] Based on the new uses corresponding to the optimized urban land, the urban land will be re-zoned and managed.

[0045] Please see Figure 2 , Figure 2 This invention provides a schematic diagram of a big data-based urban planning and construction land zoning control system.

[0046] like Figure 2 As shown in the figure, an urban planning and construction land zoning control system based on big data proposed in this embodiment of the invention includes: a data acquisition module 201, a feature extraction module 202, a dynamic zoning module 203, a conflict potential module 204, a land use optimization module 205, and a zoning control module 206.

[0047] The data acquisition module 201 is used to collect multi-dimensional data on urban planning and construction land in real time.

[0048] The multidimensional data includes at least land use, population flow, and economic activity in each area of ​​the city. In some embodiments, the multidimensional data can be collected from at least two data sources, including satellite remote sensing, IoT sensors, mobile devices, and social media. Land use data refers to the use of various types of land in each area of ​​the city, including residential, commercial, industrial, public facilities, and green space uses. This type of data can be acquired through various methods, such as satellite remote sensing, drone aerial photography, IoT sensors, and Geographic Information Systems (GIS). These technologies can provide high-precision geospatial information and monitor the land use status of various areas of the city in real time. By dynamically collecting land use data, the system can quickly understand changes in land use, such as a change in an area from industrial to commercial use, or the development of green space into residential areas. This helps identify trends in land use demand, allowing for advance planning and avoiding unreasonable land use conflicts.

[0049] Population flow data refers to the migration, density, and activity trajectories of people in different areas of a city over different time periods. It reflects the patterns of people's movement and life in the city, encompassing behaviors such as work, residence, and consumption. This type of data can be collected through mobile devices (such as cell phone signals and GPS), public transportation systems (such as subway and bus card swipe records), IoT sensors (such as camera surveillance and smart streetlights), and social media platforms. These data sources can capture changes in population flow at different times and locations. Through dynamic monitoring of population flow, the system can identify high-density population clusters and determine population flow trends within the city. For example, when an area attracts a large population due to the construction of new commercial facilities, urban planners can adjust the land use and supporting infrastructure planning of that area to improve efficiency and reduce traffic congestion.

[0050] Economic activity data primarily reflects the economic operation of various urban areas, including consumption levels, business distribution, commercial activities, and industrial development. It represents the economic vitality of different urban regions. Economic activity data can be obtained in real time through various channels, such as bank transaction records, online payment platforms, shop sales data, tax data, and business activities and advertising information on social media platforms. Furthermore, IoT devices (such as smart meters and gas meters) can help monitor regional electricity and water consumption, indirectly reflecting the intensity of economic activity in that area. By dynamically collecting economic activity data, the system can identify which areas are experiencing rapid economic growth, where economic activity is lower, and even predict potential economic hotspots. This allows urban planners to adjust land use zoning in advance, such as increasing commercial land in more economically active areas or considering optimizing infrastructure or introducing new industries in areas with slower economic development.

[0051] In some embodiments, the data acquisition module 201 can also be used to: clean, denoise and standardize the multidimensional data to enhance the quality and consistency of the multidimensional data.

[0052] Data cleaning refers to identifying and processing errors, redundancies, incompleteness, or invalid parts of data to ensure its accuracy and integrity. Since multidimensional data may come from different sources (such as satellite remote sensing, IoT sensors, social media, public transportation systems, etc.), this data may contain null values, duplicates, incorrect formats, or illogical data. For example, GPS location data may lack location information for certain time periods due to signal loss, or sensor data may contain inconsistent timestamps. Cleaning steps typically include removing duplicate data, filling in missing values, and correcting errors. For example, missing traffic flow data can be filled by interpolation using data from adjacent time periods. Duplicate records can be merged. Furthermore, data cleaning may involve format conversion, unifying the format of data from different data sources for subsequent analysis. Data cleaning improves the accuracy and integrity of multidimensional data, ensuring that all data used for analysis conforms to the expected format and eliminating potential errors or redundant data.

[0053] Denoising refers to removing random, unpredictable, or irrelevant interference information from data to enhance its effectiveness. Noisy data refers to anomalous data that is irrelevant to the analysis objective or interferes with the analysis results. In multi-source data, noise may originate from multiple sources. For example, satellite remote sensing data may be blurred due to cloud cover, sensor data may be affected by electromagnetic interference or equipment malfunction, and social media data may contain unstructured information irrelevant to the analysis (such as advertisements, noisy text, etc.). Denoising methods differ depending on the type of noise. For example, for sensor data, filtering algorithms (such as Kalman filtering and average filtering) can be used to smooth the data and reduce the impact of noise on subsequent analysis. For satellite remote sensing data, image processing techniques can be used to remove clouds or other interference factors. For text data, denoising may involve text processing techniques (such as removing stop words, irrelevant characters, etc.). Denoising can filter out irrelevant or useless data noise, ensuring that the analysis results are not interfered with and improving the accuracy and stability of the prediction model.

[0054] Standardization refers to processing data from different sources and of different types into a unified format and scale to ensure that all types of data are comparable and consistent on the same dimension. Multidimensional data may have different units and dimensions. For example, land use intensity may be measured in square meters or square kilometers, while population density is measured in people per square kilometer, and economic activity data may be measured in currency. Differences in different data dimensions can lead to inconsistencies in modeling and analysis, affecting model performance. Standardization typically employs two methods: Normalization: scaling the data to a uniform range (e.g., [0,1]) allows data from different units or scales to be compared on the same scale. Z-score Normalization: transforming data into a standard distribution with a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation. Standardization ensures consistency in feature extraction and model building for data from different sources, preventing bias in analysis due to features being too large or too small in scale. Standardization also improves model training speed and prediction accuracy.

[0055] The cleaning, denoising, and standardization of multidimensional data in this invention are key steps in ensuring the successful implementation of urban planning land use zoning control methods. These three steps improve data quality, providing a high-quality data foundation for subsequent feature extraction, model building, and optimization, while also ensuring data consistency and reliability. This makes urban planning analysis more accurate and effective, helping planners make informed decisions.

[0056] This invention provides comprehensive and accurate information support for the dynamic management of urban planning and land use zoning by collecting multidimensional data in real time, including three key dimensions: land use, population flow, and economic activity. This data can be acquired in real time through various high-tech means, ensuring that urban planners can make timely decisions in a rapidly changing urban environment. By integrating this multidimensional data, the system can dynamically assess urban development needs, predict future trends, and optimize land use layout accordingly, thereby achieving efficient use of land resources and sustainable urban development.

[0057] The feature extraction module 202 is used to extract key features from the multidimensional data.

[0058] Key characteristics can include at least urban land use intensity, population density, and economic activity index. Land use intensity refers to the degree of development of land resources within a specific area, including factors such as construction density, building area and height, and green coverage. It reflects the degree of intensive urban land use. Land use intensity data is widely available, acquired through satellite remote sensing, drone imagery, Geographic Information Systems (GIS), and IoT devices (such as sensors), allowing for real-time monitoring of urban land development and utilization. Spatial analysis techniques and machine learning algorithms can extract land use characteristics from this data for different areas. For example, using indicators such as building density, floor area ratio, and green space ratio, the system can calculate the land use intensity for each area. These characteristics form the basis for assessing the degree of regional development and determining whether land use planning needs adjustment. Land use intensity helps urban planners measure the efficiency and intensity of current land use. Areas with high utilization intensity may require optimized resource allocation, while areas with low utilization intensity may have potential development value.

[0059] Population density refers to the number of people per unit area, usually expressed as per square kilometer. It reflects the distribution and concentration of population in different areas of a city and is a key indicator for urban functional layout and land demand. Population density data can be dynamically collected and monitored from sources such as mobile device data (e.g., cell phone signals, GPS positioning), intelligent transportation systems (e.g., bus card and subway card swipe records), and social media activity data. Internet of Things (IoT) sensors (e.g., surveillance cameras, smart streetlights) can also provide real-time urban population flow data. By cleaning, fusing, and analyzing data from different sources, the population distribution characteristics of each area can be extracted. For example, mobile device location data can provide real-time population density distribution, while bus card swipe records can show dynamic population flow patterns. Through this data, urban planners can understand the population concentration in each area. Population density is an indispensable element in urban land planning, determining the distribution of demand for residential, commercial, and public service facilities (e.g., hospitals, schools, transportation hubs). High-population-density areas require more infrastructure and services, while low-population-density areas may need to attract more people through optimized land use.

[0060] The economic activity index is a comprehensive indicator measuring the economic vitality of a region, reflecting the intensity of economic activities such as business activity, consumption levels, business operations, and industrial structure. Data for the economic activity index can be obtained through various channels, such as online payment and transaction records, sales data from shops and businesses, electricity and water consumption monitoring, business activity information from social media, and government tax data. Furthermore, data from logistics and transportation systems can indirectly reflect the prosperity of economic activity. By analyzing the above economic data, the intensity of economic activity in each region can be calculated. For example, online payment and transaction records can reflect consumer behavior, while electricity and water consumption monitoring can reflect the operational status of businesses and shops. After cleaning and standardization, this data can be used to construct the economic activity index for each region. The economic activity index is an important basis for measuring the level of regional economic development and directly affects the functional zoning and commercial layout of urban land. Regions with high economic activity may require more commercial facilities and basic services, while regions with low economic activity may be planned as development zones or have new commercial projects introduced to revitalize the economy. In addition to the three key characteristics mentioned above, other features closely related to urban planning can be extracted from multidimensional data, such as:

[0061] Traffic flow characteristics: Traffic flow and congestion characteristics are extracted through intelligent transportation systems and GPS data to optimize road layout and public transportation systems.

[0062] Environmental quality characteristics: By extracting regional environmental quality data such as air quality monitoring and green coverage, we can help cities consider environmental protection and sustainable development when planning.

[0063] Social service demand characteristics: Extract social service demand characteristics by analyzing the usage of medical, educational, and cultural facilities to ensure that urban planning fully considers residents' public service needs.

[0064] These extracted key features (such as land use intensity, population density, and economic activity index) serve as fundamental data for urban planning, construction land management, and optimization. They help urban planners comprehensively understand the current situation and future needs of each area, thereby enabling them to make informed planning decisions. The extraction process for these key features requires the use of advanced technologies such as big data analytics and machine learning to ensure that the extracted data is accurate, real-time, and dynamically reflects urban development and changes.

[0065] This invention processes and analyzes multidimensional data to extract key features, such as land use intensity, population density, and economic activity index, providing fundamental and quantifiable indicators for urban planning. These features help urban planners understand the current land use status, population distribution, and dynamic changes in economic activities in different areas, thereby enabling them to scientifically and rationally manage land zoning and optimize resource allocation, ultimately promoting efficient urban development and sustainability.

[0066] The dynamic zoning module 203 is used to construct a dynamic land use zoning model based on the key features, and to determine the dynamic zoning index of each region based on the dynamic land use zoning model.

[0067] The dynamic zoning index is used to predict future land use trends in the corresponding area. In some embodiments, the dynamic zoning module 203 is further specifically used for:

[0068] Obtain the climate change index for the region;

[0069] Obtain multiple influencing factors that affect the regional dynamic zoning index, and the average value of the multiple influencing factors;

[0070] The dynamic zoning index of the region is determined based on the climate change index of the region, the multidimensional data, the multiple influencing factors, and the average value of the multiple influencing factors.

[0071] In some embodiments, the dynamic partitioning index is represented as:

[0072]

[0073] Where DZI is the dynamic zoning index for the region, LUI is land use intensity, PD is population density, EAI is the economic activity index, l is the number of the influencing factor, and F l This is the l-th influencing factor, t is the time variable, CCI is the climate change index, α, β, γ, δ, ε, ζ, η, μ are the first, second, third, fourth, fifth, sixth, seventh, and eighth weights, respectively, and θ, τ, λ, ν, and ω are the first, second, third, fourth, fifth, and sixth adjustment parameters, respectively, and F avg It is the average value of each influencing factor.

[0074] In the specific implementation, α×LUI θ It is the nonlinear effect of land use intensity, where LUI is land use intensity, reflecting the degree of development and utilization of land resources within a region. Its nonlinear function is α×LUI. θThis indicates that the indicator does not linearly affect the dynamic zoning index. The parameter θ controls the degree of this influence; when θ > 1, an increase in land use intensity accelerates regional development, while θ < 1 indicates that the impact of land use intensity on the region gradually decreases. α is the first weight, representing the relative importance of land use intensity to regional zoning. The higher the land use intensity, the greater the potential for regional development.

[0075] β×ln(PD+1) represents the logarithmic effect of population density, where PD is the population density, reflecting the degree of population concentration in the area. High population density usually means a higher demand for land resources, but high-density areas may lead to resource shortages and environmental pressures. Therefore, the logarithmic form ln(PD+1) is used to reflect the gradually decreasing impact of population density growth on the zoning index. β is the second weight, representing the importance of population density in the overall model. For some highly urbanized areas, the impact of population density will be more significant.

[0076] This is the non-linear effect of the Economic Activity Index (EAI), which reflects the intensity of economic activity within a region. Similar to land use intensity, this factor is expressed through an exponential function. This indicates its nonlinear effect. The second adjustment parameter controls the impact of the economic activity index on regional dynamics. γ is the third weight, representing the contribution of economic activity to the regional zoning index. Regions with more active economic activity typically have higher development potential and resource demands.

[0077] δ×∑(ω i ×F l F is a linear combination of other influencing factors. l This represents the l-th influencing factor, which may include other specific regional influencing factors such as infrastructure development, educational resources, and medical conditions. ω i δ is the weight of the i-th influencing factor, reflecting its importance in the partitioning model. δ is the fourth weight, used to control the overall impact of multiple factors on the partitioning index.

[0078] ε×sin(τ×t) represents the influence of time-periodic changes, indicating the periodic characteristics of the dynamic zoning index over time. It is commonly used to reflect natural seasonal changes, urban development cycles, etc. For example, land use may fluctuate periodically with seasonal or policy changes during certain periods. τ is a time adjustment parameter that controls the length of the period, while ε is the fifth weight, determining the intensity of the influence of time-periodic changes.

[0079] ζ is the integral of the land use intensity change rate, reflecting the trend of land use intensity change over the entire time period T. The rate of increase or decrease in land use intensity affects the future development potential of a region. ζ is the sixth weight, representing the importance of land use intensity change to the dynamic zoning index. If the land use intensity change is large, the region's dynamic zoning index may rise or fall rapidly.

[0080] η×exp(-λ×∑(∣F l -F avg |)) represents the exponential decay of factor deviation, reflecting the influence of certain key influencing factors F. l Compared with the average level F avg The degree of deviation. If a certain influencing factor deviates significantly from the average, it will have a significant impact on regional dynamics, but this impact is mitigated through exponential decay, expressed as exp(-λ×∑(∣F). l -F avg ∣)), this effect gradually decreases.

[0081] μ×(1-exp(-ν×CCI)) represents the long-term impacts of climate change, where CCI is the climate change index, indicating the potential impact of climate on regional development. It uses an exponential decay form to show that the impacts of climate change gradually become apparent over time. ν is a moderating parameter that controls the rate at which the impacts of climate change on the region manifest. μ is the eighth weight, representing the importance of climate change to the dynamic zoning index. This term is primarily used to assess the long-term impacts of climate change, such as its damage to agriculture, the ecological environment, and adaptive adjustments.

[0082] This invention comprehensively quantifies a region's dynamic zoning index by considering the multi-dimensional influences of land use, population, economic activities, time cycles, and environmental factors. Each variable, through different mathematical forms and weight combinations, reflects the relative importance and complex interrelationships of various factors in urban land planning. The formula considers not only static spatial dimensions but also dynamic changes over time, particularly in terms of land use intensity and climate change, reflecting the long-term development trends of the region.

[0083] The conflict potential module 204 is used to determine the conflict potential index between each pair of areas based on the dynamic zoning index corresponding to each pair of areas of different land use types.

[0084] In some embodiments, the conflict potential module 204 can also be used for:

[0085] Obtain the incompatibility, importance, and distance between the two regions;

[0086] Obtain the environmental sensitivity and policy friendliness of the two regions;

[0087] The conflict potential index between the two regions is determined based on their incompatibility, importance, and distance, as well as their environmental sensitivity, policy friendliness, and dynamic zoning index.

[0088] In some embodiments, the conflict potential index is expressed as:

[0089]

[0090] Wherein, CPI is the conflict potential index, and i and j are the codes for two different land use types, I ij W represents the incompatibility between the i-th land use type area and the j-th land use type area. ij The importance of the area with land use type i and the area with land use type j, d ij R is the distance between the i-th land use type area and the j-th land use type area, R is the influence radius, t is the time variable, and DZI is the distance between the i-th land use type area and the j-th land use type area. i DZI is the dynamic zoning index for the i-th land use type region. j ED is the dynamic zoning index for the j-th land use type region. i ED j These are the environmental sensitivities of the i-th land use type area and the j-th land use type area, respectively, PF. i PF j ω, σ, ρ, κ, ψ, ξ, and φ are the policy friendliness of the region with land use type i and the region with land use type j, respectively, and the sixth, seventh, eighth, ninth, tenth, eleventh, and twelfth adjustment parameters, respectively.

[0091] In the specific implementation, It is a basic conflict assessment, I ij This represents the incompatibility between the i-th and j-th land use types, reflecting the degree of functional or environmental conflict between the two types of land use. A higher value indicates stronger incompatibility. W ij This represents the importance weight between the i-th and j-th land use types, used to balance the relative importance of different land use types in regional planning. For example, a conflict between commercial and residential areas may be more significant than a conflict between green space and residential areas, therefore its weight will be greater. ij This is the distance between the i-th land use type area and the j-th land use type area. The greater the distance, the smaller the actual impact of the conflict may be. This term uses an exponential decay function. This indicates that the impact of conflict gradually decreases with increasing distance. R is the radius of influence, representing how far away the impact of conflict can be ignored. The larger the R value, the wider the range of conflict impact. This part calculates the basic conflict level between land use classes i and j, taking into account incompatibility, importance weight, and the attenuation effect of distance.

[0092] 1 + σ × sin(ω × t) represents the periodic effect over time, where t is a time variable reflecting the dynamic changes in conflict over time. Some land use conflicts may fluctuate at different times; for example, seasonal variations may affect land use in certain areas. ω is the sixth adjustment parameter, controlling the time period and reflecting the frequency of periodic changes in conflict over time. σ is the seventh adjustment parameter, an amplitude adjustment parameter used to adjust the magnitude of time fluctuations. The larger σ is, the greater the impact of time fluctuations on conflict potential. This section expresses the periodic changes in conflict potential over time, simulating the seasonal or periodic characteristics of certain land use conflicts.

[0093] 1-ρ×exp(-κ×∣DZI i -DZI j |) represents the impact of dynamic partition index differences, DZI i DZI and DZI are the dynamic zoning indices for land use categories i and j, respectively, reflecting the comprehensive dynamic status of these two land use categories (including land use intensity, population density, economic activity, etc.). When DZI... i DZI j A significant difference between the two land use types indicates substantial discrepancies in their development needs and resource utilization, potentially leading to greater conflict. ρ and κ are the eighth and ninth adjustment parameters, respectively. ρ adjusts for the maximum impact of DZI differences on conflict potential. This term is represented by an exponential decay function; as DZI differences increase, conflict potential gradually decreases, demonstrating a trend of conflict mitigation. κ controls the rate of change in the impact of DZI differences on conflict. This section indicates that when the dynamic zoning index differences between the two land use types are large, conflict may be more pronounced, and this effect is adjusted through exponential decay.

[0094] It is a synergistic effect of environmental sensitivity, ED i ×ED j This represents the synergistic effect of environmental sensitivity between land use types i and j, meaning that the higher the environmental sensitivity of the two land use types, the greater the potential for environmental conflict. This term uses a square root function to represent the product of the two sensitivities, emphasizing their combined impact. This section illustrates the role of environmental factors in land use conflicts, particularly in areas with high environmental sensitivity, where such conflicts are especially pronounced. ψ is the tenth moderating parameter, controlling the intensity of the influence of environmental sensitivity on conflict potential. This represents the synergistic effect of the environmental sensitivity of land use types i and j, meaning that the higher the environmental sensitivity of the two land use types, the greater the potential environmental conflict. This term uses a square root function to represent the product of the two sensitivities, emphasizing their combined impact. This section illustrates the role of environmental factors in land use conflicts, particularly in areas with high environmental sensitivity, where such conflicts are especially pronounced.

[0095] It is the moderating effect of policy friendliness, PF i PF j ξ represents the policy friendliness of land use categories i and j, respectively, reflecting the priority or encouragement level of these two land use categories under current policy support. A greater difference in policy friendliness may lead to more prominent conflicts in planning. ξ is the eleventh adjustment parameter, controlling the intensity of the impact of policy friendliness differences on conflict potential. φ is the twelfth adjustment parameter, used to adjust the degree of influence of policy friendliness differences. A power function is used to enhance or weaken the effect of policy friendliness differences on conflict potential.

[0096] This section explains the impact of policy support on land use conflicts. The greater the policy differences, the greater the conflict potential; conversely, higher policy consistency reduces the likelihood of conflict. This invention quantifies the Conflict Potential Index (CPI) between different land use types using a complex, multi-dimensional computational model. It considers the interaction of various influencing factors, including: incompatibility and importance between land use types; distance decay effect; periodic changes over time; the moderating effect of differences in Dynamic Zoning Index (DZI) on conflict; the synergistic effect of environmental sensitivity; and the impact of differences in policy friendliness. These factors, through different functional forms (such as exponential decay, logarithmic adjustment, and power-law adjustment) and adjustment parameters, work together to form a multi-dimensional, multi-level conflict potential evaluation model. This model enables a more accurate assessment of potential conflicts in urban land use planning, providing a scientific basis for optimizing land use.

[0097] The land use optimization module 205 is used to optimize and adjust the land use of each land use type area by combining the objective function with the conflict potential index, so as to obtain the optimized urban land use.

[0098] In some embodiments, the utilization optimization module 205 is further specifically used for:

[0099] Obtain the area and value coefficient of each land use type;

[0100] Obtain the scores, indicator weights, and public participation of each assessment indicator for the region being evaluated;

[0101] Obtain the city's sustainable development index, biodiversity index, target biodiversity index, and green infrastructure index;

[0102] Obtain the importance, current consumption, and maximum sustainable consumption of each resource type in the city;

[0103] Based on the area and value coefficient of each land use type, the scores, weights, and public participation of each assessment indicator, the city's sustainable development index, biodiversity index, target biodiversity index, and green infrastructure index, the importance, current consumption, and maximum sustainable consumption of each resource type in the city, an optimization objective function is constructed. Based on the optimization objective function, land use is optimized and adjusted for each land use type to obtain the optimized urban land use.

[0104] In some embodiments, the optimization objective function is expressed as:

[0105]

[0106] Where OFV is the value of the objective function, A i V represents the area of ​​each land use type. i These are the value coefficients for different land use types in a region. CPI is the conflict potential index. max It is the maximum acceptable conflict potential index, q is the evaluation indicator number, and E q S is the score of the qth evaluation indicator. q It is the weight of each evaluation indicator, P q These are the public participation levels for each assessment indicator. SI is the Sustainable Development Index, and BI is the Biodiversity Index. target The target is the biodiversity index, GI is the green infrastructure index, and R is the biodiversity index. i The importance of each resource type, C i This represents the current consumption of each resource type, C. imax ω is the maximum sustainable consumption of each resource type, λ1, λ2, λ3, λ4, λ5, λ6, and λ7 are the first, second, third, fourth, fifth, sixth, and seventh balance coefficients, respectively, β is the second weight, γ is the third weight, δ is the fourth weight, ε is the fifth weight, and η is the seventh weight.

[0107] In the specific implementation, λ1×∑(A i ×V i ×(1+ε i ×sin(ω i ×t))) is the periodic change in the area and value coefficient of land use type, A i This represents the area of ​​each land use type, reflecting the actual space occupied by different land use types within the planned area. iThis is the value coefficient for each land use type, representing the economic or social value that each type of land use brings to urban planning. ε i ×sin(ω i ×t) represents the periodic fluctuation of land value over time. ε i It is the fifth weight, controlling the amplitude of this fluctuation, ω. i This is the sixth adjustment parameter, which determines the frequency of land value fluctuations. This parameter represents the direct contribution of the area and value of different land use types to the optimization objective. By considering the periodic changes in land value, the dynamic value of land in different periods can be better reflected.

[0108] This is a non-linear effect of the conflict potential index, CPI, which represents the intensity of potential conflict between different land use types. max This is the maximum acceptable conflict potential index, used to define the upper limit of conflict impact. β is an adjustment parameter that controls the nonlinear impact of conflict potential on the optimization objective. This part represents the impact of the conflict potential index on the optimization objective. By reducing the CPI, the optimization system can reduce potential conflicts in land use and improve land use efficiency. This term is expressed exponentially, meaning that the closer the conflict is to the upper limit, the greater the negative impact on the optimization objective.

[0109] λ3×∑(E q ×S q ×ln(1+P q E is the weighted logarithmic effect of the evaluation index. q S is the score of the q-th evaluation indicator, reflecting the performance of a specific land use type in certain aspects (such as economic, environmental, and social). q P represents the weight of the corresponding evaluation indicator, used to indicate the relative importance of that indicator in the overall optimization objective. q This is the public participation rate in the evaluation indicator, representing the public's level of attention and participation in the land use plan, ln(1+P) q A logarithmic function is used to handle the increase in public participation, thus mitigating its marginal impact as public participation increases. This section reflects the contribution of public participation and multidimensional assessment to the optimization objective. By integrating the scores and weights of the assessment indicators, the system can fully consider the multifaceted impacts of public opinion and different land use types during the optimization process.

[0110] It is a dynamic change in the sustainable development index. This is the rate of change of the sustainable development index over time. Integrating this term allows us to assess the region's sustainable development trend over the entire time period. This term represents the importance of regional sustainable development throughout the optimization process. λ4 is the fourth balancing coefficient of this term, controlling the impact of sustainability on the optimization objectives. Through this part, the optimization system ensures the long-term sustainability of land use.

[0111] λ5×exp(-γ×∣BI-BI target | is the exponential decay of biodiversity differences, and BI is the biodiversity index of the current region. target This is the preset biodiversity target value. γ is the third weight, controlling the impact of biodiversity differences on the optimization target. This term reflects the gap between the current biodiversity level of the region and the expected target. It is calculated using the exponential decay function exp(-γ×|BI-BI). target | indicates that the greater the difference in biodiversity, the greater the negative impact on the optimization objective. Optimizing the system requires adjusting land use patterns to maintain or enhance biodiversity as much as possible to achieve the target value.

[0112] λ6×(1-exp(-δ×GI)) represents the saturation effect of the green infrastructure index, where GI is the green infrastructure index, reflecting the level of green infrastructure within a region. δ is the fourth weight, controlling the impact of the green infrastructure index on the optimization objective. This term represents the contribution of green infrastructure to land use optimization. The exponential function 1-exp(-δ×GI) indicates that the positive impact of the green infrastructure index on the optimization objective gradually increases, but the growth rate gradually approaches saturation as GI increases, reflecting the diminishing marginal utility of resources.

[0113] λ7×∑(R i ×(1-exp(-η i ×(C imax -C i R represents the diminishing marginal utility of resource consumption. i is the importance weight of the i-th type of resource, representing the priority of different resources in optimization. C i This is the current consumption of resources, C. imax This is the maximum sustainable consumption of the resource. η i This is the seventh weight, controlling the impact of resource consumption on the optimization objective. This part reflects the marginal effect of resource consumption in the optimization process. As resources approach their maximum sustainable consumption level, resource utilization efficiency gradually decreases, and the optimization objective will gradually shift towards reducing excessive consumption of the resource to ensure sustainability.

[0114] The Optimization Function Value (OFV) is a comprehensive function used to assess and guide the optimal allocation of urban land use. It integrates multiple dimensions of factors, including land area and value, land use conflicts, public participation, sustainable development, biodiversity, green infrastructure, and resource consumption. Through different weights and adjustment parameters, the influence of these factors acts non-linearly on the optimization objective, ensuring that land use plans not only achieve economic benefits but also minimize conflicts, maintain the ecological environment, and promote sustainable development.

[0115] The zoning control module 206 is used to re-zone and control the urban land according to the new uses corresponding to the optimized urban land.

[0116] It is understandable that optimized urban land use refers to the land use that has been replanned and allocated for different areas of the city after multi-dimensional optimization, including data analysis, conflict identification, economic and environmental assessments. These uses include, but are not limited to, residential, commercial, industrial, public service, and green space.

[0117] In this invention, the optimization is based on multi-dimensional data including land use intensity, population density, economic activity index, environmental sensitivity, and conflict potential index. Through the extraction and analysis of these key features, the optimization algorithm can generate the most reasonable new land use allocation suggestions for each urban area. For example, in areas with high population density and high economic activity index, the area can be optimized for commercial or mixed-use development; in areas with low population density and high environmental sensitivity, the area can be optimized for green space or ecological protection zones.

[0118] Rezoning refers to the redivision of urban land based on optimized new uses, clearly defining each area as a different functional zone. Zoning is a spatial planning method aimed at dividing the functions of different urban areas to ensure that various land uses can meet the diverse needs of urban development, while reducing conflicts caused by functional incompatibility.

[0119] For example, high-intensity development areas (such as commercial and industrial zones) may be planned for higher-density development, while low-intensity development areas may be planned for residential or public service areas. In densely populated areas, planners may adjust land use to meet the needs of housing, transportation, and service facilities. Areas with high economic activity will see an increase in commercial and industrial land as needed, while areas with lower economic activity may be reallocated for ecological or public uses. The purpose of zoning is to optimize the layout of urban functions, improve the efficiency of land resource use, reduce conflicts, and promote coordinated development between regions. Through rezoning, problems such as the imbalance between land supply and demand and the irrational allocation of resources in the process of urbanization can be effectively addressed.

[0120] Zoning control refers to the effective supervision of the development, use and management of various types of land on the basis of re-division, to ensure that the functions of each area can be reasonably realized and to avoid unreasonable or excessive use.

[0121] In some embodiments, specific land uses can be defined for each zone, restricting development activities beyond their planned uses. For example, industrial development is prohibited on commercial land, and large-scale commercial construction is prohibited in residential areas. Zoning control requires restrictions on development density, building height, and floor area ratio in different areas to ensure compliance with overall urban planning. For instance, strict restrictions on building height and floor area ratio may be imposed in ecological protection zones and low-population-density areas, while higher development intensity may be permitted in commercial and high-density residential areas. In areas with high environmental sensitivity (such as water source protection zones, green spaces, and ecological protection zones), zoning control also requires the formulation of strict environmental protection measures to prevent over-development from damaging the ecosystem. Simultaneously, for areas with high resource consumption, control must also consider the rational allocation and management of water, electricity, and energy. Through a real-time monitoring system, the actual use of various zones can be dynamically managed, promptly identifying and correcting development behaviors that do not conform to planned uses. This real-time monitoring and feedback mechanism ensures the dynamism and flexibility of zoning control, allowing adjustments to be made according to the needs of urban development.

[0122] As urban development needs evolve and data becomes more updated, zoning control should not be a static, one-off operation, but rather a dynamic adjustment process. Continuous monitoring of real-time data (such as population flow, changes in economic activity, and environmental changes) allows for timely adjustments to zoning plans to address unforeseen circumstances or long-term development goals. During the rezoning process, prior conflict identification algorithms are also used to assess potential conflicts between different land use types. For example, if conflicts such as traffic congestion or environmental pollution exist between a commercial area and a residential area, zoning control will require adjusting the transition area between the two, adding green belts or buffer zones to reduce conflict.

[0123] Optimized rezoning ensures that the use of each plot of land aligns with the actual needs of urban development, thereby maximizing land use efficiency. Optimized zoning reduces the number of idle land and inefficiently developed areas, promoting the rational allocation of urban resources. Rezoning helps eliminate potential conflicts between different land use types, especially in areas with significant differences in land use characteristics. Zoning control, through clear land use delineation and environmental protection measures, reduces interference between different functional land uses while protecting the ecological environment of sensitive areas. Real-time data-driven zoning adjustment and control mechanisms make urban planning more flexible and dynamic. Planners can respond promptly to the challenges of population growth, economic changes, and environmental pressures, thereby ensuring the sustainability of urban development.

[0124] This invention, through optimized analysis of multidimensional data, enables planners to redefine appropriate land uses for different areas of a city and ensure their implementation through reasonable control measures. This method effectively improves land resource utilization efficiency, reduces land use conflicts, and enhances the city's sustainable development capabilities.

[0125] In some embodiments, the partition management module 206 can also be used for:

[0126] Continuously evaluate the effectiveness of the control measures implemented by rezoning the urban land use;

[0127] The control effect is sent to the dynamic land use zoning model for iterative optimization.

[0128] In this invention, continuous evaluation refers to the periodic or real-time monitoring of the actual effectiveness of urban land use rezoning and control measures after their implementation, to determine whether the expected goals have been achieved. This process includes evaluating the effects on multiple dimensions such as land use efficiency, population distribution, economic development, and environmental protection.

[0129] Assessing the actual development and use of different land use types helps determine whether land use efficiency has improved after rezoning. For example, has the amount of idle land been reduced? Does the development of commercial areas align with planning goals? Has the problem of inefficient land use been resolved? Real-time monitoring of population flow data can assess whether the population density after rezoning is consistent with planning expectations, whether overly concentrated population distribution has been improved, and whether public services and residential facilities meet actual needs. Monitoring economic activity indices (such as business activity, consumption levels, and business operations) can determine whether the planning of commercial and industrial land use types has successfully promoted regional economic development and met the city's economic growth goals. The environmental protection effects of rezoning can be evaluated, especially in highly environmentally sensitive areas, to determine whether the negative environmental impacts of development have been reduced. Simultaneously, resource consumption (such as water, electricity, and energy) is within controllable limits and aligns with the city's sustainable development goals.

[0130] In some embodiments, continuous evaluation can rely on the ongoing collection of real-time data, including dynamic data from IoT sensors, satellite remote sensing, social media, public transportation systems, and so on. This data can provide specific, quantifiable metrics for the evaluation. Evaluation can be conducted using various data analysis tools and machine learning algorithms, such as trend analysis, time-series analysis, and anomaly detection. The system will compare actual results with planning objectives and automatically identify potential problems and gaps. For example, if economic activity in a business district fails to meet expectations, the system will prompt adjustments to be made.

[0131] The feedback mechanism involves sending the actual control effects and problems discovered during the assessment process to the dynamic land use zoning model. This allows the model to be adjusted and optimized based on the latest actual data, thereby achieving iterative improvement of the zoning plan. This process ensures that urban planning schemes can be flexibly adjusted and continuously optimized in line with urban development.

[0132] If land use efficiency in certain areas fails to meet expectations, the system can feed this data back to the dynamic land use zoning model to adjust land use allocation. For example, if commercial land in a certain area is not fully developed, the model can reassess the land use of that area and consider whether to increase or decrease the commercial area. If population density and resource use do not conform to planning, the system will provide this information, such as overpopulation, traffic congestion, or excessive resource consumption. This feedback can prompt the model to reassess the layout of transportation, housing, and public service facilities and make adjustments based on actual conditions. Through a feedback mechanism, the system can also feed back potential conflicts between land use types (such as environmental pressures and incompatible land use functions) to the model. Based on this, the model can optimize the allocation of adjacent land use types to avoid future conflicts.

[0133] In some embodiments, the primary method of data feedback is through big data analytics and automated systems, comparing the monitored land use zoning effects (such as land use, population flow, economic activity, and environmental quality) with the model. Upon receiving the feedback data, the model, combined with current real-time data, automatically identifies areas and parameters requiring optimization. The goal of the feedback is to ensure the dynamic adjustment capability of the zoning scheme, enabling flexible allocation and optimization of urban land use based on actual conditions. This feedback mechanism not only promptly corrects deficiencies in zoning but also anticipates potential future urban development problems, allowing for proactive adjustments.

[0134] Iterative optimization refers to the dynamic land use zoning model repeatedly adjusting and optimizing based on received feedback information, so that the zoning plan continuously approaches the optimal state. This is a continuous improvement process; as time goes by and data accumulates, the model's prediction accuracy and planning effectiveness will become increasingly higher.

[0135] Based on feedback information, the model can adjust land use in certain areas. For example, if the population density of an area is growing too rapidly, the model can optimize the functional zoning of that area, increasing residential land or public service facilities, and reducing other uses that do not meet population needs. If the model identifies potential conflicts between different land use types (such as noise and traffic conflicts between commercial and residential areas), these conflicts can be reduced by optimizing zoning or adding buffer zones. For areas identified in the feedback as having inefficient or excessive resource utilization, the model can adjust the functional planning of that area to reduce resource pressure, while also reducing energy consumption through optimized design. During the optimization process, heuristic algorithms, genetic algorithms, machine learning, and other techniques can be used to iteratively improve the model. These algorithms can automatically adjust model parameters based on feedback information and make predictions based on the latest data, proposing new zoning suggestions. For example, through historical data and real-time feedback, the system can dynamically adjust traffic flow distribution, land development priorities, and so on.

[0136] In some embodiments, continuous evaluation and feedback mechanisms enable the zoning model to possess high dynamism and flexibility. Urban development is dynamic and constantly changing; feedback mechanisms can capture these changes, incorporate them into the model, and make timely optimizations and adjustments, thereby ensuring the adaptability of urban planning. This cyclical feedback and iterative optimization process not only solves short-term problems but also ensures the achievement of long-term urban development goals through continuous optimization and adjustment. For example, through multiple feedback and optimization cycles, the city's land use patterns will tend towards optimization, resource allocation will become more rational, and environmental protection measures will gradually improve.

[0137] As can be seen from the above, through real-time data monitoring, evaluation, and feedback, the system of this invention can automatically identify problems in land use and feed them back to the model for optimization and adjustment. This process ensures that urban land use zoning schemes can be flexibly adjusted and continuously optimized as urban development changes, thereby achieving efficient use of land resources, reducing land use conflicts, and promoting sustainable urban development.

[0138] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 3 As shown, this embodiment of the invention proposes an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it performs the following steps:

[0139] Real-time collection of multi-dimensional data on urban planning and construction land; the multi-dimensional data includes at least land use, population flow, and economic activities in each area of ​​the city.

[0140] Key features are extracted from the multidimensional data; the key features include at least the city's land use intensity, population density, and economic activity index.

[0141] A dynamic land use zoning model is constructed based on the key features, and a dynamic zoning index for each region is determined based on the dynamic land use zoning model; the dynamic zoning index is used to predict the future land use trend of the corresponding region.

[0142] Based on the dynamic zoning index corresponding to each pair of areas of different land use types, determine the conflict potential index between each pair of areas;

[0143] By optimizing the objective function and combining it with the conflict potential index, the land use of each land use type is optimized and adjusted to obtain the optimized urban land use.

[0144] Based on the new uses corresponding to the optimized urban land, the urban land will be re-zoned and managed.

[0145] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0146] Those skilled in the art will understand that embodiments of the present invention can provide system or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

Claims

1. A big data-based urban planning and construction land zoning control system, characterized in that, The system includes: The data acquisition module is used to collect multi-dimensional data on urban planning and construction land in real time; the multi-dimensional data includes at least land use, population flow and economic activities in each area of ​​the city. The feature extraction module is used to extract key features from the multidimensional data; the key features include at least the city's land use intensity, population density, and economic activity index. A dynamic zoning module is used to construct a dynamic land use zoning model based on the key features, and to determine a dynamic zoning index for each region based on the dynamic land use zoning model. The dynamic zoning index is used to predict the future land use trend of the corresponding region. The dynamic zoning module is also specifically used to: obtain the climate change index of the region; obtain multiple influencing factors that affect the dynamic zoning index of the region, and the average value of the multiple influencing factors; and determine the dynamic zoning index of the region based on the climate change index of the region, the multidimensional data, the multiple influencing factors, and the average value of the multiple influencing factors. The conflict potential module is used to determine the conflict potential index between each pair of regions based on the dynamic zoning index corresponding to each pair of regions of different land use types. Specifically, it is also used to: obtain the incompatibility, importance, and distance between the two regions; obtain the environmental sensitivity and policy friendliness of the two regions; and determine the conflict potential index between the two regions based on the incompatibility, importance, and distance between the two regions, the environmental sensitivity, policy friendliness, and dynamic zoning index of the two regions. The land use optimization module is used to optimize and adjust the land use of various land use types in areas by combining the objective function with the conflict potential index, so as to obtain the optimized urban land use. The zoning control module is used to re-zone and control the urban land according to the new uses corresponding to the optimized urban land.

2. The urban planning and construction land zoning control system based on big data as described in claim 1, characterized in that, The application optimization module is specifically used for: Obtain the area and value coefficient of each land use type; Obtain the scores, indicator weights, and public participation of each assessment indicator for the region being evaluated; Obtain the city's sustainable development index, biodiversity index, target biodiversity index, and green infrastructure index; Obtain the importance, current consumption, and maximum sustainable consumption of each resource type in the city; Based on the area and value coefficient of each land use type, the scores, weights, and public participation of each assessment indicator, the city's sustainable development index, biodiversity index, target biodiversity index, and green infrastructure index, the importance, current consumption, and maximum sustainable consumption of each resource type in the city, an optimization objective function is constructed. Based on the optimization objective function, land use is optimized and adjusted for each land use type to obtain the optimized urban land use.

3. The urban planning and construction land zoning control system based on big data as described in claim 2, characterized in that, The partition control module is also specifically used for: Continuously evaluate the effectiveness of the control measures implemented by rezoning the urban land use; The control effect is sent to the dynamic land use zoning model for iterative optimization.

4. The urban planning and construction land zoning control system based on big data according to claim 3, characterized in that, The data acquisition module is also specifically used to clean, denoise, and standardize the multidimensional data to enhance its quality and consistency.

5. The urban planning and construction land zoning control system based on big data according to any one of claims 1-4, characterized in that, The multidimensional data is collected from at least two data sources, including satellite remote sensing, IoT sensors, mobile devices, and social media.

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