Urban planning construction land partition management and control system based on big data

Abstract

The invention relates to the technical field of big data, and provides a big-data-based urban planning and construction land zoning management and control system, which comprises a data acquisition module used for acquiring multi-dimensional data of urban planning and construction lands in real time; the feature extraction module is used for extracting key features from the multi-dimensional data; the dynamic partitioning module is used for constructing a dynamic land use partitioning model according to the key features and determining a dynamic partitioning index of each region based on the dynamic land use partitioning model; the conflict potential module is used for determining a conflict potential index between every two regions according to the dynamic partition indexes corresponding to every two regions of different land use types; the application optimization module is used for obtaining the optimized urban land; the zoning management and control module is used for zoning and managing and controlling the urban land again according to the new purpose corresponding to the optimized urban land; according to the invention, the accuracy, adaptability and sustainability of urban planning construction land zoning management and control can be improved.

Description

Technical Field

[0001] The present 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 Art

[0002] Nowadays, the zoning control of urban planning and construction land mainly relies on traditional spatial planning methods, such as land use division through geographic information system (GIS), combined with expert experience and planning policies, to formulate detailed plans for zoning control. This method usually divides urban land into different functional areas, such as residential areas, commercial areas, industrial areas, etc., based on historical data and planning goals, supplemented by strict land control and management policies to ensure the rational allocation and use of land resources.

[0003] However, the traditional zoning control method mainly relies on historical data and subjective judgment of experts, lacks effective integration of real-time data, and is difficult to timely reflect the dynamic changes of urban development. Furthermore, the traditional land use zoning method is difficult to accurately identify potential land use conflicts and future development trends, and cannot provide decision makers with all-round, data-driven intelligent support, which in turn affects the efficient use and sustainable development of urban land resources. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a city planning and construction land zoning control system and electronic equipment based on big data, which can improve the accuracy, adaptability and sustainability of city planning and construction land zoning control.

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

[0006] The present 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] Obtaining climate change indices for the region;

[0009] Acquire multiple influencing factors affecting the regional dynamic partition index, and an average value of the multiple influencing factors;

[0010] The dynamic zoning index of the region is determined according to 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 also specifically used for:

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

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

[0014] The conflict potential index between the two regions is determined according to the incompatibility, importance and distance between the two regions, the environmental sensitivity, policy friendliness and dynamic zoning index of the two regions.

[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 evaluation indicator used to evaluate the area;

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

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

[0020] According to the area and value coefficient of each land use type, the score, indicator weight and public participation of each evaluation indicator, the sustainable development index, biodiversity index, target biodiversity index and green infrastructure index of the city, the importance, current consumption and maximum sustainable consumption of each resource type in the city, an optimization objective function is constructed, and based on the optimization objective function, the land use of each land use type is optimized to obtain optimized urban land.

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

[0022] Continue to evaluate the effectiveness of the control of rezoning of said urban land;

[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 through at least two data sources of satellite remote sensing, Internet of Things sensors, mobile devices, and social media.

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

[0027] The beneficial effects of the present invention are:

[0028] (1) The present invention acquires multi-dimensional data such as urban land use, population flow, and economic activities in real time through multi-source data collection channels, enabling planners to dynamically and in real time understand urban changes and make more accurate land use planning decisions. Compared with the traditional planning method that relies on historical data, it significantly improves the timeliness of data and narrows the gap between the actual situation of urban development and planning.

[0029] (2) The present invention can extract key features such as land use intensity, population density, and economic activity index from massive data and capture complex multidimensional relationships, which not only improves the efficiency of data analysis but also helps planners identify future trends and predict land use changes. The data-driven dynamic prediction capability significantly enhances the foresight of urban planning.

[0030] (3) The conflict identification algorithm of the present invention can analyze and evaluate the potential conflicts between different land use types through the dynamic zoning index (DZI), so as to discover the incompatibility between land use types in advance and avoid potential problems in land use, such as ecological damage or resource waste. In addition, based on the conflict identification results, the heuristic algorithm is used to optimize the land use, which can ensure that while maximizing the efficiency of land use, conflicts and contradictions between regions are minimized.

[0031] In summary, the present invention realizes the intelligent, dynamic and efficient zoning of urban planning land through big data and machine learning technology. Through real-time data collection, multi-dimensional feature extraction, conflict identification and optimization adjustment, the accuracy, adaptability and sustainability of urban land use can be significantly improved, meeting the needs of dynamic planning and intelligent decision-making in the context of rapid development of modern cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A scene diagram of a city planning and construction land zoning control system based on big data provided by the present invention;

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

[0034] Figure 3 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention. DETAILED DESCRIPTION

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

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

[0037] It should be noted that Figure 1 The scenario diagram of a city planning and construction land zoning control system based on big data is only an example. The terminal, server and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0038] Among them, the terminal can be used for:

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

[0040] Extracting key features from the multidimensional data; the key features at least include the land use intensity, population density and economic activity index of the city;

[0041] Constructing a dynamic land use zoning model according to the key features, and determining a dynamic zoning index for each of the regions based on the dynamic land use zoning model; the dynamic zoning index is used to predict future land use trends in the corresponding region;

[0042] Determine the conflict potential index between each two areas according to the dynamic zoning index corresponding to each two areas of different land use types;

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

[0044] According to the new use corresponding to the optimized urban land, the urban land is rezoned and controlled.

[0045] See also Figure 2 , Figure 2 A schematic diagram of the structure of a big data-based urban planning and construction land zoning control system provided by the present invention.

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

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

[0048] Among them, the multidimensional data includes at least land use, population flow and economic activities in each area of ​​the city. In some embodiments, the multidimensional data can be collected through at least two data sources in satellite remote sensing, Internet of Things sensors, mobile devices, and social media. Among them, land use data refers to the use of various types of land in each area in the city, including residential areas, commercial areas, industrial areas, public facilities, and green spaces. Such data can be obtained in a variety of ways, such as satellite remote sensing, drone aerial photography, Internet of Things 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 certain area from industrial use to commercial use, or green land being developed into a residential area. This helps to identify trend changes in land demand, plan in advance, and avoid unreasonable land use conflicts.

[0049] Among them, population mobility data refers to the population migration, density and activity trajectory of various areas of the city in different time periods. It reflects the patterns of people's movement and life in the city, covering behaviors such as work, residence, and consumption. Such data can be collected through mobile devices (such as mobile phone signals and GPS), public transportation systems (such as subway and bus card swipe records), IoT sensors (such as camera monitoring and smart light poles), and social media platforms. These data sources can capture changes in population mobility at different times and places. Through dynamic monitoring of population mobility, the system can identify high-density population gathering areas and determine population mobility trends within urban areas. For example, when an area attracts a large number of people due to the construction of new commercial facilities, urban planners can adjust the land use and supporting facilities planning of the area accordingly to improve efficiency and reduce traffic pressure.

[0050] Among them, economic activity data mainly reflects the economic operation status of various regions in the city, including consumption levels, enterprise distribution, commercial activities and industrial development. It represents the economic vitality of different regions in the city. Economic activity data can be obtained in real time through multiple channels, such as bank transaction records, online payment platforms, store sales data, tax data, and commercial activities and advertising information on social media platforms. In addition, IoT devices (such as smart meters and gas meters) can also help monitor the electricity and water consumption of the region, indirectly reflecting the intensity of economic activities in the region. By dynamically collecting economic activity data, the system can identify which regions have rapid economic growth and where economic vitality is low, and even predict potential economic hotspots. In this way, urban planners can adjust the functional zoning of land in advance, such as increasing commercial land in areas with more active economic activities, or considering optimizing infrastructure or introducing new industries in areas with slowing economic development.

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

[0052] Among them, data cleaning refers to identifying and processing errors, redundancies, incompleteness or invalid parts in the data to ensure the accuracy and integrity of the data. Since multidimensional data may come from different sources (such as satellite remote sensing, IoT sensors, social media, public transportation systems, etc.), these data may contain null values, duplicates, incorrect formats or illogical data. For example, GPS positioning data may lack location information for certain time periods due to signal loss, or sensor data may contain inconsistent timestamps. The cleaning steps usually include deleting duplicate data, filling missing values, correcting erroneous information, etc. For example, for missing traffic flow data, data from adjacent time periods can be interpolated and supplemented. For duplicate records, they can be merged. In addition, data cleaning may also involve format conversion to unify the data format from different data sources for subsequent analysis. Data cleaning improves the accuracy and completeness of multidimensional data, ensures that all data used for analysis conforms to the expected format, and eliminates potential errors or redundant data.

[0053] Among them, denoising refers to removing random, unpredictable or irrelevant interference information from data to enhance the validity of the data. Noise data refers to abnormal data that is irrelevant to the analysis target or interferes with the analysis results. In multi-source data, noise may come from many aspects. For example, satellite remote sensing data may be affected by cloud cover, resulting in blurred images, sensor data may be affected by electromagnetic interference or equipment failure, and social media data may contain unstructured information irrelevant to the analysis (such as advertisements, noisy text, etc.). Different denoising methods can be used for different types 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 technology can be used to remove clouds or other interference factors. For text data, denoising may involve text processing technology (such as removing stop words, irrelevant characters, etc.). Denoising can filter out irrelevant or useless data noise, ensure that the analysis results are not interfered with, and improve the accuracy and stability of the prediction model.

[0054] Among them, standardization refers to the unified format and scale processing of data from different sources and types to ensure that all types of data are comparable and consistent in 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 may be measured in people / square kilometer, and economic activity data may be in monetary units. Differences in different data dimensions will lead to inconsistencies in modeling and analysis, affecting the effectiveness of the model. Standardization usually uses two methods: Normalization: Scale the data to a uniform range (such as [0,1]) so that data of different units or magnitudes can be compared at the same scale. Z-score Normalization: Convert the data to a standard distribution with a mean of 0 and a standard deviation of 1 by subtracting the mean of the data and dividing by the standard deviation. Standardization ensures that data from different sources are consistent when extracting features and building models, preventing some features from being biased in analysis due to excessively large or small dimensions. Standardization can also improve the training speed and prediction accuracy of the model.

[0055] The present invention cleans, denoises and standardizes multidimensional data, which is a key step to ensure the successful implementation of the urban planning land zoning control method. Through these three steps, the data quality is improved, which not only provides a high-quality data foundation for subsequent feature extraction, model construction and optimization adjustment, but also ensures the consistency and reliability of the data, thereby making urban planning analysis more accurate and effective, helping planners make scientific decisions.

[0056] The present invention provides comprehensive and accurate information support for the dynamic management of urban planning and land use zoning through real-time multi-dimensional data, including three key dimensions: land use, population mobility, and economic activities. These data can be acquired in real time through a variety of high-tech means, ensuring that urban planners can make timely decisions in a rapidly changing urban environment. By integrating these multi-dimensional data, the system can dynamically evaluate urban development needs, predict future trends, and optimize land use layout based on this, 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 multi-dimensional data.

[0058] Among them, the key characteristics may at least include the city's land use intensity, population density and economic activity index. Land use intensity refers to the degree of development of land resources in a specific area, including factors such as the density of construction, the floor area and height of buildings, and the green coverage rate. It reflects the intensive degree of urban land use. The data sources of land use intensity are extensive and can be obtained through satellite remote sensing, drone images, geographic information systems (GIS) and Internet of Things devices (such as sensors) to monitor the development and utilization status of urban land in real time. Through spatial analysis technology and machine learning algorithms, land use characteristics of different regions can be extracted from these data. For example, through indicators such as building density, floor area ratio, and green space ratio, the system can calculate the land use intensity of each region. These characteristics are the basis for evaluating the degree of regional development and judging whether land use planning needs to be adjusted. Land use intensity can help urban planners measure the efficiency and intensity of current land use. Areas with high utilization intensity may need to optimize resource allocation, while areas with low intensity utilization may have potential development value.

[0059] Among them, population density refers to the number of people per unit area, usually expressed in units of per square kilometer. It reflects the distribution and aggregation of population in various areas of the city and is a key indicator of urban functional layout and land demand. Population density data can be dynamically collected and monitored through sources such as mobile device data (such as mobile phone signals, GPS positioning), intelligent transportation systems (such as bus cards and subway card swiping records), and social media activity data. IoT sensors (such as surveillance cameras, smart light poles, etc.) can also provide real-time urban population flow data. By cleaning, integrating and analyzing data from different sources, the population distribution characteristics of each area can be extracted. For example, the positioning data of mobile devices can provide real-time population density distribution, and bus card swiping records can show the dynamic flow pattern of the crowd. Through these data, urban planners can understand the population aggregation status of each area. Population density is an indispensable element in urban land planning. It determines the demand distribution of residential, commercial facilities and public service facilities (such as hospitals, schools, transportation hubs, etc.). High population density areas require more infrastructure and services, while low population density areas may need to attract more people by optimizing land use.

[0060] Among them, the economic activity index is a comprehensive indicator to measure the economic vitality of a region, reflecting the intensity of economic activities such as business activities, consumption levels, enterprise operations and industrial structure in the region. The data of the economic activity index can be obtained through various channels, such as online payment and transaction records, sales data of shops and enterprises, electricity and water consumption monitoring, business activity information on social media, and government tax data. In addition, data from logistics and transportation systems can also indirectly reflect the prosperity of economic activities. By analyzing the above economic data, the intensity of economic activities in each region can be calculated. For example, online payment and transaction records can reflect consumption behavior, and electricity and water consumption monitoring can reflect the operating conditions of enterprises and shops. After cleaning and standardization, these data can be used to construct the economic activity index of 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 introduce new commercial projects to activate economic vitality. In addition to the above three key features, other features closely related to urban planning can be extracted from multidimensional data, such as:

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

[0062] Environmental quality characteristics: Extract regional environmental quality through air quality monitoring, green space coverage and other environmental data to help cities consider environmental protection and sustainable development when planning.

[0063] Social service demand characteristics: Extract social service demand characteristics through the use of medical, educational and cultural facilities to ensure that residents' public service needs are fully considered in urban planning.

[0064] The extracted key features (such as land use intensity, population density, and economic activity index) are the basic data for urban planning and construction land management and optimization. They help urban planners fully understand the current situation and future needs of each area and make scientific planning decisions. The extraction process of these key features requires the use of advanced technologies such as big data analysis and machine learning to ensure that the extracted data is accurate, real-time, and can dynamically reflect the development and changes of the city.

[0065] The present invention processes and analyzes multidimensional data to extract key features, such as land use intensity, population density and economic activity index, providing basic and quantifiable indicators for urban planning. These features help urban planners understand the current status of land use, population distribution and dynamic changes in economic activities in different regions, and then scientifically and rationally carry out land zoning control and optimize resource allocation, thereby promoting efficient development and sustainability of cities.

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

[0067] The dynamic partition index is used to predict the future land use trend of the corresponding area. In some embodiments, the dynamic partition module 203 is also specifically used to:

[0068] Obtaining climate change indices for the region;

[0069] Acquire multiple influencing factors affecting the regional dynamic partition index, and an average value of the multiple influencing factors;

[0070] The dynamic zoning index of the region is determined according to 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 partition index is expressed as:

[0072]

[0073] Among them, DZI is the dynamic zoning index of the region, LUI is the land use intensity, PD is the population density, EAI is the economic activity index, l is the number value of the influencing factor, F l is the lth influencing factor, t is the time variable, CCI is the climate change index, α, β, γ, δ, ε, ζ, η, μ are the first weight, second weight, third weight, fourth weight, fifth weight, sixth weight, seventh weight and eighth weight respectively, θ, τ, λ, ν, ω are the first adjustment parameter, the second adjustment parameter, the third adjustment parameter, the fourth adjustment parameter, the fifth adjustment parameter and the sixth adjustment parameter, respectively. avg 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. LUI is land use intensity, which reflects the development and utilization degree of land resources in a region. Its nonlinear function α×LUI θIt shows that this indicator does not affect the dynamic zoning index linearly. The parameter θ controls the degree of this influence. When θ>1, the increase in land use intensity will accelerate the development of the region. When θ<1, the influence of land use intensity on the region gradually decreases. α is the first weight, which indicates 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) is the logarithmic effect of population density, where PD is the population density, which is used to reflect the population concentration in the area. High population density usually means a higher demand for land resources, but in high-density areas it 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, which indicates the importance of population density in the entire model. For some areas with a high degree of urbanization, the impact of population density will be more significant.

[0076] It is the nonlinear effect of the economic activity index. EAI stands for economic activity index, which is used to reflect the intensity of economic activities in the region. Similar to land use intensity, this factor is expressed through an exponential function. represents its nonlinear effect, is the second adjustment parameter, controlling the impact of the economic activity index on regional dynamics. γ is the third weight, indicating the contribution of economic activity to the regional zoning index. Regions with more active economic activities usually have higher development potential and resource demand.

[0077] δ×∑(ω i ×F l ) is a linear combination of other influencing factors, F l Represents the lth influencing factor, including, for example, other specific regional influencing factors such as infrastructure construction, educational resources, and medical conditions. i is the weight of the i-th influencing factor, reflecting its importance in the partition model. δ is the fourth weight, which is used to control the overall impact of multiple factors on the partition index.

[0078] ε×sin(τ×t) is the influence of time periodic changes, which indicates the periodic characteristics of the dynamic partition index over time. It is usually used to reflect natural seasonal changes, urban development cycles, etc. For example, in certain periods of time, land use will fluctuate periodically with seasonal or policy changes. τ is the time adjustment parameter that controls the length of the cycle, and ε is the fifth weight that determines the intensity of the influence of time periodic changes.

[0079] It is the integral of the rate of change of land use intensity, which reflects the changing trend of land use intensity in the entire time period T. The rate of increase or decrease of land use intensity will affect the future development potential of the region. ζ is the sixth weight, which indicates the importance of land use intensity change to the dynamic zoning index. If the land use intensity changes greatly, the dynamic zoning index of the region may rise or fall rapidly.

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

[0081] μ×(1-exp(-ν×CCI)) is the long-term impact of climate change. CCI is the climate change index, which indicates the potential impact of climate on regional development. It uses exponential decay to indicate that the impact of climate change gradually emerges over time. v is the adjustment parameter that controls the speed at which the impact of climate change on the region emerges. μ is the eighth weight, which indicates the importance of climate change to the dynamic zoning index. This item is mainly used to evaluate the long-term impact of climate change, such as the damage or adaptive adjustment of climate change to agriculture and the ecological environment.

[0082] The present invention comprehensively quantifies the dynamic zoning index of a region by comprehensively considering the impact of multiple dimensions such as land use, population, economic activities, time cycle and environmental factors. Each variable reflects the relative importance and complex interrelationships of various factors in urban land planning through different mathematical forms and weight combinations. Each term in the formula not only considers the static spatial dimension, but also combines the dynamic changes over time, especially in terms of land use intensity and climate change, reflecting the long-term trend of the future development of the region.

[0083] The conflict potential module 204 is used to determine the conflict potential index between every two areas of different land use types according to the dynamic partition index corresponding to every two areas.

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

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

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

[0087] The conflict potential index between the two regions is determined according to the incompatibility, importance and distance between the two regions, the environmental sensitivity, policy friendliness and dynamic zoning index of the two regions.

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

[0089]

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

[0091] In the specific implementation, is the basic conflict assessment, I ij Indicates the incompatibility between the i-th land use type and the j-th land use type, reflecting the degree of conflict between the two types of land use in terms of function or environment. The higher the value, the stronger the incompatibility between the two. ij is the importance weight between the i-th land use type and the j-th land use type, which is used to balance the relative importance of different land use types in regional planning. For example, the conflict between commercial areas and residential areas may be more important than the conflict between green areas and residential areas, so its weight will be greater. ij 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 To indicate that as the distance increases, the impact of the conflict gradually decreases. R is the impact radius, which indicates how far the impact of the conflict can be ignored. The larger the R value, the wider the impact of the conflict. This section calculates the basic conflict degree between the i-th and j-th land uses, taking into account incompatibility, importance weights, and the attenuation effect of distance.

[0092] 1+σ×sin(ω×t) is the time periodic impact, t is the time variable, which is used to reflect the dynamic changes of conflicts over time. Some land use conflicts may fluctuate in different periods. For example, seasonal changes may affect land use in certain areas. ω is the sixth adjustment parameter, which is an adjustment parameter for controlling the time period, reflecting the frequency of periodic changes in the conflict in the time dimension. σ is the seventh adjustment parameter, which is an amplitude adjustment parameter used to adjust the amplitude of time fluctuations. The larger σ is, the greater the impact of time fluctuations on the conflict potential. This part expresses the periodic changes of conflict potential over time and simulates the seasonal or periodic characteristics of some land use conflicts.

[0093] 1-ρ×exp(-κ×|DZI i -DZI j ∣) is the impact of the dynamic partition index difference, DZI i DZI and DZI are the dynamic zoning indexes of the i-th and j-th land use types, respectively, reflecting the comprehensive dynamic status of these two types of land use (including land use intensity, population density, economic activities, etc.). i , DZI j When the gap between them is large, it means that there are large differences in development needs and resource use between the two types of land use, which may lead to greater conflicts. ρ and κ are the eighth and ninth adjustment parameters, respectively. ρ is used to adjust the maximum impact of DZI differences on conflict potential. This term is represented by an exponential decay function. As the DZI difference increases, the conflict potential gradually decreases, showing a trend of weakening conflicts. κ is used to control the speed of change of the impact of DZI differences on conflicts. This section shows that when the dynamic zoning index difference between the two land use types is large, the conflict may be more obvious, and this impact adjusts its effect through exponential decay.

[0094] is the synergistic effect of environmental sensitivity, ED i ×ED j Represents the synergistic effect of the environmental sensitivity of the i-th and j-th land use types, that is, the higher the environmental sensitivity of the two land use types, the greater the environmental conflict that may be caused. This term represents the product of the two sensitivities through a square root function, emphasizing their joint influence. This part illustrates the role of environmental factors in land use conflicts, especially in areas with high environmental sensitivity, where such conflicts are particularly significant. ψ is the tenth adjustment parameter, which controls the intensity of the impact of environmental sensitivity on conflict potential. It is the synergistic effect of the environmental sensitivity of the i-th and j-th land use types, that is, the higher the environmental sensitivity of the two land use types, the greater the environmental conflict that may be caused. This term expresses the product of the two sensitivities through a square root function, emphasizing their joint impact. This section illustrates the role of environmental factors in land use conflicts, especially in areas with high environmental sensitivity, where such conflicts are particularly significant.

[0095] is the moderating effect of policy friendliness, PF i PF j are the policy friendliness of the i-th and j-th land use types, respectively, reflecting the priority or encouragement of these two types of land use under the current policy support. The greater the difference in policy friendliness, the more prominent the conflict in planning may be. ξ is the eleventh adjustment parameter, which controls the intensity of the impact of policy friendliness differences on conflict potential. φ is the twelfth adjustment parameter, which is used to adjust the degree of impact of policy friendliness differences. The 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 difference, the greater the potential for conflict; conversely, when policy consistency is high, the possibility of conflict decreases. It can be understood that the present invention quantifies the conflict potential index (CPI) between different land use types through a multi-dimensional complex calculation model, taking into account the interaction of multiple influencing factors, including: incompatibility and importance between land use types; the decay effect of distance; periodic changes in time; the regulatory effect of differences in dynamic zoning index (DZI) on conflicts; the synergistic effect of environmental sensitivity; and the impact of differences in policy friendliness. Each factor works together with the adjustment parameters through different functional forms (such as exponential decay, logarithmic adjustment, and power adjustment) to form a multi-dimensional and multi-level conflict potential evaluation model. Through this model, potential conflicts in urban land planning can be evaluated more accurately, providing a scientific basis for optimizing land use.

[0097] The land use optimization module 205 is used to optimize the land use of each land use type by optimizing the objective function in combination with the conflict potential index to obtain optimized urban land.

[0098] In some embodiments, the usage optimization module 205 is further specifically configured to:

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

[0100] Obtain the scores, indicator weights and public participation of each evaluation indicator used to evaluate the area;

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

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

[0103] According to the area and value coefficient of each land use type, the score, indicator weight and public participation of each evaluation indicator, the sustainable development index, biodiversity index, target biodiversity index and green infrastructure index of the city, the importance, current consumption and maximum sustainable consumption of each resource type in the city, an optimization objective function is constructed, and based on the optimization objective function, the land use of each land use type is optimized to obtain optimized urban land.

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

[0105]

[0106] Among them, OFV is the optimization objective function value, A i is the area of ​​each land use type, V i is the value coefficient of the area of ​​each land use type, CPI is the conflict potential index, CPI max is the maximum acceptable conflict potential index, q is the number of the evaluation indicator, E q is the score of the qth evaluation metric, S q is the indicator weight of each evaluation indicator, P q is the public participation of each evaluation indicator, SI is the sustainable development index, BI is the biodiversity index, target is the target biodiversity index, GI is the green infrastructure index, R i is the importance of each resource type, C i is the current consumption of each resource type, C imax is the maximum sustainable consumption of each resource type, ω is the sixth adjustment parameter, λ1, λ2, λ3, λ4, λ5, λ6, λ7 are the first balance coefficient, the second balance coefficient, the third balance coefficient, the fourth balance coefficient, the fifth balance coefficient, the sixth balance coefficient, and the seventh balance coefficient 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 of the area and value coefficient of land use type, A i It is the area of ​​each land use type, reflecting the actual space occupied by different land use types in the planning area. iIt is the value coefficient of each land use type, indicating the economic or social value brought by each type of land use in urban planning. i ×sin(ω i ×t) is the periodic fluctuation of land value over time. i is the fifth weight, controlling the amplitude of the fluctuation, ω i It is the sixth adjustment parameter, which determines the frequency of land value fluctuations. This item represents the direct contribution of the area and value of different land types to the optimization goal. By considering the periodic changes in land value, the dynamic value of land in different periods can be better reflected.

[0108] It is the nonlinear impact of the conflict potential index. CPI is the conflict potential index, which indicates the potential conflict intensity between different land use types. max is the maximum acceptable conflict potential index, which is used to calibrate the upper limit of the conflict impact. β is the adjustment parameter that controls the nonlinear impact of the 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 the potential conflicts in land use and improve land use efficiency. This item is expressed in exponential form, which means 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 )) is the weighted logarithmic effect of the evaluation indicator, E q It is the score of the qth evaluation indicator, reflecting the performance of a specific land use type in certain aspects (such as economy, environment, and society). q is the weight of the corresponding evaluation index, which is used to indicate the relative importance of the index in the overall optimization goal. q is the public participation of the evaluation index, indicating the public's attention and participation in the land use planning. q ) uses a logarithmic function to handle the growth of public participation to weaken its marginal impact as public participation increases. This part reflects the contribution of public participation and multidimensional evaluation to the optimization goal. By integrating the scores and weights of the evaluation indicators, the system can fully consider the multi-faceted impacts of public opinions and different land use types in the optimization process.

[0110] is the dynamic change of the sustainable development index, is the rate of change of the sustainability index over time. By integrating this term, the sustainability trend of the region over the entire time period can be evaluated. This term indicates the importance of sustainable development in the region in the entire optimization process. λ4 is the fourth balance coefficient of this term, which controls the impact of sustainability on the optimization goal. Through this part, the optimization system ensures that land use is sustainable in the long term over time.

[0111] λ5×exp(-γ×|BI-BI target ∣) is the exponential decay of biodiversity differences, BI is the biodiversity index of the current area, BI target is the preset biodiversity target value. γ is the third weight, which controls the impact of biodiversity differences on the optimization target. This term reflects the gap between the current biodiversity level in the region and the expected target. target ∣), indicating that the greater the difference in biodiversity, the greater the negative impact on the optimization target. The optimization system needs to adjust land use to maintain or improve biodiversity as much as possible to achieve the target value.

[0112] λ6×(1-exp(-δ×GI)) is the saturation effect of the green infrastructure index. GI is the green infrastructure index, which reflects the level of green infrastructure in the region. δ is the fourth weight, which controls the impact of the green infrastructure index on the optimization target. 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 target gradually increases, but the growth rate gradually tends to saturation with the increase of GI, reflecting the diminishing marginal effect of resources.

[0113] λ7×∑(R i ×(1-exp(-η i ×(C imax -C i )))) is the diminishing marginal effect of resource consumption, R i is the importance weight of the i-th resource, indicating the priority of different resources in optimization. i is the current resource consumption, C imax is the maximum sustainable consumption of the resource. i It is the seventh weight, which controls the impact of resource consumption on the optimization goal. This part reflects the marginal effect of resource consumption in the optimization process. As resources approach their maximum sustainable consumption, resource utilization efficiency gradually decreases, and the optimization goal will gradually tend to reduce excessive consumption of the resource to ensure sustainability.

[0114] The optimization objective function value (OFV) is a comprehensive function used to evaluate and guide the optimal allocation of urban land use. It integrates factors from multiple dimensions, 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 on the optimization target in a nonlinear way, ensuring that the land use plan can not only achieve economic benefits, but also minimize conflicts, maintain the ecological environment, and achieve sustainable development.

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

[0116] It can be understood that the optimized urban land use refers to the land use that is replanned and allocated for different areas of the city after multi-dimensional optimization such as data analysis, conflict identification, economic and environmental assessment, etc. These uses include but are not limited to residential, commercial, industrial, public services, green space, etc.

[0117] In the present invention, the basis for optimization includes multi-dimensional data such as land use intensity, population density, economic activity index, environmental sensitivity, conflict potential index, etc. By extracting and analyzing these key features, the optimization algorithm can generate the most reasonable new use allocation suggestions for each urban area. For example, in areas with high population density and high economic activity index, they can be optimized as commercial or mixed-use areas; in areas with low population density and high environmental sensitivity, they can be optimized as green spaces or ecological protection areas.

[0118] Rezoning refers to the re-division of urban land according to the optimized new use, and the identification of different functional land in each area. Zoning is a spatial planning method that aims to divide the functions of different areas of the city to ensure that various types of land can meet the different needs of urban development, while reducing conflicts caused by incompatible functions.

[0119] For example, in high-intensity use areas (such as commercial areas and industrial areas), they may be planned as higher-density development areas, while low-intensity use areas may be planned as residential areas or public service areas. In areas with high population density, planners may adjust land use to meet the needs of residential, transportation and service facilities. Areas with high economic activity will increase commercial and industrial land as needed, while areas with weak economic activity may be reallocated for ecological or public use. The purpose of zoning is to optimize the functional layout of the city, improve the efficiency of land resource use, reduce conflicts, and promote coordinated development among regions. Through rezoning, we can effectively deal with problems such as imbalance in land supply and demand and irrational resource allocation in the urbanization process.

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

[0121] In some embodiments, specific land uses can be specified for each subdistrict to restrict development activities that exceed the planned use. For example, commercial land cannot be used for industrial development, and residential areas cannot be used for large-scale commercial construction. In zoning control, it is necessary to restrict the development density, building height, floor area ratio, etc. of different areas to ensure compliance with the overall urban planning. For example, in ecological protection areas and low population density areas, strict restrictions may be imposed on building height and floor area ratio, while in commercial areas and high-density residential areas, higher development intensity may be allowed. In areas with high environmental sensitivity (such as water source protection areas, green spaces, ecological protection areas, etc.), zoning control also needs to formulate strict environmental protection measures to prevent excessive development from damaging the ecosystem. At the same time, for areas with high resource consumption, control also needs to take into account the reasonable allocation and management of water, electricity, and energy. Through the real-time monitoring system, the actual use of various subdistricts is dynamically managed, and development behaviors that do not conform to the planned use are discovered and corrected in a timely manner. This real-time monitoring and feedback mechanism ensures the dynamic and flexibility of zoning control, and can be adjusted with the needs of urban development.

[0122] As urban development needs change and data is updated, zoning control should not be a static one-time operation, but a dynamic adjustment process. Through continuous monitoring of real-time data (such as population mobility, changes in economic activities, environmental changes, etc.), zoning plans can be adjusted in a timely manner to respond to emergencies or long-term development plans. In the process of rezoning, the previous conflict identification algorithm will also be used to evaluate potential conflicts between different land use types. For example, if there is a conflict such as traffic congestion or environmental pollution between a commercial area and a residential area, zoning control will need to adjust the transition area between the two and increase green belts or buffer zones to reduce conflicts.

[0123] Through optimized rezoning, it is possible to ensure that the use of each piece of land meets the actual needs of urban development, thereby maximizing land use efficiency. Optimized zoning reduces the number of idle land and inefficient development areas, and promotes the rational allocation of urban resources. Rezoning helps eliminate potential conflicts between different land use types, especially in areas with large differences in land use nature. Zoning control reduces interference between different functional land uses through clear use division and environmental protection measures, while protecting the ecological environment of sensitive areas. Zoning adjustment and control mechanisms based on real-time data make urban planning more flexible and dynamic. Planners can respond to the challenges of population growth, economic changes and environmental pressures in a timely manner, thereby ensuring the sustainability of urban development.

[0124] By optimizing and analyzing multidimensional data, the present invention enables planners to re-determine appropriate land uses for different areas of the city and ensure the implementation of these uses through reasonable management and control measures. This method effectively improves the utilization efficiency of land resources, reduces land use conflicts, and enhances the sustainable development capacity of the city.

[0125] In some embodiments, the partition control module 206 may also be used to:

[0126] Continue to evaluate the effectiveness of the control of rezoning of said urban land;

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

[0128] In this invention, continuous evaluation means that after the implementation of urban land rezoning and control, the actual implementation effect of these measures is monitored regularly or in real time to determine whether they have achieved the expected goals. This process includes evaluating the effects of multiple dimensions such as land use efficiency, population distribution, economic development, and environmental protection.

[0129] Evaluate the actual development and use of different land types to determine whether the efficiency of land use has been improved after rezoning. For example, whether the amount of idle land has been reduced, whether the development of commercial areas meets the goals in the plan, and whether the problem of inefficient land use has been solved. By real-time monitoring of population flow data, it can be evaluated whether the population density after rezoning is consistent with the planning expectations, whether the overly concentrated population distribution has been improved, and whether the facilities in public services and residential areas meet actual needs. By monitoring economic activity indexes (such as business activity, consumption levels, business operations, etc.), it can be determined whether the planning of commercial, industrial and other land types has successfully promoted the development of the regional economy and whether it has met the city's economic growth goals. The environmental protection effect after rezoning can be evaluated, especially in areas with high environmental sensitivity, to determine whether the negative impact of development on the environment has been reduced. At the same time, whether the consumption of resources (such as water, electricity, and energy) is within a controllable range and meets the city's sustainable development goals.

[0130] In some embodiments, continuous evaluation can rely on the continuous collection of real-time data, including dynamic data from IoT sensors, satellite remote sensing, social media, public transportation systems, etc. These data can provide specific and quantifiable indicators for evaluation. Evaluation can be performed through a variety of data analysis tools and machine learning algorithms, such as trend analysis, time series analysis, anomaly detection, etc. The system will compare the actual results with the planned goals and automatically identify potential problems and gaps. For example, if the economic activity in a business district fails to meet expectations, the system will prompt that adjustments are needed.

[0131] The feedback mechanism refers to sending the actual control effects and problems found during the evaluation process to the dynamic land use zoning model, so that the model can be adjusted and optimized according to the latest actual data, thereby achieving iterative improvement of the zoning plan. This process ensures that the urban planning plan can be flexibly adjusted and continuously optimized as the city develops.

[0132] If the land use efficiency in some areas does not meet the expected target, the system can feed such data back to the dynamic land use zoning model to adjust the allocation of land use. For example, if the commercial land in a certain area is not fully developed, the model can re-evaluate the land use in the area and consider whether the commercial area needs to be increased or reduced. If the population density and resource use are inconsistent with the plan, the system will feedback this information, such as excessive population concentration, traffic congestion, or excessive resource consumption. These feedbacks can prompt the model to re-evaluate the layout of transportation, housing, and public service facilities and make adjustments based on actual conditions. Through the feedback mechanism, the system feeds back potential conflicts between land use types (such as environmental pressure, incompatible land use functions, etc.) to the model. The model can optimize the allocation of adjacent land use types based on this to avoid future conflicts.

[0133] In some embodiments, the main method of data feedback is to compare the monitored land zoning effects (such as land use, population mobility, economic activities, environmental quality, etc.) with the model through big data analysis and automation systems. After receiving the feedback data, the model automatically identifies the areas and parameters that need to be optimized in combination with the current real-time data. The goal of the feedback is to ensure the dynamic adjustment capability of the zoning scheme so that urban land can be flexibly allocated and optimized according to actual conditions. This feedback mechanism can not only correct deficiencies in zoning in a timely manner, but also foresee possible urban development problems in the future, so as to make early adjustments.

[0134] Iterative optimization refers to the dynamic land use zoning model repeatedly adjusting and optimizing according to the feedback information received, so that the zoning planning continues to move towards the optimal state. This is a process of continuous improvement. With the passage of time and the accumulation of data, the prediction accuracy and planning effect of the model will become higher and higher.

[0135] Based on feedback information, the model can adjust the land use of certain areas. For example, if the population density of an area is growing too fast, the model can optimize the functional zoning of the area, increase residential land or public service facilities, and reduce other uses that do not meet the needs of the population. 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 buffers. For areas with inefficient or over-consumed resources found in the feedback, the model can adjust the functional planning of the area, reduce resource pressure, and reduce energy consumption by optimizing the design. During the optimization process, heuristic algorithms, genetic algorithms, machine learning and other technologies can be used to iteratively improve the model. These algorithms can automatically adjust model parameters based on feedback information, make predictions based on the latest data, and propose new zoning recommendations. For example, through historical data and real-time feedback, the system can dynamically adjust traffic flow distribution, land development priorities, etc.

[0136] In some embodiments, continuous evaluation and feedback mechanisms can be used to make the zoning model highly dynamic and flexible. Urban development is dynamic, and the feedback mechanism can capture these changes, incorporate them into the model, and optimize and adjust them in a timely manner to ensure the adaptability of urban planning. This process of cyclic feedback and iterative optimization can not only solve short-term problems, but also ensure the realization of the city's long-term development goals through continuous optimization and adjustment. For example, through multiple feedback and optimization cycles, the city's land use pattern will tend to be optimal, resource allocation will be more reasonable, and environmental protection measures will gradually improve.

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

[0138] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides 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, the following steps are implemented:

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

[0140] Extracting key features from the multidimensional data; the key features at least include the land use intensity, population density and economic activity index of the city;

[0141] Constructing a dynamic land use zoning model according to the key features, and determining a dynamic zoning index for each of the regions based on the dynamic land use zoning model; the dynamic zoning index is used to predict future land use trends in the corresponding region;

[0142] Determine the conflict potential index between each two areas according to the dynamic zoning index corresponding to each two areas of different land use types;

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

[0144] According to the new use corresponding to the optimized urban land, the urban land is rezoned and controlled.

[0145] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0146] Those skilled in the art will appreciate that embodiments of the present invention may provide a system or computer program product. Therefore, the present invention may take the form of a complete hardware embodiment, a complete 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 comprises: A data acquisition module is used to collect multi-dimensional data of urban planning and construction land in real time; the multi-dimensional data at least includes land use, population flow and economic activities in each area of ​​the city; A feature extraction module, used to extract key features from the multidimensional data; the key features at least include the land use intensity, population density and economic activity index of the city; A dynamic zoning module, used to construct a dynamic land use zoning model according to the key features, and determine a dynamic zoning index for each of the regions 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; A conflict potential module, for determining a conflict potential index between each two areas according to the dynamic zoning index corresponding to each two areas of different land use types; A use optimization module is used to optimize the land use of areas of various land use types by optimizing the objective function in combination with the conflict potential index to obtain optimized urban land; The zoning control module is used to re-zon and control the urban land according to the new use corresponding to the optimized urban land.

2. According to the big data-based urban planning and construction land zoning control system of claim 1, it is characterized in that: The dynamic partitioning module is also specifically used for: Obtaining climate change indices for the region; Acquire multiple influencing factors affecting the regional dynamic partition index, and an average value of the multiple influencing factors; The dynamic zoning index of the region is determined according to the climate change index of the region, the multidimensional data, the multiple influencing factors, and the average value of the multiple influencing factors.

3. The urban planning and construction land zoning control system based on big data according to claim 2 is characterized in that: The conflict potential module is also specifically used for: Obtaining incompatibility, importance and distance between the two regions; Obtain the environmental sensitivity and policy friendliness of the two areas; The conflict potential index between the two regions is determined according to the incompatibility, importance and distance between the two regions, the environmental sensitivity, policy friendliness and dynamic zoning index of the two regions.

4. The urban planning and construction land zoning control system based on big data according to claim 3 is characterized in that: The usage 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 evaluation indicator used to evaluate the area; Obtain the sustainable development index, biodiversity index, target biodiversity index and green infrastructure index of the city; Obtain the importance, current consumption, and maximum sustainable consumption of each resource type in the city; According to the area and value coefficient of each land use type, the score, indicator weight and public participation of each evaluation indicator, the sustainable development index, biodiversity index, target biodiversity index and green infrastructure index of the city, the importance, current consumption and maximum sustainable consumption of each resource type in the city, an optimization objective function is constructed, and based on the optimization objective function, the land use of each land use type is optimized to obtain optimized urban land.

5. The urban planning and construction land zoning control system based on big data according to claim 4 is characterized in that: The partition control module is also specifically used for: Continue to evaluate the effectiveness of the control of rezoning of said urban land; The control effect is sent to the dynamic land use zoning model for iterative optimization.

6. The urban planning and construction land zoning control system based on big data according to claim 5 is characterized in that: The data acquisition module is also specifically used to: perform cleaning, denoising and standardization on the multi-dimensional data to enhance the quality and consistency of the multi-dimensional data.

7. The urban planning and construction land zoning control system based on big data according to any one of claims 1 to 6, characterized in that: The multidimensional data is collected through at least two data sources of satellite remote sensing, Internet of Things sensors, mobile devices, and social media.

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