A Visualization Intelligent Big Data Urban Planning Decision Support Management Method and System

Abstract

The present invention provides a visualization intelligent big data urban planning decision support management method and system, which relates to the technical field of big data management. The method includes: acquiring multi-dimensional data related to urban planning; preprocessing the multi-dimensional data; establishing a correlation analysis model between urban elements, and calculating the interaction influence factors between different urban element data through it to form an urban element correlation matrix; extracting features from the urban element correlation matrix, screening out urban element combinations, and calculating the weight values of different urban element combinations by using a causal inference method; performing site selection optimization calculation on the target planning area, and calculating the comprehensive fitness scores of each alternative area; sorting each alternative area, selecting the alternative area with the highest comprehensive fitness score as the target area, and generating a visualization urban planning decision report. The present invention can provide dynamic analysis content, which is convenient for providing decision-making basis for urban planning.

Description

Technical Field

[0001] The present invention relates to the technical field of big data management, and in particular to a visual intelligent big data urban planning decision support management method and system. Background Art

[0002] Currently, the application of big data technology in urban planning and decision management has been relatively common. Existing technologies usually comprehensively process information on multiple aspects such as urban traffic, environment, and population distribution through data collection, storage, analysis, and visual display to support urban managers in formulating reasonable planning schemes. Specifically, common methods include using Internet of Things devices to collect urban dynamic data, storing and processing it through cloud computing or distributed databases, and presenting the analysis results using Geographic Information System (GIS) or three-dimensional visualization technology to make urban planning schemes more intuitive and scientific. For example, in traffic management, existing technologies use historical traffic flow data and real-time monitoring data, combined with machine learning algorithms to predict future road congestion conditions, thereby optimizing traffic signal control strategies. However, although existing technologies have played an important role in improving data processing capabilities and assisting decision-making, there are still certain limitations.

[0003] In existing big data urban planning decision support technologies, there is a problem of insufficient mining of data relevance, resulting in the interactive effects between different urban elements not being fully reflected. For example, in the planning of smart communities, existing systems often analyze factors such as population density, distribution of commercial facilities, and traffic flow separately, lacking in-depth modeling of the interactions between these data. Taking the location selection of a newly built commercial complex in a certain city as an example, existing methods may conduct preliminary screening based on population density and consumption capacity, but fail to fully consider the impact of surrounding traffic flow, parking lot capacity, and public transportation accessibility on the business district's vitality, resulting in problems such as lower-than-expected footfall and insufficient business vitality after the location selection. This defect makes it difficult to achieve precise optimization of urban planning decisions, thus affecting the rational allocation of urban resources. Summary of the Invention

[0004] The purpose of the present invention is to provide a visual intelligent big data urban planning decision support management method and system, aiming to solve the problems mentioned in the background art.

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

[0006] In a first aspect, a visual intelligent big data urban planning decision support management method, characterized in that the method includes:

[0007] Obtain multi-dimensional data related to urban planning, including population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data; preprocess the multi-dimensional data, including format standardization, missing value filling, and data deduplication, to obtain a structured data set;

[0008] According to the structured data set, establish a correlation analysis model between urban elements, and calculate the interaction impact factors between different urban element data through it to form an urban element correlation matrix;

[0009] Extract the characteristics of the urban element correlation matrix, screen out urban element combinations, and use the causal inference method to calculate the weight values of different urban element combinations to obtain a decision-making impact factor weight matrix;

[0010] According to the decision-making impact factor weight matrix, perform site selection optimization calculations on the target planning area, and calculate the comprehensive fitness scores of each alternative area;

[0011] According to the comprehensive fitness scores, sort each alternative area, select the alternative area with the highest comprehensive fitness score as the target area, and generate a visual urban planning decision report, including the geographical location of the target area, the composition of the comprehensive fitness score, the results of the urban element correlation analysis, and the site selection optimization calculation process.

[0012] Preferably, the step of establishing a correlation analysis model between urban elements according to the structured data set and calculating the interaction impact factors between different urban element data through it to form an urban element correlation matrix includes:

[0013] According to the structured data set, extract urban element data to obtain basic element data;

[0014] Conduct data feature analysis on the basic element data, calculate the distribution rules of each urban element in different regions to obtain element feature data;

[0015] According to the element feature data, calculate the correlation relationship between urban elements to obtain element correlation data;

[0016] According to the element correlation data, calculate the interaction impact factors between different elements to form element interaction data; where,

[0017] ,

[0018] is the interaction impact factor between urban element and urban element ; is the weight coefficient of urban element in state and satisfies , is the number of samples, is the number of spatial positions, is the time step, is the urban element and the urban element the weighted mutual information entropy between them, is the spatial correlation coefficient, is the temporal correlation coefficient, , are adjustment coefficients, satisfying ; where,

[0019] , is the urban element and the urban element at state the joint probability at that point, , are respectively the urban element and the urban element at state the independent probabilities at that point;

[0020] , and are respectively the values of the urban element and the urban element at the spatial position ; and are respectively the means of the urban element and the urban element over all spatial positions;

[0021] , and are respectively the values of the urban element and the urban element at time ; and are respectively the means of the urban element and the urban element over all time points;

[0022] Construct an urban element association matrix based on the element interaction data.

[0023] Preferably, perform feature extraction on the urban element association matrix, screen out urban element combinations, and use a causal inference method to calculate the weight values of different urban element combinations to obtain a decision influence factor weight matrix, including:

[0024] Screen the data of the urban element correlation matrix, remove the urban elements with low correlation, and obtain the screened element data;

[0025] Conduct causal inference analysis on the screened element data, calculate the causal relationships between urban elements, and obtain the causal analysis data;

[0026] According to the causal analysis data, calculate the influence weights of different urban element combinations, and obtain the influence weight data;

[0027] Construct a decision influence factor weight matrix based on the influence weight data.

[0028] Preferably, the calculation formula for the influence weight of the urban element combination is:

[0029] ,

[0030] is the influence weight of the urban element combination, is the urban element at state the probability that the target variable occurs, satisfying = 1, is the urban element at state the expected value of the target variable , takes or , , is the urban element different states, is the urban element weight coefficient, satisfying 0.

[0031] Preferably, according to the decision influence factor weight matrix, conduct site selection optimization calculation for the target planning area, and calculate the comprehensive fitness scores of each alternative area, including:

[0032] Extract the data of the target planning area according to the decision influence factor weight matrix to obtain the regional element data;

[0033] Evaluate the regional element data, calculate the basic fitness scores of each alternative area, and obtain the preliminary score data;

[0034] According to the preliminary score data, analyze the competition relationships between alternative areas, calculate the competition influence factors, and obtain the competition correction data;

[0035] Based on the accessibility of key resources to the target planning area, calculate the resource accessibility correction factor to obtain the resource correction data;

[0036] Calculate the environmental constraint factor based on the environmental constraint factors in the target planning area to obtain environmental correction data:

[0037] Calculate the comprehensive fitness score of the alternative areas according to the preliminary score data, competition correction data, resource correction data, and environmental correction data to obtain the comprehensive fitness score data.

[0038] Preferably, the calculation formula for the comprehensive fitness score is:

[0039] ,

[0040] is the comprehensive fitness score of the alternative area , is the basic fitness score of the alternative area , is the competition influence factor between the alternative area and the competition area , is the weight coefficient of the competition area , satisfying , is the weight coefficient of the resource center , satisfying , is the Euclidean distance from the central position of the alternative area to the resource center , is the weight coefficient of the environmental factor , satisfying , is the weight coefficient of the environmental factor at the data sample for the influence value of the alternative area , is the number of competition influence factors, is the number of resource centers, is the number of environmental factors, is the number of data samples.

[0041] Preferably, according to the comprehensive fitness score, sort each alternative area, select the alternative area with the highest comprehensive fitness score as the target area, and generate a visual urban planning decision report, including:

[0042] Extract the alternative area with the highest score according to the comprehensive fitness score data to obtain the target area data;

[0043] Conduct a feature analysis on the target area data and extract the key urban element information to obtain the area feature data;

[0044] Analyze the advantages and disadvantages of the target area in different urban element dimensions based on the regional characteristic data to obtain regional evaluation data;

[0045] Generate visual planning decision data according to the regional evaluation data in combination with the urban element correlation matrix;

[0046] Visualize the visual planning decision data and output it to the urban planning management system.

[0047] In a second aspect, a visual intelligent big data urban planning decision support management system, the system includes:

[0048] A data acquisition and processing module, configured to obtain multi-dimensional data related to urban planning, including population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data; preprocess the multi-dimensional data, including format standardization, missing value filling, and data deduplication, to obtain a structured data set;

[0049] An urban element correlation module, configured to establish a correlation analysis model between urban elements according to the structured data set, and calculate the interaction influence factors between different urban element data through it to form an urban element correlation matrix;

[0050] An urban element analysis module, configured to extract features from the urban element correlation matrix, screen out urban element combinations, and calculate the weight values of different urban element combinations using a causal inference method to obtain a decision influence factor weight matrix;

[0051] An urban element analysis module, configured to perform site selection optimization calculation on the target planning area according to the decision influence factor weight matrix, and calculate the comprehensive fitness scores of each alternative area;

[0052] A decision report generation module, configured to rank each alternative area according to the comprehensive fitness score, select the alternative area with the highest comprehensive fitness score as the target area, and generate a visual urban planning decision report, including the geographical location of the target area, the composition of the comprehensive fitness score, the results of the urban element correlation analysis, and the site selection optimization calculation process.

[0053] The above solution of the present invention has at least the following beneficial effects:

[0054] In the context of the extensive application of big data technology in urban planning and decision-making management, although existing technologies can collect, store, analyze, and visually display information such as urban traffic, environment, and population distribution, there are still certain limitations in mining data correlations, resulting in the failure to fully reflect the interactive effects between different urban elements. For example, in the process of smart community planning or commercial complex site selection, existing methods often independently analyze population density, commercial facility distribution, and traffic flow, without comprehensively considering the causal relationships and interactions among these factors, which affects the accuracy of planning decisions. This method addresses this issue by proposing a visual intelligent big data urban planning decision support management method. By establishing a correlation analysis model between urban elements and calculating the interactive influence factors between different urban element data, an urban element correlation matrix is formed, thus making up for the deficiencies of existing technologies in element correlation analysis.

[0055] First, in the data processing stage, this method constructs a multi-dimensional data input source by obtaining population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data to ensure the comprehensiveness and accuracy of the data. Compared with existing technologies that rely on single or a few key indicators for decision-making, this method uses a richer set of urban element data, enabling the planning scheme to fully consider various influencing factors in urban development. In addition, through data preprocessing operations such as format standardization, missing value filling, and data deduplication, the data quality is ensured, the impact of data redundancy on decision-making calculations is reduced, and the reliability of data analysis is improved.

[0056] Second, this method breaks through the limitations of existing technologies that only conduct single-factor analysis by establishing a correlation analysis model between urban elements. In existing methods, commercial facility site selection may mainly be based on population density and consumption ability for decision-making, while ignoring the impact of key factors such as traffic flow, parking lot capacity, and public transportation accessibility on the activity of the business district, resulting in insufficient business vitality after site selection. This method quantifies and visualizes the interaction relationships between different urban elements by calculating the interactive influence factors between urban element data and constructing an urban element correlation matrix. For example, in a certain planning area, there may be a positive correlation between population density and commercial facility distribution, while traffic flow may have a non-linear impact on commercial attractiveness, and these interactions can be accurately reflected in the urban element correlation matrix, making urban planning more accurate and efficient.

[0057] In addition, this method further enhances the intelligence level of decision-making optimization. By extracting features from the urban element correlation matrix, screening out combinations of highly correlated urban elements, and using causal inference methods to calculate the weight values of different urban element combinations, it can accurately identify the relative importance of different factors in planning decisions. In contrast, existing technologies mainly rely on static data analysis and fail to deeply explore the causal relationships between data, resulting in a low adaptability of planning schemes. This method calculates the influence weights of urban element combinations through causal inference, can dynamically adjust the priorities of different elements, and thus ensure that the planning scheme better meets the actual needs. For example, in different types of urban planning, commercial site selection may be more dependent on population density and consumption capacity, while transportation hub planning may be more dependent on traffic flow and public transportation accessibility. This method can automatically adjust the weight parameters of the decision-making model according to specific planning objectives, making the final site selection scheme more targeted.

[0058] Finally, this method also conducts a comprehensive fitness score for multiple alternative areas in the target planning area through site selection optimization calculation, thereby improving the accuracy of site selection decisions. In existing technologies, site selection optimization usually only ranks based on certain static indicators, ignoring the competitive relationships between different regions and the impact of resource distribution. This method corrects the basic fitness score by calculating the competition impact factor, resource accessibility correction factor, and environmental restriction factor of the alternative area, so that the final comprehensive fitness score can more accurately reflect the true suitability of the alternative area. For example, in the site selection of a commercial complex, the population density in a certain area may be high, but if there are already multiple competing commercial projects in the surrounding area and the market competition pressure is large, the fitness score may be negatively affected. This method can incorporate these competition factors into the site selection evaluation scope through the calculation of the competition impact factor, making the final decision more scientific and reasonable.

[0059] In summary, this method effectively makes up for the deficiencies of existing technologies in aspects such as insufficient mining of urban element correlations, weak causal relationship recognition ability, and inaccurate site selection optimization by establishing an urban element correlation analysis model, calculating interaction impact factors, using causal inference methods to calculate the weight matrix, and optimizing the site selection scheme based on multi-factor analysis. This method not only improves the scientificity and rationality of urban planning decisions but also enhances the urban managers' ability to understand and utilize planning data through visualizing urban planning decision reports, thus contributing to the efficient development of smart cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flowchart of a visual intelligent big data urban planning decision support management method provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully communicated to those skilled in the art.

[0062] As Figure 1 shown, an embodiment of the present invention provides a visualization intelligent big data urban planning decision support management method, and the method includes:

[0063] Obtain multi-dimensional data related to urban planning, including population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data; preprocess the multi-dimensional data, including format standardization, missing value filling, and data deduplication, to obtain a structured data set;

[0064] According to the structured data set, establish a correlation analysis model between urban elements, and calculate the interaction impact factors between different urban element data through it to form an urban element correlation matrix;

[0065] Extract features from the urban element correlation matrix, screen out urban element combinations, and use the causal inference method to calculate the weight values of different urban element combinations to obtain a decision impact factor weight matrix;

[0066] According to the decision impact factor weight matrix, perform site selection optimization calculation on the target planning area, and calculate the comprehensive fitness scores of each alternative area;

[0067] According to the comprehensive fitness scores, rank each alternative area, select the alternative area with the highest comprehensive fitness score as the target area, and generate a visualization urban planning decision report, including the geographical location of the target area, the composition of the comprehensive fitness score, the analysis results of urban element correlation, and the site selection optimization calculation process.

[0068] In the embodiment of the present invention, this method can effectively improve the scientificity and feasibility of urban planning, and improve the accuracy of site selection optimization based on multi-dimensional data analysis. By obtaining multi-dimensional data related to urban planning, including population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data, it is possible to fully grasp the distribution of key elements within the urban area and ensure the comprehensiveness and accuracy of the data. These data cover important factors affecting the layout of urban functional areas and provide reliable data support for subsequent planning decisions. During the data acquisition process, the data can be updated in real time through various channels such as urban management systems, remote sensing data, and traffic monitoring systems to ensure the timeliness and accuracy of the data.

[0069] The process of data preprocessing can further improve data quality and ensure the reliability of model analysis. Standardize the formats of multi-dimensional data to make data from different sources have a consistent structure, avoiding information loss or calculation errors caused by mismatched data formats. Meanwhile, for the operation of filling missing values, methods such as mean filling, interpolation, or prediction based on historical data can be adopted to ensure data integrity and improve the calculation accuracy of the model. In addition, through data deduplication, the interference of duplicate data on the analysis results can be effectively avoided, ensuring the authenticity and reliability of data statistics. After these processes, the obtained structured dataset has a high-quality calculation basis, providing accurate data input for subsequent urban planning analysis.

[0070] Based on the structured dataset, establish an analysis model for the correlation between urban elements, enabling urban planning to no longer be limited to the independent analysis of a single element, but rather to consider the interaction and influence between different elements. There may be complex interaction relationships between urban element data, such as the attracting effect of population density on commercial facilities, the impact of traffic flow on parking demand, etc. By constructing an urban element correlation matrix, these interaction relationships can be quantitatively described, enabling a scientific assessment of the correlation degree of each element during the planning process. In the process of calculating the interaction influence factor, statistical analysis methods, machine learning models, or causal inference methods can be adopted to reveal the deep-seated correlations between different urban elements, thereby more accurately predicting the future change trends of urban elements.

[0071] The construction of the decision-making influence factor weight matrix further improves the rationality of site selection optimization. After screening out combinations of urban elements with strong correlations, calculate the weight values of different combinations of urban elements through causal inference methods, which can clarify the influence degree of each element on the final site selection decision. For example, during the process of selecting a commercial area site, population density and traffic flow may be the main decision-making factors, while in the planning of industrial areas, land availability and infrastructure construction may carry higher weights. By calculating the weight matrix, the importance of each element can be adjusted according to different planning scenarios, making the planning scheme more in line with actual needs.

[0072] Finally, through this method, location optimization calculations are carried out for the target planning area, which can comprehensively evaluate the optional areas in the city based on multi-dimensional data, avoiding one-sidedness caused by single-index decision-making. The calculation of the comprehensive fitness score takes into account the interaction of multiple urban elements, making the finally selected area more reasonable in the overall planning. By ranking each alternative area, the advantages and disadvantages of different location options can be objectively evaluated, ensuring that the finally selected area has the optimal fitness and reducing resource waste caused by unreasonable planning. At the same time, a visual urban planning decision-making report is generated, enabling planners to intuitively understand the advantages and disadvantages of different location options, thereby improving the transparency and interpretability of decision-making. This method not only enhances the scientific nature of urban planning but also its feasibility in practical applications.

[0073] Among them, the acquisition of multi-dimensional data related to urban planning includes population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data; preprocessing of the multi-dimensional data, including format standardization, missing value filling, and data deduplication, to obtain a structured data set, specifically including:

[0074] The main data types include:

[0075] Population density is an important parameter in urban planning, usually used to measure the population distribution in different areas to assist in decision-making such as commercial site selection and the layout of public service facilities.

[0076] Data source: It can be obtained through census data, mobile signaling data, and socio-economic survey data.

[0077] Data characteristics: Stored in a grid form (such as 100m×100m or 1km×1km), and the unit is usually "persons per square kilometer".

[0078] Application scenarios: It can be used to analyze the residential density, commuting needs, commercial needs, etc. of residents.

[0079] The spatial distribution of commercial facilities directly affects the economic vitality of urban areas and is crucial for commercial site selection and land use planning.

[0080] Data source: It can be obtained through government statistical data, online map APIs such as Amap, Baidu, Google Maps, enterprise registration information, etc.

[0081] Data characteristics: Usually include the geographical coordinates of commercial facilities, categories (such as retail, catering, entertainment), scale (such as business area, number of employees), etc.

[0082] Application scenarios: It can be used to analyze the formation mechanism of business districts, the commercial competition pattern, and the development potential of commercial areas.

[0083] Traffic flow data is used to describe traffic movement in different areas, which can assist in optimizing road planning, public transportation layout, and business district vitality analysis.

[0084] Data sources: Sources include traffic monitoring systems (cameras, radars), GPS devices, mobile phone signaling data, etc.

[0085] Data characteristics: It can be stored in the form of time series, such as "hourly traffic volume", "traffic pressure during peak hours", etc., and can be segmented according to different transportation modes, such as walking, cycling, bus, and private car.

[0086] Application scenarios: It can be used for traffic bottleneck analysis, travel pattern prediction, and assessment of the accessibility of commercial facilities.

[0087] The capacity and distribution of parking lots affect urban traffic mobility and are also of great significance for optimizing the location selection of commercial and residential areas.

[0088] Data sources: Mainly from parking management systems, government planning data, Internet of Things (IoT) sensor monitoring systems, etc.

[0089] Data characteristics: Include parking lot location, total number of parking spaces, utilization rate, charging standard, etc.

[0090] Application scenarios: It can be used to evaluate the carrying capacity of commercial areas, optimize the location selection of parking lots, and reduce traffic congestion problems.

[0091] Public transportation accessibility measures the convenience for residents or visitors in different areas to reach specific locations and is one of the core indicators in urban planning.

[0092] Data sources: Can be obtained through bus companies, subway operators, urban planning management systems, or by using open data such as GTFS data format.

[0093] Data characteristics: Include the locations of bus stops and subway stops, line coverage, departure intervals, transfer times, etc.

[0094] Application scenarios: Used to evaluate the convenience of public transportation, optimize the layout of stops, and enhance the attractiveness of public transportation.

[0095] The data formats of different data sources may be incompatible. For example: Inconsistent coordinate systems: Some data may use the WGS84 coordinate system, while others may use the GCJ-02 coordinate system, and unified conversion is required. Different unit standards: For example, traffic flow may be in "vehicles / hour", while some systems use "vehicles / minute", and conversion to a unified unit is needed. Non-uniform data storage structures: For example, some data is stored in CSV format, while some is stored in JSON or a database, and conversion to a standardized database format is required. During the process of format standardization, the data storage format is usually unified to make all data conform to a unified structure and ensure that data from different sources can be used compatibly. For example, all geographical data can be converted to GeoJSON or Shapefile format for unified management in a GIS system.

[0096] For filling missing values, the following methods are usually adopted: Interpolation method: For time series data such as traffic flow, linear interpolation or moving average methods can be used to fill in the missing values. Inference from neighboring regions: For spatial data such as population density, speculation can be made based on the data of adjacent regions. Machine learning prediction: Based on historical data and relevant variables, a model can be trained to predict the missing values. For example, a regression model can be used to predict the density of commercial facilities in a certain area.

[0097] The deduplication methods include:

[0098] Deduplication based on geographical location: If the coordinates of two data points are very close, such as the error is less than 10 meters and their attributes are basically the same, they can be merged into one data point. Deduplication based on unique identifiers: If the data contains unique IDs, such as enterprise registration numbers, camera numbers, deduplication can be performed based on the IDs. Deduplication based on similarity matching: Use fuzzy matching technology to determine whether records with similar names are the same entity. After format standardization, missing value filling, and deduplication processing, a high-quality structured dataset is finally obtained. This dataset is usually stored in the form of a table or a database and has unified data fields.

[0099] Among them, the alternative areas are used to refer to the candidate areas within or near the boundaries of the target planning area that meet specific site selection conditions and have potential development value. The alternative areas are used for site selection optimization calculations to screen out the optimal target areas;

[0100] The target planning area is used to refer to the overall planning scope set by the urban planning and management department or decision-makers. The target planning area is used to define the geographical boundary of the site selection optimization and ensure that the site selection calculation is only carried out within this scope. The scope of the target planning area can be set according to administrative divisions, functional zoning, economic development needs, or urban expansion plans;

[0101] Divide different geographical units within the target planning area, and use the grid method or the method based on existing administrative divisions for segmentation to form multiple alternative areas. The grid method is used to divide the target planning area into regular geographical units. For example, it can be divided into grid units of 100m×100m or 1km×1km, while the method based on administrative divisions can directly use existing streets, communities, or functional areas as alternative areas to reduce duplicate calculations and geographical errors.

[0102] In a preferred embodiment of the present invention, based on the structured data set, establish an association analysis model between urban elements, and calculate the interaction influence factors between different urban element data through it to form an urban element association matrix, including:

[0103] Extract urban element data from the structured data set to obtain element basic data;

[0104] Conduct data feature analysis on the element basic data, calculate the distribution rules of each urban element in different regions, and obtain element feature data;

[0105] Calculate the association relationship between urban elements based on the element feature data to obtain element correlation data;

[0106] Calculate the interaction influence factors between different elements based on the element correlation data to form element interaction data; where,

[0107] ,

[0108] is the urban element and the urban element the interaction influence factor between them, is the weight coefficient of the urban element in state satisfies , is the number of samples, is the number of spatial positions, is the time step, is the urban element and the urban element the weighted mutual information entropy between them, is the spatial correlation coefficient, is the time correlation coefficient, , are adjustment coefficients, satisfying ; where,

[0109] , is the urban element and the urban element in state The joint probability at and are the independent probabilities of urban element and urban element in state respectively;

[0110] , and are the values of urban element and urban element at spatial location respectively, and are the means of urban element and urban element at all spatial locations;

[0111] , and are the values of urban element and urban element at time respectively, and are the means of urban element and urban element at all time points;

[0112] Construct an urban element association matrix based on the element interaction data.

[0113] In the embodiments of the present invention, during the urban planning process, the interaction between urban elements plays a key role in the planning decision-making. There are complex interaction relationships between different elements. For example, the distribution of commercial facilities is closely related to the population density, the accessibility of public transportation affects the travel mode, and the parking lot capacity affects the traffic flow, etc. In order to more scientifically analyze the associations between these elements, an association analysis model between urban elements is constructed. This model can reveal the statistical relationships and causal influences between urban elements based on a structured data set, making the planning scheme more in line with the actual needs.

[0114] First, before constructing the urban element correlation matrix, it is necessary to extract urban element data from the structured dataset to form the basic element data. This data includes information such as population density, distribution of commercial facilities, and traffic flow in different areas of the city, ensuring the integrity and accuracy of the input data. After extracting the data, it is necessary to conduct data feature analysis to calculate the distribution rules of each urban element in different areas. There may be significant differences in the element characteristics of different areas. For example, the traffic flow in the commercial center area is much higher than that in the residential area, and areas with higher population density are usually accompanied by more commercial facilities. Through data feature analysis, these distribution rules can be more clearly identified, providing data support for subsequent correlation analysis.

[0115] Based on the characteristic data of each urban element, the correlation relationship between urban elements can be calculated to obtain the element correlation data. The correlation between different elements can be calculated through statistical methods (such as Pearson correlation coefficient, Spearman correlation coefficient), or machine learning methods can be used for modeling analysis. For example, in some cases, the distribution of commercial facilities may show a high positive correlation with population density, while the parking lot capacity may show a negative correlation with the accessibility of public transportation. By analyzing these correlations, the interaction between urban elements can be quantified, and it can be clarified which elements have a strong correlation in the urban planning process.

[0116] To further refine the interaction between urban elements, it is necessary to calculate the interaction impact factors between different elements. These impact factors can reflect how the change of one element affects other elements. For example, an increase in traffic flow may lead to an increase in commercial facilities, and the increase in commercial facilities may further affect population movement. When calculating the interaction impact factors, causal inference methods or regression analysis methods based on historical data can be used to construct a more accurate impact model. Finally, through the calculation of these interaction impact factors, element interaction data is formed, enabling urban planning to consider the complex interactions between different elements.

[0117] After completing the calculation of the element interaction data, construct an urban element correlation matrix to systematically present the degree of association between each element. This matrix can not only be used for urban planning analysis but also serve as the basic data for subsequent decision-making optimization. In the planning process, decision-makers can adjust the weights of different urban elements according to this matrix to ensure that the planning scheme can more accurately meet the needs of urban development. Through the establishment of the urban element correlation matrix, the scientific nature and rationality of urban planning are further improved, making the planning scheme more forward-looking and adaptable.

[0118] Among them, calculating the correlation relationship between urban elements based on the element characteristic data to obtain the element correlation data specifically includes:

[0119] Obtain element feature data, where the element feature data includes the spatial distribution data of each urban element in different regions, and the urban elements at least include population density, commercial facility distribution, traffic flow, parking lot capacity, and public transportation accessibility;

[0120] Based on the element feature data, extract the characteristic values of urban elements in each region. The characteristic values include the distribution density, spatial distribution uniformity, change trend, and influence range of the elements to form a regional element characteristic matrix;

[0121] Based on the regional element characteristic matrix, calculate the spatial similarity between different urban elements to obtain spatial correlation data. The spatial correlation data is used to reflect the overlapping degree of the spatial distribution of different urban elements. Among them, if the spatial correlation of two elements is high, it indicates that their distribution trends in the urban area are relatively consistent;

[0122] Based on the regional element characteristic matrix, analyze the change trends of different urban elements in the time dimension to obtain time correlation data. The time correlation data is used to describe the synchronization degree of the changes of different urban elements over time. Among them, if the time correlation of two elements is high, it indicates that their time series change patterns are similar. For example, the peak period of traffic flow and the change of the activity of commercial facilities may have a strong time correlation;

[0123] Based on the spatial correlation data and time correlation data, calculate the comprehensive correlation between different urban elements to form element correlation data. The element correlation data is used to quantify the association degree between different urban elements, so that subsequent urban planning can evaluate the interaction relationship between different elements based on these data to optimize the layout of urban functional areas and resource allocation.

[0124] Among them,

[0125] Comprehensively evaluate the interaction between urban elements by means of weighted summation and The first item is the weighted mutual information entropy , reflecting the correlation between the two elements in the state distribution, and the weight represents the importance of different states. The second and third items respectively introduce the spatial correlation coefficient and the time correlation coefficient to quantify the dependence relationship of the elements in the spatial distribution and time series, and the adjustment coefficients and , satisfying Used to balance the contributions of the spatial and temporal dimensions. Through multi-dimensional fusion, this model can more comprehensively characterize the dynamic interaction mechanism among urban elements and is applicable to multi-factor collaborative analysis in urban planning.

[0126] Among them,

[0127] Based on the concept of mutual information in information theory, it measures the statistical independence of two urban elements and . The larger the ratio of the joint probability to the independent probability , the stronger the correlation between the two elements in the state . Through negative logarithm weighted summation, the larger the final value, the higher the dependence between the two elements. This method can effectively identify the non-linear relationship between elements and provide a quantitative basis for the analysis of element collaboration or conflict in the urban system.

[0128] Among them, Drawing on the form of the Pearson correlation coefficient, it calculates the linear correlation between two elements in terms of spatial position. The numerator is the covariance, and the denominator is the product of the standard deviations, with the result ranging from [-1, 1]. A positive value indicates a co-directional change in the spatial distribution, and a negative value indicates an opposite change. By introducing the spatial mean , and the global mean , , the formula distinguishes the local and global spatial characteristics and is applicable to the analysis of the spatial agglomeration effect or diffusion pattern of urban elements.

[0129] Among them, is similar in structure to the spatial correlation coefficient but focuses on the time dimension. By comparing the fluctuation trends of two elements in the time series, it quantifies their dynamic correlation. The means and represent the reference values within the time window, and the denominator is standardized to ensure the comparability of the results. This method can identify the lag effect or periodic collaborative change between elements and provide support for urban dynamic monitoring and prediction.

[0130] In a preferred embodiment of the present invention, the feature extraction of the urban element correlation matrix, screening of urban element combinations, and calculation of the weight values of different urban element combinations using the causal inference method to obtain the decision influence factor weight matrix include:

[0131] Performing data screening on the urban element correlation matrix to remove low-correlation urban elements and obtaining the screened element data;

[0132] Performing causal inference analysis on the screened element data to calculate the causal relationships between urban elements and obtaining the causal analysis data;

[0133] Calculate the influence weights of different combinations of urban elements based on the causal analysis data to obtain the influence weight data;

[0134] Construct a decision influence factor weight matrix based on the influence weight data.

[0135] In the embodiment of the present invention, in urban planning decision-making, the interaction between urban elements has a crucial impact on site selection optimization. However, the relationship between urban elements is not just a simple correlation, but a complex causal relationship. For example, the increase in commercial facilities may be due to the growth of population density, and in turn, the density of commercial facilities will also affect the population flow pattern. Therefore, in order to more accurately evaluate the actual impact of urban elements, it is necessary to extract the features of the urban element correlation matrix, screen out the element combinations that truly have a decisive impact, and use causal inference methods to calculate the weight values of different combinations of urban elements to form a decision influence factor weight matrix. This process can effectively improve the scientific nature of urban planning, making site selection optimization no longer a simple analysis based on static data, but able to fully consider the interaction between different elements, thus making more accurate planning decisions.

[0136] First, perform data screening on the urban element correlation matrix to remove urban elements with low correlation degrees, which can avoid the interference of irrelevant or weakly related factors on the final decision. In urban planning data, not all elements are equally important for site selection optimization. For example, in the planning of a commercial area, the density of commercial facilities, population density, and traffic flow may be key factors, while the impact of the area of park green space may be relatively small. Therefore, by screening out elements with low correlation degrees, it can be ensured that only the urban elements that have an important impact on the decision are retained, thereby improving the calculation efficiency and accuracy of the model.

[0137] Next, perform causal inference analysis on the screened urban element data to clarify the causal relationship between different elements. Traditional statistical correlation analysis can only show whether two variables have a certain linear or non-linear relationship, but cannot distinguish the causal relationship. For example, there may be a high correlation between population density and commercial facility density, but this does not mean that there is a direct causal connection between the two. Therefore, the introduction of causal inference methods can infer which elements are the key factors that truly affect other elements based on historical data, experimental data, or structured causal models. For example, in the process of causal inference, it can be found that the accessibility of public transportation is a direct factor affecting the distribution of commercial facilities, and the density of commercial facilities further affects the housing prices and population flow in the surrounding area. This in-depth causal analysis enables site selection optimization to be more in line with the actual situation of urban development, rather than simply relying on the co-variation relationship in statistics.

[0138] Based on the results of causal inference analysis, the influence weights of different combinations of urban elements can be calculated to determine the contribution degree of each element in site selection optimization. The influence weights of different elements vary according to specific planning requirements. For example, in residential area planning, the green space rate may occupy a higher weight, while in commercial area site selection, the weights of traffic flow and population density are higher. By calculating the influence weights, it can be ensured that in different site selection scenarios, the selected optimal area meets the planning requirements of specific types, rather than using the same decision criteria across the board.

[0139] Finally, based on the calculated influence weight data, a decision influence factor weight matrix is constructed, making the site selection optimization model more adaptable and intelligent. This matrix can serve as the core data for subsequent optimization calculations, enabling the model to dynamically adjust the element weights when facing different types of urban planning requirements, ensuring that the finally selected area can meet the actual planning needs. In addition, the construction of this matrix can also improve the transparency of the planning scheme, enabling urban planners to clearly understand the influence of different elements on decisions, so as to make decisions more in line with the urban development direction in actual operations.

[0140] In summary, through the feature extraction of urban elements and causal inference analysis, not only can the most influential urban elements be screened out, but also the influence degree of different elements on site selection optimization can be quantified, thus constructing a more accurate and intelligent urban planning decision-making model. This method can effectively improve the scientific nature of planning, expand the analysis of urban elements from simple correlation research to causal inference, provide more accurate data support for urban development, and improve the rationality and feasibility of site selection optimization.

[0141] In a preferred embodiment of the present invention, the calculation formula for the influence weight of the urban element combination is:

[0142] ,

[0143] is the influence weight of the urban element combination, is the urban element at state when the probability of the target variable occurring satisfies = 1, is the urban element at state when the expected value of the target variable , takes or , , is the urban element in different states, is the urban element The weight coefficient satisfies 0;

[0144] , is the total number of data samples, is the value of the target variable in the data sample at; is the urban factor in the data sample at the value;

[0145] , is the urban factor when the value is the target variable the value probability.

[0146] In the embodiments of the present invention, the comprehensive impact of the factor combination on the target variable is calculated through a causal inference framework. The core is the intervention effect , indicating the marginal impact of the change in the state of the urban factor on . Combining the conditional probability and the weight coefficient , the formula combines statistical association and causal contribution to avoid confounding bias.

[0147] Among them, calculates the conditional probability of the occurrence of the target variable when the urban factor is in the state in a data-driven manner. The numerator is the sum of the products of and in all samples, and the denominator is the sum of , which is essentially a variant of weighted average. Its core logic is to use the presence or intensity of as the weight to amplify the contribution of co-occurring with it, so as to reflect the statistical relevance of to .

[0148] Among them, based on the "intervention" concept in causal inference, calculates the expected value of the target variable when the urban factor is forced to be in a specific state . Different from the ordinary conditional expectation, the intervention expectation directly reflects by severing the confounding path between and other variables The causal effect. In the formula represents under the intervention takes the value of probability. This method can effectively eliminate confounding bias and provide theoretical support for policy intervention or factor regulation.

[0149] Among them, in the formula, and the specific meanings of:

[0150] represents the state of a certain urban element after change, such as new addition, improvement, optimization, etc.

[0151] represents the benchmark state of a certain urban element, such as no change, original state, policy not implemented, etc.

[0152] The purpose of influencing the weight calculation is to evaluate the impact of a certain element change on the urban system. Therefore, it is necessary to construct two states of "with change" and "without change" for comparison. By calculating (the state where the element has changed) and (the state where the element has not changed) on a specific urban area, the impact difference can be quantified to measure the contribution of a certain element to urban planning. For example, when evaluating the impact of the construction of a certain subway station on commercial vitality, represents "the subway station has been built", represents "the subway station has not been built yet", and the difference between the two can measure the impact degree of the subway station on the distribution of commercial facilities.

[0153] In a preferred embodiment of the present invention, the location optimization calculation of the target planning area is performed according to the decision influence factor weight matrix, and the comprehensive fitness scores of each alternative area are calculated, including:

[0154] Extract the data of the target planning area according to the decision influence factor weight matrix to obtain regional element data;

[0155] Evaluate the regional element data, calculate the basic fitness scores of each alternative area, and obtain preliminary score data;

[0156] Analyze the competition relationship between alternative areas according to the preliminary score data, calculate the competition influence factor, and obtain competition correction data;

[0157] Based on the accessibility of key resources in the target planning area, calculate the resource accessibility correction factor to obtain resource correction data;

[0158] Based on the environmental constraint factors of the target planning area, calculate the environmental restriction factor to obtain environmental correction data:

[0159] Based on the preliminary scoring data, competition correction data, resource correction data, and environmental correction data, calculate the comprehensive fitness score of the alternative areas to obtain the comprehensive fitness score data.

[0160] In the embodiments of the present invention, in urban planning, reasonable site selection optimization is an important link to improve the scientificity of the urban functional area layout. Site selection optimization based on multi-dimensional data can not only improve land use efficiency, but also promote the reasonable distribution of various urban resources, making the development of each region more in line with the overall urban planning requirements. By analyzing the target planning area in detail and calculating the comprehensive fitness score of each alternative area according to the decision-making influence factor weight matrix, it can provide intuitive and scientific site selection basis for urban decision-makers, ensuring that the finally selected area has the best adaptability.

[0161] First, based on the decision-making influence factor weight matrix, extract the data of the target planning area to construct a comprehensive regional element data set. This data set contains various urban element information within the target planning area, such as population density, traffic flow, distribution of commercial facilities, accessibility of public resources, etc. The extraction of these data enables the analysis of site selection optimization to be based on complete data, ensuring the accuracy of subsequent calculations.

[0162] Evaluating the regional element data and calculating the basic fitness score of each alternative area is an important step in site selection optimization. In this process, the basic adaptability of each alternative area can be calculated based on the weights of each urban element. For example, an area with high population density and high commercial facility density is usually more suitable for commercial use, while an area with low population density and high green space coverage rate may be more suitable for an ecological protection area. By calculating the basic fitness score, candidate areas that meet the planning requirements can be initially screened out, providing a basis for subsequent optimization calculations.

[0163] To further improve the accuracy of site selection optimization, it is necessary to analyze the competition relationship between alternative areas and calculate the competition influence factor. There may be competition relationships between different areas of the city. For example, two adjacent commercial areas may compete for the same flow of people and market share. If there are already multiple similar facilities around a certain area, the competition pressure of this area is relatively large, and its fitness score may be reduced accordingly. By calculating the competition influence factor, areas with advantages or disadvantages in market competition can be identified, thus avoiding planning and construction in areas with excessive competition.

[0164] In addition to the competitive relationship, resource accessibility is also an important factor affecting site selection optimization. Urban planning needs to ensure the reasonable distribution of key resources to improve the overall accessibility of the region. Therefore, it is necessary to calculate the accessibility of alternative areas to key resources to obtain resource correction data. For example, during the process of shopping mall site selection, the accessibility of subway stations, bus hubs, and main roads are important considerations. If an alternative area is far from the main transportation hub, its resource accessibility may be low, thus affecting its comprehensive fitness score. By calculating resource accessibility, it can be ensured that site selection optimization fully considers transportation and infrastructure factors, improving the rationality of site selection.

[0165] In addition, urban planning also needs to fully consider the constraints of environmental factors on the suitability of alternative areas. Different urban areas may be subject to different types of environmental constraints, such as noise pollution, air quality, flood risk, etc. If an alternative area is located in a region with severe air pollution or in a zone vulnerable to natural disasters, its fitness score may be reduced accordingly. By analyzing the environmental impacts on each alternative area, it can be ensured that the site selection plan meets the requirements of sustainable development, thus avoiding adverse effects caused by environmental problems.

[0166] Finally, by comprehensively calculating the basic fitness score, competition correction data, resource correction data, and environmental correction data, the comprehensive fitness score of alternative areas can be obtained, and each alternative area can be ranked based on the scoring results. The area with the highest score will be used as the optimal site selection area, providing the final decision-making basis for urban planning. This method can ensure the scientificity and rationality of site selection optimization, making urban planning decisions more intelligent and data-driven, and improving the utilization efficiency of land resources in practical applications. At the same time, it optimizes the urban functional layout and improves the overall urban operation efficiency.

[0167] In a preferred embodiment of the present invention, the calculation formula for the comprehensive fitness score is:

[0168] ,

[0169] is the comprehensive fitness score of alternative area , is the basic fitness score of alternative area , is the competition impact factor between alternative area and competition area , is the weight coefficient of competition area , satisfying , is the weight coefficient of resource center , satisfying , Is the central position of the alternative area To the resource center The Euclidean distance of Is the environmental factor The weight coefficient of, satisfying , Is the environmental factor At the data sample The influence value of the alternative area , such as PM2.5 concentration, noise level, etc., Is the number of competitive influence factors, Is the number of resource centers, Is the number of environmental factors, Is the number of data samples;

[0170] , Is the weight coefficient of the urban element , Is the alternative area The value of the urban element within , Is the number of urban elements;

[0171] , Is the competitive area At the data sample The competition intensity with the alternative area , which can represent merchant density, market share, etc., Is the competitive area In the alternative area The average competition intensity of all data samples within.

[0172] In the embodiment of the present invention, the scoring formula evaluates the fitness of the alternative area from four dimensions: basic, competitive, resource, and environment . The basic score Is the linear weighted sum of urban elements; the competition term Measures the competition intensity between regions through variance; the resource term reflects the resource accessibility through the reciprocal of the distance; the environment term quantifies negative factors such as pollution through the mean. The weight coefficients satisfy normalization to ensure the flexibility of the model. This formula can support decision-making scenarios such as urban site selection and functional zoning, and balance the interests of multiple parties.

[0173] Among them, Is the basic part of the comprehensive score, directly performing weighted summation on the urban element . The weight Reflects the importance of each element, such as traffic convenience, population density, etc. Its logic is simple and intuitive, facilitating quick calculation.

[0174] Among them, Calculate the competition area through variance For the alternative areas of the competition intensity. The larger the variance, the more intense the fluctuation of the competition intensity and the higher the uncertainty between regions. Combining weights , the model can dynamically adjust the impact of competition on the comprehensive score and is applicable to risk assessment in commercial site selection or market analysis.

[0175] Among them, the competition area refers to the area with similar functions or market competition relationships with the alternative area. They may be inside or outside the target planning area, but they will all affect the market performance of the alternative area. For example, the competition area can be existing shopping malls, office buildings, commercial streets, office areas, etc., which may compete with the alternative area for the same target customers.

[0176] In a preferred embodiment of the present invention, according to the comprehensive fitness score, each alternative area is sorted, the alternative area with the highest comprehensive fitness score is selected as the target area, and a visual urban planning decision report is generated, including:

[0177] According to the comprehensive fitness score data, extract the alternative area with the highest score to obtain the target area data;

[0178] Conduct feature analysis on the target area data, extract key urban element information, and obtain regional feature data;

[0179] According to the regional feature data, analyze the advantages and disadvantages of the target area in different urban element dimensions to obtain regional evaluation data;

[0180] According to the regional evaluation data, combined with the urban element correlation matrix, generate visual planning decision data;

[0181] Visualize the visual planning decision data and output it to the urban planning management system.

[0182] In the embodiment of the present invention, in the urban planning decision-making process, the calculation of the comprehensive fitness score provides a scientific quantitative basis for site selection optimization. However, simply calculating the fitness score is not sufficient to directly be used for decision-making. It is also necessary to further analyze the score results in order to clarify the specific advantages and disadvantages of the alternative areas, so as to make a reasonable final site selection decision. By sorting the comprehensive fitness scores of each alternative area, the suitability of different areas can be intuitively compared to ensure that the optimal area is finally selected as the target area. In addition, in order to improve the transparency and interpretability of the planning decision-making, it is necessary to generate a visual urban planning decision report so that planners can more intuitively understand the basis and results of the site selection optimization.

[0183] Based on the comprehensive fitness score data, extracting the alternative area with the highest score can ensure that the final site selection plan conforms to the optimal result of the comprehensive evaluation of multi-dimensional data. During the calculation process of the comprehensive fitness score, multiple urban elements and their interactions have been considered, such as population density, distribution of commercial facilities, traffic flow, resource accessibility, environmental impact, etc. Therefore, the alternative area with the highest score usually means that it has relative advantages in the evaluation dimensions of multiple key elements. This process effectively avoids the uncertainty of subjective decision-making, making the site selection optimization more scientific and data-driven. In the specific operation process, all alternative areas can be sorted according to the fitness score, and the area with the highest fitness score can be selected as the final target area. This method can ensure the rationality of the site selection optimization and improve the accuracy of urban planning at the same time.

[0184] Conducting feature analysis on the target area data and extracting key urban element information can further improve the accuracy of site selection optimization. Within the selected target area, each urban element may have different distribution characteristics. For example, a certain area may have a high population density but a low density of commercial facilities, while another area may have the opposite characteristics. Therefore, after finally determining the target area, it is necessary to further analyze the distribution of urban elements within this area to evaluate its future development potential. Through data analysis, the key factors affecting the comprehensive fitness score of the target area can be identified, and a reasonable planning scheme can be formulated based on these factors. For example, during the commercial site selection process, if the traffic flow in the target area is high but the parking lot capacity is small, then it can be considered to increase parking facilities to optimize the commercial layout.

[0185] Analyzing the advantages and disadvantages of the target area in different urban element dimensions based on the regional characteristic data can make the planning scheme more targeted. Different regions may show different characteristics in different urban element dimensions. For example, a certain area may have a high commercial value but may have deficiencies in infrastructure construction. By quantitatively analyzing the advantages and disadvantages of the target area, more accurate information can be provided for urban planners so that corresponding optimization measures can be taken. For example, if the fitness score of the target area is mainly affected by resource accessibility, then in the planning scheme, key consideration can be given to improving the transportation infrastructure in this area to enhance its overall adaptability. On the contrary, if the comprehensive fitness score of the target area is relatively high but there are certain risks in environmental factors, then corresponding ecological optimization strategies can be adopted, such as increasing the green space coverage rate or reducing the entry of highly polluting industries, so as to enhance the sustainable development ability of the region.

[0186] Combined with the urban element correlation matrix, visual planning decision-making data is generated to make the planning results more intuitive and operable. The urban element correlation matrix provides the interaction and influence relationships between different urban elements, enabling planners to more deeply understand the comprehensive impact of each element on the target area. For example, if the population density in the target area is high while the distribution of commercial facilities is relatively sparse, the urban element correlation matrix may show that the commercial development potential in this area is high. Through this analysis, planners can prioritize increasing the layout of commercial facilities in this area in future urban development strategies to meet the needs of population growth. In addition, the visual planning decision-making data can intuitively display the composition of the comprehensive fitness score of the target area, the results of the urban element correlation analysis, and the process of site selection optimization calculation, enabling planners to more clearly understand the impact of different factors on the final decision.

[0187] The visual planning decision-making data is visually processed and output to the urban planning management system, enabling the decision-making results to be efficiently applied in the urban management and planning system. In the process of modern urban planning, data visualization is one of the key technologies to improve planning efficiency and accuracy. By converting urban planning decision-making data into intuitive visual reports, planners can more quickly understand complex data structures and make more accurate decisions. Visual processing can be carried out in various ways, such as map heatmaps, trend analysis charts, 3D urban modeling, etc., to display different characteristics of the target area. For example, the spatial distribution of population density can be shown through a heatmap, or the possible impact of future planning schemes can be shown through 3D modeling. In addition, this data can be integrated into the urban planning management system as an important reference for future urban development planning, thus supporting more long-term urban planning decisions.

[0188] In summary, by sorting the comprehensive fitness scores and extracting the target area, it can be ensured that the final site selection decision conforms to the optimal scheme of multi-dimensional data comprehensive evaluation. Combining urban element feature analysis and visual processing of decision-making data can improve the transparency and accuracy of site selection optimization, making the planning scheme more targeted. At the same time, through the integration of the urban planning management system, the long-term availability of data can be improved, making the urban planning decision-making system more intelligent and data-driven, thus promoting the sustainable development of the city.

[0189] An embodiment of the present invention also provides a visual intelligent big data urban planning decision-making support management system, and the system includes:

[0190] A data acquisition and processing module, configured to obtain multi-dimensional data related to urban planning, including population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data; preprocess the multi-dimensional data, including format standardization, missing value filling, and data deduplication, to obtain a structured data set;

[0191] The urban element association module is used to establish an association analysis model between urban elements based on the structured data set, calculate the interaction influence factors between different urban element data through it, and form an urban element association matrix;

[0192] The urban element analysis module is used to extract the features of the urban element association matrix, screen out the urban element combinations, and calculate the weight values of different urban element combinations by using the causal inference method to obtain the decision influence factor weight matrix;

[0193] The urban element analysis module is used to perform site selection optimization calculation on the target planning area according to the decision influence factor weight matrix, and calculate the comprehensive fitness scores of each alternative area;

[0194] The decision report generation module is used to sort each alternative area according to the comprehensive fitness score, select the alternative area with the highest comprehensive fitness score as the target area, and generate a visual urban planning decision report, including the geographical location of the target area, the composition of the comprehensive fitness score, the results of the urban element association analysis, and the site selection optimization calculation process.

[0195] It should be noted that this system corresponds to the above method, and all implementation methods in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0196] The embodiment of the present invention also provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the method as described above. All implementation methods in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0197] The embodiment of the present invention also provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is made to execute the method as described above. All implementation methods in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0198] The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A visualization intelligent big data urban planning decision support management method, characterized in that, The method includes: Obtain multi-dimensional data related to urban planning, including population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data; preprocess the multi-dimensional data, including format standardization, missing value filling, and data deduplication, to obtain a structured data set; According to the structured data set, establish a correlation analysis model between urban elements, and calculate the interaction influence factors between different urban element data through it to form an urban element correlation matrix; Extract features from the urban element correlation matrix, screen out urban element combinations, and use causal inference methods to calculate the weight values of different urban element combinations to obtain a decision influence factor weight matrix; According to the decision influence factor weight matrix, perform site selection optimization calculations on the target planning area, and calculate the comprehensive fitness scores of each alternative area; According to the comprehensive fitness scores, sort each alternative area, select the alternative area with the highest comprehensive fitness score as the target area, and generate a visual urban planning decision report, including the geographical location of the target area, the composition of the comprehensive fitness score, the results of the correlation analysis of urban elements, and the site selection optimization calculation process; The extracting features from the urban element correlation matrix, screening out urban element combinations, and using causal inference methods to calculate the weight values of different urban element combinations to obtain a decision influence factor weight matrix includes: Perform data screening on the urban element correlation matrix, remove low-correlation urban elements, and obtain screened element data; Perform causal inference analysis on the screened element data, calculate the causal relationships between urban elements, and obtain causal analysis data; According to the causal analysis data, calculate the influence weights of different urban element combinations to obtain influence weight data; According to the influence weight data, construct a decision influence factor weight matrix; The calculation formula for the influence weight of the urban element combination is: ， is the influence weight of the urban element combination, is the urban element at state when the probability of the target variable occurring satisfies = 1, is the urban element at state when the expected value of the target variable is takes or , , is the urban element in different states, is the weight coefficient of the urban element satisfying 0, is the number of urban elements in the urban element combination.

2. The visualized intelligent big data urban planning decision-making support management method according to claim 1, wherein, The establishing a correlation analysis model between urban elements according to the structured data set, and calculating the interaction influence factors between different urban element data through it to form an urban element correlation matrix includes: Extract urban element data according to the structured data set to obtain element basic data; Perform data feature analysis on the element basic data, calculate the distribution rules of each urban element in different regions, and obtain element feature data; According to the element feature data, calculate the correlation relationships between urban elements to obtain element correlation data; According to the element correlation data, calculate the interaction influence factors between different elements to form element interaction data; where ， is the urban element and the urban element the interaction influence factor between them is the weight coefficient of the urban element at the state satisfying , is the number of states is the number of spatial positions is the time step is the urban element and the urban element the weighted mutual information entropy between them is the spatial correlation coefficient is the temporal correlation coefficient , are the adjustment coefficients, satisfying ; among them, , is the urban element and the urban element in the state at the joint probability, , are respectively the urban element and the urban element in the state at the independent probability; , and are the values of urban element and urban element at the spatial location respectively, and are the mean values of urban element and urban element at all spatial locations; , and are the values of urban element and urban element at time respectively, and are the means of urban element and urban element at all time points; According to the element interaction data, construct an urban element correlation matrix.

3. A visualized intelligent big data urban planning decision-making support management method according to claim 1, characterized in that, The performing site selection optimization calculations on the target planning area according to the decision influence factor weight matrix, and calculating the comprehensive fitness scores of each alternative area includes: Extract the data of the target planning area according to the decision influence factor weight matrix to obtain regional element data; Evaluate the regional element data, calculate the basic fitness scores of each alternative area to obtain preliminary score data; According to the preliminary score data, analyze the competition relationships between alternative areas, calculate the competition influence factors to obtain competition correction data; Calculate the resource accessibility of each alternative area based on the accessibility of key resources to the target planning area, and obtain resource correction data; Analyze the degree of restriction of environmental factors on the suitability of alternative areas based on the environmental impacts on each alternative area within the target planning area, and obtain environmental correction data: Calculate the comprehensive fitness score of the alternative areas according to the preliminary score data, competition correction data, resource correction data, and environmental correction data, and obtain comprehensive fitness score data.

4. A visual intelligent big data urban planning decision-making support management method according to claim 3, characterized in that, The calculation formula for the comprehensive fitness score is: ， is the comprehensive fitness score of the alternative area , is the basic fitness score of the alternative area , is the alternative area and the competition impact factor between the competition area , is the competition area 's weight coefficient, satisfying , is the weight coefficient of the resource center , satisfying , is the central position of the alternative area to the resource center 's Euclidean distance is the weight coefficient of the environmental factor , satisfying , is the environmental factor at the data sample the impact value on the alternative area , is the number of competition impact factors is the number of resource centers is the number of environmental factors is the number of data samples 5. A visualized intelligent big data urban planning decision support management method according to claim 4, characterized in that, According to the comprehensive fitness score, sort the alternative areas, select the alternative area with the highest comprehensive fitness score as the target area, and generate a visual urban planning decision report, including: Extract the alternative area with the highest score according to the comprehensive fitness score data to obtain target area data; Conduct a feature analysis on the target area data, extract key urban element information, and obtain regional feature data; Analyze the advantages and disadvantages of the target area in different urban element dimensions according to the regional feature data to obtain regional evaluation data; Generate visual planning decision data according to the regional evaluation data in combination with the urban element correlation matrix; Perform visualization processing on the visual planning decision data and output it to the urban planning management system.

6. A visual intelligent big data urban planning decision-making support management system, characterized in that, Applied to the method described in any one of claims 1 to 5, the system includes: A data acquisition and processing module, configured to obtain multi-dimensional data related to urban planning, including population density data, commercial facility distribution data, traffic flow data, parking lot capacity data, and public transportation accessibility data; perform preprocessing on the multi-dimensional data, including format standardization, missing value filling, and data deduplication, to obtain a structured data set; An urban element correlation module, configured to establish a correlation analysis model between urban elements according to the structured data set, and calculate the interaction impact factors between different urban element data through it to form an urban element correlation matrix; An urban element analysis module, configured to extract features from the urban element correlation matrix, screen out urban element combinations, and calculate the weight values of different urban element combinations using a causal inference method to obtain a decision impact factor weight matrix; An urban element analysis module, configured to perform site selection optimization calculation on the target planning area according to the decision impact factor weight matrix, and calculate the comprehensive fitness score of each alternative area; A decision report generation module, configured to sort the alternative areas according to the comprehensive fitness score, select the alternative area with the highest comprehensive fitness score as the target area, and generate a visual urban planning decision report, including the geographical location of the target area, the composition of the comprehensive fitness score, the analysis results of urban element correlation, and the site selection optimization calculation process.

7. A computing device, characterized in that, Including: One or more processors; A storage device for storing one or more programs, which when executed by the one or more processors cause the one or more processors to implement the method described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, A program is stored in the computer-readable storage medium, and when the program is executed by a processor, the method described in any one of claims 1 to 5 is implemented.

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