A geographic information-based intelligent city municipal planning method and system

Abstract

This invention provides a geographic information-based intelligent urban planning method and system, relating to the field of data processing technology. The method includes: acquiring urban spatial data from different data sources, including topography, land use, building distribution, transportation networks, population distribution, and environmental indicators; analyzing the urban spatial data, including spatial overlay analysis, buffer analysis, and network analysis, to identify key issues and potential risks in urban planning, thereby obtaining data analysis results. This invention can comprehensively and accurately analyze urban spatial data, automatically generate optimized urban planning schemes, and update and adjust them in real time to improve urban traffic flow, environmental quality, and the efficiency of public facility utilization.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a smart city municipal planning method and system based on geographic information. Background Technology

[0002] Traditional planning methods rely on limited data sources and simple statistical analysis techniques, making it difficult to comprehensively and accurately integrate and analyze multi-source, multi-scale urban spatial data.

[0003] For example, when planning a transportation network, only basic data such as road layout and traffic flow may be considered, while factors that significantly influence transportation demand, such as topography, land use, and population distribution, are ignored. This lack of data integration and analysis may result in planning schemes that lack scientific rigor and foresight.

[0004] In environmental planning, traditional methods may focus only on direct data such as pollutant emissions and environmental quality monitoring, failing to adequately consider the impact of indirect factors such as climate change and ecological sensitivity on environmental planning. This one-sided data analysis may result in environmental planning schemes that are ill-equipped to meet the challenges of future environmental changes.

[0005] Traditional planning methods often rely on human experience and trial and error to optimize and adjust planning schemes, lacking scientific and efficient optimization methods.

[0006] For example, in road planning schemes, it may be necessary to judge parameters such as road width and intersection layout based on experience, and such judgments are often influenced by personal subjective factors.

[0007] In terms of public facility layout, traditional methods may only consider basic factors such as the service radius and population coverage of facilities, while failing to fully consider the synergistic effects between facilities, the actual needs of residents, and future urban development trends. This rigid planning approach may lead to an irrational layout of public facilities, making it difficult to meet the increasingly diverse needs of residents. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a smart urban municipal planning method and system based on geographic information, which realizes in-depth mining and analysis of urban spatial data and can more accurately identify key issues and potential risks in urban municipal planning.

[0009] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0010] Firstly, a smart urban planning method based on geographic information, the method comprising:

[0011] Acquire urban spatial data from different data sources, including topography, land use, building distribution, transportation network, population distribution, and environmental indicators.

[0012] The analysis of urban spatial data includes spatial overlay analysis, buffer analysis, and network analysis to identify key issues and potential risks in urban planning and obtain data analysis results.

[0013] Based on the data analysis results, the differential evolution algorithm is used to simulate, predict and optimize different urban planning schemes. Through the differences between populations and evolutionary strategies, multiple planning schemes are iteratively optimized to automatically generate the final urban planning scheme that includes road planning, public facility layout, environmental protection strategies and disaster risk assessment.

[0014] The final urban planning scheme is evaluated to obtain the evaluation results of the final urban planning scheme in terms of urban traffic flow, environmental quality, and the utilization efficiency of public facilities.

[0015] Establish a dynamic update process for municipal planning outcomes, and update and adjust the planning outcomes in real time based on newly acquired data and feedback on the effects of the implementation of urban planning schemes.

[0016] Furthermore, urban spatial data is analyzed, including spatial overlay analysis, buffer analysis, and network analysis, to identify key issues and potential risks in urban planning, yielding data analysis results, including:

[0017] Acquire urban spatial data, including land use layers, transportation network layers, location data of urban facilities such as schools and hospitals, and location data of rivers and industrial areas. Overlay the data from different layers to identify the relationship between land use and transportation, and obtain the overlay analysis results.

[0018] Based on the overlay analysis results, specific patterns or anomalous areas in urban space are identified, including the overlap of high-density population areas and traffic congestion areas, or the close proximity of industrial areas and residential areas. Buffer zones are set for specific patterns or anomalous areas, including schools, hospitals, rivers, and industrial areas.

[0019] Within the buffer zone, environmental characteristics, including air quality, noise levels, population distribution, and traffic conditions, are analyzed, and the impact of facilities or areas on the surrounding environment is assessed. Potential risks within the buffer zone are identified, including the impact of noise from schools on surrounding residents, or the potential threat of pollutant emissions from industrial areas to the surrounding environment, in order to obtain buffer zone analysis results.

[0020] A city transportation network model is constructed, including road networks, rail transit lines, and public transportation lines. Traffic flow data, road capacity information, and public transportation operation data are used to assess the accessibility, connectivity, and congestion of the transportation network, and to identify bottleneck nodes, congestion points, and areas with insufficient public transportation services in the transportation network to obtain network analysis results.

[0021] Based on the results of spatial overlay analysis, buffer zone analysis, and network analysis, key issues and potential risks in urban planning were identified. Key issues include irrational land use, traffic congestion, insufficient public facilities, and environmental pollution; potential risks include disaster risks and social conflict risks.

[0022] Furthermore, an urban transportation network model is constructed, including road networks, rail transit lines, and public transportation lines. Traffic flow data, road capacity information, and public transportation operation data are used to assess the accessibility, connectivity, and congestion levels of the transportation network. Bottleneck nodes, congestion points, and areas with insufficient public transportation services are identified to obtain network analysis results, including:

[0023] Obtain information on urban roads, including road type, length, width, number of lanes, and speed limit attributes, and obtain route maps, station information, operating hours, and train frequency for subway and light rail transit.

[0024] The data of roads, rail transit and public transportation lines are integrated into a network topology, where nodes represent intersections or stations and edges represent road segments or track segments.

[0025] Assign corresponding attributes to nodes and edges in the network topology, including road type, length, capacity, speed limit, rail transit line type, and public transportation schedule, and calculate the path between any two points in the network topology to evaluate network connectivity and identify isolated nodes or subnetworks.

[0026] Using the assigned traffic flow data and road capacity information attributes, the congestion level of each road or rail segment is calculated to obtain a congestion index, and bottleneck nodes and congestion points are identified.

[0027] Based on public transportation operation data, the coverage, frequency, and occupancy rate of public transportation services are assessed to identify areas with insufficient service. Based on the congestion index, network analysis results are obtained, including the accessibility, connectivity, congestion level, and areas with insufficient public transportation services.

[0028] Furthermore, the formula for calculating the congestion index is: ;

[0029] in, Indicates the congestion index; Indicates actual traffic flow; Indicates road capacity; Indicates the basic congestion coefficient; Indicates the speed deviation coefficient; This indicates the actual speed at which the vehicle is traveling on the road; Indicates the preset speed; This indicates the actual vehicle density on the road; This indicates the desired vehicle density on the road. Indicates the time deviation coefficient; This indicates the actual time required for a vehicle to travel across the road. Indicates the expected time for a vehicle to travel along the road; Indicates the weather impact coefficient; This indicates the weather impact index.

[0030] Furthermore, based on the data analysis results, the differential evolution algorithm is used to simulate, predict, and optimize different urban planning schemes. Through differences among populations and evolutionary strategies, multiple planning schemes are iteratively optimized to automatically generate a final urban planning scheme that includes road planning, public facility layout, environmental protection strategies, and disaster risk assessment.

[0031] The parameters of the differential evolution algorithm are set, including population size, crossover probability and mutation factor, and the objective function and constraints of the municipal planning problem are determined based on the data analysis results.

[0032] A set of initial municipal planning schemes is randomly generated. Each scheme is a vector containing information on road planning, public facility layout, environmental protection strategies, and disaster risk assessment.

[0033] The objective function is used to simulate and predict each initial municipal planning scheme, and the impact on urban traffic flow, environmental quality and public facility utilization efficiency is calculated to obtain the objective function value.

[0034] Repeated mutation, crossover, and selection operations are performed until a preset number of iterations are reached. The final urban planning scheme, i.e., the final solution, is then determined from the final population based on the objective function value.

[0035] Furthermore, the formula for calculating the objective function is as follows: ;

[0036] in, Representation scheme The objective function value; , , Indicates the weighting coefficient; Representation scheme Average traffic speed under these conditions; Representation scheme The congestion index below; Representation scheme Average traffic delay time; Representation scheme The air quality index below; The baseline value representing the air quality index; Indicates the number of monitoring points; Indicates the first Noise intensity measured at each monitoring point; This represents the maximum value of the noise level; Representation scheme The water quality index below; A baseline value indicating water quality status; Representation scheme The frequency of use of public facilities; Indicates the total capacity of public facilities; Representation scheme The service coverage of public facilities; Indicates the total area of ​​the study region; Representation and Scheme The relevant utility value.

[0037] Furthermore, the final urban planning scheme is evaluated to obtain assessment results regarding urban traffic flow, environmental quality, and the efficiency of public facility utilization, including:

[0038] Obtain the final municipal planning scheme, which includes information on road planning, public facility layout, environmental protection strategies, and disaster risk assessment. Extract key features from the final municipal planning scheme, including road network structure, traffic signal settings, location and capacity of public facilities, green coverage, and environmental protection measures.

[0039] Key features are input into a pre-set evaluation model, and the key features are analyzed and calculated according to the learning patterns and rules to generate prediction results, including indicators of urban traffic flow, environmental quality and public facility utilization efficiency.

[0040] Furthermore, a dynamic updating process for municipal planning outcomes will be established. Based on newly acquired data and feedback on the effects of the planning scheme implementation, the planning outcomes will be updated and adjusted in real time, including:

[0041] Receive newly acquired data and feedback on the effects of urban planning schemes after implementation, and determine the triggering conditions for dynamic updates, including regular updates and updates triggered by data changes;

[0042] The newly acquired data and feedback on the effects of the planning scheme are integrated and analyzed to evaluate the implementation effect of the planning scheme and determine whether to adjust or update the planning scheme, so as to obtain the processing results of the new data and feedback.

[0043] Based on the processing results of the new data and feedback, determine whether the triggering conditions for dynamic updates are met; if so, initiate the update process to update and adjust the municipal planning scheme in real time.

[0044] Secondly, a geographic information-based intelligent urban planning system includes:

[0045] The data acquisition module is used to acquire urban spatial data from different data sources, including topographic data, land use data, building distribution data, transportation network data, population distribution data, and environmental indicator data.

[0046] The data analysis module is used to analyze the acquired urban spatial data. The analysis methods include spatial overlay analysis, buffer analysis, and network analysis to identify key issues and potential risks in urban planning and obtain data analysis results.

[0047] The planning optimization module is used to simulate, predict, and optimize different municipal planning schemes based on data analysis results using differential evolution algorithms. Through the differences between populations and evolutionary strategies, it iteratively optimizes multiple planning schemes and automatically generates municipal planning schemes that include multiple aspects such as road planning, public facility layout, environmental protection strategies, and disaster risk assessment.

[0048] The scheme evaluation module is used to evaluate municipal planning schemes. The evaluation content includes urban traffic flow, environmental quality, and the utilization efficiency of public facilities, in order to obtain evaluation results.

[0049] The dynamic update module is used to establish a dynamic update process for municipal planning results. Based on newly acquired data and feedback on the effects of the implementation of urban planning schemes, the planning results are updated and adjusted in real time.

[0050] Thirdly, a computing device includes:

[0051] One or more processors;

[0052] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

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

[0054] By integrating urban spatial data from various sources, including topography, land use, building distribution, transportation networks, population distribution, and environmental indicators, this method provides comprehensive and accurate foundational data support. This helps to gain a deeper understanding of the current state of the city and provides a solid foundation for urban planning.

[0055] By employing techniques such as spatial overlay analysis, buffer analysis, and network analysis, this method can effectively identify key issues and potential risks in urban planning. This helps planners anticipate potential challenges and take corresponding preventative measures. Through differential evolution algorithms to simulate, predict, and optimize multiple urban planning schemes, this method can automatically generate final urban planning solutions encompassing road planning, public facility layout, environmental protection strategies, and disaster risk assessment. This automated generation method not only improves planning efficiency but also ensures the scientific validity and feasibility of the planning scheme.

[0056] A comprehensive evaluation of the final urban planning scheme was conducted, including aspects such as urban traffic flow, environmental quality, and the efficiency of public facility utilization. This helps planners understand the implementation effects of the plan and make timely adjustments and optimizations. Simultaneously, a dynamic update process for urban planning outcomes was established, ensuring that planning results can be updated and adjusted in real time as the city develops and data changes. This provides city managers with scientific and accurate decision support, helping them make more informed urban planning decisions. Furthermore, the introduction of intelligent technologies and algorithms has improved the intelligence level of urban planning, promoting the development of smart cities. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating an intelligent urban municipal planning method based on geographic information, provided by an embodiment of the present invention.

[0058] Figure 2 This is a schematic diagram of a geographic information-based intelligent urban planning system provided by an embodiment of the present invention. Detailed Implementation

[0059] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0060] like Figure 1 As shown, an embodiment of the present invention proposes a smart city municipal planning method based on geographic information, the method comprising the following steps:

[0061] Step 11: Obtain urban spatial data from different data sources, including topography, land use, building distribution, transportation network, population distribution, and environmental indicators.

[0062] Step 12: Analyze the urban spatial data, including spatial overlay analysis, buffer analysis, and network analysis, to identify key issues and potential risks in urban planning and obtain data analysis results.

[0063] Step 13: Based on the data analysis results, use the differential evolution algorithm to simulate, predict and optimize different municipal planning schemes. Through the differences between populations and evolutionary strategies, iteratively optimize multiple planning schemes and automatically generate the final urban municipal planning scheme that includes road planning, public facility layout, environmental protection strategies and disaster risk assessment.

[0064] Step 14: Evaluate the final urban planning scheme to obtain the evaluation results of the final urban planning scheme in terms of urban traffic flow, environmental quality, and public facility utilization efficiency.

[0065] Step 15: Establish a dynamic update process for municipal planning results. Based on newly acquired data and feedback on the effects of implementing urban planning schemes, update and adjust the planning results in real time.

[0066] In this embodiment of the invention, by integrating data from different data sources, including topography, land use, building distribution, transportation networks, population distribution, and environmental indicators, comprehensive and diverse basic information is provided for urban planning, ensuring the accuracy and reliability of the planning. The data from different sources complement each other, overcoming the limitations of a single data source and making the description of urban spatial data more complete and detailed. Through spatial overlay analysis, buffer analysis, and network analysis, key issues and potential risks in urban planning can be accurately identified, providing a scientific basis for planning decisions. The data analysis results reveal urban spatial characteristics and problems, enabling urban planning to more effectively address the actual needs and challenges of urban development.

[0067] Differential evolutionary algorithms iteratively optimize multiple planning schemes by leveraging differences and evolutionary strategies among populations, significantly improving the efficiency and quality of optimization. The automatically generated final urban planning schemes, encompassing road planning, public facility layout, environmental protection strategies, and disaster risk assessment, are more scientific, rational, and feasible. By evaluating indicators such as urban traffic flow, environmental quality, and public facility utilization efficiency, the planning effects can be quantified, providing an intuitive reference for planning decisions.

[0068] The evaluation results reflect the effectiveness of the planning scheme in a timely manner, which helps planners to adjust and optimize the scheme in a timely manner and ensure the smooth achievement of the planning objectives.

[0069] By establishing a dynamic update process, the city planning outcomes are ensured to be updated and adjusted in real time as the city develops and data changes, maintaining the timeliness and accuracy of the planning results. This dynamic update process allows city planning to adapt more flexibly to changes and challenges in urban development, improving the adaptability and sustainability of the plans.

[0070] In a preferred embodiment of the present invention, step 11 above, which involves acquiring urban spatial data from different data sources, including topography, land use, building distribution, transportation network, population distribution, and environmental indicator data, may include:

[0071] In this embodiment of the invention, specific objectives of urban planning are clearly defined, such as traffic optimization, land use adjustment, and environmental protection. This will determine the types of data required. Based on the planning objectives, the required data types are listed, including topographic data, land use data, building distribution data, transportation network data, population distribution data, and environmental indicator data. Government agencies (such as urban planning bureaus, environmental protection bureaus, and statistics bureaus) provide authoritative urban spatial data, while commercial companies, research institutions, or open-source projects also provide useful urban spatial data. For certain specific data, such as topographic details or environmental indicators, on-site investigations are required.

[0072] Download the required data from official or third-party data sources, or submit data requests, and conduct on-site data collection using tools such as GPS devices, drones, and ground surveying instruments. Integrate data from different data sources. Build a city spatial database using Geographic Information System (GIS) software or Database Management System (DBMS).

[0073] In a preferred embodiment of the present invention, step 12 above, which analyzes urban spatial data, including spatial overlay analysis, buffer analysis, and network analysis, to identify key issues and potential risks in urban planning and obtain data analysis results, may include:

[0074] Step 121: Obtain urban spatial data, including land use layer, transportation network layer, location data of urban facilities such as schools and hospitals, location data of rivers and industrial areas, and overlay the data of different layers to identify the relationship between land use and transportation in order to obtain the overlay analysis results.

[0075] Step 122: Based on the overlay analysis results, identify specific patterns or abnormal areas in the urban space, including the overlap of high-density population areas and traffic congestion areas, or the close proximity of industrial areas and residential areas, and set buffer zones for specific patterns or abnormal areas, including schools, hospitals, rivers, and industrial areas.

[0076] Step 123: Within the buffer zone, analyze environmental characteristics, including air quality, noise levels, population distribution, and traffic conditions, and assess the impact of facilities or areas on the surrounding environment. Identify potential risks within the buffer zone, including the impact of noise from schools on surrounding residents, or the potential threat of pollutant emissions from industrial areas to the surrounding environment, in order to obtain buffer zone analysis results.

[0077] Step 124: Construct an urban transportation network model, including road network, rail transit lines and public transportation lines. Use traffic flow data, road capacity information and public transportation operation data to evaluate the accessibility, connectivity and congestion of the transportation network, identify bottleneck nodes, congestion points and areas with insufficient public transportation services in the transportation network, and obtain network analysis results.

[0078] Step 125: Based on the results of spatial overlay analysis, buffer zone analysis, and network analysis, identify key issues and potential risks in urban planning. Key issues include irrational land use, traffic congestion, insufficient public facilities, and environmental pollution; potential risks include disaster risks and social conflict risks.

[0079] In this embodiment of the invention, land use layers, transportation network layers, location data of urban facilities (schools, hospitals), and location data of rivers and industrial areas are extracted from an urban spatial database. Using overlay analysis tools in GIS software, the land use layer and the transportation network layer are overlaid. Overlay rules, such as intersection, tangency, or containment, are set to identify the relationship between land use and transportation, generating an overlay analysis result layer that displays the spatial relationship between land use and the transportation network. The overlay analysis result layer is analyzed to identify overlapping areas between high-density population areas and traffic congestion areas, and the proximity of industrial areas and residential areas is recorded.

[0080] Step 122: Based on the overlay analysis results, identify specific patterns in urban space, such as the overlap between high-density population areas and traffic congestion areas. Identify anomalous areas, such as the proximity of industrial areas and residential areas. For specific patterns or anomalous areas, such as schools, hospitals, rivers, and industrial zones, establish reasonable buffer zones.

[0081] Use the buffer tool in GIS software to generate a buffer layer based on the set range.

[0082] Step 123: Within the buffer zone, extract data on air quality, noise levels, population distribution, and traffic conditions. Use analysis tools in GIS software to perform spatial analysis on the extracted data. Assess the impact of facilities or areas on the surrounding environment, such as the impact of noise pollution from schools on nearby residents. Identify potential risks within the buffer zone, such as the potential threat of pollutant emissions from industrial areas to the surrounding environment. Generate a buffer zone analysis result layer to display potential risk areas. Analyze the buffer zone analysis result layer to identify the specific location and type of potential risks.

[0083] Step 124: Using network analysis tools in GIS software, construct an urban transportation network model, including road networks, rail transit lines, and public transportation lines. Import traffic flow data, road capacity information, and public transportation operation data. Assess the accessibility, connectivity, and congestion level of the transportation network, identify bottleneck nodes, congestion points, and areas with insufficient public transportation services, and generate a network analysis result layer to display key issues and areas of the transportation network. Analyze the network analysis result layer to identify specific problems and areas within the transportation network.

[0084] Step 125: Based on the results of spatial overlay analysis, buffer zone analysis, and network analysis, identify key issues in urban planning, such as irrational land use, traffic congestion, insufficient public facilities, and environmental pollution. Identify potential risks, such as disaster risks (e.g., floods, earthquakes) and social conflict risks, and assess and classify these risks by combining historical urban data and expert opinions. Integrate the identified key issues and potential risks into a report, proposing recommendations and solutions for these issues and risks.

[0085] Suppose we are analyzing municipal planning data for a medium-sized city:

[0086] First, we acquired the city's land use layer, transportation network layer, school and hospital location data, and river and industrial area location data. Then, we used GIS software to overlay and analyze these layers, discovering that high-density population areas overlapped with traffic congestion areas, and that industrial areas were adjacent to residential areas.

[0087] Buffer zones were established for these specific patterns or abnormal areas, and air quality, noise levels, population distribution, and traffic conditions were analyzed within these buffer zones. The analysis revealed that noise pollution from schools exceeded the tolerance range of surrounding residents, and that pollutant emissions from industrial areas posed a potential threat to the surrounding environment. An urban transportation network model was constructed, and traffic flow data, road capacity information, and public transportation operation data were used to evaluate the network. The analysis identified bottleneck nodes and congestion points within the network, and insufficient public transportation services in certain areas.

[0088] Based on these analyses, key issues and potential risks in urban planning were identified, including irrational land use, traffic congestion, inadequate public facilities, environmental pollution, and potential disaster and social conflict risks. These findings were compiled into a report, which proposes recommendations and solutions to address these issues.

[0089] Through in-depth machine analysis of urban spatial data, planning decision-makers can gain a more scientific understanding of key issues and potential risks in urban planning, leading to more informed decisions. Identified key issues and potential risks provide planners with clear directions and goals for improvement, making planning more targeted and effective. Addressing key issues and potential risks in urban planning can promote sustainable urban development and improve residents' quality of life. The system can process large amounts of urban spatial data quickly and accurately, and conduct in-depth analysis, improving planning efficiency. By identifying potential risks in advance and developing corresponding countermeasures, risks during planning implementation can be reduced.

[0090] In another preferred embodiment of the present invention, step 124 above, which involves constructing an urban transportation network model, including road networks, rail transit lines, and public transportation lines, and using traffic flow data, road capacity information, and public transportation operation data to assess the accessibility, connectivity, and congestion level of the transportation network, and identifying bottleneck nodes, congestion points, and areas with insufficient public transportation services in the transportation network to obtain network analysis results, may include:

[0091] Step 1241: Obtain information on roads within the city, including road type, road length, width, number of lanes, and speed limit attributes; and obtain route maps, station information, operating hours, and train frequencies for subway and light rail transit.

[0092] Step 1242: Integrate road, rail transit, and public transportation line data into a network topology, where nodes represent intersections or stations, and edges represent road segments or rail segments.

[0093] Step 1243: Assign corresponding attributes to nodes and edges in the network topology, including road type, length, capacity, speed limit, rail transit line type, and public transportation schedule, and calculate the path between any two points in the network topology to evaluate the network connectivity and identify isolated nodes or subnetworks.

[0094] Step 1244: Using the assigned traffic flow data and road capacity information attributes, calculate the congestion level for each road or rail segment, obtain the congestion index, and identify bottleneck nodes and congestion points.

[0095] Step 1245: Based on public transportation operation data, assess the coverage, frequency, and occupancy rate of public transportation services, identify areas with insufficient services, and obtain network analysis results based on the congestion index, including the accessibility, connectivity, congestion level, and areas with insufficient public transportation services.

[0096] In this embodiment of the invention, road layers are extracted from an urban spatial database to obtain the type, length, width, number of lanes, and speed limit attributes of each road, ensuring the completeness and accuracy of the road data, and supplementing or correcting missing or erroneous data. Route maps of rail transit such as subways and light rails are obtained, including route directions, station locations, and names. Operational data such as rail transit operating hours and train frequencies are collected to ensure the real-time nature and accuracy of the data.

[0097] Step 1242 defines road intersections, rail transit stations, etc., as nodes in the network topology.

[0098] Assign a unique identifier to each node and record its location coordinates. Define road segments, track segments, etc., as edges in the network topology. Assign a unique identifier to each edge and record its start and end nodes. Use GIS software or a programming language to construct the network topology, ensuring the correct connections between nodes and edges.

[0099] Step 1243: For road intersection nodes, assign their location coordinates (such as latitude and longitude) as basic attributes. For rail transit station nodes, in addition to location coordinates, they can also be assigned attributes such as station name and line. For road segment edges, assign their road type (such as highway, urban arterial road, branch road, etc.), length, capacity (i.e., the maximum number of vehicles that can pass through per unit time), speed limit, etc. For rail segment edges, assign their rail transit line type (such as subway, light rail), length, train speed (or average speed), train interval, etc.

[0100] Dijkstra's algorithm is a commonly used shortest path algorithm, suitable for weighted graphs, where edge weights are non-negative. In this scenario, the length of the road segment, travel time (considering speed limits and traffic flow), or travel time of rail transit are used as edge weights.

[0101] Set the distance from the starting node to itself to 0, and the distance to other nodes to infinity. For each unvisited node, select the node closest to the starting node, update the distances to its neighbors, and repeat this process until all nodes have been visited. Return the shortest path from the starting node to the target node and its distance.

[0102] set up For the network topology, where For a set of nodes, Let the boundary set be an edge set. For any two points... , Dijkstra's algorithm calculates arrive shortest path distance .initialization , For each unvisited node If an edge exists ,and (in for If the predecessor node is updated, then update .

[0103] If for any two points , There exists a path from arrive If there are paths to the network, then the network is said to be connected.

[0104] If there exists at least one pair of points , This makes it impossible to obtain from arrive If the path is not clear, then the network is said to be disconnected.

[0105] The Dijkstra algorithm is used to calculate the path between any two points. If a path cannot be calculated between a pair of points, it is identified as an isolated node or a subnetwork. All node pairs are traversed. The path is calculated using Dijkstra's algorithm. If for a given pair of nodes... If the algorithm returns no solution or the path distance is infinite, then... and Belonging to different subnetworks or It is an isolated node.

[0106] Step 1244: Using traffic flow data and road capacity information, calculate the congestion level of each road or rail segment. The congestion level can be represented by a congestion index, such as the ratio of traffic flow to road capacity.

[0107] Based on the congestion level calculation results, road or rail segments with high congestion indices are identified. The starting and ending points of these congested segments are analyzed to identify bottleneck nodes.

[0108] Step 1245: Based on public transportation operation data, assess the coverage, frequency, and occupancy rate of public transportation services. Use GIS software or a programming language to calculate the service area of ​​each station and identify areas with insufficient service. Combine the above analysis results to determine the accessibility, connectivity, congestion level, and areas with insufficient public transportation services in the transportation network. Present the analysis results in the form of charts, reports, etc.

[0109] Suppose we are analyzing the transportation network of a large city. First, we acquire road information within the city, including road type, length, width, number of lanes, and speed limits, as well as route maps, station information, operating hours, and train frequencies for rail transit such as subways and light rail. This data is then integrated into a network topology, where nodes represent intersections or stations, and edges represent road segments or track segments. Each node and edge is assigned corresponding attributes, such as road type, length, and capacity.

[0110] Graph theory algorithms were used to calculate the path between any two points in the network topology, assessing network connectivity and identifying isolated nodes or subnetworks. Simultaneously, traffic flow data and road capacity information were used to calculate the congestion level of each road or rail segment, deriving a congestion index and identifying bottleneck nodes and congestion points. Public transportation service coverage, frequency, and occupancy rates were assessed based on public transportation operation data, identifying areas with insufficient service. Combining these analysis results, the accessibility, connectivity, congestion level, and areas with insufficient public transportation service in the transportation network were determined, and these results were presented to planning decision-makers in the form of charts and reports.

[0111] In-depth analysis of urban transportation networks allows planners to gain a more scientific understanding of the network's current state and problems, leading to more informed decision-making. Identifying bottlenecks, congestion points, and areas with inadequate public transportation services provides planners with clear directions and targets for improvement, facilitating the optimization of transportation resource allocation. Addressing problems in the transportation network, such as congestion and bottlenecks, can improve traffic efficiency and reduce traffic delays and congestion time. Optimizing the transportation network helps reduce traffic emissions and energy consumption, promoting sustainable urban development. Identifying areas with inadequate public transportation services and implementing corresponding improvement measures can enhance the quality of public transportation services, meeting the travel needs of more citizens.

[0112] In another preferred embodiment of the present invention, the formula for calculating the congestion index is: ;

[0113] in, Indicates the congestion index; Indicates actual traffic flow; Indicates road capacity; Indicates the basic congestion coefficient; Indicates the speed deviation coefficient; This indicates the actual speed at which the vehicle is traveling on the road; Indicates the preset speed; This indicates the actual vehicle density on the road; This indicates the desired vehicle density on the road. Indicates the time deviation coefficient; This indicates the actual time required for a vehicle to travel across the road. Indicates the expected time for a vehicle to travel along the road; Indicates the weather impact coefficient; This indicates the weather impact index.

[0114] In this embodiment of the invention, the number of vehicles on the road is collected in real time using traffic monitoring equipment (such as cameras and sensors). The maximum number of vehicles a road can accommodate is determined based on road design and specifications. Based on historical traffic data and experience, a baseline coefficient is set. This reflects the level of congestion. The actual speed of vehicles is obtained through GPS data and traffic signal systems. And set an ideal preset speed. Calculate the actual vehicle density on the road using sensors and traffic models. Vehicle density under ideal conditions The actual travel time of vehicles is recorded through a traffic monitoring system. It calculates the expected travel time based on road length and preset speed. Obtain real-time weather data (such as rainfall, snowfall, visibility, etc.) from meteorological departments and set an impact index based on its degree of influence on traffic. .

[0115] Calculate the ratio of the deviation between the actual driving speed and the preset speed, i.e. Calculate the ratio of the deviation between the actual wheel density and the expected wheel density, i.e. Calculate the ratio of the actual transit time to the expected transit time, i.e. Based on historical data analysis, the extent to which speed deviations affect congestion is determined. The impact of time deviation on traffic flow was analyzed, and appropriate coefficients were set. Based on the specific impact of weather on traffic (such as slippery roads caused by rain), set reasonable coefficients. .

[0116] Substitute all collected and processed data into the formula Calculate the congestion index This index is used to reflect the current level of road congestion.

[0117] Real-time data collection and analysis enable accurate assessment of road congestion, providing a scientific basis for traffic management. Traffic management departments can adjust traffic signals and release road condition information promptly based on congestion indices, guiding vehicles to divert appropriately and improving overall traffic efficiency. In the event of severe weather or unforeseen incidents causing changes in traffic conditions, management strategies can be quickly adjusted to mitigate the impact on traffic. Long-term data accumulation and analysis can help optimize road design and planning, improving road utilization efficiency and safety. By providing accurate traffic information, it helps the public plan optimal travel routes, reducing travel time and costs.

[0118] In a preferred embodiment of the present invention, step 13 above, based on the data analysis results, uses a differential evolution algorithm to simulate, predict, and optimize different urban planning schemes. Through differences between populations and evolutionary strategies, iterative optimization of multiple planning schemes is performed to automatically generate a final urban planning scheme that includes road planning, public facility layout, environmental protection strategies, and disaster risk assessment. This may include:

[0119] Step 131: Set the parameters of the differential evolution algorithm, including population size, crossover probability and mutation factor, and determine the objective function and constraints of the municipal planning problem based on the data analysis results.

[0120] Step 132: Randomly generate a set of initial municipal planning schemes. Each scheme is a vector containing information on road planning, public facility layout, environmental protection strategies, and disaster risk assessment.

[0121] Step 133: Simulate and predict each initial municipal planning scheme through the objective function, calculate the impact on urban traffic flow, environmental quality, and public facility utilization efficiency, and obtain the objective function value;

[0122] Repeated mutation, crossover, and selection operations are performed until a preset number of iterations are reached. The final urban planning scheme, i.e., the final solution, is then determined from the final population based on the objective function value.

[0123] In this embodiment of the invention, the number of individuals participating in evolution is determined, and the probability of gene exchange between individuals is controlled based on the complexity of the problem and computational resources. This affects the diversity of solutions, determines the magnitude of mutation operations, and influences the ability to explore the search space. Based on the data analysis results, an objective function is constructed to quantify the merits of urban planning schemes.

[0124] Step 132: For each individual (municipal planning scheme), randomly generate specific parameters for road planning, public facility layout, environmental protection strategies, and disaster risk assessment.

[0125] Step 133: Using traffic simulation software, environmental assessment models, and other tools, simulate each scheme to predict its future performance. Collect simulation data, such as traffic flow, pollutant emissions, and public facility access volume. Based on the simulation results, calculate the score of each scheme on the objective function, considering the comprehensive effect of all sub-objectives (traffic, environment, facility utilization). For each individual in the population, select three different individuals (usually randomly selected), and calculate new mutant individuals using a mutation factor. Based on the crossover probability, determine the gene exchange method between the mutant individuals and the original individuals, generate experimental individuals, compare the objective function values ​​of the experimental individuals with those of the original individuals, and retain the better-performing individuals for the next generation. Repeat the mutation, crossover, and selection operations until the preset number of iterations is reached, and select the individual with the optimal objective function value from the final population as the final urban planning scheme.

[0126] Suppose we are processing a population containing 100 initial municipal planning schemes, each scheme containing the following information:

[0127] Road planning: road width and traffic signal configuration.

[0128] Public facility layout: location and capacity of schools and hospitals.

[0129] Environmental protection strategies: green space distribution and pollution control measures.

[0130] Disaster risk assessment: flood control facilities, earthquake safety zone delineation.

[0131] First, 100 scenarios are randomly generated, and each scenario is predicted using traffic simulation software and an environmental model. Based on the prediction results, the objective function value for each scenario is calculated, such as traffic flow (measured by average commute time), environmental quality (measured by air quality index), and public facility utilization efficiency (measured by facility access volume).

[0132] The differential evolution algorithm is used to perform mutation, crossover, and selection operations. For each individual, three different individuals are randomly selected, and new mutated individuals are generated using a mutation factor. Next, based on the crossover probability, the gene exchange method between the mutated individuals and the original individuals is determined, generating experimental individuals. Finally, the objective function values ​​of the experimental individuals and the original individuals are compared, and the individuals with better performance are retained for the next generation. This process is repeated multiple times until a preset number of iterations is reached. Ultimately, the individual with the optimal objective function value is selected from the final population as the final urban planning scheme.

[0133] Differential evolutionary algorithms can efficiently explore the solution space and find the final urban planning scheme. Through the design of the objective function, multiple aspects such as transportation, environment, and public facility utilization can be comprehensively considered to achieve integrated optimization. The algorithm can adapt to different urban planning problems; by adjusting parameters and the objective function, it can flexibly handle various complex situations. The generated final urban planning scheme can provide a scientific basis for government decision-making, improving decision-making efficiency and accuracy. By optimizing environmental protection strategies and disaster risk assessments, it can promote sustainable urban development and improve the quality of life for residents.

[0134] In a preferred embodiment of the present invention, the formula for calculating the objective function is: ;

[0135] in, Representation scheme The objective function value; , , Indicates the weighting coefficient; Representation scheme Average traffic speed under these conditions; Representation scheme The congestion index below; Representation scheme Average traffic delay time; Representation scheme The air quality index below; The baseline value representing the air quality index; Indicates the number of monitoring points; Indicates the first Noise intensity measured at each monitoring point; This represents the maximum value of the noise level; Representation scheme The water quality index below; A baseline value indicating water quality status; Representation scheme The frequency of use of public facilities; Indicates the total capacity of public facilities; Representation scheme The service coverage of public facilities; Indicates the total area of ​​the study region; Representation and Scheme The relevant utility value.

[0136] In this embodiment of the invention, the average traffic speed of each scheme is collected. Congestion Index Average traffic delay time This data can be obtained through traffic monitoring systems, GPS data, and traffic reports. (This is used to obtain the air quality index.) Noise intensity at each monitoring point Water quality index This data is provided by environmental protection departments or related sensor networks. It collects information on the frequency of use of public facilities. Service coverage and related utility values A survey can be conducted through public facility management agencies and user feedback to set a baseline value for the air quality index. Maximum noise level Water quality baseline values Total capacity of public facilities and the total area of ​​the study area. Weighting coefficients are set according to the importance of each indicator. , , These weights can be determined through historical data analysis or decision analysis methods.

[0137] calculate Efficiency used to assess traffic conditions, calculation Used to assess environmental quality and calculate This is used to evaluate the utilization efficiency and service level of public facilities. Substituting the calculated sub-indicators into the objective function formula, the calculation for each scheme is performed. objective function value Compare the objective function values ​​of different schemes. To determine the final recommended solution.

[0138] By comprehensively considering factors such as transportation, environment, and public facilities, this method can fully evaluate the advantages and disadvantages of various options, avoiding the one-sidedness of evaluation based on a single indicator. This provides decision-makers with a scientific basis, helps them select the final solution, and improves decision-making efficiency and accuracy. Optimizing the allocation of options can effectively utilize resources, reduce waste, and improve the efficiency and service level of public facilities. Considering environmental and air quality factors in option selection helps promote green development, reduce environmental pollution, and improve residents' quality of life. Evaluating transportation indicators can identify traffic bottlenecks, optimize traffic flow, improve traffic efficiency, and reduce congestion and delays. This method can adjust weights and indicators as needed to adapt to the specific needs of different cities and regions, exhibiting good scalability and flexibility.

[0139] In a preferred embodiment of the present invention, step 14 above, which evaluates the final urban planning scheme to obtain evaluation results of the final urban planning scheme in terms of urban traffic flow, environmental quality, and public facility utilization efficiency, may include:

[0140] Step 141: Obtain the final municipal planning scheme, which includes information on multiple aspects such as road planning, public facility layout, environmental protection strategies, and disaster risk assessment. Extract key features from the final municipal planning scheme, including road network structure, traffic signal settings, location and capacity of public facilities, green coverage rate, and environmental protection measures.

[0141] Step 142: Input the key features into the preset evaluation model, analyze and calculate the key features according to the learning patterns and rules, and generate prediction results, including indicators of urban traffic flow, environmental quality and public facility utilization efficiency.

[0142] In this embodiment of the invention, the final municipal planning scheme is read from the output of the optimization algorithm. The scheme includes information on multiple aspects such as road planning, public facility layout, environmental protection strategies, and disaster risk assessment.

[0143] Key features extracted are:

[0144] Road network structure: Information such as road type (e.g., expressway, arterial road, secondary arterial road, local road), width, and connection relationship are used to construct a road network topology map.

[0145] Traffic signal setup: location and timing scheme of traffic lights.

[0146] Location and capacity of public facilities: Information such as the location coordinates, service area, and capacity (e.g., number of beds, number of seats) of public facilities such as schools, hospitals, parks, and sports facilities.

[0147] Green coverage rate: The green area, greening types (such as lawns, trees, and flower beds) and distribution map within the planning area are used to calculate the green coverage rate.

[0148] Environmental protection measures: Information on the location, treatment capacity, and operation strategies of environmental protection facilities such as air purification facilities, sewage treatment plants, and garbage treatment plants, as well as overall environmental protection policies and standards.

[0149] Step 142: Based on the assessment requirements, select an appropriate assessment model, such as a traffic simulation model, an environmental assessment model, or a public facility usage prediction model. These models can be based on physics, statistics, or machine learning. Organize the extracted key features according to the model's requirements, such as converting road network structure into topology data, traffic signal settings into time series or event data, and public facility locations and capacities into geospatial data. Use the traffic simulation model to simulate vehicle travel on the urban road network and calculate indicators such as average speed, congestion index, and traffic delay time. Analyze the impact of traffic signal settings on traffic flow and assess the rationality of signal timing. Use the environmental assessment model to predict indicators such as air quality index, noise level, and water quality based on information such as green coverage and environmental protection measures, considering the impact of urban expansion, industrial emissions, and traffic exhaust on environmental quality. Use the public facility usage prediction model to predict indicators such as usage frequency and service coverage based on information such as the location, capacity, and service area of ​​public facilities, analyze the rationality of public facility layout, and assess whether it meets residents' needs. The results obtained from analysis and calculation are compiled into reports or visualizations, including indicators of urban traffic flow, environmental quality, and the efficiency of public facility utilization.

[0150] Assuming the final urban planning scheme has been obtained and the following key features have been extracted:

[0151] Road network structure: A topology diagram including main roads, secondary roads, and branch roads, with the width of each road known.

[0152] Traffic signal setup: A traffic light will be installed every 500 meters on the main road, and the timing scheme has been determined.

[0153] Location and capacity of public facilities: The location and capacity of public facilities such as schools, hospitals, and parks are known.

[0154] Green coverage rate: The green coverage rate within the planned area is 30%.

[0155] Environmental protection measures: Equipped with air purification facilities, sewage treatment plants, etc., and environmental protection policies comply with national standards.

[0156] These key features are input into a pre-defined evaluation model. The traffic simulation model, based on road network structure and traffic signal settings, simulates vehicle movement and calculates average speed, congestion index, and traffic delay time. The environmental assessment model, based on green coverage and environmental protection measures, predicts air quality index, noise levels, and water quality. The public facility usage prediction model, based on the location and capacity of public facilities, predicts usage frequency and service coverage. Finally, a detailed evaluation report is generated, including indicators of urban traffic flow, environmental quality, and public facility utilization efficiency, along with corresponding visualization charts.

[0157] By extracting key features and inputting them into an evaluation model, machines can comprehensively assess the performance of final urban planning schemes in terms of urban traffic flow, environmental quality, and the efficiency of public facility utilization. The evaluation results provide decision-makers with a scientific basis, helping them understand the advantages and disadvantages of the schemes and make more informed decisions.

[0158] Based on the assessment results, the plan can be further optimized and adjusted to improve the overall performance of the city and the quality of life for residents. By assessing the utilization efficiency of public facilities, resources can be allocated more rationally, avoiding waste and redundant construction. Assessing environmental quality indicators helps promote sustainable urban development and reduce environmental pollution and ecological damage. Automating the assessment process improves efficiency and accuracy, saving manpower and time costs.

[0159] In a preferred embodiment of the present invention, step 15 above, establishing a dynamic update process for municipal planning results, and updating and adjusting the planning results in real time based on newly acquired data and feedback on the effects of the planning scheme implementation, may include:

[0160] Step 151: Receive newly acquired data and feedback on the effects of the implementation of the urban planning scheme, and determine the triggering conditions for dynamic updates, including regular updates and updates triggered by data changes;

[0161] Step 152: Integrate and analyze the newly acquired data and feedback on the effects of the planning scheme after its implementation, evaluate the implementation effect of the planning scheme, and determine whether to adjust or update the planning scheme in order to obtain the processing results of the new data and feedback.

[0162] Step 153: Based on the processing results of the new data and effect feedback, determine whether the triggering conditions for dynamic updates are met; if so, start the update process to update and adjust the municipal planning scheme in real time.

[0163] In this embodiment of the invention, newly acquired data information from various data sources, such as traffic flow data, environmental quality monitoring data, and public facility usage data, is automatically received through a preset data interface or API. Simultaneously, feedback on the effects of urban planning schemes after implementation is received, including feedback from resident surveys, expert evaluations, and monitoring system reports. Based on preset rules, triggering conditions for dynamic updates are determined. These conditions include regular updates (e.g., monthly or quarterly updates) and updates triggered by data changes (e.g., updates triggered when traffic flow exceeds a threshold or environmental quality deteriorates to a certain extent).

[0164] Step 152 involves integrating the received new data and feedback to form a unified data format and structure. Data analysis tools are then used to analyze the new data and feedback. For example, comparing traffic flow data before and after implementation assesses the effectiveness of traffic improvement; comparing environmental quality monitoring data evaluates the effectiveness of environmental protection measures; and comparing public facility usage data assesses the rationality of facility layout. Based on the data analysis results, the implementation effect of the planning scheme is evaluated. If the implementation effect meets the expected goals, no adjustment or update to the planning scheme is needed; if the implementation effect does not meet the expected goals, adjustment or update to the planning scheme needs to be considered. Based on the evaluation results, the processing outcome of the new data and feedback is determined. This includes whether the planning scheme needs adjustment, what specific content needs adjustment, and the extent of the adjustment.

[0165] Step 153: Based on the triggering conditions determined in Step 151, determine whether the current conditions for dynamic updates are met. For example, if the current time is a scheduled update point, or if new data indicates that traffic flow exceeds a preset threshold, then the triggering conditions are met. If the triggering conditions are met, the update process is initiated. This includes retrieving the current planning scheme from the database, modifying the planning scheme based on the processing results, saving the modified planning scheme back to the database, and notifying relevant personnel. During the update process, the municipal planning scheme is updated and adjusted in real time to ensure that the planning scheme always remains consistent with the actual situation.

[0166] Assuming that newly acquired traffic flow data, environmental quality monitoring data, and resident survey feedback following the implementation of the urban planning scheme have been received, a dynamic update will be initiated based on preset trigger conditions.

[0167] Newly acquired traffic flow data, environmental quality monitoring data, and resident survey feedback were received via a data interface. This data was integrated into a unified data format and structure. Data analysis tools were used to analyze the integrated data. Comparison of traffic flow data before and after implementation revealed that traffic flow on certain road sections exceeded preset thresholds; comparison of environmental quality monitoring data showed that environmental quality in some areas had deteriorated. Based on resident survey feedback, it was learned that some residents were dissatisfied with the use of public facilities. Taking all the above analysis results into account, the assessment concluded that the implementation of the planning scheme did not achieve the expected goals.

[0168] Based on the assessment results, the processing outcomes for the new data and feedback were determined: adjustments to the road widening plan, increased investment in environmental protection facilities, and optimization of the public facility layout were required. Simultaneously, it was determined that the current conditions for dynamic updates were met (due to traffic flow exceeding preset thresholds and a deterioration in environmental quality). The update process was initiated, retrieving the current planning scheme from the database. Based on the processing results, the system modified the planning scheme: adjusting the road widening plan, increasing investment in environmental protection facilities, and optimizing the public facility layout. The modified planning scheme was saved back to the database, and relevant personnel were notified.

[0169] Through a dynamic update process, new data and information can be received in real time, allowing for continuous updates and adjustments to planning schemes, ensuring their timeliness and accuracy. This process enables planning schemes to adapt to changes and needs in urban development, exhibiting greater adaptability and flexibility. Data analysis and evaluation provide decision-makers with a scientific basis, helping them make more optimized and informed decisions. The dynamic update process ensures the rationality and effectiveness of planning schemes, improves resource utilization efficiency, and avoids waste and redundant construction. By optimizing the layout of public facilities and improving environmental quality, the dynamic update process can enhance residents' quality of life and satisfaction. Ultimately, the dynamic update process contributes to promoting sustainable urban development, reducing environmental pollution and ecological damage, and achieving coordinated economic, social, and environmental development.

[0170] like Figure 2 As shown, embodiments of the present invention also provide a geographic information-based intelligent urban planning system, comprising:

[0171] The data acquisition module is used to acquire urban spatial data from different data sources, including topographic data, land use data, building distribution data, transportation network data, population distribution data, and environmental indicator data.

[0172] The data analysis module is used to analyze the acquired urban spatial data. The analysis methods include spatial overlay analysis, buffer analysis, and network analysis to identify key issues and potential risks in urban planning and obtain data analysis results.

[0173] The planning optimization module is used to simulate, predict, and optimize different municipal planning schemes based on data analysis results using differential evolution algorithms. Through the differences between populations and evolutionary strategies, it iteratively optimizes multiple planning schemes and automatically generates municipal planning schemes that include multiple aspects such as road planning, public facility layout, environmental protection strategies, and disaster risk assessment.

[0174] The scheme evaluation module is used to evaluate municipal planning schemes. The evaluation content includes urban traffic flow, environmental quality, and the utilization efficiency of public facilities, in order to obtain evaluation results.

[0175] The dynamic update module is used to establish a dynamic update process for municipal planning results. Based on newly acquired data and feedback on the effects of the implementation of urban planning schemes, the planning results are updated and adjusted in real time.

[0176] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0177] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0178] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A smart city municipal planning method based on geographic information, characterized in that, The method includes: Acquire urban spatial data from different data sources, including topography, land use, building distribution, transportation network, population distribution, and environmental indicators. The analysis of urban spatial data, including spatial overlay analysis, buffer analysis, and network analysis, identifies key issues and potential risks in urban planning, yielding data analysis results, including: Acquire urban spatial data, including land use layers, transportation network layers, location data of urban facilities such as schools and hospitals, and location data of rivers and industrial areas. Overlay the data from different layers to identify the relationship between land use and transportation, and obtain the overlay analysis results. Based on the overlay analysis results, specific patterns or anomalous areas in urban space are identified, including the overlap of high-density population areas and traffic congestion areas, or the close proximity of industrial areas and residential areas. Buffer zones are set for specific patterns or anomalous areas, including schools, hospitals, rivers, and industrial areas. Within the buffer zone, environmental characteristics, including air quality, noise levels, population distribution, and traffic conditions, are analyzed, and the impact of facilities or areas on the surrounding environment is assessed. Potential risks within the buffer zone are identified, including the impact of noise from schools on surrounding residents, or the potential threat of pollutant emissions from industrial areas to the surrounding environment, in order to obtain buffer zone analysis results. A city transportation network model is constructed, including road networks, rail transit lines, and public transportation lines. Traffic flow data, road capacity information, and public transportation operation data are used to assess the accessibility, connectivity, and congestion levels of the transportation network. Bottleneck nodes, congestion points, and areas with insufficient public transportation services are identified to obtain network analysis results, including: Obtain information on urban roads, including road type, length, width, number of lanes, and speed limit attributes, and obtain route maps, station information, operating hours, and train frequency for subway and light rail transit. The data of roads, rail transit and public transportation lines are integrated into a network topology, where nodes represent intersections or stations and edges represent road segments or track segments. Assign corresponding attributes to nodes and edges in the network topology, including road type, length, capacity, speed limit, rail transit line type, and public transportation schedule, and calculate the path between any two points in the network topology to evaluate network connectivity and identify isolated nodes or subnetworks. Using the assigned traffic flow data and road capacity information attributes, the congestion level of each road or rail segment is calculated to obtain a congestion index, and bottleneck nodes and congestion points are identified. Based on public transportation operation data, the coverage, frequency, and occupancy rate of public transportation services are assessed to identify areas with insufficient service. Network analysis results are obtained based on the congestion index, including the accessibility, connectivity, congestion level, and areas with insufficient public transportation service. The formula for calculating the congestion index is: ； in, Indicates the congestion index; Indicates actual traffic flow; Indicates road capacity; Indicates the basic congestion coefficient; Indicates the speed deviation coefficient; This indicates the actual speed at which the vehicle is traveling on the road; Indicates the preset speed; This indicates the actual vehicle density on the road; This indicates the desired vehicle density on the road. Indicates the time deviation coefficient; This indicates the actual time required for a vehicle to travel across the road. Indicates the expected time for a vehicle to travel along the road; Indicates the weather impact coefficient; Indicates the weather impact index; Based on the results of spatial overlay analysis, buffer zone analysis, and network analysis, key issues and potential risks in urban planning were identified. Key issues include irrational land use, traffic congestion, insufficient public facilities, and environmental pollution; potential risks include disaster risks and social conflict risks. Based on the data analysis results, the differential evolution algorithm is used to simulate, predict and optimize different urban planning schemes. Through the differences between populations and evolutionary strategies, multiple planning schemes are iteratively optimized to automatically generate the final urban planning scheme that includes road planning, public facility layout, environmental protection strategies and disaster risk assessment. The final urban planning scheme is evaluated to obtain the evaluation results of the final urban planning scheme in terms of urban traffic flow, environmental quality, and the efficiency of public facility utilization. Establish a dynamic update process for municipal planning outcomes, and update and adjust the planning outcomes in real time based on newly acquired data and feedback on the effects of the implementation of urban planning schemes.

2. The intelligent urban planning method based on geographic information according to claim 1, characterized in that, Based on data analysis results, the differential evolution algorithm is used to simulate, predict, and optimize different urban planning schemes. Through inter-population differences and evolutionary strategies, multiple planning schemes are iteratively optimized, automatically generating a final urban planning scheme that includes road planning, public facility layout, environmental protection strategies, and disaster risk assessment. The parameters of the differential evolution algorithm are set, including population size, crossover probability and mutation factor, and the objective function and constraints of the municipal planning problem are determined based on the data analysis results. A set of initial municipal planning schemes is randomly generated. Each scheme is a vector containing information on road planning, public facility layout, environmental protection strategies, and disaster risk assessment. The objective function is used to simulate and predict each initial municipal planning scheme, and the impact on urban traffic flow, environmental quality and public facility utilization efficiency is calculated to obtain the objective function value. Repeated mutation, crossover, and selection operations are performed until a preset number of iterations are reached. The final urban planning scheme, i.e., the final solution, is then determined from the final population based on the objective function value.

3. The intelligent urban planning method based on geographic information according to claim 2, characterized in that, The formula for calculating the objective function is: ； in, Representation scheme The objective function value; , , Indicates the weighting coefficient; Representation scheme Average traffic speed under these conditions; Representation scheme The congestion index below; Representation scheme Average traffic delay time; Representation scheme The air quality index below; The baseline value representing the air quality index; Indicates the number of monitoring points; Indicates the first Noise intensity measured at each monitoring point; This represents the maximum value of the noise level; Representation scheme The water quality index below; A baseline value indicating water quality status; Representation scheme The frequency of use of public facilities; Indicates the total capacity of public facilities; Representation scheme The service coverage of public facilities; Indicates the total area of ​​the study region; Representation and Scheme The relevant utility value.

4. The intelligent urban planning method based on geographic information according to claim 3, characterized in that, The final urban planning scheme is evaluated to obtain assessment results on urban traffic flow, environmental quality, and the efficiency of public facility utilization, including: Obtain the final municipal planning scheme, including information on road planning, public facility layout, environmental protection strategies, and disaster risk assessment, and extract key features from the final municipal planning scheme, including road network structure, traffic signal settings, location and capacity of public facilities, green coverage, and environmental protection measures. Key features are input into a pre-set evaluation model, and the key features are analyzed and calculated according to the learning patterns and rules to generate prediction results, including indicators of urban traffic flow, environmental quality and public facility utilization efficiency.

5. The intelligent urban planning method based on geographic information according to claim 4, characterized in that, Establish a dynamic updating process for municipal planning outcomes. Based on newly acquired data and feedback on the effects of planning scheme implementation, update and adjust planning outcomes in real time, including: Receive newly acquired data and feedback on the effects of urban planning schemes after implementation, and determine the triggering conditions for dynamic updates, including regular updates and updates triggered by data changes; The newly acquired data and feedback on the effects of the planning scheme are integrated and analyzed to evaluate the implementation effect of the planning scheme and determine whether to adjust or update the planning scheme, so as to obtain the processing results of the new data and feedback. Based on the processing results of the new data and feedback, determine whether the triggering conditions for dynamic updates are met; if so, initiate the update process to update and adjust the municipal planning scheme in real time.

6. A geographic information-based intelligent urban planning system, wherein the system implements the method as described in any one of claims 1 to 5, characterized in that, include: The data acquisition module is used to acquire urban spatial data from different data sources, including topographic data, land use data, building distribution data, transportation network data, population distribution data, and environmental indicator data. The data analysis module is used to analyze the acquired urban spatial data. The analysis methods include spatial overlay analysis, buffer analysis, and network analysis to identify key issues and potential risks in urban planning and obtain data analysis results. The planning optimization module is used to simulate, predict, and optimize different municipal planning schemes based on data analysis results using differential evolution algorithms. Through the differences between populations and evolutionary strategies, it iteratively optimizes multiple planning schemes and automatically generates municipal planning schemes that include multiple aspects such as road planning, public facility layout, environmental protection strategies, and disaster risk assessment. The scheme evaluation module is used to evaluate municipal planning schemes. The evaluation content includes urban traffic flow, environmental quality, and the utilization efficiency of public facilities, in order to obtain evaluation results. The dynamic update module is used to establish a dynamic update process for municipal planning results. Based on newly acquired data and feedback on the effects of the implementation of urban planning schemes, the planning results are updated and adjusted in real time.

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

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