Intelligent city municipal planning method and system based on geographic information

Abstract

The invention provides an intelligent urban municipal planning method and system based on geographic information, and relates to the technical field of data processing, and the method comprises the steps: obtaining urban spatial data of different data sources, including terrain, land utilization, building distribution, traffic network, population distribution and environment index data; and analyzing the urban spatial data, including spatial overlay analysis, buffer analysis and network analysis, and identifying key problems and potential risks in urban municipal planning to obtain a data analysis result. The urban space data can be comprehensively and accurately analyzed, the optimized municipal planning scheme is automatically generated and updated and adjusted in real time, and the urban traffic fluency, the environment quality and the public facility utilization efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for intelligent city municipal planning based on geographic information. Background Art

[0002] Traditional planning methods rely on limited data sources and simple statistical analysis methods, which makes it difficult to comprehensively and accurately integrate and analyze multi-source and 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 have a significant impact on transportation demand, such as topography, land use, and population distribution, may be ignored. This lack of data integration and analysis may lead to a lack of scientificity and foresight in planning solutions.

[0004] In environmental planning, traditional methods may only focus on direct data such as pollutant emissions and environmental quality monitoring, but fail to fully consider the impact of indirect factors such as climate change and ecological sensitivity on environmental planning. This one-sided data analysis may make it difficult for environmental planning programs to cope with the challenges of future environmental changes.

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

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

[0007] In terms of the layout of public facilities, traditional methods may only consider basic factors such as the service radius of the facilities and the population covered, but fail to fully consider the synergy between facilities, the actual needs of residents, and the future trend of urban development. This rigid planning approach may lead to an unreasonable layout of public facilities and make it difficult to meet the growing and diverse needs of residents. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a method and system for intelligent urban municipal planning 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] In order to solve the above technical problems, the technical solution of the present invention is as follows: In a first aspect, a method for smart city municipal planning based on geographic information is provided, the method comprising: Obtain urban spatial data from different data sources, including topography, land use, building distribution, transportation network, population distribution and environmental indicators; Analyze urban spatial data, including spatial overlay analysis, buffer zone analysis, and network analysis, to identify key issues and potential risks in urban municipal planning and obtain data analysis results; Based on the data analysis results, the differential evolution algorithm is used to simulate, predict and optimize different municipal planning schemes. Through the differences between populations and evolutionary strategies, multiple planning schemes are iteratively optimized to automatically generate the final urban municipal planning scheme including road planning, public facilities layout, environmental protection strategy, and disaster risk assessment. Evaluate the final urban municipal planning scheme and obtain the evaluation results of the final urban municipal planning scheme in terms of urban traffic flow, environmental quality, and efficiency of public facilities utilization; Establish a dynamic update process for municipal planning results, and update and adjust planning results in real time based on newly acquired data and information and feedback on the effects of the implementation of urban municipal planning programs.

[0010] Furthermore, the urban spatial data is analyzed, including spatial overlay analysis, buffer zone analysis, and network analysis, to identify key issues and potential risks in urban municipal planning, so as to obtain data analysis results, including: Obtain urban spatial data, including land use layers, transportation network layers, urban facilities, schools, hospital location data, river and industrial area location data, and overlay data from different layers to identify the relationship between land use and transportation, so as to obtain overlay analysis results; Based on the results of the overlay analysis, 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 ranges for specific patterns or abnormal areas, including schools, hospitals, rivers, and industrial areas; Within the buffer zone, analyze environmental characteristics, including air quality, noise level, 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 school noise on surrounding residents, or the potential threat of pollutant emissions from industrial areas to the surrounding environment, to obtain buffer zone analysis results; Construct an urban transportation network model, including road networks, rail transit lines and public transportation lines, and 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; Based on the results of spatial overlay analysis, buffer zone analysis, and network analysis, key issues and potential risks in urban municipal planning are identified. Key issues include irrational land use, traffic congestion, insufficient public facilities, and environmental pollution; potential risks include disaster risks, social conflict risks, etc.

[0011] Furthermore, we build an urban transportation network model, including road networks, rail transit lines and public transportation lines, and 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, including: Obtain information about roads in the city, including road type, road length, width, number of lanes, speed limit attributes, and obtain route maps, station information, operating hours, and train frequencies for subway and light rail transit; Integrate road, rail and public transport line data 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, public transportation frequency, and calculate the path between any two points in the network topology to evaluate the connectivity of the network and identify isolated nodes or sub-networks; Using the assigned traffic flow data and road capacity information attributes, the congestion level of each road or track segment is calculated to obtain the congestion index and identify bottleneck nodes and congestion points; Based on public transportation operation data, the coverage, frequency and load factor of public transportation services are evaluated, areas with insufficient services are identified, and based on the congestion index, network analysis results are obtained, including the accessibility, connectivity, congestion level of the transportation network and areas with insufficient public transportation services.

[0012] Furthermore, the calculation formula of the congestion index is: ; in, represents the congestion index; Indicates actual traffic flow; Indicates road capacity; represents the basic congestion coefficient; Indicates the speed deviation coefficient; Indicates the actual speed of the vehicle on the road; Indicates the preset speed; Indicates the actual vehicle density on the road; represents the expected vehicle density on the road; represents the time deviation coefficient; Indicates the actual time required for a vehicle to pass through the road; Indicates the expected time for a vehicle to pass the road; represents the weather influence coefficient; Represents the weather impact index.

[0013] Furthermore, based on the data analysis results, the differential evolution algorithm is used to simulate, predict and optimize different municipal planning schemes. Through the differences between populations and evolutionary strategies, multiple planning schemes are iteratively optimized to automatically generate the final urban municipal planning scheme including road planning, public facilities layout, environmental protection strategy, and disaster risk assessment, including: 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; A set of initial municipal planning schemes are randomly generated, each of which is a vector containing information on road planning, public facilities layout, environmental protection strategies, and disaster risk assessment; Through the objective function, each initial municipal planning scheme is simulated and predicted, and the impact on urban traffic flow, environmental quality, and public facilities utilization efficiency is calculated to obtain the objective function value; The mutation, crossover and selection operations are repeated until the preset number of iterations is reached, and the final municipal planning scheme, i.e., the final solution, is determined from the final population according to the objective function value.

[0014] Furthermore, the calculation formula of the objective function is: ; in, Representation scheme The objective function value of , , represents the weight coefficient; Representation scheme Average traffic speed under Representation scheme The congestion index under Representation scheme Average traffic delay time under Representation scheme Air quality index under Indicates the baseline value of the air quality index; Indicates the number of monitoring points; Indicated in The noise intensity measured at each monitoring point; Indicates the maximum value of the noise level; Representation scheme Water quality index under A benchmark value indicating water quality conditions; Representation scheme Frequency of use of public facilities; It represents the total capacity of public facilities; Representation scheme The service coverage of public facilities; represents the total area of ​​the study area; Representation and solution The associated utility value.

[0015] Furthermore, the final urban municipal planning scheme is evaluated to obtain the evaluation results of the final urban municipal planning scheme in terms of urban traffic flow, environmental quality, and efficiency of public facilities utilization, including: Obtain the final municipal planning scheme, including information on road planning, public facilities 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, public facilities location and capacity, green coverage, and environmental protection measures; The key features are input into the preset evaluation model, and the key features are analyzed and calculated according to the learned patterns and rules to generate prediction results, including indicators of urban traffic smoothness, environmental quality and public facilities utilization efficiency.

[0016] Furthermore, a dynamic update process for municipal planning results is established to update and adjust planning results in real time based on newly acquired data and information and feedback on the effects of planning schemes after implementation, including: Receive newly acquired data information and feedback on the effects of the implementation of urban municipal planning programs, and determine the trigger conditions for dynamic updates, including regular updates and updates triggered by data changes; Integrate and analyze the newly acquired data and information and the feedback on the implementation of the planning scheme, evaluate the implementation effect of the planning scheme, and determine whether to adjust or update the planning scheme to obtain the processing results of the new data and effect feedback; Based on the processing results of new data and effect feedback, determine whether the trigger conditions for dynamic updates are met; if met, start the update process to update and adjust the municipal planning plan in real time.

[0017] In the second aspect, a smart city municipal planning system based on geographic information includes: The data acquisition module is used to obtain urban spatial data from different data sources, including terrain 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 zone analysis, and network analysis to identify key issues and potential risks in urban municipal 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 the differential evolution algorithm. It iteratively optimizes multiple planning schemes through differences between populations and evolutionary strategies, and automatically generates municipal planning schemes that include road planning, public facility layout, environmental protection strategies, and disaster risk assessment. The program evaluation module is used to evaluate the municipal planning program. The evaluation content includes urban traffic flow, environmental quality, and public facilities utilization efficiency to obtain the evaluation results; The dynamic update module is used to establish a dynamic update process for municipal planning results, and to update and adjust the planning results in real time based on newly acquired data information and feedback on the effects of the implementation of urban municipal planning programs.

[0018] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0019] The above solution of the present invention includes at least the following beneficial effects: By integrating urban spatial data from different data sources, including topography, land use, building distribution, transportation network, population distribution, and environmental indicators, this method provides comprehensive and accurate basic data support, which helps to gain a deeper understanding of the current situation of the city and provides a solid foundation for municipal planning.

[0020] Using spatial overlay analysis, buffer zone analysis, network analysis and other technical means, this method can effectively identify key issues and potential risks in urban municipal planning. This helps planners to foresee possible challenges in advance and take corresponding preventive measures. Through the differential evolution algorithm to simulate, predict and optimize multiple municipal planning schemes, this method can automatically generate the final urban municipal planning scheme including road planning, public facilities layout, environmental protection strategy, disaster risk assessment and other aspects. This automated generation method not only improves planning efficiency, but also ensures the scientificity and feasibility of the planning scheme.

[0021] The final urban municipal planning scheme was comprehensively evaluated, including urban traffic flow, environmental quality, and efficiency of public facilities. This helps planners understand the implementation effect of the planning scheme and make timely adjustments and optimizations. At the same time, a dynamic update process for municipal planning results was established to ensure that planning results can be updated and adjusted in real time as the city develops and data changes. It provides scientific and accurate decision-making support for city managers, helping them make more informed municipal planning decisions. At the same time, by introducing intelligent technologies and algorithms, the intelligence level of municipal planning has been improved, promoting the development of smart cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of a smart city municipal planning method based on geographic information provided by an embodiment of the present invention.

[0023] Figure 2 It is a schematic diagram of a smart city municipal planning system based on geographic information provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The 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 accompanying 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 to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0025] like Figure 1 As shown, an embodiment of the present invention provides a smart city municipal planning method based on geographic information, the method comprising the following steps: Step 11, obtaining urban spatial data from different data sources, including topography, land use, building distribution, transportation network, population distribution and environmental indicator data; Step 12: Analyze urban spatial data, including spatial overlay analysis, buffer zone analysis, and network analysis, to identify key issues and potential risks in urban municipal planning, so as to obtain data analysis results; Step 13, based on the data analysis results, use the differential evolution algorithm to simulate, predict and optimize different municipal planning schemes, iteratively optimize multiple planning schemes through the differences between populations and evolutionary strategies, and automatically generate the final urban municipal planning scheme including road planning, public facilities layout, environmental protection strategy, and disaster risk assessment; Step 14, evaluating the final urban municipal planning scheme to obtain evaluation results of the final urban municipal planning scheme in terms of urban traffic flow, environmental quality, and public facilities utilization efficiency; Step 15: Establish a dynamic update process for municipal planning results, and update and adjust the planning results in real time based on newly acquired data and information and feedback on the effects of the implementation of the urban municipal planning program.

[0026] In an embodiment of the present invention, by integrating data such as terrain, land use, building distribution, transportation network, population distribution and environmental indicators from different data sources, comprehensive and diverse basic information is provided for urban municipal planning, ensuring the accuracy and reliability of planning. Data from different data sources complement each other, making up for the limitations of a single data source, making the description of urban spatial data more complete and detailed. Through technical means such as spatial overlay analysis, buffer zone analysis, and network analysis, key issues and potential risks in urban municipal planning can be accurately identified, providing a scientific basis for planning decisions. The data analysis results reveal the characteristics and problems of urban space, enabling municipal planning to more specifically address the actual needs and challenges of urban development.

[0027] The differential evolution algorithm iteratively optimizes multiple planning schemes through differences between populations and evolutionary strategies, greatly improving the optimization efficiency and quality of planning schemes. The final urban municipal planning scheme automatically generated, including road planning, public facility layout, environmental protection strategy, disaster risk assessment, etc., is more scientific, reasonable and feasible. By evaluating indicators such as urban traffic flow, environmental quality, and public facility utilization efficiency, the planning effect can be quantified, providing an intuitive reference for planning decisions.

[0028] The evaluation results timely reflect the implementation effect of the planning scheme, which helps planners to adjust and optimize the planning scheme in a timely manner and ensure the smooth realization of planning goals.

[0029] By establishing a dynamic update process, we ensure that municipal planning results can be updated and adjusted in real time as the city develops and data changes, thus maintaining the timeliness and accuracy of planning results. The dynamic update process enables municipal planning to adapt more flexibly to changes and challenges in urban development, improving the adaptability and sustainability of planning.

[0030] In a preferred embodiment of the present invention, the above step 11, obtaining urban spatial data from different data sources, including terrain, land use, building distribution, transportation network, population distribution and environmental index data, may include: In the embodiment of the present invention, the specific goals of urban municipal planning are clarified, such as traffic optimization, land use adjustment, environmental protection, etc. This will determine what types of data are needed. According to the planning goals, the required data types are listed, including terrain data, land use data, building distribution data, transportation network data, population distribution data, and environmental indicator data. Government agencies (such as the Urban Planning Bureau, Environmental Protection Bureau, Statistics Bureau, etc.) provide authoritative urban spatial data, and commercial companies, research institutions or open source projects also provide useful urban spatial data. For some specific data, such as terrain details or environmental indicators, field surveys are required.

[0031] Download the required data from official or third-party data sources, or submit data requests, and collect data on the spot using tools such as GPS devices, drones, and ground survey instruments. Integrate data from different data sources. Use geographic information system (GIS) software or database management system (DBMS) to build an urban spatial database.

[0032] In a preferred embodiment of the present invention, the above step 12, analyzing the urban spatial data, including spatial overlay analysis, buffer zone analysis, and network analysis, identifying key issues and potential risks in urban municipal planning, and obtaining data analysis results, may include: Step 121, obtaining urban spatial data, including land use layers, transportation network layers, urban facilities, schools, hospital location data, river and industrial area location data, and superimposing data of different layers to identify the relationship between land use and transportation, so as to obtain superimposed analysis results; Step 122, based on the overlay analysis results, identifying 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 setting buffer ranges for specific patterns or abnormal areas, including schools, hospitals, rivers, and industrial areas; Step 123, within the buffer zone, analyze environmental characteristics, including air quality, noise level, population distribution, and traffic conditions, and evaluate the impact of facilities or areas on the surrounding environment, identify potential risks within the buffer zone, including the impact of school noise on surrounding residents, or the potential threat of pollutant emissions from industrial areas to the surrounding environment, to obtain a buffer zone analysis result; Step 124, constructing an urban transportation network model, including a road network, rail transit lines, and public transportation lines, and using 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, so as to obtain network analysis results; Step 125, based on the results of spatial overlay analysis, buffer zone analysis, and network analysis, identify key issues and potential risks in urban municipal planning. Key issues include unreasonable land use, traffic congestion, insufficient public facilities, and environmental pollution; potential risks include disaster risks, social conflict risks, etc.

[0033] In an embodiment of the present invention, a land use layer, a traffic network layer, location data of urban facilities (schools, hospitals), and location data of rivers and industrial areas are extracted from an urban spatial database. The land use layer and the traffic network layer are superimposed using the overlay analysis tool in the GIS software. Overlay rules, such as intersection, tangency, or inclusion, are set to identify the relationship between land use and traffic, and an overlay analysis result layer is generated to display the spatial relationship between land use and the traffic network. The overlay analysis result layer is analyzed to identify the overlapping areas of high-density population areas and traffic congestion areas, and to record the close proximity of industrial areas and residential areas.

[0034] Step 122, based on the overlay analysis results, identify specific patterns in the urban space, such as the overlap of high-density population areas and traffic congestion areas. Identify abnormal areas, such as the close proximity of industrial areas and residential areas. Set reasonable buffer ranges for specific patterns or abnormal areas, such as schools, hospitals, rivers, and industrial areas.

[0035] Use the buffer tool in the GIS software to generate a buffer layer based on the set range.

[0036] Step 123, within the buffer zone, extract data such as air quality, noise level, population distribution, and traffic conditions, and use the analysis tools in the GIS software to perform spatial analysis on the extracted data. Evaluate the impact of facilities or areas on the surrounding environment, such as the impact of the school's noise impact range on surrounding 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, and display potential risk areas. Analyze the buffer zone analysis result layer to identify the specific location and type of potential risks.

[0037] Step 124, using the network analysis tool in the GIS software, construct an urban transportation network model, including road networks, rail transit lines and public transportation lines, and import traffic flow data, road capacity information and public transportation operation data. Evaluate the accessibility, connectivity and congestion level of the transportation network, identify bottleneck nodes, congestion points and areas with insufficient public transportation services in the transportation network, generate a network analysis result layer, and display key problems and areas of the transportation network. Analyze the network analysis result layer to identify specific problems and areas of the transportation network.

[0038] Step 125, based on the results of spatial overlay analysis, buffer zone analysis, and network analysis, identify key issues in urban municipal planning, such as unreasonable land use, traffic congestion, insufficient public facilities, environmental pollution, etc. Identify potential risks, such as disaster risks (such as floods, earthquakes, etc.), social conflict risks, etc., and evaluate and classify potential risks in combination with urban historical data and expert opinions. Integrate the identified key issues and potential risks into a report, and propose suggestions and solutions for the key issues and potential risks.

[0039] Suppose you are analyzing the municipal planning data of a medium-sized city: First, we obtained the city's land use layer, transportation network layer, school and hospital location data, river and industrial area location data. Then, we used GIS software to overlay these layers and found that high-density population areas overlapped with traffic congestion areas, and industrial areas were adjacent to residential areas.

[0040] Buffer zones were set for these specific patterns or abnormal areas, and air quality, noise levels, population distribution, and traffic conditions were analyzed within the buffer zones. It was found that the noise impact of the school 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 the transportation network was evaluated using traffic flow data, road capacity information, and public transportation operation data. It was found that there were bottleneck nodes and congestion points in the transportation network, and that public transportation services were insufficient in some areas.

[0041] Based on these analysis results, key issues and potential risks in urban municipal planning were identified, including irrational land use, traffic congestion, inadequate public facilities, environmental pollution, and potential disaster risks and social conflict risks. These results were integrated into a report, and recommendations and solutions to these issues were proposed.

[0042] Through in-depth analysis of urban spatial data by machines, planning decision makers can have a more scientific understanding of key issues and potential risks in urban municipal planning, and thus make more informed decisions. The identified key issues and potential risks provide planners with clear directions and goals for improvement, making planning more targeted and effective. By solving key issues and potential risks in urban municipal planning, the sustainable development of cities can be promoted and the quality of life of residents can be improved. Large amounts of urban spatial data can be processed quickly and accurately, and in-depth analysis can be performed, improving planning efficiency. By identifying potential risks in advance and formulating corresponding countermeasures, risks in the planning implementation process can be reduced.

[0043] In another preferred embodiment of the present invention, the above step 124 constructs an urban transportation network model, including a road network, rail transit lines and public transportation lines, and uses 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, so as to obtain network analysis results, which may include: Step 1241, obtaining information about roads in the city, including road type, road length, width, number of lanes, speed limit attributes, and obtaining line maps, station information, operating hours, and train frequencies of subway and light rail transit; Step 1242, integrating the road, rail transit and public transportation line data into a network topology structure, wherein nodes represent intersections or stations, and edges represent road segments or track segments; Step 1243, assigning corresponding attributes to nodes and edges in the network topology, including road type, length, capacity, speed limit, rail transit line type, public transportation frequency, and calculating the path between any two points in the network topology to evaluate the connectivity of the network and identify isolated nodes or sub-networks; Step 1244, using the assigned traffic flow data and road capacity information attributes, calculate the congestion level for each road or track segment, obtain the congestion index, and identify bottleneck nodes and congestion points; Step 1245, based on the public transportation operation data, evaluate the coverage, frequency and load factor of the public transportation service, identify the areas with insufficient services, and obtain network analysis results based on the congestion index, including the accessibility, connectivity, congestion level of the transportation network and the areas with insufficient public transportation services.

[0044] In the embodiment of the present invention, the road layer is extracted from the urban spatial database to obtain the type, length, width, number of lanes and speed limit attributes of each road, ensure the integrity and accuracy of the road data, and complete or correct the missing or erroneous data. Obtain the route map of subways, light rails and other rail transit, including the route direction, station location and name. Collect the operation data of rail transit such as operating hours and train frequency to ensure the real-time and accuracy of the data.

[0045] Step 1242, defining road intersections, rail transit stations, etc. as nodes in the network topology structure.

[0046] 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 programming language to build the network topology, and ensure the connection relationship between nodes and edges is correct.

[0047] Step 1243, for road intersection nodes, assign their location coordinates (such as longitude and latitude) as basic attributes; for rail transit station nodes, in addition to location coordinates, attributes such as station name and line to which they belong can also be assigned; for road segment edges, assign their road type (such as expressway, urban trunk road, branch road, etc.), length, capacity (i.e., the maximum number of vehicles that can pass through per unit time), speed limit and other attributes; for track segment edges, assign their track line type (such as subway, light rail), length, train speed (or average speed), frequency interval and other attributes.

[0048] The Dijkstra algorithm is a commonly used shortest path algorithm that is applicable to weighted graphs, and the edge weights are non-negative. In this scenario, the length of the road segment, the travel time (considering the speed limit and traffic flow), or the travel time of rail transit is used as the edge weight.

[0049] Set the distance from the starting node to itself to 0, and the distance to other nodes to infinity. Select the node closest to the starting node from the nodes that have not been visited, update the distances of its adjacent nodes, 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.

[0050] set up is the network topology, where is a node set, is an edge set. For any two points , , Dijkstra algorithm calculation arrive The shortest path distance .initialization , For each unvisited node , if there is an edge ,and (in for 's predecessor node), then update .

[0051] If for any two points , , there is a arrive If there is a path, the network is said to be connected.

[0052] If there is at least one pair of points , , so that there is no arrive The network is said to be disconnected if there is no path.

[0053] The path between any two points is calculated using the Dijkstra algorithm. If a path cannot be calculated between a pair of points, it is identified as an isolated node or subnetwork. , use Dijkstra algorithm to calculate the path. If for a pair of nodes , the algorithm returns no solution or the path distance is infinite, then and Belong to different subnetworks or An isolated node.

[0054] Step 1244, using the traffic flow data and road capacity information, calculate the congestion level of each road or track segment. The congestion level can be represented by a congestion index, such as the ratio of traffic flow to road capacity.

[0055] Based on the congestion calculation results, the road or track sections with higher congestion index are identified. The starting and ending nodes of these congested sections are analyzed to identify bottleneck nodes.

[0056] Step 1245, based on the public transportation operation data, evaluate the coverage, frequency and full load rate of public transportation services. Use GIS software or programming language to calculate the service range of each station and count the areas with insufficient services. Combining the above analysis results, the accessibility, connectivity, congestion level and insufficient areas of public transportation services of the transportation network are obtained. The analysis results are presented in the form of charts, reports, etc.

[0057] Suppose you are analyzing the transportation network of a large city. First, obtain the road information in the city, including road type, length, width, number of lanes, and speed limit attributes, as well as the route map, station information, operating hours, and train frequency of rail transit such as subways and light rails. Integrate this data into a network topology, where nodes represent intersections or stations, and edges represent road segments or track segments. It assigns corresponding attributes to each node and edge, such as road type, length, capacity, etc.

[0058] The graph theory algorithm is used to calculate the path between any two points in the network topology, evaluate the connectivity of the network, and identify isolated nodes or sub-networks. At the same time, the congestion level of each road or track section is calculated using traffic flow data and road capacity information, and the congestion index is obtained, and bottleneck nodes and congestion points are identified. The coverage, frequency and full load rate of public transportation services are evaluated based on public transportation operation data, and areas with insufficient services are identified. Based on the above analysis results, the accessibility, connectivity, congestion level and insufficient areas of public transportation services of the transportation network are obtained, and these results are presented to planning decision makers in the form of charts, reports, etc.

[0059] Through in-depth analysis of the urban transportation network, planning decision makers can have a more scientific understanding of the current status and problems of the transportation network, and thus make more informed decisions. The identified bottleneck nodes, congestion points, and areas with insufficient public transportation services provide planners with clear improvement directions and goals, which helps to optimize the allocation of transportation resources. By solving problems in the transportation network, such as congestion and bottlenecks, it is possible to improve traffic efficiency and reduce traffic delays and congestion time. Optimizing the transportation network helps reduce traffic emissions and energy consumption, and promotes the sustainable development of the city. By identifying areas with insufficient public transportation services and taking corresponding improvement measures, the service quality of public transportation can be improved to meet the travel needs of more citizens.

[0060] In another preferred embodiment of the present invention, the calculation formula of the congestion index is: ; in, represents the congestion index; Indicates actual traffic flow; Indicates road capacity; represents the basic congestion coefficient; Indicates the speed deviation coefficient; Indicates the actual speed of the vehicle on the road; Indicates the preset speed; Indicates the actual vehicle density on the road; represents the expected vehicle density on the road; represents the time deviation coefficient; Indicates the actual time required for a vehicle to pass through the road; Indicates the expected time for a vehicle to pass the road; represents the weather influence coefficient; Represents the weather impact index.

[0061] In the embodiment of the present invention, the number of vehicles on the road is collected in real time by traffic monitoring equipment (such as cameras, sensors). . Determine the maximum number of vehicles that the road can accommodate based on road design and specifications . Based on historical traffic data and experience, set a basic coefficient To reflect the level of congestion. The actual speed of vehicles is obtained through GPS data and traffic signal system. , and set an ideal preset speed . Use sensors and traffic models to calculate the actual vehicle density on the road and ideal vehicle density . The actual travel time of vehicles is recorded through the traffic monitoring system , and calculate the expected travel time based on the road length and preset speed Obtain real-time weather data (such as rainfall, snow, visibility, etc.) from the meteorological department and set the impact index based on its impact on traffic .

[0062] Calculate the deviation ratio between the actual driving speed and the preset speed, that is, , calculate the deviation ratio between the actual wheel density and the expected wheel density, that is, , calculate the deviation ratio between the actual passing time and the expected passing time, that is, . Determine the impact of speed deviation on congestion based on historical data analysis , analyze the impact of time deviation on traffic flow and set appropriate coefficients , according to the specific impact of weather on traffic (such as slippery roads due to rainy days), set a reasonable coefficient .

[0063] Substitute all collected and processed data into the formula , calculate the congestion index , this index is used to reflect the current road congestion level.

[0064] Through real-time data collection and analysis, the congestion of roads can be accurately assessed, providing a scientific basis for traffic management. Traffic management departments can adjust traffic signals and release road condition information in a timely manner according to the congestion index, guide vehicles to divert reasonably, and improve overall traffic efficiency. When traffic conditions change due to bad weather or emergencies, management strategies can be adjusted quickly to reduce the impact on traffic. Long-term data accumulation and analysis can help optimize road design and planning, and improve road use efficiency and safety. By providing accurate traffic information, it helps the public plan the best travel routes and reduce travel time and costs.

[0065] In a preferred embodiment of the present invention, the above step 13 uses a differential evolution algorithm to simulate, predict and optimize different municipal planning schemes based on the data analysis results, and iteratively optimizes multiple planning schemes through differences between populations and evolutionary strategies to automatically generate a final urban municipal planning scheme including road planning, public facilities layout, environmental protection strategy, and disaster risk assessment, which may include: Step 131, setting the parameters of the differential evolution algorithm, including population size, crossover probability and mutation factor, and determining the objective function and constraint conditions of the municipal planning problem based on the data analysis results; Step 132, randomly generating a set of initial municipal planning schemes, each of which is a vector containing road planning, public facilities layout, environmental protection strategy, and disaster risk assessment information; Step 133, simulate and predict each initial municipal planning scheme through the objective function, calculate the impact on urban traffic smoothness, environmental quality, and public facilities utilization efficiency, and obtain the objective function value; The mutation, crossover and selection operations are repeated until the preset number of iterations is reached, and the final municipal planning scheme, i.e., the final solution, is determined from the final population according to the objective function value.

[0066] In the embodiment of the present invention, the number of individuals participating in the evolution is determined, and the probability of gene exchange between individuals is controlled according to the complexity of the problem and the setting of computing resources, which affects the diversity of solutions, determines the amplitude of the mutation operation, and affects the exploration ability of the search space. Based on the data analysis results, an objective function is constructed to quantify the advantages and disadvantages of the municipal planning scheme.

[0067] Step 132, for each individual (municipal planning scheme), specific parameters of road planning, public facilities layout, environmental protection strategy, and disaster risk assessment are randomly generated.

[0068] Step 133, use tools such as traffic simulation software and environmental assessment models to simulate each plan and predict its performance in the future. Collect simulation data such as traffic flow, pollutant emissions, and visits to public facilities. Based on the simulation results, calculate the score of each plan on the objective function, considering the comprehensive effect of all sub-goals (traffic, environment, and facility utilization). For each individual in the population, select three different individuals (usually randomly selected) and calculate new mutant individuals through mutation factors. According to the crossover probability, determine the gene exchange method between the mutant individual and the original individual, generate test individuals, compare the objective function values ​​of the test individuals and the original individuals, and retain individuals with better performance to enter the next generation. Repeat the mutation, crossover, and selection operations until the preset number of iterations is reached, and select the individual with the best objective function value from the final population as the final municipal planning plan.

[0069] Suppose you are processing a population of 100 initial municipal planning proposals, each of which contains the following information: Road planning: road width, traffic signal configuration.

[0070] Layout of public facilities: location and capacity of schools and hospitals.

[0071] Environmental protection strategies: green space distribution, pollution control measures.

[0072] Disaster risk assessment: flood control facilities, earthquake safety zone demarcation.

[0073] First, the 100 scenarios were randomly generated, and traffic simulation software and environmental models were used to predict each scenario. Based on the prediction results, the objective function values ​​of each scenario were calculated, such as traffic flow (measured by average commuting time), environmental quality (measured by air quality index), and public facility utilization efficiency (measured by facility visits).

[0074] Perform the mutation, crossover, and selection operations of the differential evolution algorithm. For each individual, three different individuals are randomly selected, and new mutant individuals are generated through the mutation factor. Then, according to the crossover probability, the gene exchange method between the mutant individual and the original individual is determined to generate the test individual. Finally, the objective function value of the test individual is compared with that of the original individual, and the individuals with better performance are retained to enter the next generation. This process is repeated many times until the preset number of iterations is reached. Finally, the individual with the best objective function value is selected from the final population as the final municipal planning solution.

[0075] The differential evolution algorithm can efficiently explore the solution space and find the final municipal planning scheme. Through the design of the objective function, multiple aspects such as transportation, environment, and public facilities utilization can be comprehensively considered to achieve comprehensive optimization. The algorithm can adapt to different municipal planning problems and can flexibly respond to various complex situations by adjusting parameters and objective functions. The generated final municipal planning scheme can provide a scientific basis for government decision-making and improve decision-making efficiency and accuracy. By optimizing environmental protection strategies and disaster risk assessment, the sustainable development of the city can be promoted and the quality of life of residents can be improved.

[0076] In a preferred embodiment of the present invention, the calculation formula of the objective function is: ; in, Representation scheme The objective function value of , , represents the weight coefficient; Representation scheme Average traffic speed under Representation scheme The congestion index under Representation scheme Average traffic delay time under Representation scheme Air quality index under Indicates the baseline value of the air quality index; Indicates the number of monitoring points; Indicated in The noise intensity measured at each monitoring point; Indicates the maximum value of the noise level; Representation scheme Water quality index under A benchmark value indicating water quality conditions; Representation scheme Frequency of use of public facilities; It represents the total capacity of public facilities; Representation scheme The service coverage of public facilities; represents the total area of ​​the study area; Representation and solution The associated utility value.

[0077] In this embodiment of the present invention, the average traffic speed of each solution is collected. , congestion index , average traffic delay time . This data can be obtained through traffic monitoring systems, GPS data and traffic reports. Get the Air Quality Index , Noise intensity at each monitoring point , Water Quality Index These data are provided by environmental protection departments or related sensor networks. Collect the frequency of use of public facilities 、Service coverage , and the associated utility values . This can be done through surveys by public utility management agencies and user feedback. Setting a baseline value for the air quality index , the maximum value of the noise level , Baseline values ​​of water quality , the total capacity of public facilities , and the total area of ​​the study area . According to the importance of each indicator, set the weight coefficient , , These weights can be determined through historical data analysis or decision analysis methods.

[0078] calculate Used to evaluate the efficiency of traffic conditions, calculate , used to assess environmental quality, calculate , which is used to evaluate the efficiency and service level of public facilities. Substitute the above calculated sub-items into the objective function formula to calculate each plan The objective function value of . Compare the objective function values ​​of different solutions to determine the final recommendation.

[0079] By comprehensively considering factors such as transportation, environment and public facilities, the advantages and disadvantages of each plan can be comprehensively evaluated, avoiding the one-sidedness of single indicator evaluation, providing scientific basis for decision makers, helping to select the final plan, and improving decision-making efficiency and accuracy. By optimizing the configuration of the plan, resources can be effectively utilized, waste can be reduced, and the efficiency and service level of public facilities can be improved. Considering factors such as environment and air quality in the selection of plans will help promote green development, reduce environmental pollution, and improve the quality of life of residents. Through the evaluation of traffic indicators, traffic bottlenecks can be identified, traffic flows can be optimized, traffic efficiency can be improved, and congestion and delays can be reduced. This method can adjust weights and indicators as needed to adapt to the specific needs of different cities and regions, and has good scalability and flexibility.

[0080] In a preferred embodiment of the present invention, the above step 14, evaluating the final urban municipal planning scheme, and obtaining the evaluation results of the final urban municipal planning scheme in terms of urban traffic smoothness, environmental quality, and public facilities utilization efficiency, may include: Step 141, obtaining the final municipal planning scheme, including information on road planning, public facilities layout, environmental protection strategy, and disaster risk assessment, and extracting key features from the final municipal planning scheme, including road network structure, traffic signal settings, public facilities location and capacity, green coverage, and environmental protection measures; Step 142, input the key features into the preset evaluation model, analyze and calculate the key features according to the learned patterns and rules, and generate prediction results, including indicators of urban traffic smoothness, environmental quality and public facility utilization efficiency.

[0081] In an embodiment of the present invention, the final municipal planning scheme is read from the output of the optimization algorithm, and the scheme includes information on road planning, public facilities layout, environmental protection strategy, disaster risk assessment, and other aspects.

[0082] The key features extracted are: Road network structure: road type (such as expressways, main roads, secondary roads, branch roads), width, connection relationships and other information to build a road network topology map.

[0083] Traffic signal settings: location and timing of traffic lights.

[0084] Location and capacity of public facilities: location coordinates, service scope, capacity (such as number of beds and seats) and other information of public facilities such as schools, hospitals, parks, and sports facilities.

[0085] Green coverage rate: Green area, green type (such as lawns, trees, flower beds) and distribution map within the planning area are used to calculate the green coverage rate.

[0086] Environmental protection measures: information on the location, treatment capacity, operation strategy, etc. 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.

[0087] Step 142, according to the evaluation requirements, select a suitable evaluation model, such as a traffic simulation model, an environmental evaluation model, a public facility use prediction model, etc. These models can be based on physics, statistics or machine learning. The extracted key features are sorted according to the format required by the model, such as converting the road network structure into topological map data, converting the traffic signal settings into time series or event data, and converting the location and capacity of public facilities into geospatial data. Use the traffic simulation model to simulate the driving process of vehicles 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 evaluate the rationality of signal timing. Use the environmental evaluation model to predict indicators such as air quality index, noise level, water quality, etc. based on information such as green coverage and environmental protection measures, and consider the impact of factors such as urban expansion, industrial emissions, and traffic exhaust on environmental quality. Use the public facility use prediction model to predict indicators such as usage frequency and service coverage based on information such as the location, capacity, and service scope of public facilities, analyze the rationality of public facility layout, and evaluate whether it meets the needs of residents. The results of analysis and calculation are organized into reports or visual charts, including indicators of urban traffic flow, environmental quality and efficiency of public facilities utilization.

[0088] Assume that the final municipal planning scheme has been obtained and the following key features have been extracted: Road network structure: A topological map including main roads, secondary roads, and branch roads, with the width of each road known.

[0089] Traffic signal setting: A traffic signal will be set up every 500 meters on the main road, and the timing plan has been determined.

[0090] Location and capacity of public facilities: The location and capacity of public facilities such as schools, hospitals, and parks are known.

[0091] Green coverage rate: The green coverage rate in the planning area is 30%.

[0092] Environmental protection measures: Air purification facilities, sewage treatment stations, etc. are installed, and environmental protection policies are in line with national standards.

[0093] These key features are input into the preset evaluation model. The traffic simulation model simulates the vehicle driving process according to the road network structure and traffic signal settings, and calculates the average speed, congestion index and traffic delay time. The environmental assessment model predicts the air quality index, noise level and water quality according to the green coverage rate and environmental protection measures. The public facility use prediction model predicts the frequency of use and service coverage according to the location and capacity of public facilities. Finally, a detailed evaluation report is generated, which includes indicators of urban traffic smoothness, environmental quality and public facility utilization efficiency, as well as corresponding visualization charts.

[0094] By extracting key features and inputting them into the evaluation model, the machine can comprehensively evaluate the performance of the final municipal planning scheme in terms of urban traffic flow, environmental quality, and efficiency of public facilities utilization. The evaluation results provide a scientific basis for decision makers, helping them understand the advantages and disadvantages of the scheme and make more informed decisions.

[0095] Based on the evaluation results, the plan can be further optimized and adjusted to improve the overall performance of the city and the quality of life of residents. By evaluating the utilization efficiency of public facilities, resources can be allocated more reasonably to avoid waste and duplication. Evaluating environmental quality indicators helps promote the sustainable development of cities and reduce environmental pollution and ecological damage. Automating the evaluation process improves the efficiency and accuracy of the evaluation and saves manpower and time costs.

[0096] In a preferred embodiment of the present invention, the above step 15, establishing a dynamic update process for municipal planning results, updating and adjusting the planning results in real time according to the newly acquired data information and the feedback on the effect after the implementation of the planning scheme, may include: Step 151, receiving newly acquired data information and feedback on the effect of the implementation of the urban municipal planning scheme, and determining the triggering conditions for dynamic update, including regular update and update triggered by data change; Step 152, integrating and analyzing the newly acquired data information and the effect feedback after the implementation of the planning scheme, evaluating the implementation effect of the planning scheme, and determining whether to adjust or update the planning scheme to obtain the processing results of the new data and effect feedback; Step 153, based on the processing results of the new data and effect feedback, determine whether the triggering conditions for dynamic update are met; if met, start the update process to update and adjust the municipal planning plan in real time.

[0097] In the embodiment of the present invention, newly acquired data information from various data sources is automatically received through a preset data interface or API, such as traffic flow data, environmental quality monitoring data, public facility usage data, etc. At the same time, feedback on the effects of the implementation of the urban municipal planning scheme is received, which comes from resident surveys, expert evaluations, monitoring system reports, etc. According to the preset rules, the trigger conditions for dynamic updates are determined. These conditions include regular updates (such as monthly or quarterly updates), data change trigger updates (such as when traffic flow exceeds a threshold, environmental quality deteriorates to a certain extent, trigger updates), etc.

[0098] Step 152, integrate the received new data information and effect feedback to form a unified data format and structure. Use data analysis tools to analyze the new data information and effect feedback. For example, by comparing the traffic flow data before and after implementation, evaluate the traffic improvement effect; by comparing the environmental quality monitoring data, evaluate the effectiveness of environmental protection measures; by comparing the public facility usage data, evaluate the rationality of the facility layout, etc. According to the data analysis results, evaluate the implementation effect of the planning scheme. If the implementation effect reaches the expected goal, there is no need to adjust or update the planning scheme; if the implementation effect does not reach the expected goal, it is necessary to consider adjusting or updating the planning scheme. According to the evaluation results, determine the processing results of the new data and effect feedback. This includes whether the planning scheme needs to be adjusted, what specific content to adjust, and the degree of adjustment.

[0099] Step 153, based on the trigger conditions determined in step 151, determine whether the trigger conditions for dynamic update are currently met. For example, if the current time point is a regular update time point, or if new data indicates that the traffic flow exceeds a preset threshold, the trigger condition is met. If the trigger condition is met, the update process is started. This includes extracting 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. In the update process, the municipal planning scheme is updated and adjusted in real time to ensure that the planning scheme is always consistent with the actual situation.

[0100] Assume that newly acquired traffic flow data and environmental quality monitoring data, as well as resident survey feedback after the implementation of the city's municipal planning scheme, have been received. Based on the preset trigger conditions, a dynamic update is decided.

[0101] Newly acquired traffic flow data and environmental quality monitoring data, as well as resident survey feedback, were received through the data interface. These data were integrated to form a unified data format and structure. The integrated data was analyzed using data analysis tools. By comparing the traffic flow data before and after implementation, it was found that the traffic flow of some sections exceeded the preset threshold; by comparing the environmental quality monitoring data, it was found that the environmental quality of some areas had deteriorated. According to the resident survey feedback, it was learned that some residents were dissatisfied with the use of public facilities. Combining the above analysis results, it was evaluated that the implementation effect of the planning scheme did not meet the expected goals.

[0102] Based on the evaluation results, the processing results of the new data and effect feedback were determined: the road widening plan in the planning scheme needs to be adjusted, the investment in environmental protection facilities needs to be increased, the layout of public facilities needs to be optimized, etc. At the same time, it is determined that the trigger conditions for dynamic updates are currently met (because the traffic flow exceeds the preset threshold and the environmental quality has deteriorated). The update process was started, and the current planning scheme was extracted from the database. Based on the processing results, the machine modified the planning scheme: the road widening plan was adjusted, the investment in environmental protection facilities was increased, the layout of public facilities was optimized, etc. The modified planning scheme was saved back to the database and the relevant personnel were notified.

[0103] Through the dynamic update process, new data and information can be received in real time, and the planning scheme can be updated and adjusted in real time to ensure the real-time and accuracy of the planning scheme. The dynamic update process enables the planning scheme to adapt to the changes and needs of urban development, with higher adaptability and flexibility. Through data analysis and evaluation, it can provide scientific basis for decision makers to help them make more optimized and wise decisions. The dynamic update process can ensure the rationality and effectiveness of the planning scheme, improve resource utilization efficiency, and avoid waste and duplication of construction. By optimizing the layout of public facilities and improving environmental quality, the dynamic update process can improve the quality of life and satisfaction of residents. The dynamic update process helps promote the sustainable development of cities, reduce environmental pollution and ecological damage, and achieve coordinated development of economy, society and environment.

[0104] like Figure 2 As shown, an embodiment of the present invention further provides a smart city municipal planning system based on geographic information, comprising: The data acquisition module is used to obtain urban spatial data from different data sources, including terrain 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 zone analysis, and network analysis to identify key issues and potential risks in urban municipal 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 the differential evolution algorithm. It iteratively optimizes multiple planning schemes through differences between populations and evolutionary strategies, and automatically generates municipal planning schemes that include road planning, public facility layout, environmental protection strategies, and disaster risk assessment. The program evaluation module is used to evaluate the municipal planning program. The evaluation content includes urban traffic flow, environmental quality, and public facilities utilization efficiency to obtain the evaluation results; The dynamic update module is used to establish a dynamic update process for municipal planning results, and to update and adjust the planning results in real time based on newly acquired data information and feedback on the effects of the implementation of urban municipal planning programs.

[0105] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0106] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0107] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as 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 comprises: Obtain urban spatial data from different data sources, including topography, land use, building distribution, transportation network, population distribution and environmental indicators; Analyze urban spatial data, including spatial overlay analysis, buffer zone analysis, and network analysis, to identify key issues and potential risks in urban municipal planning and obtain data analysis results; Based on the data analysis results, the differential evolution algorithm is used to simulate, predict and optimize different municipal planning schemes. Through the differences between populations and evolutionary strategies, multiple planning schemes are iteratively optimized to automatically generate the final urban municipal planning scheme including road planning, public facilities layout, environmental protection strategy, and disaster risk assessment. Evaluate the final urban municipal planning scheme and obtain the evaluation results of the final urban municipal planning scheme in terms of urban traffic flow, environmental quality, and efficiency of public facilities utilization; Establish a dynamic update process for municipal planning results, and update and adjust planning results in real time based on newly acquired data and information and feedback on the effects of the implementation of urban municipal planning programs.

2. The method for smart city municipal planning based on geographic information according to claim 1, characterized in that: Analyze urban spatial data, including spatial overlay analysis, buffer zone analysis, and network analysis, to identify key issues and potential risks in urban municipal planning, and obtain data analysis results, including: Obtain urban spatial data, including land use layers, transportation network layers, urban facilities, schools, hospital location data, river and industrial area location data, and overlay data from different layers to identify the relationship between land use and transportation, so as to obtain overlay analysis results; Based on the results of the overlay analysis, 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 ranges for specific patterns or abnormal areas, including schools, hospitals, rivers, and industrial areas; Within the buffer zone, analyze environmental characteristics, including air quality, noise level, 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 school noise on surrounding residents, or the potential threat of pollutant emissions from industrial areas to the surrounding environment, to obtain buffer zone analysis results; Construct an urban transportation network model, including road networks, rail transit lines and public transportation lines, and 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; Based on the results of spatial overlay analysis, buffer zone analysis, and network analysis, key issues and potential risks in urban municipal planning are identified. Key issues include irrational land use, traffic congestion, insufficient public facilities, and environmental pollution; potential risks include disaster risks, social conflict risks, etc.

3. The smart city municipal planning method based on geographic information according to claim 2 is characterized in that: Construct an urban transportation network model, including road networks, rail transit lines and public transportation lines, and 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, including: Obtain information about roads in the city, including road type, road length, width, number of lanes, speed limit attributes, and obtain route maps, station information, operating hours, and train frequencies for subway and light rail transit; Integrate road, rail and public transport line data 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, public transportation frequency, and calculate the path between any two points in the network topology to evaluate the connectivity of the network and identify isolated nodes or sub-networks; Using the assigned traffic flow data and road capacity information attributes, the congestion level of each road or track segment is calculated to obtain the congestion index and identify bottleneck nodes and congestion points; Based on public transportation operation data, the coverage, frequency and load factor of public transportation services are evaluated, areas with insufficient services are identified, and based on the congestion index, network analysis results are obtained, including the accessibility, connectivity, congestion level of the transportation network and areas with insufficient public transportation services.

4. The method for smart city municipal planning based on geographic information according to claim 3 is characterized in that: The calculation formula of congestion index is: ; in, represents the congestion index; Indicates actual traffic flow; Indicates road capacity; represents the basic coefficient of congestion; Indicates the speed deviation coefficient; Indicates the actual speed of the vehicle on the road; Indicates the preset speed; Indicates the actual vehicle density on the road; represents the expected vehicle density on the road; represents the time deviation coefficient; Indicates the actual time required for a vehicle to pass through the road; Indicates the expected time for a vehicle to pass the road; represents the weather influence coefficient; Represents the weather impact index.

5. The method for smart city municipal planning based on geographic information according to claim 4, characterized in that: According to the data analysis results, the differential evolution algorithm is used to simulate, predict and optimize different municipal planning schemes. Through the differences between populations and evolutionary strategies, multiple planning schemes are iteratively optimized to automatically generate the final urban municipal planning scheme including road planning, public facilities layout, environmental protection strategy, and disaster risk assessment, including: 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; A set of initial municipal planning schemes are randomly generated, each of which is a vector containing information on road planning, public facilities layout, environmental protection strategies, and disaster risk assessment; Through the objective function, each initial municipal planning scheme is simulated and predicted, and the impact on urban traffic flow, environmental quality, and public facilities utilization efficiency is calculated to obtain the objective function value; The mutation, crossover and selection operations are repeated until the preset number of iterations is reached, and the final municipal planning scheme, i.e., the final solution, is determined from the final population according to the objective function value.

6. The method for smart city municipal planning based on geographic information according to claim 5, characterized in that: The calculation formula of the objective function is: ; in, Representation scheme The objective function value of , , represents the weight coefficient; Representation scheme Average traffic speed under Representation scheme The congestion index under Representation scheme Average traffic delay time under Representation scheme Air quality index under Indicates the baseline value of the air quality index; Indicates the number of monitoring points; Indicated in The noise intensity measured at each monitoring point; Indicates the maximum value of the noise level; Representation scheme Water quality index under A benchmark value indicating water quality conditions; Representation scheme Frequency of use of public facilities; It represents the total capacity of public facilities; Representation scheme The service coverage of public facilities; represents the total area of ​​the study area; Representation and solution The associated utility value.

7. The method for smart city municipal planning based on geographic information according to claim 6, characterized in that: Evaluate the final urban municipal planning scheme and obtain the evaluation results of the final urban municipal planning scheme in terms of urban traffic flow, environmental quality, and efficiency of public facilities utilization, including: Obtain the final municipal planning scheme, including information on road planning, public facilities 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, public facilities location and capacity, green coverage, and environmental protection measures; The key features are input into the preset evaluation model, and the key features are analyzed and calculated according to the learned patterns and rules to generate prediction results, including indicators of urban traffic smoothness, environmental quality and public facilities utilization efficiency.

8. The method for smart city municipal planning based on geographic information according to claim 7, characterized in that: Establish a dynamic update process for municipal planning results, and update and adjust planning results in real time based on newly acquired data and information and feedback from the implementation of planning solutions, including: Receive newly acquired data information and feedback on the effects of the implementation of urban municipal planning programs, and determine the trigger conditions for dynamic updates, including regular updates and updates triggered by data changes; Integrate and analyze the newly acquired data and information and the feedback on the implementation of the planning scheme, evaluate the implementation effect of the planning scheme, and determine whether to adjust or update the planning scheme to obtain the processing results of the new data and effect feedback; Based on the processing results of new data and effect feedback, determine whether the trigger conditions for dynamic updates are met; if met, start the update process to update and adjust the municipal planning plan in real time.

9. A smart city municipal planning system based on geographic information, the system implementing the method as claimed in any one of claims 1 to 8, characterized in that: include: The data acquisition module is used to obtain urban spatial data from different data sources, including terrain 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 zone analysis, and network analysis to identify key issues and potential risks in urban municipal 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 the differential evolution algorithm. It iteratively optimizes multiple planning schemes through differences between populations and evolutionary strategies, and automatically generates municipal planning schemes that include road planning, public facility layout, environmental protection strategies, and disaster risk assessment. The program evaluation module is used to evaluate the municipal planning program. The evaluation content includes urban traffic flow, environmental quality, and public facilities utilization efficiency to obtain the evaluation results; The dynamic update module is used to establish a dynamic update process for municipal planning results, and to update and adjust the planning results in real time based on newly acquired data information and feedback on the effects of the implementation of urban municipal planning programs.

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

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