A smart city planning method and system based on digital twinning

Abstract

The application discloses a kind of wisdom city planning method and system based on digital twinning, it is related to city planning technical field, it solves the technical problem that it is lack of the automatic generation and matching of index constraint list to depend on artificial interpretation regulation text, planning suggestion can ignore key constraint condition, lead to scheme unfeasible, simultaneously, scheme subjectivity is strong, lack unified quantitative model;By NLP technology real-time comparison planning suggestion and regulation clause, conflict index is automatically marked, combined with planning condition and preliminary index calculation result, the project planning suggestion generated strictly follows policy framework, avoids human interpretation error or risk of violation of regulations.Establish the collaborative quantitative evaluation model of single building and regional landscape, evaluate the landscape effect of planning suggestion from spatial layout, visual corridor, skyline, color coordination and other dimensions. Through quantitative score, the fusion degree of project and surrounding environment is directly reflected, and the deviation of subjective evaluation is avoided.

Description

Technical Field

[0001] This invention belongs to the field of urban planning, specifically a smart city planning method and system based on digital twins. Background Technology

[0002] Digital twin is a technological system that uses technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence to construct virtual mapping models of physical entities (such as cities, buildings, and transportation systems), enabling two-way interaction between the virtual and real worlds, dynamic simulation, and intelligent optimization. The deep integration of digital twin technology with urban planning concepts is driving the transformation of planning models from "traditional experience-based" to "modern intelligent," necessitating the development of smart city planning solutions based on digital twins.

[0003] Traditional planning relies on manual interpretation of legal texts and lacks automated generation and matching of indicator constraint lists. Planning recommendations may overlook key constraints, leading to infeasible solutions. It also relies on expert experience or public perception, resulting in highly subjective solutions. Furthermore, it lacks a unified quantitative model, making it inefficient and prone to errors. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art; to this end, this invention proposes a smart city planning method and system based on digital twins to solve the technical problems of relying on manual interpretation of regulatory texts, lack of automated generation and matching of indicator constraint lists, planning suggestions may ignore key constraints, resulting in infeasible solutions, and strong subjectivity of solutions, lack of a unified quantitative model.

[0005] To address the above problems, the first aspect of this invention provides a smart city planning method and system based on digital twins, comprising the following steps: By using NLP technology to analyze the basic information of urban planning projects, matching the corresponding statutory planning texts and non-statutory planning texts from the planning basis database, extracting binding and guiding indicators, and generating an indicator constraint list; The system automatically retrieves project surveying data and operational performance data, and generates preliminary indicators by analyzing core indicators of planning conditions; Based on the list of indicator constraints and the preliminary indicator calculation results of the planning conditions, project planning suggestions are generated. By analyzing the matching degree between the project planning suggestions and the planning basis under multi-objective optimization constraints, the project planning suggestions are optimized and verified. By analyzing the unity of individual units and regions, a quantitative evaluation model for landscape synergy is established to assess the landscape coordination of project planning recommendations, and a landscape indicator adjustment formula is set to optimize the indicators of project planning recommendations. During the project construction process, the project is divided into blocks, and the actual landscape data of the completed blocks is obtained. This data is then compared with the actual project planning proposals. By analyzing the fit between the landscape implementation and the proposed solutions, the parameters of the landscape indicator adjustment formula are adjusted accordingly.

[0006] Optionally, in one example of the above aspects, NLP technology is used to parse the basic information of urban planning projects, match the corresponding statutory planning texts and non-statutory planning texts from the planning basis database, extract binding indicators and guiding indicators, and generate an indicator constraint list, including the following steps: For statutory planning documents, integrate territorial spatial planning documents and regulatory detailed planning documents, extract land use location, nature and scale fields, and construct a structured planning database; For non-statutory planning documents, they are incorporated into special planning documents and industrial planning documents, and their planned land use location, target development nature and target development scale are marked and added to the structured planning database; The NLP (Natural Language Processing) tools were used to segment and tag the legal and non-legal planning texts in the structured planning database. The binding and guiding indicators are set as key entity information. The NER named entity recognition model is trained to identify key entity information in statutory planning texts and non-statutory planning texts in the structured planning database, and the key entity information is marked and associated with the corresponding values ​​or ranges. Spatial overlay analysis is performed between the coordinates or address of the project site and the boundaries of the plots in the structured planning database to determine the planning scope to which the project site belongs; Retrieve the mandatory clauses for the corresponding land parcels from the statutory planning texts within the planning scope of the structured planning database, and their marked key entity information, and mark them as hard constraints; Retrieve the guiding requirements for the corresponding land parcels and their marked key entity information from non-statutory planning texts within the planning scope of the structured planning database, marking them as soft constraints; statutory planning texts specifically include: territorial spatial planning and control planning. Hard and soft constraints will be extracted and linked to land use locations in the structured planning database to generate a list of indicator constraints.

[0007] Optionally, in one example of the above aspects, the system automatically retrieves project surveying data and operational performance data, and generates preliminary indicators by analyzing core indicators of planning conditions, including the following steps: The system automatically retrieves project surveying data and operational performance data. Project surveying data includes topography, buildings, and transportation; operational performance data includes the load of surrounding public services and traffic efficiency. Preliminary indicators are generated by analyzing the core indicators of the planning conditions.

[0008] Optionally, in one example of the above aspects, based on the list of indicator constraints and the preliminary indicator calculation results of the planning conditions, a project planning recommendation is generated, including the following steps: Import the preliminary indicators of the project planning conditions, correlate and screen them with the values ​​in the indicator constraint list, mark the data that do not meet the numerical requirements in the indicator constraint list, and send a manual modification prompt. By using GIS tools, the project land boundary is spatially overlaid with the plot area and surrounding facilities in the structured planning database to clarify the locational relationship of the project land boundary; The existing surrounding facilities at the project site boundary are marked, and alternative planning schemes for the project are generated, including: The compliance plan includes: planning the project land use based on the preliminary indicators of the screened project planning conditions; including the surrounding facilities marked on the project land boundary in the project planning surrounding facilities; and requiring the surrounding facilities to be implemented in accordance with the minimum standards in the indicator constraint list. The quality plan includes: planning the project land use based on the preliminary indicators of the project planning conditions after screening; excluding the surrounding facilities marked on the project land boundary from the surrounding facilities in the project planning; and requiring the surrounding facilities to be implemented in accordance with the minimum standards in the indicator constraint list. The hybrid optimization scheme includes: planning the project land use based on the preliminary indicators of the screened project planning conditions; excluding surrounding facilities already marked at the project land boundary from the planned surrounding facilities, and requiring surrounding facilities to meet the minimum standards in the indicator constraint list; simultaneously, while meeting the numerical requirements in the indicator constraint list, selecting several indicators from the preliminary indicators of the project planning conditions to set key guiding optimization indicators, optimizing the original preliminary indicator values ​​by 10%. The NER model is used to extract the location, nature, and scale of land use from the text of the project planning proposal, and then compare them with the planning nature and scale of the corresponding plots in the structured planning database to verify consistency.

[0009] Optionally, in one example of the above aspects, the location, nature, and size of the land use are extracted from the text of the project planning proposal using a NER model, and compared with the planning nature and size of the corresponding plot in the structured planning database to verify consistency, including: By using dependency parsing, the constraint relationships between key entity information and constraint indicators are extracted; Based on the constraints between key entity information and binding indicators, the location, nature, and scale of the project land are compared with the location, planning nature, and scale of the corresponding plots in the structured planning database. The cosine similarity of the corresponding location, nature, and scale texts is calculated, and the results are weighted and summed to obtain the consistency coefficient. Data with consistency coefficients below the threshold are marked with an early warning and a manual modification prompt is sent.

[0010] Optionally, in one example of the above aspects, by analyzing the matching degree between the project planning recommendations and the planning basis under multi-objective optimization constraints, the project planning recommendations are optimized and verified, including the following steps: We set optimization constraint ranges for various indicators in the alternative solutions proposed in the project planning, and calculate compliance verification coefficients by analyzing the multi-objective optimization constraints to analyze the matching degree between the project planning recommendations and the planning basis. ; Where: Com represents the optimization verification result of the indicators in the alternative solutions proposed in the project planning, [In min,k In max,k ] represents the optimization constraint interval for the k-th indicator. min,k To find the minimum value of the optimization constraint for the k-th index, In max,k Let be the maximum value of the optimization constraint for the k-th indicator, where k∈(1,2,…,m), and m is the total number of indicators; The system will issue warnings and make corrections for anomalies. If Com=0, the system will automatically mark the abnormal indicators, push the conflict basis, and provide correction plans for compliance, quality and hybrid optimization based on historical cases and through the big language model, and send a manual review prompt; otherwise, it will determine that the alternative plan does not need to be modified.

[0011] Optionally, in one example of the above aspects, by analyzing the unity of individual units and regions, a quantitative evaluation model for landscape synergy is established to assess the landscape coordination of the project planning recommendations, and a landscape index adjustment formula is set to optimize the indicators of the project planning recommendations, including the following steps: Landscape parameters of the project area and surrounding areas were collected, and a quantitative evaluation model for landscape synergy was established by analyzing both individual and regional parameters. Where L represents the overall coordination degree of landscape collaboration; Lv represents the visual coordination score; Le represents the ecological integration score; Lf represents the functional adaptability score; ωv represents the weight of the evaluation dimension, ωe represents the ecological integration weight, and ωf represents the functional adaptability weight, which are set according to the key points of urban planning. By establishing a quantitative evaluation model for landscape synergy, the comprehensive coordination degree of landscape synergy is calculated, the landscape coordination of project planning recommendations is evaluated, and early warnings are issued for project planning recommendations with a comprehensive coordination degree of landscape synergy below the threshold.

[0012] Optionally, in one example of the above aspects, setting a landscape index adjustment formula to optimize the indicators of the project planning recommendations includes the following steps: For project planning recommendations that have passed the landscape harmony assessment, the indicators of the project planning recommendations are optimized by setting landscape indicator adjustment formulas.

[0013] Optionally, in one example of the above aspects, during the project construction process, the project is divided into blocks, the actual landscape data of the completed blocks is obtained, and compared with the actual implemented project planning proposal. By analyzing the landscape implementation fit, the parameters of the landscape indicator adjustment formula are adjusted accordingly, including the following steps: During the project construction process, the project is divided into blocks, the actual landscape data of the completed blocks is obtained, and the target value of landscape synergy and coordination is incorporated into the block-based formula for block analysis. Under the constraints of calculation, the range of the target value of landscape synergy and coordination degree for each block in the project area is calculated. The minimum value within the range is averaged to obtain the optimized target value of landscape synergy and coordination degree, and the corresponding parameters of the original landscape index adjustment formula are replaced.

[0014] According to another aspect of this disclosure, a smart city planning system based on digital twins is provided, which adopts a smart city planning method based on digital twins as described above to realize smart city planning based on digital twins.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention generates a list of indicator constraints or preliminary indicators through data integration in a digital twin environment, forming a closed loop of "design-simulation-optimization" to reduce repeated modifications and resource waste in the physical world. Quantitative analysis based on surveying data and operational performance provides objective evidence for planning recommendations, avoiding subjective decision-making biases. NLP technology is used to compare planning recommendations with regulatory clauses in real time, automatically marking conflicting indicators such as exceeding the floor area ratio or failing to meet green space requirements, thus reducing legal risks.

[0016] This invention utilizes NLP technology to analyze a planning basis database, extracting binding and guiding indicators from statutory and non-statutory planning texts to form an indicator constraint list. Combined with the preliminary indicator calculation results of the planning conditions, the generated project planning recommendations strictly adhere to the policy framework, avoiding errors in human interpretation or the risk of violations. A collaborative quantitative evaluation model for individual buildings and the regional landscape is established, assessing the landscape effect of the planning recommendations from dimensions such as spatial layout, visual corridors, skyline, and color harmony. Quantitative scoring intuitively reflects the integration of the project with its surrounding environment, avoiding biases in subjective evaluation. During project construction, actual landscape data is obtained through block division and compared with the planning recommendations to analyze implementation fit. Based on feedback on deviations, the landscape indicator formula parameters are adjusted, forming a closed loop of "planning-construction-feedback-optimization" to ensure the effective implementation of the plan. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figures 1-2 The first aspect of this invention provides a smart city planning method and system based on digital twins, comprising the following steps: By using NLP technology to analyze core information such as land location, nature, and scale of urban planning projects, the corresponding statutory and non-statutory planning texts are matched from the planning basis database to extract binding and guiding indicators and generate an indicator constraint list. The system automatically retrieves project surveying data and operational performance data, and generates preliminary indicators by analyzing core indicators of planning conditions; Based on the list of indicator constraints and the preliminary indicator calculation results of the planning conditions, project planning suggestions are generated. By analyzing the matching degree between the project planning suggestions and the planning basis under multi-objective optimization constraints, the project planning suggestions are optimized and verified. By analyzing the unity of individual units and regions, a quantitative evaluation model for landscape synergy is established to assess the landscape coordination of project planning recommendations, and a landscape indicator adjustment formula is set to optimize the indicators of project planning recommendations. During the project construction process, the project is divided into blocks, and the actual landscape data of the completed blocks is obtained. This data is then compared with the actual project planning proposals. By analyzing the fit between the landscape implementation and the proposed solutions, the parameters of the landscape indicator adjustment formula are adjusted accordingly.

[0021] Specifically, in this embodiment, core information such as land location, nature, and scale is automatically extracted using natural language processing technology. It then matches statutory and non-statutory planning texts from the planning basis database to ensure that planning recommendations strictly adhere to the policy framework. For example, binding indicators such as plot ratio and green space ratio are extracted to avoid human interpretation errors. The system calls upon surveying and mapping data and operational performance data for cross-validation with the indicator constraint list, generating preliminary indicators that better reflect actual conditions and reducing the risk of a disconnect between planning and implementation.

[0022] The entire process, from information analysis to indicator generation and recommendation output, is automated, replacing traditional manual surveys, text comparisons, and indicator calculations, significantly shortening the planning time. For example, Xiong'an New Area uses a digital twin platform to record the entire lifecycle of its planning schemes, improving efficiency. The digital twin model can dynamically access urban operational data, enabling planning recommendations to quickly respond to urban changes.

[0023] In a digital twin environment, data integration generates a list of indicator constraints or preliminary indicators, forming a closed loop of "design-simulation-optimization," reducing repeated modifications and resource waste in the physical world. Quantitative analysis based on surveying data and operational performance provides objective evidence for planning recommendations, avoiding subjective decision-making biases. 3D visualization technology can be used to present planning schemes, allowing managers to intuitively compare the advantages and disadvantages of different options. By analyzing data such as population distribution and traffic flow, the layout of public facilities can be optimized, reducing resource idleness or over-concentration. Digital twin models can simulate extreme situations, allowing for advance planning of emergency resource reserves and evacuation routes, enhancing urban resilience.

[0024] By using NLP technology to compare planning recommendations with regulatory clauses in real time, conflicting indicators such as exceeding the floor area ratio or failing to meet the green space ratio are automatically marked, reducing legal risks. Construction plans are tested in a virtual environment to identify the risk of project rework in advance.

[0025] By analyzing the planning basis database using NLP technology, binding and guiding indicators are extracted from statutory and non-statutory planning documents to form an indicator constraint list. Combined with the preliminary indicator calculation results of the planning conditions, the generated project planning recommendations strictly adhere to the policy framework, avoiding errors in human interpretation or the risk of violations. For example, in historical preservation areas, the system can automatically identify building height restrictions to ensure that the planning recommendations meet protection requirements.

[0026] A collaborative quantitative evaluation model for individual buildings and the surrounding landscape is established to assess the landscape effects of planning recommendations from dimensions such as spatial layout, visual corridors, skyline, and color harmony. Quantitative scoring intuitively reflects the degree of integration between the project and its surrounding environment, avoiding biases from subjective evaluations. For example, the model can analyze the impact of new buildings on the skyline of historic districts and propose height adjustment suggestions.

[0027] During the planning recommendation generation phase, the system uses a multi-objective optimization algorithm based on a list of indicator constraints to balance economic, social, and environmental goals. For example, in commercial district planning, it can simultaneously optimize floor area ratio and traffic flow to avoid congestion caused by high-density development.

[0028] During project construction, actual landscape data is obtained through block division and compared with the proposed planning scheme to analyze its suitability for implementation. Based on feedback on deviations, the landscape indicator formula parameters are adjusted, forming a closed loop of "planning-construction-feedback-optimization" to ensure the effective implementation of the plan. For example, if the actual greening rate is lower than the planned value, the system can automatically increase the greening indicator requirements for subsequent blocks.

[0029] Digital twin technology maps project construction progress in real time, enabling phased evaluation through block division. If a construction deviation is detected in a certain block, the system can immediately trigger an alert and generate adjustment plans to prevent problems from accumulating. The optimization and verification of planning recommendations not only target the current project but also consider future urban development needs. For example, by simulating population growth and traffic flow changes, the flexibility of planning recommendations is assessed to ensure the long-term feasibility of the plan.

[0030] In one embodiment of the present invention, NLP technology is used to parse basic information of urban planning projects, match corresponding statutory planning texts and non-statutory planning texts from the planning basis database, extract binding indicators and guiding indicators, and generate an indicator constraint list, including the following steps: For statutory planning documents, the land use spatial planning documents and control detailed planning documents are integrated, and the land use location, nature and scale fields are extracted to construct a structured planning database. In this embodiment, the correspondence between "plot number-land use nature-plot ratio" in the control plan is stored in a relational database.

[0031] For non-statutory planning documents, they are incorporated into special planning documents and industrial planning documents, and their planned land use location, target development nature and target development scale are marked and added to the structured planning database; The NLP (Natural Language Processing) tools were used to segment and tag the legal and non-legal planning texts in the structured planning database. The binding and guiding indicators are set as key entity information. The NER named entity recognition model is trained to identify key entity information in statutory planning texts and non-statutory planning texts in the structured planning database, and the key entity information is marked and associated with the corresponding values ​​or ranges. Spatial overlay analysis is performed between the coordinates or address of the project site and the boundaries of the plots in the structured planning database to determine the planning scope to which the project site belongs; Retrieve the mandatory clauses for the corresponding land parcels from the statutory planning texts within the planning scope of the structured planning database, and their marked key entity information, and mark them as hard constraints; Retrieve the guiding requirements for the corresponding land parcels and their marked key entity information from non-statutory planning texts within the planning scope of the structured planning database, marking them as soft constraints; statutory planning texts specifically include: territorial spatial planning and control planning. Hard and soft constraints will be extracted and linked to land use locations in the structured planning database to generate a list of indicator constraints.

[0032] In this embodiment, the generation of the indicator list includes: The extracted hard and soft constraints are categorized into tables based on indicator type (binding / guiding), indicator name and value / range, planning basis, and the format of the matching plots. Compare the indicators of the same plot of land in different plans (such as the control plan requiring a green space ratio of 30%, while the special plan requires 35%), and mark the conflicting items for manual review; Use charts or maps to aid decision-making.

[0033] In one embodiment of the present invention, the system automatically retrieves project surveying data and operational performance data, and generates preliminary indicators by analyzing core indicators of planning conditions, including the following steps: The system automatically retrieves project surveying data and operational performance data. Project surveying data includes topography, buildings, and transportation; operational performance data includes the load of surrounding public services and traffic efficiency. By analyzing the core indicators of the planning conditions, preliminary indicators are generated: ; Where: Inn is the preliminary planning condition indicator (such as plot ratio and building height), Inga is the upper limit of the indicator specified by the statutory plan, β is the operation performance correction coefficient, with a value of 0.1~0.3, and the coefficient is smaller for better performance; Pera is the actual operation performance of the surrounding area, including: public service coverage and traffic efficiency; Pert is the operation performance target value, γ is the current density correction coefficient, with a value of 0.2~0.4, and the coefficient is larger for higher density; Deac is the current building density of the surrounding area, and Dest is the planned standard building density.

[0034] In one embodiment of the present invention, a project planning suggestion is generated based on the indicator constraint list and the preliminary indicator calculation results of the planning conditions, including the following steps: Import the preliminary indicators of the project planning conditions, correlate and screen them with the values ​​in the indicator constraint list, mark the data that do not meet the numerical requirements in the indicator constraint list, and send a manual modification prompt. By using GIS tools, the project land boundary is spatially overlaid with the plot area and surrounding facilities in the structured planning database to clarify the locational relationship of the project land boundary; The existing surrounding facilities at the project site boundary are marked, and alternative planning schemes for the project are generated, including: The compliance plan includes: planning the project land use based on the preliminary indicators of the screened project planning conditions; including the surrounding facilities marked on the project land boundary in the project planning surrounding facilities; and requiring the surrounding facilities to be implemented in accordance with the minimum standards in the indicator constraint list. The quality plan includes: planning the project land use based on the preliminary indicators of the project planning conditions after screening; excluding the surrounding facilities marked on the project land boundary from the surrounding facilities in the project planning; and requiring the surrounding facilities to be implemented in accordance with the minimum standards in the indicator constraint list. The hybrid optimization scheme includes: planning the project land use based on the preliminary indicators of the screened project planning conditions; excluding surrounding facilities already marked at the project land boundary from the planned surrounding facilities, and requiring surrounding facilities to meet the minimum standards in the indicator constraint list; simultaneously, while meeting the numerical requirements in the indicator constraint list, selecting several indicators from the preliminary indicators of the project planning conditions to set key guiding optimization indicators, optimizing the original preliminary indicator values ​​by 10%. The NER model is used to extract the location, nature, and scale of land use from the text of the project planning proposal, and then compare them with the planning nature and scale of the corresponding plots in the structured planning database to verify consistency.

[0035] In one embodiment of the present invention, the location, nature, and scale of land use are extracted from the text of project planning suggestions using a NER model, and compared with the planning nature and scale of the corresponding plots in a structured planning database to verify consistency, including: Dependency parsing is used to extract the constraint relationships between key entity information and binding indicators; for example, residential land must meet the requirement of a green space ratio of ≥30%. Based on the constraints between key entity information and binding indicators, the location, nature, and scale of the project land are compared with the location, planning nature, and scale of the corresponding plots in the structured planning database. The cosine similarity of the corresponding location, nature, and scale texts is calculated, and the results are weighted and summed to obtain the consistency coefficient. Data with consistency coefficients below the threshold are marked with an early warning and a manual modification prompt is sent.

[0036] In one embodiment of the present invention, the matching degree between the project planning suggestions and the planning basis is analyzed under multi-objective optimization constraints, and the project planning suggestions are optimized and verified, including the following steps: We set optimization constraint ranges for various indicators in the alternative solutions proposed in the project planning, and calculate compliance verification coefficients by analyzing the multi-objective optimization constraints to analyze the matching degree between the project planning recommendations and the planning basis. ; Where: Com represents the optimization verification result of the indicators in the alternative solutions proposed in the project planning, [In min,k In max,k ] represents the optimization constraint interval for the k-th indicator. min,k To find the minimum value of the optimization constraint for the k-th index, In max,k Let be the maximum value of the optimization constraint for the k-th indicator, where k∈(1,2,…,m), and m is the total number of indicators; The system provides early warnings and corrections for anomalies. If Com=0, the system automatically marks the abnormal indicators, pushes the basis for the conflict, and provides correction solutions for compliance, quality and hybrid optimization based on historical cases through a large language model, and sends a manual review prompt.

[0037] In one embodiment of the present invention, by analyzing the unity of individual entities and regions, a landscape synergy quantitative evaluation model is established to assess the landscape coordination of the project planning recommendations, and a landscape index adjustment formula is set to optimize the indicators of the project planning recommendations, including the following steps: Landscape parameters of the project area and surrounding areas were collected, and a quantitative evaluation model for landscape synergy was established by analyzing both individual and regional parameters. ; Where L represents the overall coordination degree of landscape collaboration; Lv represents the visual coordination score. ; Le represents the score for ecological integration. ; Lf represents the functional adaptability score. ; ωv is the weight of the evaluation dimension, ωe is the weight of ecological integration, and ωf is the weight of functional adaptation, which are set according to the key points of urban planning; in this embodiment, the visual coordination weight ωv=0.4, the ecological integration weight ωe=0.3, and the functional adaptation weight ωf=0.3. Hpr is the building height of the project, and Hav is the average building height of the surrounding area. Sts represents the similarity between the project's architectural style and its surroundings. In this embodiment, the similarity Sts between the project's architectural style and its surroundings is obtained by calculating the cosine similarity between the key semantic features of the actual project's architectural style and the key semantic features of the architectural styles of the surrounding buildings. Coc represents the color contrast between the project building and its surroundings. A drone is used to take 360-degree panoramic photos of the project building and its surrounding buildings from a preset distance. The multiple photos are then stitched together into a complete 360-degree panoramic image using PTGui software. Color histograms of the project building and its surrounding areas are extracted. Based on the selected contrast calculation method, in this embodiment, the standard deviation method, variance method, or CIEDE2000 color difference method can be selected to calculate the color contrast between the project building and its surrounding buildings. Grec is the regional green space connectivity index. The overall connectivity index (IIC) of green space patches is calculated using Conefor software, with a value ranging from 0 to 1. Grep represents the project's vegetation coverage rate, Grea represents the average vegetation coverage rate of the area surrounding the project, and Fum represents the fit between the project's land use function and the regional planning. In this embodiment, the fit between the project's land use function and the regional planning is obtained by calculating the cosine similarity between the semantic features of the actual project's land use function and the semantic features of the regional planning's land use function. Spac is the connection length between the project's public space and the regional public space, and Spat is the total length of the project's public space and the regional public space. By establishing a quantitative evaluation model for landscape synergy, the comprehensive coordination degree of landscape synergy is calculated, the landscape coordination of project planning recommendations is evaluated, and early warnings are issued for project planning recommendations with a comprehensive coordination degree of landscape synergy below the threshold.

[0038] In one embodiment of the present invention, setting a landscape index adjustment formula to optimize the indicators of project planning recommendations includes the following steps: For project planning recommendations that have passed the landscape harmony assessment, the indicators of the project planning recommendations are optimized by setting landscape indicator adjustment formulas: ; Wherein: Ina is the optimized landscape-related index, Lta is the target value of landscape synergy and coordination (preset to 80 points in this embodiment); Lcu is the landscape synergy and coordination degree of the current scheme; and Int is the initial planning index.

[0039] In one embodiment of the present invention, during the project construction process, the project is divided into blocks, the actual landscape data of the completed blocks is obtained, and compared with the actual implemented project planning proposal. By analyzing the landscape implementation fit, the parameters of the landscape indicator adjustment formula are adjusted accordingly. This includes the following steps: During project construction, the project was divided into blocks, and actual landscape data of the completed blocks were obtained. The landscape synergy and coordination target value Lta was incorporated into the block-based formula for block analysis. ; For each block in the project area, establish the following landscape coordination constraint: stLta≥80; For each block in the project area, establish the target constraint: stFit≥90%; Where, Lactual represents the actual landscape coordination degree of each block in the project area, and Fit represents the optimization target coefficient of each block in the project area; Under the constraints of calculation, the range of the target value Lta for landscape synergy and coordination degree of each block in the project area is determined. The minimum value within the range is averaged to obtain the optimized target value Lta for landscape synergy and coordination degree. The corresponding parameters of the original landscape index adjustment formula are then replaced. The optimized landscape-related indicators of the undeveloped blocks are recalculated using the adjusted landscape index adjustment formula.

[0040] In one embodiment of the present invention, the system employs a digital twin-based smart city planning method as described above to realize digital twin-based smart city planning, including: Constraint Establishment Module: Using NLP technology, the module analyzes core information such as land location, nature, and scale of urban planning projects, matches corresponding statutory and non-statutory planning texts from the planning basis database, extracts binding and guiding indicators, and generates a list of indicator constraints. Correlation Calculation Module: The system automatically calls up project surveying data and operational performance data, and generates preliminary indicators by analyzing core indicators of planning conditions; The planning generation and verification module includes: Planning Recommendation Generation Unit: Based on the indicator constraint list and the preliminary indicator calculation results of the planning conditions, it generates project planning recommendations; Compliance verification unit: By analyzing the matching degree between project planning recommendations and planning basis under multi-objective optimization constraints, the unit optimizes and verifies the project planning recommendations. Landscape Harmony Calculation Module: By analyzing the unity of individual units and regions, a quantitative evaluation model for landscape harmony is established to assess the landscape harmony of the project planning recommendations, and a landscape index adjustment formula is set to optimize the indicators of the project planning recommendations. Landscape effect tracking and feedback module: During the project construction process, the project is divided into blocks, the actual landscape data of the completed blocks is obtained, and compared with the actual project planning suggestions. By analyzing the fit of landscape implementation, the parameters of the landscape indicator adjustment formula are adjusted accordingly.

[0041] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A smart city planning method based on digital twins, characterized in that, Includes the following steps: By using NLP technology to analyze the basic information of urban planning projects, matching the corresponding statutory planning texts and non-statutory planning texts from the planning basis database, extracting binding and guiding indicators, and generating an indicator constraint list; The system automatically retrieves project surveying data and operational performance data, and generates preliminary indicators by analyzing core indicators of planning conditions; Based on the list of indicator constraints and the preliminary indicator calculation results of the planning conditions, project planning suggestions are generated. By analyzing the matching degree between the project planning suggestions and the planning basis under multi-objective optimization constraints, the project planning suggestions are optimized and verified. By analyzing the unity of individual units and regions, a quantitative evaluation model for landscape synergy is established to assess the landscape coordination of project planning recommendations, and a landscape indicator adjustment formula is set to optimize the indicators of project planning recommendations. During the project construction process, the project is divided into blocks, and the actual landscape data of the completed blocks is obtained. This data is then compared with the actual project planning proposals. By analyzing the fit between the landscape implementation and the proposed solutions, the parameters of the landscape indicator adjustment formula are adjusted accordingly. The system automatically retrieves project surveying and mapping data and operational performance data, and generates preliminary indicators by analyzing core planning conditions, including the following steps: The system automatically retrieves project surveying data and operational performance data. Project surveying data includes topography, buildings, and transportation; operational performance data includes the load of surrounding public services and traffic efficiency. By analyzing the core indicators of the planning conditions, preliminary indicators are generated: ; Where: Inn is the preliminary indicator of planning conditions, Inga is the upper limit of the indicator specified by the statutory plan, β is the operation performance correction coefficient, Pera is the actual operation performance of the surrounding area, Pert is the operation performance target value, γ is the current density correction coefficient, Deac is the current building density of the surrounding area, and Dest is the planned standard building density. The NER model is used to extract land use location, nature, and scale from the text of project planning proposals. This information is then compared with the planning nature and scale of corresponding plots in the structured planning database to verify consistency, including: By using dependency parsing, the constraint relationships between key entity information and constraint indicators are extracted; Based on the constraint relationship between key entity information and binding indicators, the location, nature, and scale of the project land are compared with the location, planning nature, and scale of the corresponding plots in the structured planning database. The cosine similarity of the corresponding location, nature, and scale texts is calculated, and the results are weighted and summed to obtain the consistency coefficient. Data with consistency coefficients below the threshold are marked with warnings and manual modification prompts are sent. By analyzing the matching degree between project planning recommendations and planning basis under multi-objective optimization constraints, the project planning recommendations are optimized and verified, including the following steps: We set optimization constraint ranges for various indicators in the alternative solutions proposed in the project planning, and calculate compliance verification coefficients by analyzing the multi-objective optimization constraints to analyze the matching degree between the project planning recommendations and the planning basis. ; Where: Com represents the optimization verification result of the indicators in the alternative solutions proposed in the project planning, [In min,k In max,k ] represents the optimization constraint interval for the k-th indicator. min,k To find the minimum value of the optimization constraint for the k-th index, In max,k Let be the maximum value of the optimization constraint for the k-th indicator, where k∈(1,2,…,m), and m is the total number of indicators; The system will issue warnings and make corrections for anomalies. If Com=0, the system will automatically mark the abnormal indicators, push the conflict basis, and provide correction plans for compliance, quality and hybrid optimization based on historical cases and through the big language model, and send a manual review prompt; otherwise, it will determine that the alternative plan does not need to be modified.

2. The smart city planning method based on digital twins according to claim 1, characterized in that, By using NLP technology to analyze basic information of urban planning projects, matching corresponding statutory and non-statutory planning documents from the planning basis database, extracting binding and guiding indicators, and generating an indicator constraint list, the following steps are included: For statutory planning documents, integrate territorial spatial planning documents and regulatory detailed planning documents, extract land use location, nature and scale fields, and construct a structured planning database; For non-statutory planning documents, they are incorporated into special planning documents and industrial planning documents, and their planned land use location, target development nature and target development scale are marked and added to the structured planning database; The NLP (Natural Language Processing) tools were used to segment and tag the legal and non-legal planning texts in the structured planning database. The binding and guiding indicators are set as key entity information. The NER named entity recognition model is trained to identify key entity information in statutory planning texts and non-statutory planning texts in the structured planning database, and the key entity information is marked and associated with the corresponding values ​​or ranges. Spatial overlay analysis is performed between the coordinates or address of the project site and the boundaries of the plots in the structured planning database to determine the planning scope to which the project site belongs; Retrieve the mandatory clauses for the corresponding land parcels from the statutory planning texts within the planning scope of the structured planning database, and their marked key entity information, and mark them as hard constraints; Retrieve the guiding requirements for the corresponding land parcels and their marked key entity information from the non-statutory planning texts within the planning scope of the structured planning database, and mark them as soft constraints; Hard and soft constraints will be extracted and linked to land use locations in the structured planning database to generate a list of indicator constraints.

3. The smart city planning method based on digital twins according to claim 1, characterized in that, Based on the list of indicator constraints and the preliminary indicator calculation results of the planning conditions, project planning recommendations are generated, including the following steps: Import the preliminary indicators of the project planning conditions, correlate and screen them with the values ​​in the indicator constraint list, mark the data that do not meet the numerical requirements in the indicator constraint list, and send a manual modification prompt. By using GIS tools, the project land boundary is spatially overlaid with the plot area and surrounding facilities in the structured planning database to clarify the locational relationship of the project land boundary; The existing surrounding facilities at the project site boundary are marked, and alternative planning schemes for the project are generated, including: The compliance plan includes: planning the project land use based on the preliminary indicators of the screened project planning conditions; including the surrounding facilities marked on the project land boundary in the project planning surrounding facilities; and requiring the surrounding facilities to be implemented in accordance with the minimum standards in the indicator constraint list. The quality plan includes: planning the project land use based on the preliminary indicators of the project planning conditions after screening; excluding the surrounding facilities marked on the project land boundary from the surrounding facilities in the project planning; and requiring the surrounding facilities to be implemented in accordance with the minimum standards in the indicator constraint list. The hybrid optimization scheme includes: planning the project land use based on the preliminary indicators of the screened project planning conditions; excluding surrounding facilities already marked at the project land boundary from the planned surrounding facilities, and requiring surrounding facilities to meet the minimum standards in the indicator constraint list; simultaneously, while meeting the numerical requirements in the indicator constraint list, selecting key guiding optimization indicators from the preliminary indicators of the project planning conditions, optimizing them by 10% based on the original preliminary indicator values ​​of the project planning conditions; The NER model is used to extract the location, nature, and scale of land use from the text of the project planning proposal, and then compare them with the planning nature and scale of the corresponding plots in the structured planning database to verify consistency.

4. The smart city planning method based on digital twins according to claim 1, characterized in that, By analyzing the unity of individual units and the region, a quantitative evaluation model for landscape synergy is established to assess the landscape coordination of project planning recommendations. Furthermore, a landscape indicator adjustment formula is set to optimize the indicators of the project planning recommendations, including the following steps: Landscape parameters of the project area and surrounding areas were collected, and a quantitative evaluation model for landscape synergy was established by analyzing both individual and regional parameters. ； Where L represents the overall coordination degree of landscape collaboration; Lv represents the visual coordination score. ; Le represents the score for ecological integration. ; Lf represents the functional adaptability score. ; ωv is the weight of the evaluation dimension, ωe is the weight of the ecosystem integration, and ωf is the weight of the functional adaptation. Hpr is the building height of the project, Hav is the average building height of the surrounding area; Sts is the similarity of the project's architectural style with the surrounding area; Coc is the contrast of the project's building color with the surrounding area; Grec is the regional green space connectivity index; Grep is the vegetation coverage rate of the project, Grea is the average vegetation coverage rate of the surrounding area; Fum is the fit between the project's land use function and the regional planning; Spac is the connection length between the project's public space and the regional public space, Spat is the total length of the regional public space between the project's public space and the regional public space; By establishing a quantitative evaluation model for landscape synergy, the comprehensive coordination degree of landscape synergy is calculated, the landscape coordination of project planning recommendations is evaluated, and early warnings are issued for project planning recommendations with a comprehensive coordination degree of landscape synergy below the threshold.

5. A smart city planning method based on digital twins according to claim 1, characterized in that, To optimize the indicators recommended in the project planning, the following steps are involved in setting up a landscape indicator adjustment formula: For project planning recommendations that have passed the landscape harmony assessment, the indicators of the project planning recommendations are optimized by setting landscape indicator adjustment formulas: ; Where: Ina is the optimized landscape-related index, Lta is the target value of landscape synergy and coordination, Lcu is the landscape synergy and coordination degree of the current scheme, and Int is the initial planning index.

6. A smart city planning method based on digital twins according to claim 5, characterized in that, During project construction, the project is divided into blocks, and the actual landscape data of the completed blocks is obtained. This data is then compared with the actual implemented project planning proposals. By analyzing the fit between the landscape implementation and the proposed solutions, the parameters of the landscape indicator adjustment formula are adjusted accordingly. This process includes the following steps: During project construction, the project was divided into blocks, and actual landscape data of the completed blocks were obtained. The landscape synergy and coordination target value Lta was incorporated into the block-based formula for block analysis. ; For each block in the project area, establish the following landscape coordination constraint: stLta≥80; For each block in the project area, establish the target constraint: stFit≥90%; Where, Lactual represents the actual landscape coordination degree of each block in the project area, and Fit represents the optimization target coefficient of each block in the project area; Under the constraints of calculation, the range of the target value Lta for landscape synergy and coordination degree of each block in the project area is determined. The minimum value within the range is averaged to obtain the optimized target value Lta for landscape synergy and coordination degree, and the corresponding parameters of the original landscape index adjustment formula are replaced.

7. A smart city planning system based on digital twins, characterized in that, The system employs a digital twin-based smart city planning method as described in any one of claims 1-6 to achieve digital twin-based smart city planning, comprising: Constraint Establishment Module: Analyzes basic information of urban planning projects using NLP technology, matches corresponding statutory and non-statutory planning texts from the planning basis database, extracts binding and guiding indicators, and generates a list of indicator constraints; Correlation Calculation Module: The system automatically calls up project surveying data and operational performance data, and generates preliminary indicators by analyzing core indicators of planning conditions; The planning generation and verification module includes: Planning Recommendation Generation Unit: Based on the indicator constraint list and the preliminary indicator calculation results of the planning conditions, it generates project planning recommendations; Compliance verification unit: By analyzing the matching degree between project planning recommendations and planning basis under multi-objective optimization constraints, the unit optimizes and verifies the project planning recommendations. Landscape Harmony Calculation Module: By analyzing the unity of individual units and regions, a quantitative evaluation model for landscape harmony is established to assess the landscape harmony of the project planning recommendations, and a landscape index adjustment formula is set to optimize the indicators of the project planning recommendations. Landscape effect tracking and feedback module: During the project construction process, the project is divided into blocks, the actual landscape data of the completed blocks is obtained, and compared with the actual project planning suggestions. By analyzing the fit of landscape implementation, the parameters of the landscape indicator adjustment formula are adjusted accordingly.

Images