Land use planning based scenario dynamic urban model system
By constructing a contextualized dynamic urban model system, the problems of fragmented data analysis, static evaluation, and untraceable decision-making in traditional urban planning have been solved. This system enables real-time simulation and optimization of urban planning, improving planning efficiency and the standardization of results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 虞振亚
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-19
AI Technical Summary
Traditional urban planning methods struggle to cope with the complex and ever-changing dynamic development of cities. Fragmented data analysis, static assessments, and untraceable decision-making processes lead planners to rely on experience and static indicators, lacking real-time simulation and optimization driven by full data.
Construct a scenario-based dynamic urban model system based on land use planning, including a spatial data module, a land tagging module, a scenario simulation module, a dynamic feedback module, an intelligent recommendation module, a collaborative operation module, and a results generation module, to achieve data fusion, real-time simulation, intelligent optimization, and version management.
It shortens the planning analysis cycle from monthly to near real-time, enhances the foresight and conflict avoidance capabilities of planning schemes, supports multi-party collaborative discussions and knowledge accumulation, and ensures the standardization and traceability of results.
Smart Images
Figure CN122242004A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of urban land planning technology, and in particular to a contextualized dynamic urban model system based on land use planning. Background Technology
[0002] Urban planning, especially land-use planning, is a complex and systematic project that requires the coordinated management of multiple objectives, including environmental protection, economic development, social equity, and infrastructure carrying capacity, within limited land space. With the deepening of urbanization and the increasing emphasis on sustainable development, traditional planning methods based on experience and static drawings are increasingly revealing their limitations, failing to address the complexity and uncertainty of dynamic urban development. Currently, the following are the main technical bottlenecks in planning and decision support: The fragmented analysis process hinders scientific decision-making. Planning decisions rely on multi-source, heterogeneous data, including geographic information, environmental baseline data, and socioeconomic and infrastructure data. However, this data is often scattered across different departments, with inconsistent formats and standards, forming "data silos." Traditional technical processes require planners to invest significant time in tedious data collection, cleaning, registration, and integration. During the analysis phase, simulation models from different disciplines (such as traffic forecasting and environmental impact assessment) typically operate independently, making data exchange difficult and model serial computation time-consuming. This makes comprehensive analysis a high-cost, low-frequency activity. The scientific validation of planning schemes often lags behind or even deviates from the scheme design process, and decisions still largely rely on qualitative experience and static indicators, lacking quantitative, real-time simulation and extrapolation driven by comprehensive data.
[0003] The static nature of scheme evaluation hinders dynamic optimization. Existing planning support tools (such as GIS and CAD) primarily serve as "drawing" and "static analysis" tools. Once a scheme is formulated, its potential comprehensive impacts (such as the long-term pollution risks of new industrial land to surrounding residential areas and traffic congestion caused by large-scale residential development) are difficult to assess quickly and intuitively. Even when assessments are conducted, the results are mostly static reports or drawings, failing to provide immediate and visual feedback during scheme adjustments. The lack of an interactive interface between planners and schemes that reflects "hypothesis-outcome" in real time leads to slow iterations in the planning optimization process and an inability to proactively avoid potential, cross-disciplinary spatial conflicts (such as conflicts between land use layout and environmental capacity).
[0004] The decision-making process is not traceable, making it difficult to ensure collaboration and knowledge accumulation. Planning typically involves multiple rounds of revisions and team collaboration. Traditional document management models make it difficult to clearly record and trace the evolution path of the plan, the differences between versions, and the decision-making basis behind each revision (especially decisions based on simulation results). This results in a lack of objective process records for plan comparison, version confusion in team collaboration, and the loss of valuable planning logic and experience after the project ends. It also fails to form an accumulative and reusable decision-making knowledge base, hindering the iterative improvement of the overall technical capabilities of the planning industry.
[0005] Therefore, there is an urgent need in this field for a contextualized dynamic urban model system based on land use planning. This system should be able to achieve closed-loop management of the entire process from data to solutions, from simulation to feedback, and from collaboration to results, thereby transforming urban planning from a "depicting art" based on static blueprints into a "deductive science" based on dynamic simulation. The present invention is proposed precisely to address the aforementioned core challenges. Summary of the Invention
[0006] To address the shortcomings of existing technologies, this invention provides a contextualized dynamic urban model system based on land use planning, thereby solving the technical problems mentioned in the background section.
[0007] To achieve the above objectives, the present invention provides the following technical solution: A contextualized dynamic urban model system based on land use planning includes the following modules; Spatial data module: Loads the digital base map of the target area, and then synchronously integrates geographic information data, environmental baseline data, socio-economic data and infrastructure data; Land Tagging Module: Maintains a structured urban land planning technology land use tag library, which includes a hierarchical tagging system from macro to micro levels. Scenario simulation module: Its operation is triggered by events that complete attribute definition through the land tag library and attribute binding module or by direct user commands; Dynamic feedback module: After receiving the raw data packets output by the engine, its internal visualization rendering components immediately start working according to predefined rules; Intelligent recommendation module: continuously monitors the status of the feedback device, and immediately initiates up-and-down analysis when a specific type of conflict is detected; Collaborative Operation Module: Using the timeline as the axis, it automatically records every land parcel definition and modification from the attribute binding module, every trigger and result snapshot from the scenario simulation engine, and every suggestion adoption record from the intelligent recommendation module; Results generation module: Automatically collects base map data from the spatial database module, final scheme attribute data from the land label library and attribute binding module, and all analysis results data generated by the scenario simulation engine based on the final scheme.
[0008] In one possible implementation, within the spatial data module, all data undergoes coordinate system standardization, format standardization, and spatial registration, and is organized according to a unified timestamp and spatial index to form a multi-layered, structured urban land planning technology data base.
[0009] In one possible implementation, in the land labeling module, when a user assigns a label to a specific patch on the map through the interactive interface, this module activates the patch and associates it with a dynamic attribute form. The detailed attributes defined by the user here are encapsulated by this module into a unique, structured urban land planning technology feature vector for that plot. This urban land planning technology feature vector is the sole and authoritative data input source for all subsequent scenario simulations.
[0010] In one possible implementation, in the scenario simulation module, after the engine starts, each sub-module works synchronously: the environment simulation sub-module calls the wind field data in the spatial database module, and combines it with the urban land planning technology emission level urban land planning technology defined by the attribute binding module for a specific industrial plot, to run the atmospheric pollutant diffusion model in real time and output the concentration distribution field.
[0011] In one possible implementation, within the scenario simulation module, the traffic simulation submodule reads attributes such as population density, number of jobs, etc., of each plot of land, combines them with road network data, performs traffic demand forecasting and allocation, and outputs traffic load.
[0012] In one possible implementation, the pollution concentration field is rendered as a dynamically spreading color cloud map in the dynamic feedback module, overlaying the affected plots; traffic load data is mapped to road color and thickness variations; and its diagnostic analysis component quickly parses the data packets, generates structured text alerts, and prominently identifies the core conflict area.
[0013] In one possible implementation, the intelligent recommendation module combines planning rules from the knowledge base, optimization algorithms, and the context of the current planning scheme to generate specific and executable correction suggestions.
[0014] In one possible implementation, the intelligent recommendation module suggests that suggestions be accurately pushed to relevant plots or panels in the form of interactive controls. The user's action of adopting the suggestion will be directly used as a new input command and passed back to the land tag library and attribute binding module or planning map, thereby initiating the next round of optimization cycle from modification of urban land planning technical attributes to simulation to feedback of urban land planning technology.
[0015] In one possible implementation, within the collaborative operation module, all operations, data, and states are encapsulated as individual urban land planning technology version nodes, forming a non-linear, branching decision tree. This module allows users to revert to any historical node at any time, view the complete planning scenario and simulation data at that time, and support the derivation of new planning branches from that point, or the comparison of key indicator differences between different branches.
[0016] In one possible implementation, the output generation module uses embedded mapping templates and a report engine to generate land use planning maps, various special analysis maps, and comprehensive planning implementation evaluation reports that conform to industry standards with a single click, completing the output closed loop from dynamic simulation and deduction to static legal results.
[0017] Beneficial effects compared to existing technologies: 1. This solution establishes a complete automated workflow encompassing data fusion, intent definition, scenario simulation, dynamic feedback, intelligent recommendation, version management, and output. The spatial data module automatically integrates multi-source heterogeneous data to form a standardized template, while the land tagging module seamlessly converts abstract user intents into machine-computable feature vectors. The scenario simulation module then drives multiple professional models in parallel for real-time calculations. This process replaces the extensive manual data processing, serial calculations of single models, and result integration work required in traditional planning, shortening planning analysis from monthly or weekly cycles to near real-time, while ensuring the rigor and repeatability of the analysis process, significantly improving the scientific basis and efficiency of planning. 2. In this solution, the system achieves real-time evaluation and intelligent optimization of planning effectiveness through dynamic feedback and intelligent recommendation modules. Simulation results are no longer just background data, but are rendered in real time as intuitive dynamic conflict maps and structured diagnostic reports, allowing planners to see the immediate potential impact of the solution. When the system identifies high-risk conflicts, the intelligent recommendation module proactively intervenes, generating specific source governance or spatial optimization suggestions based on the knowledge base, and pushing them as interactive controls. This transforms the planning process from formulation and static evaluation to a dynamic iterative cycle of simulation, feedback, and optimization, greatly enhancing the foresight, conflict avoidance capabilities, and targeted nature of multi-party collaborative discussions in the planning scheme. 3. In this solution, the collaborative operation module acts as a time machine for the system, automatically recording each operation, simulation, and state in the form of a non-linear decision tree, forming a complete version history. Users can rewind to any historical node at any time to view, compare, or derive new branches, providing strong support for multi-solution comparison, team collaborative iteration, and accountability. Finally, the results generation module can aggregate all final data and results with one click, automatically generating planning diagrams, analysis diagrams, and evaluation reports that conform to industry standards through embedded templates, achieving an automatic closed loop from dynamic deduction to standardized legal results. This not only ensures the standardization of the results but also systematically accumulates the tacit knowledge in the planning process. Attached Figure Description
[0018] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.
[0019] Figure 1 This is a schematic diagram of the system structure of the present invention; Detailed Implementation
[0020] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can also be implemented in various different forms, and therefore the present invention is not limited to the embodiments described below. The technical solution in this application embodiment is to solve the problems mentioned in the background art, and the overall idea is as follows: Example: Please refer to Figure 1 As shown, this embodiment introduces a contextualized dynamic urban model system based on land use planning, which includes the following modules: Spatial Data Module: This module forms the data foundation for all system analyses. Its core function lies in the automatic collection, standardization, and fusion of heterogeneous spatial data from various sources. It first loads a digital base map of the target area, then simultaneously integrates geographic information data (topography, elevation), environmental baseline data (multi-year meteorological statistics, including generated vector wind fields; hydrological data; soil types), socioeconomic data (current population, industrial distribution), and infrastructure data (transportation networks, municipal pipelines). All data undergoes coordinate system standardization, format standardization, and spatial registration within this module, and is organized according to a unified timestamp and spatial index, forming a multi-layered, structured urban land planning technology data base. The output of this module is a standardized comprehensive database containing spatial geometry and attribute information, which can be directly accessed by all other modules.
[0021] Land Tagging Module: This module provides users with interactive tools for spatial intervention and transforms abstract intentions into system-calcifiable parameters. It maintains a structured urban land use tag library, encompassing a hierarchical tagging system from macro (e.g., residential land use) to micro (e.g., Class I residential land use - R1). When a user assigns a tag (e.g., M2 industrial land use) to a specific patch on the map via the interface, this module activates that patch and associates it with a dynamic attribute form. The detailed attributes defined by the user (e.g., industry type: chemical industry; pollutant emission level: Level III; plot ratio: 2.0) are encapsulated by this module into a unique, structured feature vector for that plot. This feature vector serves as the sole and authoritative data input source for all subsequent scenario simulations, ensuring a lossless conversion from user intentions to machine-understandable parameters.
[0022] Scenario Simulation Module: This module is the core computing and analysis center of the system, consisting of a series of parallel simulation sub-modules. Its operation is triggered by events defined by the land tag library and attribute binding module, or by direct user commands. After the engine starts, each sub-module works synchronously: the environmental simulation sub-module calls wind field data from the spatial database module and, combined with the urban land planning emission levels defined by the attribute binding module for specific industrial plots, runs an atmospheric pollutant diffusion model in real time, outputting a concentration distribution field. The traffic simulation sub-module reads attributes such as population density and employment numbers for each plot, combines them with road network data, performs traffic demand prediction and allocation, and outputs traffic load. The public service module calculates the supply and demand matching degree of facilities. The calculation results (simulation data field) of all sub-modules are packaged in real time, ready for output.
[0023] Dynamic Feedback Module: This module is part of the system's urban land planning technology sensory system. It is responsible for transforming the calculation results generated by the scenario simulation engine into intuitive, multimodal, real-time feedback. After receiving the raw data packets output by the engine, its internal visualization rendering component immediately works according to predefined rules (such as concentration thresholds corresponding to red alerts): rendering the pollution concentration field as a dynamically spreading color cloud map, overlaying it on the affected plots; mapping traffic load data to road color and thickness variations; simultaneously, its diagnostic analysis component quickly parses the data packets, generating structured text alerts (such as urban land planning technology conflict warning: industrial area upwind affecting residential area, expected number of days exceeding standards 18 days urban land planning technology), and highlighting the core conflict area in a conspicuous manner (such as flashing, highlighting). The output of this module is no longer raw data, but a highly visualized urban land planning technology conflict map urban land planning technology and urban land planning technology diagnostic report urban land planning technology.
[0024] Intelligent Recommendation Module: This module serves as the system's decision support brain for urban land planning technology. Its activation directly depends on the high-conflict status signals output by the dynamic feedback mechanism. It continuously monitors the feedback mechanism's status, and when it detects a specific type of conflict (such as severe environmental risks), it immediately initiates upstream and downstream analysis. This module combines planning rules from the knowledge base, optimization algorithms, and the context of the current planning scheme to generate specific and actionable corrective suggestions. For example, regarding environmental conflicts, it generates suggestions for source control (adjusting emission level parameters of pollution sources) and spatial optimization (recommending more suitable layout locations). These suggestions are precisely pushed to relevant plots or panels in the form of interactive controls. User actions that adopt suggestions (such as clicking to adjust emission levels) are directly used as new input commands, passed back to the land tag library and attribute binding module or planning map, thus initiating the next round of optimization cycle from attribute modification to simulation to feedback.
[0025] Collaborative Operation Module: This module acts as a recorder of the entire urban land planning process, ensuring traceability and collaboration. From system startup, it automatically records every land parcel definition and modification from the attribute binding module, every trigger and result snapshot from the scenario simulation engine, and every suggestion adoption record from the intelligent recommendation module, all along a timeline. All these operations, data, and states are encapsulated as version nodes, forming a non-linear, branching decision tree. This module allows users to revert to any historical point at any time, view the complete planning scenario and simulation data at that time, and support the derivation of new planning branches from that point, or the comparison of key indicator differences between different branches. This function provides core technical support for multi-scheme comparison and team collaborative iteration.
[0026] Result Generation Module: This module is the final output port of the system workflow, responsible for transforming the dynamic interactive process into standardized, deliverable results. It is invoked after the optimization cycle ends and the user confirms the final solution. It automatically aggregates base map data from the spatial database module, final solution attribute data from the land tag library and attribute binding module, and all analysis results data generated by the scenario simulation engine based on the final solution. Through embedded mapping templates and a reporting engine, it generates, with one click, industry-standard land use planning maps, various specialized analysis maps (such as noise contour maps and public facility service area maps), and comprehensive planning implementation evaluation reports, completing the closed loop from dynamic simulation to static legally binding results.
[0027] The above examples illustrate the data-driven linkage mechanism of the system's seven core modules: from data fusion laying the foundation, to defining input parameters to trigger scenario simulations for calculation, to dynamic feedback and visualization of results to stimulate intelligent recommendations and provide solutions, the entire process is recorded and traced by version management, and finally, the results generation module outputs standardized products. Each module strictly adheres to the principle of upstream output triggering downstream initiation of urban land planning technology, forming a highly automated and intelligent closed loop for planning decision support.
[0028] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.
Claims
1. A contextualized dynamic urban model system based on land use planning, characterized in that, Includes the following modules; Spatial data module: Loads the digital base map of the target area, and then synchronously integrates geographic information data, environmental baseline data, socio-economic data and infrastructure data; Land Tagging Module: Maintains a structured urban land planning technology land use tag library, which includes a hierarchical tagging system from macro to micro levels. Scenario simulation module: Its operation is triggered by events that complete attribute definition through the land tag library and attribute binding module or by direct user commands; Dynamic feedback module: After receiving the raw data packets output by the engine, its internal visualization rendering components immediately start working according to predefined rules; Intelligent recommendation module: continuously monitors the status of the feedback device, and immediately initiates up-and-down analysis when a specific type of conflict is detected; Collaborative Operation Module: Using the timeline as the axis, it automatically records every land parcel definition and modification from the attribute binding module, every trigger and result snapshot from the scenario simulation engine, and every suggestion adoption record from the intelligent recommendation module; Results generation module: Automatically collects base map data from the spatial database module, final scheme attribute data from the land label library and attribute binding module, and all analysis results data generated by the scenario simulation engine based on the final scheme.
2. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the spatial data module, all data undergoes coordinate system standardization, format standardization, and spatial registration, and is organized according to a unified timestamp and spatial index to form a multi-layered, structured urban land planning technology data base.
3. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the land labeling module, when a user assigns a label to a specific patch on the map through the interactive interface, this module activates the patch and associates it with a dynamic attribute form. The detailed attributes defined by the user here are encapsulated by this module into a unique, structured urban land planning technology feature vector for that plot. This urban land planning technology feature vector is the only and authoritative data input source for all subsequent scenario simulations.
4. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the scenario simulation module, after the engine starts, each sub-module works synchronously: the environment simulation sub-module calls the wind field data in the spatial database module, and combines it with the urban land planning technology emission level urban land planning technology defined by the attribute binding module for a specific industrial plot, to run the atmospheric pollutant diffusion model in real time and output the concentration distribution field.
5. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the scenario simulation module, the traffic simulation submodule reads the urban land planning technology, population density, number of urban land planning technology jobs, and other attributes of each plot, and combines them with road network data to predict and allocate traffic demand, and output traffic load.
6. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the dynamic feedback module, the pollution concentration field is rendered as a dynamically spreading color cloud map, overlaying the affected plots; traffic load data is mapped as changes in road color and thickness; at the same time, its diagnostic analysis component quickly parses data packets, generates structured text alerts, and prominently identifies the core conflict area.
7. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the intelligent recommendation module, specific and actionable correction suggestions are generated by combining planning rules, optimization algorithms, and the context of the current planning scheme in the knowledge base.
8. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the intelligent recommendation module, suggestions are accurately pushed to relevant plots or panels in the form of interactive controls. When a user adopts a suggestion, it will be directly used as a new input command and passed back to the land tag library and attribute binding module or planning map, thereby initiating the next round of optimization cycle from modification of urban land planning technical attributes to simulation to feedback of urban land planning technology.
9. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the collaborative operation module, all operations, data, and status are encapsulated as individual urban land planning technology version nodes, forming a non-linear, branching decision tree. This module allows users to revert to any historical node at any time, view the complete planning scenario and simulation data at that time, and support the derivation of new planning branches from that point, or the comparison of key indicator differences between different branches.
10. The contextualized dynamic urban model system based on land use planning as described in claim 1, characterized in that, In the results generation module, the embedded mapping templates and report engine can generate land use planning maps, various special analysis maps, and comprehensive planning implementation evaluation reports that conform to industry standards with one click, completing the output closed loop from dynamic simulation and deduction to static legal results.