An urban low-carbon livable planning index intelligent monitoring optimization system

Abstract

The application discloses a kind of urban low-carbon livable planning index intelligent monitoring optimization systems, including following module: data acquisition module, the multi-source heterogeneous data of city planning area is obtained, the multi-source heterogeneous data includes land use data, traffic flow data, energy consumption data, building information data, meteorological environment data and population activity data, thus, time series change trend and dynamic spatial distribution feature are organically fused, can accurately identify high carbon emission key area, provide reliable and accurate support basis for low-carbon planning decision, pay attention to the comprehensive balance of low-carbon development, livable quality and economic benefit, introduce efficient optimization technology, flexibly adjust each target weight, significantly improve the adaptability and flexibility of scheme, adapt to the differentiated development needs of different cities, can accurately identify the spatial conflict area of multiple planning indexes in the same geographical space, while establishing scientific quantification determination system, reduce the rework cost of planning landing.

Description

Technical Field

[0001] This application relates to the technical field of smart city management, and in particular to an intelligent monitoring and optimization system for urban low-carbon and livable planning indicators. Background Technology

[0002] With the continuous advancement of urbanization, low-carbon development and livable construction have become core demands for high-quality urban development. Urban low-carbon and livable planning, as a key means of coordinating urban ecology, economy, and people's livelihoods, directly impacts the level of sustainable urban development and the quality of life for residents through its scientific rigor and precision. Currently, the field of urban planning is gradually introducing various monitoring and optimization technologies in an attempt to achieve synergistic progress between low-carbon goals and livable experiences, but many shortcomings still exist.

[0003] First, existing carbon emission prediction methods are mostly limited to a single dimension. Some only focus on time-series trends, emphasizing the assessment of future increases or decreases in total carbon emissions, or only analyze static spatial distribution characteristics, focusing on identifying current high-emission areas. This makes it difficult to accurately identify and dynamically pinpoint key high-carbon emission areas in planning practice. At the same time, model construction generally relies on traditional machine learning algorithms, the carbon emission accounting system lacks scientific standards, and data screening and feature extraction are insufficient, resulting in large prediction errors and failing to provide reliable support for low-carbon planning decisions.

[0004] Secondly, traditional planning indicator optimization is often single-objective-oriented, neglecting the synergistic coupling and comprehensive balance between low-carbon development, livability, and economic benefits. This results in insufficient feasibility of the optimization schemes and high implementation difficulty. The optimization process relies excessively on human experience, making it difficult to achieve global optimization under multiple constraints. Furthermore, it cannot flexibly adjust the target weights according to the development positioning and strategic preferences of different cities, resulting in weak adaptability and flexibility of the schemes.

[0005] Furthermore, when assessing the coordination of multiple planning indicators within the same geographical space, issues such as contradictions and spatial incompatibility between indicators are still mainly identified through manual investigation. This is not only inefficient but also makes it difficult to accurately identify the spatial conflict locations of planning indicators, and it is impossible to scientifically quantify the type, level, and degree of impact of conflicts.

[0006] Application content

[0007] This application aims to address, at least to some extent, the technical problems in the related art.

[0008] To achieve the above objectives, this application proposes an intelligent monitoring and optimization system for urban low-carbon and livable planning indicators, comprising the following modules:

[0009] The data acquisition module acquires multi-source heterogeneous data of the urban planning area, including land use data, traffic flow data, energy consumption data, building information data, meteorological and environmental data, and population activity data.

[0010] A carbon emission prediction module is connected to the data acquisition module. Based on the multi-source heterogeneous data, a carbon emission prediction model is constructed to predict the spatiotemporal distribution of carbon emissions in the urban planning area and output the carbon emission prediction results.

[0011] The planning indicator optimization module is connected to the carbon emission prediction module. Based on the carbon emission prediction results and combined with the preset low-carbon livable planning indicator constraints, it uses a multi-objective optimization algorithm to optimize and solve the urban planning indicators, and generates an optimized planning indicator scheme.

[0012] The spatial conflict detection module is connected to the planning index optimization module. It performs spatial conflict detection on the planning index optimization scheme, identifies conflict areas of different planning indicators in spatial layout, and outputs spatial conflict detection results.

[0013] An environmental quality monitoring module is connected to the data acquisition module to acquire real-time environmental quality monitoring data of the urban planning area and perform relevant analysis and processing. The real-time environmental quality monitoring data includes at least air quality data, noise data, and thermal environment data.

[0014] The decision output module is connected to the planning indicator optimization module, the spatial conflict detection module, and the environmental quality monitoring module, respectively. It integrates the planning indicator optimization scheme, the spatial conflict detection results, and the real-time environmental quality monitoring data to generate and output decision support information for urban low-carbon and livable planning.

[0015] Specifically, the data acquisition module includes a data interface unit, which connects to the land and space planning database, traffic monitoring platform, energy management system, building information modeling platform, meteorological monitoring station and population statistics platform to realize real-time capture and batch import of multi-source heterogeneous data.

[0016] Specifically, the carbon emission prediction module includes the following units: a carbon emission accounting unit, which calculates the carbon emissions of different industry sectors and land use types in the urban planning area based on the carbon emission factor method; a spatiotemporal feature extraction unit, which extracts spatiotemporal impact features of carbon emissions such as land use intensity, population density, road network density, and building volume ratio from multi-source heterogeneous data; and a spatiotemporal prediction unit, which builds a prediction model based on a deep learning network, inputs the spatiotemporal impact features, and outputs the spatial distribution and time series prediction results of carbon emissions.

[0017] Specifically, the planning index optimization module includes the following units: a constraint and function construction unit, which configures the constraints of low-carbon livable planning indicators and, based on the carbon emission prediction results and the constraints, constructs a multi-objective optimization function with the optimization objectives of minimizing carbon emissions, maximizing livability, and optimizing economic benefits; an optimization solution unit, which uses a multi-objective intelligent optimization algorithm to solve the multi-objective optimization function and generate an optimal solution set, wherein the multi-objective intelligent optimization algorithm includes particle swarm optimization algorithm, genetic algorithm, or non-dominated sorting genetic algorithm; and a scheme generation unit, which selects planning index optimization schemes that meet preset preference conditions from the optimal solution set.

[0018] Specifically, the spatial conflict detection module includes the following units: a spatial data fusion unit, which maps the various planning indicators in the planning indicator optimization scheme to a unified geospatial grid to generate a spatial distribution layer of planning indicators; a conflict detection and evaluation unit, which performs spatial overlay analysis and spatial topological relationship detection on the spatial distribution layer of planning indicators based on a conflict rule base, identifies grid cells with conflicts and conflict types, and quantifies the severity of conflicts, calculating the conflict index and the proportion of conflict area; and a conflict report generation unit, which generates a spatial conflict detection report, which includes the spatial location of the conflict area, the conflict type, the conflict severity, and conflict mitigation suggestions.

[0019] Specifically, the environmental quality monitoring module includes the following units: a real-time monitoring data acquisition unit, which acquires real-time environmental quality monitoring data of the urban planning area from the data acquisition module; an environmental quality assessment and alarm unit, which compares the real-time environmental quality monitoring data with preset environmental quality standard thresholds, assesses the environmental quality level of the urban planning area, and generates abnormal alarm information and triggers an early warning mechanism when the real-time environmental quality monitoring data exceeds the environmental quality standard thresholds; and a spatiotemporal change analysis unit, which performs spatiotemporal trend analysis on the real-time environmental quality monitoring data to identify hotspots and patterns of environmental quality changes.

[0020] Specifically, the decision output module visualizes the planning indicator optimization scheme in the form of maps, charts, and indicator tables. In the visualization, the spatial conflict detection results are marked on the spatial layout map of the planning indicator optimization scheme, and a comparative analysis report of planning indicators before and after optimization is generated.

[0021] In summary, the beneficial effects of the intelligent monitoring and optimization system for urban low-carbon and livable planning indicators proposed in this application are as follows:

[0022] 1. By organically integrating temporal trends with dynamic spatial distribution characteristics, it can accurately identify key areas with high carbon emissions, taking into account both the assessment of future total carbon emissions and the positioning of key areas. At the same time, it optimizes the model construction method, establishes a scientific and standardized carbon emission accounting system, effectively reduces prediction errors, and provides reliable and accurate support for low-carbon planning decisions.

[0023] 2. Emphasizing a comprehensive balance between low-carbon development, livability, and economic benefits, the introduction of efficient optimization technologies enables global optimization under multiple constraints. Furthermore, it allows for flexible adjustment of the weights of various objectives based on the development positioning and strategic preferences of different cities, significantly enhancing the adaptability and flexibility of the solution and catering to the differentiated development needs of different cities.

[0024] 3. It can accurately identify the spatial conflict locations of multiple planning indicators within the same geographical space, and establish a scientific quantitative judgment system. It can accurately quantify and analyze the conflict type, conflict level and impact, providing a clear direction for the coordination and adjustment of planning indicators, avoiding blind adjustments and reducing the rework cost of planning implementation. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0026] Figure 1 This is a flowchart of an intelligent monitoring and optimization system for urban low-carbon and livable planning indicators according to this application.

[0027] Figure 2 This application presents a flowchart of the carbon emission prediction module of an intelligent monitoring and optimization system for urban low-carbon and livable planning indicators.

[0028] Figure 3 This application presents a flowchart of the planning indicator optimization module of an intelligent monitoring and optimization system for urban low-carbon and livable planning indicators.

[0029] Figure 4 This is a flowchart of the spatial conflict detection module of an intelligent monitoring and optimization system for urban low-carbon and livable planning indicators, as described in this application. Detailed Implementation

[0030] To make the technical means, inventive features, objectives, and effects of this application easier to understand, the application is further described below with reference to specific illustrations. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0031] The present application will now be described in further detail with reference to the accompanying drawings.

[0032] like Figure 1As shown in the embodiment of this application, an intelligent monitoring and optimization system for urban low-carbon and livable planning indicators includes the following modules: a data acquisition module, which acquires multi-source heterogeneous data of the urban planning area, including land use data, traffic flow data, energy consumption data, building information data, meteorological environment data, and population activity data.

[0033] Specifically, based on multi-source data fusion technology, the barriers between data from different fields are broken down. Through standardized data collection protocols, comprehensive data on the core dimensions of urban planning can be captured. Among them, land use data reflects the basic spatial layout of the city, transportation and energy data are associated with the core sources of carbon emissions, and building, meteorological and population data support the collaborative analysis of livability and low carbon. The various types of data complement and verify each other, and build the basic data foundation for urban low-carbon and livable planning.

[0034] This module achieves comprehensive data coverage, avoids planning deviations caused by a single data dimension, ensures the scientific nature of monitoring and optimization, and enables the synchronous acquisition of multi-source heterogeneous data. It can realize a comprehensive and three-dimensional perception of the city's operational status, providing high-quality data support for subsequent carbon emission prediction, indicator optimization and other processes.

[0035] The carbon emission prediction module, connected to the data acquisition module, constructs a carbon emission prediction model based on multi-source heterogeneous data, predicts the spatiotemporal distribution of carbon emissions in urban planning areas, and outputs the carbon emission prediction results.

[0036] Specifically, using multi-source heterogeneous data as input and combining it with the inherent laws of urban carbon emissions, a predictive model that takes into account both time and spatial dimensions is constructed through data preprocessing and feature correlation analysis. Concrete data such as land use and traffic flow are transformed into carbon emission-related feature variables. Through model training and iteration, accurate predictions of carbon emissions in different areas of the city in the future can be achieved.

[0037] This module enables spatiotemporal prediction of carbon emissions, which can not only clarify the temporal variation pattern of carbon emissions, but also accurately locate high carbon emission areas, providing clear targets for optimizing planning indicators. At the same time, the model built based on multi-source data has higher prediction accuracy and can effectively avoid prediction bias caused by the distortion of single data, providing a reliable decision-making basis for low-carbon planning.

[0038] The planning indicator optimization module is connected to the carbon emission prediction module. Based on the carbon emission prediction results and combined with the preset low-carbon and livable planning indicator constraints, it uses a multi-objective optimization algorithm to optimize and solve the urban planning indicators, and generates an optimized planning indicator scheme.

[0039] Specifically, the core constraints of low-carbon and livable planning should be clarified first (such as total carbon emission thresholds, livability evaluation standards, and land use compliance requirements). With carbon emission prediction results as the core reference, a multi-objective optimization function should be constructed to minimize carbon emissions, maximize livability, and optimize economic benefits. The Pareto optimal solution set should be solved through a multi-objective optimization algorithm. Then, the optimal solution should be selected in combination with actual planning needs to achieve a synergistic balance between the three major objectives of low carbon, livability, and economy.

[0040] This module breaks away from the limitations of prioritizing a single objective in traditional planning, taking into account low-carbon development, livability, and economic benefits, avoiding the loss of one aspect for another. The application of multi-objective optimization algorithms can efficiently solve the optimal solution under multiple constraints. Compared with manual optimization, it not only improves optimization efficiency but also avoids human experience bias. The generated optimization schemes are more scientific and operable. At the same time, the objective weights can be flexibly adjusted according to different planning preferences to adapt to the development needs of different cities.

[0041] The spatial conflict detection module is connected to the planning index optimization module. It performs spatial conflict detection on the planning index optimization scheme, identifies conflict areas in the spatial layout of different planning indicators, and outputs the spatial conflict detection results.

[0042] Specifically, based on geographic information system spatial analysis technology, the optimized planning indicators (such as land use type, transportation network layout, building density, etc.) are mapped to a unified geospatial grid. Through spatial overlay analysis and topological relationship detection, the adaptability of different indicators in the same spatial area is compared. Combined with a pre-set conflict rule base (such as distance constraints between industrial land and residential land, and layout conflicts between transportation networks and ecological green spaces), conflict areas are identified, conflict types are determined, and the severity of conflicts is assessed through quantitative indicators (conflict index, conflict area ratio).

[0043] This module enables visualized conflict detection of spatial layout of planning indicators, significantly improving efficiency. It can accurately locate conflict areas and their root causes, preventing spatial layout contradictions after the planning scheme is implemented (such as conflicts between industrial pollution and residential livability, or between traffic congestion and land use). At the same time, through quantitative assessment of conflict severity, it can provide targeted directions for conflict mitigation, reduce the rework costs of planning implementation, and ensure the feasibility of planning schemes.

[0044] The environmental quality monitoring module is connected to the data acquisition module to acquire real-time environmental quality monitoring data of the urban planning area and perform relevant analysis and processing. The real-time environmental quality monitoring data includes at least air quality data, noise data, and thermal environment data.

[0045] Specifically, by connecting to environmental monitoring terminals (such as air quality monitoring stations, noise sensors, thermal environment recorders, etc.), environmental indicator data of urban planning areas are captured in real time. Data quality is ensured by using technologies such as data cleaning and outlier removal. Then, through comparative analysis (comparing with environmental quality standard thresholds) and spatiotemporal trend analysis, real-time monitoring, level assessment and change prediction of environmental quality are achieved.

[0046] Real-time and precise monitoring of environmental quality can promptly detect environmental anomalies (such as excessive air quality or noise) and quickly trigger early warning mechanisms, providing timely feedback for planning adjustments. At the same time, through spatiotemporal trend analysis, hotspots and patterns of environmental quality changes can be identified, clarifying the correlation between environmental quality and planning indicators, providing environmental support for optimizing planning indicators, and ensuring the implementation of livable planning schemes.

[0047] The decision output module is connected to the planning indicator optimization module, the spatial conflict detection module, and the environmental quality monitoring module, respectively. It integrates the planning indicator optimization scheme, the spatial conflict detection results, and the real-time environmental quality monitoring data to generate and output decision support information for urban low-carbon and livable planning.

[0048] Specifically, data integration and visualization technologies are used to standardize the output results of multiple modules (optimization schemes, conflict detection results, and environmental monitoring data). The data is presented intuitively through map annotations, charts, and indicator tables. At the same time, a comparative analysis report before and after optimization is generated, clearly showing the effect of planning optimization, existing problems, and improvement directions, providing decision-makers with comprehensive and clear decision-making basis.

[0049] This module breaks down the isolation of data from different modules, enabling the integrated presentation of multi-dimensional information, reducing the information acquisition costs for decision-makers, and providing intuitive and easy-to-understand visualization methods (maps, charts, etc.), allowing non-professional decision-makers to quickly grasp the core content of the plan; the comparative analysis before and after optimization can clearly demonstrate the value of planning optimization, while also identifying potential problems such as spatial conflicts and environmental quality, providing strong decision support for the final implementation of the planning scheme, and improving the scientific nature and efficiency of planning decisions.

[0050] In one embodiment of this application, the data acquisition module includes a data interface unit, which interfaces with a land and space planning database, a traffic monitoring platform, an energy management system, a building information modeling platform, a meteorological monitoring station, and a population statistics platform to realize real-time capture and batch import of multi-source heterogeneous data.

[0051] It should be noted that a standardized interface protocol is adopted to achieve seamless integration with existing data platforms in various fields. Interface adaptation technology is used to solve the problems of inconsistent data formats and transmission protocols between different platforms. At the same time, a dual mechanism of real-time capture and batch import is built. Real-time capture meets the needs of dynamic monitoring, while batch import meets the needs of historical data backtracking and batch analysis, ensuring the flexibility and comprehensiveness of data acquisition.

[0052] It eliminates the need to reconstruct the existing data platform, reducing system construction costs, while enabling real-time data updates and batch acquisition, balancing dynamic monitoring and historical analysis needs; the standardized design of the interface units allows for flexible integration with new data platforms, improving system scalability and ensuring data timeliness and integrity.

[0053] In one embodiment of this application, such as Figure 2 As shown, the carbon emission prediction module includes the following units: a carbon emission accounting unit, which calculates the carbon emissions of different industries and land use types in the urban planning area based on the carbon emission factor method; a spatiotemporal feature extraction unit, which extracts the spatiotemporal impact features of carbon emissions, such as land use intensity, population density, road network density, and building volume ratio, from multi-source heterogeneous data; and a spatiotemporal prediction unit, which builds a prediction model based on a deep learning network, inputs the spatiotemporal impact features, and outputs the spatial distribution and time series prediction results of carbon emissions.

[0054] It should be noted that the carbon emission accounting unit achieves accurate quantification of carbon emissions through the carbon emission factor method (i.e., calculating the emissions of various carbon sources based on carbon emission coefficients of different industries and land use types, combined with activity level data); the spatiotemporal feature extraction unit uses feature engineering technology to screen out spatiotemporal features strongly correlated with carbon emissions from multi-source data, eliminate redundant information, and provide high-quality input for the prediction model; the spatiotemporal prediction unit adopts a deep learning network, using CNN to extract spatial features and LSTM to capture time series patterns, to achieve accurate prediction of the spatiotemporal distribution of carbon emissions.

[0055] The carbon emission accounting unit adopts the mature factor method, which has high accounting accuracy and strong adaptability, and can cover the carbon emission accounting needs of different industries and land use types. The spatiotemporal feature extraction unit can eliminate redundant data, improve model training efficiency and prediction accuracy. The application of deep learning networks, compared with traditional machine learning models, can better capture the spatiotemporal correlation patterns of carbon emissions, and the prediction results are more accurate and have more reference value, which can provide precise targeted guidance for subsequent planning optimization.

[0056] In one embodiment of this application, such as Figure 3As shown, the planning index optimization module includes the following units: a constraint and function construction unit, which configures the constraints of low-carbon livable planning indicators and constructs a multi-objective optimization function with the optimization objectives of minimizing carbon emissions, maximizing livability, and optimizing economic benefits based on carbon emission prediction results and constraints; an optimization solution unit, which uses a multi-objective intelligent optimization algorithm to solve the multi-objective optimization function and generate an optimal solution set. The multi-objective intelligent optimization algorithm includes particle swarm optimization algorithm, genetic algorithm, or non-dominated sorting genetic algorithm; and a scheme generation unit, which selects planning index optimization schemes that meet preset preference conditions from the optimal solution set.

[0057] It should be noted that the constraint and function construction unit, in conjunction with relevant urban planning standards, low-carbon development goals, livability requirements, and economic benefit baselines, clarifies the constraint scope of planning indicators (such as the upper limit of building volume ratio, the threshold of total carbon emissions, etc.), and quantifies the three major optimization objectives into mathematical functions to construct a multi-objective optimization model; the optimization solution unit, through intelligent optimization algorithms, simulates biological evolution or group cooperation mechanisms to efficiently search for the optimal solution set within the constraints, ensuring the diversity and optimality of the solution set; the scheme generation unit, in conjunction with actual planning preferences (such as some cities prioritizing low carbon, while others prioritize livability), selects the scheme that best meets the needs from the optimal solution set.

[0058] The constraint and function construction unit ensures that the optimization scheme meets policy requirements and actual needs, avoids deviation from the optimization direction, and the multi-objective intelligent optimization algorithm has high solution efficiency and excellent solution set quality. It can quickly handle complex optimization problems with multiple constraints and multiple objectives. Compared with traditional optimization methods, it has stronger applicability. The scheme generation unit is flexible and can adjust the screening conditions according to the development preferences of different cities to generate personalized optimization schemes and improve the adaptability of planning.

[0059] In one embodiment of this application, such as Figure 4 As shown, the spatial conflict detection module includes the following units: a spatial data fusion unit, which maps various planning indicators in the planning indicator optimization scheme to a unified geospatial grid to generate a spatial distribution layer of planning indicators; a conflict detection and evaluation unit, which performs spatial overlay analysis and spatial topological relationship detection on the spatial distribution layer of planning indicators based on a conflict rule base, identifies grid units with conflicts and conflict types, and quantifies the severity of conflicts, calculating the conflict index and the proportion of conflict area; and a conflict report generation unit, which generates a spatial conflict detection report, which includes the spatial location of the conflict area, the conflict type, the conflict severity, and conflict mitigation suggestions.

[0060] It should be noted that the spatial data fusion unit maps different types of planning indicators (such as land use, transportation, and ecology) to a unified grid through geographic coordinate calibration and data standardization, achieving unified alignment of spatial data. The conflict detection and assessment unit, based on a pre-set conflict rule base (such as distance constraints between prohibited industrial land and residential land and high-noise traffic networks within ecological protection zones), compares the spatial layout of different indicators through overlay analysis, identifies spatial conflicts through topological relationship detection, and then calculates the conflict index (reflecting the severity of the conflict) and the conflict area ratio (reflecting the scope of the conflict's impact) through quantitative formulas. The conflict report generation unit integrates the detection results, combines the conflict type and severity, and generates targeted mitigation suggestions, providing specific guidance for planning adjustments.

[0061] The spatial data fusion unit achieves spatial unification of different planning indicators, avoiding misjudgments of conflicts caused by inconsistencies in coordinates and formats; the conflict detection and assessment unit enables accurate identification and quantitative assessment of conflicts, which is more objective and scientific than traditional qualitative judgments, and can clearly define the scope and severity of the conflict's impact; the conflict report generation unit provides complete detection results and mitigation suggestions, eliminating the need for additional analysis by decision-makers, and directly providing actionable directions for planning adjustments, thereby improving the efficiency of planning implementation.

[0062] In one embodiment of this application, the environmental quality monitoring module includes the following units: a real-time monitoring data acquisition unit, which acquires real-time environmental quality monitoring data of the urban planning area from the data acquisition module; an environmental quality assessment and alarm unit, which compares the real-time environmental quality monitoring data with preset environmental quality standard thresholds, assesses the environmental quality level of the urban planning area, and generates abnormal alarm information and triggers an early warning mechanism when the real-time environmental quality monitoring data exceeds the environmental quality standard thresholds; and a spatiotemporal change analysis unit, which performs spatiotemporal trend analysis on the real-time environmental quality monitoring data to identify hotspot areas and change patterns of environmental quality changes.

[0063] It should be noted that the real-time monitoring data acquisition unit connects to the data acquisition module through a data interface to achieve real-time synchronization of environmental quality data and ensure the timeliness of the data; the environmental quality assessment and alarm unit presets national or local environmental quality standard thresholds, judges the environmental quality level through data comparison, and triggers an early warning mechanism (such as SMS alarm, platform pop-up) when the data exceeds the standard to promptly remind relevant personnel; the spatiotemporal change analysis unit uses time series analysis, spatial hotspot analysis and other technologies to explore the temporal change trend (such as monthly and quarterly changes) and spatial distribution pattern (such as the clustering characteristics of high pollution areas) of environmental quality data, and clarifies the dynamic changes in environmental quality.

[0064] The real-time monitoring data acquisition unit ensures the timeliness of environmental quality monitoring and can quickly capture environmental changes; the assessment and alarm unit enables rapid early warning of environmental quality anomalies, facilitating timely control measures to prevent the continuous deterioration of environmental quality and ensure livability; the spatiotemporal change analysis unit can accurately identify hotspots and patterns of environmental quality changes, providing precise environmental guidance for optimizing planning indicators, while also providing targeted directions for environmental management and improving the efficiency of environmental governance.

[0065] In one embodiment of this application, the decision output module visualizes the planning index optimization scheme in the form of maps, charts and index tables. In the visualization, the spatial conflict detection results are marked on the spatial layout map of the planning index optimization scheme, and a comparative analysis report of planning indicators before and after optimization is generated.

[0066] It should be noted that visualization technologies (such as GIS map rendering and chart generation tools) are used to transform the spatial layout and core indicator data of the planning indicator optimization scheme into intuitive forms such as maps, bar charts, line charts, and tables. At the same time, the spatial conflict detection results (conflict areas and conflict types) are overlaid on the spatial layout map in different colors and markers to achieve the visual location of conflict areas. By comparing the planning indicators (such as total carbon emissions, livability score, and environmental quality level) before and after optimization, a comparative analysis report is generated to clearly present the optimization effect.

[0067] The visual presentation method is intuitive and easy to understand, allowing for a quick grasp of the core content and spatial layout of the planning scheme; the visual marking of conflict results allows for quick location of conflict areas, facilitating targeted adjustments; the comparative analysis before and after optimization clearly demonstrates the value of planning optimization, identifies the advantages and disadvantages of the optimized scheme, provides decision-makers with a clear and comprehensive reference for their final decision, and facilitates the promotion and implementation of the planning scheme.

[0068] It should be noted that, in this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0069] The present application and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present application. The actual structure is not limited to this. In conclusion, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the present application, such design should fall within the protection scope of the present application.

Claims

1. An intelligent monitoring and optimization system for urban low-carbon and livable planning indicators, characterized in that, Includes the following modules: The data acquisition module acquires multi-source heterogeneous data of the urban planning area, including land use data, traffic flow data, energy consumption data, building information data, meteorological and environmental data, and population activity data. A carbon emission prediction module is connected to the data acquisition module. Based on the multi-source heterogeneous data, a carbon emission prediction model is constructed to predict the spatiotemporal distribution of carbon emissions in the urban planning area and output the carbon emission prediction results. planning The indicator optimization module is connected to the carbon emission prediction module. Based on the carbon emission prediction results and combined with the preset low-carbon livable planning indicator constraints, it uses a multi-objective optimization algorithm to optimize and solve the urban planning indicators, and generates an optimized planning indicator scheme. The spatial conflict detection module is connected to the planning index optimization module. It performs spatial conflict detection on the planning index optimization scheme, identifies conflict areas of different planning indicators in spatial layout, and outputs spatial conflict detection results. An environmental quality monitoring module is connected to the data acquisition module to acquire real-time environmental quality monitoring data of the urban planning area and perform relevant analysis and processing. The real-time environmental quality monitoring data includes at least air quality data, noise data, and thermal environment data. The decision output module is connected to the planning indicator optimization module, the spatial conflict detection module, and the environmental quality monitoring module, respectively. It integrates the planning indicator optimization scheme, the spatial conflict detection results, and the real-time environmental quality monitoring data to generate and output decision support information for urban low-carbon and livable planning.

2. The intelligent monitoring and optimization system for urban low-carbon and livable planning indicators according to claim 1, characterized in that, The data acquisition module includes a data interface unit, which connects to the land and space planning database, traffic monitoring platform, energy management system, building information modeling platform, meteorological monitoring station and population statistics platform to realize real-time capture and batch import of multi-source heterogeneous data.

3. The intelligent monitoring and optimization system for urban low-carbon and livable planning indicators according to claim 1, characterized in that, The carbon emission prediction module includes the following units: The carbon emission accounting unit calculates the carbon emissions of different industry sectors and land use types in urban planning areas based on the carbon emission factor method. The spatiotemporal feature extraction unit extracts spatiotemporal impact features of carbon emissions, such as land use intensity, population density, road network density, and building volume ratio, from multi-source heterogeneous data. The spatiotemporal prediction unit constructs a prediction model based on a deep learning network, takes spatiotemporal influence characteristics as input, and outputs the spatial distribution and time series prediction results of carbon emissions.

4. The intelligent monitoring and optimization system for urban low-carbon and livable planning indicators according to claim 1, characterized in that, The planning indicator optimization module includes the following units: The constraint and function construction unit configures the constraints of low-carbon livable planning indicators, and constructs a multi-objective optimization function with the optimization objectives of minimizing carbon emissions, maximizing livability, and optimizing economic benefits based on the carbon emission prediction results and the constraints. The optimization unit uses a multi-objective intelligent optimization algorithm to solve the multi-objective optimization function and generate the optimal solution set. The multi-objective intelligent optimization algorithm includes particle swarm optimization algorithm, genetic algorithm or non-dominated sorting genetic algorithm. The scheme generation unit selects planning index optimization schemes that meet preset preference conditions from the optimal solution set.

5. The intelligent monitoring and optimization system for urban low-carbon and livable planning indicators according to claim 1, characterized in that, The spatial conflict detection module includes the following units: The spatial data fusion unit maps each planning indicator in the planning indicator optimization scheme onto a unified geospatial grid, generating a spatial distribution layer of planning indicators. The conflict detection and evaluation unit performs spatial overlay analysis and spatial topology detection on the spatial distribution layer of the planning indicators based on the conflict rule base, identifies grid cells with conflicts and conflict types, quantifies the severity of conflicts, and calculates the conflict index and the proportion of conflict area. The conflict report generation unit generates a spatial conflict detection report, which includes the spatial location of the conflict area, the type of conflict, the severity of the conflict, and conflict mitigation suggestions.

6. The intelligent monitoring and optimization system for urban low-carbon and livable planning indicators according to claim 1, characterized in that, The environmental quality monitoring module includes the following units: The real-time monitoring data acquisition unit acquires real-time environmental quality monitoring data of the urban planning area from the data acquisition module. The environmental quality assessment and alarm unit compares the real-time environmental quality monitoring data with the preset environmental quality standard threshold to assess the environmental quality level of the urban planning area, and generates abnormal alarm information and triggers the early warning mechanism when the real-time environmental quality monitoring data exceeds the environmental quality standard threshold. The spatiotemporal change analysis unit performs spatiotemporal trend analysis on real-time environmental quality monitoring data to identify hotspots and patterns of environmental quality changes.

7. The intelligent monitoring and optimization system for urban low-carbon and livable planning indicators according to claim 1, characterized in that, The decision output module visualizes the planning indicator optimization scheme in the form of maps, charts, and indicator tables. In the visualization, the spatial conflict detection results are marked on the spatial layout map of the planning indicator optimization scheme, and a comparative analysis report of planning indicators before and after optimization is generated.

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