Urban climate environment rapid evaluation system and method thereof

Abstract

The invention relates to the technical field of urban climatic science, in particular to an urban climatic environment rapid evaluation system and method. According to the technical scheme, the system comprises a data acquisition module, an index standardization and feature extraction module, an index weighting and dynamic parameter adjustment module and a climatic environment comprehensive index generation module. According to the method, based on meteorological and environmental multi-source data of an urban area, a multi-dimensional evaluation index set is firstly constructed through standardization and normalization processing, and then a fuzzy logic harmonic function and a non-linear harmonic function are utilized to identify the coupling relation and the cooperation strength among the indexes; and meanwhile, a dynamic optimization mechanism is introduced to adaptively adjust each weight coefficient according to the real-time index sensitivity, an urban climate environment index is finally generated, and spatial visual expression and grade early warning are realized. According to the method, rapid evaluation and trend judgment of the urban climate system state can be realized, and the method is suitable for urban planning, climate risk monitoring and toughness analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of urban climatology, and particularly to a rapid assessment system and method for urban climate environment. Background Art

[0002] With the acceleration of urbanization, the urban ecological environment and climate system are facing multiple pressures. Problems such as the enhancement of the urban heat island effect, the increase in the concentration of atmospheric pollutants, the frequent occurrence of extreme meteorological events, and the insufficient proportion of urban green space are becoming increasingly prominent. The superposition of these factors seriously affects the quality of life of urban residents and the sustainable operation ability of the urban system.

[0003] Currently, although there are some environmental monitoring systems for the collection and display of urban meteorological data, the following main problems exist: Single evaluation dimension and lack of comprehensiveness: Most existing evaluation methods usually only perform static analysis on single indicators such as temperature, PM2.5, or humidity, lacking the ability to uniformly process and integrate multi-source climate data and unable to reflect the overall health status of the urban climate system.

[0004] Lack of dynamic weight and critical response mechanism: Traditional models often adopt fixed weights or linear weighting methods, unable to identify the critical characteristics of index changes, especially being slow to respond under extreme weather or sudden environmental events and lacking an intelligent adjustment mechanism.

[0005] Simple model calculation and ignoring non-linear climate coupling effects: Effects such as the heat island mitigation effect of urban green space and the impact of wind speed on pollution diffusion are often ignored, unable to accurately reflect the feedback mechanism of the urban natural ecosystem.

[0006] Insufficient visualization and early warning functions: Current systems mostly stay in the stage of result display, lacking the linkage ability with the management system and unable to achieve real-time risk identification and trend early warning.

[0007] Therefore, we propose a rapid assessment system and method for urban climate environment to solve the existing problems. Summary of the Invention

[0008] The object of the present invention is to propose a rapid assessment system and method for urban climate environment aiming at the problems in the background art.

[0009] To achieve the above object, the present invention provides the following technical solution: A rapid assessment system for urban climate environment, comprising: A data acquisition module for collecting multi-source climate environment data within the urban area, where the climate environment data includes air temperature, humidity, wind speed, PM2.5 concentration, surface albedo, urban heat island intensity index, and green space rate; An index standardization and feature extraction module is used to standardize the climate environment data and extract feature variables; An index weighting and dynamic parameter adjustment module is used to construct a non - linear harmonic function to process the standardized indexes and dynamically and adaptively adjust the index weights based on the function gradient; A comprehensive climate environment index generation module is used to construct a comprehensive evaluation function of the urban climate environment based on the weighted processing results and generate a comprehensive climate environment index by combining the urban heat island intensity and the green space rate; A visualization and report output module is used to grade the evaluation results and output the corresponding visualization graphics and text reports.

[0010] Preferably, the harmonic function adopted in the index weighting and dynamic parameter adjustment module is an S - type non - linear response function, which is constructed based on an exponential growth form and is used to map the standardized climate environment indexes to interval values to reflect the rapid risk amplification effect of the indexes when approaching their critical values; among them, the steepness of the function curve is controlled by the slope adjustment parameter, and the inflection point of the function change is set by the index risk threshold, so as to realize the differential modeling of the critical sensitivity of different indexes.

[0011] Preferably, the dynamic weight adjustment mechanism quantifies the marginal influence degree of each index based on the absolute value calculation method of partial derivatives, and obtains the weight ratio of each index through normalization processing. This weight allocation method can be updated according to the real - time changes of climate disturbance events, ensuring that the system has a sensitive response ability to sudden meteorological extremes and avoiding the evaluation distortion problem caused by artificially fixed weights.

[0012] Preferably, the construction of the comprehensive climate environment index takes into account the coupling relationship between the urban heat island effect and the urban green space coverage level, and adopts a ratio function representation with an inhibition mechanism. When the urban green space rate increases, the influence value of the heat island intensity on the comprehensive climate environment index will show a decreasing trend, so as to truly reflect the actual role of urban ecological infrastructure in alleviating high - temperature aggregation.

[0013] Preferably, the system also includes a data interaction interface module for cross - system integration, which supports data exchange with third - party urban management systems, geographic information system platforms, meteorological monitoring systems and disaster warning systems through application programming interfaces, and realizes the visual display of climate assessment results, climate map overlay, zoned alarm push and historical data archiving management.

[0014] A rapid urban climate environment assessment method includes the following steps: Step 1: Collect multi - source urban climate environment data, including temperature, humidity, wind speed, PM2.5 concentration, albedo, heat island intensity and green space rate; Step 2: Standardize the collected indicator data to obtain dimensionless standard indicators; Step 3: Map each standardized indicator by constructing a non-linear function, and calculate the dynamic weight of each indicator based on the gradient of the mapping function; Step 4: Aggregate all indicators with weights to form the urban climate harmony function value; Step 5: Combine the urban heat island intensity index and the green space rate to calculate the comprehensive urban climate environment index; Step 6: Divide the comprehensive index into multiple levels and output the graphical evaluation results and reports.

[0015] Preferably, the parameter setting method of the non-linear response function is adaptively set according to the distribution characteristics of various climate indicators in historical meteorological events. Among them, the parameter for controlling the steepness of the function is obtained by calculating the historical volatility, and the parameter for defining the risk critical point is set by statistically analyzing the typical values of extreme weather events. The method has the ability of urban customization and can adapt to the environmental assessment needs of different geographical and climatic conditions.

[0016] Preferably, the weight factor used to calculate the climate harmony function in the method will be periodically updated according to the change of the real-time collected data. The update period can be flexibly set according to the application scenario, including daily update, hourly update, and update triggered by meteorological events. This mechanism enables the method to adapt to rapid climate change scenarios, such as extreme meteorological conditions like heatwaves, high temperature warnings, and cold snaps, and ensures that the evaluation results are real-time and forward-looking.

[0017] Preferably, the calculation results of the comprehensive climate environment index can be used for longitudinal comparative analysis in multiple regions and multiple time dimensions, including the analysis of the climate risk evolution trend of the same city at different times and the analysis of the climate environment score differences between different cities in the same period. At the same time, combined with the historical mean and volatility, a climate anomaly discrimination mechanism and a trend warning curve are formed.

[0018] Preferably, the evaluation level division intervals include five levels: excellent, good, average, poor, and extremely poor, and the corresponding index ranges are: Excellent: 0.85∼1.00; Good: 0.65∼0.85; Average: 0.45∼0.65; Poor: 0.25∼0.45; Extremely poor: 0.00∼0.25.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: Multi-dimensional climate index integration to enhance the comprehensiveness of assessment: The system integrates multiple climate and ecological indicators such as air temperature, humidity, wind speed, PM2.5, heat island index, and green space rate, and uses standardization and feature extraction technologies to construct a unified assessment framework, improving the comprehensive reflection ability of the assessment model on the overall urban climate state; Introduce a non-linear response model to enhance the ability to identify critical changes: Use an adjustable S-shaped response function to map the indicators, set risk thresholds and slope parameters, which can effectively capture the sharp changes of key indicators when approaching the risk critical point, and significantly improve the system's ability to predict extreme climate changes; Dynamic weight calculation mechanism to enhance the sensitivity and adaptability of the system: Automatically update the weight distribution through the normalization method of the derivative of the indicator function, enabling the system to dynamically adjust the assessment structure according to real-time data, enhancing the model's response ability and self-adaptive ability to sudden environmental events; Construct an urban climate harmony index to support scientific decision-making: The comprehensive index model integrates multiple indicators into an urban climate environment comprehensive index (CCEI), and introduces a coupling factor of green space rate and heat island effect, making the assessment results more scientific and of practical guiding value, and helping urban managers formulate precise ecological intervention measures for weak areas; Result visualization and risk grading output to improve management efficiency: The system supports graphical expression of assessment results, including color scale maps, heat maps, radar maps, etc., and divides them into multiple levels according to the index value, realizing intuitive identification and early warning prompts for the climate risk levels of different urban areas, and improving the level of refined urban climate governance; Strong adaptability and high scalability: The system supports access to multiple data sources and algorithm modules, can be applied to environmental governance scenarios in different climate zones and different urban scales, has good compatibility and expandability, and is suitable for the construction of smart cities and the establishment of long-term mechanisms for coping with climate change. Brief Description of the Drawings

[0020] Figure 1 It is a flow diagram of the present invention. Detailed Embodiments

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1 As Figure 1As shown in the figure, the present invention provides a rapid urban climate environment assessment system and method. The rapid urban climate environment assessment system includes the following modules: Data acquisition module This module is used to obtain multi-source climate data of the urban area. The data sources include surface meteorological stations, remote sensing satellite platforms, environmental monitoring networks, and unmanned meteorological observation equipment. The collected indicators include but are not limited to: Air temperature; Relative humidity; Wind speed; PM2.5 concentration; Surface albedo; Urban heat island intensity index; Urban green space rate.

[0023] Data standardization and feature extraction module This module normalizes and makes the original data dimensionless, so that data with different dimensions can enter a unified evaluation model for comparison. In this embodiment, a standardization processing method is adopted, and characteristic variables such as annual average value, volatility, extreme value difference, and occurrence frequency are extracted for each indicator.

[0024] Index weighting and dynamic parameter adjustment module This module performs mapping processing on each standardized index through a non-linear function model. The mapping function adopts an exponential S-shaped response model. By setting the risk threshold and response slope of each index, it can sensitively identify the criticality change of the index. The harmonic function used in the index weighting and dynamic parameter adjustment module is an S-shaped non-linear response function, which is constructed based on an exponential growth form and is used to map the standardized climate environment index to an interval value to reflect the rapid risk amplification effect when the index approaches its critical value; among them, the steepness of the function curve is controlled by the slope adjustment parameter, and the inflection point of the function change is set by the index risk threshold, so as to realize differential modeling of the critical sensitivity of different indexes.

[0025] In practical applications, taking PM2.5 as an example, assuming its threshold is 75 μg / m³ and the corresponding standardized value is 0.65, if this index in a certain area exceeds this value, the system will automatically increase its function slope to improve the sensitivity of the overall evaluation.

[0026] The index weights adopt a dynamic update mechanism, using the normalized result of the function partial derivative as the weight factor, so that the model can quickly respond to sudden climate fluctuations, improve the timeliness and accuracy of the evaluation. The dynamic weight adjustment mechanism is based on the absolute value calculation method of the partial derivative, quantifies the marginal influence degree of each index, and obtains the weight ratio of each index through normalization processing. This weight allocation method can be updated according to the real-time changes of climate disturbance events, ensuring that the system has a sensitive response ability to sudden meteorological extremes, and at the same time avoiding the evaluation distortion problem caused by artificially fixed weights.

[0027] Climate reconciliation index generation module After all indicators are non-linearly transformed and weighted and integrated, the system will construct a comprehensive evaluation function to describe the overall urban climate state. The function value will be coupled with the urban heat island effect and green space rate factors, and finally generate the "Comprehensive Urban Climate Environment Index (CCEI)". The construction of the comprehensive climate environment index takes into account the coupling relationship between the urban heat island effect and the urban green space coverage level, and adopts a ratio function representation with an inhibition mechanism. When the urban green space rate increases, the influence value of the heat island intensity on the comprehensive climate environment index will show a decreasing trend, so as to truly reflect the actual role of urban ecological infrastructure in alleviating high-temperature aggregation.

[0028] The system also includes a data interaction interface module for cross-system integration. This module supports data exchange with third-party urban management systems, geographic information system platforms, meteorological monitoring systems and disaster warning systems through the application programming interface method, and realizes the visual display of climate assessment results, climate map overlay, zonal alarm push and historical data archiving management.

[0029] In this embodiment, the CCEI value is calculated as follows: First, calculate the harmonic function value F of the weighted output of each index; Then introduce the heat island effect factor and the green space coverage correction factor; Finally, obtain an index value between 0 and 1. The higher the value, the better the urban climate state.

[0030] Visualization and report output module This module divides the evaluation results into five grades: excellent, good, average, poor, and extremely poor, and presents the climate condition differences in each region in the form of radar charts, color scale charts and climate heat maps, and generates graphic reports for urban managers, planning departments and the public to browse and reference.

[0031] The method supporting this system includes the following steps: Step 1: Urban climate data collection Collect various climate factor data of the target urban area through a sensor network and a remote sensing platform, and automatically classify and store them as a structured climate index data table. The collected indicators include, but are not limited to: Air temperature; relative humidity; wind speed; PM2.5 concentration; surface albedo; urban heat island intensity index; urban green space rate.

[0032] Step 2: Data standardization processing Process the collected original data through the standard deviation normalization algorithm to make its mean approach 0 and the standard deviation be 1, ensuring that indicators with different units and dimensions can be jointly used in the calculation model. Its mathematical expression is as follows:

[0033] Where: : The th original climate indicator; , : The historical mean and standard deviation respectively, used for standardization.

[0034] Step 3: Feature extraction and mapping transformation Apply an exponential non-linear function to each indicator for range mapping. The mapping function has "inflection point sensitivity" and can be flexibly adjusted according to different cities. For example, when the temperature exceeds a certain risk critical point, the mapping function value rises rapidly, reflecting a significant increase in heat risk. Introduce a "fuzzy logic harmonic function" to identify the coupling relationship of key indicators:

[0035] Where: : The total number of indicators; : The weight parameter; : The non-linear harmonic function.

[0036] The non-linear harmonic function is a Sigmoid function with S-shaped characteristics. Its parameter setting method is adaptively set according to the distribution characteristics of various climate indicators in historical meteorological events. Among them, the parameter used to control the steepness of the function is obtained by calculating the historical volatility, and the parameter used to define the risk critical point is set by statistically analyzing the typical values of extreme weather events. The method has the ability of urban customization and can adapt to the environmental assessment needs of different geographical and climatic conditions. Its mathematical expression is:

[0037] Where: is the A standardized climate indicator; is the slope adjustment factor, used to control the sensitivity of the function response; is the critical value of the indicator, used to determine whether the indicator is in the safe, transitional or risk interval; The function at = has the maximum rate of change, achieving an amplified response to critical climate perturbations.

[0038] This function form ensures the "rapid responsiveness" of the system to extreme weather indicators, different from the balance and hysteresis of traditional linear assessment methods.

[0039] Step 4: Dynamic weight allocation Calculate the partial derivative of the marginal impact of each mapped indicator value on the overall assessment result and normalize it to obtain the weight distribution under the current climate state, supporting on-demand adjustment. The weight factor used to calculate the climate reconciliation function in the method will be updated periodically according to the change of real-time collected data. The update period can be flexibly set according to the application scenario, including daily update, hourly update, and update triggered by meteorological events. This mechanism enables the method to adapt to rapidly changing climate scenarios, such as extreme meteorological conditions like heatwaves, high-temperature warnings, and cold snaps, and ensures that the assessment result has real-time and forward-looking properties. The mathematical expression of the dynamic optimization mechanism is as follows:

[0040] Where: is the urban climate reconciliation function; is the th standardized indicator; The weight calculation is the normalized partial derivative, with dynamic adaptability and robustness characteristics; The mechanism can respond in real time to the automatic increase or suppression of the weights of mutating climate indicators.

[0041] This mechanism enables the system to have an adaptive adjustment ability to address the problem of unbalanced weight allocation under different cities, seasons, and climate backgrounds.

[0042] Step 5: Generate the comprehensive climate environment index Construct an integrated climate reconciliation function, combine the green space coverage rate and the heat island intensity, and use a composite function model to generate the comprehensive urban climate environment index . The closer the value of this index is to 1, the healthier the urban climate system is and the stronger the ecological resilience is. The final output is an urban climate environment index , defined as:

[0043] in: , is the proportional adjustment factor (experience recommended value: =0.7, =0.3); : Heat island-green space harmonic function, defined as:

[0044] represents the intensity of urban heat island; It indicates the green space ratio per unit area; : Green space buffer coefficient, reflecting the ability of green space to alleviate urban heat island.

[0045] The comprehensive index ranges from [0,1], where the closer to 1, the better the climate environment; The above formula shows that the higher the green area ratio, The weaker the impact on the system, the more helpful it is to quantify the buffering effect of ecological infrastructure on the climate environment.

[0046] The calculation results of the comprehensive climate and environmental index can be used for longitudinal comparative analysis in multiple regions and time dimensions, including analysis of the evolution trend of climate risks in different periods in the same city and analysis of differences in climate and environmental scores between different cities in the same period. At the same time, combined with historical means and volatility, a climate anomaly discrimination mechanism and trend warning curve can be formed.

[0047] Step 6: Evaluation, Grading and Visualization according to The climate environment level is numerically divided, and when the assessment results are output, a graphical display of the index level is achieved through a graphical interface, including the use of color-scale maps to visualize the thermal distribution of urban space, or the use of radar maps to express the risks of each indicator in three dimensions. It also supports the export of assessment reports in text format (such as PDF) or structured data format (such as JSON or XML) for subsequent archiving or further calls by other information systems.

[0048] If the level of a certain area is "poor" or "extremely poor", the system will link the meteorological warning module to automatically trigger relevant prompts.

[0049] The evaluation level is divided into five levels: excellent, good, fair, poor and very poor, and the corresponding index range is: Excellent: 0.85∼1.00; Good: 0.65∼0.85; Normal: 0.45∼0.65; Poor: 0.25 to 0.45; Very poor: 0.00 to 0.25.

[0050] The above specific embodiments are only several preferred embodiments of the present invention. Based on the technical solution of the present invention and the relevant revelations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

[0051] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention.

Claims

1. A rapid assessment system for urban climate environment, characterized in that: include: A data collection module is used to collect multi-source climate and environmental data in the urban area, wherein the climate and environmental data include temperature, humidity, wind speed, PM2.5 concentration, surface albedo, urban heat island intensity index and green space ratio; An index standardization and feature extraction module, used to standardize the climate and environmental data and extract feature variables; The indicator weighting and dynamic parameter adjustment module is used to construct a nonlinear harmonic function to process the standardized indicators and to dynamically and adaptively adjust the indicator weights based on the function gradient; The climate environment comprehensive index generation module is used to construct a comprehensive evaluation function of the urban climate environment based on the weighted processing results, and to generate a comprehensive climate environment index by combining the urban heat island intensity and green space ratio; The visualization and report output module is used to classify the evaluation results and output corresponding visualization graphics and text reports.

2. The urban climate environment rapid assessment system according to claim 1 is characterized by: The harmonic function used in the indicator weighting and dynamic parameter adjustment module is an S-type nonlinear response function, which is constructed based on an exponential growth form and is used to map standardized climate and environmental indicators to interval values, so as to reflect the rapid risk amplification effect of the indicators when they are close to their critical values; wherein the steepness of the function curve is controlled by the slope adjustment parameter, and the inflection point of the function change is set by the indicator risk threshold, thereby achieving differentiated modeling of the critical sensitivity of different indicators.

3. The urban climate environment rapid assessment system according to claim 1 is characterized by: The dynamic weight adjustment mechanism is based on the absolute value calculation method of partial derivatives, quantifies the marginal impact of each indicator, and obtains the weight ratio of each indicator through normalization. The weight distribution method can be updated according to the real-time changes of climate disturbance events to ensure that the system has the ability to respond sensitively to sudden meteorological extremes, while avoiding the evaluation distortion caused by artificially fixed weights.

4. The urban climate environment rapid assessment system according to claim 1 is characterized by: The construction of the comprehensive climate and environmental index takes into account the coupling relationship between the urban heat island effect and the urban green space coverage level, and adopts a ratio function representation method with an inhibition mechanism. When the urban green space ratio increases, the impact value of the heat island intensity on the comprehensive climate and environmental index will show a decreasing trend, thereby truly reflecting the actual role of urban ecological infrastructure in alleviating high temperature accumulation.

5. The urban climate environment rapid assessment system according to claim 1 is characterized by: The system also includes a data interaction interface module for cross-system integration, which supports data exchange with third-party urban management systems, geographic information system platforms, meteorological monitoring systems and disaster warning systems through application programming interfaces, to achieve visual display of climate assessment results, climate map overlay, zone alarm push and historical data archiving management.

6. A method for rapid assessment of urban climate environment, characterized in that: The steps include: Step 1: Collect multi-source urban climate and environmental data, including temperature, humidity, wind speed, PM2.5 concentration, albedo, heat island intensity, and green space ratio; Step 2: Standardize the collected index data to obtain dimensionless standard index; Step 3: Map each standardized indicator by constructing a nonlinear function, and calculate the dynamic weight of each indicator based on the gradient of the mapping function; Step 4: Weight all indicators and summarize them to form the urban climate harmonic function value; Step 5: Calculate the comprehensive index of urban climate environment by combining the urban heat island intensity index and green space ratio; Step 6: Divide the comprehensive index into multiple levels and output graphical evaluation results and reports.

7. A method for rapid assessment of urban climate environment according to claim 6, characterized in that: The parameter setting method of the nonlinear response function is adaptively set according to the distribution characteristics of various climate indicators in historical meteorological events, wherein the parameters used to control the steepness of the function are obtained by calculating the historical volatility, and the parameters used to define the risk critical point are set by statistically analyzing the typical values ​​of extreme weather events. The method has the ability to be customized for cities and can adapt to the environmental assessment needs of different geographical and climatic conditions.

8. A method for rapid assessment of urban climate environment according to claim 6, characterized in that: The weight factors used to calculate the climate harmonic function in the method will be periodically updated according to the changes in the real-time collected data. The update period can be flexibly set according to the application scenario, including daily updates, hourly updates, and updates triggered by meteorological events. The mechanism enables the method to adapt to rapid climate change scenarios and ensure that the evaluation results are real-time and forward-looking.

9. A method for rapid assessment of urban climate environment according to claim 6, characterized in that: The calculation results of the comprehensive climate and environmental index can be used for longitudinal comparative analysis in multiple regions and time dimensions, including analysis of climate risk evolution trends in different periods in the same city and analysis of differences in climate and environmental scores between different cities in the same period. At the same time, combined with historical means and volatility, a climate anomaly discrimination mechanism and trend warning curve can be formed.

10. A method for rapid assessment of urban climate environment according to claim 6, characterized in that: The evaluation grading interval includes five levels: excellent, good, average, poor and very poor, and the corresponding index range is: Excellent: 0.85∼1.00; Good: 0.65∼0.85; Normal: 0.45∼0.65; Poor: 0.25∼0.45; Range: 0.00∼0.25.

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