Cold region city blue-green space thermal environment influence factor identification and efficiency measurement system

Abstract

The invention relates to the technical field of blue-green space planning, in particular to a cold region city blue-green space thermal environment influence factor identification and efficiency measurement system, which comprises a data acquisition unit, a factor extraction unit, a heat flow evaluation unit, an efficiency measurement unit and a planning countermeasure generation unit. The factor extraction unit uses multiple analysis technologies to screen key factors, the heat flow evaluation unit quantifies seasonal influence and adjustment efficiency of a blue-green space on a thermal environment based on a heat source-sink theory, and the efficiency measurement unit quantifies thermal environment adjustment efficiency and sorts the thermal environment adjustment efficiency in combination with an economic cost and ecological service value evaluation method. According to the cold region city blue-green space planning method and system, influence factors can be accurately recognized, a scientific basis is provided for cold region city blue-green space planning, cooperative improvement of the urban ecological environment and the thermal environment is achieved, and sustainable development of a cold region city is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of blue-green space planning, and more specifically, to a system for identifying influencing factors and measuring the effectiveness of the thermal environment of blue-green space in cold-region cities. Background Art

[0002] Blue-green space planning is an important technology. In the field of blue-green space planning and thermal environment improvement in cold-region cities, it is crucial to accurately identify the key factors affecting the thermal environment by blue-green space. However, traditional methods have many insurmountable defects.

[0003] The thermal environment of cold-region cities is affected by the combined effects of factors such as vegetation coverage, water area, shape index of blue-green space, land use type, and local climate conditions, forming an extremely complex system. Traditional methods mostly rely on empirical judgment or correlation analysis, which cannot cope with such complex data and diverse influencing factors. When analyzing the relationship between vegetation coverage and the thermal environment, it is unable to consider the differences in the impact of the changing growth state of vegetation in different seasons on the thermal environment, as well as the synergistic effects of surrounding water bodies, buildings, and other factors. This leads to inaccurate results when determining the key factors affecting the thermal environment. Based on the inaccurate identification results of key factors for blue-green space planning, the planning lacks pertinence, cannot fully exert the regulatory role of blue-green space on the thermal environment, is difficult to alleviate the urban heat island effect and optimize the local climate, and ultimately causes problems such as waste of resources and poor planning effects. To solve this technical problem, we provide a system for identifying influencing factors and measuring the effectiveness of the thermal environment of blue-green space in cold-region cities. Summary of the Invention

[0004] The purpose of the present invention is to provide a system for identifying influencing factors and measuring the effectiveness of the thermal environment of blue-green space in cold-region cities to solve the problems raised in the above background art.

[0005] 1. Since traditional methods are difficult to accurately identify the key factors affecting the thermal environment of blue-green space in cold-region cities, resulting in a lack of pertinence in blue-green space planning and the inability to effectively regulate the thermal environment, in this case, the factor extraction unit preprocesses the data using a two-dimensional dimensionality reduction method combining principal component analysis and independent component analysis, screens the key factors using grey relational analysis and stepwise regression analysis, and also uses spatial recognition technologies such as local spatial autocorrelation analysis based on geographically weighted regression to accurately determine the key factors, providing a basis for scientific blue-green space planning and improving the regulatory effect of blue-green space on the thermal environment.

[0006] 2. Since the traditional method cannot comprehensively quantify the thermal environment regulation efficiency and level of the blue-green space in cold-region cities and it is difficult to evaluate its comprehensive benefits, this case establishes a full-life cycle cost accounting model for the construction and maintenance of blue-green space through an effectiveness measurement unit, uses the emergy analysis-based method to evaluate the ecological service value, constructs comprehensive benefit indicators, and uses the analytic hierarchy process and grey comprehensive evaluation method to quantify the thermal environment regulation efficiency and level of different seasonal blue-green space combination forms, which can comprehensively evaluate the comprehensive benefits of blue-green space, provide quantitative support for the reasonable planning and optimization of blue-green space, and realize the efficient utilization of resources.

[0007] To achieve the above objectives, a thermal environment impact factor identification and effectiveness measurement system for the blue-green space in cold-region cities is provided, including a data acquisition unit, a factor extraction unit, a heat flow assessment unit, an effectiveness measurement unit, and a planning countermeasure generation unit;

[0008] The data acquisition unit is used to collect relevant basic data such as the surface temperature, land use classification, blue-green space distribution, and local climate zoning of cold-region cities;

[0009] The factor extraction unit extracts the key factors affecting the urban thermal environment from the blue-green space based on mathematical statistics analysis and spatial recognition technology;

[0010] The heat flow assessment unit constructs a "heat flow" measurement system based on the heat "source-sink" theory to quantify the seasonal impact of the blue-green space on the urban thermal environment and evaluate its regulation effectiveness;

[0011] The effectiveness measurement unit combines the economic cost and ecological service value evaluation methods to quantify the thermal environment regulation efficiency and level of different seasonal blue-green space combination forms and form a comprehensive value ranking;

[0012] The planning countermeasure generation unit proposes a planning and regulation path for the blue-green space that adapts to the climate characteristics of cold-region cities according to the factor identification and effectiveness measurement results.

[0013] As a further improvement of this technical solution, when the factor extraction unit extracts key factors based on mathematical statistics analysis:

[0014] The two-dimensional dimensionality reduction method of principal component analysis combined with independent component analysis is used to preprocess the multi-source data related to the blue-green space, and grey relational analysis is used to determine the correlation degree between each potential factor and the urban thermal environment index, and the top 20% with close correlation are selected as the preliminary key factor set, and then the stepwise regression analysis method is used to screen out the key factors affecting the thermal environment from the preliminary key factor set, and a regression equation is constructed to obtain the final key factors.

[0015] As a further improvement of this technical solution, when the factor extraction unit uses the spatial recognition technology:

[0016] Adopt the local spatial autocorrelation analysis method based on geographically weighted regression, and combine the spatial weight matrix to identify the spatial heterogeneity characteristics of the impact of blue-green space on the thermal environment;

[0017] At the same time, use the spatial clustering algorithm to conduct clustering analysis on the blue-green space and its surrounding areas, and divide the spatial clusters with similar thermal environment response characteristics according to the clustering results.

[0018] As a further improvement of this technical solution, when the heat flow evaluation unit constructs the "heat flow" measurement system based on the heat "source-sink" theory:

[0019] Introduce the multi-source remote sensing data fusion technology, comprehensively combine the surface cover information obtained from the optical remote sensing image and the surface temperature information reflected by the thermal infrared remote sensing data to construct a high-resolution heat "source-sink" spatial distribution map for identifying the boundary between the blue-green space and other urban land use types;

[0020] Establish a physical model of heat flow transmission based on the energy balance principle, dynamically simulate the heat exchange process between the blue-green space and the surrounding environment, obtain the heat conduction equation, and then solve the heat conduction equation by the finite difference method to quantify the heat flow intensity of the blue-green space as a heat "source" or heat "sink" in different seasons and different time periods.

[0021] As a further improvement of this technical solution, when the heat flow evaluation unit evaluates the regulation efficiency of the blue-green space:

[0022] Adopt a method combining comparative experiments and scenario simulations to set multiple comparison scenarios, and use the constructed heat flow measurement system to simulate the evolution process of the urban thermal environment under different scenarios respectively;

[0023] Evaluate the actual efficiency of the blue-green space in alleviating the urban heat island effect and regulating the local climate by comparing the surface temperature distribution and the change of heat island intensity under different scenarios;

[0024] At the same time, introduce sensitivity analysis, analyze the influence degree of changing the key parameters of the blue-green space on the regulation efficiency, determine the key factors and optimization directions for improving the regulation efficiency, and the key parameters include vegetation coverage, water area and shape index.

[0025] As a further improvement of this technical solution, when the efficiency measurement unit combines the economic cost and ecological service value evaluation methods:

[0026] Establish a full-life cycle cost accounting model for the construction and maintenance of the blue-green space, and use the cost-benefit analysis method to discount each cost according to the time series to obtain the present value of the total cost of the blue-green space during its life cycle;

[0027] For the evaluation of the value of ecosystem services, the method of evaluating the value of ecosystem service functions based on emergy analysis is used to convert the ecosystem service functions provided by the blue-green space into a unified emergy unit, and combined with the local emergy-money ratio, it is converted into monetary value. By comparing the economic cost and the value of ecosystem services, a comprehensive benefit index of the blue-green space is constructed for the quantitative evaluation of its thermal environment regulation efficiency.

[0028] As a further improvement of this technical solution, when the efficacy measurement unit quantifies the thermal environment regulation efficiency and level of the blue-green space combination form under different seasonal conditions:

[0029] The analytic hierarchy process is used to construct an evaluation index system. The area ratio, connectivity, vegetation type diversity, and water body fluidity of the blue-green space are used as the criterion layer indicators, and the thermal environment regulation efficiency is used as the target layer to construct a judgment matrix and determine the weights of each indicator;

[0030] Based on the grey comprehensive evaluation method and combined with the monitoring data of different seasons, a comprehensive evaluation of the blue-green space combination form is carried out to form a comprehensive value ranking.

[0031] As a further improvement of this technical solution, when the planning countermeasure generation unit proposes the planning and regulation path of the blue-green space according to the factor identification and efficacy measurement results:

[0032] A spatial optimization model based on the genetic algorithm is adopted, with the goal of maximizing the thermal environment regulation efficacy of the blue-green space and minimizing the economic cost. The constraint conditions of the key factors and other restrictions of urban planning are incorporated into the model, and the optimal blue-green space layout scheme is searched in the solution space through the selection, crossover, and mutation operations of the genetic algorithm;

[0033] At the same time, combined with the public participation geographic information system technology, the use demands and preference information of citizens for the blue-green space are collected and converted into the optimization goals of the spatial layout.

[0034] Compared with the prior art, the beneficial effects of the present invention:

[0035] In the system for identifying influencing factors and measuring the effectiveness of the thermal environment of blue-green spaces in cold-region cities, the factor extraction unit uses two-dimensional dimensionality reduction, grey relational analysis, and stepwise regression analysis techniques to extract the key factors affecting the thermal environment, overcoming the deficiencies of traditional methods and making the blue-green space planning more targeted. The heat flow assessment unit constructs a measurement system based on the heat "source-sink" theory to quantify the seasonal thermal environment impact and regulation effectiveness of blue-green spaces, providing a basis for understanding its heat regulation mechanism. The effectiveness measurement unit combines economic cost and ecological service value evaluation methods to quantify the thermal environment regulation efficiency and level of different seasonal blue-green space combinations, comprehensively considering economic and ecological benefits. The planning countermeasure generation unit uses a spatial optimization model based on genetic algorithms and public participation geographic information system technology to search for the optimal blue-green space layout plan, enhancing the ability of blue-green spaces in cold-region cities to regulate the thermal environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a block diagram of the overall working process of the present invention.

[0037] The meanings of the various reference numerals in the figure are as follows:

[0038] 1. Data acquisition unit; 2. Factor extraction unit; 3. Heat flow assessment unit; 4. Effectiveness measurement unit; 5. Planning countermeasure generation unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] 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 of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0040] The present invention provides a system for identifying influencing factors and measuring the effectiveness of the thermal environment of blue-green spaces in cold-region cities. Please refer to Figure 1 as shown, which includes a data acquisition unit 1, a factor extraction unit 2, a heat flow assessment unit 3, an effectiveness measurement unit 4, and a planning countermeasure generation unit 5;

[0041] The data acquisition unit 1 is used to collect relevant basic data such as surface temperature, land use classification, blue-green space distribution, and local climate zoning of cold-region cities;

[0042] The factor extraction unit 2 extracts the key factors affecting the urban thermal environment from the blue-green space based on mathematical statistics analysis and spatial recognition technology;

[0043] When the factor extraction unit 2 extracts key factors based on mathematical statistics analysis:

[0044] A two-dimensional dimensionality reduction method combining principal component analysis and independent component analysis is used to preprocess multi-source data related to blue-green space. Grey relational analysis is adopted to determine the degree of association between each potential factor and urban thermal environment indicators, and the top 20% with close association are selected as the preliminary key factor set. Then, the stepwise regression analysis method is used to screen out the key factors affecting the thermal environment from the preliminary key factor set, and a regression equation is constructed to obtain the final key factors;

[0045] Standardize the collected multi-source data related to blue-green space, such as land surface temperature, vegetation coverage, water area, etc., to eliminate the influence of dimensions between different variables, calculate the covariance matrix of the standardized data, obtain eigenvalues and eigenvectors through eigenvalue decomposition, sort according to the size of the eigenvalues, select the first few principal components with a cumulative contribution rate of 80%-90%, project the original data onto these principal components to achieve the first dimensionality reduction of the data. Take the dimension-reduced data as the input, use the independent component analysis algorithm to find the independent components of the data to maximize the independence between components, further extract the independent features in the data through independent component analysis to complete the two-dimensional dimensionality reduction. After the dimensionality reduction process, the data dimension is significantly reduced and the computational complexity is greatly reduced, providing a better data basis for subsequent factor screening;

[0046] Take the land surface temperature as the reference sequence X0, and take each potential factor sequence after dimensionality reduction as the comparison sequence X i(i = 1, 2,..., n), calculate the correlation coefficient between each comparison sequence and the reference sequence at each moment, calculate the average value of the correlation coefficients of each comparison sequence to obtain the correlation degree between the comparison sequence and the reference sequence, sort each potential factor according to the magnitude of the correlation degree, and select the factors with the top 20% of the correlation degree ranking as the preliminary key factor set. There may still be some factors in the preliminary key factor set that have insignificant effects on the thermal environment. Through stepwise regression analysis, factors can be gradually introduced or excluded while considering the multicollinearity between factors, and the key factors with significant effects on the thermal environment can be screened out. Set the significance levels for introducing and excluding factors. Take the urban thermal environment index as the dependent variable and the factors in the preliminary key factor set as independent variables. The initial model does not contain any independent variables. Select the factor with the highest correlation with the dependent variable from the preliminary key factor set and introduce it into the model for regression analysis. Calculate the regression coefficient and significance level. If the significance level of this factor is less than the significance level for introducing factors, then retain it in the model; otherwise, do not introduce this factor. After introducing a new factor, check the significance levels of the factors already existing in the model. If the significance level of a certain factor is greater than the significance level for excluding factors, then remove it from the model until no factors can be introduced or excluded, obtaining the final regression model. According to the final regression model, obtain the regression coefficients of each key factor and construct a regression equation. The goodness of fit of the regression equation is relatively high, which can provide more targeted suggestions for the regulation of the urban thermal environment.

[0047] When the factor extraction unit 2 applies the spatial recognition technology:

[0048] Adopt the local spatial autocorrelation analysis method based on geographically weighted regression, combine the spatial weight matrix to identify the spatial heterogeneity characteristics of the impact of blue-green space on the thermal environment, collect relevant data on blue-green space (such as vegetation coverage, water area, etc.) and thermal environment data (such as surface temperature), and spatially process them, convert them into vector or raster data in a geographic information system, construct a spatial weight matrix to reflect the interaction intensity between spatial points, use local indices for local spatial autocorrelation analysis, calculate the local index for each spatial unit, identify high-high, low-low, high-low, and low-high clustering regions existing in the space through calculating the local index, that is, regions with significant spatial autocorrelation. Taking the thermal environment index as the dependent variable and the relevant factors of blue-green space as the independent variables, establish a geographically weighted regression model, and use weighted least squares to estimate the local regression coefficients of the geographically weighted regression model, determine the bandwidth parameter through cross-validation to obtain the optimal geographically weighted regression model, analyze the local regression coefficients obtained from the geographically weighted regression model and the results of local spatial autocorrelation analysis, identify the spatial heterogeneity characteristics of the impact of blue-green space on the thermal environment. At the same time, use a spatial clustering algorithm to conduct clustering analysis on the blue-green space and its surrounding areas, divide spatial clusters with similar thermal environment response characteristics according to the clustering results, select characteristic variables related to blue-green space and thermal environment, such as the proportion of blue-green space, surface temperature, heat flux, etc., standardize these variables to eliminate the influence of dimension. The spatial clustering algorithm conducts clustering based on the density of data points and requires setting two key parameters, namely the neighborhood radius and the minimum number of points. Input the standardized spatial data into the spatial clustering algorithm for clustering analysis. The algorithm will divide them into different clusters and noise points according to the density of data points. For each data point, calculate the number of points within its neighborhood radius. If the number of points is greater than or equal to the minimum number of points, then this point is a core point. If a point is not a core point but is within the neighborhood of a certain core point, then this point is a boundary point. Otherwise, this point is a noise point. The core point and the points that can be reached by its density form a cluster. According to the results of clustering analysis, divide the area corresponding to each cluster into a spatial cluster, conduct statistical analysis on the thermal environment response characteristics of each spatial cluster, such as calculating the average surface temperature, the proportion of blue-green space, etc., summarize the characteristics of each cluster, use indicators such as the silhouette coefficient to verify the clustering results, and evaluate the quality of clustering. If the clustering effect is not ideal, the parameters of the clustering algorithm can be adjusted or other clustering algorithms can be selected to re-conduct the analysis. The spatial clustering algorithm can automatically identify spatial clusters with similar thermal environment response characteristics, provide an intuitive basis for the zonal management and planning of the urban thermal environment, and help improve the pertinence and effectiveness of urban thermal environment regulation.

[0049] The heat flux assessment unit 3 constructs a "heat flux" measurement system based on the heat "source-sink" theory to quantify the seasonal impact of blue-green space on the urban thermal environment and evaluate its regulation efficiency.

[0050] When the heat flow evaluation unit 3 constructs a "heat flow" measurement system based on the heat "source-sink" theory:

[0051] Introduce the multi-source remote sensing data fusion technology, comprehensively integrate the surface cover information obtained from optical remote sensing images and the surface temperature information reflected by thermal infrared remote sensing data, construct a high-resolution heat "source-sink" spatial distribution map to identify the boundaries between blue-green spaces and other urban land use types, collect optical remote sensing images and thermal infrared remote sensing data of the same area and the same time phase, perform radiometric correction, geometric correction, and atmospheric correction on the optical images to eliminate sensor errors, topographic effects, and atmospheric interference, perform radiometric calibration and atmospheric correction on the thermal infrared data, and convert it into surface temperature data. Use the optical remote sensing images and adopt an object-oriented classification method to extract surface cover information. Segment the images into objects with similar spectral and texture characteristics through multi-scale segmentation, and then classify based on the spectral, shape, texture, and other characteristics of the objects to identify blue-green spaces (such as vegetation, water bodies) and other urban land use types (such as buildings, roads). Adopt a data fusion algorithm to fuse the high spatial resolution of the optical remote sensing images and the surface temperature information of the thermal infrared remote sensing data. During the fusion process, ensure that the key information of the two types of data is retained to generate a high-resolution fused image, which contains both detailed surface cover information and accurate surface temperature information. According to the surface temperature information in the fused image, set a temperature threshold, define the areas with higher temperatures as heat "sources", and the areas with lower temperatures as heat "sinks". Combine the surface cover information to accurately identify the boundaries between blue-green spaces and other urban land use types, construct a heat "source-sink" spatial distribution map, and improve the accuracy of subsequent heat flow calculation and analysis;

[0052] Based on the energy balance principle, establish a physical model of heat flow transmission, dynamically simulate the heat exchange process between the blue-green space and the surrounding environment, obtain the heat conduction equation, and then solve the heat conduction equation by the finite difference method to quantify the heat flow intensity of the blue-green space as a heat "source" or a heat "sink" in different seasons and different time periods. According to the energy balance principle, the energy budget relationship between the blue-green space and the surrounding environment can be expressed as R n =H + LE + G, where R n is the net radiation flux, H is the sensible heat flux, LE is the latent heat flux, G is the soil heat flux. Calculate each flux item respectively. According to Fourier's law of heat conduction, the conduction of heat flow in a medium can be described by the heat conduction equation. For two-dimensional cases, the heat conduction equation is Where T is the temperature, t is the time, x and y are the spatial coordinates, and α is the thermal diffusivity. By combining the energy balance equation and the boundary conditions, the heat conduction equation is corrected and improved to obtain a heat conduction equation applicable to the heat exchange between the blue-green space and the surrounding environment. The heat conduction equation is discretized in space and time, and the finite difference method is used to approximate the partial derivatives with differences. Through the discretization process, the heat conduction equation is transformed into an algebraic equation system. According to the discretized algebraic equation system, combined with the initial conditions and boundary conditions, an iterative method is used to solve the equation system to obtain the temperature distribution at different spatial positions and times. According to the temperature gradient and the thermal conductivity, the heat flux intensity is calculated. By calculating the heat flux intensity between the blue-green space and the surrounding environment in different seasons and at different times, the role of the blue-green space as a heat "source" or heat "sink" is quantified, providing a scientific basis for evaluating the regulation effect of the blue-green space on the urban heat environment and helping to formulate reasonable urban planning and ecological protection strategies.

[0053] When the heat flux evaluation unit 3 evaluates the regulation efficiency of the blue-green space:

[0054] A method combining comparative experiments and scenario simulations is used to set up multiple comparison scenarios, and the constructed heat flux measurement system is used to simulate the evolution process of the urban heat environment under different scenarios respectively, accurately revealing the influence mechanism of changes in factors such as the area and layout of the blue-green space on the urban heat environment, helping urban planners intuitively understand the heat environment benefits of different planning strategies, and assisting in decision-making;

[0055] By comparing the surface temperature distribution and the change of the heat island intensity under different scenarios, the actual efficiency of the blue-green space in alleviating the urban heat island effect and regulating the local climate is evaluated. After completing the heat environment simulation of each scenario, the surface temperature data and the corresponding heat island intensity data at different times and in different regions are extracted from the simulation results, and these data are classified and sorted according to the dimensions of scenario, time, and space to construct a multi-dimensional data set for subsequent comparative analysis;

[0056] At the same time, sensitivity analysis is introduced. By changing the key parameters of the blue-green space, the degree of its influence on the regulation efficiency is analyzed, and the key factors and optimization directions for improving the regulation efficiency are determined. The key parameters include vegetation coverage, water area, and shape index;

[0057] Selected vegetation coverage, water body area, and shape index are used as key analysis parameters. According to the current situation and planning potential of the city, a reasonable change range for each parameter is determined. For example, the vegetation coverage increases from the current 30% in 5% steps to 60%, the water body area is adjusted within the range of 0.5 - 2 times the existing water area, and the shape index is changed by altering the boundary shape so that its value varies between 1 - 5. Each time, only one key parameter is changed while keeping other parameters unchanged. Using the heat flux measurement system and scenario simulation method, the evolution of the urban thermal environment under different parameter values is simulated. The above process is repeated, and for different values of each parameter, corresponding thermal environment index data such as surface temperature distribution and heat island intensity are obtained. The local sensitivity analysis method, such as calculating partial derivatives or relative sensitivity indices, is used to quantify the influence degree of each parameter change on the thermal environment indices. For example, calculate the reduction amplitude of the heat island intensity when the vegetation coverage increases by 1%, and the average reduction amplitude of the surface temperature when the water body area doubles. The parameters are sorted according to the sensitivity size to determine the key factors for improving the adjustment efficiency. Combining the simulation results, the action mechanism of the key factors is deeply analyzed. For example, how does the increase in vegetation coverage improve the thermal environment through transpiration heat dissipation, shading, etc., so as to clarify the optimization direction of the blue-green space, such as preferentially increasing vegetation coverage in the core area of the urban heat island and optimizing the water body shape to enhance heat exchange, providing a quantitative basis for the urban blue-green space planning.

[0058] The effectiveness measurement unit 4 combines the economic cost and ecological service value evaluation methods to quantify the thermal environment regulation efficiency and level of the blue-green space combination form under different seasonal conditions, and forms a comprehensive value ranking.

[0059] When the effectiveness measurement unit 4 combines the economic cost and ecological service value evaluation methods:

[0060] Establish a full-life cycle cost accounting model for the construction and maintenance of the blue-green space, and use the cost-benefit analysis method to discount each cost according to the time series to obtain the total present value of the cost of the blue-green space during its life cycle.

[0061] For the evaluation of ecological service value, use the ecological system service function value evaluation method based on emergy analysis to convert the ecological service functions provided by the blue-green space into a unified emergy unit, and combine the local emergy-money ratio to convert it into monetary value. By comparing the economic cost and ecological service value, a comprehensive benefit index of the blue-green space is constructed for the quantitative evaluation of its thermal environment regulation efficiency.

[0062] Identify the main energy inputs (such as solar radiation energy, wind energy, etc.) and material inputs (such as precipitation, soil nutrients, etc.) in the blue-green space ecosystem, as well as energy outputs (such as the energy fixed by plant photosynthesis, the energy dissipated by water evaporation, etc.) and material outputs (such as the nutrients output by vegetation litter, the sediment carried away by surface runoff, etc.). Through means such as field monitoring, literature research, and model calculation, quantify the values of these energy flows and material flows. For example, use a photosynthetically active radiation meter to measure the solar radiation energy absorbed by the blue-green space vegetation, record the evaporation amount of the water body and related energy losses through a hydrological monitoring station. According to the energy conversion rate, convert different energy flows and material flows into solar energy values. The energy conversion rate refers to the solar energy value contained in each unit of a certain energy or material flow, such as the energy conversion rate of each joule of electric energy, the energy conversion rate of each gram of soil organic matter, etc. These conversion rates can be found in professional energy value databases. After converting the energy flows and material flows corresponding to various ecological service functions in the blue-green space one by one and accumulating them, the total solar energy value is obtained, representing the total energy value of its ecological service functions. Combining with the local energy value-money ratio, convert the solar energy value into monetary value. The energy value-money ratio reflects the energy value contained in each unit of currency in the local economic system and is obtained by statistically analyzing the ratio of the total annual energy value usage to the gross domestic product in the local area. Construct a comprehensive benefit index I = E / C, where E is the ecological service value of the blue-green space and C is the present value of the total cost. If this index is greater than 1, it indicates that the ecological service value exceeds the economic cost input and the benefit is good; if the index is close to 1, it means the income and expenditure are balanced; if the index is less than 1, it means the cost is too high and the benefit is poor. Further analyze the relationship between the comprehensive benefit index and the thermal environment regulation efficiency of the blue-green space. Through statistical analysis of historical data or simulation data, explore the contribution degree of the blue-green space to thermal environment regulation indicators such as reducing the urban heat island intensity and stabilizing the surface temperature at different levels of the comprehensive benefit index. If it is found that the higher the comprehensive benefit index of the blue-green space area, the higher the thermal environment regulation efficiency, it indicates that this area performs well in the coordination of economy and ecology. Otherwise, optimization and adjustment are needed to achieve the sustainable development of the urban ecology and economy, effectively improve the quality of residents' living environment, and maximize the input-output benefit of the blue-green space construction.

[0063] When the effectiveness measurement unit 4 quantifies the thermal environment regulation efficiency and level of the blue-green space combination form under different seasonal conditions:

[0064] The analytic hierarchy process is used to construct an evaluation index system. The area ratio, connectivity, vegetation type diversity, and water body fluidity of the blue-green space are used as the criterion layer indicators, and the thermal environment regulation efficiency is used as the target layer to construct a judgment matrix, and the weights of each index are determined. It is clear that the target layer is the thermal environment regulation efficiency, and the criterion layer includes the area ratio, connectivity, vegetation type diversity, and water body fluidity of the blue-green space. These criterion layer indicators affect the thermal environment regulation efficiency from different aspects. The area ratio reflects the scale of the blue-green space, the connectivity affects the heat flow exchange, the vegetation type diversity helps to enhance the ecological function, and the water body fluidity can promote heat dissipation. The pairwise comparison is carried out according to the relative importance between the indicators, and the judgment matrix of the criterion layer relative to the target layer is constructed according to the comparison results. The maximum eigenvalue of the judgment matrix is calculated, and the consistency index is calculated. The average random consistency index is searched, and the consistency ratio is calculated. If the consistency ratio is less than 0.1, it is considered that the judgment matrix has satisfactory consistency, otherwise, the judgment matrix needs to be adjusted again. The eigenvector calculation is carried out on the judgment matrix to obtain the eigenvector and the corresponding weight;

[0065] Based on the grey comprehensive evaluation method and combined with the monitoring data of different seasons, the comprehensive evaluation of the blue-green space combination form is carried out to form a comprehensive value ranking. The area ratio, connectivity, vegetation type diversity, and water body fluidity and other indicators of the blue-green space are monitored in different seasons to obtain the corresponding data. The data is standardized, and the optimal values of each index are composed into a reference sequence. The index data of each blue-green space combination form under different seasons are composed into a comparison sequence. The correlation coefficient between the comparison sequence and the reference sequence on each index is calculated. According to the index weights determined by the analytic hierarchy process, the comprehensive evaluation value of each blue-green space combination form is calculated. According to the size of the comprehensive evaluation value, the blue-green space combination forms under different seasons are ranked. The higher the comprehensive evaluation value, the higher the thermal environment regulation efficiency of this combination form and the higher the level, so as to accurately quantify the thermal environment regulation efficiency of the blue-green space combination form under different seasonal conditions, form a scientific and reasonable comprehensive value ranking, and provide a decision-making basis for urban planning and blue-green space optimization.

[0066] The planning countermeasure generation unit 5 proposes a blue-green space planning and regulation path that adapts to the climate characteristics of cold-region cities according to the factor identification and effectiveness measurement results.

[0067] When the planning countermeasure generation unit 5 proposes a blue-green space planning and regulation path according to the factor identification and effectiveness measurement results:

[0068] A spatial optimization model based on the genetic algorithm is adopted, with the goal of maximizing the thermal environment regulation efficiency of the blue-green space and minimizing the economic cost. The constraint conditions of the key factors and other restrictions of urban planning are incorporated into the model, and the optimal blue-green space layout plan is searched in the solution space through the selection, crossover, and mutation operations of the genetic algorithm;

[0069] Define the evaluation function E for the thermal environment regulation efficiency pb , which can be constructed based on the comprehensive benefit index obtained from the previous efficiency measurement unit 4 and data such as the thermal environment regulation efficiency. Define the economic cost evaluation function C pb , which can refer to the life cycle cost accounting model, consider the various costs of the construction and maintenance of the blue-green space, and construct the objective function F = ω1E - ω2C, where ω1 and ω2 are weight coefficients that can be adjusted according to the actual situation to balance the importance of the thermal environment regulation efficiency and the economic cost. According to the results of the factor extraction unit 2, key factors such as the area ratio, connectivity, vegetation type diversity, and water body fluidity of the blue-green space are used as constraint conditions and combined with the urban planning restriction conditions to encode the layout scheme of the blue-green space. Each coding bit represents whether a spatial unit is a blue-green space. Randomly generate a certain number of initial layout schemes as the population, evaluate the individuals in the population according to the objective function value, select the individuals with higher fitness as the parents, and participate in the subsequent crossover and mutation operations. Perform crossover operations on the selected parent individuals to generate new offspring individuals, and perform mutation operations on the offspring individuals to increase the diversity of the population. Repeat the selection, crossover, and mutation operations until the termination condition is met to obtain the optimal layout scheme of the blue-green space.

[0070] At the same time, combined with the public participation geographic information system technology, collect the usage needs and preference information of citizens for the blue-green space and transform it into the optimization goal of the spatial layout. The public participation geographic information system technology can fully collect the opinions and suggestions of citizens, integrate the public needs into the blue-green space planning, make the planning closer to the actual needs of citizens, improve the feasibility and satisfaction of the planning. By combining the public needs in the planning, it can increase the usage frequency and satisfaction of citizens for the blue-green space, enhance the sense of identity and participation of citizens in the urban ecological environment construction, and promote the sustainable development of the city.

[0071] In the present invention, the data acquisition unit 1 collects multi-source basic data, the factor extraction unit 2 uses a variety of analysis techniques to screen key factors, the heat flow evaluation unit 3 quantifies the seasonal impact and regulation efficiency of the blue-green space on the thermal environment based on the heat "source-sink" theory, the efficiency measurement unit 4 combines the economic cost and ecological service value evaluation methods to quantify the thermal environment regulation efficiency and rank them, and the planning countermeasure generation unit 5 uses genetic algorithms and public participation techniques to formulate the planning regulation path. The present invention can accurately identify the influencing factors, provide a scientific basis for the blue-green space planning of cold-region cities, realize the coordinated improvement of the urban ecological environment and the thermal environment, and promote the sustainable development of cold-region cities.

[0072] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. Cold-region urban blue-green space thermal environment impact factor identification and effectiveness measurement system, characterized in that, It includes a data acquisition unit (1), a factor extraction unit (2), a heat flow evaluation unit (3), a performance measurement unit (4), and a planning countermeasure generation unit (5); The data acquisition unit (1) is used to collect relevant basic data such as the surface temperature, land use classification, blue-green space distribution, and local climate zoning of cold-region cities; The factor extraction unit (2) extracts the key factors affecting the urban thermal environment from the blue-green space based on mathematical statistics analysis and spatial recognition technology; The heat flow evaluation unit (3) constructs a "heat flow" measurement system based on the heat "source-sink" theory, which is used to quantify the seasonal impact of the blue-green space on the urban thermal environment and evaluate its regulation efficiency; The performance measurement unit (4) combines the economic cost and ecological service value evaluation methods to quantify the thermal environment regulation efficiency and level of the blue-green space combination forms under different seasonal conditions, and forms a comprehensive value ranking; The planning countermeasure generation unit (5) proposes a blue-green space planning and regulation path adapted to the climate characteristics of cold-region cities according to the factor identification and performance measurement results; 2. The cold region urban blue-green space thermal environment impact factor identification and effectiveness measurement system according to claim 1, characterized in that When the factor extraction unit (2) extracts key factors based on mathematical statistics analysis: It uses a two-dimensional dimensionality reduction method combining principal component analysis and independent component analysis to preprocess the multi-source data related to the blue-green space, and uses grey relational analysis to determine the correlation degree between each potential factor and the urban thermal environment index, selects the top 20% with close correlation as the preliminary key factor set, and then uses the stepwise regression analysis method to screen out the key factors affecting the thermal environment from the preliminary key factor set, and constructs a regression equation to obtain the final key factors; 3. The cold-region urban blue-green space thermal environment impact factor identification and effectiveness measurement system according to claim 2, characterized in that, When the factor extraction unit (2) uses spatial recognition technology: It adopts a local spatial autocorrelation analysis method based on geographically weighted regression, and combines the spatial weight matrix to identify the spatial heterogeneity characteristics of the blue-green space affecting the thermal environment; At the same time, it uses a spatial clustering algorithm to conduct clustering analysis on the blue-green space and its surrounding areas, and divides spatial groups with similar thermal environment response characteristics according to the clustering results; 4. The cold region urban blue-green space thermal environment impact factor identification and effectiveness measurement system according to claim 1, characterized in that When the heat flow evaluation unit (3) constructs a "heat flow" measurement system based on the heat "source-sink" theory: It introduces a multi-source remote sensing data fusion technology, combines the surface cover information obtained from optical remote sensing images and the surface temperature information reflected by thermal infrared remote sensing data to construct a high-resolution heat "source-sink" spatial distribution map, which is used to identify the boundaries between the blue-green space and other urban land use types; Based on the energy balance principle, it establishes a physical model of heat flow transmission, dynamically simulates the heat exchange process between the blue-green space and the surrounding environment, obtains the heat conduction equation, and then solves the heat conduction equation by the finite difference method to quantify the heat flow intensity of the blue-green space as a heat "source" or heat "sink" in different seasons and different time periods; 5. The cold-region urban blue-green space thermal environment impact factor identification and efficacy measurement system according to claim 4, wherein When the heat flow evaluation unit (3) evaluates the regulation efficiency of the blue-green space: It uses a method combining comparative experiments and scenario simulations to set multiple comparison scenarios, and uses the constructed heat flow measurement system to simulate the evolution process of the urban thermal environment under different scenarios respectively; It evaluates the actual efficiency of the blue-green space in alleviating the urban heat island effect and regulating the local climate by comparing the surface temperature distribution and the change of heat island intensity under different scenarios; Meanwhile, sensitivity analysis is introduced. By changing the key parameters of the blue-green space, the influence degree on the regulation efficiency is analyzed, and the key factors and optimization directions for improving the regulation efficiency are determined. The key parameters include vegetation coverage, water area, and shape index.

6. The cold region urban blue-green space thermal environment impact factor identification and effectiveness measurement system according to claim 1, characterized in that, When the efficiency measurement unit (4) combines the economic cost and the ecological service value evaluation method: Establish a full-life cycle cost accounting model for the construction and maintenance of the blue-green space, and use the cost-benefit analysis method to discount each cost according to the time series to obtain the present value of the total cost of the blue-green space during its life cycle; For the evaluation of the ecological service value, use the ecosystem service function value evaluation method based on emergy analysis to convert the ecological service functions provided by the blue-green space into a unified emergy unit, and combine with the local emergy-money ratio to convert it into monetary value. By comparing the economic cost and the ecological service value, a comprehensive benefit index of the blue-green space is constructed for the quantitative evaluation of its thermal environment regulation efficiency.

7. The cold-region urban blue-green space thermal environment impact factor identification and effectiveness measurement system according to claim 6, characterized in that, When the efficiency measurement unit (4) quantifies the thermal environment regulation efficiency and level of the blue-green space combination form under different seasonal conditions: Use the analytic hierarchy process to construct an evaluation index system. Take the area ratio, connectivity, vegetation type diversity, and water body fluidity of the blue-green space as the criterion layer indicators, and the thermal environment regulation efficiency as the target layer to construct a judgment matrix and determine the weights of each indicator; Based on the grey comprehensive evaluation method and combined with the monitoring data of different seasons, conduct a comprehensive evaluation of the blue-green space combination form to form a comprehensive value ranking.

8. The cold region urban blue-green space thermal environment impact factor identification and effectiveness measurement system according to claim 1, characterized in that, When the planning countermeasure generation unit (5) proposes the blue-green space planning and regulation path according to the factor identification and efficiency measurement results: Adopt a spatial optimization model based on the genetic algorithm, with the goal of maximizing the thermal environment regulation efficiency of the blue-green space and minimizing the economic cost. Incorporate the constraint conditions of the key factors and other restrictions of the urban planning into the model, and search for the optimal blue-green space layout plan in the solution space through the selection, crossover, and mutation operations of the genetic algorithm; Meanwhile, combine the public participation geographic information system technology to collect the usage needs and preference information of citizens for the blue-green space, and convert them into the optimization goals of the spatial layout.

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