Blue-green space thermal environment impact factor identification and efficiency measurement system for cold region cities

Abstract

The present application 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; in the present application, the data acquisition unit collects multi-source basic data, the factor extraction unit uses various analysis techniques to screen key factors, the heat flow evaluation unit quantifies the seasonal influence and adjustment efficiency of the blue-green space on the thermal environment based on the heat "source-sink" theory, the efficiency measurement unit quantifies the thermal environment adjustment efficiency and sorts it by combining economic cost and ecological service value evaluation methods, and the planning countermeasure generation unit uses genetic algorithm and public participation technology to develop planning regulation paths; the present application can accurately identify influence factors, provide scientific basis for cold region city blue-green space planning, realize the coordinated improvement of urban ecological environment and thermal environment, and promote the sustainable development of cold region cities.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blue-green space planning, in particular to a cold region city blue-green space thermal environment influencing factor identification and efficiency measurement system. BACKGROUND

[0002] Blue-green space planning is an important technology, and in the field of cold region city blue-green space planning and thermal environment improvement, accurately identifying the key factors of blue-green space affecting the thermal environment is crucial. However, traditional methods have many defects that are difficult to overcome.

[0003] The thermal environment of a cold region city is influenced by the mutual influence of factors such as vegetation coverage, water area, shape index of blue-green space, land use type, and local climate conditions, forming a very complex system. Traditional methods rely on experience or correlation analysis, which cannot cope with such complex data and multiple influencing factors. When analyzing the relationship between vegetation coverage and thermal environment, the differences in the influence of vegetation growth state changes on thermal environment in different seasons, as well as the synergistic effect of surrounding water bodies and buildings, cannot be considered. This results in inaccurate identification of key factors affecting the thermal environment, and based on the inaccurate identification results of key factors, blue-green space planning lacks pertinence and cannot fully play the role of blue-green space in regulating the thermal environment, making it difficult to alleviate the urban heat island effect and optimize the local climate, ultimately causing resource waste and poor planning results. In order to solve this technical problem, we provide a cold region city blue-green space thermal environment influencing factor identification and efficiency measurement system. SUMMARY

[0004] The purpose of the present application is to provide a cold region city blue-green space thermal environment influencing factor identification and efficiency measurement system to solve the problems raised in the background.

[0005] 1. Since traditional methods cannot accurately identify the key factors of cold region city blue-green space affecting the thermal environment, leading to lack of pertinence in blue-green space planning and inability to effectively regulate the thermal environment, the present case uses a double-dimensional dimension reduction method of principal component analysis combined with independent component analysis to preprocess data through a factor extraction unit, uses grey correlation analysis and stepwise regression analysis to screen key factors, and also uses spatial identification techniques such as local spatial autocorrelation analysis based on geographic weighted regression, to accurately determine the key factors, providing a basis for scientific planning of blue-green space and improving the regulation 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 blue-green space in cold region cities, it is difficult to evaluate its comprehensive benefits, therefore, the case establishes a full life cycle cost accounting model of blue-green space construction and maintenance through the efficiency measurement unit, evaluates the ecological service value based on the energy analysis method, constructs the comprehensive benefit index, quantifies the thermal environment regulation efficiency and level of blue-green space combination form in different seasons by using the analytic hierarchy process and gray comprehensive evaluation method, can comprehensively evaluate the comprehensive benefits of blue-green space, provides quantitative support for reasonable planning and optimization of blue-green space, realizes efficient use of resources.

[0007] In order to achieve the above purpose, a cold region city blue-green space thermal environment influence factor identification and efficiency measurement system is provided, comprising a data acquisition unit, a factor extraction unit, a heat flow evaluation unit, an efficiency measurement unit and a planning countermeasure generation unit;

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

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

[0010] The heat flow evaluation unit constructs a "heat flow" measurement system based on the heat "source-sink" theory, which is used to quantify the seasonal influence of blue-green space on urban thermal environment and evaluate its regulation efficiency;

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

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

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

[0014] A double-dimension dimension reduction method combining principal component analysis with independent component analysis is used to preprocess the multi-source data related to blue-green space, and a gray correlation analysis is used to determine the correlation degree between each potential factor and the urban thermal environment index, and the top 20% of closely related factors are selected as the preliminary key factor set, then a stepwise regression analysis method is used to select the key factors affecting the thermal environment from the preliminary key factor set, and a regression equation is used to obtain the final key factors.

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

[0016] The local spatial autocorrelation analysis method based on the geographic weighted regression is adopted, and the spatial weight matrix is combined to identify the spatial heterogeneity characteristics of the blue-green space influencing the thermal environment.

[0017] Meanwhile, the spatial clustering algorithm is used for clustering analysis of the blue-green space and the surrounding area, and the spatial groups with similar thermal environment response characteristics are divided according to the clustering results.

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

[0019] The multi-source remote sensing data fusion technology is introduced to integrate the land cover information obtained from optical remote sensing images and the land temperature information reflected by thermal infrared remote sensing data, and a high-resolution heat "source-sink" spatial distribution map is constructed to identify the boundary between the blue-green space and other urban land types.

[0020] Based on the energy balance principle, a physical model of heat flow transmission is established to 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 the technical solution, when the heat flow evaluation unit evaluates the regulation efficiency of the blue-green space:

[0022] A plurality of comparison scenarios are set by combining the comparison experiment and the scenario simulation, and the heat flow measurement system constructed is used to simulate the evolution process of the urban thermal environment under different scenarios.

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

[0024] Meanwhile, sensitivity analysis is introduced to analyze the influence degree of the key parameters of the blue-green space on the regulation efficiency, determine the key factors and optimization direction for improving the regulation efficiency, and the key parameters include vegetation coverage, water area and shape index.

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

[0026] A full life cycle cost accounting model for blue-green space construction and maintenance is established, and a cost-benefit analysis method is used to discount each cost according to the time sequence to obtain the present value of the total cost of the blue-green space in its life cycle.

[0027] For the evaluation of ecological service value, the ecosystem service function value evaluation method based on energy analysis is used to convert the ecological service function provided by the blue-green space into a unified energy value unit, and the local energy value-money ratio is combined to convert into monetary value.

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

[0029] The analytic hierarchy process is used to construct the evaluation index system, the area proportion, connectivity, vegetation type diversity and water flow of the blue-green space are taken as the criterion layer indexes, the thermal environment regulation efficiency is taken as the target layer to construct the judgment matrix, and the weight of each index is determined;

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

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

[0032] The spatial optimization model based on genetic algorithm is adopted, the maximum thermal environment regulation performance of the blue-green space and the minimum economic cost are taken as the objective function, the constraint conditions of key factors and other limitation conditions of urban planning are included in the model, and the optimal blue-green space layout scheme is searched in the solution space through the selection, crossover and mutation operations of genetic algorithm;

[0033] At the same time, the public participation geographic information system technology is combined to collect the use demand and preference information of citizens for the blue-green space, and the information is converted into the optimization target of spatial layout.

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

[0035] In the system for identifying and measuring the efficiency of the blue-green space thermal environment influencing factors in cold region cities, the factor extraction unit extracts the key factors influencing the thermal environment by using two-dimensional dimension reduction, grey correlation analysis and stepwise regression analysis, overcomes the shortcomings of the traditional method, and makes the blue-green space planning more targeted. The heat flow evaluation unit constructs a measurement system based on the heat "source-sink" theory to quantify the seasonal thermal environment influence and adjustment efficiency of the blue-green space, provides a basis for understanding the thermal adjustment mechanism, the efficiency measurement unit quantifies the thermal environment adjustment efficiency and grade of different seasonal blue-green space combination forms by combining the economic cost and ecological service value evaluation method, comprehensively considers the economic and ecological benefits, the planning countermeasure generation unit searches for the optimal blue-green space layout scheme by using the spatial optimization model of the genetic algorithm and the public participation geographic information system technology, and improves the adjustment capacity of the blue-green space of the cold region cities to the thermal environment. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 It is the overall workflow diagram of the present application.

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

[0038] 1, data acquisition unit; 2, factor extraction unit; 3, heat flow evaluation unit; 4, efficiency measurement unit; 5, planning countermeasure generation unit. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0040] The present application provides a system for identifying and measuring the efficiency of the blue-green space thermal environment influencing factors in cold region cities, please refer to Figure 1 As shown in the figure, it includes a data acquisition unit 1, a factor extraction unit 2, a heat flow evaluation unit 3, an efficiency measurement unit 4 and a planning countermeasure generation unit 5.

[0041] The data acquisition unit 1 is used to collect the ground temperature, land use classification, blue-green space distribution and local climate zoning and other related basic data of the cold region city;

[0042] The factor extraction unit 2 extracts the key factors influencing 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 the key factors based on mathematical statistics analysis:

[0044] The blue-green space related multi-source data is preprocessed by using the double-dimension dimension reduction method of principal component analysis combined with independent component analysis, and the correlation degree between each potential factor and the urban thermal environment index is determined by using grey correlation analysis, the top 20% of the closely related factors are selected as the preliminary key factor set, and the stepwise regression analysis method is used to screen the key factors of the thermal environment from the preliminary key factor set, and a regression equation is constructed to obtain the final key factors;

[0045] The blue-green space related multi-source data collected, such as land surface temperature, vegetation coverage, water area, etc., is standardized to eliminate the influence of different variables on the dimension, the covariance matrix of the standardized data is calculated, the eigenvalues and eigenvectors are obtained by eigenvalue decomposition, the eigenvalues are sorted according to their size, the top few principal components whose cumulative contribution rate reaches 80%-90% are selected, the original data is projected onto these principal components, and the first dimension reduction of the data is realized. The reduced data is used as input, and the independent component analysis algorithm is used to find the independent components of the data, so that the independence between the components is maximized. The independent features in the data are further extracted by independent component analysis, and the double-dimension dimension reduction is completed. After dimension reduction, the data dimension is significantly reduced, the computational complexity is greatly reduced, and a better data basis is provided for the subsequent factor selection;

[0046] The land surface temperature is taken as the reference sequence X0, and the potential factor sequences after dimension reduction are taken as the comparison sequences X i(i = 1, 2,..., n), the correlation coefficient of each comparison sequence and the reference sequence at each time is calculated, the correlation coefficient of each comparison sequence is averaged to obtain the correlation degree of the comparison sequence and the reference sequence, the potential factors are sorted according to the correlation degree, and the top 20% of the factors in the correlation degree are selected as the preliminary key factor set. The factors in the preliminary key factor set may still have some factors that have no significant influence on the thermal environment. Through stepwise regression analysis, factors can be gradually introduced or removed under the consideration of multiple collinearity between factors, and key factors that have a significant influence on the thermal environment are screened out. The significance level of the introduced factors and the removed factors is set, the urban thermal environment index is used as the dependent variable, the factors in the preliminary key factor set are used as the independent variable, and the initial model does not include any independent variable. The factor with the highest correlation with the dependent variable is selected from the preliminary key factor set and introduced into the model, regression analysis is performed, the regression coefficient and the significance level are calculated, if the significance level of the factor is less than the significance level of the introduced factor, the factor is retained in the model, otherwise, the factor is not introduced. After introducing a new factor, the significance level of the existing factor in the model is checked, if the significance level of a certain factor is greater than the significance level of the removed factor, the factor is removed from the model, until no factor can be introduced or removed, the final regression model is obtained, the regression coefficient of each key factor is obtained according to the final regression model, and the regression equation is constructed. The regression equation has a high fitting degree and can provide more targeted suggestions for the regulation of the urban thermal environment.

[0047] The factor extraction unit 2 uses spatial recognition technology:

[0048] The local spatial autocorrelation analysis method based on the geographic weighted regression is used to identify the spatial heterogeneity characteristics of the blue-green space influencing the thermal environment. Spatial weight matrix is used to identify the spatial heterogeneity characteristics of the blue-green space influencing the thermal environment. The blue-green space related data (such as vegetation coverage, water area, etc.) and thermal environment data (such as land surface temperature) are collected and spatialized to vector or raster data in geographic information system. The spatial weight matrix is used to reflect the interaction strength between spatial points. The local index is used for local spatial autocorrelation analysis. The local index of each spatial unit is calculated. The high-high, low-low, high-low and low-high cluster areas in the space, i.e. the areas with significant spatial autocorrelation, are identified. The thermal environment index is used as the dependent variable, and the blue-green space related factors are used as the independent variable. The geographic weighted regression model is established. The local regression coefficient of the geographic weighted regression model is estimated by using the weighted least squares method. The bandwidth parameter is determined by cross-validation to obtain the optimal geographic weighted regression model. The local regression coefficient obtained by the geographic weighted regression model and the local spatial autocorrelation analysis result are analyzed to identify the spatial heterogeneity characteristics of the blue-green space influencing the thermal environment. The spatial clustering algorithm is used for clustering analysis of the blue-green space and its surrounding area. According to the clustering results, the spatial groups with similar thermal environment response characteristics are divided. The characteristic variables related to the blue-green space and the thermal environment are selected, such as the proportion of blue-green space, land surface temperature, heat flux, etc. The standardized treatment is performed on these variables to eliminate the influence of dimension. The spatial clustering algorithm is based on the density of data points for clustering. Two key parameters need to be set, i.e. the neighborhood radius and the minimum number of points. The standardized spatial data is input into the spatial clustering algorithm for clustering analysis. The algorithm will divide the data points into different clusters and noise points according to their density. For each data point, the number of points within the neighborhood radius is calculated. If the number of points is greater than or equal to the minimum number of points, the point is a core point. If a point is not a core point, but is within the neighborhood of a core point, the point is a boundary point. Otherwise, the point is a noise point. The core points and their density reachable points form a cluster. According to the clustering analysis results, each cluster is divided into a spatial group. The thermal environment response characteristics of each spatial group are statistically analyzed, such as calculating the average land surface temperature, the proportion of blue-green space, etc. The characteristics of each group are summarized. The clustering results are verified by using indicators such as silhouette coefficient to 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 for reanalysis. The spatial clustering algorithm can automatically identify spatial groups with similar thermal environment response characteristics, which provides intuitive basis for the zoning management and planning of urban thermal environment, and helps to improve the pertinence and effectiveness of urban thermal environment regulation.

[0049] 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 influence of blue-green space on urban thermal environment and evaluate its regulation efficiency.

[0050] The heat flow evaluation unit 3 in the construction of the "heat flow" measurement system based on the heat "source-sink" theory:

[0051] The multi-source remote sensing data fusion technology is introduced, the surface cover information obtained by the optical remote sensing image and the surface temperature information reflected by the thermal infrared remote sensing data are comprehensively used, a high-resolution heat "source-sink" spatial distribution map is constructed, which is used to identify the boundary between the blue-green space and other urban land types, the optical remote sensing image and the thermal infrared remote sensing data of the same region and the same phase are collected, the optical image is subjected to radiation correction, geometric correction and atmospheric correction to eliminate sensor errors, terrain influence and atmospheric interference, the thermal infrared data is subjected to radiation calibration and atmospheric correction to convert it into surface temperature data, the surface cover information is extracted from the optical remote sensing image by using the object-oriented classification method, the image is segmented into objects with similar spectral and texture characteristics through multi-scale segmentation, and then the objects are classified based on the spectral, shape and texture characteristics to identify the blue-green space (such as vegetation and water body) and other urban land types (such as building and road), the data fusion algorithm is used to fuse the high spatial resolution of the optical remote sensing image and the surface temperature information of the thermal infrared remote sensing data, in the fusion process, the key information of the two kinds of data is ensured to be retained, and a high-resolution fused image is generated, which contains detailed surface cover information and accurate surface temperature information, according to the surface temperature information in the fused image, a temperature threshold is set, the area with higher temperature is defined as a heat "source", and the area with lower temperature is defined as a heat "sink", the boundary between the blue-green space and other urban land types is accurately identified by combining the surface cover information, and the heat "source-sink" spatial distribution map is constructed, which improves the accuracy of subsequent heat flow calculation and analysis;

[0052] Based on the energy balance principle, a physical model of heat flow transmission is established to 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 at different times, according to the energy balance principle, the energy balance relationship between the blue-green space and the surrounding environment can be expressed as R n = H + LE + G, wherein R n is the net radiation flux, H is the sensible heat flux, LE is the latent heat flux, and G is the soil heat flux, each flux is calculated, according to the Fourier heat conduction law, the conduction of heat flow in the medium can be described by the heat conduction equation, for the two-dimensional case, the heat conduction equation is where T is temperature, t is time, x, y are spatial coordinates, and a is the thermal diffusion coefficient. The heat conduction equation is modified and improved by combining the energy balance equation and the boundary conditions to obtain a heat conduction equation applicable to the heat exchange between blue-green space and the surrounding environment. The heat conduction equation is discretized in space and time, and the partial derivatives are approximated using finite difference method. Through discretization, the heat conduction equation is converted into an algebraic equation system. According to the discretized algebraic equation system, combined with the initial conditions and boundary conditions, the equation system is solved using an iterative method to obtain the temperature distribution at different spatial locations and times. According to the temperature gradient and the thermal conductivity, the heat flow intensity is calculated. By calculating the heat flow intensity between blue-green space and the surrounding environment in different seasons and at different times, the role of blue-green space as a heat "source" or heat "sink" is quantified, providing a scientific basis for evaluating the regulating effect of blue-green space on urban thermal environment, and helping to develop reasonable urban planning and ecological protection strategies.

[0053] The heat flow evaluation unit 3 evaluates the regulating efficiency of blue-green space as follows:

[0054] A comparison experiment combined with scenario simulation is used to set up multiple comparison scenarios. The heat flow measurement system is used to simulate the evolution of urban thermal environment under different scenarios, accurately revealing the influence mechanism of changes in blue-green space area, layout, and other factors on urban thermal environment, helping urban planners to intuitively understand the thermal environment benefits of different planning strategies, and assisting decision-making;

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

[0056] At the same time, sensitivity analysis is introduced to analyze the influence of key parameters of blue-green space on the regulating efficiency, determine the key factors and optimization direction for improving the regulating efficiency, and the key parameters include vegetation coverage, water area, and shape index.

[0057] The selected vegetation coverage, water area, and shape index are taken as key analysis parameters. According to the current situation and planning potential of the city, the reasonable variation range of each parameter is determined, for example, the vegetation coverage is increased by 5% from the current 30% to 60%, the water area is adjusted within the range of 0.5-2 times the existing water area, and the shape index is changed by changing the boundary shape, so that its value changes between 1-5. Only one key parameter is changed each time, and the other parameters remain unchanged. The heat flow measurement system and scenario simulation method are used to simulate the evolution of the urban thermal environment under different parameter values. For each parameter value, the corresponding ground temperature distribution, heat island intensity, and other thermal environment index data are obtained. The local sensitivity analysis method, such as calculating the partial derivative or relative sensitivity index, is used to quantify the influence of each parameter change on the thermal environment index. For example, the heat island intensity is reduced by 1% for each increase in vegetation coverage, and the average decrease in ground temperature is calculated when the water area is doubled. According to the sensitivity size, the parameters are sorted to determine the key factors for improving the adjustment efficiency. Combined with the simulation results, the action mechanism of the key factors is analyzed in depth, such as how the increase of vegetation coverage improves the thermal environment through transpiration heat dissipation, shading, etc., so as to clarify the optimization direction of blue-green space, such as preferentially increasing vegetation coverage in the core area of urban heat island and optimizing water body shape to enhance heat exchange, etc. The quantitative basis is provided for urban blue-green space planning.

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

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

[0060] A full life cycle cost accounting model of blue-green space construction and maintenance is established, and the cost-benefit analysis method is used to discount each cost according to the time sequence to obtain the present value of the total cost of blue-green space within its life cycle;

[0061] For ecological service value evaluation, the ecosystem service function value evaluation method based on energy analysis is used to convert the ecological service function provided by blue-green space into a unified energy unit. Combined with the local energy value-money ratio, it is converted into monetary value. The comprehensive benefit index of blue-green space is constructed by comparing the economic cost and ecological service value, which is used for quantitative evaluation of its thermal environment adjustment efficiency;

[0062] The main energy input (such as solar radiation energy, wind energy, etc.) and material input (such as precipitation, soil nutrients, etc.) in the blue-green space ecosystem, as well as the energy output (such as the energy fixed by plant photosynthesis, the energy dissipated by water evaporation, etc.) and the material output (such as the nutrients output by plant litter, the sediment carried away by surface runoff, etc.), are identified, and the values of these energy flows and material flows are quantified through field monitoring, literature research, and model calculation. For example, the solar radiation energy absorbed by the blue-green space vegetation is measured using a photosynthetically active radiation instrument, the evaporation amount and related energy loss of the water body are recorded through a hydrological monitoring station, and different energy flows and material flows are converted into solar energy values according to the energy value conversion rate. The energy value conversion rate refers to the solar energy value contained in each unit of energy or material flow, such as the energy value conversion rate of each joule of electrical energy, the energy value conversion rate of each gram of soil organic matter, etc. These conversion rates can be consulted from professional energy value databases. After converting the energy flows and material flows corresponding to each ecological service function of the blue-green space, the total solar energy value is obtained by adding up, representing the total amount of energy value of the ecological service function. Combined with the local energy value-currency ratio, the solar energy value is converted into monetary value. The energy value-currency ratio reflects the amount of energy value contained in a unit of currency in the local economic system, which is obtained by calculating the ratio of the total annual energy value used to the gross domestic product. The comprehensive benefit index I = E / C is constructed, where E is the ecological service value of the blue-green space, and C is the total cost present value. If the index is greater than 1, it means that the ecological service value exceeds the economic cost input, and the benefit is good. If the index is close to 1, it means that the income and expenditure are balanced. If the index is less than 1, it means that the cost is too high, and the benefit is poor. By further analyzing the relationship between the comprehensive benefit index and the thermal environment regulation efficiency of the blue-green space, the contribution of the blue-green space to reducing the intensity of urban heat island and stabilizing the ground temperature under different levels of comprehensive benefit index is explored through statistical analysis of historical data or simulation data. If it is found that the higher the comprehensive benefit index of the blue-green space region, the higher the thermal environment regulation efficiency, it means that the region performs well in economic and ecological coordination. Otherwise, optimization and adjustment are needed to achieve sustainable development of urban ecology and economy, and to effectively improve the quality of residents' living environment, so as to maximize the input-output benefit of blue-green space construction.

[0063] The performance measurement unit 4 quantifies the thermal environment regulation efficiency and grade 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 proportion, connectivity, vegetation type diversity and water body flow of the blue-green space are taken as the criterion layer indexes, a judgment matrix is constructed for the target layer of thermal environment regulation efficiency, and the weights of the indexes are determined. The target layer is the thermal environment regulation efficiency, and the criterion layer includes the area proportion, connectivity, vegetation type diversity and water body flow of the blue-green space. The criterion layer indexes affect the thermal environment regulation efficiency from different aspects. The area proportion reflects the size of the blue-green space, the connectivity affects the heat exchange, the vegetation type diversity helps to enhance the ecological function, and the water body flow promotes the heat dissipation. According to the relative importance between the indexes, a judgment matrix of the criterion layer relative to the target layer is constructed, the maximum eigenvalue of the judgment matrix is calculated, the consistency index is calculated, the average random consistency index is found, 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, the characteristic vector of the judgment matrix is calculated, and the characteristic vector and the corresponding weight are obtained;

[0065] Based on the gray comprehensive evaluation method and combined with the monitoring data in different seasons, the combination forms of the blue-green space are comprehensively evaluated to form a comprehensive value ranking. The area proportion, connectivity, vegetation type diversity and water body flow of the blue-green space in different seasons are monitored to obtain corresponding data. The data is standardized processed, the optimal values of the indexes are taken as a reference series, the index data of the blue-green space combination forms in different seasons are taken as comparison series, the correlation coefficients of the comparison series and the reference series in the indexes are calculated, the comprehensive evaluation values of the blue-green space combination forms are calculated according to the index weights determined by the analytic hierarchy process, and the blue-green space combination forms in different seasons are ranked according to the comprehensive evaluation values. The higher the comprehensive evaluation value is, the higher the thermal environment regulation efficiency of the combination form is, and the higher the grade is. The thermal environment regulation efficiency of the blue-green space combination form in different seasons is accurately quantified, a scientific and reasonable comprehensive value ranking is formed, and a decision basis is provided for urban planning and optimization of the blue-green space.

[0066] The planning countermeasure generation unit 5 proposes a blue-green space planning regulation path suitable for the climate characteristics of the cold region city according to the factor identification and the efficiency measurement result.

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

[0068] A space optimization model based on a genetic algorithm is adopted, the maximum thermal environment regulation efficiency of the blue-green space and the minimum economic cost are taken as the objective functions, the constraint conditions of the key factors and other limitation conditions of the urban planning are included in 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;

[0069] define the thermal environment regulation performance evaluation function E pb The function can be constructed based on the comprehensive benefit index obtained by the previous performance measurement unit 4 and the thermal environment regulation efficiency data, define the economic cost evaluation function C pb The function can refer to the whole life cycle cost accounting model, consider various costs of blue-green space construction and maintenance, construct the target function F=ω 1 E-ω 2 C, wherein ω 1 And ω 2 Are weight coefficients, can be adjusted according to actual situation, to balance the importance of thermal environment regulation performance and economic cost, according to the result of factor extraction unit 2, the area proportion, connectivity, vegetation type diversity, water flow of blue-green space key factor are taken as constraint conditions and combined with urban planning restriction condition, the layout scheme of blue-green space is coded, each coding bit represents whether a space unit is blue-green space, a certain number of initial layout schemes are randomly generated as a population, the individuals in the population are evaluated according to the target function value, select the individuals with higher fitness as parents to participate in the subsequent crossover and mutation operation, the selected parent individuals are crossed to generate new offspring individuals, the offspring individuals are mutated to increase the diversity of the population, repeat the selection, crossover and mutation operation until the termination condition is met, and the optimal blue-green space layout scheme is obtained.

[0070] At the same time, the public participation geographic information system technology is combined to collect the use demand and preference information of citizens on blue-green space, which is converted into the optimization target of space layout. The public participation geographic information system technology can fully collect the opinions and suggestions of citizens, integrate the public demand into the blue-green space planning, make the planning more close to the actual demand of citizens, improve the feasibility and satisfaction of the planning, and improve the use frequency and satisfaction of citizens on the blue-green space by planning combined with the public demand, enhance the identification and participation of citizens on the construction of urban ecological environment, and promote the sustainable development of the city.

[0071] In the application, the multi-source basic data is collected through the data acquisition unit 1, the key factors are screened by the factor extraction unit 2 using various analysis technologies, the seasonal influence and regulation performance of the blue-green space on the thermal environment are quantified based on the heat "source-sink" theory, the thermal environment regulation efficiency is quantified and sorted by combining the economic cost and ecological service value evaluation method, and the planning countermeasure generation unit 5 utilizes the genetic algorithm and the public participation technology to formulate the planning regulation path, so that the application can accurately identify the influence factors, provide a scientific basis for the blue-green space planning of the cold city, realize the collaborative improvement of the urban ecological environment and the thermal environment, and promote the sustainable development of the cold city.

[0072] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A system for identifying and measuring the efficiency of factors affecting the thermal environment of blue-green space in a cold city, characterized in that, It comprises a data collection unit (1), a factor extraction unit (2), a heat flow evaluation unit (3), an efficiency measurement unit (4) and a planning countermeasure generation unit (5); The data collection unit (1) is used for collecting the ground temperature, land use classification, blue-green space distribution and local climate zoning and other related basic data of the cold region city; The factor extraction unit (2) extracts the key factors affecting the urban thermal environment from the blue-green space based on mathematical statistical analysis and spatial recognition technology; The heat flow evaluation unit (3) constructs a "heat flow" measurement system based on the "source-sink" theory, which is used to quantify the seasonal influence of blue-green space on urban thermal environment and evaluate its regulation efficiency. When constructing the "heat flow" measurement system based on the "source-sink" theory, the heat flow evaluation unit (3): Introduces multi-source remote sensing data fusion technology to integrate the ground cover information obtained from optical remote sensing images and the ground temperature information reflected from thermal infrared remote sensing data to construct a high-resolution heat "source-sink" spatial distribution map for identifying the boundary between blue-green space and other urban land types; Based on the energy balance principle, a physical model of heat flow transmission is established to dynamically simulate the heat exchange process between blue-green space and the surrounding environment, obtain the heat conduction equation, and then solve the heat conduction equation by finite difference method to quantify the heat flow intensity of blue-green space as a heat "source" or "sink" in different seasons and time periods; When evaluating the regulation efficiency of blue-green space, the heat flow evaluation unit (3): Uses a method combining comparative experiments and scenario simulation to set up multiple comparison scenarios, and uses the constructed heat flow measurement system to simulate the evolution process of urban thermal environment under different scenarios; By comparing the ground temperature distribution and heat island intensity changes under different scenarios, the actual efficiency of blue-green space in alleviating urban heat island effect and regulating local climate is evaluated; At the same time, sensitivity analysis is introduced to analyze the influence degree of key parameters of blue-green space on regulation efficiency, determine the key factors and optimization direction for improving regulation efficiency, and the key parameters include vegetation coverage, water area and shape index; The efficiency measurement unit (4) combines economic cost and ecological service value evaluation method to quantify the thermal environment regulation efficiency and grade of blue-green space combination form in different seasons, and forms a comprehensive value ranking; The planning countermeasure generation unit (5) proposes a blue-green space planning regulation path suitable for the climate characteristics of cold region cities according to the factor recognition and efficiency measurement results.

2. The cold region city blue-green space thermal environment impact factor identification and efficiency measure system according to claim 1, characterized in that, When extracting key factors based on mathematical statistical analysis, the factor extraction unit (2): Uses a two-dimensional dimension reduction method combining principal component analysis and independent component analysis to preprocess the multi-source data related to blue-green space, and uses grey correlation analysis to determine the correlation degree between each potential factor and urban thermal environment index, selects the top 20% of closely related factors as the preliminary key factor set, and then uses stepwise regression analysis method to select the key factors affecting thermal environment from the preliminary key factor set and construct a regression equation to obtain the final key factors.

3. The cold region city blue-green space thermal environment impact factor identification and efficiency measure system according to claim 2, characterized in that, When using spatial recognition technology, the factor extraction unit (2): The local spatial autocorrelation analysis method based on the geographic weighted regression is used to identify the spatial heterogeneity of the blue-green space and its influence on the thermal environment by combining the spatial weight matrix. Meanwhile, the spatial clustering algorithm is used to analyze the clustering of the blue-green space and its surrounding areas, and the spatial groups with similar thermal environment response characteristics are divided according to the clustering results.

4. The cold region city blue-green space thermal environment impact factor identification and efficiency measurement system according to claim 1, characterized in that, The performance measurement unit (4) combines the economic cost and ecological service value evaluation method: A full life cycle cost accounting model for blue-green space construction and maintenance is established, and the cost-benefit analysis method is used to discount each cost according to the time sequence to obtain the total cost present value of the blue-green space in its life cycle. For the evaluation of ecological service value, the ecosystem service function value evaluation method based on energy analysis is used to convert the ecological service function provided by the blue-green space into a unified energy unit, and combined with the local energy value-money ratio, it is converted into monetary value. By comparing the economic cost and the ecological service value, the comprehensive benefit index of the blue-green space is constructed, which is used for the quantitative evaluation of the thermal environment regulation efficiency.

5. The cold region city blue-green space thermal environment impact factor identification and efficiency measure system according to claim 4, characterized in that, When the performance measurement unit (4) quantifies the thermal environment regulation efficiency and grade of the blue-green space combination form under different seasonal conditions: The analytic hierarchy process is used to construct the evaluation index system, taking the area proportion, connectivity, vegetation type diversity, and water flow of the blue-green space as the criterion layer index, and the thermal environment regulation efficiency as the target layer to construct the judgment matrix and determine the weight of each index. Based on the gray 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 the comprehensive value ranking.

6. The cold region city blue-green space thermal environment impact factor identification and efficiency measure system according to claim 1, characterized in that, When the planning countermeasure generation unit (5) proposes the blue-green space planning control path according to the factor identification and performance measurement results: The spatial optimization model based on genetic algorithm is used to maximize the thermal environment regulation performance of the blue-green space and minimize the economic cost as the objective function, and the constraint conditions of key factors and other restrictions of urban planning are included in the model. Through the selection, crossover, and mutation operations of genetic algorithm, the optimal blue-green space layout scheme is searched in the solution space. At the same time, combined with the public participation geographic information system technology, the use demand and preference information of citizens for the blue-green space are collected and converted into the optimization target of spatial layout.

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