A vertical greening cooling benefit evaluation method, a terminal device and a storage medium

Abstract

The present application relates to a kind of vertical greening cooling benefit evaluation method, terminal equipment and storage medium, the method includes: the historical data of vertical greening site is collected;Based on the data collected, the simulation model corresponding to the vertical greening site is constructed by ENVI-met;On the basis of simulation model, the required multiple scenes to be analyzed and a control scene are set, the simulation model is run and the prediction results of each scene are output;The prediction results of each scene are evaluated, and the optimal scene is selected out.The present application can be applicable to the quantitative vertical greening cooling benefit evaluation of block scale, and the influence on relieving urban heat island and high temperature can be better reflected.

Description

Technical Field

[0001] This invention relates to the field of urban ecological environment assessment, and in particular to a method, terminal equipment and storage medium for assessing the cooling benefits of vertical greening. Background Technology

[0002] Vertical greening refers to the greening of vertical surfaces using plants. It encompasses a system of plants, substrates, and components, also known as green walls, vertical gardens, or three-dimensional greening. While the concept of vertical greening has a long history, dating back to the Hanging Gardens of Babylon in the 6th century BC, research on it has only emerged in the last forty years. With urban development and increasing environmental problems, the growing risks of urban heat islands and high temperatures have exacerbated the contradiction between people's physiological and psychological demand for green space and the insufficient supply of land. This has prompted planners and engineers to explore possible solutions for building facades. On the one hand, vertical greening is a supplementary means of green space on building facades, covering the increasing vertical impermeable surfaces in cities. It can provide multiple ecological benefits such as cooling, energy conservation and emission reduction, air purification, and carbon sequestration without occupying limited land, while also beautifying the urban environment, demonstrating good development potential. On the other hand, the development and application of vertical greening are still in the exploratory stage and are limited by high construction and maintenance costs. Therefore, in urban planning and design, we should strengthen the construction of scientific and rational vertical greening to better achieve sustainable urban development.

[0003] Quantitative analysis of the cooling benefits of vertical greening is fundamental for a scientific understanding, rational planning, and project evaluation of vertical greening. However, how to reasonably and effectively assess its cooling benefits, thereby promoting planning and design, remains a major issue limiting the development of vertical greening. Existing research is limited in its application to block-scale and above-scale studies that directly impact planning and design. The influence of block-scale factors such as spatial layout and building form on the cooling benefits of vertical greening remains unclear and insufficient to guide rational vertical greening planning and design in specific cases.

[0004] Numerical simulation is currently an important tool for assisting planning and design research. At present, the scale of vertical greening construction is relatively small, and it is difficult to find suitable test sites in real-world environments for many scenarios. Therefore, numerical simulation is a suitable choice to compensate for the limitations of real-world case studies. ENVI-met is a CFD three-dimensional microclimate simulation software based on fluid dynamics, capable of simulating the interaction between surface, vegetation, and air in urban environments. It can comprehensively simulate steady-state and transient thermal comfort conditions by integrating solar radiation and wind flow processes, possessing advantages in high simulation capabilities and high resolution. It is currently widely used in urban planning, landscape, architecture, and environmental fields. Compared with other CFD models, its V4.4 and later versions have added a dedicated high-precision vertical greening module, which can simulate the impact of vertical greening on outdoor microclimates. However, numerical simulation methods still have some shortcomings. The standardization of model workflows and deficiencies in model validation need to be addressed. Existing studies commonly suffer from low-quality input parameters, missing model validation steps, misuse of validation indicators, and a single evaluation index for cooling benefits, affecting the credibility of simulation results and the comprehensiveness of evaluation results. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a method for evaluating the cooling benefits of vertical greening, a terminal device, and a storage medium.

[0006] The specific plan is as follows:

[0007] A method for evaluating the cooling benefits of vertical greening includes the following steps:

[0008] S1: Collect historical data of vertical greening sites;

[0009] S2: Based on the collected data, construct a simulation model of the vertical greening site using ENVI-met;

[0010] S3: Based on the simulation model, set up multiple scenarios to be analyzed and one control scenario, run the simulation model and output the prediction results for each scenario;

[0011] S4: Evaluate the prediction results for each scenario and select the optimal scenario;

[0012] When conducting assessments at the block scale, the assessment process includes the following steps:

[0013] S401: Calculate the average change of parameters in the study area and the average change of parameters in the upstream and downstream areas for each scenario to be analyzed, and obtain the first evaluation index by weighted summation based on the two changes. When the first evaluation index is greater than the preset first evaluation index threshold, select the scenario to be analyzed when the first evaluation index is the largest as the optimal scenario; otherwise, proceed to S402.

[0014] S402: Calculate the four values ​​of the effective change area ratio of parameters and the average effective change of parameters in the study area corresponding to each scenario to be analyzed, and the effective change area ratio of parameters and the average effective change of parameters in the upstream and downstream areas. Based on the four values, perform a weighted summation to obtain the second evaluation index, and select the scenario to be analyzed with the largest second evaluation index as the optimal scenario.

[0015] Furthermore, the types of data collected should include the parameters required for building the simulation model and the verification variables output after the simulation model is run.

[0016] Furthermore, meteorological parameters are collected through meteorological stations, which are located in areas with open surroundings, no obvious building shade, and a surface that is homogeneous with the surrounding environment.

[0017] Furthermore, the simulation model selects the meteorological data from historical data that is closest to the typical meteorological day in the meteorological parameter settings.

[0018] Furthermore, each season corresponds to a typical meteorological day, and the meteorological data for the typical meteorological day are determined according to the corresponding environmental design standards for each region.

[0019] Furthermore, in selecting meteorological data that are closest to typical weather days, the distance between two meteorological data points is calculated using Euclidean distance, and the smallest distance is considered the closest.

[0020] Furthermore, the parameters used in step S4 for evaluation are air temperature, relative humidity, wall temperature, or a combination of multiple parameters.

[0021] Furthermore, the average variation of parameters in the study area The calculation formula is:

[0022]

[0023] Where i represents the i-th grid within the study area, and m represents the number of grids contained within the study area. This represents the parameter value of the i-th grid output by the model when the scene is the control scene. This represents the parameter value of the i-th grid output by the model when the scene is the n-th scene to be analyzed;

[0024] Average variation of parameters in upstream and downstream regions The calculation formula is:

[0025]

[0026] Where j represents the j-th grid in the upstream or downstream region, and k represents the number of grids contained in the upstream or downstream region. This represents the parameter value of the j-th grid in the upstream region output by the model when the scene is the n-th scene to be analyzed. This represents the parameter value of the j-th grid in the downstream region of the model output when the scene is the n-th scene to be analyzed.

[0027] Furthermore, the calculation method for the effective change area ratio of parameters is as follows: based on the preset effective change threshold, the number of grids in the region whose parameter change is greater than the effective change threshold is counted, and the ratio of the counted grids to the total number of grids in the region is used as the effective change area ratio of parameters.

[0028] The method for calculating the average effective change of parameters is as follows: extract the parameter values ​​corresponding to all grids in the region where the parameter change is greater than the effective change threshold, and then average them to obtain the average effective change of parameters.

[0029] The region is either the study area or the upstream and downstream regions. The upstream and downstream regions include the upstream region and the downstream region, and the upstream region and the downstream region contain the same number of grids.

[0030] A terminal device for evaluating the cooling benefits of vertical greening includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described in the embodiments of the present invention.

[0031] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above in the embodiments of the present invention.

[0032] The present invention adopts the above technical solution, which can be applied to the quantitative evaluation of the cooling effect of vertical greening at the block scale, and can better reflect its impact on alleviating urban heat island and high temperature. Attached Figure Description

[0033] Figure 1 The diagram shown is a flowchart of Embodiment 1 of the present invention.

[0034] Figure 2 The figure shown is a schematic diagram of the visualization cross-section of the model results in this embodiment. Detailed Implementation

[0035] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments, and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention.

[0036] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0037] Example 1:

[0038] This invention provides a method for evaluating the cooling benefits of vertical greening, such as... Figure 1 As shown, the method includes the following steps:

[0039] S1: Collect historical data of vertical greening sites.

[0040] The collected data should include parameters required for building the simulation model and validation variables output after the simulation model runs. The parameters required for building the simulation model are used to construct the model, and the validation variables output after the simulation model runs are used to evaluate the quality of the model. In this embodiment, the data types include material (including plant, building, and soil) parameters, meteorological parameters, etc. Those skilled in the art can choose the specific type according to actual needs, and no limitation is made here. Tables 1 and 2 show the input parameters required for model construction set in this embodiment.

[0041] Table 1

[0042]

[0043] Table 2

[0044]

[0045]

[0046] Different parameters require different instruments for data acquisition. The selection of instruments should be based on the following factors: whether the measurement accuracy meets the requirements, whether the size and specifications are easy to install, whether the data acquisition and storage methods are easy to use, whether the power supply of the instrument is stable, and whether the cost is within the budget.

[0047] The selection of data collection locations for each variable followed these principles: 1. The vertical greening was in good condition and covered a large area. 2. The locations met the conditions for control and replication experiments, meaning there were both exposed building facades and vertical green walls with similar environments for control, and the areas were large enough for replication. The distance between the collection points should not be too far, and control and replication should be carried out in homogeneous environments as much as possible. 3. The locations were not affected by significant interference factors, such as prolonged building shading or heat exhaust from air conditioning units. Concealed locations with low pedestrian traffic were chosen to minimize human interference.

[0048] Meteorological parameters are collected through weather stations. Some studies obtain background meteorological data from nearby urban weather stations, which are relatively far from the actual measurement site. Microclimates vary significantly in heterogeneous environments, and distant urban weather stations cannot accurately reflect the meteorological background of the actual measurement site. Therefore, it is necessary to select a suitable location at the measurement site to install a small weather station to collect the required meteorological data. The weather station should ideally be located in an area with open surroundings, no significant building shading, and a surface homogeneous with the surrounding environment, free from significant interference. Appropriate sensors are installed according to the parameters to be monitored. Commonly used meteorological parameters include air temperature, relative humidity, wind speed, wind direction, solar radiation, and rainfall.

[0049] For the data in Tables 1 and 2 that could not be obtained through on-site monitoring, the remaining required data were supplemented by searching for empirical values ​​in literature, selecting default values ​​for the model, and seeking help from relevant departments and units.

[0050] Furthermore, it is necessary to determine the measurement dates, times, monitoring cycles, and data acquisition frequencies based on the research objectives. The season, day / night cycle, and weather conditions of interest in the research should also be determined. The cooling benefits of vertical greening are significantly affected by weather and other factors; therefore, longer monitoring periods are more conducive to reducing individual case errors and reflecting the true patterns of change. Excessively high data acquisition frequencies can quickly deplete the instrument's data storage capacity and battery power, while excessively low frequencies may fail to reflect the temporal patterns of change. A suitable monitoring frequency should be selected based on research needs and instrument performance; a frequency of 0.5 hours or 1 hour is recommended.

[0051] S2: Based on the collected data, construct a simulation model of the vertical greening site using ENVI-met.

[0052] The construction of the simulation model in this embodiment includes the following steps:

[0053] S21: Set material parameters in the DB Manager submodule of ENVI-met. Use the soil, plant, building, and other parameters collected in S1 to construct the specified materials for this project in the DB Manager.

[0054] S22: Build a physical space model based on the real environment in the Spaces module of ENVI-met.

[0055] First, set the number of grids and grid resolution according to the size of the vertical greening site. Buffer boundaries need to be reserved in both the horizontal and vertical directions to reduce interference with the model results.

[0056] The ENVI-met model requires a long runtime. To improve efficiency and reduce runtime loss, this can be achieved by selecting an appropriate resolution and setting the equidistant mesh to a scaling mesh in the vertical direction. Less critical vertical heights can be scaled up proportionally, with the tallest building height often used as the starting point for scaling. The number of nested meshes in the horizontal direction should be at least half the height of the nearest building, and the upper boundary height in the vertical direction should be at least twice the height of the tallest building. Next, import the site plan as the base map, and combine it with building heights, structures, materials, vegetation heights, and underlying surface materials to perform a 3D reconstruction model in Spaces.

[0057] S23: Set meteorological parameters in the Forcing Manager submodule of ENVI-met.

[0058] It is recommended to select typical meteorological days within the monitoring period for the input meteorological parameters to improve the representativeness of the model results. The selection of typical meteorological days can be based on the classification in environmental design standards. Taking the calculation of typical summer meteorological days as an example, based on the meteorological parameters of typical summer meteorological days in major cities provided in the standards, the measured daily meteorological data are compared with the reference typical meteorological days to select the most similar days. The specific method is as follows:

[0059] Four parameters—dry-bulb temperature (Ta), relative humidity (RH), total irradiance (SW), and wind speed (WS)—were selected as the main reference meteorological variables. The building climate zone type and typical daily meteorological characteristics of the area where the vertical greening is located were determined. Distance correlation analysis was performed between the daily average values ​​of each meteorological parameter collected by the meteorological station and the typical daily meteorological parameters of this type of building climate zone. Distance correlation analysis measures the similarity between samples or variables; the standardized Euclidean distance method was selected to eliminate the influence of meteorological parameter values ​​from different dimensions. Finally, the typical summer meteorological day that best matches the typical climate characteristics of the local area was determined, and the meteorological value of that day was used as the boundary condition input into the model. It is particularly important to note that the meteorological parameters required in Forcing Manager must be input strictly according to the format and units given in the software; air temperature units must be converted from °C to K.

[0060] S24. Set the run file in the ENVI-guide submodule of ENVI-met.

[0061] The ENVI-guide module offers two modes: simple forced and full forced. The full forced mode provides higher model accuracy than the simple forced mode, so it is the preferred choice. The results during the initialization phase of model execution may be unstable. To improve the accuracy of the model results, the simulation period should be extended, and simulation results should be read after at least 6 hours of execution.

[0062] S24. Run the model in the ENVI-core submodule of ENVI-met and output the results in Leonardo.

[0063] Calculations are performed in the ENVI-core module, and the resulting files can be extracted and visualized in the Leonardo module. The model assigns values ​​to each mesh, and the results are presented in raster format. Due to the large number of meshes involved in the 3D model, using Python code in DataStudio to batch read the required values ​​greatly improves data preprocessing efficiency, allowing for more flexible subsequent data analysis.

[0064] Furthermore, after the simulation model is built, it needs to be validated. The validation variable results (air temperature, relative humidity, wall temperature, etc.) output by the model are compared with the corresponding validation variable data in the collected historical data. The model is evaluated using existing evaluation indicators (such as consistency index, root mean square error, systematic root mean square error, unsystematic root mean square error, mean absolute error, and mean deviation error). If the requirements are not met, the parameters in the simulation model are modified until the requirements are met.

[0065] S3: Based on the simulation model, set up multiple scenarios to be analyzed and one control scenario, run the simulation model and output the prediction results for each scenario.

[0066] The various scenarios to be analyzed are different scenarios under the influence of a single factor (such as coverage), that is, only one parameter changes, while other parameters remain the same.

[0067] S4: Evaluate the prediction results for each scenario and select the optimal scenario.

[0068] The model output file contains the values ​​assigned to each grid within the physical space model. With the help of Python code, the numerical values ​​of the target variables for all grids in n scenarios can be output quickly and accurately.

[0069] Different evaluation scales need to be used depending on the application scenario.

[0070] (1) When evaluating based on the quadrat scale, calculate the air temperature drop ΔT. a That is, the air temperature T around the vertical green wall g Temperature T around the exposed building exterior walls b The difference. Calculate the surface temperature drop ΔT. s That is, the surface temperature S of the vertical greening wall. g Temperature S of exposed building exterior wall surface b The difference. Based on ΔT a With ΔT sThe results of the weighted summation are used for scenario evaluation.

[0071] The calculation formula is as follows:

[0072] ΔT a =T b -T g

[0073] ΔT s =S b -S g

[0074] In the formula, T b Indicates the temperature around the exposed exterior walls of a building, T g Indicates the air temperature around the vertical greening point; S b S represents the surface temperature of the bare wall. g The surface temperature of the green wall.

[0075] (2) When the assessment is based on the block scale, the assessment process includes the following steps:

[0076] S401: Calculate the average change of parameters (air temperature, relative humidity, or wall temperature) in the study area and the average change of parameters in the upstream and downstream areas for each scenario to be analyzed, and obtain the first evaluation index by weighted summation based on the two changes. When the first evaluation index is greater than the preset threshold of the first evaluation index, select the scenario to be analyzed when the first evaluation index is the largest as the optimal scenario; otherwise, proceed to S402.

[0077] Average variation of parameters in the study area The calculation formula is:

[0078]

[0079] Where i represents the i-th grid within the study area, and m represents the number of grids contained within the study area. This represents the parameter value of the i-th grid output by the model when the scene is the control scene. This represents the parameter value of the i-th grid output by the model when the scene is the n-th scene to be analyzed;

[0080] Average variation of parameters in upstream and downstream regions The calculation formula is:

[0081]

[0082] Where j represents the j-th grid in the upstream or downstream region, and k represents the number of grids contained in the upstream or downstream region. This represents the parameter value of the j-th grid in the upstream region output by the model when the scene is the n-th scene to be analyzed. This represents the parameter value of the j-th grid in the downstream region of the model output when the scene is the n-th scene to be analyzed.

[0083] S402: Calculate the four values ​​of the effective change area ratio of parameters and the average effective change of parameters in the study area corresponding to each scenario to be analyzed, and the effective change area ratio of parameters and the average effective change of parameters in the upstream and downstream areas. Based on the four values, perform a weighted summation to obtain the second evaluation index, and select the scenario to be analyzed with the largest second evaluation index as the optimal scenario.

[0084] When the primary evaluation indicator cannot effectively compare the differences in benefits between scenarios, the effective change in parameter area percentage (A) is selected. e and the average effective change of parameters T e The benefits of vertical greening need to be evaluated. Due to the large number of grids and short time steps in the simulation results, and the small change in the vertical greening area, the method of taking the mean and then comparing may easily smooth out the numerical differences between scenarios due to the large denominator, which may result in an inability to effectively compare the benefits between scenarios.

[0085] The calculation method for the effective change area ratio is as follows: based on the preset effective change threshold, the number of grids in the region whose parameter change is greater than the effective change threshold is counted, and the ratio of the counted grids to the total number of grids in the region is taken as the effective change area ratio.

[0086] The method for calculating the average effective change of parameters is as follows: extract the parameter values ​​corresponding to all grids in the region where the parameter change is greater than the effective change threshold, and then average them to obtain the average effective change of parameters.

[0087] like Figure 2 Therefore, this is a schematic diagram of the visualization interface for the model results. The region is either the study area (the middle part) or the upstream and downstream regions (the left and right sides of the study area, determined by the prevailing wind direction). The upstream and downstream regions include the upstream region and the downstream region, and the upstream region and the downstream region contain the same number of grids.

[0088] Indicators based on quadrat-scale assessments can only evaluate the cooling effect around a single vertical green wall, without considering its impact on the overall air temperature of the study area. However, using indicators based on block-scale assessments to evaluate the impact of vertical greening on the overall temperature of the study area more effectively reflects the facility's role in mitigating urban heat islands and the hazards of high temperatures.

[0089] This invention combines field observation and numerical simulation to construct a quantitative method for evaluating the cooling benefits of vertical greening at the street block scale. First, this method expands the research scale of the cooling benefits of vertical greening. Past research on the cooling benefits of vertical greening has largely focused on the quadrat scale, with limited research on the more macroscopic street block scale that directly impacts planning and design. Numerical simulation allows for the exploration of cooling benefits under different scenarios at the street block scale, providing guidance for rational planning and layout. Second, this method improves the accuracy of the research results. Past studies, especially those related to numerical simulation of vertical greening, often suffer from inadequate research procedures and methods, easily reducing the quality of results. Combining field observation with numerical simulation and establishing standardized procedures reduces errors at each step of the research process, improving the accuracy of the results. Finally, this method supplements and constructs new indicators, enhancing the ability to characterize the cooling benefits of vertical greening. Compared to the past focus on the ambient temperature near the vertical green wall, the newly added study on the average change of parameters in the study area and the average change of parameters in the upstream and downstream areas pays more attention to the overall ambient temperature change in the study area, which can better reflect its impact on mitigating the urban heat island and high temperature. The area ratio of effective change of parameters and the average effective change of parameters, by defining effective thresholds, can more detailed and effectively compare the differences between scenarios.

[0090] Example 2:

[0091] The present invention also provides a terminal device for evaluating the cooling benefits of vertical greening, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the method embodiment described above in Embodiment 1 of the present invention.

[0092] Furthermore, as an executable solution, the vertical greening cooling benefit assessment terminal device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The vertical greening cooling benefit assessment terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above-described composition of the vertical greening cooling benefit assessment terminal device is merely an example and does not constitute a limitation on the vertical greening cooling benefit assessment terminal device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the vertical greening cooling benefit assessment terminal device may also include input / output devices, network access devices, buses, etc., and this embodiment of the invention does not limit this.

[0093] Furthermore, as an executable solution, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices. The general-purpose processor can be a microprocessor or any conventional processor. This processor serves as the control center of the vertical greening cooling benefit assessment terminal equipment, connecting all parts of the equipment via various interfaces and lines.

[0094] The memory can be used to store the computer programs and / or modules. The processor, by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory, realizes various functions of the vertical greening cooling benefit assessment terminal device. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0095] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the embodiments of the present invention.

[0096] If the modules / units integrated in the vertical greening cooling benefit assessment terminal equipment are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc.

[0097] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for evaluating the cooling benefits of vertical greening, characterized in that, Includes the following steps: S1: Collect historical data of vertical greening sites; S2: Based on the collected data, construct a simulation model of the vertical greening site using ENVI-met; S3: Based on the simulation model, set up multiple scenarios to be analyzed and one control scenario, run the simulation model and output the prediction results for each scenario; S4: Evaluate the prediction results for each scenario and select the optimal scenario. The parameters used in the evaluation are air temperature, relative humidity, wall temperature, or a combination of multiple parameters. When conducting assessments at the block scale, the assessment process includes the following steps: S401: Calculate the average change of parameters in the study area and the average change of parameters in the upstream and downstream areas for each scenario to be analyzed, and obtain the first evaluation index by weighted summation based on the two changes. When the first evaluation index is greater than the preset first evaluation index threshold, select the scenario to be analyzed when the first evaluation index is the largest as the optimal scenario; otherwise, proceed to S402. S402: Calculate the four values ​​of the effective change area ratio of parameters and the average effective change of parameters in the study area corresponding to each scenario to be analyzed, and the effective change area ratio of parameters and the average effective change of parameters in the upstream and downstream areas. Based on the four values, perform a weighted summation to obtain the second evaluation index. Select the scenario to be analyzed with the largest second evaluation index as the optimal scenario. Among them, the average change of parameters in the study area The calculation formula is: in, i Indicates the first within the study area i One grid, m This indicates the number of grid cells contained within the study area. This indicates the first output of the model when the scene is the reference scene. i The parameter values ​​of each grid, This indicates the output of the model when the scene is the nth scene to be analyzed. i The parameter values ​​for each grid; Average variation of parameters in upstream and downstream regions The calculation formula is: in, j Indicates the first term within the upstream or downstream region. j One grid, k This indicates the number of grid cells contained in the upstream or downstream region. This represents the upstream region output by the model when the scene is the nth scene to be analyzed. j The parameter values ​​of each grid, This represents the downstream region output by the model when the scene is the nth scene to be analyzed. j The parameter values ​​for each grid; The calculation method for the effective change area ratio is as follows: based on the preset effective change threshold, the number of grids in the region whose parameter change is greater than the effective change threshold is counted, and the ratio of the counted grids to the total number of grids in the region is taken as the effective change area ratio.

2. The method for evaluating the cooling benefits of vertical greening according to claim 1, characterized in that: The types of data collected should include the parameters required for building the simulation model and the verification variables output after the simulation model is run.

3. The method for evaluating the cooling benefits of vertical greening according to claim 1, characterized in that: In the meteorological parameter settings, the simulation model selects the meteorological data that is closest to the typical meteorological day from historical data. The selection of the meteorological data that is closest to the typical meteorological day is calculated by Euclidean distance between the two meteorological data, and the smallest distance is the closest.

4. The method for evaluating the cooling benefits of vertical greening according to claim 3, characterized in that: Each season corresponds to a typical meteorological day, and the meteorological data for the typical meteorological day are determined according to the corresponding environmental design standards for each region.

5. The method for evaluating the cooling benefits of vertical greening according to claim 1, characterized in that: The method for calculating the average effective change of parameters is as follows: extract the parameter values ​​corresponding to all grids in the region where the parameter change is greater than the effective change threshold, and then average them to obtain the average effective change of parameters. The region is either the study area or the upstream and downstream regions. The upstream and downstream regions include the upstream region and the downstream region, and the upstream region and the downstream region contain the same number of grids.

6. A terminal device for evaluating the cooling benefits of vertical greening, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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