Classical garden space environment evaluation method based on landscape visual quality and thermal comfort coupling model

Abstract

The invention discloses a classical garden space environment evaluation method based on a landscape visual quality and thermal comfort coupling model. The classical garden space environment evaluation method comprises the steps that garden basic data, microclimate actual measurement data, landscape beauty degree scores, thermal sensation voting data and visual quality-thermal comfort weight scores of multiple target measurement points in a target classical garden are acquired; obtaining a landscape visual quality grade map based on the landscape beauty degree score and the garden basic data; obtaining a thermal comfort level map based on the thermal feeling voting data and the microclimate measured data; and based on visual quality-thermal comfort weight scoring, determining a final weight for coupling analysis, and performing weighted overlay analysis on the landscape visual quality grade map and the thermal comfort grade map to generate a coupling grading map of the target classical garden. The visual quality and thermal comfort coupling evaluation method accurately adapts to classical garden space and aesthetic characteristics, gives consideration to space-time dynamic changes and considers crowd perception differences, and provides a scientific basis for classical garden protection.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of landscape planning and design and environmental science, and more particularly to a classical garden space environment evaluation method based on a landscape visual quality and thermal comfort coupling model. BACKGROUND

[0002] As an important cultural heritage, the value of classical gardens lies in their unique spatial aesthetics and pleasant physical environment. Scientific evaluation and sustainable protection of classical gardens require comprehensive consideration of two key dimensions: landscape visual quality and human thermal comfort. However, existing technologies have deficiencies in evaluating and coupling these two dimensions, making it difficult to meet the needs of fine evaluation and improvement of classical gardens.

[0003] In terms of landscape visual quality evaluation, existing technologies have developed various evaluation paradigms and quantitative methods. Early research focused on the construction of evaluation systems, proposing visual management systems (VMS) based on quantitative indicators, and capturing public aesthetic preferences through questionnaires or landscape element analysis. Subsequent research further divided landscape perception into different schools, including the expert paradigm relying on professional teams, the psychophysical school establishing the relationship between environmental elements and aesthetic functions, the cognitive school analyzing emotional arousal mechanisms, and the experiential school focusing on individual perception. In terms of technical application, geographic information system (GIS) spatial analysis, eye tracking, virtual reality (VR) simulation, and other tools have been used to quantify visual appeal. However, existing visual quality evaluation systems have obvious deficiencies when applied to classical gardens: first, their evaluation indicators are mostly derived from the summary of urban open spaces or natural landscapes, failing to fully consider unique gardening elements such as "leak windows", "moon hole doors", and "borrowed scenery" in classical gardens, and the dynamic visual experience they bring; for example, the assessment of "landscape prominence" does not consider the guiding and blocking effects of rockeries, rockeries, and curved corridors on sight lines; second, existing evaluations are mostly static analyses, making it difficult to reflect the dynamic influence of seasonal changes (such as summer greenery and autumn foliage) and day-night changes (such as early morning and evening oblique light and midday strong light) on garden visual levels; finally, subjective and objective evaluations are disconnected, with expert scoring based on morphological indicators deviating from actual public perception, such as some high-expert-score nodes being unsuitable for tourists in summer, but not being included in thermal environment coordination analysis, making the evaluation results lack practical guidance value.

[0004] In the field of human thermal comfort research, the Predicted Mean Vote (PMV) model and the Physiologically Equivalent Temperature (PET) based on the heat balance theory have become the mainstream evaluation tools. Microclimate simulation software (such as ENVI-met, Rayman) is widely used to predict outdoor thermal environment. Related researches have confirmed the positive role of vegetation shade, water body, and paving materials in improving local thermal comfort, and pointed out the necessity of regional correction of thermal comfort indicators. Although the research in this field has extended from urban space to garden environment, there are still technical gaps in classical garden scenarios: first, existing PET, PMV and other indicators are mostly based on global or regional scale, and have not been fully localized calibrated for specific regions such as hot summer and cold winter, resulting in application bias; second, the thermal regulation mechanism of traditional wisdom such as "mountain heat storage", "curved corridor wind guide", "water temperature regulation" in classical gardens has not been fully quantified and associated with modern thermal comfort indicators; third, in the complex terrain of classical gardens (such as the rock group in the Liuyuan Garden and the winding water system), the simulation values of wind environment in the lake center and temperature field around the rocks deviate from the measured values, and most of the researches only cover summer, lacking analysis of thermal comfort characteristics in winter and spring, which is difficult to support full-season evaluation.

[0005] In recent years, the coupling research of visual quality and thermal comfort has begun to attract attention. Existing research has preliminarily revealed the synergistic effect of vegetation density, paving materials and other factors on visual satisfaction and thermal environment, and has attempted to build a comprehensive evaluation model in urban parks and other spaces. However, when applying the coupling evaluation to classical gardens, there are still limitations in existing technology: first, existing models are mostly directly transplanted from the framework of urban parks, and have not fully considered the unique spatial logic and scale of classical gardens such as "quiet and secluded in winding paths" and "mountains and forests within a short distance", resulting in insufficient spatial adaptability; second, existing researches are mostly static or single-point superimposed analysis, and have not systematically revealed the dynamic influence of seasonal and diurnal changes on the coupling relationship between the two; finally, existing evaluation models usually use average weights, and have not fully considered the differences in perception preferences of different tourist groups (such as different ages and genders), resulting in deviation between evaluation results and actual experience.

[0006] Therefore, how to accurately adapt the visual quality and thermal comfort coupling evaluation method to the spatial and aesthetic characteristics of classical gardens, consider the dynamic changes in time and space, and take into account the differences in perception of different groups of people, so as to provide scientific basis for the protection and improvement of classical gardens, is a problem that needs to be solved by those skilled in the art. SUMMARY

[0007] In view of the above problems, the present application provides a classical garden space environment evaluation method based on a landscape visual quality and thermal comfort coupling model, to at least solve some of the technical problems mentioned in the background art.

[0008] In order to achieve the above object, the present application adopts the following technical solutions: The present application provides a classical garden space environment evaluation method based on a landscape visual quality and thermal comfort coupling model, characterized by comprising the following steps: Obtaining garden basic data, microclimate measured data and tourist perception data of a plurality of target measuring points in a target classical garden; the tourist perception data includes landscape beauty degree score, thermal sensation voting data and visual quality-thermal comfort weight score; Based on the landscape beauty degree score and the garden basic data, a landscape visual quality grade map is generated; Based on the thermal sensation voting data and the microclimate measured data, a thermal comfort grade map is generated; Based on the visual quality-thermal comfort weight score, the maximum weight for coupling analysis of landscape visual quality and thermal comfort is determined, and the landscape visual quality grade map and the thermal comfort grade map are weighted and superimposed for analysis based on the maximum weight, to generate a coupling classification map of the target classical garden.

[0009] Further, The garden basic data includes spatial feature data and elevation data; The microclimate measured data includes air temperature, relative humidity, wind speed and wind direction data measured at the plurality of target measuring points within a preset time period in summer and autumn.

[0010] Further, based on the landscape beauty degree score and the garden basic data, a landscape visual quality grade map is generated; specifically comprising: Based on the landscape beauty degree score and the garden basic data, basic visual elements and landscape visual sensitivity are obtained, and a visual evaluation index system is constructed; The weights of each index in the visual evaluation index system are determined by using fuzzy analytic hierarchy process; Based on the weights, a visual quality comprehensive index is obtained by weighted calculation; According to the visual quality comprehensive index, the visual quality grade is divided to obtain a landscape visual quality grade map.

[0011] Further, based on the landscape beauty degree score and the garden basic data, basic visual elements and landscape visual sensitivity are obtained, and a visual evaluation index system is constructed; specifically comprising: By performing GIS spatial analysis on the garden basic data, basic visual elements are obtained; the basic visual elements include visual range, visual point and water area openness; Based on the garden basic data, relative distance sensitivity and relative slope sensitivity are obtained; and based on the landscape beauty degree score, landscape prominence is obtained; The relative distance sensitivity, the relative slope sensitivity and the landscape conspicuity are taken as landscape visual sensitivity; Based on the basic visual elements and the landscape visual sensitivity, a visual evaluation index system is constructed.

[0012] Further, based on the thermal sensation vote data and microclimate measured data, a thermal comfort level map is generated; specifically including: Based on the microclimate measured data, a physiological equivalent temperature distribution map is generated by microclimate simulation software; The physiological equivalent temperature PET value of each target measuring point is obtained from the physiological equivalent temperature distribution map; The physiological equivalent temperature PET value is linearly regressed and fitted with the thermal sensation vote data of the corresponding target measuring point, a localized PET-TSV relationship model is established, and a local comfort threshold range is determined based on the PET-TSV relationship model; According to the local comfort threshold range, the thermal comfort level is divided to obtain the thermal comfort level map.

[0013] Further, the determination of the maximum weight for coupling analysis of landscape visual quality and thermal comfort specifically includes: A hierarchical structure model including target layer, criterion layer and index layer is constructed by using fuzzy analytic hierarchy process; Through triangular fuzzy number scale and Yager fuzzy preference relationship method, the visual quality-thermal comfort weight scoring of tourists of different time periods, different genders and different ages is combined to establish a space-time-group dynamic weight system; Based on the space-time-group dynamic weight system, the maximum weight of landscape visual quality and thermal comfort is determined.

[0014] Further, the space-time-group dynamic weight system is embodied as: In summer, the weight of thermal comfort is higher than that of visual quality; In autumn, the weight of visual quality is higher than that of thermal comfort; In the same season, female tourists give higher weight to thermal comfort than male tourists, and young tourists give higher weight to visual quality than middle-aged and old tourists.

[0015] Further, it also includes: based on the different coupling type regions identified by the coupling classification map, corresponding classical garden environment improvement strategies are proposed.

[0016] Further, the coupling type region includes: high visual-high thermal comfort coupling type, high visual-low thermal comfort coupling type, low visual-high thermal comfort coupling type, and low visual-low thermal comfort coupling type.

[0017] Further, the classical garden environment promotion strategy includes building elements, plant elements, rock elements and water elements.

[0018] Through the above technical solution, compared with the prior art, the classical garden space environment evaluation method based on the coupling model of landscape visual quality and thermal comfort is provided, and has the following beneficial effects: (1) The traditional method analyzes the landscape visual quality or thermal comfort in isolation, and cannot reveal the internal relationship between the two, resulting in the evaluation result being inconsistent with the actual experience. The application quantitatively correlates and superimposes the visual aesthetic and physiological feeling in a unified framework by constructing a "visual-thermal comfort" two-dimensional dynamic coupling model. This not only can accurately identify the "high visual- low thermal comfort" contradictory space (such as the core area of the view in summer due to high temperature, leading to poor experience of tourists), but also can provide a new scientific perspective and decision basis for garden environment optimization from the angle of "visual-physiological" synergistic gain, and fundamentally solves the problem that the traditional single-dimensional evaluation cannot reflect the comprehensive experience of tourists.

[0019] (2) The application generates dynamic coupling classification maps in summer and autumn at different times by integrating GIS spatial analysis and ENVI-met microclimate dynamic simulation, so as to quantitatively reveal the spatio-temporal evolution law of visual quality and thermal comfort. This spatio-temporal dynamic evaluation capability enables the application to provide scientific garden visiting suggestions for tourists in different times and seasons, and to provide precise data support for adaptive management of gardens (such as seasonal vegetation configuration and high-temperature period facility allocation).

[0020] (3) The traditional evaluation adopts "averaging" weight and ignores the perception preference differences of different tourist groups. The application introduces fuzzy analytic hierarchy process (FAHP) and combines detailed tourist questionnaires to establish a time-space-group dynamic weight system, successfully quantifying the influence of key demographic variables such as gender and age on visual and thermal comfort perception weight. This makes the evaluation result no longer a general average, but can fit the actual feelings of different tourist groups, greatly improving the humanistic care and scientific accuracy of the evaluation.

[0021] (4) In the visual evaluation, the application innovatively includes water area openness, landscape eye-catching degree and other indexes derived from classical garden elements; in the thermal comfort evaluation, the linear regression fitting of PET and TSV is realized to achieve the localization calibration of the thermal comfort threshold. This tailor-made index system and localization standard ensure that the evaluation result can accurately reflect the real environment quality of the classical garden, and solve the problem of inappropriate use of urban parks or western standards.

[0022] (5) The application proposes specific and operable optimization strategies closely combined with the four elements of classical gardens (architecture, water body, plants, and rocks) for different problem areas identified by coupling analysis. The bidirectional gain of visual quality and thermal comfort is achieved, forming a replicable and generalizable methodology, which provides direct technical tools and practical examples for the scientific protection, fine management, and sustainable utilization of classical gardens.

[0023] The technical solutions of the application will be further described in detail below with the help of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0025] Figure 1 The flowchart of the classical garden space environment evaluation method based on the coupling model of landscape visual quality and thermal comfort provided by the embodiment of the present application is shown.

[0026] Figure 2 The physiological equivalent temperature distribution diagram generated by the summer temperature and humidity parameter input provided by the embodiment of the present application is shown.

[0027] Figure 3 The physiological equivalent temperature distribution diagram generated by the autumn temperature and humidity parameter input provided by the embodiment of the present application is shown.

[0028] Figure 4 The linear fitting analysis diagram of summer PET and TSV provided by the embodiment of the present application is shown.

[0029] Figure 5 The linear fitting analysis diagram of autumn PET and TSV provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below with the help of the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0031] Embodiment one: The embodiment of the present application discloses a classical garden space environment evaluation method based on a coupling model of landscape visual quality and thermal comfort, as shown inFigure 1 as shown, comprising the following steps: S1, obtaining garden basic data, microclimate measured data and tourist perception data of a plurality of target measuring points in a target classical garden; the tourist perception data includes landscape beauty degree score, thermal sensation voting data and visual quality-thermal comfort weight score; S2, generating a landscape visual quality grade map based on the landscape beauty degree score and the garden basic data; S3, generating a thermal comfort grade map based on the thermal sensation voting data and the microclimate measured data; S4, determining the maximum weight for coupling analysis of landscape visual quality and thermal comfort based on the visual quality-thermal comfort weight score, and performing weighted superposition analysis on the landscape visual quality grade map and the thermal comfort grade map based on the maximum weight, to generate a coupling classification map of the target classical garden.

[0032] It should be noted that the above identifiers S1-S4 are only for subsequent description and do not limit the execution order of each step. Next, each of the above steps will be described in detail.

[0033] In the above step S1, the garden basic data, microclimate measured data and tourist perception data of a plurality of target measuring points in the target classical garden are obtained; specifically: (1) Selecting a plurality of target measuring points in the target classical garden: Among them, the target measuring point is arranged according to the principle of spatial heterogeneity, covering three types of space types of open space (such as lawn area), semi-enclosed space (such as Xiangxian Xuan), and enclosed space (such as the south courtyard of Wufeng Xiangguan), and taking into account the difference of underlying surface (light color stone brick, grassland) and vegetation canopy density gradient (low: 0-20%, medium: 41-70%, high: 71-100%); In the embodiment of the present application, 24 typical scenic spots in the target classical garden are selected as target measuring points.

[0034] (2) Experimental instruments: 1) Microclimate measuring instrument: NH193MINI portable weather meter, measurement accuracy: air temperature ±0.5℃ (resolution 0.1℃), relative humidity ±2% (resolution 0.1%), wind speed ±3% (resolution 0.1m / s), used for collecting microclimate parameters of measuring points; 2) Spatial analysis software: ArcGIS 10.2, used for landscape visual quality evaluation, including surface analysis and visual range analysis module; ENVI-met 4.4.4, used for thermal comfort simulation, supporting 2m precision grid PET temporal and spatial distribution calculation; 3) Statistical and weight calculation tools: SPSS 25.0, used for data correlation and regression analysis; YAAHP software for fuzzy analytic hierarchy process (FAHP) weight calculation.

[0035] (3) Data types: 1) Landscape basic data: The landscape basic data includes: spatial feature data, elevation data, and high-precision boundary vector data of the garden; wherein the spatial feature data is data collected for multiple target measurement points, such as spatial name, canopy density, underlying surface composition, and facility configuration; the elevation data is high-resolution DEM data generated by using the Kriging spatial interpolation method, which makes up for the problem of insufficient precision of 30m resolution DEM; In the embodiment of the present application, the elevation data is generated by using the Kriging interpolation method to generate 2m resolution raster elevation data, covering simulated elevation points (covering rockeries, water bodies, and flat ground) laid out through field reconnaissance, and after being cut by the garden boundary vector, is used for visual analysis in ArcGIS; the spatial feature data is obtained by using field photography + table recording, and the spatial types (open / semi-enclosed / enclosed), canopy density (obtained by visual observation combined with analysis by photographing software), underlying surface composition (light-colored stone bricks / grass coverage), and facility configuration (landscape pavilion / seats / sculptures) of 24 measurement points are counted to establish a measurement point spatial feature database. The spatial features of the target measurement points are shown in Table 1 as follows: Table 1: Spatial features of measurement points

[0036] 2) Microclimate measurement data: The microclimate measurement data includes: air temperature, relative humidity, wind speed, and wind direction data measured at multiple target measurement points within preset time periods in summer and autumn; In the embodiment of the present application, continuous stable weather in early July (July 9-11 for formal investigation) is selected for summer, and continuous stable weather in mid-October (October 5-7 for formal investigation) is selected for autumn, and four typical time periods of 8:00, 12:00, 15:00, and 17:00 are focused on every day, and 1 hour is taken as the data collection flexible window for each time period; a handheld weather meter is held at a standard height of 1.5m above the ground at the measurement point to obtain microclimate measurement data such as air temperature, relative humidity, wind speed, and wind direction, and after the values are stable, they are recorded, and each measurement point is measured three times for each time period to take the average value to ensure data accuracy; and environmental information including the underlying surface, vegetation coverage, and building shelter around the synchronous measurement point is recorded synchronously for subsequent simulation parameter calibration; The actual measurement time arrangement of the garden in the embodiment of the present application is shown in Table 2 as follows: Table 2: Actual measurement time arrangement of the garden

[0037] 3) Tourist perception data: The tourist perception data includes: landscape beauty degree score obtained through questionnaire survey, thermal sensation voting data and visual quality-thermal comfort weight score; in the embodiment of the application, 482 valid questionnaires (summer 242, autumn 240) are conducted, including tourist basic information (gender: male 43%, female 57%; age: teenager 18% / young 10% / middle-aged 26% / old 46%), thermal sensation voting (TSV, ASHRAE seven-level scale: -3 to 3 points), landscape beauty degree score (low: 0-1 point, medium: 1-2 points, high: 2-3 points), visual quality-thermal comfort weight score (9-level scale); the combination of "self-filling + interview filling" is adopted (old tourists are filled by others), so as to avoid repeated filling by the same tourists and ensure the independence of samples in each period and each measuring point. In the embodiment of the application, the survey results of visual quality and thermal comfort satisfaction are shown in Table 3; the visual quality-thermal comfort weight score is shown in Table 4; Table 3: Visual quality and thermal comfort satisfaction survey

[0038] Table 4: Comparison of importance of visual quality and thermal comfort

[0039] In the above step S2, based on the landscape beauty degree score and the garden basic data, a landscape visual quality grade map is generated; specifically including: (1) Based on the landscape beauty degree score and the garden basic data, the basic visual elements and the landscape visual sensitivity are obtained, and a visual evaluation index system is constructed; specifically: 1) Based on the ArcGIS software, the GIS spatial analysis is performed on the garden basic data to obtain the basic visual elements; the basic visual elements include the view V (area proportion of visible area), view point S (ratio of visible view points to total view points) and water area openness W va (projected area of visible water area); 2) Based on the garden basic data, the relative distance sensitivity and the relative slope sensitivity are obtained; wherein: For the relative distance sensitivity, the near view 3 times weight, the middle view 2 times weight and the far view 1 times weight are adopted; the relative distance sensitivity is expressed as: S d =3 N 1+2 N 2+ N 3 Wherein, S dIndicates relative distance sensitivity; N 1 indicates the score for the quantity or importance of landscape elements in the foreground; N 2 indicates the score for the quantity or importance of landscape elements in the midground; N 3 indicates the score for the quantity or importance of landscape elements in the background; Relative slope sensitivity is expressed as: S a =sin α (0°≤ α ≤90°) in, S a Indicates relative slope sensitivity; α Indicates the angle of incidence of the line of sight; 3) Obtain landscape prominence based on landscape beauty score statistics. S n Divided into high / medium / low sensitivity zones; 4) Relative distance sensitivity, relative slope sensitivity, and landscape visibility are used as landscape visual sensitivity; the relationship between landscape visual sensitivity and relative slope is shown in Table 5 below: Table 5: Relationship between sensitivity and relative slope

[0040] 5) Construct a visual evaluation index system based on basic visual elements and landscape visual sensitivity; (2) The weights of each index in the visual evaluation index system are determined by fuzzy hierarchical analysis (FAHP); in this embodiment of the invention, the weight of basic visual elements is 0.37, the weight of landscape visual sensitivity is 0.63, and the weight of prominence is the highest at 0.265. (3) Based on the weights, the comprehensive visual quality index of the evaluation index system is obtained by weighted calculation. VI : VI = V × w 1+ S × w 2+ W va × w 3+ S d × w 4+ S a × w 5+ S n × w 6 in, w 1 represents the field of view V The weights;w 2 indicates the viewpoint S The weights; w 3 indicates the openness of the water area. W va The weights; w 4 indicates relative distance sensitivity S d The weights; w 5 indicates relative slope sensitivity S a The weights; w 6 indicates the visibility of the landscape. S n The weights; The weights of the visual evaluation index system in this embodiment of the invention are shown in Table 6 below: Table 6: Weights of Visual Evaluation Index System

[0041] (4) Based on the comprehensive visual quality index, the visual quality level is divided to obtain the landscape visual quality level map; in the embodiment of the present invention, it is specifically divided into four levels of visual space: best, better, average and poor.

[0042] In step S3 above, a thermal comfort level map is generated based on thermal sensation voting data and microclimate measurement data; specifically, this includes: (1) Based on the measured microclimate data, a physiological equivalent temperature distribution map is generated by microclimate simulation software; In this embodiment of the invention, the grid adopts a grid precision of 2m×4m×4m, and a 200×200×20 three-dimensional model is constructed. The geographic coordinates are locked to the latitude and longitude of the target classical garden. The underlying surface material is: WW deep water material for water bodies, gray concrete for paving, and LO standard soil for soil. The parameter simulation starts at 7:00 (preheating for 1 hour) and ends at 19:00. The data recording interval is 60 minutes. The measured temperature, humidity, and wind speed average values ​​of 24 measuring points are input. The solar radiation is automatically generated by the software based on the geographic coordinates. The physiological equivalent temperature distribution diagram generated by inputting summer temperature and humidity parameters is shown below. Figure 2 As shown; a schematic diagram of the physiologically equivalent temperature distribution generated by inputting autumn temperature and humidity parameters is shown below. Figure 3 As shown; (2) Obtain the physiological equivalent temperature (PET) values ​​of each target measurement point from the physiological equivalent temperature distribution map; (3) Linear regression fitting was performed on the physiological equivalent temperature (PET) values ​​and the thermal sensation voting data of the corresponding target measurement points to establish a localized PET-TSV relationship model, and the localized comfort threshold range was determined based on the PET-TSV relationship model. In this embodiment of the invention, during the linear regression fitting process, the summer heat perception voting data is TSV=0.4803PET. 15.092, the coefficient of determination is R. 2 =0.8454; Autumn: Thermal Sensitivity Voting Data is TSV=0.7158PET 19.085, the coefficient of determination is R. 2 =0.9106; The determined localized comfort threshold range is: 30.4-32.4℃ in summer; 26-27.58℃ in autumn; The schematic diagram of the linear fitting analysis of PET and TSV in summer is shown below. Figure 4 As shown; Schematic diagram of linear fitting analysis of PET and TSV in autumn. Figure 5 As shown in Table 7; the PET range for summer thermal comfort adaptation adjustment is shown in Table 8. Table 7: Summer Thermal Comfort Adaptability Adjustment PET Range

[0043] Table 8: PET Range for Autumn Thermal Comfort Adaptability Adjustment

[0044] (4) Divide thermal comfort into thermal comfort levels according to the localized comfort threshold range to obtain a thermal comfort level map; in the embodiments of the present invention, it is specifically divided into four levels of thermal comfort space: comfortable, relatively comfortable, relatively uncomfortable, and extremely uncomfortable.

[0045] Step S4 above specifically includes: (1) Based on the visual quality-thermal comfort weighted scoring, determine the final weights used for the coupled analysis of landscape visual quality and thermal comfort; specifically including: A hierarchical model consisting of a target layer, a criterion layer, and an indicator layer was constructed using the fuzzy hierarchical analysis method (FAHP). The target layer is a coupled evaluation of visual and thermal comfort; the criterion layer is visual quality and thermal comfort; and the indicator layer is the field of view, viewpoint, water area, relative distance sensitivity, relative slope sensitivity, landscape visibility, and physiological equivalent temperature (PET). By using the triangular fuzzy numerical scale and Yager's fuzzy preference relation method, and combining the visual quality-thermal comfort weight scores of tourists of different time periods, genders, and ages (e.g., 0.81 for women and 0.64 for men in the afternoon thermal comfort in summer), a spatiotemporal-group dynamic weight system is established. This spatiotemporal-group dynamic weight system is reflected in the following ways: in summer, the weight of thermal comfort is higher than that of visual quality; in autumn, the weight of visual quality is higher than that of thermal comfort; in the same season, female tourists assign a higher weight to thermal comfort than male tourists, and teenage tourists assign a higher weight to visual quality than middle-aged and elderly tourists.

[0046] Based on the spatiotemporal-group dynamic weighting system, the final weights of landscape visual quality and thermal comfort are determined.

[0047] (2) Based on the final weight, the landscape visual quality level map and the thermal comfort level map are weighted and superimposed to generate a coupled grading map of the target classical garden; Specifically, visual quality level maps and thermal comfort level maps are imported into ArcGIS. Using a weighted overlay method (visual quality weight 0.37, thermal comfort weight 0.63), combined with dynamic weight coefficients, coupled grading maps for different time periods in summer and autumn are generated to identify four types of coupled areas: "high visual-high thermal comfort", "high visual-low thermal comfort", "low visual-high thermal comfort", and "low visual-low thermal comfort".

[0048] Example 2: This second embodiment, based on the first embodiment, further includes: proposing corresponding strategies for enhancing the classical garden environment based on the different coupling type regions identified by the coupling hierarchy diagram; wherein: (1) The coupling type regions include: high visual-high thermal comfort coupling type, high visual-low thermal comfort coupling type, low visual-high thermal comfort coupling type, and low visual-low thermal comfort coupling type; (2) The strategies for enhancing the classical garden environment include architectural elements, plant elements, rock elements, and water elements; in the embodiments of this invention: 1) Regarding architectural elements: Wisteria, climbing plants such as climbing roses are planted in the corridor area, with a foliage shading rate of 70% in summer and no obstruction of the tower's shadow in winter; traditional bamboo blinds are transformed into sliding adjustable blinds, and the eaves projection depth is calculated based on the winter solstice shadow angle, taking into account both shading and framing effects; Sedum lineare and Sedum sarmentosum are installed on the west wall of the building as a vertical green wall, reducing the wall surface temperature by 8-10℃.

[0049] 2) Regarding plant elements: the ratio of evergreen to deciduous trees is 6:4. The upper layer is selected from ginkgo (12-15m high) and camphor (10-12m high) to provide the main shade. The middle layer is selected from pittosporum (1.5-2m high) and nandina (1-1.5m high) to form a transition layer. The ground cover is selected from liriope (0.3-0.5m high) and saxifrage (0.2-0.3m high) to enhance surface evaporation. The green coverage rate in winter is ≥50%, and the proportion of colorful foliage trees in autumn is 35%, which solves the problem of monotonous seasonal appearance.

[0050] 3) Regarding the rock elements: Ivy is planted along the ridgeline of the Huangshi artificial mountain (covering 40% of the rock body), which reduces the surface temperature by 3-4℃ through leaf transpiration; a 10cm thick layer of permeable gravel (5-10mm in diameter) is laid at the foot of the mountain to create a cool and humid microclimate by storing water and evaporating through the pores; maidenhair ferns and pteridium fern are added to create a visual contrast between the delicate leaf shape and the rugged texture of the rocks.

[0051] 4) Regarding water features: Set up ecological floating islands covering 15% of the area (planted with water lilies and calamus) to improve water transparency to over 1.2m and restore the "tower reflection"; install atomizing nozzles (3m spacing, 5-10μm droplets) along the revetment, which will be activated during the high-temperature period in summer to reduce the surrounding temperature by 2-3℃; use 3000K underwater LED lights at night to simulate moonlight and extend the comfortable tour time in the waterfront space.

[0052] In this embodiment of the invention, a tourist landscape environment satisfaction questionnaire (7-point scale: -3 to 3 points) was used to verify the coupling evaluation results. The mean satisfaction of 24 measurement points was correlated with the coupling level (e.g., the mean satisfaction of "high coupling" area in autumn was 2.2±0.3, and that of "low coupling" area was 0.5±0.2). Pearson correlation analysis (r>0.85, P<0.01) was used to verify the reliability of the evaluation system. At the same time, the coupling evaluation results were used to formulate improvement strategies. The visual quality score (28% improvement) and PET value (1.8℃ decrease) before and after the implementation of the strategies were compared to verify the effectiveness of the method in practice.

[0053] The classical garden spatial environment evaluation method based on the coupling model of landscape visual quality and thermal comfort provided in the above embodiments of the present invention has the following uses: (1) Provide a scientific quantitative assessment tool for the coupling relationship between visual quality and thermal comfort in classical garden landscapes. By generating a level map of six visual indicators such as field of view and viewpoint through GIS spatial analysis, and combining it with the PET thermal comfort distribution level map output by the ENVI-met model, a dynamic coupling level map is generated through overlay analysis. This can accurately identify different coupling types of areas in the garden, such as "high visual quality - high thermal comfort" and "low visual quality - low thermal comfort", filling the limitations of traditional single-dimensional evaluation and revealing the coupling law between the two in the spatiotemporal dimension (such as the high-quality visual space in autumn expanding by 12-15% compared to summer, and the proportion of areas with thermal discomfort at noon in summer exceeding 40%).

[0054] (2) Guide tourists to choose time and space to optimize the recreational experience. By quantifying the visual quality and thermal comfort weights of different times in the morning, noon, afternoon and evening in summer and autumn, and clarifying the perceptual differences of different gender and age groups (such as women being more thermally sensitive than men in summer and teenagers having more prominent visual preferences), we can provide tourists with scientific suggestions on the time of day (such as morning and evening in autumn, and early morning / evening in summer) and space (such as the core water feature area in the middle of Lingering Garden) to improve the viewing experience and comfort of staying.

[0055] (3) Provide targeted strategies and practical paths for improving the environment of classical gardens. For the garden elements (buildings, water bodies, plants, rocks), spatial types and areas with low superposition effects identified in the coupled evaluation, we propose optimization modes for synergistic visual quality and thermal comfort (such as vegetation gradient configuration, water body shape adjustment and building shading optimization), which can be directly applied to the environmental improvement design of similar classical gardens. This invention has successfully applied the method to Taying Garden, achieving its dual gains in visual and thermal comfort.

[0056] (4) Provide data support for classical garden management decisions. The constructed spatiotemporal dynamic perception model can quantify the changing characteristics of landscape perception during the day and night of the four seasons, providing a scientific basis for garden management departments to formulate refined management measures such as time-sharing reservations, seasonal guided tours, and resource allocation (such as adding shading facilities during high-temperature periods), and promoting the scientific and sustainable development of classical garden protection and utilization.

[0057] Next, taking Taying Garden as an example, we will further explain in detail the classical garden spatial environment evaluation method based on the coupling model of landscape visual quality and thermal comfort provided in the above embodiments of the present invention.

[0058] 1. Overview and Current Issues of Tayingyuan Park: The Pagoda Shadow Garden covers an area of ​​0.8 hectares. During its restoration in the 1980s, it preserved the spatial layout of the Ming Dynasty. Its core feature is the "pagoda shadow reflected in the pond" visual axis, borrowing the view of the Tiger Hill Pagoda. The buildings are mainly hipped roofs and curved roofs, the artificial hill is made of yellow stones, and the water feature is the Huanyun Pond. The main visitors are young and middle-aged people aged 30-45 (52.6%, inclined towards photography) and seniors over 60 (28.3%, inclined towards leisure). In summer afternoons, due to the high temperature (over 32°C), visitor comfort levels decrease, and people tend to gather in shaded areas. In spring and autumn, the visitors are more evenly distributed. Visitors' visual preferences are concentrated on the pagoda's reflection in the water and the abundant plant landscape.

[0059] The core problems of the current situation can be categorized into three types: visual quality deviation type spaces lack plant coverage, have a monotonous color scheme and lack depth; thermal comfort deviation type spaces lack shade from trees, have a high proportion of hard paving and high humidity in summer; and visual-thermal comfort deviation type spaces have both of the first two problems, with PET exceeding 35°C in summer. In addition, there are additional problems such as the break in cultural heritage (multiple renovations have resulted in the loss of the "green phoenix trees and tall bamboos" artistic conception of the Ming Dynasty), the disconnect between modern facilities and traditional style, monotonous seasonal plant phases, and weathering of the core landscape (fluctuations in the water quality of Tayingbang, aging of brick and wood buildings, and wear and tear on artificial mountains and stone carvings).

[0060] 2. Application of the coupled model of landscape visual quality and thermal comfort in Tower Shadow Garden: The coupling model takes "protecting the borrowed view corridor" as its core premise, classifies spatial superposition effects, and coordinates the implementation with garden elements, focusing on solving the coordination problem between "the integrity of the borrowed view" and "thermal comfort adaptation": 2.1 Spatial optimization based on overlay effect: (1) High-space superposition effect (core viewing nodes, such as the Tower Shadow Pavilion viewing platform). Visually, the "tower-water-stone" borrowed scenery corridor is strictly preserved, and the height of vegetation within the field of vision is controlled to ≤3.5m to avoid obstructing the tower shadow; for thermal comfort, retractable intelligent shading (imitation bamboo curtain material, which dynamically adjusts the shading range according to the sun's altitude angle), vertical greening (ivy and creeping fig covering the surface of the rockery) and micro-atomizing nozzles embedded in the stone crevices are adopted. At high temperatures, it not only creates a "smoky and cloud-like artistic conception" but also reduces the temperature of PET by 3-4℃.

[0061] (2) Overlapping effect in the space (waterfront walkway, roadside tree area). Visually, prune overly dense bamboo groves and replant with autumn-colored trees such as tallow trees and beech trees to enhance the seasonal layering; for thermal comfort, use permeable gabion revetment to improve water evaporation efficiency, and combine it with tree canopy (shading coefficient 0.4-0.6) to increase the wind speed in the waterfront area by 0.5-0.8 m / s, thus alleviating the stuffy feeling.

[0062] (3) Superimposed effect in low space (sparse woodland and grassland north of the artificial hill pavilion). Visually, Japanese maple, red maple (visible foliage layer) and holly (evergreen basal layer) are added to avoid visual emptiness in winter; thermal comfort is constructed by a three-level structure of "deciduous trees (ginkgo, 4-5m high under branches) - semi-evergreen shrubs (nanand bamboo) - permeable gravel + liriope (ground cover layer)", and the PET under the forest is controlled at 30.4-32.4℃ in summer (summer comfort threshold).

[0063] (4) Spaces with poor superposition effect (entrance and exit plazas, logistics passages). Visually, an ecological buffer zone of "camphor-forsythia-permeable paving" is set up, and modern facilities are covered with traditional "swastika pattern" grid walls; for thermal comfort, a retractable sunshade film is installed, and trumpet creeper and trachelospermum green walls are set up along the pedestrian flow path to guide the humid airflow through the high temperature area and reduce the surface temperature by 5-6℃.

[0064] 2.2 Coupled Design Based on Garden Elements: (1) Architectural elements. The corridor area is planted with climbing plants such as wisteria and climbing rose, which provide 70% shade in summer and do not block the shadow of the tower in winter. The traditional bamboo curtain is transformed into a sliding adjustable curtain, and the eaves projection depth is calculated according to the winter solstice shadow angle to take into account both shading and framing effects. The west wall of the building is set with a vertical green wall of sedum and creeping sedum to reduce the wall surface temperature by 8-10℃.

[0065] (2) Plant elements. The evergreen:decaying ratio is 6:4. The upper layer is selected from ginkgo (12-15m high) and camphor (10-12m high) to provide the main shade. The middle layer is selected from pittosporum (1.5-2m high) and nandina (1-1.5m high) to form a transition layer. The ground cover is selected from liriope (0.3-0.5m high) and saxifrage (0.2-0.3m high) to enhance surface evaporation. The green coverage rate in winter is ≥50%, and the proportion of colorful foliage trees in autumn is 35%, which solves the problem of monotonous seasonal phases.

[0066] (3) Rock elements. Ivy is planted along the ridge of the Huangshi artificial mountain (covering 40% of the rock body), which reduces the surface temperature by 3-4℃ through leaf transpiration; a 10cm thick layer of permeable gravel (5-10mm in diameter) is laid at the foot of the mountain to create a cool and humid microclimate by storing water and evaporating through the pores; maidenhair fern and pteridium fern are added to create a visual contrast between the delicate leaf shape and the rugged texture of the rocks.

[0067] (4) Water elements. 15% of Huanyun Pond is equipped with ecological floating islands (planted with water lilies and calamus) to improve water transparency to more than 1.2m and restore the "tower reflection". Atomizing nozzles (3m spacing, 5-10μm mist particles) are installed along the revetment and activated during the high temperature period in summer to reduce the surrounding temperature by 2-3℃. At night, 3000K underwater LED lights are used to simulate moonlight to extend the comfortable tour time in the water-friendly space.

[0068] 3. Brief differences from the implementation at Liuyuan Garden: The Taying Garden (0.8 hectares, 16 monitoring points) is centered on the borrowed scenery of Tiger Hill Pagoda. The coupled model focuses on "borrowed scenery corridor control". The optimization objects are mostly small-scale spaces (sparse woodland and grassland, entrances and exits). The thermal comfort measures are relatively lightweight (atomizing sprinklers, permeable gabions). The Liuyuan Garden (23,300 square meters, 24 monitoring points) has a complex space (divided into four areas, including the eastern building complex and the central landscape garden). The model focuses on the whole-area coupled evaluation (including 6 visual indicators such as field of view and viewpoint + PET simulation). The thermal comfort measures are more systematic (composite shading, building ventilation shafts, ground source heat pump assisted permeable paving).

[0069] In terms of plant arrangement, the Pagoda Shadow Garden emphasizes "seasonal compensation and harmonious borrowing of scenery," strictly controlling the height of vegetation to avoid obstructing the pagoda's shadow; the Lingering Garden focuses on "community stability and shading efficiency," with no fixed proportion of evergreen and deciduous plants, ensuring visual transparency through thinning of mid-level shrubs. Regarding cultural preservation, the Pagoda Shadow Garden focuses on restoring the artistic conception of "the pagoda's shadow entering the pond," minimizing interference from modern facilities; the Lingering Garden emphasizes the preservation of the "integration of residence and garden" layout, restoring building facades (such as the traditional "one hemp, five ash" technique), and maintaining a visual sequence of "changing scenery with each step."

[0070] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0071] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating a classical garden space environment based on a coupling model of landscape visual quality and thermal comfort, characterized in that, The method comprises the following steps: obtaining garden basic data, microclimate measured data and tourist perception data of a plurality of target measuring points in a target classical garden; the tourist perception data comprises landscape beauty score, thermal sensation voting data and visual quality-thermal comfort weight score; generating a landscape visual quality grade map based on the landscape beauty score and the garden basic data; generating a thermal comfort grade map based on the thermal sensation voting data and the microclimate measured data; determining the maximum weight for coupling analysis of landscape visual quality and thermal comfort based on the visual quality-thermal comfort weight score, and performing weighted superposition analysis on the landscape visual quality grade map and the thermal comfort grade map based on the maximum weight to generate a coupling classification map of the target classical garden.

2. The classical garden space environment evaluation method based on a landscape visual quality and thermal comfort coupling model according to claim 1, characterized in that: the garden basic data comprises spatial feature data and elevation data; the microclimate measured data comprises air temperature, relative humidity, wind speed and wind direction data measured at the plurality of target measuring points within a preset time period in summer and autumn. 3.The classical garden space environment evaluation method based on the coupling model of landscape visual quality and thermal comfort according to claim 1, characterized in that, The method comprises the following steps: based on the landscape beauty score and the garden basic data, obtaining basic visual elements and landscape visual sensitivity, and constructing a visual evaluation index system; determining the weight of each index in the visual evaluation index system by using a fuzzy analytic hierarchy process; based on the weight, obtaining a visual quality comprehensive index by weighted calculation; dividing the visual quality level according to the visual quality comprehensive index to obtain a landscape visual quality grade map.

4. The classical garden space environment evaluation method based on the coupling model of landscape visual quality and thermal comfort according to claim 3, characterized in that, The method comprises the following steps: obtaining basic visual elements by performing GIS spatial analysis on the garden basic data; the basic visual elements comprise visual range, visual point and water area openness; based on the garden basic data, obtaining relative distance sensitivity and relative slope sensitivity; and obtaining landscape prominence based on the landscape beauty score statistics; taking the relative distance sensitivity, relative slope sensitivity and landscape prominence as landscape visual sensitivity; based on the basic visual elements and the landscape visual sensitivity, constructing a visual evaluation index system. 5.The classical garden space environment evaluation method based on the coupling model of landscape visual quality and thermal comfort according to claim 1, characterized in that, The method comprises the following steps: based on the microclimate measured data, generating a physiological equivalent temperature distribution map by using a microclimate simulation software; obtaining the physiological equivalent temperature PET value of each target measuring point from the physiological equivalent temperature distribution map; performing linear regression fitting on each physiological equivalent temperature PET value and the corresponding target measuring point thermal sensation voting data to establish a local PET-TSV relationship model, and determining a local comfort threshold range based on the PET-TSV relationship model; dividing the thermal comfort degree according to the local comfort threshold range to obtain a thermal comfort grade map. 6.The classical garden space environment evaluation method based on the coupling model of landscape visual quality and thermal comfort according to claim 1, characterized in that, The determination of the maximum weight for coupling analysis of landscape visual quality and thermal comfort, specifically comprising: Adopting fuzzy analytic hierarchy process to construct a hierarchical structure model including target layer, criterion layer and index layer; Through triangular fuzzy number scale and Yager fuzzy preference relation method, combining the visual quality-thermal comfort weight scoring of tourists of different time periods, different genders and different ages, a space-time-group dynamic weight system is established; Based on the space-time-group dynamic weight system, the maximum weight for coupling analysis of landscape visual quality and thermal comfort is determined. 7.The classical garden space environment evaluation method based on the coupled model of landscape visual quality and thermal comfort according to claim 6, characterized in that, The space-time-group dynamic weight system is embodied as: In summer, the weight of thermal comfort is higher than that of visual quality; In autumn, the weight of visual quality is higher than that of thermal comfort; In the same season, female tourists give higher weight to thermal comfort than male tourists, and young tourists give higher weight to visual quality than middle-aged and old tourists. 8.The classical garden space environment evaluation method based on the coupling model of landscape visual quality and thermal comfort according to claim 1, characterized in that, Also including: Based on the different coupling type areas identified by the coupling classification map, corresponding classical garden environment improvement strategies are proposed. 9.The classical garden space environment evaluation method based on the coupled model of landscape visual quality and thermal comfort according to claim 8, characterized in that, The coupling type areas include: high visual-high thermal comfort coupling type, high visual-low thermal comfort coupling type, low visual-high thermal comfort coupling type, and low visual-low thermal comfort coupling type. 10.The classical garden space environment evaluation method based on the coupling model of landscape visual quality and thermal comfort according to claim 8, characterized in that, The classical garden environment improvement strategies include architectural elements, plant elements, rock elements and water elements.

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