Ecological landscaping design optimization method

Abstract

The invention discloses an ecological landscaping design optimization method, relates to the technical field of landscaping design, and realizes comprehensive extraction and integration of factors influencing visual experience by constructing an initial view feature vector F [omega]. Furthermore, by constructing an optimal vegetation height adjustment vector Hopt and combining with a view barrier matrix Um of a view spot generated by fitting, not only can a key vegetation unit which causes actual shielding to a viewpoint sight be accurately identified, but also the influence can be quantified as a view barrier index U (k), and an optimized vegetation optimization density vector Pp is obtained accordingly, so that the optimal vegetation height adjustment vector Pp is obtained. And finally calculating a comprehensive view optimization index Q. According to the processing process, the landscaping design is converted into a system optimization process based on data driving and model reasoning from traditional empirical judgment, the method has obvious advantages in the aspects of calculability, structure expressivity and visual patency improvement, and the scientificity and decision-making efficiency of a landscaping design scheme are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of garden greening design, in particular to an ecological garden greening design optimization method. Background Art

[0002] Ecological environmental development is an essential component of modern urban development. Landscape planning, as a core area of ​​ecological construction, is dedicated to enhancing the ecological functions, aesthetic value, and sustainable development capabilities of urban green spaces. Within the sub-areas of landscape planning, ecological landscaping focuses on the rational allocation of natural elements such as vegetation, topography, and water bodies to enhance the ecological stability and landscape diversity of green spaces. Within this field, landscape view optimization has gradually become an important research direction, affecting not only the viewing experience of gardens but also the interaction between humans and nature. For example, in diverse settings such as parks, wetlands, urban greenways, and scenic areas, a reasonable landscape view design can guide visitors' viewing routes, enhance the walking experience, and make green spaces not only part of the ecosystem but also highly attractive cultural and leisure spaces.

[0003] In the invention of Chinese patent application No. CN202311840936.0, an ecological garden greening optimization method is disclosed, which specifically relates to the field of garden landscape design technology, including the following steps: step one, collecting target garden information and clarifying the greening area; step two, collecting regional information of the greening area in step one, and summarizing the collected information into a greening area information set; step three, collecting greening plant information and summarizing it into a greening plant information set; step four, performing analysis operations based on the greening area information set and the greening plant information set to obtain a suitable plant planting value ZZi, and sorting according to the suitable plant planting value ZZi to obtain a suitable plant species sorting form; the present invention can improve the ecological benefits of the greening area. By collecting target garden information, clarifying the greening area, and collecting regional information and greening plant information for the greening area, the ecological status of the greening area can be better understood, and a scientific and reasonable basis can be provided for the selection and planting of greening plants.

[0004] Therefore, current ecological landscaping designs have significant shortcomings in optimizing landscape views. Traditional greening planning primarily focuses on vegetation adaptability, ecological restoration, and biodiversity, but pays less attention to visual transparency and landscape layering. In many urban greening and park landscape designs, trees, shrubs, and groundcover plants are often planted in a uniformly dense or randomly distributed pattern, lacking scientific adjustments based on human visual experience. This approach can easily lead to visual obstruction, whereby overly dense trees or shrubs obscure the landscape in some areas, limiting the view and making it difficult for visitors to fully appreciate the natural scenery. Furthermore, in some urban green spaces, parks, and scenic areas, the uniform plant height configuration lacks layering, resulting in a lack of depth in the landscape and a lack of rich visual depth between near and far. For areas requiring expansive views, such as lakes, mountains, and park central plazas, existing landscaping methods can result in some key attractions being obscured by trees, diminishing the aesthetic value of the garden space and limiting interaction between people and the environment. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an ecological landscaping design optimization method, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an ecological landscaping design optimization method, comprising the following steps:

[0007] S1. Collect three-dimensional terrain data and existing vegetation distribution data of the garden greening to form a terrain feature vector T and a vegetation feature matrix P. Collect observation points k of the garden greening to form a viewpoint dataset V. Calculate the initial line of sight patency index Ω0. Integrate the terrain feature vector T, the vegetation feature matrix P, the viewpoint dataset V, and the initial line of sight patency index Ω0 to obtain the initial field of view feature vector FΩ.

[0008] S2, calculating the optimal visual height of each observation point k in the viewpoint dataset V based on the initial field of view feature vector FΩ, and forming the optimal vegetation height adjustment vector Hopt;

[0009] S3. Based on the obtained optimal vegetation height adjustment vector Hopt, fit it with the terrain characteristic vector T and the vegetation characteristic matrix P to calculate the viewing obstacle index U(k) of each observation point k, and after integration, obtain the visual obstacle matrix Um of the viewing point;

[0010] S4. Optimizing the vegetation planting density of the landscaping by combining the obtained visual field obstruction matrix Um and the optimal vegetation height adjustment vector Hopt to obtain the vegetation optimization density vector Pp;

[0011] S5. Calculate the comprehensive field of view optimization index Q by combining the vegetation optimization density vector Pp, the field of view obstruction matrix Um, and the optimal vegetation height adjustment vector Hopt, and compare it with the preset optimization threshold Qth to determine whether to enter iterative optimization and decision execution.

[0012] Preferably, S1 includes S11, S12, S13 and S14;

[0013] S11, collecting three-dimensional terrain data and existing vegetation distribution data of the garden greening area in real time through lidar mapping, GIS system and remote sensing imagery, and forming a terrain feature vector T and a vegetation feature matrix P;

[0014] Wherein, the terrain feature vector T includes T={t1, t2, ..., tn}, ti represents the feature vector of terrain point i, and n represents the total number of terrain feature points;

[0015] The characteristic vector ti of terrain point i specifically includes the elevation h(ti), slope s(ti) and surface reflectivity λ(ti) at terrain point i;

[0016] The vegetation feature matrix P includes P = {hp(j), dp(j), λp(j)|j∈Mp}, where Mp represents the total number of vegetation, hp(j) represents the height of vegetation j, dp(j) represents the crown diameter of vegetation j, and λp(j) represents the leaf density of vegetation j.

[0017] Preferably, S12, collecting observation points k of the landscape, where observation points k include roadsides, overpasses, walkways, viewing platforms, lakeside areas, and rest areas, and extracting the hierarchical heights of observation points k, including ground view angles, elevated view angles, and long-distance view points. Furthermore, extracting the viewing angle range of observation point k between 30° and 120° to form a viewpoint dataset V;

[0018] Among them, observation point collection includes using laser rangefinders, GIS systems and panoramic cameras to collect the layer height, coordinates, viewing angle range and viewing distance information of observation points;

[0019] The viewpoint dataset V includes V = {((xu(k), yu(k), hu(k), Ou(k), du(k))|k∈Mu}, where (xu(k), yu(k) represent the geographic coordinates of viewpoint k, hu(k) represents the height of viewpoint k, Ou(k) represents the viewing direction of viewpoint k, du(k) represents the maximum viewing distance of viewpoint k; Mu represents the total number of observation points.

[0020] Preferably, S13, calculating the maximum visible area Atotal(k) and the initial blocked area Ablock(k) of the observation point k based on the obtained viewpoint dataset V, and obtaining the initial sight line unobstructedness index Ω0 by calculating the maximum visible area Atotal(k) and the initial blocked area Ablock(k), reflecting the initial sight line unobstructedness of the entire landscaping;

[0021] The maximum visible area Atotal(k) is obtained by the following calculation formula:

[0022] Atotal(k)=π*du(k) 2 ;

[0023] Where, π represents the circumference constant, which is 3.14;

[0024] The initial occlusion area Ablock(k) is obtained by the following calculation formula:

[0025] Ablock(k)=∑ j∈Mp f(hp(j),dp(j),λp(j),Ou(k));

[0026] Where f represents the comprehensive function, which is used to estimate the occlusion contribution of vegetation j to observation point k;

[0027] The initial sight line patency index Ω0 is obtained by the following calculation formula:

[0028]

[0029] Where w(k) represents the weight coefficient of observation point k, which is preset according to the flow of people and functional level at observation point k;

[0030] S14. Based on the acquired terrain feature vector T, vegetation feature matrix P, viewpoint data set V and initial sight line patency index Ω0, integration is performed to acquire the set V and the initial sight line patency index Ω0, and obtain the initial sight line patency index Ω0.

[0031] Preferably, S2 includes S21;

[0032] S21. Based on the initial field of view feature vector FΩ, the viewpoint dataset V and the vegetation feature matrix P are extracted. Based on the spatial relationship between the viewpoint dataset V and the vegetation feature matrix P, the optimal viewing height required for unobstructed observation point k is obtained by performing a weighted average estimate of the height, leaf density, and spatial direction angle of the vegetation within a specific angle range in front of the observation point k, and the optimal vegetation height adjustment vector Hopt is constructed.

[0033] The optimal vegetation height adjustment vector Hopt is specifically Hopt = {(Hopt(1), Hopt(1), ..., Hopt(k)) | k∈Mu};

[0034] The optimal vegetation height adjustment vector Hopt is obtained by the following calculation formula:

[0035]

[0036] Where Hopt(k) represents the optimal vegetation height adjustment vector at observation point k, P(k) represents the subset of the vegetation feature matrix P that has an occlusion relationship with observation point k, P(j)∈P(k) indicates that the vegetation feature matrix P of vegetation j belongs to the subset of the vegetation feature matrix P at observation point k, cos represents the cosine function, φ(k,j) represents the direction angle between vegetation j and observation point k, and when cos(φ(k,j))=0°, it means that vegetation j is directly in front of observation point k. When the main direction is deviated from, the direction angle φ(k,j) between vegetation j and observation point k changes adaptively, and the influence of vegetation j is synchronously and adaptively adjusted.

[0037] Preferably, S3 includes S31 and S32;

[0038] S31. Based on the obtained optimal vegetation height adjustment vector Hopt, the optimal vegetation height adjustment vector Hopt(k) at the observation point k is extracted, and fitting is performed based on the terrain characteristic vector T and the vegetation characteristic matrix P of the observation point k area to calculate the viewing obstruction index U(k) at the observation point k.

[0039] The viewing obstacle index U(k) at observation point k is obtained by the following calculation formula:

[0040]

[0041] Where U(k, j) represents the viewing obstruction index of vegetation j at observation point k, κ(k, j) represents the terrain occlusion factor, which specifically represents the additional impact of terrain slope and height difference on vegetation j at observation point k, u1, u2, and u3 represent weight factors, specifically u1 is used to balance the weight factor of height exceeding the limit, u2 is used to balance the weight factor of vegetation j density, and u3 is used to balance the weight factor of terrain occlusion, and u1+u2+u3=1. The specific value is set by the user.

[0042] Preferably, S32, based on the obtained viewing obstruction index U(k) at the observation point k, all observation points are summarized, and the specific occlusion contribution relationship between the observation point k and the vegetation j is expressed in matrix form, and the visual field obstruction matrix Um of the viewing point is obtained. The size of the visual field obstruction matrix Um is Mu*Mp, and the occlusion intensity relationship between all observation points and the vegetation is recorded;

[0043] The specific matrix form of the visual field obstruction matrix Um is as follows:

[0044]

[0045] Preferably, S4 includes S41;

[0046] S41. Extracting the occlusion contribution of vegetation j to observation point k from the obtained visual field obstruction matrix Um, and then calculating the density adjustment required for the relative occlusion intensity of vegetation j in combination with the optimal vegetation height adjustment vector Hopt, thereby optimizing the planting density of landscaping vegetation and obtaining the vegetation optimization density vector Pp.

[0047] The vegetation optimization density vector Pp is specifically Pp = {Pp(1), Pp(2), ..., Pp(j) | j∈Mp};

[0048] The vegetation optimization density vector Pp is obtained by the following calculation formula:

[0049]

[0050] Where Pp(j) represents the optimized planting density of vegetation j, Orig(j) represents the original planting density of vegetation j, which is set by the initial landscaping planning, U(k, j) represents the viewing obstacle index of vegetation j at observation point k, hp(j) represents the height of vegetation j, and Hopt(k) represents the optimal vegetation height adjustment vector at observation point k.

[0051] Preferably, S5 includes S51 and S52;

[0052] S51, combining the vegetation optimization density vector Pp, the visual field obstruction matrix Um and the optimal vegetation height adjustment vector Hopt to calculate the comprehensive visual field optimization index Q to reflect the visual field after the landscaping is improved;

[0053] The comprehensive vision optimization index Q is obtained by the following calculation formula:

[0054]

[0055] Where Mp represents the total number of vegetation, Mu represents the total number of observation points, and U(k, j) represents the viewing obstacle index of vegetation j at observation point k. represents the mean value of vegetation obstruction, represents the vegetation height deviation, (1-Pp(j)) represents the density reduction degree term, q1, q2 and q3 represent the weight coefficients of the vegetation obstacle mean, vegetation height deviation and density reduction degree term respectively, and q1+q2+q3=1. The specific value is set by the user.

[0056] Preferably, S52, comparing the obtained comprehensive field of view optimization index Q with a preset optimization threshold Qth to obtain a comparison result, and judging whether to enter iterative optimization and decision execution according to the comparison result;

[0057] The comparison results are obtained through the following comparison methods:

[0058] When the comprehensive field of view optimization index Q ≤ the optimization threshold Qth, the comparison result is passed and the iterative optimization phase is not entered. Instead, the decision execution phase is entered, including generating vegetation height adjustment suggestions based on the optimal vegetation height adjustment vector Hopt, generating vegetation density adjustment suggestions based on the vegetation optimization density vector Pp, and generating blocked observation point intervention suggestions based on the field of view obstruction matrix Um.

[0059] When the comprehensive vision optimization index Q is less than the optimization threshold Qth, it means that the comparison result is not passed, and the decision execution environment is not entered. Instead, the iterative optimization phase is entered, including executing S2 to S4 for iterative adjustment.

[0060] The present invention provides an ecological landscaping design optimization method, which has the following beneficial effects:

[0061] (1) By constructing the initial visual field feature vector FΩ, the comprehensive extraction and integration of factors affecting the visual experience are achieved. Furthermore, by constructing the optimal vegetation height adjustment vector Hopt and combining it with the visual field obstruction matrix Um of the viewing point generated by fitting, not only can the key vegetation units that actually block the view of the viewpoint be accurately identified, but the impact can also be quantified as the viewing obstruction index U(k), and the optimized vegetation optimization density vector Pp is obtained based on this, and finally the comprehensive visual field optimization index Q is calculated. The above processing process transforms the garden greening design from the traditional reliance on empirical judgment to a system optimization process based on data drive and model reasoning, effectively solving the common problems in existing garden design, such as "lack of quantitative support for visual field experience", "imbalance between greening density and landscape value", and "difficulty in dynamically judging the pros and cons of greening configuration". Compared with the traditional method that relies on manual experience, the present invention has obvious advantages in terms of computability, structural expression and improvement of visual smoothness, significantly improving the scientific nature and decision-making efficiency of garden greening design schemes.

[0062] (2) By collecting and modeling the three-dimensional terrain and multi-dimensional vegetation features in the garden greening area, a multi-dimensional structured data foundation including the terrain feature vector T, the vegetation feature matrix P and the viewpoint dataset V was constructed. In particular, by comprehensively recording the viewing range, viewpoint height and maximum viewing distance of the observation point k under different scene conditions, the viewpoint dataset V can truly reflect the actual multi-level and multi-directional visual contact paths of the crowd in the park. At the same time, by comparing the maximum visible area Atotal(k) and the initial blocked area Ablock(k), combined with the spatial distribution characteristics of vegetation and the directional occlusion relationship, an initial line of sight unobstructedness index Ω0 based on the observation point was established for the first time. This makes the unobstructedness index not only have an area geometry basis, but also have the ability to estimate the weight of the physical properties of vegetation. On this basis, by obtaining the initial field of view eigenvector FΩ, a comprehensive modeling data foundation with spatial accuracy, angular relationships and ecological parameters is provided for subsequent model calculations based on visual optimization. This breaks through the core bottlenecks of "data isolation, weak spatial correlation, and inability to quantitatively evaluate initial transparency" in traditional greening layout, and significantly improves the data integrity and scientific nature of the analysis starting point before garden design optimization.

[0063] (3) By constructing a comprehensive visual field optimization index Q, a multi-factor collaborative evaluation mechanism is realized in the process of landscape design optimization, which can simultaneously integrate the three key factors of spatial density control, occlusion intensity and height control reflected by the vegetation optimization density vector Pp, the visual field obstruction matrix Um and the optimal vegetation height adjustment vector Hopt. A data-driven stage judgment strategy is introduced into the landscape design optimization process, which not only avoids invalid and repeated manual subjective judgments, but also can enter the decision execution link or automatically trigger the iterative optimization process according to the comparison results, forming a complete closed loop of optimization → evaluation → feedback → decision-making. This mechanism significantly enhances the intelligent response capability of the landscape design method, enabling it to maintain continuous optimization capabilities and controllable goal achievement in dynamically changing or complex multi-source environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a schematic diagram of the steps of an ecological landscaping design optimization method of the present invention;

[0065] Figure 2 Schematic diagram of density change trend of some vegetation numbers after multiple rounds of optimization. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0067] Example 1

[0068] The present invention provides an ecological landscaping design optimization method, please refer to Figure 1 , including the following steps:

[0069] S1. Collect three-dimensional terrain data and existing vegetation distribution data of the garden greening to form a terrain feature vector T and a vegetation feature matrix P. Collect observation points k of the garden greening to form a viewpoint dataset V. Calculate the initial line of sight patency index Ω0. Integrate the terrain feature vector T, the vegetation feature matrix P, the viewpoint dataset V, and the initial line of sight patency index Ω0 to obtain the initial field of view feature vector FΩ.

[0070] S2, calculating the optimal visual height of each observation point k in the viewpoint dataset V based on the initial field of view feature vector FΩ, and forming the optimal vegetation height adjustment vector Hopt;

[0071] S3. Based on the obtained optimal vegetation height adjustment vector Hopt, fit it with the terrain characteristic vector T and the vegetation characteristic matrix P to calculate the viewing obstacle index U(k) of each observation point k, and after integration, obtain the visual obstacle matrix Um of the viewing point;

[0072] S4. Optimizing the vegetation planting density of the landscaping by combining the obtained visual field obstruction matrix Um and the optimal vegetation height adjustment vector Hopt to obtain the vegetation optimization density vector Pp;

[0073] S5. Calculate the comprehensive field of view optimization index Q by combining the vegetation optimization density vector Pp, the field of view obstruction matrix Um, and the optimal vegetation height adjustment vector Hopt, and compare it with the preset optimization threshold Qth to determine whether to enter iterative optimization and decision execution.

[0074] In this embodiment, the initial visual field feature vector FΩ is formed by constructing a terrain feature vector T, a vegetation feature matrix P, a viewpoint dataset V, and an initial visual field unobstruction index ΩO. This allows for the comprehensive extraction and integration of factors influencing the visual experience. Furthermore, by constructing the optimal vegetation height adjustment vector Hopt and combining it with the fitted visual field obstruction matrix Um for the viewpoint, it is possible to accurately identify key vegetation units that actually obstruct the viewpoint's line of sight and quantify this effect as a visual obstruction index U(k). This is then used to obtain the optimized vegetation density vector Pp, and ultimately calculate the comprehensive visual field optimization index Q. This process transforms landscape design from traditional reliance on empirical judgment to a systematic optimization process based on data-driven and model-based reasoning. It effectively addresses the common issues in existing landscape design, including the lack of quantitative support for visual field unobstruction, the imbalance between greening density and landscape value, and the difficulty in dynamically assessing the quality of greening configurations. In particular, the present invention realizes the automatic iterative optimization and adjustment decision output of the overall greening system through the comparison control mechanism of the comprehensive visual field optimization index Q and the optimization threshold Qth, making the garden design computable, evaluable and closed-loop adaptive control capabilities. On the basis of improving visual transparency and ornamental value, it takes into account ecological functions and planting rationality, and is highly systematic and innovative.

[0075] Compared with traditional methods that rely on manual experience, this invention has obvious advantages in computability, structural expression and visual fluency, significantly improving the scientific nature and decision-making efficiency of landscaping design plans.

[0076] Example 2

[0077] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: S1 includes S11, S12, S13 and S14;

[0078] S11, collecting three-dimensional terrain data and existing vegetation distribution data of the garden greening area in real time through lidar mapping, GIS system and remote sensing imagery, and forming a terrain feature vector T and a vegetation feature matrix P;

[0079] Wherein, the terrain feature vector T includes T={t1, t2, ..., tn}, ti represents the feature vector of terrain point i, and n represents the total number of terrain feature points;

[0080] The characteristic vector ti of terrain point i specifically includes the elevation h(ti), slope s(ti) and surface reflectivity λ(ti) at terrain point i;

[0081] The vegetation feature matrix P includes P = {hp(j), dp(j), λp(j)|j∈Mp}, where Mp represents the total number of vegetation, hp(j) represents the height of vegetation j, dp(j) represents the crown diameter of vegetation j, and λp(j) represents the leaf density of vegetation j.

[0082] S12. Collect observation points k of the landscape, including roadside, overpass, pedestrian path, viewing platform, lakeside, and rest area. Extract the hierarchical heights of observation point k, including ground view, elevated view, and long-distance view. Then extract the viewing angle range of observation point k between 30° and 120° to form the viewpoint dataset V.

[0083] The settings for ground view angle, elevated view angle and long-distance view point are usually: ground view angle (1.5m), elevated view angle (3m-5m) and long-distance view point (10m+);

[0084] Among them, observation point collection includes using laser rangefinders, GIS systems and panoramic cameras to collect the layer height, coordinates, viewing angle range and viewing distance information of observation points;

[0085] The viewpoint dataset V includes V = {((xu(k), yu(k), hu(k), Ou(k), du(k))|k∈Mu}, where (xu(k), yu(k) represents the geographic coordinates of viewpoint k, hu(k) represents the altitude of viewpoint k, Ou(k) represents the viewing direction of viewpoint k, specifically represents the main visual range of viewpoint k, du(k) represents the maximum visual distance of viewpoint k, specifically represents the farthest green area that viewpoint k can observe; Mu represents the total number of observation points.

[0086] S13. Calculate the maximum visible area Atotal(k) and the initial blocked area Ablock(k) of the observation point k based on the obtained viewpoint dataset V. By calculating the maximum visible area Atotal(k) and the initial blocked area Ablock(k), obtain the initial sight line unobstructedness index Ω0, which reflects the initial sight line unobstructedness of the entire landscaping.

[0087] The maximum visible area Atotal(k) is obtained by the following calculation formula:

[0088] Atotal(k)=π*du(k) 2 ;

[0089] Where π is the circumference constant, which is 3.14. The significance of this formula is that it simulates an ideal unobstructed visible circular area with the observation point k as the center and the maximum visible distance as the radius, which is used to establish a comparison benchmark for the subsequent "how much is blocked";

[0090] The initial occlusion area Ablock(k) is obtained by the following calculation formula:

[0091] Ablock(k)=∑ j∈Mp f(hp(j),dp(j),λp(j),Ou(k));

[0092] Where f represents a comprehensive function, which is specifically used to estimate the occlusion contribution of vegetation j to observation point k. The comprehensive function f is usually obtained by modeling and experimental data fitting, and is taken as: f = α*hp(j)*dp(j)*λp(j)*δ(Op(j), Ou(k)), Op(j) represents the angle of vegetation j, specifically the angle between the center of vegetation j and the direction of observation point k, and δ represents the view matching function, which determines whether vegetation j is within the view range of observation point k, and the output is 0 or 1. The significance of this formula is: by traversing all vegetation, combining their physical characteristics with the spatial relationship of observation point k, the actual obstructed field of view area of ​​each observation point k is estimated for subsequent patency assessment;

[0093] The initial sight line patency index Ω0 is obtained by the following calculation formula:

[0094]

[0095] Where w(k) represents the weight coefficient of observation point k, which is preset according to the flow of people and functional level at observation point k;

[0096] S14. Based on the acquired terrain feature vector T, vegetation feature matrix P, viewpoint data set V and initial sight line patency index Ω0, integration is performed to acquire the set V and the initial sight line patency index Ω0, and obtain the initial sight line patency index Ω0.

[0097] In this embodiment, by collecting and modeling three-dimensional terrain and multidimensional vegetation features in a landscaped area, a multidimensional structured data foundation was constructed, including a terrain feature vector T, a vegetation feature matrix P, and a viewpoint dataset V. In particular, by comprehensively recording the viewing angle range, viewpoint height, and maximum viewing distance of observation point k under different scene conditions (such as ground, elevated, and distant), the viewpoint dataset V can truly reflect the actual multi-level and multi-directional visual contact paths of people in the park. Simultaneously, by comparing the maximum visible area Atotal(k) with the initial blocked area Ablock(k), and combining the spatial distribution characteristics of vegetation with directional occlusion relationships, an initial visual accessibility index Ω0 based on the observation point was established for the first time. This provides an accessibility index that not only has an area geometry basis but also provides the ability to estimate the weights of vegetation physical properties. On this basis, by obtaining the initial field of view eigenvector FΩ, a comprehensive modeling data foundation with spatial accuracy, angular relationships and ecological parameters is provided for subsequent model calculations based on visual optimization. This breaks through the core bottlenecks of "data isolation, weak spatial correlation, and inability to quantitatively evaluate initial transparency" in traditional greening layout, and significantly improves the data integrity and scientific nature of the analysis starting point before garden design optimization.

[0098] Example 3

[0099] This embodiment is explained in Example 2, please refer to Figure 1 , specifically: S2 includes S21;

[0100] S21. Based on the initial field of view feature vector FΩ, the viewpoint dataset V and the vegetation feature matrix P are extracted. Based on the spatial relationship between the viewpoint dataset V and the vegetation feature matrix P, the optimal viewing height required for unobstructed observation point k is obtained by performing a weighted average estimate of the height, leaf density, and spatial direction angle of the vegetation within a specific angle range in front of the observation point k, and the optimal vegetation height adjustment vector Hopt is constructed.

[0101] The optimal vegetation height adjustment vector Hopt is specifically Hopt = {(Hopt(1), Hopt(1), ..., Hopt(k)) | k∈Mu};

[0102] The optimal vegetation height adjustment vector Hopt is obtained by the following calculation formula:

[0103]

[0104] Where Hopt(k) represents the optimal vegetation height adjustment vector at observation point k, P(k) represents the subset of the vegetation feature matrix P that has an occlusion relationship with observation point k, P(j)∈P(k) indicates that the vegetation feature matrix P of vegetation j belongs to the subset of the vegetation feature matrix P at observation point k, cos represents the cosine function, φ(k,j) represents the direction angle between vegetation j and observation point k, when cos(φ(k,j))=0°, it means that vegetation j is directly in front of observation point k. When the main direction is deviated from, the direction angle φ(k,j) between vegetation j and observation point k changes adaptively, and the influence of vegetation j is synchronously and adaptively adjusted.

[0105] Table 1. Vegetation feature matrix corresponding to observation point k. Subset P = {P1, P2, P3};

[0106]

[0107] Calculate and obtain Hopt(k)=1 / 3*{5.0*0.8*0.9659+6.0*0.6*0.8192+4.5*0.7*0.5}≈2.80;

[0108] The ideal maximum vegetation height in the area corresponding to observation point k is about 2.80.

[0109] S3 includes S31 and S32;

[0110] S31. Based on the obtained optimal vegetation height adjustment vector Hopt, the optimal vegetation height adjustment vector Hopt(k) at the observation point k is extracted, and fitting is performed based on the terrain characteristic vector T and the vegetation characteristic matrix P of the observation point k area to calculate the viewing obstruction index U(k) at the observation point k.

[0111] The viewing obstacle index U(k) at observation point k is obtained by the following calculation formula:

[0112]

[0113] Where U(k, j) represents the viewing obstruction index of vegetation j at observation point k, κ(k, j) represents the terrain occlusion factor, which specifically represents the additional impact of terrain slope and height difference on vegetation j at observation point k, u1, u2, and u3 represent weight factors, specifically u1 is used to balance the weight factor of height exceeding the limit, u2 is used to balance the weight factor of vegetation j density, and u3 is used to balance the weight factor of terrain occlusion, and u1+u2+u3=1. The specific value is set by the user.

[0114] In this embodiment, based on the spatial correlation between the viewpoint dataset V extracted from the initial field of view feature vector FΩ and the vegetation feature matrix P, a weighted fusion of vegetation height, leaf density, and directional angle within a specific angular range in front of each observation point k is performed. The resulting optimal vegetation height adjustment vector Hopt dynamically reflects the ideal unobstructed line of sight height requirements at different locations and observation angles. In particular, within the overlapping region of multi-angle visual fields, it effectively captures subtle changes in occlusion effects from different directions, thereby improving the angular resolution of regional visibility judgment. Furthermore, by jointly fitting Hopt(k) with the terrain feature vector T and the vegetation feature matrix P within the region of observation point k, a viewing obstruction index U(k) is constructed. Comprehensive evaluation factors including height deviation, vegetation leaf density, and terrain slope are introduced. This allows obstruction judgment to be refined beyond visibility within the visual range to the specific occlusion intensity under the current desired height and terrain interference conditions, significantly enhancing sensitivity to visual obstruction in micro-topography environments and complex planting structures. This process can provide a highly robust spatial occlusion quantification basis for subsequent density adjustment and optimization, solving the problem in traditional methods that it is difficult to accurately perceive the degree of local visual interference under irregular terrain and dynamic field of view, and has stronger regional adaptability and structure recognition capabilities.

[0115] Example 4

[0116] This embodiment is explained in Example 3, please refer to Figure 1 Specifically: S32, based on the obtained viewing obstruction index U(k) at the observation point k, summarize all observation points, and express the specific occlusion contribution relationship between the observation point k and the vegetation j in matrix form, obtain the visual field obstruction matrix Um of the viewing point, the size of the visual field obstruction matrix Um is Mu*Mp, and the occlusion intensity relationship between all observation points and the vegetation is recorded;

[0117] The specific matrix form of the visual field obstruction matrix Um is as follows:

[0118]

[0119] Among them, each matrix element is specifically Where (.) + If the value is less than 0, it will be taken as 0 to prevent the influence of negative numbers.

[0120] S4 includes S41;

[0121] S41. Extracting the occlusion contribution of vegetation j to observation point k from the obtained visual field obstruction matrix Um, and then calculating the density adjustment required for the relative occlusion intensity of vegetation j in combination with the optimal vegetation height adjustment vector Hopt, thereby optimizing the planting density of landscaping vegetation and obtaining the vegetation optimization density vector Pp.

[0122] The vegetation optimization density vector Pp is specifically Pp = {Pp(1), Pp(2), ..., Pp(j) | j∈Mp};

[0123] The vegetation optimization density vector Pp is obtained by the following calculation formula:

[0124]

[0125] Where Pp(j) represents the optimized planting density of vegetation j, Orig(j) represents the original planting density of vegetation j, which is set by the initial landscaping planning, U(k, j) represents the viewing obstacle index of vegetation j at observation point k, hp(j) represents the height of vegetation j, and Hopt(k) represents the optimal vegetation height adjustment vector at observation point k.

[0126] In this embodiment, based on the constructed view obstruction matrix Um, the pairwise occlusion contribution intensity U(k, j) between observation point k and vegetation j can be comprehensively recorded in a matrix structure. This not only provides fine-grained view impact data, but also breaks through the limitation of traditional garden design that only allows for global average occlusion assessment. For the first time, it achieves explicit quantification of the occlusion relationship between "multiple viewpoints → multiple vegetation", with stronger occlusion traceability and local controllability. Furthermore, by combining the height deviation relationship between the optimal vegetation height adjustment vector Hopt(k) and the actual vegetation height hp(j), and superimposing the occlusion contribution coefficient in the view obstruction matrix Um, the vegetation optimized density vector Pp is dynamically adjusted and generated based on the original planting density Orig(j). This eliminates the limitation of density optimization to the average processing of the entire area, and instead responsively adjusts the specific occlusion performance and expected height deviation of each vegetation unit, significantly improving the matching accuracy between planting density and sight quality during greening layout. This mechanism provides a comprehensive optimization method for actual garden configuration with the coordinated capabilities of interference recognition, target response, and density control, while maintaining the ecological effect of green space and achieving the differentiated control goal of local visibility.

[0127] Example 5

[0128] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: S5 includes S51 and S52;

[0129] S51, combining the vegetation optimization density vector Pp, the visual field obstruction matrix Um and the optimal vegetation height adjustment vector Hopt to calculate the comprehensive visual field optimization index Q to reflect the visual field after the landscaping is improved;

[0130] The comprehensive vision optimization index Q is obtained by the following calculation formula:

[0131]

[0132] Where Mp represents the total number of vegetation, Mu represents the total number of observation points, and U(k, j) represents the viewing obstacle index of vegetation j at observation point k. Indicates the mean value of vegetation obstruction, specifically indicating the negative impact of the vegetation on the overall vision of the park. It represents the vegetation height deviation, specifically whether the overall height of the vegetation is still higher than the recommended value at different viewpoints. The larger the value, the more likely the tree is "too tall". (1-Pp(j)) represents the density reduction degree item. Specifically, if the density has been adjusted to the minimum (such as 0), this item is 1, indicating that the adjustment is in place. If it is still the original density (such as 1), this item is 0, indicating that no adjustment has been made. q1, q2, and q3 represent the weight coefficients of the vegetation obstacle mean, vegetation height deviation, and density reduction degree items, respectively, and q1+q2+q3=1. The specific value is set by the user.

[0133] S52, comparing the obtained comprehensive field of view optimization index Q with a preset optimization threshold Qth to obtain a comparison result, and determining whether to enter iterative optimization and decision execution based on the comparison result;

[0134] The comparison results are obtained through the following comparison methods:

[0135] When the comprehensive field of view optimization index Q ≤ the optimization threshold Qth, the comparison result is passed and the iterative optimization phase is not entered. Instead, the decision execution phase is entered, including generating vegetation height adjustment suggestions based on the optimal vegetation height adjustment vector Hopt, generating vegetation density adjustment suggestions based on the vegetation optimization density vector Pp, and generating blocked observation point intervention suggestions based on the field of view obstruction matrix Um.

[0136] When the comprehensive vision optimization index Q is less than the optimization threshold Qth, it means that the comparison result is not passed, and the decision execution environment is not entered. Instead, the iterative optimization phase is entered, including executing S2 to S4 for iterative adjustment.

[0137] In this embodiment, the constructed comprehensive view optimization index Q implements a multi-factor collaborative evaluation mechanism for landscape design optimization. This mechanism simultaneously integrates three key factors: spatial density control, occlusion intensity, and height control, as reflected by the vegetation optimization density vector Pp, the view obstruction matrix Um, and the optimal vegetation height adjustment vector Hopt. The comprehensive view optimization index Q, composed of the vegetation obstruction mean, vegetation height deviation, and density reduction, not only accurately reflects the overall improvement in the park's view but also allows for flexible configuration of weight coefficients q1, q2, and q3 to accommodate different design objectives, such as transparency, aesthetics, or ecological coverage. This enhances the method's parameter control adaptability. Furthermore, the automatic comparison mechanism between the comprehensive view optimization index Q and the optimization threshold Qth introduces a data-driven stage-by-stage decision-making strategy for the landscape design optimization process. This not only avoids ineffective and repetitive manual subjective judgments but also enables the decision execution phase or automatically triggers the iterative optimization process based on the comparison results, forming a complete closed loop of optimization → evaluation → feedback → decision-making. This mechanism significantly enhances the intelligent response capability of landscaping design methods, enabling them to maintain continuous optimization capabilities and controllability of target achievement in dynamically changing or complex multi-source environments.

[0138] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An ecological landscaping design optimization method, characterized by: The following steps are involved: S1. Collect three-dimensional terrain data and existing vegetation distribution data of the garden greening to form a terrain feature vector T and a vegetation feature matrix P. Collect observation points k of the garden greening to form a viewpoint dataset V. Calculate the initial line of sight patency index Ω0. Integrate the terrain feature vector T, the vegetation feature matrix P, the viewpoint dataset V, and the initial line of sight patency index Ω0 to obtain the initial field of view feature vector FΩ. S2, calculating the optimal visual height of each observation point k in the viewpoint dataset V based on the initial field of view feature vector FΩ, and forming the optimal vegetation height adjustment vector Hopt; S3. Based on the obtained optimal vegetation height adjustment vector Hopt, fit it with the terrain characteristic vector T and the vegetation characteristic matrix P to calculate the viewing obstacle index U(k) of each observation point k, and after integration, obtain the visual obstacle matrix Um of the viewing point; S4. Optimizing the vegetation planting density of the landscaping by combining the obtained visual field obstruction matrix Um and the optimal vegetation height adjustment vector Hopt to obtain the vegetation optimization density vector Pp; S5. Calculate the comprehensive field of view optimization index Q by combining the vegetation optimization density vector Pp, the field of view obstruction matrix Um, and the optimal vegetation height adjustment vector Hopt, and compare it with the preset optimization threshold Qth to determine whether to enter iterative optimization and decision execution.

2. The ecological landscaping design optimization method according to claim 1, characterized in that: S1 includes S11, S12, S13 and S14; S11, collecting three-dimensional terrain data and existing vegetation distribution data of the garden greening area in real time through lidar mapping, GIS system and remote sensing imagery, and forming a terrain feature vector T and a vegetation feature matrix P; Wherein, the terrain feature vector T includes T={t1, t2, ..., tn}, ti represents the feature vector of terrain point i, and n represents the total number of terrain feature points; The characteristic vector ti of terrain point i specifically includes the elevation h(ti), slope s(ti) and surface reflectivity λ(ti) at terrain point i; The vegetation feature matrix P includes P = {hp(j), dp(j), λp(j)|j∈Mp}, where Mp represents the total number of vegetation, hp(j) represents the height of vegetation j, dp(j) represents the crown diameter of vegetation j, and λp(j) represents the leaf density of vegetation j.

3. The ecological landscaping design optimization method according to claim 2, characterized in that: S12. Collect observation points k of the landscape, including roadside, overpass, pedestrian path, viewing platform, lakeside, and rest area. Extract the hierarchical heights of observation point k, including ground view, elevated view, and long-distance view. Then extract the viewing angle range of observation point k between 30° and 120° to form the viewpoint dataset V. Among them, observation point collection includes using laser rangefinders, GIS systems and panoramic cameras to collect the layer height, coordinates, viewing angle range and viewing distance information of observation points; The viewpoint dataset V includes V = {((xu(k), yu(k), hu(k), Ou(k), du(k))|k∈Mu}, where (xu(k), yu(k) represent the geographic coordinates of viewpoint k, hu(k) represents the height of viewpoint k, Ou(k) represents the viewing direction of viewpoint k, du(k) represents the maximum viewing distance of viewpoint k; Mu represents the total number of observation points.

4. The ecological landscaping design optimization method according to claim 3, characterized in that: S13. Calculate the maximum visible area Atotal(k) and the initial blocked area Ablock(k) of the observation point k based on the obtained viewpoint dataset V. By calculating the maximum visible area Atotal(k) and the initial blocked area Ablock(k), obtain the initial sight line unobstructedness index Ω0, which reflects the initial sight line unobstructedness of the entire landscaping. The maximum visible area Atotal(k) is obtained by the following calculation formula: Atotal(k)=π*du(k) 2 ; Where, π represents the circumference constant, which is 3.14; The initial occlusion area Ablock(k) is obtained by the following calculation formula: Ablock(k)=∑ j∈Mp f(hp(j),dp(j),λp(j),Ou(k)); Where f represents the comprehensive function, which is used to estimate the occlusion contribution of vegetation j to observation point k; The initial sight line patency index ΩO is obtained by the following calculation formula: Where w(k) represents the weight coefficient of observation point k, which is preset according to the flow of people and functional level at observation point k; S14. Based on the acquired terrain feature vector T, vegetation feature matrix P, viewpoint data set V and initial sight line patency index Ω0, integration is performed to acquire the set V and the initial sight line patency index Ω0, and obtain the initial sight line patency index Ω0.

5. The ecological landscaping design optimization method according to claim 4 is characterized by: S2 includes S21; S21. Based on the initial field of view feature vector FΩ, the viewpoint dataset V and the vegetation feature matrix P are extracted. Based on the spatial relationship between the viewpoint dataset V and the vegetation feature matrix P, the optimal viewing height required for unobstructed observation point k is obtained by performing a weighted average estimate of the height, leaf density, and spatial direction angle of the vegetation within a specific angle range in front of the observation point k, and the optimal vegetation height adjustment vector Hopt is constructed. The optimal vegetation height adjustment vector Hopt is specifically Hopt = {(Hopt(1), Hopt(1), ..., Hopt(k)) | k∈Mu}; The optimal vegetation height adjustment vector Hopt is obtained by the following calculation formula: Where Hopt(k) represents the optimal vegetation height adjustment vector at observation point k, P(k) represents the subset of the vegetation feature matrix P that has an occlusion relationship with observation point k, P(j)∈P(k) indicates that the vegetation feature matrix P of vegetation j belongs to the subset of the vegetation feature matrix P at observation point k, cos represents the cosine function, φ(k,j) represents the direction angle between vegetation j and observation point k, and when cos(φ(k,j))=0°, it means that vegetation j is directly in front of observation point k. When the main direction is deviated from, the direction angle φ(k,j) between vegetation j and observation point k changes adaptively, and the influence of vegetation j is synchronously and adaptively adjusted.

6. The ecological landscaping design optimization method according to claim 5, characterized in that: S3 includes S31 and S32; S31. Based on the obtained optimal vegetation height adjustment vector Hopt, the optimal vegetation height adjustment vector Hopt(k) at the observation point k is extracted, and fitting is performed based on the terrain characteristic vector T and the vegetation characteristic matrix P of the observation point k area to calculate the viewing obstruction index U(k) at the observation point k. The viewing obstacle index U(k) at observation point k is obtained by the following calculation formula: Where U(k, j) represents the viewing obstruction index of vegetation j at observation point k, κ(k, j) represents the terrain occlusion factor, which specifically represents the additional impact of terrain slope and height difference on vegetation j at observation point k, u1, u2, and u3 represent weight factors, specifically u1 is used to balance the weight factor of height exceeding the limit, u2 is used to balance the weight factor of vegetation j density, and u3 is used to balance the weight factor of terrain occlusion, and u1+u2+u3=1. The specific value is set by the user.

7. The ecological landscaping design optimization method according to claim 6, characterized in that: S32. Based on the obtained view obstruction index U(k) at observation point k, summarize all observation points and express the specific occlusion contribution relationship between observation point k and vegetation j in matrix form to obtain the view obstruction matrix Um of the view point. The size of the view obstruction matrix Um is Mu*Mp, and the occlusion intensity relationship between all observation points and vegetation is recorded; The specific matrix form of the visual field obstruction matrix Um is as follows:

8. The ecological landscaping design optimization method according to claim 7, characterized in that: S4 includes S41; S41. Extracting the occlusion contribution of vegetation j to observation point k from the obtained visual field obstruction matrix Um, and then calculating the density adjustment required for the relative occlusion intensity of vegetation j in combination with the optimal vegetation height adjustment vector Hopt, thereby optimizing the planting density of landscaping vegetation and obtaining the vegetation optimization density vector Pp. The vegetation optimization density vector Pp is specifically Pp = {Pp(1), Pp(2), ..., Pp(j) | j∈Mp}; The vegetation optimization density vector Pp is obtained by the following calculation formula: Where Pp(j) represents the optimized planting density of vegetation j, Orig(j) represents the original planting density of vegetation j, which is set by the initial landscaping planning, U(k, j) represents the viewing obstacle index of vegetation j at observation point k, hp(j) represents the height of vegetation j, and Hopt(k) represents the optimal vegetation height adjustment vector at observation point k.

9. The ecological landscaping design optimization method according to claim 8, characterized in that: S5 includes S51 and S52; S51, combining the vegetation optimization density vector Pp, the visual field obstruction matrix Um and the optimal vegetation height adjustment vector Hopt to calculate the comprehensive visual field optimization index Q to reflect the visual field after the landscaping is improved; The comprehensive vision optimization index Q is obtained by the following calculation formula: Where Mp represents the total number of vegetation, Mu represents the total number of observation points, and U(k, j) represents the viewing obstacle index of vegetation j at observation point k. represents the mean value of vegetation obstruction, represents the vegetation height deviation, (1-Pp(j)) represents the density reduction degree term, q1, q2 and q3 represent the weight coefficients of the vegetation obstacle mean, vegetation height deviation and density reduction degree term respectively, and q1+q2+q3=1. The specific value is set by the user.

10. The ecological landscaping design optimization method according to claim 9, characterized in that: S52, comparing the obtained comprehensive field of view optimization index Q with a preset optimization threshold Qth to obtain a comparison result, and determining whether to enter iterative optimization and decision execution based on the comparison result; The comparison results are obtained through the following comparison methods: When the comprehensive field of view optimization index Q ≤ the optimization threshold Qth, the comparison result is passed and the iterative optimization phase is not entered. Instead, the decision execution phase is entered, including generating vegetation height adjustment suggestions based on the optimal vegetation height adjustment vector Hopt, generating vegetation density adjustment suggestions based on the vegetation optimization density vector Pp, and generating blocked observation point intervention suggestions based on the field of view obstruction matrix Um. When the comprehensive vision optimization index Q is less than the optimization threshold Qth, it means that the comparison result is not passed, and the decision execution environment is not entered. Instead, the iterative optimization phase is entered, including executing S2 to S4 for iterative adjustment.

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