1. bookAHEAD OF PRINT
Dettagli della rivista
License
Formato
Rivista
eISSN
2444-8656
Prima pubblicazione
01 Jan 2016
Frequenza di pubblicazione
2 volte all'anno
Lingue
Inglese
access type Accesso libero

The Spatial Form of Digital Nonlinear Landscape Architecture Design Based on Computer Big Data

Pubblicato online: 15 Dec 2021
Volume & Edizione: AHEAD OF PRINT
Pagine: -
Ricevuto: 16 Jun 2021
Accettato: 24 Sep 2021
Dettagli della rivista
License
Formato
Rivista
eISSN
2444-8656
Prima pubblicazione
01 Jan 2016
Frequenza di pubblicazione
2 volte all'anno
Lingue
Inglese
Abstract

Condensing the multi-dimensional digital model of green urban design, and constructing a digital method system of it progressively layer by layer. Based on this research background, the dissertation designs the spatial form of landscape architecture based on the data visualisation of nonlinear technology. The article uses the colour zoning method to design the actual scene of the garden landscape with nonlinear parameteriszation. The simulation result analyses that the proposed nonlinear algorithm has realised the efficiency improvement purpose of landscape architecture design.

Keywords

MSC 2010

Introduction

Before the emergence of landscape ecology, the status of landscape architecture belonged to architecture. Landscape design serves the construction and provides a place for leisure and entertainment in the crowded city. With the emergence of landscape ecology, the discipline of landscape architecture has gradually moved towards a self-independent development path. Landscape architecture in this period went hand in hand with architecture and urban planning, and each developed in its professional field [1]. This separation of disciplines destroys the diversity of places and humans to a certain extent. Due to the fragmentation of disciplines in the Chinese context, part of the landscape design is still attached to architecture, and the landscape is fragmented and homogeneous. Design pursues single issues such as form, function, style, and ecology within the discipline, and the limitations brought about by the separation of disciplines are becoming increasingly apparent.

Nonlinear science is regarded as “another revolution” in the history of science in the 20th century. It has almost spread to all fields of natural sciences, humanities, and social sciences and is changing people's traditional views of the real world. The scientific community believes that the research of nonlinear science has great scientific significance, and nonlinearity is the source of complexity and variety in nature [2]. The development of nonlinear science has also caused profound changes in philosophy and methodology. Our contribution to its exploration is creating a new subject field. Still, the more far-reaching significance is that it has brought about a significant change in the outlook on nature, the outlook on science, the methodology, and even thinking. Nonlinear interaction has a more intrinsic and essential meaning than linear interaction. Linear interaction is only a highly simplified and approximate treatment of nonlinear interaction. Information technology closely related to nonlinear science is developing rapidly, making today's society a transformation from a post-industry to an information society. Information technology is based on computer, communication and control technology. In recent years, information technology has developed database technology, artificial intelligence, expert system, remote sensing technology, geographic information system, global positioning system, computer-aided decision-making system, automatic control technology, monitoring technology, multimedia technology, computer network technology and other applications. And these technologies have widely influenced all aspects of people's lives.

With the continuous advancement of ecological landscape construction and improved living environment, people's quality standards for landscape construction are constantly improving. So we need to carry out multi-dimensional nonlinear landscape design. Combining the functional positioning of the landscape to optimise the structure of multi-dimensional nonlinear landscape design can improve its urban design. The multi-dimensional nonlinear landscape design method is of great significance in improving the rationality of architectural design and improving the living environment of human settlements [3]. The planning and design of landscape construction based on its development orientationand government policy-oriented factors can help us improve the spatial expression ability. The article uses computer vision with structured image analysis and parameter simulation methods to optimise multi-dimensional design. This paper proposes a multi-dimensional nonlinear landscape design method based on parameterised models to improve the quantitative analysis ability.

Multi-dimensional nonlinear landscape image analysis
Multi-dimensional nonlinear landscape image sampling

To achieve a multi-dimensional nonlinear landscape design, we need to perform model parameter analysis and feature extraction. We construct a dynamic feature distribution model of multi-dimensional nonlinear landscape images through block matching detection and fusion recognition of multi-dimensional nonlinear landscape images [4]. The article combines the fuzzy associated feature point detection method to optimise the extraction of multi-dimensional nonlinear landscape data. We combine fuzzy feature extraction methods to optimise the collection and feature recognition of multi-dimensional nonlinear landscape images. This article uses the pixel feature point area reconstruction method for multi-dimensional nonlinear landscape image processing and information fusion. The geometric invariant moment generation model for constructing a multi-dimensional nonlinear landscape image is Dif(C1,C2)=minυiC1,υjC2,(υi,υj)Eω[(υi,υj)+FE] Dif({C_1},{C_2}) = \mathop {\min }\limits_{{\upsilon _i} \in {C_1},{\upsilon _j} \in {C_2},({\upsilon _i},{\upsilon _j}) \in E} \omega [({\upsilon _i},{\upsilon _j}) + {F_E}] FE=lnIlnD {F_E} = {{\ln I} \over {\ln D}} represents the sampling point of multi-dimensional nonlinear landscape image features. We use the virtual scene space vision planning method to simulate multi-dimensional nonlinear landscape design parameters to construct the spatial regional distribution model of multi-dimensional nonlinear landscape images [5]. Multidimensional nonlinear landscape regional fusion is performed in the gradient direction and combined with the template matching method to obtain the edge information of the multi-dimensional nonlinear landscape image as: Gx(x,y,t)=u(x,y,t)x {G_x}(x,y,t) = {{\partial u(x,y,t)} \over {\partial x}} Gy(x,y,t)=u(x,y,t)y {G_y}(x,y,t) = {{\partial u(x,y,t)} \over {\partial y}} We decompose the edge information of the landscape image into two components along the gradient direction. The article uses Xi,j to represent the edge pixel distribution set of the rational distribution of the landscape at the central pixel point (i, j). At the same time, we use the visual feature distributed reconstruction method to make multi-dimensional nonlinear landscape parameterised design decisions [6]. The length L = xmaxxmin, widthW = ymaxymin, and height H = zmaxzmin of the landscape area distribution. We use the template block region matching method, traverse all sub-blocks, and combine high-resolution information fusion technology to simulate multi-dimensional nonlinear landscape parameters to obtain the scale-space: Mi,j=med(Xi1,j1Xi,jXi+1,j+1) {M_{i,j}} = med({X_{i - 1,j - 1}} \cdots {X_{i,j}} \cdots {X_{i + 1,j + 1}}) Then there are: Fi,j=(1,|Xi,jMi,j|T0,|Xi,jMi,j|<T {F_{i,j}} = \left( {\matrix{ {1,} \hfill & {|{X_{i,j}} - {M_{i,j}}| \ge T} \hfill \cr {0,} \hfill & {|{X_{i,j}} - {M_{i,j}}| < T} \hfill \cr } } \right. The article combines the frame matching method to perform multi-dimensional nonlinear landscape block detection and feature matching, and the inter-frame pixel set is Ic. The adjacent frame is denoted as NFc = {n : cknc+k}. We established a multi-dimensional nonlinear landscape parameter template matching model to obtain the plane pheromone of nonlinear landscape design as G(x, y, t). among them: u(x,y,t)=G(x,y,t) u(x,y,t) = G(x,y,t) Multi-dimensional nonlinear landscape image sampling in multi-scale space. We perform nonlinear landscape design based on the image sampling results.

Parametric simulation of multi-dimensional nonlinear landscape design

The thesis uses the virtual space visual planning method to simulate multi-dimensional nonlinear landscape design [7]. Combining the distribution characteristics of each pixel to perform multi-dimensional nonlinear landscape image similarity analysis, the expression of the spatial and regional feature matching model for landscape design is: F=p˜(x,y)=p(x,y)(υ(x)υ(y))1/2 F = \widetilde p(x,y) = p(x,y){\left( {{{\upsilon (x)} \over {\upsilon (y)}}} \right)^{1/2}} The matrix X is used to express the neighboring phase points and establish the entropy weight feature distribution set to realise the parameterised design of a multi-dimensional nonlinear landscape. We obtain the pixel covariance function of the multi-dimensional nonlinear landscape under the multi-scale eigendecomposition mode as: p(x,y)=k(x,y)υ(x)υ(x,y)=yk(x,y) \matrix{ {p(x,y) = {{k(x,y)} \over {\upsilon (x)}}} \hfill \cr {\upsilon (x,y) = \sum\limits_y k(x,y)} \hfill \cr } We take the pixel point i as the centre to perform the affine invariant region segmentation of the multi-dimensional nonlinear landscape image [8]. According to the number of observations in the training set, the training function satisfies 0 ≤ ω(i, j) ≤ 1 and jΩω(i,j)=1 \sum\limits_{j \in \Omega } \omega (i,j) = 1 . Initialise the prior shape to obtain the parameter distribution matrix of the multi-dimensional nonlinear landscape design: D=[Ix2IxIyIxIyIy2] D = \left[ {\matrix{ {I_x^2} & {{I_x}{I_y}} \cr {{I_x}{I_y}} & {I_y^2} \cr } } \right] Based on the parameter simulation results of multi-dimensional nonlinear landscape design, regional reconstruction is carried out. The parameter vectorisation feature extraction of multi-dimensional nonlinear landscape design is carried out by combining the RGB feature decomposition method.

Multi-dimensional nonlinear landscape design optimisation

We combined the RGB feature decomposition method to perform parameterised segmentation of multi-dimensional nonlinear landscape models [9]. The thesis extracts the fuzzy degree matching feature quantity of the multi-dimensional nonlinear landscape image. The estimated value of the parameters of the salient feature points in the inner and outer areas of the target edge of the landscape image is: NLM[g(i)]=jΩω(i,j)g(j) NLM[g(i)] = \sum\limits_{j \in \Omega } \omega (i,j)g(j) Based on the idea of metric learning, the three-dimensional feature reconstruction parameters of multi-dimensional nonlinear landscape images are uniformly distributed [10]. That is, the pixel sequence satisfies nN(0,σn2) n \in N(0,\sigma _n^2) . The paper uses a parameterised model for structural analysis to find the second moment of multi-dimensional nonlinear landscape design: μpq=m=1Mn=1N(xx.)p(yy.)qf(x,y) {\mu _{pq}} = \sum\limits_{m = 1}^M \sum\limits_{n = 1}^N {(x - \mathop x\limits^. )^p}{(y - \mathop y\limits^. )^q}f(x,y) We use first-order moments m01 and m02 to denote the edge blur characteristics of multi-dimensional nonlinear landscape images, respectively [11]. We use the block fusion technology to obtain the edge template area of the landscape design that satisfies the normal distribution in the nc × nr sub-blocks to obtain the fine-grained feature point extraction results of the landscape image: I1=n20n02n112n004 {I_1} = {{{n_{20}}{n_{02}} - n_{11}^2} \over {n_{00}^4}} Emij=k=0255emkij E_m^{ij} = \sum\limits_{k = 0}^{255} e_{mk}^{ij} emkij={pklogpkpk00,pk=0 e_{mk}^{ij} = \left\{ {\matrix{ { - {p_k}\log {p_k}} & {{p_k} \ne 0} \cr {0,} & {{p_k} = 0} \cr } } \right. pk is the vectorised dimension of the spatial data of the landscape image; m = 1, 2,···, N. We extract parameterised models of multi-dimensional nonlinear landscape views [12]. The vector quantisation feature set of the multi-dimensional nonlinear landscape image obtained based on the parameterised model method to achieve the multi-dimensional nonlinear landscape design is: pij(A)={ωijωi,ijeijA0ijeijA1j:eijAωijωi,i=j {p_{ij}}(A) = \left\{ {\matrix{ {{{{\omega _{ij}}} \over {{\omega _i}}},} & {i \ne j{e_{ij}} \in A} \cr 0 & {i \ne j{e_{ij}} \in A} \cr {1 - {{\sum\limits_{j:{e_{ij}} \in A} {\omega _{ij}}} \over {{\omega _i}}},} & {i = j} \cr } } \right. We use the block template matching method to perform the parameterized segmentation of the multi-dimensional: x(k)=[x1(k),x2(k),,xm(k)],i=1,2,,m x(k) = [{x_1}(k),{x_2}(k), \cdots ,{x_m}(k)],i = 1,2, \cdots ,m k is the sampling node of multi-dimensional nonlinear landscape data; xi(k) is the data stream. The realisation process of the whole model is shown in Figure 1.

Fig. 1

Landscape design implementation process

Experimental test analysis

The experiment adopts a Matlab simulation design. The sample pixel scale of landscape images is 100~ 600. The pixel distribution of the landscape image is 300 × 300. The modulus of metric learning is 100. The number of iterations is 1200. The descriptive statistical analysis results of the correlation parameters are shown in Table 1.

Descriptive statistical analysis results

Number of samples Landscape relevance Similarity coefficient Contribution level Blur factor
100 0.723 0.456 0.775 0.532
200 0.434 0.556 0.565 0.545
300 0.531 0.434 0.454 0.554
400 0.534 0.643 0.554 0.545
500 0.422 0.434 0.324 0.544
600 0.345 0.454 0.455 0.434

According to the descriptive statistical analysis results in Table 1, multi-dimensional nonlinear landscape image sampling is performed. We use the virtual scene space visual planning method to simulate multi-dimensional nonlinear landscape design parameters to achieve landscape optimisation design [13]. The design effect results are shown in Figures 2–4. Analysing Figures 2–4, we know that our multi-dimensional nonlinear landscape design method has a better effect.

Fig. 2

The design results of the park landscape parameterised structural features

Fig. 3

Parameterised results of architectural landscape design

Fig. 4

Result of parameter design of a country house

The test regression analysis value and test value, and other parameter results are shown in Table 2. Analysing Table 2 shows that we adopt the method of this paper to carry out multi-dimensional nonlinear landscape design with better visual feature expression ability. The parameter distribution structured fitting accuracy is high.

Regression analysis value and test value

Variable name Mean Standard value F test value
Landscape planning structure 0.456 1.432 0.456
Habitat satisfaction level 0754 1.544 0.656
Environmental risk factor 0.467 1.676 0.265
Ecological improvement level 0.435 1.545 0.645
Landscape construction scale 0.567 0.545 0.367
Decision evaluation value 0.365 0.567 0.655
Overall planning configuration efficiency 0.545 0.545 0.345
Regression standard deviation 0.567 0.753 0.655
Conclusion

This paper proposes a multi-dimensional nonlinear landscape design method based on a parameterised model to extract a multi-dimensional nonlinear landscape view model. We use parameteried model methods to achieve multi-dimensional lnonlinear landscape design. The article uses the computer vision image analysis method to carry out multi-dimensional nonlinear landscape design and constructs a parametric analysis model of multi-dimensional nonlinear landscape design. We combine fuzzy feature extraction methods to optimise the collection and feature recognition of multi-dimensional nonlinear landscape images. The analysis shows that we have a solid ability to express visual characteristics of multi-dimensional nonlinear landscape design using this method. Moreover, the landscape design effect obtained by this method is better.

Fig. 1

Landscape design implementation process
Landscape design implementation process

Fig. 2

The design results of the park landscape parameterised structural features
The design results of the park landscape parameterised structural features

Fig. 3

Parameterised results of architectural landscape design
Parameterised results of architectural landscape design

Fig. 4

Result of parameter design of a country house
Result of parameter design of a country house

Regression analysis value and test value

Variable name Mean Standard value F test value
Landscape planning structure 0.456 1.432 0.456
Habitat satisfaction level 0754 1.544 0.656
Environmental risk factor 0.467 1.676 0.265
Ecological improvement level 0.435 1.545 0.645
Landscape construction scale 0.567 0.545 0.367
Decision evaluation value 0.365 0.567 0.655
Overall planning configuration efficiency 0.545 0.545 0.345
Regression standard deviation 0.567 0.753 0.655

Descriptive statistical analysis results

Number of samples Landscape relevance Similarity coefficient Contribution level Blur factor
100 0.723 0.456 0.775 0.532
200 0.434 0.556 0.565 0.545
300 0.531 0.434 0.454 0.554
400 0.534 0.643 0.554 0.545
500 0.422 0.434 0.324 0.544
600 0.345 0.454 0.455 0.434

Giblin, B., Cataneo, M., Moews, B., & Heymans, C. On the road to per cent accuracy–II. Calibration of the nonlinear matter power spectrum for arbitrary cosmologies. Monthly Notices of the Royal Astronomical Society., 2019. 490(4): 4826–4840 GiblinB. CataneoM. MoewsB. HeymansC. On the road to per cent accuracy–II. Calibration of the nonlinear matter power spectrum for arbitrary cosmologies Monthly Notices of the Royal Astronomical Society 2019 490 4 4826 4840 10.1093/mnras/stz2659 Search in Google Scholar

Scaife, C. I., Singh, N. K., Emanuel, R. E., Miniat, C. F., & Band, L. E. Non-linear quickflow response as indicators of runoff generation mechanisms. Hydrological Processes., 2020. 34(13): 2949–2964 ScaifeC. I. SinghN. K. EmanuelR. E. MiniatC. F. BandL. E. Non-linear quickflow response as indicators of runoff generation mechanisms Hydrological Processes 2020 34 13 2949 2964 10.1002/hyp.13780 Search in Google Scholar

Scaife, C. I., Singh, N. K., Emanuel, R. E., Miniat, C. F., & Band, L. E. Non-linear quickflow response as indicators of runoff generation mechanisms. Hydrological Processes., 2020. 34(13): 2949–2964 ScaifeC. I. SinghN. K. EmanuelR. E. MiniatC. F. BandL. E. Non-linear quickflow response as indicators of runoff generation mechanisms Hydrological Processes 2020 34 13 2949 2964 10.1002/hyp.13780 Search in Google Scholar

Kimberley, A., Bullock, J. M., & Cousins, S. A. Unbalanced species losses and gains lead to nonlinear trajectories as grasslands become forests. Journal of Vegetation Science., 2019. 30(6): 1089–1098 KimberleyA. BullockJ. M. CousinsS. A. Unbalanced species losses and gains lead to nonlinear trajectories as grasslands become forests Journal of Vegetation Science 2019 30 6 1089 1098 10.1111/jvs.12812 Search in Google Scholar

Sulaiman, T., Bulut, H. & Atas, S. Optical solitons to the fractional Schrödinger-Hirota equation. Applied Mathematics and Nonlinear Sciences., 2019. 4(2): 535–542 SulaimanT. BulutH. AtasS. Optical solitons to the fractional Schrödinger-Hirota equation Applied Mathematics and Nonlinear Sciences 2019 4 2 535 542 10.2478/AMNS.2019.2.00050 Search in Google Scholar

Kaur, D., Agarwal, P., Rakshit, M. & Chand, M. Fractional Calculus involving (p, q)-Mathieu Type Series. Applied Mathematics and Nonlinear Sciences., 2020.5(2):15–34. KaurD. AgarwalP. RakshitM. ChandM. Fractional Calculus involving (p, q)-Mathieu Type Series Applied Mathematics and Nonlinear Sciences 2020 5 2 15 34. 10.2478/amns.2020.2.00011 Search in Google Scholar

Chon, H., & Sim, J. From design thinking to design knowing: An educational perspective. Art, Design & Communication in Higher Education., 2019. 18(2): 187–200 ChonH. SimJ. From design thinking to design knowing: An educational perspective Art, Design & Communication in Higher Education 2019 18 2 187 200 10.1386/adch_00006_1 Search in Google Scholar

Toubeau, J. F., Iassinovski, S., Jean, E., Parfait, J. Y., Bottieau, J., De Grève, Z., & Vallée, F. Nonlinear hybrid approach for the scheduling of merchant underground pumped hydro energy storage. IET Generation, Transmission & Distribution., 2019. 13(21): 4798–4808 ToubeauJ. F. IassinovskiS. JeanE. ParfaitJ. Y. BottieauJ. De GrèveZ. ValléeF. Nonlinear hybrid approach for the scheduling of merchant underground pumped hydro energy storage IET Generation, Transmission & Distribution 2019 13 21 4798 4808 10.1049/iet-gtd.2019.0204 Search in Google Scholar

Abramczyk, H., Brozek-Pluska, B., Jarota, A., Surmacki, J., Imiela, A., & Kopec, M. A look into the use of Raman spectroscopy for brain and breast cancer diagnostics: linear and nonlinear optics in cancer research as a gateway to tumor cell identity. Expert review of molecular diagnostics., 2020. 20(1): 99–115 AbramczykH. Brozek-PluskaB. JarotaA. SurmackiJ. ImielaA. KopecM. A look into the use of Raman spectroscopy for brain and breast cancer diagnostics: linear and nonlinear optics in cancer research as a gateway to tumor cell identity Expert review of molecular diagnostics 2020 20 1 99 115 10.1080/14737159.2020.1724092 Search in Google Scholar

Alam, M. N. K., Roussel, P., Heyns, M., & Van Houdt, J. Positive nonlinear capacitance: The origin of the steep subthreshold-slope in ferroelectric FETs. Scientific reports., 2019. 9(1): 1–9 AlamM. N. K. RousselP. HeynsM. Van HoudtJ. Positive nonlinear capacitance: The origin of the steep subthreshold-slope in ferroelectric FETs Scientific reports 2019 9 1 1 9 10.1038/s41598-019-51237-2 Search in Google Scholar

Callaghan, C. T., Bino, G., Major, R. E., Martin, J. M., Lyons, M. B., & Kingsford, R. T. Heterogeneous urban green areas are bird diversity hotspots: insights using continental-scale citizen science data. Landscape Ecology., 2019.34(6): 1231–1246 CallaghanC. T. BinoG. MajorR. E. MartinJ. M. LyonsM. B. KingsfordR. T. Heterogeneous urban green areas are bird diversity hotspots: insights using continental-scale citizen science data Landscape Ecology 2019 34 6 1231 1246 10.1007/s10980-019-00851-6 Search in Google Scholar

Heidari, A., Esposito, J., & Caissutti, A. The quantum entanglement dynamics induced by non–linear interaction between a moving nano molecule and a two–mode field with two–photon transitions using reduced von neumann entropy and jaynes–cummings model for human cancer cells, tissues and tumors diagnosis. Int J Crit Care Emerg Med., 2019.5(2): 071–084 HeidariA. EspositoJ. CaissuttiA. The quantum entanglement dynamics induced by non–linear interaction between a moving nano molecule and a two–mode field with two–photon transitions using reduced von neumann entropy and jaynes–cummings model for human cancer cells, tissues and tumors diagnosis Int J Crit Care Emerg Med 2019 5 2 071 084 10.23937/2474-3674/1510071 Search in Google Scholar

Maier, R., & Robson, K. Exploring university-to-college transfer in Ontario: A qualitative study of nonlinear post-secondary mobility. Canadian Journal of Higher Education/Revue canadienne d’enseignement supérieur., 2020. 50(1): 82–94 MaierR. RobsonK. Exploring university-to-college transfer in Ontario: A qualitative study of nonlinear post-secondary mobility Canadian Journal of Higher Education/Revue canadienne d’enseignement supérieur 2020 50 1 82 94 10.47678/cjhe.v50i1.188609 Search in Google Scholar

Articoli consigliati da Trend MD

Pianifica la tua conferenza remota con Sciendo