# Application of Computer Simulation Analysis in Green City Garden Plant Landscape Design

Authors: Jing Zhang
Journal: Scientific Programming
 (2021)

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## Abstract

As an important component of urban landscape elements, garden plants have the role of improving urban ecological environment and forming landscape effects. Digital landscape is a high-simulation model of the system combined with computer hardware and software systems, which allow analysis of the application of computer simulation in the analysis of the value of urban landscape design. In this paper, we propose a “Task-Crowd-Process-Evaluation” computer simulation analysis framework to support management decision, which can form 2D or 3D spatial data, realize real-time and statistical analysis, make use of the multidimensional, efficient, and humanized human living environment, and make the environment landscape planning more reasonable and practical. The simulation shows that the effective use of urban vegetation can not only beautify the urban landscape, but also play the function of rainwater utilization.


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## Body

## 1. Introduction

Modern landscape design, instead of being “scenic” as in the case of human vision, should be creatively transformed with the spirit of place to create a subversive, avant-garde, and modern landscape [1]. While urban landscapes are often based on the modeling of mountains and water to create natural and pristine landscapes, the integration of landscape plants helps to create a “new landscape” that belongs to the urban space [2]. In this picture, based on the artificial landscape in the site, in order to achieve the integration of landscape, life, and landscape, different colorful garden plants are planted in the upper space of the rockery and trees are used to make the rockery more vibrant. Around the landscape, colorful flowering plants are arranged to create a relaxed and natural courtyard atmosphere, making the landscape have a rich and natural form [3, 4].In the design of urban plant scene, some local designs are very important, such as plant size, structure, surface, and color effect, for example, a mixture of craftsmanship, plant network guidelines, and area conditions [5]. Therefore, vegetation design is the key technology in the use area of urban leisure center. The analysis of urban life in urban climate data, fast-paced panic sense and thought weight of artworks, and urban display climate can reduce people’s physical and psychological weight, relaxation, and urban scenes and can lead individuals to engage in healthy exercise, thus enhancing the consumption of urban scenes [6]. On this premise, the design of urban vegetation landscape and urban social population structure are further analyzed, which can improve the quality of life of urban residents.After completing the project research and forecast, computer simulation analysis can help planners in the process of completing the landscape master planning and design, with the help of certain models, quickly analyze the actual situation of the base, grasp the entire project in all aspects, and accurately control each process [7, 8]. Construction layout and implementation clearly understand the details of the process. The concept of landscape planning and design is a starting point compared with the conceptual design and landscape planning and design drawings [9, 10]. Designers use immersion computer simulation analysis to draw concept drawings for landscape planning and design [11]. In the interaction between virtual and reality, the landscape designer puts himself in the virtual environment of landscape design and is able to find information about the design plan quickly and accurately, so that he can establish an analysis and evaluation model to master the design more accurately and improve the success rate of the design [12, 13].Based on this, this paper proposes a “Task-Crowd-Process-Evaluation” computer simulation [14] and analysis framework to support management decision making, which can form spatial data, realize real-time and statistical analysis, and utilize the multidimensional, efficient and humanized human living environment. It makes the environmental landscape planning more reasonable and practical. Effective use of urban vegetation for the construction process of the urban landscape as well as landscaping and visual beautification optimizes the ecosystem; these functions in the landscape design, rain gardens, can achieve roof gardens and urban greening. Designated style evaluation of urban park plant landscape and how to carry out urban park plant display have hypothetical and realistic guidance.The contributions of this article are as follows:(1)
A “Task-Crowd-Process-Evaluation” computer simulation analysis framework is proposed, which can form two-dimensional or three-dimensional spatial data, realize real-time and statistical analysis, and make use of the multidimensional, efficient, and humanized human living environment to make environmental landscape planning more reasonable and practical.(2)
In the process of individual innovation amplitude, the element dimension introduces the variable of individual innovation amplitude, and the number of decision-making dimensions for each individual adjustment problem solution is represented by IA. Individuals searching for optimal problem solutions will randomly adjust IA dimensions of their own problem solutions.(3)
We have done a lot of experiments to verify the effectiveness of the scheme in this paper, and the results can produce 3D simulation diagrams. And the solution in this article can be the guide of the designer.


### 1.1. Related Work

To improve the complex social living environment, urban landscape is closely related to human survival. Many have explored explicitly in the design of courtyard plants in a hypothetical premise meadow that exploits the possibilities of environmental areas, which are important to guide and regulate the use of decree aspirations [6, 15]. There is knowledge of urban botanical landscapes and recreation centers. The most effective method of making urban park botanical landscapes is resourceful initiative and specific assumptions [16]. Throughout the design, 94 examples were coordinated by determining the structure of the tree arrangement little by little [17]. A number of photographs were taken from each point at each checkpoint, and arbitrarily selected photographs were used as evaluation tests [18]. There is a tendency to show their work in various ways, namely, landscape effects. Panoramic renderings of gardens were drawn by hand or by computer [19]. It also provides a strong verbal communication bridge for the viewer in a visual way.


## 1.1. Related Work

To improve the complex social living environment, urban landscape is closely related to human survival. Many have explored explicitly in the design of courtyard plants in a hypothetical premise meadow that exploits the possibilities of environmental areas, which are important to guide and regulate the use of decree aspirations [6, 15]. There is knowledge of urban botanical landscapes and recreation centers. The most effective method of making urban park botanical landscapes is resourceful initiative and specific assumptions [16]. Throughout the design, 94 examples were coordinated by determining the structure of the tree arrangement little by little [17]. A number of photographs were taken from each point at each checkpoint, and arbitrarily selected photographs were used as evaluation tests [18]. There is a tendency to show their work in various ways, namely, landscape effects. Panoramic renderings of gardens were drawn by hand or by computer [19]. It also provides a strong verbal communication bridge for the viewer in a visual way.


## 2. Simulation Model Construction

### 2.1. Basic Model

In the study of how groundsheet supports management decisions, a “Task-Crowd-Process-Evaluation” (TCPE) framework [14] for groundsheet operations was proposed. Process and evaluation are shown in Figure 1. This framework incorporates three subjects: the groundsheet issuer, the platform, and the recipient, and can fully depict the essential characteristics of the groundsheet operation process, which mainly consists of four stages:(1)
Crowdsourcing Task Generation. The issuer chooses a problem that the enterprise needs to solve and entrusts it to a groundsheet platform, which organizes and analyzes it to form a groundsheet task [20].(2)
Crowdsourcing Participant Identification. The groundsheet platform publishes the task on its website or pushes it directly to the market [21].(3)
Crowdsourcing Participants Determination. The groundsheet platform publishes the task on its website or pushes it directly to the problem solvers on the platform (the receiver), the groundsheet participants choose whether to accept the task, and all participants who accept the groundsheet task form a network to solve the problem [22, 23].(4)
Participants Solve the Problem. All participants who accept the task start to solve the problem and in the process of solving the problem, they can communicate and share knowledge with other problem solvers through the platform function [24, 25].(5)
Crowdsourcing Performance Evaluation. Within the specified time of completion of the groundsheet task, participants submit the final problem solution to the groundsheet platform, and the platform evaluates it according to certain criteria and requirements and sends one or several best solutions back to the issuer [26].Figure 1
Framework of TCPE groundsheet operation process.This study adopts a model-oriented modeling approach, combining the NK model [27] and the TCPE framework, and constructs a simulation model to model the process of groundsheet problem solving based on the four basic elements of the framework: task, quality, process, and evaluation:(1)
Modeling of Task Elements. If a new product consists of N parts, it is essentially an N-dimensional array of decisions. Since each decision of a task has several different choices, in order to simplify the problem space structure, two choices are usually set [11, 28], denoted by 0 and 1, so the groundsheet task has 2 N potential problem solutions and forms the problem space of the groundsheet task. The complexity of the task is determined by the value of K. The larger the value of K, the higher the complexity of the task.(2)
Mass Element Modeling. When a groundsheet task is released, it is assumed that there are M individuals participating in this groundsheet activity, and each individual has to submit a problem solution. Since individual problem solutions correspond to groundsheet tasks, an N-dimensional array O=O1O2⋯O⋯ON is used to represent them, where O1∈0,1.(3)
Modeling of Process Elements. The process is a series of activities that participants perform after receiving the task. In competition-based groundsheet, participants randomly adjust one or several dimensions of the existing problem solution through several iterations of innovative search based on their initial problem solution, and if a high-quality problem solution is formed, it replaces the old one; otherwise, it remains unchanged [29].(4)
Modeling of Assessment Elements. The assessment includes both individual problem solution quality assessment and groundsheet performance assessment. In general, the quality of an individual problem solution can be represented by the performance of the solution of the problem space. Assume that a decision i in a problem solution has a value of ai. Since the contribution of each decision to the performance of the problem solution ci is influenced by the values of other K decisions in addition to its own value [18, 30] and the values of K decisions are represented by vector ak, the contribution of decision i to the performance of the problem solution is calculated as follows:(1)Pi=ciai,ak.The overall performance of a problem solution is represented by the average contribution size of N decisions [26], which is calculated as follows:(2)P=∑i=1Nai,akN.Regarding groundsheet performance assessment, this paper draws on Javadi Khasraghi’s (2014) study [31], which uses the average quality of participants’ solution submissions to reflect groundsheet performance. Reference [32] argues that the sender does not always select the best solution, so the average performance of participants’ submissions is a robust measure to circumvent the failure to select the best solution. The calculation formula is as follows:(3)CP=∑i=1NpN.


### 2.2. New Features

(1)
Individual Innovation Amplitude. In the process factor dimension, the individual innovation magnitude variable is introduced, which is defined as the number of problem solution decision dimensions that an individual adjusts each time and is expressed as IA. The solution of the optimal problem can be searched through individual random adjustment, and the performance of the adjusted solution can be observed for improvement. The adjusted problem solution is adopted; otherwise, it remains unchanged.(2)
Individual Limited Rationality Level. In the mass factor dimension, the individual limited rationality level variable is introduced. According to existing research, finite rationality is defined as an individual’s partial attention to the problem space [24]. Individuals have a parsimonious space whose dimension is smaller than the problem space, and they construct the optimal parsimonious space based on their past knowledge and experience, i.e., individual cognitive representations, which reflect the source of their competitive advantage. The individual cognitive representation dimension is assumed to be N1, and the ratio of cognitive representation dimensions (N1/N) is used to represent the individual’s limited rationality level (BRL).Based on [32] modeling of the finite rationality level of individuals, each decision configuration in the cognitive representation space has one decision configuration in the groundsheet task problem space corresponding to it, and it is assumed that the fitness value of each point in the cognitive representation space is equal to the average fitness value of the points in the groundsheet task problem space corresponding to that point, and individuals choose the one with the largest fitness value in the cognitive representation space as their cognitive representation. The individual chooses one of the decision configurations with the highest fitness value as his or her cognitive representation.(3)
Individual Limited Rationality Bias. The variable was introduced in the mass factor dimension. Due to different regions, personal upbringing, and professional backgrounds, individuals have different knowledge and experience, i.e., different individual cognitive representations. According to the existing literature, individual limited rationality bias is defined as the degree of accuracy of cognitive representations [18, 33], which is expressed by BRB.Generally, N1 consists of some key decision dimensions and other random decision dimensions, where the number of key decision dimensions is determined by BRB; BRB =N1∗(1-BRB). Individuals select N1∗(1-BRB) key decision dimensions from N dimensions and N1∗BRB random decision dimensions from the remaining (N–N1∗(1–BRB)-dimensions to construct cognitive representations. Among them, the key decision dimensions are identified based on the influence of each decision item in the groundsheet task, and the influence is determined by the number of other decisions influenced by each decision. If several decisions have the same influence, one decision is randomly selected as the key decision dimension to build the cognitive representation.


## 2.1. Basic Model

In the study of how groundsheet supports management decisions, a “Task-Crowd-Process-Evaluation” (TCPE) framework [14] for groundsheet operations was proposed. Process and evaluation are shown in Figure 1. This framework incorporates three subjects: the groundsheet issuer, the platform, and the recipient, and can fully depict the essential characteristics of the groundsheet operation process, which mainly consists of four stages:(1)
Crowdsourcing Task Generation. The issuer chooses a problem that the enterprise needs to solve and entrusts it to a groundsheet platform, which organizes and analyzes it to form a groundsheet task [20].(2)
Crowdsourcing Participant Identification. The groundsheet platform publishes the task on its website or pushes it directly to the market [21].(3)
Crowdsourcing Participants Determination. The groundsheet platform publishes the task on its website or pushes it directly to the problem solvers on the platform (the receiver), the groundsheet participants choose whether to accept the task, and all participants who accept the groundsheet task form a network to solve the problem [22, 23].(4)
Participants Solve the Problem. All participants who accept the task start to solve the problem and in the process of solving the problem, they can communicate and share knowledge with other problem solvers through the platform function [24, 25].(5)
Crowdsourcing Performance Evaluation. Within the specified time of completion of the groundsheet task, participants submit the final problem solution to the groundsheet platform, and the platform evaluates it according to certain criteria and requirements and sends one or several best solutions back to the issuer [26].Figure 1
Framework of TCPE groundsheet operation process.This study adopts a model-oriented modeling approach, combining the NK model [27] and the TCPE framework, and constructs a simulation model to model the process of groundsheet problem solving based on the four basic elements of the framework: task, quality, process, and evaluation:(1)
Modeling of Task Elements. If a new product consists of N parts, it is essentially an N-dimensional array of decisions. Since each decision of a task has several different choices, in order to simplify the problem space structure, two choices are usually set [11, 28], denoted by 0 and 1, so the groundsheet task has 2 N potential problem solutions and forms the problem space of the groundsheet task. The complexity of the task is determined by the value of K. The larger the value of K, the higher the complexity of the task.(2)
Mass Element Modeling. When a groundsheet task is released, it is assumed that there are M individuals participating in this groundsheet activity, and each individual has to submit a problem solution. Since individual problem solutions correspond to groundsheet tasks, an N-dimensional array O=O1O2⋯O⋯ON is used to represent them, where O1∈0,1.(3)
Modeling of Process Elements. The process is a series of activities that participants perform after receiving the task. In competition-based groundsheet, participants randomly adjust one or several dimensions of the existing problem solution through several iterations of innovative search based on their initial problem solution, and if a high-quality problem solution is formed, it replaces the old one; otherwise, it remains unchanged [29].(4)
Modeling of Assessment Elements. The assessment includes both individual problem solution quality assessment and groundsheet performance assessment. In general, the quality of an individual problem solution can be represented by the performance of the solution of the problem space. Assume that a decision i in a problem solution has a value of ai. Since the contribution of each decision to the performance of the problem solution ci is influenced by the values of other K decisions in addition to its own value [18, 30] and the values of K decisions are represented by vector ak, the contribution of decision i to the performance of the problem solution is calculated as follows:(1)Pi=ciai,ak.The overall performance of a problem solution is represented by the average contribution size of N decisions [26], which is calculated as follows:(2)P=∑i=1Nai,akN.Regarding groundsheet performance assessment, this paper draws on Javadi Khasraghi’s (2014) study [31], which uses the average quality of participants’ solution submissions to reflect groundsheet performance. Reference [32] argues that the sender does not always select the best solution, so the average performance of participants’ submissions is a robust measure to circumvent the failure to select the best solution. The calculation formula is as follows:(3)CP=∑i=1NpN.


## 2.2. New Features

(1)
Individual Innovation Amplitude. In the process factor dimension, the individual innovation magnitude variable is introduced, which is defined as the number of problem solution decision dimensions that an individual adjusts each time and is expressed as IA. The solution of the optimal problem can be searched through individual random adjustment, and the performance of the adjusted solution can be observed for improvement. The adjusted problem solution is adopted; otherwise, it remains unchanged.(2)
Individual Limited Rationality Level. In the mass factor dimension, the individual limited rationality level variable is introduced. According to existing research, finite rationality is defined as an individual’s partial attention to the problem space [24]. Individuals have a parsimonious space whose dimension is smaller than the problem space, and they construct the optimal parsimonious space based on their past knowledge and experience, i.e., individual cognitive representations, which reflect the source of their competitive advantage. The individual cognitive representation dimension is assumed to be N1, and the ratio of cognitive representation dimensions (N1/N) is used to represent the individual’s limited rationality level (BRL).Based on [32] modeling of the finite rationality level of individuals, each decision configuration in the cognitive representation space has one decision configuration in the groundsheet task problem space corresponding to it, and it is assumed that the fitness value of each point in the cognitive representation space is equal to the average fitness value of the points in the groundsheet task problem space corresponding to that point, and individuals choose the one with the largest fitness value in the cognitive representation space as their cognitive representation. The individual chooses one of the decision configurations with the highest fitness value as his or her cognitive representation.(3)
Individual Limited Rationality Bias. The variable was introduced in the mass factor dimension. Due to different regions, personal upbringing, and professional backgrounds, individuals have different knowledge and experience, i.e., different individual cognitive representations. According to the existing literature, individual limited rationality bias is defined as the degree of accuracy of cognitive representations [18, 33], which is expressed by BRB.Generally, N1 consists of some key decision dimensions and other random decision dimensions, where the number of key decision dimensions is determined by BRB; BRB =N1∗(1-BRB). Individuals select N1∗(1-BRB) key decision dimensions from N dimensions and N1∗BRB random decision dimensions from the remaining (N–N1∗(1–BRB)-dimensions to construct cognitive representations. Among them, the key decision dimensions are identified based on the influence of each decision item in the groundsheet task, and the influence is determined by the number of other decisions influenced by each decision. If several decisions have the same influence, one decision is randomly selected as the key decision dimension to build the cognitive representation.


## 3. Green Urban Landscaping Design Process

### 3.1. Landscape Design Analysis

Through the classification of urban land use, the land types around the city can be divided into forest land, cultivated land, water area and wetland, other urban construction land, road traffic land, and other land use types. The land change can be seen from Figure2. We can clearly know that urban land can be planned systematically. As can be seen from the data change in Figure 3, from 1995 to 2019, with the increase in urban construction area, the road network density gradually increases and the forest land area decreases. Among them, the water area decreased rapidly, by 31.93%, respectively. The construction land of other cities in Yuhang district increased by 25.96%, and the land type in Yuhang District changed little. In terms of municipal landscaping, the construction and development of urban landscaping have directly driven the rapid growth of the landscaping industry. From 2003 to 2010, the average annual growth rate was 38.34%, growing very rapidly; from 2011 to 2014, the investment in fixed assets of landscaping in Hangzhou was generally maintained at a high level.Figure 2
Urban land use change of (a) 1995; (b) 2000; (c) 2005; (d) 2010; (e) 2015.Figure 3
Land use change by urban wards from 1995 to 2019.Cities are in a period of rapid industrialization and urbanization. On the one hand, there is a huge demand for high energy consumption products such as steel and cement; on the other hand, the improvement of residents’ quality of life is accompanied by the increase in living energy consumption, which makes China’s carbon emission reduction face arduous challenges. Therefore, how to effectively develop low-carbon economy and balance the relationship between China’s urbanization process and carbon emission will become China’s sustainable development in the future. This paper takes China’s urbanization and carbon dioxide emission as an example [23]. With the increasingly prominent contradiction between environmental problems and economic development, “low-carbon economy” has become a topic of increasing concern by governments and academic circles. The general situation of urbanization and carbon dioxide emission is analyzed through statistical data. As shown in Figure 4, the stability test of urbanization rate and carbon dioxide emission during 1995–2019 is carried out. The empirical test results show that there is a long-term equilibrium relationship between the two. Urbanization plays a positive role in the fluctuation of carbon dioxide. For different regions, due to the different economic structure and development status of different provinces and cities, the force of urbanization on carbon dioxide emission is also different.Figure 4
Spatial variation of urban carbon emission intensity, 1995–2019.From Figure3, it can be seen that before 2000, high carbon emission areas were mainly concentrated in the central area of the main urban area. After 2000, patches gradually began to migrate to the southeast and northeast. Until 2019, the high carbon spots no longer appear in the central area of the main urban area. The study also found that high carbon showed spatial aggregation before 2000 and gradually dispersed in space after 2000.As can be seen from Figure5, before 2010, high carbon emission areas were mainly concentrated in the main urban areas. After 2010, patches began to migrate. In 2019, high carbon points will no longer appear in the main urban area. Such data provide a reference for our urban greening landscape design and then design the greening density of the city according to different carbon emissions [34].Figure 5
Spatial variation of urban carbon sink density (1995, 2010, and 2019).
(a)(b)(c)As can be seen from Figure6, carbon emissions decreased gradually every year from 1995 to 2000 and basically disappeared in the main urban area after 2000. High carbon areas began to develop gradually in the northwest and southwest. The carbon emissions of urban residents’ living consumption and residents’ breathing can be distributed according to the urban vegetation density. According to the carbon emission of urban land, the road network and road vegetation density can be designed. Through the progress of urban greening, even if the carbon emission of construction land in various regions increases, the vegetation density increases more, which can make the city full of plants.Figure 6
Carbon emissions changes of 1995–2019.


## 3.1. Landscape Design Analysis

Through the classification of urban land use, the land types around the city can be divided into forest land, cultivated land, water area and wetland, other urban construction land, road traffic land, and other land use types. The land change can be seen from Figure2. We can clearly know that urban land can be planned systematically. As can be seen from the data change in Figure 3, from 1995 to 2019, with the increase in urban construction area, the road network density gradually increases and the forest land area decreases. Among them, the water area decreased rapidly, by 31.93%, respectively. The construction land of other cities in Yuhang district increased by 25.96%, and the land type in Yuhang District changed little. In terms of municipal landscaping, the construction and development of urban landscaping have directly driven the rapid growth of the landscaping industry. From 2003 to 2010, the average annual growth rate was 38.34%, growing very rapidly; from 2011 to 2014, the investment in fixed assets of landscaping in Hangzhou was generally maintained at a high level.Figure 2
Urban land use change of (a) 1995; (b) 2000; (c) 2005; (d) 2010; (e) 2015.Figure 3
Land use change by urban wards from 1995 to 2019.Cities are in a period of rapid industrialization and urbanization. On the one hand, there is a huge demand for high energy consumption products such as steel and cement; on the other hand, the improvement of residents’ quality of life is accompanied by the increase in living energy consumption, which makes China’s carbon emission reduction face arduous challenges. Therefore, how to effectively develop low-carbon economy and balance the relationship between China’s urbanization process and carbon emission will become China’s sustainable development in the future. This paper takes China’s urbanization and carbon dioxide emission as an example [23]. With the increasingly prominent contradiction between environmental problems and economic development, “low-carbon economy” has become a topic of increasing concern by governments and academic circles. The general situation of urbanization and carbon dioxide emission is analyzed through statistical data. As shown in Figure 4, the stability test of urbanization rate and carbon dioxide emission during 1995–2019 is carried out. The empirical test results show that there is a long-term equilibrium relationship between the two. Urbanization plays a positive role in the fluctuation of carbon dioxide. For different regions, due to the different economic structure and development status of different provinces and cities, the force of urbanization on carbon dioxide emission is also different.Figure 4
Spatial variation of urban carbon emission intensity, 1995–2019.From Figure3, it can be seen that before 2000, high carbon emission areas were mainly concentrated in the central area of the main urban area. After 2000, patches gradually began to migrate to the southeast and northeast. Until 2019, the high carbon spots no longer appear in the central area of the main urban area. The study also found that high carbon showed spatial aggregation before 2000 and gradually dispersed in space after 2000.As can be seen from Figure5, before 2010, high carbon emission areas were mainly concentrated in the main urban areas. After 2010, patches began to migrate. In 2019, high carbon points will no longer appear in the main urban area. Such data provide a reference for our urban greening landscape design and then design the greening density of the city according to different carbon emissions [34].Figure 5
Spatial variation of urban carbon sink density (1995, 2010, and 2019).
(a)(b)(c)As can be seen from Figure6, carbon emissions decreased gradually every year from 1995 to 2000 and basically disappeared in the main urban area after 2000. High carbon areas began to develop gradually in the northwest and southwest. The carbon emissions of urban residents’ living consumption and residents’ breathing can be distributed according to the urban vegetation density. According to the carbon emission of urban land, the road network and road vegetation density can be designed. Through the progress of urban greening, even if the carbon emission of construction land in various regions increases, the vegetation density increases more, which can make the city full of plants.Figure 6
Carbon emissions changes of 1995–2019.


## 4. Simulation Results

Plants have cultural quality and symbolic meanings, and landscape architecture belongs to urban infrastructure. Combining the humanistic attributes of landscape architecture in urban landscape design and reasonably applying different garden plants can improve the overall landscape design layout, enhance the cultural value of the landscape, and create a landscape that is suitable for the humanistic urban environment. In Figure7, combining the mood and characteristics of the buildings in the urban landscape, in order to better match the architectural landscape to achieve the effect of cultural nurturing, attention is paid to the biodiversity of the environment when incorporating garden plants, the essence of mountain and forest scenery is inherited and developed, and flowers or shrubs with brighter colors are combined with some taller and more luxuriant native plants, forming a beautiful landscape picture with poetic sentiment [15]. It allows visitors to experience the cultural heritage of the city while traveling and sightseeing.Figure 7
Urban vegetation landscapes.Seasonality is an important characteristic of garden plants. Different plants will show different states in different seasons, and the same plants will show different forms in different seasons. Therefore, in urban landscape design, designers select different kinds of plants for landscape creation to increase the ornamental nature of the urban landscape and reflect the vitality that changes in the four seasons. In this urban landscape design, with the help of tree species composition and topographic changes, regular planting techniques are used, and evergreen herbaceous plants are selected for landscape ground cover, which can further improve the ornamental effect while enhancing the urban green space coverage [25]. On the basis of color block shaping space, we follow the principle of locality and choose seasonal plants to match in the rear view treatment, which helps to form the landscape phenomenon of changing seasons and show the landscape characteristics of regular order, making the overall space more dynamic and interesting.As shown in Figure8, the scene simulated in this paper has a shaded grass and trees of different colors, where different trees and plants are set against each other, allowing people to enjoy the visual changes while enjoying the elegant scenery. Specifically, different planting techniques are used, and the main form of planning is the art of arrangement so that the plants become lines of points and form several corridors of sight lines with large areas of open flat land. In order to strengthen the sense of ritual and mystery of the space and to give full play to the effect of height and undulation, plants are planted on the slope with a certain horizontal height, and the difference of height and perspective links the space into a unified whole, creating a richly layered landscape space. The use of garden plants to create ornamental attractions is the value of garden plants in urban landscape design highlights.Figure 8
Vegetation generation pattern.Plant elements can be combined with other spatial elements to create interesting spatial relationships, forming a visually impeccable landscape. In the picture, the central fountain is the key landscape in the urban landscape. In order to alleviate the visual monotony and tedium, the designer has constructed a geometrically interesting plant landscape directly in front of it to create a fresh environment. In addition, a large number of courtyard plants with different colors are also incorporated around it, and the main planting form is column planting, creating the visual effect of a romantic garden together with the central fountain landscape, forming a three-dimensional spatial hierarchy of high and low staggering.


## 5. Conclusions

Digital information is particularly important for landscape design, allowing for human-computer interaction. In this paper, we propose a “Task-Crowd-Process-Evaluation” computer simulation and analysis framework to study how groundsheet can support management decisions, which can form two-dimensional or three-dimensional spatial data, realize real-time and statistical analysis, and make use of the multidimensional, efficient, and humanized human living environment to make the environmental landscape planning more reasonable and practical. Reasonable application of different garden plants can enhance the overall landscape design layout, enhance the cultural value of the landscape, and create a suitable landscape with the human urban environment.



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*Source: 9422417-2021-10-08.xml*