Method and device for constructing water-based green vegetation pattern in semi-arid region

Abstract

The invention discloses a semi-arid region vegetation pattern construction method and device, and relates to the technical field of vegetation pattern construction and semi-arid region ecological restoration, and the method comprises the steps: obtaining water resource data, vegetation data and climate data of a semi-arid region; according to the water resource data, performing water resource evaluation on the semi-arid region to obtain a water resource evaluation result; according to the vegetation data and the climate data, performing vegetation water demand analysis on the semi-arid region to obtain a vegetation water demand analysis result; carrying out vegetation suitability evaluation on the semi-arid region, and screening vegetation species matched with water resource conditions of the semi-arid region; and carrying out vegetation pattern design on the semi-arid region, and determining a vegetation pattern design scheme so as to conveniently carry out vegetation planting operation on the semi-arid region. According to the method, reasonable matching of vegetation construction and water resources in the semi-arid region can be achieved, and the stability and the ecological service function of a regional ecological system are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of vegetation pattern construction and ecological restoration in semi-arid areas, and in particular to a method and device for constructing a water-based green vegetation pattern in semi-arid areas. Background Art

[0002] Semi-arid regions, characterized by scarce and unevenly distributed precipitation, high evaporation, and fragile ecosystems, face numerous challenges in ecological restoration and vegetation establishment. On the one hand, water shortages in these regions severely restrict vegetation growth and distribution; on the other hand, unscientific planting patterns can easily lead to vegetation degradation and increased desertification. Therefore, scientifically and rationally establishing vegetation patterns in semi-arid regions is conducive to ecological restoration.

[0003] Currently, existing vegetation pattern construction methods are generally unsuitable for semi-arid regions. They struggle to properly match vegetation construction with water resources, effectively improving the stability and ecological services of these ecosystems. Therefore, a pressing technical challenge in this field is to develop a precise vegetation pattern construction method that is adaptable to the unique water conditions of semi-arid regions, effectively matching vegetation construction with water resources and improving the stability and ecological services of regional ecosystems. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for constructing a water-based green vegetation pattern in semi-arid areas, which can achieve a reasonable match between vegetation construction and water resources in semi-arid areas, and effectively improve the stability of the regional ecosystem and the ecological service function.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for constructing a water-based green vegetation pattern in a semi-arid area, which specifically comprises the following steps:

[0007] Obtain water resources data, vegetation data and climate data for semi-arid areas.

[0008] A water resource assessment is conducted on the semi-arid area based on the water resource data to obtain a water resource assessment result.

[0009] A vegetation water demand analysis is performed on the semi-arid area according to the vegetation data and the climate data to obtain a vegetation water demand analysis result.

[0010] Based on the water resources assessment results and the vegetation water demand analysis results, a vegetation suitability evaluation is performed on the semi-arid area, and vegetation species that match the water resources conditions of the semi-arid area are screened to obtain a vegetation screening result.

[0011] According to the vegetation screening results, a vegetation pattern design is performed on the semi-arid area to determine a vegetation pattern design scheme; the vegetation pattern design scheme is used as a vegetation planting scheme to facilitate vegetation planting operations in the semi-arid area.

[0012] Optionally, the water resource data includes precipitation data, surface water flow data, and groundwater level and reserve data.

[0013] Conducting a water resources assessment on the semi-arid region based on the water resources data to obtain a water resources assessment result specifically includes the following steps:

[0014] Based on the precipitation data, a time series analysis method is used to determine the seasonal variation pattern of precipitation in the semi-arid region and the cycle of good and bad years of precipitation.

[0015] Based on the seasonal variation pattern of precipitation in the semi-arid region and the cycle of good and bad years of precipitation, the temporal distribution of water resources in the semi-arid region is evaluated.

[0016] Using the spatial analysis tools of the geographic information system, interpolation analysis is performed on the surface water flow data and the groundwater level and storage data to draw a regional water resources spatial distribution map.

[0017] Based on the spatial distribution map of water resources in the region, evaluate the distribution of water resources in different geographical locations in the semi-arid area.

[0018] Based on the temporal distribution of water resources in the semi-arid region and the distribution of water resources in different geographical locations, the total amount of water resources in the semi-arid region and its temporal and spatial distribution characteristics are determined as the water resources assessment result.

[0019] Optionally, performing a vegetation water demand analysis on the semi-arid region based on the vegetation data and the climate data to obtain a vegetation water demand analysis result specifically includes the following steps:

[0020] Based on the vegetation data, the water requirements and water requirement frequencies of various vegetation types in the semi-arid region at various growth stages are determined, including the water requirements during the germination period, the water requirements during the germination period, the water requirements during the growth period, the water requirements during the growth period, the water requirements during the dormancy period, and the water requirement frequency during the dormancy period.

[0021] According to the water demand and water demand frequency of various vegetation in the semi-arid area at various growth stages and the climate data, the effects of temperature, humidity and wind speed on vegetation water demand are analyzed, and a vegetation water demand model is established.

[0022] Based on the vegetation water demand model, the vegetation water demand of each type of vegetation in the semi-arid area is calculated as the vegetation water demand analysis result.

[0023] Optionally, the vegetation water demand model is expressed as:

[0024] ET = P + IRD - ΔS;

[0025] Where ET is the vegetation water requirement, P is the precipitation, I is the irrigation amount, R is the surface runoff, D is the deep infiltration, and ΔS is the change in soil water storage.

[0026] Optionally, based on the water resources assessment result and the vegetation water requirement analysis result, a vegetation suitability evaluation is performed on the semi-arid area, and vegetation species that match the water resources conditions of the semi-arid area are screened out to obtain a vegetation screening result, which specifically includes the following steps:

[0027] Based on the water resource assessment results and the vegetation water demand analysis results, vegetation species that match the water resource conditions of the semi-arid area are screened out.

[0028] Establish a suitable vegetation screening index system, including drought resistance indicators, ecological function indicators and economic value indicators.

[0029] Based on the suitable vegetation screening index system, the entropy weight-TOPSIS method is used to conduct a comprehensive quantitative evaluation of each selected vegetation species that matches the water resource conditions of the semi-arid area, and a comprehensive evaluation result is calculated as the vegetation screening result.

[0030] Optionally, based on the vegetation screening result, a vegetation pattern design is performed for the semi-arid area to determine a vegetation pattern design scheme, specifically comprising the following steps:

[0031] According to the vegetation screening result, a spatial analysis algorithm is used to divide the vegetation planting area in the semi-arid region to obtain a vegetation planting area division result.

[0032] According to the results of the vegetation planting area division, a multi-objective optimization algorithm is used to optimize the vegetation pattern of each area divided in the semi-arid area with the highest water resource utilization efficiency, the best ecological function and the maximum economic value as the objective function, to determine the optimal vegetation planting layout and community structure combination, and obtain the vegetation pattern design plan.

[0033] Optionally, after the step of designing a vegetation pattern for the semi-arid area according to the vegetation screening result and determining a vegetation pattern design scheme, the method for constructing a water-based green vegetation pattern in a semi-arid area further comprises the following steps:

[0034] According to the vegetation pattern in the vegetation pattern design plan, vegetation planting operations are carried out in the semi-arid area.

[0035] In the second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor executes the computer program to implement the steps of the method for constructing a water-based green vegetation pattern in a semi-arid area as described in any one of the above-mentioned methods.

[0036] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for constructing a water-based green vegetation pattern in a semi-arid area as described in any one of the above.

[0037] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for constructing a water-based green vegetation pattern in a semi-arid area.

[0038] According to the specific embodiments provided in this application, this application has the following technical effects:

[0039] The present application provides a method and device for constructing a water-based green vegetation pattern in a semi-arid area. By sequentially conducting water resource assessment, vegetation water demand analysis, and vegetation suitability evaluation in the semi-arid area, vegetation species that match the water resource conditions in the semi-arid area are screened out, thereby achieving a reasonable match between vegetation construction and water resources in the semi-arid area. By scientifically screening suitable vegetation and rationally designing the vegetation pattern, a more accurate and reliable vegetation pattern design scheme can be obtained, which is conducive to carrying out more efficient and more suitable vegetation planting operations, and can improve the success rate and stability of vegetation construction, realize the efficient use of water resources, enhance the self-repair ability and ecological service function of the ecosystem in the semi-arid area, and promote the sustainable development of regional ecology, economy and society. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 This is an application environment diagram of a method for constructing a water-based green vegetation pattern in a semi-arid area provided in one embodiment of the present application.

[0042] Figure 2A flow chart of a method for constructing a water-based green vegetation pattern in a semi-arid area provided in one embodiment of the present application.

[0043] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0045] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0046] The method for constructing a water-based green vegetation pattern in a semi-arid area provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. The data storage system can store water resource data, vegetation data, and climate data for the semi-arid region that server 104 needs to process. The data storage system can be set up separately, integrated on server 104, or placed on a cloud or other server. Terminal 102 can send the water resource data, vegetation data, and climate data for the semi-arid region to server 104. After receiving the water resource data, vegetation data, and climate data for the semi-arid region, server 104 performs a water resource assessment for the semi-arid region based on the water resource data to obtain a water resource assessment result; performs a vegetation water demand analysis for the semi-arid region based on the vegetation data and climate data to obtain a vegetation water demand analysis result; performs a vegetation suitability assessment for the semi-arid region and selects vegetation species that match the water resource conditions of the semi-arid region; and performs a vegetation pattern design for the semi-arid region to determine a vegetation pattern design plan. Server 104 can provide feedback on the resulting vegetation pattern design plan to terminal 102. In addition, in some embodiments, the method for constructing a water-based green vegetation pattern in a semi-arid area can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform water resource assessment, vegetation water demand analysis, vegetation suitability evaluation, and vegetation pattern design on the water resource data, vegetation data, and climate data of the semi-arid area. The server 104 can also obtain the water resource data, vegetation data, and climate data of the semi-arid area from the data storage system, and perform water resource assessment, vegetation water demand analysis, vegetation suitability evaluation, and vegetation pattern design on the water resource data, vegetation data, and climate data of the semi-arid area.

[0047] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0048] In an exemplary embodiment, Figure 2As shown, a method for constructing a water-based greening type vegetation pattern in a semi-arid area is provided, wherein water-based greening means that in vegetation construction, the scale and method of greening are determined according to the carrying capacity of water resources. This embodiment sequentially conducts water resource assessment, vegetation water demand analysis, vegetation suitability evaluation and vegetation pattern design in the semi-arid area, thereby screening out vegetation species that match the water resource conditions in the semi-arid area, and designs a suitable vegetation pattern for it, and formulates a vegetation pattern design plan that meets the water resource carrying capacity, thereby achieving the goal of water-based greening. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are specifically included:

[0049] Step S1: Acquire water resource data, vegetation data, and climate data in a semi-arid area.

[0050] In this embodiment, water resource data includes precipitation data, surface water flow data, and groundwater level and storage data. Precipitation data primarily includes precipitation; surface water flow data includes irrigation volume and surface runoff; and groundwater level and storage data includes deep seepage and changes in soil water storage. Vegetation data includes vegetation name, vegetation type, water demand, water demand frequency, and growth cycle. Climate data includes the temperature, humidity, and wind speed of the vegetation growth environment.

[0051] Step S2: Conduct a water resource assessment on the semi-arid area based on the water resource data to obtain a water resource assessment result.

[0052] Step S3: performing vegetation water demand analysis on the semi-arid area based on the vegetation data and the climate data to obtain a vegetation water demand analysis result.

[0053] Step S4: Based on the water resources assessment result and the vegetation water demand analysis result, the vegetation suitability of the semi-arid area is evaluated, and vegetation species that match the water resources conditions of the semi-arid area are screened to obtain a vegetation screening result.

[0054] Step S5: Based on the vegetation screening results, a vegetation pattern design is performed for the semi-arid region to determine a vegetation pattern design scheme. The vegetation pattern design scheme serves as a vegetation planting scheme, providing a vegetation pattern for each region of the semi-arid region for vegetation planting operations. This allows vegetation planting operations to be performed in the semi-arid region according to the vegetation pattern for each region in the vegetation pattern design scheme, with vegetation species that match water resource conditions being planted in each region of the semi-arid region.

[0055] In this embodiment, step S2 performs a water resource assessment on the semi-arid area based on the water resource data to obtain a water resource assessment result, which specifically includes the following steps:

[0056] Step S21: Based on the precipitation data, a time series analysis method is used to determine the seasonal variation pattern of precipitation in the semi-arid region and the cycles of good and bad years.

[0057] Step S22: Evaluate the temporal distribution of water resources in the semi-arid region based on the seasonal variation pattern of precipitation in the semi-arid region and the cycles of good and bad years of precipitation.

[0058] Step S23: Using the spatial analysis tool of the Geographic Information System (GIS), interpolation analysis is performed on the surface water flow data and the groundwater level and storage data to draw a regional water resources spatial distribution map.

[0059] Step S24: Evaluate the distribution of water resources in the semi-arid region at different geographical locations based on the regional water resources spatial distribution map.

[0060] Step S25: Determine the total amount of water resources in the semi-arid region and their temporal and spatial distribution characteristics based on the temporal distribution of water resources in the semi-arid region and the distribution of water resources in different geographical locations, as the water resources assessment result.

[0061] In this embodiment, step S3 performs vegetation water demand analysis on the semi-arid region based on the vegetation data and the climate data to obtain a vegetation water demand analysis result, which specifically includes the following steps:

[0062] Step S31: Determine the water requirement and water requirement frequency of each vegetation in the semi-arid area at each growth stage based on the vegetation data, including the water requirement in the budding period, the water requirement in the budding period, the water requirement in the growing period, the water requirement in the growing period, the water requirement in the dormant period, and the water requirement in the dormant period.

[0063] Step S32: Analyze the effects of temperature, humidity, and wind speed on vegetation water demand based on the water demand and water demand frequency of various vegetation types in the semi-arid area at various growth stages and the climate data, and establish a vegetation water demand model.

[0064] Step S33: Calculate the vegetation water demand of each type of vegetation in the semi-arid area based on the vegetation water demand model as the vegetation water demand analysis result.

[0065] In this embodiment, step S4 performs vegetation suitability evaluation on the semi-arid region based on the water resources assessment result and the vegetation water requirement analysis result, and screens out vegetation species that match the water resources conditions of the semi-arid region to obtain a vegetation screening result, which specifically includes the following steps:

[0066] Step S41: Screen out vegetation species that match the water resource conditions of the semi-arid area based on the water resource assessment result and the vegetation water requirement analysis result.

[0067] Step S42: Establish a suitable vegetation screening index system.

[0068] In this embodiment, the suitable vegetation screening index system includes drought tolerance, ecological function, and economic value. Drought tolerance includes indicators such as root depth, leaf water retention capacity, and water use efficiency. Ecological function includes windbreak and sand fixation, soil improvement, and carbon sequestration. Economic value includes indicators such as market price and yield.

[0069] Step S43: Based on the suitable vegetation screening index system, the entropy weight-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is used to perform a comprehensive quantitative evaluation of each selected vegetation species that matches the water resource conditions in the semi-arid area, and a comprehensive evaluation result is calculated as the vegetation screening result.

[0070] In this embodiment, step S5 performs vegetation pattern design for the semi-arid area based on the vegetation screening result and determines a vegetation pattern design scheme, which specifically includes the following steps:

[0071] Step S51: Based on the vegetation screening result, a spatial analysis algorithm is used to divide the vegetation planting area in the semi-arid region to obtain a vegetation planting area division result.

[0072] Step S52: Based on the vegetation planting area division results, a multi-objective optimization algorithm is used to optimize the vegetation pattern of each area divided in the semi-arid area with the highest water resource utilization efficiency, the best ecological function and the maximum economic value as the objective function, to determine the optimal vegetation planting layout and community structure combination, and obtain the vegetation pattern design plan.

[0073] In this embodiment, in step S5, a vegetation pattern design is performed for the semi-arid area based on the vegetation screening result. After the vegetation pattern design scheme is determined, the method for constructing a water-based green vegetation pattern in the semi-arid area further comprises the following steps:

[0074] Step S6: Planting vegetation in the semi-arid area according to the vegetation pattern in the vegetation pattern design scheme.

[0075] In order to make the technical solution of this embodiment clearer, the specific implementation process of the technical solution of this embodiment is described in detail below in the form of examples, including the following steps.

[0076] Step 1: Water resources assessment.

[0077] Before water resource assessment, this embodiment first collects water resource data, vegetation data, climate data, etc. in the semi-arid area, and conducts a comprehensive analysis of these data to assess the total amount of regional water resources and their temporal and spatial distribution characteristics.

[0078] Specifically, precipitation data were analyzed using time series analysis to identify seasonal patterns in precipitation, cycles between good and bad years, and other characteristics, in order to assess the temporal distribution of water resources. Using spatial analysis tools from a geographic information system, surface water flow data and groundwater levels and storage data were interpolated to create a regional water resource spatial distribution map, identifying differences in water resource distribution across geographic locations. By comparing water resource data from different years and seasons, a comprehensive assessment of the total regional water resources and their temporal and spatial distribution was conducted, identifying areas with relatively abundant and scarce water resources, providing basic data support for subsequent vegetation pattern design.

[0079] The data source of this embodiment is mainly public data on the Internet. The information is open, authoritative, and easy to obtain. The data source information is detailed in Table 1.

[0080] Table 1 Data source information

[0081]

[0082] Step 2: Analysis of vegetation water requirements.

[0083] Long-term field monitoring was conducted on typical vegetation types in semi-arid regions, including shrubs such as sea buckthorn, caragana, and wolfberry, as well as trees such as poplar and willow, and herbaceous plants such as stipa and chinensis. The water requirements of these vegetation types throughout their growth cycles were studied, and the water requirements and frequency of water requirements for each vegetation type in semi-arid regions were determined. These included the water requirements during the budding phase, the water requirements during the growing phase, the water requirements during the dormant phase, and the water requirements during the dormant phase. During the budding phase, vegetation primarily relies on pre-existing water stored in the soil to initiate growth, requiring relatively little water but being highly sensitive to water. During the growing phase, vegetation grows rapidly, requiring more water and requiring more water frequently. During the dormant phase, vegetation physiological activity slows down, resulting in a lower water requirement. Furthermore, rising temperatures increase transpiration, thereby increasing water requirements. Decreased humidity increases the vapor pressure difference between vegetation and the air, intensifying transpiration. Increased wind speeds accelerate the diffusion of water vapor from the vegetation surface, also leading to an increase in water requirements. Therefore, this embodiment combines climate data to analyze the impact of meteorological factors such as temperature, humidity, and wind speed on vegetation water demand, so as to establish a vegetation water demand model.

[0084] In this embodiment, when constructing the vegetation water demand model, a formula based on the water balance principle is used to express the vegetation water demand model, as shown in the following formula:

[0085] ET=P+IRD-ΔS.

[0086] Where ET is the vegetation water requirement, i.e., vegetation evapotranspiration, P is precipitation, I is irrigation, R is surface runoff, D is deep infiltration, and ΔS is the change in soil water storage.

[0087] After constructing the vegetation water demand model, this embodiment determines the parameters in the vegetation water demand model through long-term monitoring and data fitting, thereby improving the prediction accuracy of the vegetation water demand model.

[0088] Step 3: Vegetation suitability evaluation.

[0089] Based on the results of water resource assessments and vegetation water demand analysis, select vegetation species that match regional water resource conditions. Prioritize drought-tolerant and water-saving vegetation, such as those with well-developed root systems, highly keratinized leaves, and strong stomatal regulation. Evaluate the suitability of different vegetation types, taking into account their ecological functions (such as windbreak and sand fixation, water and soil conservation, and soil fertility improvement) and economic value (such as their use as feed or medicinal materials).

[0090] For ecological benefits, vegetation with lush branches and leaves, low crowns, and strong root systems that can fix soil should be selected for windbreak and sand fixation. For soil and water conservation, plants with deep and dense root systems and high above-ground coverage should be prioritized. For soil fertility improvement, vegetation with nitrogen fixation or that promotes soil microbial activity should be selected. For economic benefits, herbs such as alfalfa and Astragalus membranaceus, which can serve as high-quality fodder, or medicinal plants such as wolfberry and licorice should be selected to improve the overall benefits of vegetation development.

[0091] The quantitative standard of drought tolerance screening index in this embodiment is shown in Table 2.

[0092] Table 2 Quantitative standards for drought tolerance screening indicators

[0093]

[0094]

[0095] In this embodiment, a vegetation suitability test is conducted on the selected vegetation. The selected vegetation is planted under different site conditions (such as different soil types, slopes, slope directions, etc.), and its growth conditions, survival rates, coverage and other indicators are observed to further determine the suitability of the selected vegetation in semi-arid areas, thereby completing the vegetation suitability evaluation process.

[0096] In this embodiment, when evaluating vegetation suitability, the suitability index of each vegetation type is calculated using the following formula:

[0097]

[0098] Among them, i is the suitability index, p is the precipitation factor, s is the soil suitability factor, and c is the climate suitability factor; p max 、s max 、c max They are the maximum value of precipitation, the maximum value of soil suitability factor, and the maximum value of climate suitability factor respectively.

[0099] This example establishes a suitable vegetation screening index system, including drought tolerance indicators (such as root depth, leaf water retention capacity, and water use efficiency), ecological function indicators (such as windbreak and sand fixation, soil improvement effect, and carbon sequestration capacity), and economic value indicators (such as market price and yield). Each selected vegetation is quantitatively evaluated based on these indicators to determine its overall advantages.

[0100] Among them, the quantitative evaluation of drought tolerance indicators includes the following:

[0101] (1) Root depth measurement: Use the excavation method or root scanning technology to select representative plants in the wild, carefully excavate their roots, and measure the deepest root depth. For vegetation with deep root distribution, such as some trees and deep-rooted shrubs, the excavation depth can reach several meters; for herbaceous plants, the excavation depth is generally within the range of tens of centimeters. Based on the root depth data, vegetation is divided into deep-rooted type (root depth greater than 1 meter), medium-rooted type (root depth between 0.5-1 meter) and shallow-rooted type (root depth less than 0.5 meter), and different scores are assigned to each type. For example, deep-rooted vegetation scores 8-10 points on the root depth index, medium-rooted vegetation scores 5-7 points, and shallow-rooted vegetation scores 1-4 points.

[0102] (2) Leaf water retention capacity test: Collect fresh leaves and use the weighing method to measure the water loss and water loss rate of the leaves under natural water loss conditions. Place the leaves naturally in the room, weigh them at regular intervals (such as 1 hour), and record the changes in leaf weight. Calculate the leaf water loss rate. Leaves with low water loss rates have strong water retention capacity. Based on the water loss rate data, vegetation is divided into high water retention capacity (water loss rate less than 20%), medium water retention capacity (water loss rate between 20%-40%), and low water retention capacity (water loss rate greater than 40%), with corresponding scores of 8-10 points, 5-7 points, and 1-4 points, respectively.

[0103] (3) Water use efficiency assessment: Use photosynthetic instruments and other equipment to measure the photosynthetic rate and transpiration rate of vegetation and calculate the water use efficiency (photosynthetic rate / transpiration rate). Select sunny weather during the period of vigorous vegetation growth for measurement. For each vegetation species, measure multiple samples (e.g., 5-10) and take the average value. Based on the water use efficiency value, vegetation is divided into high efficiency (water use efficiency greater than 5), medium efficiency (water use efficiency between 3-5), and low efficiency (water use efficiency less than 3), with scores set at 8-10, 5-7, and 1-4.

[0104] The quantitative evaluation of ecological function indicators includes the following:

[0105] (1) Determination of wind and sand fixation ability: Set up a wind erosion observation site in the field, plant the vegetation to be evaluated, and evaluate its wind and sand fixation ability by measuring the changes in wind erosion before and after planting. The wind erosion amount can be measured by collecting sand particles with a sand collector and weighing them. Calculate the wind and sand fixation efficiency ((wind erosion amount before planting - wind erosion amount after planting) / wind erosion amount before planting × 100%). Based on the wind and sand fixation efficiency, the vegetation is divided into strong sand fixation type (wind and sand fixation efficiency greater than 60%), medium sand fixation type (wind and sand fixation efficiency between 30% and 60%), and weak sand fixation type (wind and sand fixation efficiency less than 30%), and is given 8-10 points, 5-7 points, and 1-4 points respectively.

[0106] (2) Evaluation of soil improvement effect: Analyze the impact of vegetation on the physical and chemical properties of the soil. In terms of physical properties, the soil bulk density, porosity and other indicators are measured. After planting vegetation, the soil bulk density decreases and the porosity increases, indicating that the soil structure has improved. In terms of chemical properties, the soil organic matter content, nitrogen, phosphorus, potassium and other nutrient contents are tested. The soil improvement effect is good if the vegetation can increase the soil organic matter and nutrient content during growth. According to the degree of soil improvement, the vegetation is divided into excellent improvement type (soil improvement effect is significant, and multiple indicators are significantly improved), good improvement type (some indicators are improved) and general improvement type (improvement effect is not obvious), with corresponding scores of 8-10 points, 5-7 points and 1-4 points.

[0107] (3) Calculation of carbon sequestration capacity: Carbon sequestration capacity is estimated by measuring vegetation biomass and soil organic carbon content. The biomass is measured using the harvesting method. At the end of the vegetation growth cycle, the aboveground parts are harvested and the underground roots are excavated and weighed. The vegetation carbon storage is calculated based on the conversion coefficient between biomass and carbon content; the soil organic carbon content is measured using chemical analysis methods. The total carbon sink is obtained by adding the vegetation carbon storage and the soil organic carbon increment. Based on the size of the total carbon sink, the vegetation is divided into high carbon sink type (total carbon sink greater than 10 tons / hectare), medium carbon sink type (total carbon sink between 5-10 tons / hectare) and low carbon sink type (total carbon sink less than 5 tons / hectare), with scores of 8-10 points, 5-7 points and 1-4 points respectively.

[0108] This example uses a screening index quantification method. For windbreak and sand fixation capability, a wind tunnel test of ≥500N / m indicates that the vegetation has excellent windbreak and sand fixation capability. For soil improvement effect, an organic matter increase rate after planting of ≥15% indicates that the vegetation has excellent soil improvement effect. For carbon sequestration capacity, an annual carbon sequestration of ≥50kg / m 2 , indicating that the carbon sequestration capacity of vegetation is excellent.

[0109] The quantitative evaluation of economic value indicators includes the following:

[0110] (1) Market price survey: Conduct market research on economically valuable vegetation products (such as medicinal materials, feed, etc.) to understand their market price fluctuation range. Based on the average market price over the past five years, high-priced vegetation (such as some precious medicinal materials) is rated 8-10 points, medium-priced vegetation (such as common feed crops) is rated 5-7 points, and low-priced vegetation is rated 1-4 points.

[0111] (2) Yield determination: Set up a sample plot of a certain area (e.g., 100 square meters) within the planting area and count the yield of the vegetation. For vegetation that can be harvested multiple times, calculate its total annual yield; for vegetation that can be harvested once, record its yield at maturity. Based on the yield data, the vegetation is divided into high-yield type (yield is more than 50% higher than the average yield of similar local vegetation), medium-yield type (yield is within ±50% of the average yield of similar local vegetation), and low-yield type (yield is less than 50% of the average yield of similar local vegetation), with corresponding scores of 8-10 points, 5-7 points, and 1-4 points.

[0112] In this embodiment, the entropy weight-TOPSIS method is used for comprehensive evaluation. The calculation formula of the entropy weight-TOPSIS method is as follows:

[0113]

[0114] Among them, W i is the comprehensive score, Y j is the indicator weight, X ij is the index score, i.e. the score of drought tolerance index, ecological function index or economic value index, maxX ij The maximum value of the indicator score.

[0115] In an exemplary embodiment, the scores of the drought tolerance index, ecological function index or economic value index of each vegetation species can also be weighted and summed to determine its comprehensive score. For example, the weight of the drought tolerance index is set to 0.4, the weight of the ecological function index is 0.4, and the weight of the economic value index is 0.2. The weights can be adjusted according to the needs of regional ecological restoration and economic development. The formula for calculating the comprehensive score is: comprehensive score = drought tolerance index score × 0.4 + ecological function index score × 0.4 + economic value index score × 0.2. The vegetation is sorted according to the comprehensive score, and the vegetation species with a comprehensive score greater than a certain threshold are screened out for use in the construction of vegetation patterns in semi-arid areas.

[0116] Step 4: Vegetation pattern design.

[0117] When designing the vegetation pattern in this embodiment, a water resource zoning method can be used to divide different vegetation planting areas, including high-water areas, medium-water areas, and low-water areas, based on regional topographic features (such as mountains, hills, plains, river valleys, etc.) and water resource distribution. For areas with relatively abundant water resources, such as high-water areas and medium-water areas, the planting density and diversity of vegetation can be appropriately increased. For areas with scarce water resources, such as low-water areas, highly drought-tolerant vegetation can be selected and the planting density can be reasonably controlled to determine the vegetation pattern design plan.

[0118] For example, in river valleys with relatively abundant water resources, where high vegetation coverage and diversity are planned, the proportion of trees and shrubs planted can be appropriately increased to enhance the stability of vegetation communities and improve their ecological services. In areas with scarce water resources, such as mountainous areas and hilltops, drought-tolerant herbs or low shrubs can be selected for planting, and planting density can be appropriately controlled to avoid excessive water consumption.

[0119] The water resource zoning method in this embodiment specifically includes the following steps:

[0120] (1) Use ArcGIS to perform Kriging interpolation to generate the spatial distribution of precipitation at a resolution of 30 m.

[0121] (2) According to the spatial distribution of precipitation, the natural break point method is used to divide the vegetation planting area, including: high water area: >400mm / a; medium water area: 200-400mm / a; low water area: <200mm / a.

[0122] This embodiment takes into account the structure of vegetation communities and constructs a multi-layered vegetation pattern that combines trees, shrubs, and grasses. As trees, vegetation with well-developed root systems and low transpiration rates is selected, with preference given to vegetation with well-developed root systems and low transpiration rates, such as Pinus sylvestris and Platycladus orientalis. These vegetation species can take root deep in semi-arid areas under limited water resources and obtain water while reducing water loss through transpiration. The spacing between trees is determined according to the characteristics of the tree species and site conditions, generally between 3 and 5 meters, forming a relatively sparse but stable superstructure. As shrubs, vegetation with strong drought-resistant and sand-fixing capabilities is selected, such as sea buckthorn and caragana, which have strong drought-resistant and sand-fixing capabilities. Sea buckthorn has a well-developed root system and strong adaptability, and can grow in poor soil and arid environments. Its fruit also has certain economic value. Caragana grows rapidly, has dense branches and leaves, and plays an important role in preventing wind and fixing sand. The planting density of shrubs is larger than that of trees, generally between 0.5 and 1 meter, forming the middle layer of the vegetation community, cooperating with trees to play functions such as preventing wind and fixing sand, and maintaining water and soil. Select herbaceous plants with broad adaptability and high coverage to form a patchwork and functionally complementary vegetation community. Select types such as Stipa grassi and Leymus chinensis that have broad adaptability and high coverage. Herbaceous plants can quickly cover the ground, reduce soil moisture evaporation, and prevent soil erosion. The planting density is determined according to the characteristics of the grass species. Generally, the sowing amount per square meter is between 10-20 grams, forming the lower structure of the vegetation community. It is staggered with the tree and shrub layer to jointly build a functionally complementary vegetation community. Combined with the ecological corridor theory, vegetation belts are planned along natural corridors such as rivers and valleys to enhance the connectivity and stability of regional ecosystems. Specifically, along natural corridors such as rivers and valleys, vegetation belts with a width of 10-50 meters are planned. Vegetation species that are resistant to flooding and whose roots can adapt to the impact of water flow, such as weeping willows and reeds, are selected in the vegetation belts. The weeping willow has a well-developed root system that can stabilize the river bank, and its branches and leaves can provide shade and habitat for aquatic organisms; reeds grow by the water, which can filter water quality, slow down the flow of water, promote sedimentation, and benefit the stability of the river bank ecosystem.

[0123] In this embodiment, a transition area can be set up between the vegetation belt and the surrounding vegetation communities, and some transitional vegetation, such as Robinia pseudoacacia, can be planted to naturally connect the vegetation belt with the surrounding environment, enhance the connectivity and stability of the regional ecosystem, and promote the migration and exchange of vegetation species between different vegetation communities.

[0124] When designing the vegetation pattern in this embodiment, a vegetation pattern optimization algorithm can also be used. First, a spatial analysis algorithm (such as spatial interpolation methods and overlay analysis operations in geographic information systems) is used to optimize the division of vegetation planting areas; then, a multi-objective optimization algorithm (such as genetic algorithms and simulated annealing algorithms) is combined with the highest water resource utilization efficiency, optimal ecological function, and maximum economic value as the objective function to optimize the vegetation pattern, determine the optimal vegetation planting layout and community structure combination, and obtain the final vegetation pattern design plan.

[0125] The spatial interpolation method utilizes spatial interpolation techniques such as Kriging interpolation in geographic information systems to interpolate regional water resource data (such as precipitation, surface water, and groundwater), soil data (such as soil type, soil fertility, and soil water retention capacity), and topographic data (such as altitude, slope, and aspect). Discrete monitoring point data is converted into continuous spatially distributed data to generate thematic maps of water resources, soil, and topography with a resolution of 30 meters or higher. For example, for precipitation data, interpolation can produce an estimated precipitation value for each grid cell (30m×30m) within the region, thereby more accurately identifying differences in the spatial distribution of water resources.

[0126] The overlay analysis operation is to overlay and analyze thematic maps of water resources, soil, and topography on the geographic information system. According to pre-set rules (such as areas with abundant water resources and fertile soil are suitable for planting vegetation with large water requirements, while areas with scarce water resources and poor soil are suitable for planting drought-resistant vegetation), the area is divided into different suitable vegetation planting areas. For example, areas with water resources greater than 500 mm and high soil fertility levels are divided into high-water-demand vegetation planting areas; areas with water resources less than 300 mm and low soil fertility levels are divided into drought-resistant vegetation planting areas. Through the overlay analysis operation, the vegetation planting suitability level of each area is determined, providing an accurate spatial basis for vegetation pattern design.

[0127] When setting the objective function for the water resource utilization efficiency objective function in this embodiment, the goal is to minimize the ratio of vegetation water requirement (ET) to available water resources (WR), i.e., min(ET / WR). Vegetation water requirement can be calculated using a vegetation water requirement model, and available water resources include the total amount of available water resources, such as precipitation, surface water, and exploitable groundwater. By optimizing the vegetation pattern, vegetation can maximize the use of limited water resources during growth, reducing water waste. For the ecological function objective function, a comprehensive ecological function evaluation index is constructed, including vegetation coverage (VC), biodiversity index (BDI), windbreak and sand fixation capacity (WSD), soil conservation capacity (SCR), etc. The objective function is max(VC×BDI×WSD×SCR), which maximizes the product of vegetation coverage, biodiversity index, windbreak and sand fixation capacity, and soil conservation capacity. By rationally configuring the vegetation community structure, the ecological service function of vegetation is improved, and the stability and anti-interference ability of the regional ecosystem are enhanced. For the economic value objective function, the economic output value (EV) of vegetation is calculated, including the market value of products such as feed, medicinal materials, and timber. The objective function is max(EV), which maximizes the economic output value of vegetation. By selecting vegetation species with high economic value and optimizing their layout, the economic benefits of vegetation construction can be maximized.

[0128] During the genetic algorithm optimization process, the population is first encoded and initialized. Parameters such as vegetation type, planting density, and planting location in the vegetation pattern design are encoded to form chromosomes. For example, binary encoding is used to represent vegetation type (00 for herbaceous plants, 01 for shrubs, and 10 for trees), while real numbers are used to represent planting density and location coordinates. An initial population is randomly generated, with the population size determined based on the complexity of the problem, typically between 50 and 100. Next, fitness evaluation is performed, calculating the fitness of each individual (vegetation pattern design) based on the set objective function. The fitness value reflects the design's performance in terms of water resource efficiency, ecological function, and economic value. Individuals with higher fitness values are more competitive within the population. Selection, crossover, and mutation operations are then performed, using strategies such as roulette wheel selection, to select individuals with higher fitness from the population as parents. Crossover is performed on these parent individuals, simulating genetic recombination in biological inheritance, to produce new offspring individuals. The crossover probability is typically set between 0.6 and 0.9. Mutation is performed on offspring individuals, altering certain genes with a certain probability (e.g., 0.01-0.1) to introduce new genetic information and prevent the algorithm from falling into a local optimal solution. Finally, iterative optimization repeats the fitness evaluation, selection, crossover, and mutation operations described above. After multiple generations (e.g., 100-500 generations), the population gradually evolves toward the optimal solution. Ultimately, the individuals with the highest fitness are obtained, indicating the optimal vegetation planting layout and community structure combination, which is the final vegetation pattern design.

[0129] During the simulated annealing optimization process, the initial solution and temperature are first set. An initial vegetation pattern design is randomly generated as the initial solution, with the initial temperature T0, the temperature drop coefficient α (generally between 0.8 and 0.99), and the termination temperature Tmin set. The initial temperature T0 must be sufficiently high to ensure that the algorithm can accept poor solutions in the initial stages and avoid falling into local optima. Next, a neighborhood solution is generated. Based on the current solution, parameters such as vegetation type, planting density, or location are randomly modified to generate a neighborhood solution. For example, the vegetation type in a certain area can be randomly replaced from herbaceous plants to shrubs, or the planting density of a certain plot can be adjusted. The acceptance criterion is then determined, and the objective function value of the neighborhood solution is calculated and compared with the objective function value of the current solution. According to the Metropolis criterion, a poor neighborhood solution is accepted with a certain probability. The probability formula is P = exp(-(ΔE / T)), where ΔE is the difference between the objective function value of the neighborhood solution and the current solution, and T is the current temperature. When the temperature is high, the probability of accepting a poor solution is high. As the temperature decreases, the probability of acceptance gradually decreases. Then, a temperature update and iterative optimization are performed, updating the temperature according to the temperature drop coefficient α, where T = T × α. The process of generating neighborhood solutions, determining the acceptance criteria, and updating the temperature is repeated until the temperature reaches the termination temperature Tmin. The resulting solution is the vegetation pattern design optimized by the simulated annealing algorithm. Through the optimized simulated annealing algorithm, it is possible to search for the global optimal or near-optimal solution within a large solution space, improving the rationality and scientific nature of vegetation pattern design.

[0130] Step 5: Vegetation planting and management.

[0131] This embodiment carries out vegetation planting operations according to the vegetation pattern design scheme obtained in step 4. The vegetation pattern design scheme clearly defines the vegetation pattern, that is, different areas in the semi-arid region can plant specific vegetation types that match the regional water resource conditions and are suitable for planting. Therefore, vegetation planting is carried out according to the vegetation pattern in the vegetation pattern design scheme. At the same time, appropriate planting time and planting methods (such as sowing, transplanting, etc.) can be selected according to actual conditions to ensure the survival rate of vegetation. Then, a vegetation monitoring system is established to regularly monitor the growth status of vegetation (such as height, diameter at breast height, coverage, etc.), soil moisture content, and water quality changes, and obtain vegetation monitoring results. Based on the vegetation monitoring results, vegetation management measures are adjusted in a timely manner, including irrigation methods and water volume, fertilization timing and dosage, pest and disease control and other vegetation management measures, so as to ensure the healthy growth of vegetation.

[0132] This embodiment, through precise water resource assessment and vegetation water demand analysis, scientifically screens suitable vegetation, rationally designs vegetation patterns, and conducts effective planting and management, to improve the success rate and stability of vegetation construction, achieve efficient use of water resources, enhance the self-repair capacity and ecological service functions of ecosystems in semi-arid areas, and promote the sustainable development of regional ecology, economy, and society.

[0133] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store water resource data, vegetation data and climate data, etc. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for constructing a water-based green vegetation pattern in a semi-arid area is implemented.

[0134] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0135] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0136] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0137] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0138] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0139] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for constructing a water-based green vegetation pattern in a semi-arid area, characterized in that: include: Obtain water resources data, vegetation data and climate data in semi-arid areas; Conducting a water resources assessment on the semi-arid region based on the water resources data to obtain a water resources assessment result; performing a vegetation water demand analysis on the semi-arid region based on the vegetation data and the climate data to obtain a vegetation water demand analysis result; Conducting a vegetation suitability evaluation for the semi-arid region based on the water resources assessment results and the vegetation water demand analysis results, and screening out vegetation species that match the water resources conditions of the semi-arid region to obtain a vegetation screening result; According to the vegetation screening results, a vegetation pattern design is performed on the semi-arid area to determine a vegetation pattern design scheme; the vegetation pattern design scheme is used as a vegetation planting scheme to facilitate vegetation planting operations in the semi-arid area.

2. The method for constructing a water-based green vegetation pattern in a semi-arid area according to claim 1, characterized in that: The water resource data includes precipitation data, surface water flow data, groundwater level and storage data; Based on the water resources data, a water resources assessment is conducted on the semi-arid region to obtain a water resources assessment result, specifically including: Based on the precipitation data, using a time series analysis method, determine the seasonal variation pattern of precipitation in the semi-arid region and the cycle of good and bad years; Assess the temporal distribution of water resources in the semi-arid region based on the seasonal variation of precipitation and the cycles of good and bad years in the semi-arid region; Using the spatial analysis tools of the geographic information system, interpolation analysis is performed on the surface water flow data and the groundwater level and storage data to draw a regional water resources spatial distribution map; Assess the distribution of water resources in different geographical locations in the semi-arid region based on the spatial distribution map of water resources in the region; Based on the temporal distribution of water resources in the semi-arid region and the distribution of water resources in different geographical locations, the total amount of water resources in the semi-arid region and its temporal and spatial distribution characteristics are determined as the water resources assessment result.

3. The method for constructing a water-based green vegetation pattern in a semi-arid area according to claim 1, characterized in that: Performing a vegetation water demand analysis on the semi-arid region based on the vegetation data and the climate data to obtain a vegetation water demand analysis result, specifically including: Determine the water requirements and water requirement frequencies of various vegetation types in the semi-arid region at various growth stages based on the vegetation data, including water requirements during the budding period, water requirements during the budding period, water requirements during the growing period, water requirements during the growing period, water requirements during the dormant period, and water requirements during the dormant period; Analyzing the effects of temperature, humidity, and wind speed on vegetation water demand based on the water demand and water demand frequency of various vegetation types in the semi-arid region at various growth stages and the climate data, and establishing a vegetation water demand model; Based on the vegetation water demand model, the vegetation water demand of each type of vegetation in the semi-arid area is calculated as the vegetation water demand analysis result.

4. The method for constructing a water-based green vegetation pattern in a semi-arid area according to claim 3, characterized in that: The expression of the vegetation water demand model is: ET = P + IRD - ΔS; Where ET is the vegetation water requirement, P is the precipitation, I is the irrigation amount, R is the surface runoff, D is the deep infiltration, and ΔS is the change in soil water storage.

5. The method for constructing a water-based green vegetation pattern in a semi-arid area according to claim 1, characterized in that: Based on the water resources assessment results and the vegetation water demand analysis results, a vegetation suitability evaluation is conducted on the semi-arid area, and vegetation species that match the water resources conditions of the semi-arid area are screened to obtain vegetation screening results, specifically including: Screening out vegetation species that match the water resource conditions of the semi-arid region based on the water resource assessment results and the vegetation water demand analysis results; Establish an index system for screening suitable vegetation, including drought tolerance, ecological function, and economic value indicators; Based on the suitable vegetation screening index system, the entropy weight-TOPSIS method is used to conduct a comprehensive quantitative evaluation of each selected vegetation species that matches the water resource conditions of the semi-arid area, and a comprehensive evaluation result is calculated as the vegetation screening result.

6. The method for constructing a water-based green vegetation pattern in a semi-arid area according to claim 1, characterized in that: Based on the vegetation screening results, a vegetation pattern design is performed for the semi-arid area, and a vegetation pattern design scheme is determined, specifically including: According to the vegetation screening result, a spatial analysis algorithm is used to divide the vegetation planting area in the semi-arid region to obtain a vegetation planting area division result; According to the results of the vegetation planting area division, a multi-objective optimization algorithm is used to optimize the vegetation pattern of each area divided in the semi-arid area with the highest water resource utilization efficiency, the best ecological function and the maximum economic value as the objective function, to determine the optimal vegetation planting layout and community structure combination, and obtain the vegetation pattern design plan.

7. The method for constructing a water-based green vegetation pattern in a semi-arid area according to claim 1, characterized in that: After designing a vegetation pattern for the semi-arid area based on the vegetation screening result and determining a vegetation pattern design scheme, the method for constructing a water-based green vegetation pattern in the semi-arid area further comprises: According to the vegetation pattern in the vegetation pattern design plan, vegetation planting operations are carried out in the semi-arid area.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for constructing a water-based green vegetation pattern in a semi-arid area as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for constructing a water-based green vegetation pattern in a semi-arid area according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for constructing a water-based green vegetation pattern in a semi-arid area according to any one of claims 1 to 7 is implemented.

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