A refined design method and system for aquatic ecosystem restoration plants

Abstract

This invention discloses a refined design method and system for aquatic ecological restoration plants, belonging to the field of aquatic ecological environment engineering technology. The method involves: integrating measured, remote sensing, and literature data to construct a basic database containing environmental and plant data; performing spatiotemporal statistical analysis on environmental factors and extracting feature values; dividing the plant planting ecological zones into four categories along the water depth gradient; establishing a carefully selected set of plant varieties for each zone through preliminary screening and refined screening based on the annual average number of super-flood-tolerant events using multi-year daily water depth time series; setting construction, area, and landscape boundary conditions; selecting varieties in descending order based on pollution removal economic indicators; constructing an optimization model with the goal of maximizing pollutant removal and constraints of engineering cost and planting area; and solving the model using the simplex method; evaluating environmental and ecological benefits through total pollutant removal and the Shannon-Wiener index. This invention achieves coordinated matching of plant configuration with dynamic environmental factors and construction timing, which can reduce greening costs.

Description

Technical Field

[0001] This invention relates to the field of water ecological environment engineering technology, specifically to a method and system for refined design of plants for water ecological restoration. Background Technology

[0002] As the core area for maintaining the material cycle and energy flow of aquatic ecosystems, ensuring biodiversity, and achieving pollutant purification, the scientific restoration and systematic construction of aquatic ecological spaces are of significant strategic importance for maintaining the health of water bodies. Aquatic ecological restoration, as a key engineering technology for addressing water pollution and ecosystem degradation, aims to gradually restore damaged water bodies to a near-natural healthy state with structural integrity, stable function, and self-sustaining capabilities through systematic engineering measures. Plant communities, as the core structural elements and functional carriers of aquatic ecological spaces, directly determine the long-term stability of river and lake ecosystems and the continued functioning of their ecosystem services through their restoration effectiveness.

[0003] In existing technologies, the design and configuration of aquatic ecological restoration plants mainly suffer from the following three problems.

[0004] Firstly, plant community configuration often relies heavily on static environmental parameters as a design basis, with insufficient consideration for the dynamic fluctuations of environmental factors. Environmental factors such as water level, water quality, temperature, and light in rivers and lakes fluctuate significantly throughout the year and over many years. In particular, daily changes in water level can cause plants to be submerged beyond their suitable water depth at specific times. Relying solely on static parameters such as the multi-year average water level for plant selection fails to reflect the true survival pressure plants face in a dynamically fluctuating environment over the long term, resulting in a significantly lower survival rate of selected plants during periods of abundant water or under extreme water level conditions compared to design expectations.

[0005] Secondly, plant design relies excessively on experience and lacks objective quantitative basis. The selection of plant species and their allocation in existing aquatic ecological restoration projects largely depend on the engineering experience and habitual preferences of designers, lacking objective tools for unified quantitative evaluation of the plant's pollutant purification efficiency, construction costs, and landscape appearance. In multi-objective and multi-constraint engineering implementation scenarios, this model struggles to ensure that the design scheme achieves the optimal balance between pollutant reduction, engineering economics, and landscape quality.

[0006] Thirdly, there is a time mismatch between the design phase and the construction phase. The available plant varieties, specifications, and unit prices vary significantly with the construction months, but traditional design methods treat plant costs as static parameters and do not incorporate construction time into the design model. This time mismatch often leads to plants specified in the design being unavailable, of incorrect specifications, or with significantly increased unit prices during actual construction, further causing problems such as decreased project implementation quality and cost overruns.

[0007] In summary, there is an urgent need for a refined design method and system for water ecological restoration plants that can take into account the dynamic fluctuations of environmental factors, provide objective quantitative evidence, and incorporate construction sequence considerations, so as to improve the scientific nature and feasibility of plant configuration and ensure the stable achievement of water ecological restoration goals. Summary of the Invention

[0008] To address the three main problems in existing technologies for aquatic ecological restoration plant configuration—insufficient consideration of dynamic fluctuations in environmental factors, over-reliance on experience in design, and misalignment between the design and construction phases—this invention provides a refined design method and system for aquatic ecological restoration plants. This method achieves coordinated matching between plant configuration and dynamic environmental factors and construction timing, improves the scientific rigor and feasibility of plant design, and ensures the stable achievement of aquatic ecological restoration goals while significantly reducing engineering costs.

[0009] To achieve the above objectives, a first aspect of the present invention provides a method for refined design of aquatic ecosystem restoration plants, comprising: By integrating measured data, satellite remote sensing data, and literature data, a basic database including environmental and plant data is constructed.

[0010] Statistical analysis of environmental factors in the spatiotemporal dimensions is performed on the environmental factor data in the basic database, an assessment of the current status of water environmental quality is conducted, and characteristic values ​​of environmental factors that have a key impact on plant growth and distribution are extracted.

[0011] Based on the water level characteristic value among the environmental factor characteristic values, and combined with the engineering design topography, geographic information system spatial analysis technology is used to divide the plant planting ecological zones along the water depth gradient.

[0012] Through preliminary screening and fine screening, a selection of plant varieties for each of the plant planting ecological zones was established.

[0013] Design boundary conditions are set, and the economic efficiency of pollutant reduction is used as an indicator. Based on the selected set of plant varieties, the optimal plant varieties for the scheme are determined. An optimization model is constructed with the maximization of pollutant removal as the objective function and engineering cost and planting area as constraints. The optimal area configuration scheme of plant varieties for each plant planting ecological zone is obtained by solving the mathematical programming method.

[0014] Based on the determined plant species and area allocation plan, a quantitative evaluation model is established to predict and evaluate the environmental and ecological benefits after the implementation of the plan.

[0015] Furthermore, the basic database is implemented collaboratively based on the QGIS platform and the MySQL database management system. Spatial data management and analysis are performed on the QGIS platform, while attribute data management and relational queries are performed on the MySQL database management system. The two are integrated and linked through geocoding or spatial location information. The environmental data includes hydrological elements, water quality elements, and climate elements. The hydrological elements are daily water level monitoring data of rivers and lakes in the project area. The water quality elements are monthly monitoring data of the water environment quality in the project area. The climate elements include the monthly average of regional sunshine hours, monthly average of sunshine intensity, and monthly average of temperature. The time series length of the environmental data is no less than 5 years.

[0016] Furthermore, the plant data includes basic attributes, preliminary attributes, refined attributes, and functional attributes. The basic attributes characterize the plant's ecological taxonomy, which includes wetland plants, emergent plants, floating-leaved plants, and submerged plants. The preliminary attributes include suitable climate zones, suitable water depth ranges, light intensity requirements, and temperature tolerance ranges. The refined attributes characterize the plant's resilience, including a flood tolerance index, quantified as the maximum number of days the plant can survive beyond its suitable water depth. The functional attributes characterize the plant's purification efficiency, economic viability, and ornamental value. Purification efficiency is characterized by the amount of pollutants absorbed per unit area of ​​plant, economic viability by the unit cost per construction month, and ornamental value by the plant's seasonal phenological characteristics.

[0017] Furthermore, the assessment of the current water environment quality includes determining the water quality category of a monitoring section and calculating the pollutant exceedance rate. The determination of the water quality category employs a single-factor evaluation method, determining the final water quality category of the monitoring section based on the single indicator category with the highest pollution level among the evaluation indicators during the evaluation period. The pollutant exceedance rate is calculated using the following formula: (1) In the formula, This represents the exceedance rate of evaluation indicator i; This indicates the number of times the concentration of evaluation indicator i exceeds the standard limit of the target water quality category within a given time period; N represents the total number of water environment monitoring times within the same time period; the evaluation indicator includes at least one of five-day biochemical oxygen demand, chemical oxygen demand, ammonia nitrogen, total phosphorus, and total nitrogen. The environmental factors with key influences on plant growth and distribution include hydrological and meteorological characteristics. The hydrological characteristics include the multi-year average water level, the multi-year average high water level, and the multi-year average low water level. The meteorological characteristics include the multi-year average air temperature, the multi-year average maximum air temperature, the multi-year average minimum air temperature, the multi-year average monthly sunshine intensity, and the multi-year average monthly sunshine duration.

[0018] Furthermore, the process of dividing the plant planting ecological zones along the water depth gradient includes the following steps: The engineering design topographic map is imported into a geographic information system platform. Using a vector-to-raster tool, elevation attributes are used as raster cell values ​​to generate design elevation raster data. Based on the difference between the water level characteristic value and the design elevation raster data, average water depth raster data for the restoration area is generated. According to preset zoning criteria, the average water depth raster data for the restoration area is reclassified to generate planting zoning planning data. The planting zoning planning data includes wetland plant zones, emergent plant zones, floating-leaved plant zones, and submerged plant zones.

[0019] Further, the preliminary screening includes: using the climate zone, water depth, temperature, light intensity, and light duration among the environmental factor characteristic values ​​as screening conditions, selecting plant varieties from a pre-stored plant library that match the preliminary attributes and environmental factor characteristic values ​​for each plant planting ecological zone, forming a preliminary selection set of plant varieties for each zone. The refined screening includes: based on the multi-year daily water depth time series of each plant planting ecological zone, using the flood tolerance index among the selected attributes as the key indicator, statistically analyzing the annual average number of flood tolerance events for each preliminary selection plant; setting a corresponding flood tolerance event threshold for each plant planting ecological zone; and removing plant varieties from the preliminary selection set of plant varieties whose annual average number of flood tolerance events exceeds the flood tolerance event threshold for the corresponding plant planting ecological zone, obtaining the selected set of plant varieties. The daily water depth of each zone is calculated using the following formula: (2) In the formula, This represents the daily water depth of zone m on day n; m is the zone number, corresponding to the wetland plant zone, emergent plant zone, floating-leaved plant zone, and submerged plant zone, respectively; n is the day number within the statistical period. This represents the daily water level monitoring value for day n. This represents the average elevation of zone m. The super-flood tolerance event is defined as follows: when the daily water depth of the zone containing the plant continuously exceeds the plant's maximum suitable water depth for more than the number of days it exceeds its flood tolerance index, it is recorded as a super-flood tolerance event, where the maximum suitable water depth is the upper limit of the plant's suitable water depth range. The annual average number of super-flood tolerance events is calculated using the following formula: (3) In the formula, This indicates the average number of events exceeding the flood tolerance level per year. This represents the number of events in year j where the daily water depth in a given area exceeds the maximum suitable water depth for plants and the duration exceeds their flood tolerance index; j is the statistical year number; J represents the total number of statistical years.

[0020] Furthermore, the design boundary conditions include construction conditions, planting area conditions, and landscape appearance conditions. The construction conditions refer to the planting time. The planting area conditions include the maximum and minimum planting area ratios for each zone. The maximum planting area for each zone is determined based on the planned area of ​​the ecological zone for plant cultivation. The minimum area ratio refers to the minimum allowable proportion of the planting area of ​​each plant variety to the maximum planting area of ​​the zone. The landscape appearance conditions include the total number of plant varieties in each zone, the number of evergreen plant species in each zone, and the number of colored plant species in each zone.

[0021] Furthermore, the economic efficiency of pollutant reduction is quantitatively characterized by a decontamination economic index, which is calculated using the following formula: (4) In the formula, This represents the economic efficiency of plant variety k in decontaminating pollutant i. This represents the amount of pollutant i absorbed per unit area by plant variety k; This represents the comprehensive construction cost of plant variety k at construction month t. The preferred plant varieties for the proposed scheme are determined as follows: Under the conditions of satisfying the construction and landscape features, based on the decontamination economic indicators, the varieties in the selected plant variety set are sorted in descending order, and the plant varieties ranking higher in the sequence are selected first to form a preferred set of plant varieties for each region. The objective function of the optimization model is: (5) The constraints of the optimization model include cost constraints, total area constraints for each partition, and minimum area percentage constraints. The cost constraints are as follows: (6) The total area constraint of the partition is: (7) The minimum area ratio constraint is: (8) In the formula, f(X) represents the total removal amount of pollutant i within partition m; X is the set of decision variables; and m is the number of the plant planting ecological partition. This represents the area where plant variety k is configured. This represents the amount of pollutant i absorbed per unit area by plant variety k; This represents the total number of plant varieties configured within partition m; ε represents the unit cost of plant variety k in construction month t; ε represents the maximum allowable total cost budget. Let represent the maximum planting area of ​​partition m; α represents the minimum area percentage. The optimization model is solved using the simplex method.

[0022] Furthermore, the prediction and assessment of the environmental and ecological benefits after the implementation of the plan includes calculating the total amount of pollutants removed using the following formula: (9) In the formula, This represents the total removal amount of pollutant of type i, expressed in kilograms per year; F represents the total number of plant species selected in the plan. This represents the area of ​​plant variety k, in square meters. The value represents the amount of pollutant i absorbed by plant variety k per unit area, expressed in grams per square meter per day; 365 is a conversion factor for unit time, representing the number of days in a year; 1000 is a conversion factor for unit mass, representing the conversion between grams and kilograms; pollutant i includes at least one of five-day biochemical oxygen demand, total nitrogen, and total phosphorus. The biodiversity index is calculated using the Shannon-Wiener index, as follows: (10) In the formula, H′ represents the biodiversity index; F represents the total number of plant species selected in the scheme; This represents the proportion of the planting area of ​​the kth plant variety to the total planting area.

[0023] A second aspect of this invention provides a refined design system for aquatic ecological restoration plants, comprising a database construction module, an environmental feature analysis module, a planting zone division module, a selected plant set establishment module, a design scheme generation module, and an ecological benefit assessment module. The database construction module integrates measured data, satellite remote sensing data, and literature data to construct a basic database including environmental and plant data. The environmental feature analysis module performs spatiotemporal statistical analysis on the environmental factor data in the basic database, conducts a water environment quality status assessment, and extracts characteristic values ​​of environmental factors that have a key impact on plant growth and distribution. The planting zone division module, based on the water level characteristic values ​​among the environmental factor characteristic values ​​and combined with the engineering design topography, uses geographic information system spatial analysis technology to divide plant planting ecological zones along water depth gradients. The selected plant set establishment module establishes a selected plant variety set for each plant planting ecological zone through preliminary and refined screening. The design scheme generation module is used to set design boundary conditions, using the economic efficiency of pollutant reduction as an indicator. Based on the selected set of plant varieties, it determines the preferred plant varieties for the scheme, constructs an optimization model with maximizing pollutant removal as the objective function and engineering cost and planting area as constraints, and solves the optimal area configuration scheme of plant varieties for each plant planting ecological zone using mathematical programming. The ecological benefit assessment module is used to establish a quantitative assessment model based on the determined plant varieties and area configuration schemes to predict and evaluate the environmental and ecological benefits after the scheme is implemented.

[0024] Compared with the prior art, the present invention has achieved the following three beneficial effects.

[0025] First, this invention introduces a refined screening mechanism based on daily water depth time series over many years. By calculating the daily water depth difference using formula (2) and statistically analyzing the annual average number of flood-tolerant events using formula (3), a quantitative coupling relationship is established between plant flood tolerance indicators and regional dynamic water level changes. Furthermore, unsuitable plants are eliminated based on regional flood-tolerant event thresholds. This mechanism overcomes the limitations of existing technologies that rely solely on static environmental parameters, enabling plant selection to reflect the real survival pressure under long-term dynamic hydrological conditions. It significantly improves the survival rate of selected plants during periods of abundant water and under extreme water level conditions, and solves the problem of insufficient consideration of dynamic fluctuations in environmental factors in existing technologies.

[0026] Second, this invention quantifies the pollutant purification efficiency and construction cost of plants into a single comparable indicator through the pollution removal economic index of formula (4). Using this indicator as the core basis, the selected plant varieties are sorted in descending order. Combined with constraints on the number of evergreen and colored plant varieties under landscape conditions, a preferred set of plant varieties for each zone is formed. Based on this, an optimization model is constructed with the objective function of maximizing pollutant removal (formula (5),) cost constraints (formula (6), total area constraints (formula (7), and minimum area ratio constraints (formula (8))) as multiple constraints. The globally optimal area configuration scheme is obtained by solving the simplex method. This mechanism provides an objective quantitative tool for plant configuration in multi-objective, multi-constraint engineering implementation scenarios, solving the problem of existing technologies over-reliance on experience and lack of quantitative basis.

[0027] Third, the present invention addresses the cost variables of the decontamination economic indicators. The design model explicitly introduces the construction month 't' as a time dimension, incorporating the characteristics of plant market availability and unit price changes over construction time. This mechanism organically links the design phase and construction period through the time-series parameter of unit cost, avoiding situations where selected plants are unavailable, do not meet specifications, or experience significant price increases during actual construction. This solves the problem of time sequence misalignment between the design and construction phases in existing technologies.

[0028] By combining the above three mechanisms, this invention achieves synergistic matching between plant configuration and dynamic environmental factors and construction timing, thereby improving the scientific rigor and feasibility of plant design. In the embodiments, while achieving the water environment management goals, this method can effectively reduce greening costs by 15%, ensuring the stable achievement of water ecological restoration goals. Attached Figure Description

[0029] Figure 1 This is a flowchart of the refined design method for aquatic ecological restoration plants according to the present invention; Figure 2 This is a structural diagram of the refined design system for aquatic ecological restoration plants according to the present invention; Figure 3 This is a diagram of the environmental factor database structure of the present invention; Figure 4 This is a diagram of the plant attribute database structure of the present invention; Figure 5 Topographic maps and topographic grid maps for example engineering designs; Figure 6 Example: Plant zoning planning map; Figure 7 The flowchart for selecting preferred plant varieties for the present invention is shown below. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the implementation of the present invention is not limited thereto.

[0031] like Figure 2 As shown, the refined design system for aquatic ecological restoration plants of the present invention includes a database construction module, an environmental feature analysis module, a planting zone division module, a selected set establishment module, a design scheme generation module, and an ecological benefit assessment module.

[0032] The database construction module is responsible for integrating measured data, satellite remote sensing data, and literature data to build a basic database including environmental and plant data. In this embodiment, the basic database is implemented collaboratively based on the QGIS platform and the MySQL database management system. The QGIS platform manages and performs spatial analysis operations on spatial data, storing spatial data such as meteorological element raster, design elevation raster, and planting zoning planning data. The MySQL database management system manages and performs relational queries on attribute data, storing structured data such as water level, water quality monitoring time series, and plant attribute tables. The two platforms are linked and integrated through geocoding or spatial location information. The environmental factor database structure is as follows: Figure 3 As shown, the structure of the plant attribute database is as follows: Figure 4 As shown.

[0033] The environmental feature analysis module is responsible for calling environmental factor data in the basic database, performing spatiotemporal statistical analysis, performing water environment quality status evaluation, calculating the exceedance rate of various pollutants according to formula (1), and extracting environmental factor feature values ​​that have a key impact on plant growth and distribution, including hydrological feature values ​​and meteorological feature values.

[0034] The planting zoning module is responsible for importing the engineering design topographic map into the QGIS platform, using the vector-to-raster tool to generate design elevation raster data, combining the multi-year average water level characteristic value to generate average water depth raster data of the restoration area, and reclassifying it according to the preset zoning criteria to generate four types of planting zoning planning data: wetland plant area, emergent plant area, floating-leaved plant area, and submerged plant area.

[0035] The module for establishing the selected set is responsible for executing two sub-processes: preliminary screening and fine screening. Preliminary screening is based on environmental factor characteristic values, selecting plants with matching initial attributes from the plant library to form a preliminary set of plant varieties for the region; fine screening is based on the daily water depth of formula (2) and the annual average number of flood-tolerant events of formula (3), combined with the regional flood-tolerant event threshold. By removing unsuitable plants, a carefully selected collection of plant varieties for each zone is obtained.

[0036] The design scheme generation module is responsible for setting construction conditions, planting area conditions, and landscape features conditions, and calculating the decontamination economic indicators of each plant variety according to formula (4). Figure 7The proposed scheme optimizes the plant variety selection process to form a set of optimal plant varieties for each zone. Based on this, an optimization model is constructed with formula (5) as the objective function and formulas (6) to (8) as constraints. The optimal area allocation scheme of plant varieties for each zone is obtained by solving the simplex method.

[0037] The ecological benefit assessment module is responsible for calculating the total amount of pollutant removal according to formula (9) and the biodiversity index according to formula (10) Shannon-Vina index, and for predicting and assessing the environmental and ecological benefits after the implementation of the plan.

[0038] like Figure 1 As shown, the refined design method for aquatic ecosystem restoration plants of the present invention includes steps S1 to S6 in sequence. This embodiment takes a lake aquatic ecosystem restoration project as an example to explain each step in detail.

[0039] Step S1: Construct a multi-source basic database Based on the geographical location and time frame of specific engineering projects, a basic database including environmental and plant data is constructed. In this embodiment, the measured water level data time series is 10 years long, the measured water quality data time series is 5 years long, the meteorological element raster data accuracy is 1 km, and the time scale is from 1990 to 2020. The structure of the environmental factor database is as follows: Figure 3 As shown, the climate elements in the environmental factor data are stored in the QGIS project database in raster data form, including nationwide temperature and solar radiation intensity raster data. To ensure the timeliness of feature analysis, the meteorological database is regularly updated with the latest data.

[0040] Plant attribute database structure as follows Figure 4 As shown. Plant attribute data serves as the foundational background database in the system and can be directly accessed. Plant data includes four categories: basic attributes, preliminary attributes, refined attributes, and functional attributes. Basic attributes characterize the ecological group affiliation of plants, including wetland plants, emergent plants, floating-leaved plants, and submerged plants. Preliminary attributes define the basic suitable growth range of plants, including suitable climate zones, suitable water depth ranges, light intensity requirements, and temperature tolerance ranges. Refined attributes characterize the stress resistance of plants, including flood tolerance indicators, which are quantified as the maximum number of days a plant can survive beyond its suitable water depth. Functional attributes characterize the purification efficiency, economic efficiency, and ornamental value of plants. Purification efficiency is characterized by the amount of pollutants absorbed per unit area of ​​plant, economic efficiency by the unit cost in different construction months, and ornamental value by the seasonal changes in the plant. To ensure the timeliness of the design scheme, the plant attribute database is updated regularly to incorporate new varieties and update construction costs in a timely manner.

[0041] Step S2: Analyze the spatiotemporal characteristics of the environment This step includes three sub-sections: hydrological characteristic analysis, meteorological element characteristic analysis, and water environment quality assessment.

[0042] Hydrological characteristic analysis. The statistical functions of a MySQL database were used to perform statistical analysis on daily water level monitoring data during the project's water area measurement period, and to calculate the multi-year average water level. Multi-year average high water level Multi-year average low water level In this embodiment, = 7.67 m, = 9.84 m, = 6.66 m.

[0043] Meteorological element characteristic analysis. In the QGIS environment, input the boundary of the project area, retrieve pre-stored meteorological element data, extract the spatial distribution of meteorological elements within the area using the spatial clipping function, and calculate key climate parameters, including multi-year average temperature, using raster statistical functions. Multi-year average maximum temperature Multi-year average minimum temperature Average annual solar radiation intensity And the average monthly sunshine duration over many years. In this embodiment, =18.2 ℃, = 29.9 ℃, = 5.5 ℃, = 15000 kJ·m -2 ·d -1 .

[0044] Water environment quality assessment. The current status assessment of water environment quality includes the determination of water quality category at monitoring sections and the calculation of pollutant exceedance rates. The determination of water quality category at monitoring sections adopts the single-factor evaluation method, which determines the final water quality category of the section based on the single indicator category with the highest pollution level among the evaluation indicators during the evaluation period. The pollutant exceedance rate is calculated according to formula (1): (1) In the formula, This represents the exceedance rate of evaluation indicator i; This represents the number of times the concentration of evaluation indicator i exceeds the standard limit for the target water quality category within a given time period; N represents the total number of water environment monitoring sessions within the same time period. In this embodiment, the evaluation target is Class III surface water environment, and the key pollutant indicators considered include chemical oxygen demand (COD), ammonia nitrogen, and total phosphorus. Calculations show that the exceedance rate for ammonia nitrogen is 13.8%, the exceedance rate for COD is 8.33%, and the exceedance rate for total phosphorus is 16.7%. Therefore, it can be determined that the most severely exceeded pollutant indicator in this embodiment is total phosphorus, and this identification result will serve as the key pollutant indicator in subsequent plant configuration optimization design.

[0045] Step S3: Divide the plant planting ecological zones Step S3 includes three sub-steps: design elevation data processing, average water depth calculation, and planting zoning planning generation.

[0046] Sub-step S3.1: Design elevation data processing. Import the DWG format CAD engineering design topographic map into the QGIS platform. Using the vector-to-raster tool, generate design elevation raster data at the set resolution, using elevation attributes as raster cell values. The spatial resolution is set based on the area and terrain complexity of the engineering region. In this embodiment, a resolution value between 0.5 meters and 2 meters is selected to balance data accuracy and computational efficiency. The design elevation raster map generated from the CAD engineering design drawings after elevation data processing is shown in the figure below. Figure 5 As shown.

[0047] Sub-step S3.2: Calculate the average water depth. This involves calling the multi-year average water level characteristic value of the river / lake section where the project is located, obtained in step S2. Perform water depth calculation in the QGIS raster calculator to generate raster data of the average water depth in the restoration area. The calculation formula is: In the formula, This is the average water depth raster data for the restoration area; The characteristic value of the multi-year average water level of the river section where the project is located; To design elevation raster data.

[0048] Sub-step S3.3: Planting zoning plan generation. Based on preset zoning criteria, the average water depth raster data is processed using the QGIS raster reclassification tool. Reclassification is performed to generate planting zoning planning data. The zoning criteria used in this embodiment are based on the physiological characteristics and growth requirements of aquatic plants in different ecological types, and the specific divisions are as follows: In wetland plant areas, when When the water level is ≤ 0 m, this area is mainly distributed above the water level or in areas that are periodically flooded, and is suitable for planting wetland plant species with strong drought resistance and short-term flood resistance.

[0049] Emergent plant area, when 0 < When the water depth is ≤ 0.6 m, this water depth range is suitable for the growth of emergent plants, which usually have long stems that allow their photosynthetic organs to extend above the water surface.

[0050] In the floating-leaved plant area, when 0.6 < When the depth is ≤ 0.9 m, the water depth is suitable for the growth of floating-leaved plants, whose leaves float on the water surface and whose roots are fixed in the bottom mud.

[0051] Submerged plant area, when 0.9 < When the water depth is ≤ 1.8 m, this water depth range is suitable for the growth of submerged plants. These plants are completely submerged in water and have specific requirements for underwater light conditions.

[0052] The planting zoning plan in this embodiment is as follows: Figure 6 As shown.

[0053] Step S4: Establish a curated collection of plant varieties for each region Step S4 includes two sub-steps: preliminary screening and fine screening.

[0054] Sub-step S4.1: Preliminary screening. This embodiment belongs to the Southern Climate Zone. The environmental indicator data of the project area obtained in steps S2 and S3 are used as query conditions for the plant attribute database. The environmental indicators specifically include in this embodiment: = 18.2 ℃, = 29.9 ℃, = 5.5 ℃, = 15000 kJ·m -2 ·d -1 .

[0055] This embodiment utilizes a MySQL database management system to construct multi-condition joint query statements to filter plant varieties from a plant attribute database that meet engineering environmental requirements. The specific filtering logic is as follows: An ecological group matching query condition is established, with the query statement `ecological_group = [corresponding ecological type]`, where ecological groups include wetland plants, emergent plants, floating-leaved plants, and submerged plants. A climate zone matching query condition is established, with the query statement `JSON_CONTAINS(suitable_climate_zones, "South")`. A temperature suitability query condition is established, with the query statement `min_temperature >= 5.5 AND max_temperature <= 29.9`. A light intensity suitability query condition is established, with the query statement `min_light_intensity >= 15000`.

[0056] Through the database query operations described above, a preliminary selection set of plant varieties for each plant planting ecological zone is generated. In this embodiment, the preliminary screening results for each zone are as follows: 23 suitable varieties are obtained for the wetland plant zone, 20 suitable varieties for the emergent plant zone, 9 suitable varieties for the floating-leaved plant zone, and 10 suitable varieties for the submerged plant zone. The preliminary selection set of plant varieties is stored in the database.

[0057] Sub-step S4.2, Refined Screening. Based on the initial screening, the preliminary selection of plant varieties is further refined by combining the dynamic characteristics of water level changes and through flood tolerance assessment. This step includes four sub-processes: calculating the daily water depth of each zone, quantifying the annual average number of flood tolerance events for the initially screened plants, setting the flood tolerance event threshold, and establishing a refined selection of plant varieties for each zone.

[0058] Subprocess S4.2.1 calculates the daily water depth of the zone. Based on historical water level data and zone topographic features, the daily water depth of the zone is calculated according to formula (2): (2) In the formula, This represents the daily water depth of zone m on day n, in meters; m is the zone number, with values ​​of 1, 2, 3, and 4, corresponding to the wetland plant zone, emergent plant zone, floating-leaved plant zone, and submerged plant zone, respectively; n is the day number within the statistical period. This represents the daily water level monitoring value for day n, in meters. This represents the average elevation of zone m, in meters.

[0059] Subprocess S4.2.2 quantifies the annual average number of superflood tolerance events for the initially screened plants. For each initially screened plant species, the maximum suitable water depth is determined based on the maximum suitable water depth recorded in the plant attribute database. Using SQL conditions to determine > Extract water from the zone where the plant is located where the water depth exceeds The time series. Identify consecutive time series exceeding [a certain threshold]. If the number of consecutive days exceeds the flood tolerance index for that plant attribute, it is counted as a super-flood tolerance event. The number of flood tolerance events is recorded annually. , where j represents the statistical year number. Based on the statistical results of each year, the average number of super-flood tolerance events per year for this plant is calculated according to formula (3): (3) In the formula, This represents the average number of events exceeding the flood tolerance standard per year. The term "J" represents the number of events in the j-th year where the daily water depth in a given region exceeds the plant's maximum suitable water depth and the duration of these events exceeds its flood tolerance index; J represents the total number of statistical years, which in this embodiment is no less than 10 years. For each plant variety obtained from the initial screening, the average annual number of flood tolerance events is quantified and stored in a new attribute field. middle.

[0060] Sub-process S4.2.3 sets the threshold for over-flooding events. A corresponding threshold for over-flooding events is set for each plant zone. The threshold for the super-flood tolerance event is related to the redundancy of plant survival rate and the cost of plant replacement in the project. Higher plant survival rate redundancy results in a lower super-flood tolerance event threshold; higher plant replacement costs also result in a lower super-flood tolerance event threshold. Generally, the super-flood tolerance event threshold for plants does not exceed 2, meaning that the average number of super-flood tolerance events per year does not exceed 2. In this embodiment, the threshold is set as: the threshold for the wetland plant area. = 1, Threshold for emergent plant areas = 2, Threshold for floating-leaved plant area = 2, Threshold for submerged plant area = 2.

[0061] Subprocess S4.2.4 establishes a carefully selected set of plant varieties for each region. Plants selected in the initial screening are excluded based on the annual average number of super-flood-tolerant events. Exceeding the corresponding partition threshold The selected plant varieties were selected for each region. In this embodiment, the number of selected varieties in each region is as follows: 10 suitable varieties for the wetland plant region, 8 suitable varieties for the emergent plant region, 6 suitable varieties for the floating-leaved plant region, and 8 suitable varieties for the submerged plant region.

[0062] Step S5: Generate plant design scheme Step S5 includes three sub-steps: setting design boundary conditions, determining the preferred plant varieties, and optimizing the plant area.

[0063] Sub-step S5.1: Set design boundary conditions. The design boundary conditions include construction conditions, planting area conditions, and landscape appearance conditions.

[0064] Construction conditions refer to the planting time, which directly affects the available plant varieties, specifications, and unit prices on the market. In this example, the planting time is November.

[0065] Planting area conditions include the maximum planting area for plants in each zone. And the minimum area ratio α. The maximum planting area of ​​the plants in the specified zone. The planting area is determined based on the planned area of ​​the planting zones as specified in step S3. In this embodiment, the maximum planting area for each zone is: = 5000 square meters, = 135,025 square meters, = 75293 square meters, = 368,430 square meters. The minimum area ratio α is set by the designer according to project requirements. Its technical purpose is to avoid the extinction of specific plants due to interspecific competition by limiting the minimum area ratio of a single plant in its respective zone. In this embodiment, α = 10%.

[0066] Landscape conditions are controlled through quantitative indicators, including the total number of plant species in each zone. Number of evergreen plant species in each zone And the number of flowering or colorful foliage plants in each zone In this embodiment, the total number of plant varieties in each zone is: = 6, = 5, = 3, = 4. The required number of evergreen plant species in each zone is as follows: ≥ 2, ≥ 2, = 0 indicates no requirements. ≥ 1. The required number of colored plant species in each zone is as follows: ≥ 2, ≥ 3, ≥ 1, = 0 indicates no requirements.

[0067] Sub-step S5.2: Determine the preferred plant varieties for the scheme. Retrieve the selected plant varieties obtained in step S4 from the MySQL database, and calculate the decontamination economic indicators of each plant in the selected set according to the following process. Retrieve the functional characteristics of the plants, including the unit cost and purification efficiency of the plants under different construction months. In this embodiment, the plant construction month is November. Search and retrieve the corresponding comprehensive construction cost of the plants under this month from the plant database, and calculate the decontamination economic indicators of the plants according to formula (4): (4) In the formula, The decontamination economic index of plant variety k for pollutant i is expressed in grams per yuan, representing the amount of pollutant that can be removed per unit cost. This represents the amount of pollutant i absorbed by plant variety k per unit area, expressed in grams per square meter. This represents the comprehensive construction cost of plant variety k at construction month t, expressed in yuan per square meter. This cost is a dynamic parameter, adjusted according to market price fluctuations during the construction season. A higher value for this decontamination economic efficiency index indicates a higher unit cost pollutant removal efficiency and better economic performance for that plant variety.

[0068] Based on the obtained pollution removal economic indicators, a three-round hierarchical screening rule was established, categorized by ecological group, constrained by landscape appearance requirements, and ranked according to pollution removal economic indicators. The optimal plant variety selection process is as follows: Figure 7 As shown, the establishment of an optimized set of wetland plants will be used as an example for illustration.

[0069] The first round of screening prioritized evergreen plants. A selection criteria were established within the chosen plant variety set, specifying that the ecological type was hygrophytes and the evergreen attribute was true. The selected plant varieties were then sorted in descending order based on their decontamination economic efficiency, and the varieties ranked highest were selected. One plant, in this embodiment = 2, save it to the preferred plant set.

[0070] The second round of screening focused on selecting the best colored plants. A selection rule was established within the chosen plant variety set, specifying that the ecological type was hygrophytes and the colored attribute was present. The selected plant varieties were then sorted in descending order based on their decontamination economic efficiency, and the varieties ranked highest were selected. One plant, in this embodiment = 2, save it to the preferred plant set.

[0071] The third round of screening is a comprehensive selection and supplement. The remaining plant varieties in the selected plant variety set that are of the wetland type are sorted in descending order according to their decontamination economic efficiency index, and the top N plants are selected, where N = - - In this embodiment, N = 2, and it is saved to the preferred plant set.

[0072] The selection of preferred plant varieties for other ecological groups was carried out according to the same rules. In this embodiment, the final set of preferred plant varieties for each zone was determined as follows: wetland plants were Juncus effusus, Siberian iris, Imperata cylindrica, Aquilaria sinensis, Reed and Dictyophora indica; emergent plants were Canna indica, Thalia dealbata, Typha orientalis, Reed and Pickerelweed; floating-leaved plants were Water chestnut, Pennywort and Water lily; submerged plants were Vallisneria natans, Potamogeton malaianus, Foxtail grass and Hydrilla verticillata.

[0073] Sub-step S5.3, Optimization of Plant Area Allocation. This sub-step, based on the completed selection of optimal plant varieties, establishes a multi-constraint optimization model to achieve the scientific allocation of plant area within each zone.

[0074] Subprocess S5.3.1: Establish the optimization objective function. Retrieve the optimal plant selection set and, for each plant planting ecological zone m, establish an optimization function with the objective of maximizing pollutant removal, calculated according to formula (5): (5) In the formula, f(X) represents the total removal amount of pollutant i within partition m; X is the set of decision variables, representing the area of ​​each plant species within partition m. This represents the area of ​​plant variety k, in square meters. This represents the amount of target pollutant i absorbed by plant variety k per unit area, expressed in grams per square meter. This represents the total number of plant species configured within partition m; m is the number of the plant planting ecological partition, with values ​​of 1, 2, 3, and 4, corresponding to the wetland plant zone, emergent plant zone, floating-leaved plant zone, and submerged plant zone, respectively. In this embodiment, the target pollutant i is total phosphorus, the pollutant with the highest exceedance rate.

[0075] Sub-process S5.3.2 sets multiple constraints. The optimization model is subject to three types of constraints to ensure the feasibility, economy, and ecological rationality of the solution.

[0076] Cost constraints. The maximum permissible cost budget is used as a constraint, according to formula (6): (6) In the formula, This represents the area of ​​plant variety k, in square meters. This represents the unit cost of plant variety k in a given construction period of month t, expressed in yuan per square meter. ε represents the total number of plant varieties configured within partition m; ε represents the maximum allowable total cost budget, in yuan.

[0077] Total area constraint for each zone. The total area of ​​all plants within a zone must not exceed the maximum allowable planting area for that zone, as per formula (7): (7) In the formula, This represents the maximum planting area for plants in zone m, expressed in square meters.

[0078] Minimum area ratio constraint. The area of ​​any plant variety must not be less than the minimum preset ratio of the area of ​​that zone, according to formula (8): (8) In the formula, α represents the minimum area percentage, expressed as a percentage, and is used to maintain biodiversity and structural stability. In this embodiment, α = 10%.

[0079] Sub-process S5.3.3 determines the cost constraint parameters. The range of cost constraints is determined using either the ideal point method or the direct engineering determination method. The ideal point method solves for the minimum cost that satisfies the area constraint condition. and the maximum cost corresponding to maximizing pollutant removal. Based on this, the theoretical scope of cost constraints is determined as follows: ≤ ε ≤ The direct determination method for engineering projects determines the cost constraint value directly based on the actual situation of the project, typically satisfying ε ≤ .

[0080] Subprocess S5.3.4, Pareto Front Generation and Optimal ε Selection. A discrete value sequence of ε is set, the optimization model is solved for each ε, the corresponding optimal solution is recorded, a curve showing the relationship between cost and pollutant removal is generated, the marginal benefit corresponding to each ε is calculated, and the ε value before the marginal benefit begins to decrease significantly is selected as the constraint value.

[0081] Subprocess S5.3.5 optimizes the model solution and stores the results. The optimization model is solved using the simplex method. This algorithm systematically searches the vertices of the feasible region to find the optimal plant area configuration scheme that maximizes the objective function while strictly satisfying all constraints. After the solution is completed, a planting area attribute field is added to the plant preference set, and the optimal configuration area of ​​each plant is stored in this attribute. The optimal configuration areas of each plant obtained in this embodiment are shown in Table 1.

[0082] It should be noted that the planting area of ​​each plant listed in Table 1 is illustrative data of this embodiment, used to illustrate the overall application process and output form of the method of the present invention; in actual engineering implementation, the final plant area configuration scheme should be obtained by strictly solving the multi-constraint optimization model described in the present invention, and the planting area of ​​each plant should meet the minimum area ratio constraint described in formula (8).

[0083] Table 1 Optimal Plant Configuration Area Table Step S6: Assess the ecological and environmental benefits Based on the determined plant species and area allocation plan, the environmental and ecological benefits achievable after the implementation of the plan are quantitatively estimated. Environmental benefits are characterized by the total amount of pollutants removed, and ecological benefits are characterized by the biodiversity index.

[0084] The total amount of pollutants removed is calculated according to formula (9): (9) In the formula, This represents the total removal amount of pollutant of type i, expressed in kilograms per year; F represents the total number of plant species selected in the plan. This represents the area of ​​plant variety k, in square meters. This represents the amount of target pollutant i absorbed per unit area by plant variety k. The daily average rate is taken as grams per square meter per day, which is equivalent to the annual cumulative amount in formula (4) after dividing the latter by 365; 365 is the unit time conversion factor, representing the number of days in a year; 1000 is the mass unit conversion factor, representing the conversion relationship between grams and kilograms. The pollutant i includes at least one of five-day biochemical oxygen demand, total nitrogen, and total phosphorus. In this embodiment, the pollutant removal amount calculated according to the obtained plant configuration scheme is: the chemical oxygen demand removal amount is 7.36 × 10 5 The annual ammonia nitrogen removal rate is 9.58 × 10⁻⁶ kg / kg. 4 The total phosphorus removal is 9.14 × 10⁻⁶ kg / year. 3 kilograms per year.

[0085] The biodiversity index is calculated using the Shannon-Wiener index, according to formula (10): (10) In the formula, H′ represents the biodiversity index; F represents the total number of plant species selected in the scheme; This represents the proportion of the planting area of ​​the kth plant species to the total planting area. A higher biodiversity index indicates a higher degree of biodiversity in the plant configuration. In this embodiment, the biodiversity index H′ = 4.44 calculated based on the obtained plant configuration is at a moderately high level, indicating that the community has good species abundance and evenness.

[0086] Based on the implementation process and results of steps S1 to S6 above, this embodiment uses the refined design method and system for aquatic ecological restoration plants described in this invention to design plant configuration, ultimately determining a planting area configuration scheme for 18 plant varieties. This scheme, while achieving the water environment governance goals, can effectively reduce greening costs by 15% compared to traditional empirical design methods. The survival rate of the selected plants under dynamic water level conditions is significantly improved, and both pollutant reduction and biodiversity maintenance effects meet expectations. This embodiment verifies the scientific validity, feasibility, and economic efficiency of the method and system described in this invention in aquatic ecological restoration projects.

[0087] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of protection of the present invention. Those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for refined design of plants for aquatic ecological restoration, characterized in that, include: By integrating measured data, satellite remote sensing data, and literature data, a basic database including environmental and plant data is constructed. Statistical analysis of environmental factors in the spatiotemporal dimensions was performed on the environmental factor data in the basic database, an assessment of the current status of water environmental quality was conducted, and characteristic values ​​of environmental factors that have a key impact on plant growth and distribution were extracted. Based on the water level characteristic value among the environmental factor characteristic values, combined with the engineering design topography, and using geographic information system spatial analysis technology, plant planting ecological zones are divided along the water depth gradient; Through preliminary screening and fine screening, a carefully selected set of plant varieties for each of the plant planting ecological zones was established. Design boundary conditions are set, and the economic efficiency of pollutant reduction is used as an indicator. Based on the selected set of plant varieties, the optimal plant varieties for the scheme are determined. An optimization model is constructed with the maximization of pollutant removal as the objective function and engineering cost and planting area as constraints. The optimal area configuration scheme of plant varieties for each plant planting ecological zone is obtained by solving the mathematical programming method. Based on the determined plant species and area allocation plan, a quantitative evaluation model is established to predict and evaluate the environmental and ecological benefits after the implementation of the plan.

2. The method for refined design of aquatic ecosystem restoration plants according to claim 1, characterized in that, The basic database is implemented collaboratively based on the QGIS platform and the MySQL database management system. Specifically, the QGIS platform is used for spatial data management and spatial analysis, while the MySQL database management system is used for attribute data management and related queries. The two are linked and integrated through geocoding or spatial location information. The environmental data includes hydrological elements, water quality elements, and climate elements; the hydrological elements are daily water level monitoring data of rivers and lakes in the project area; the water quality elements are monthly water environment quality monitoring data of the project area; the climate elements include the monthly average sunshine hours, monthly average sunshine intensity, and monthly average temperature of the area; the time series length of the environmental data is not less than 5 years.

3. The method for refined design of aquatic ecosystem restoration plants according to claim 1, characterized in that, The plant data includes basic attributes, preliminary attributes, refined attributes, and functional attributes; The basic attributes characterize the ecological taxonomy of the plant, which includes wetland plants, emergent plants, floating-leaved plants, and submerged plants. The initial selection attributes include suitable climate zones, suitable water depth ranges, light intensity requirement ranges, and temperature tolerance ranges. The selected attributes characterize the plant's stress resistance, including a flood tolerance index, which is quantified as the maximum number of days the plant can survive under conditions exceeding its suitable water depth. The functional attributes characterize the purification efficiency, economic efficiency, and ornamental value of the plant. The purification efficiency is characterized by the amount of pollutants absorbed by the plant per unit area. The economic efficiency is characterized by the unit cost in different construction months. The ornamental value is characterized by the seasonal changes of the plant.

4. The method for refined design of aquatic ecosystem restoration plants according to claim 1, characterized in that, The current status assessment of water environment quality includes the determination of water quality category of cross section and the calculation of pollutant exceedance rate; the determination of water quality category of cross section adopts the single factor evaluation method, and the final water quality category of the cross section is determined according to the single indicator category with the highest pollution degree among the evaluation indicators of the monitoring cross section during the evaluation period. The pollutant exceedance rate is calculated using the following formula: In the formula, This represents the exceedance rate of evaluation indicator i; This indicates the number of times the concentration of evaluation indicator i exceeds the standard limit of the target water quality category within a given time period; N represents the total number of water environment monitoring times within the same time period; the evaluation indicator includes at least one of five-day biochemical oxygen demand, chemical oxygen demand, ammonia nitrogen, total phosphorus, and total nitrogen; The environmental factors that have a key impact on plant growth and distribution include hydrological and meteorological characteristics. The hydrological characteristics include the multi-year average water level, the multi-year average high water level, and the multi-year average low water level. The meteorological characteristics include the multi-year average temperature, the multi-year average maximum temperature, the multi-year average minimum temperature, the multi-year average monthly sunshine intensity, and the multi-year average monthly sunshine duration.

5. The method for refined design of aquatic ecosystem restoration plants according to claim 1, characterized in that, The division of plant planting ecological zones along the water depth gradient includes: Import the engineering design topographic map into the geographic information system platform, and use the vector to raster tool to generate design elevation raster data with the elevation attribute as the raster cell value. Based on the difference between the water level characteristic value and the design elevation raster data, average water depth raster data of the restoration area is generated. According to the preset zoning criteria, the average water depth raster data of the restoration area is reclassified to generate planting zoning planning data; the planting zoning planning data includes wetland plant area, emergent plant area, floating-leaved plant area and submerged plant area.

6. The method for refined design of aquatic ecosystem restoration plants according to claim 3, characterized in that, The preliminary screening includes: using the climate zone, water depth, temperature, light intensity and light duration in the environmental factor characteristic values ​​as screening conditions, selecting plant varieties from the pre-stored plant library that match the preliminary attributes and environmental factor characteristic values ​​for each plant planting ecological zone, forming a preliminary set of plant varieties for each zone; The fine screening includes: based on the multi-year daily water depth time series of each plant planting ecological zone, using the flood tolerance index in the selected attributes as the key indicator, counting the annual average number of super flood tolerance events for each initially selected plant; setting a corresponding super flood tolerance event threshold for each plant planting ecological zone; removing plant varieties whose annual average number of super flood tolerance events exceeds the super flood tolerance event threshold of the corresponding plant planting ecological zone from the initial selection set of plant varieties in the zone, thus obtaining the selected set of plant varieties; The daily water depth of each zone is calculated using the following formula: In the formula, This represents the daily water depth of zone m on day n; m is the zone number, corresponding to the wetland plant zone, emergent plant zone, floating-leaved plant zone, and submerged plant zone, respectively; n is the day number within the statistical period. This represents the daily water level monitoring value for day n. This represents the average elevation of partition m; The super-flood tolerance event is defined as follows: when the number of consecutive days in which the daily water depth of the zone where the plant is located exceeds the maximum suitable water depth of the plant is greater than its flood tolerance index, it is recorded as a super-flood tolerance event, where the maximum suitable water depth is the upper limit of the suitable water depth range of the plant; the annual average number of super-flood tolerance events is calculated according to the following formula: In the formula, This indicates the average number of events exceeding the flood tolerance level per year. This represents the number of events in year j where the daily water depth in a given area exceeds the maximum suitable water depth for plants and the duration exceeds their flood tolerance index; j is the statistical year number; J represents the total number of statistical years.

7. The method for refined design of aquatic ecosystem restoration plants according to claim 1, characterized in that, The design boundary conditions include construction conditions, planting area conditions, and landscape appearance conditions; The construction conditions refer to the planting time of the plants; The planting area conditions include the maximum planting area and minimum area ratio of plants in the zone; wherein, the maximum planting area of ​​plants in the zone is determined based on the planned area of ​​the plant planting ecological zone, and the minimum area ratio refers to the minimum allowable proportion of the planting area of ​​each plant variety to the maximum planting area of ​​plants in the zone. The landscape features include the total number of plant species in each zone, the number of evergreen plant species in each zone, and the number of colored plant species in each zone.

8. The method for refined design of aquatic ecosystem restoration plants according to claim 7, characterized in that, The economic efficiency of pollutant reduction is quantitatively characterized by a decontamination economic index, which is calculated using the following formula: In the formula, This represents the economic efficiency of plant variety k in decontaminating pollutant i. This represents the amount of pollutant i absorbed per unit area by plant variety k; This represents the total construction cost of plant variety k at construction month t. The preferred plant varieties in the scheme are determined in the following way: under the conditions of construction and landscape appearance, based on the decontamination economic indicators, the varieties in the selected plant varieties set are arranged in descending order, and the plant varieties ranked at the top of the sequence are selected first to form a preferred set of plant varieties for the region. The objective function of the optimization model is: The constraints of the optimization model include cost constraints, total area constraints of the partitions, and minimum area percentage constraints. The cost constraint is: The total area constraint of the partition is: The minimum area ratio constraint is: In the formula, f(X) represents the total removal amount of pollutant i within partition m; X is the set of decision variables; and m is the number of the plant planting ecological partition. This represents the area where plant variety k is configured. This represents the amount of pollutant i absorbed per unit area by plant variety k; This represents the total number of plant varieties configured within partition m; ε represents the unit cost of plant variety k in construction month t; ε represents the maximum allowable total cost budget. α represents the maximum planting area of ​​plants in partition m; α represents the minimum area percentage. The optimization model is solved using the simplex method.

9. The method for refined design of aquatic ecosystem restoration plants according to claim 1, characterized in that, The prediction and evaluation of the environmental and ecological benefits after the implementation of the plan includes: Calculate the total amount of pollutants removed using the following formula: In the formula, This represents the total removal amount of pollutant of type i, expressed in kilograms per year; F represents the total number of plant species selected in the plan. This represents the area of ​​plant variety k, in square meters. The unit area absorption of pollutant i by plant variety k is expressed in grams per square meter per day; 365 is the unit time conversion factor, representing the number of days in a year; 1000 is the mass unit conversion factor, representing the conversion relationship between grams and kilograms; the pollutant i includes at least one of five-day biochemical oxygen demand, total nitrogen, and total phosphorus. The biodiversity index is calculated using the Shannon-Wiener index, as follows: In the formula, H′ represents the biodiversity index; F represents the total number of plant species selected in the scheme; This represents the proportion of the planting area of ​​the kth plant variety to the total planting area.

10. A refined design system for aquatic ecosystem restoration plants, characterized in that, include: The database construction module is used to integrate measured data, satellite remote sensing data, and literature data to build a basic database that includes environmental and plant data. The environmental feature analysis module is used to perform spatiotemporal statistical analysis on the environmental factor data in the basic database, perform water environment quality status assessment, and extract the feature values ​​of environmental factors that have a key impact on plant growth and distribution. The planting zone division module is used to divide plant planting ecological zones along the water depth gradient based on the water level characteristic value among the environmental factor characteristic values, combined with the engineering design topography, and using geographic information system spatial analysis technology. The selected collection building module is used to build a selected collection of plant varieties for each plant planting ecological zone through preliminary screening and fine screening; The design scheme generation module is used to set design boundary conditions, take the economic efficiency of pollutant reduction as an indicator, and determine the preferred plant varieties based on the selected set of plant varieties; construct an optimization model with the maximization of pollutant removal as the objective function and engineering cost and planting area as constraints, and solve it by mathematical programming to obtain the optimal area configuration scheme of plant varieties for each plant planting ecological zone. The ecological benefit assessment module is used to establish a quantitative assessment model based on the determined plant species and area configuration scheme, and to predict and assess the environmental and ecological benefits after the scheme is implemented.

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