Poplar man-made forest structure optimization and sustainable operation method and poplar man-made forest structure optimization and sustainable operation system

Abstract

The invention belongs to the technical field of poplar man-made forest cultivation, and particularly relates to a poplar man-made forest structure optimization and sustainable operation method and system. Comprising the following steps: S1, constructing a growth model of poplars, and dividing the poplars into a young stage, a middle-age stage and a near-ripening stage according to the growth model; s2, collecting site factor data; s3, dividing the research area into a plurality of site grade areas, and configuring differentiated growth parameters, rotation periods and felling intensity for each site grade area; s4, building a multi-objective linear programming model, wherein objective functions cover wood total yield maximization, operation cost minimization and ecological benefit maximization; s5, performing linear fitting on the young stage, the middle stage and the near-mature stage to obtain a linear equation corresponding to each stage, taking the linear equation in the S2 as a constraint condition to be embedded into the multi-target linear programming model, and definitely defining the forest age range of each stage; and S6, solving the multi-target linear programming model to obtain the optimal cutting area, rotation and cutting strength parameters.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of poplar plantation, and particularly relates to a poplar plantation structure optimization and sustainable management method and system. BACKGROUND

[0002] As a core tree species with fast growth and high yield in China, the poplar has a plantation area of tens of millions of hectares, and plays an important role in wood production, ecological protection and forestry industry development. The linear programming (LP) model has been widely used in poplar plantation management decision-making, such as harvesting plan formulation and resource allocation, due to its high efficiency and strong stability. However, there are two technical bottlenecks in the practical application of this model.

[0003] Firstly, there are limitations in processing non-linear growth relationships. The diameter at breast height, tree height and volume growth of poplar follow a typical S-shaped Logistic curve. However, the traditional linear programming model can only handle linear relationships, and direct application to poplar plantation management will result in a significant decrease in model accuracy, especially near the growth inflection point. Although non-linear programming (NLP) models can represent this type of non-linear curve, they have complex solution processes, large computational loads, and are prone to local optimal solutions, making them less practical and difficult to meet the efficient decision-making needs of large-scale plantation management.

[0004] Secondly, there is insufficient adaptation to spatial heterogeneity. Poplar plantations are widely distributed, and the site conditions of different plots differ significantly in soil fertility, terrain slope, altitude and climate conditions. Traditional models use a one-size-fits-all management strategy, which cannot develop targeted management plans based on differences in site conditions, resulting in underutilization of the production potential of high-quality sites, high operating costs for poor-quality sites, and low overall operating efficiency. At the same time, it is difficult to improve ecological benefits.

[0005] Existing technical solutions either ignore non-linear characteristics and force linear approximation, or simplify spatial heterogeneity processing, making it difficult to balance model accuracy and practicality, and focusing on a single management objective, which cannot achieve the coordinated improvement of economic, ecological and social benefits. Therefore, it is necessary to develop a poplar plantation management optimization technology that can simultaneously handle non-linear relationships and spatial heterogeneity, which has important theoretical value and practical application prospects. SUMMARY

[0006] In order to realize the precise and scientific management of poplar plantations, simultaneously improve wood yield, reduce operating costs and enhance ecological benefits, and solve the problems of large non-linear processing errors, extensive spatial management and unbalanced comprehensive benefits in existing technologies, the application provides a poplar plantation structure optimization and sustainable management method and system.

[0007] The application adopts the following technical scheme: a poplar plantation structure optimization and sustainable management method, comprising: S1: constructing a growth model of poplar, and dividing the poplar into three stages of juvenile stage, middle-aged stage and near-mature stage according to the growth model; S2: collecting site factor data, the site factors including soil organic matter content, slope, altitude and annual precipitation; S3: clustering the site factor data, dividing the research area into multiple site grade areas, and configuring differentiated growth parameters, rotation period and felling intensity for each site grade area; S4: building a multi-objective linear programming model, the multi-objective linear programming model covering maximization of total wood yield, minimization of management cost and maximization of ecological benefit; S5: linear fitting is performed on the juvenile stage, the middle-aged stage and the near-mature stage to obtain the linear equation corresponding to each stage, and the linear equation is embedded into the multi-objective linear programming model as a constraint condition to clearly define the forest age range of each stage; S6: solving the multi-objective linear programming model to obtain optimal felling area, rotation period and felling intensity parameters; generating two types of spatial decision maps, namely felling zoning map and under-forest economic layout map.

[0008] In some embodiments, S1 comprises: S11: constructing a growth model, the growth model expression being: wherein V is the volume per unit area; t is the forest age; K is the upper limit of volume growth, a , b is the fitting parameter; S12: calculating the second derivative of the growth model, and solving the growth inflection point by setting the second derivative to 0; S13: dividing into three stages of juvenile stage, middle-aged stage and near-mature stage according to the growth inflection point.

[0009] In some embodiments, S2 comprises: Obtaining soil samples by field grid sampling and analyzing organic matter content, and the sampling grid is arranged at a density of 500m x 500m; Extracting slope and altitude data of the research area in combination with GIS technology; Collecting long-term observation data of the surrounding weather stations to calculate the annual precipitation.

[0010] In some embodiments, S3 comprises: S31: determining the number of clusters as the number n of site grade areas; S32: The K-means clustering algorithm is used to cluster the site factor data, dividing the study area into n site grade zones, and calculating the comprehensive site quality score for each site grade zone; S33: Based on the number of site grade zones, configure growth parameters, rotation period and harvesting intensity for each site grade zone.

[0011] In some embodiments, S31 includes: Calculate the sum of squared errors (SSE) for cluster numbers k = 1 to 6. C k Let μ be the sample set of the k-th cluster. k Let the center vector of the k-th cluster be denoted as 'k'. Plot the SSE curve as a function of k. The integer approximation of the value of k when the curve shows a clear inflection point is the number of clusters.

[0012] In some embodiments, S33, the criteria for configuring growth parameters, rotation period, and harvesting intensity are as follows: All site quality zones are sorted from highest to lowest according to their "comprehensive site quality score" and designated as level 1 to level n. Growth parameters: The growth parameters of grade 1 are used as the baseline values. The growth parameters of grade i = the baseline values ​​of grade 1 × the relative coefficient of site quality. The relative coefficient of site quality = the comprehensive score of site quality of grade i / the comprehensive score of site quality of grade 1. Rotation period: Rotation period of grade i = Rotation period of grade 1 × (1 + site quality decay coefficient), where site quality decay coefficient = (comprehensive site quality score of grade 1 - comprehensive site quality score of grade i) / score of grade 1; Harvesting intensity: Harvesting intensity of grade i = benchmark harvesting intensity of grade 1 × (1 - site quality attenuation coefficient), and the harvesting intensity of all site grade areas shall not exceed the upper limit of the management technical regulations for this tree species.

[0013] In some embodiments, S4: Multi-objective linear programming models include: Model for maximizing total timber production: ; Operating cost minimization model: ; Ecological benefit maximization model: ; The constraints include: Logging volume constraints: ; Area balance constraint: ; Spatial constraints: d j ≤1000m; Site adaptability constraints: A h ≤0.6·A suitable ; Among them, V i Let A be the volume of the i-th cubic grade zone. i For the first i Logging area in site grade zone, S i For logging intensity, convert to decimal for calculation, C i For the first i Operating cost per unit area in a site grade zone E i For the first i Ecological benefit value per unit area of ​​site grade zone, dV i / dt represents the volumetric growth rate of the i-th site grade zone, A i,new Let A be the updated area of ​​the i-th site level zone. i,cut Let d be the logging area of ​​the i-th site grade zone. j Let A be the distance from the j-th logging unit to the nearest transport road. h For the area of ​​understory planting, A suitable The area is suitable for planting under the forest canopy with a canopy closure of 0.6-0.7.

[0014] In some embodiments, in S5, the linear equations corresponding to each stage are synchronously embedded into a multi-objective linear programming model as a core component of the growth law constraint: That is, when the forest age is young, the volume V ≤ 0.08t + 0.02; When the forest age is middle-aged, the timber volume V ≤ 0.15t - 0.35t; When the forest age is near maturity, the volume V ≤ 0.10t + 0.15, where t is the number of years of growth.

[0015] In some embodiments, S6 includes: solving the model using Lingo or GAMS software, transforming the multi-objective optimization into a single-objective optimization using a weighted method, and setting the solution accuracy to 10. -6 ; The solution results are overlaid with GIS geographic data for analysis, and logging zoning maps and understory planting layout maps are generated using ArcGIS to provide intuitive guidance for on-site management.

[0016] A system for optimizing the structure and sustainable management of poplar plantations, comprising: The data acquisition module is used to acquire poplar growth data, site factor data, and operating cost data; The data processing module is communicatively connected to the data acquisition module and is used to perform piecewise linearization processing on the growth model and spatial heterogeneity analysis on the site factor data. The model solving module is communicatively connected to the data processing module and is used to construct and solve a multi-objective linear programming model to generate optimal operating parameters. The decision output module is connected in communication with the model solving module and is used to output logging zoning maps, understory economic layout maps, and management plan reports.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention achieves a fundamental shift from extensive to precise management of poplar plantations by integrating nonlinear growth relationship processing with spatial heterogeneity adaptation. The method innovatively segments and linearizes the Logistic growth curve and assigns differentiated management parameters to different site grade areas based on site factor clustering results. This effectively overcomes the inherent shortcomings of traditional linear programming models, such as large errors near growth inflection points and inability to adapt to complex site conditions, significantly improving the accuracy and reliability of growth prediction and resource allocation schemes.

[0018] This invention improves timber production, reduces operating costs, and enhances ecological benefits by constructing an economic-cost-ecological multi-objective optimization model. Its output spatial decision map provides intuitive guidance for logging and understory economic activities, making management strategies both scientific and feasible, ultimately achieving synergistic development of the economic benefits and ecological sustainability of poplar plantations. Attached Figure Description

[0019] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] A method for optimizing the structure and sustainable management of poplar plantations includes: S1: Construct a growth model for poplar trees and divide them into three stages: juvenile, middle-aged, and near-mature. S11: To address the nonlinear S-curve of poplar growth, piecewise linearization is implemented. First, a Logistic growth model is constructed based on measured data: ,in V t represents the volume of timber per unit area; t represents the age of the forest. K This is the upper limit of volume growth. a , bThe fitting parameters were obtained through long-term locational observations. Ten representative standard plots were selected within the study area, each with an area of ​​1 hectare. The diameter at breast height (DBH) and tree height of all poplar trees in the plots were measured annually, and the volume per unit area was calculated.

[0022] S12: Calculate the second derivative of the growth model Solve for the growth inflection point by setting the second derivative to 0. ; S13: Based on the growth model, it is divided into three stages: juvenile stage, middle-aged stage, and near-maturity stage.

[0023] The growth inflection point t0 is the node where the poplar tree has the fastest growth rate. The stage division standard is: the juvenile stage corresponds to the forest age 1 ≤ t ≤ t 0 / 2 (Early stage of rapid growth), middle age corresponds to t 0 / 2 <t≤t0 (peak growth rate stage), near maturity corresponds to t>t0 (growth rate slows down and tends to stabilize stage); where t 0 / 2 The target rotation period is calibrated in conjunction with the specific value of t0. For example, when the target rotation period is 10 years, t0≈10 years, corresponding to 1-5 years for the juvenile stage, 6-10 years for the middle stage, and 11 years or more for the near-mature stage.

[0024] S2: Collect site factor data, including soil organic matter content, slope, altitude, and annual precipitation; use a combination of technologies to acquire site factor data: obtain soil samples and analyze organic matter content through field grid sampling, with the sampling grid laid out at a density of 500m × 500m; interpret vegetation and topographic information using remote sensing technology, and extract slope and altitude data of the study area using GIS technology; collect long-term observation data from surrounding meteorological stations to calculate annual precipitation.

[0025] S3: Cluster the site factor data to divide the study area into multiple site grade zones, and configure differentiated growth parameters, rotation period and harvesting intensity for each site grade zone; S31: Use the elbow rule to determine the number of clusters; Calculate the sum of squared errors (SSE) for cluster numbers k = 1 to 6. C k Let μ be the sample set of the k-th cluster. k Let k be the center vector of the k-th cluster. Plot the SSE curve as a function of k. The integer approximation of the value of k when the curve shows a clear inflection point is the number of clusters. In this embodiment, the number of clusters is determined to be 3, and the study area is finally divided into three site levels: excellent, medium, and poor.

[0026] S32: The K-means clustering algorithm is used to cluster the site factor data, dividing the study area into n site grade zones, and calculating the comprehensive site quality score for each site grade zone; 1. Standardize the data on soil organic matter content, slope, altitude, and annual precipitation; 2. The k-means++ algorithm is used to initialize the three cluster centers to avoid local optima caused by random initialization; 3. Calculate the Euclidean distance from each sample to the center of each cluster, and assign the sample to the cluster with the closest distance; 4. Update the cluster center to the mean vector of all samples within the cluster; 5. Repeat steps 3-4 until the number of iterations reaches 50 or the change in cluster centers is less than the convergence threshold of 10. -6 Stop the calculation and output the clustering results.

[0027] Calculate the "Comprehensive Site Quality Score" (out of 100) for each site quality zone; a higher score indicates better site quality. Based on the process of "site factor quantification → indicator standardization → weighted summation," the specific steps are as follows: Step 1: Determine the site factor index system (for poplar plantations) First, we screened the site factors that significantly affect poplar growth and formed an indicator system (taking common core factors as an example): Step 2: Standardization and weighting of site factors Since different indicators have different dimensions and value ranges, they need to be standardized first, and then the weights of each indicator (with a weight sum of 1) are determined using the Analytic Hierarchy Process (AHP): Indicator standardization: Convert the actual value of each indicator into a single-indicator score of 0-10 (taking soil thickness as an example): Soil thickness ≥80cm → 10 points; 60-80cm → 8 points; 40-60cm → 6 points; 20-40cm → 4 points; <20cm → 2 points. (Other indicators are scored similarly, with the scoring range determined by the degree of benefit to poplar growth.) Indicator weights (example): Soil factors (soil thickness, texture, organic matter): weight 0.4; Climate factors (annual average temperature, precipitation): weight 0.3; Topographic factors (elevation, slope, aspect): weight 0.3.

[0028] Step 3: Calculation of Single Indicator Score For each site unit (i.e., the smallest spatial unit to be divided, such as 10m × 10m), according to its actual site conditions and the standardization rules in step 2, calculate the single-index score for each indicator (denoted as S1, S2, ..., S). k (where k is the number of indicators).

[0029] Step 4: Calculation of overall site quality score (core formula) The overall score for each site unit (out of 100 points) is calculated using a weighted summation method: Overall site quality score = in: : The standardized score (0-10) of the i-th indicator; The weight of the i-th indicator ( ); ×10: Magnify the weighted result of 0-10 points to the full score range of 0-100 points.

[0030] If the number of site grade zones is adjusted, such as by adding or merging grade zones, it is only necessary to recalculate the relative coefficient of site quality and the attenuation coefficient to quickly adapt and generate the growth parameters, rotation period and harvesting intensity of the corresponding grade zone.

[0031] S33: Based on the number of site grade zones (n, n≥1), following the coupling principle of "site quality-growth potential-management objectives", differentiated growth parameters, rotation periods, and harvesting intensities are configured for each site grade zone, with the specific rules as follows: Growth parameters: covering core dimensions such as site index, diameter at breast height (DBH) growth rate, tree height growth rate, and volume growth, with the "site quality score of the site grade zone" as the core variable, and establishing a linear / nonlinear correlation model between parameters and site quality.

[0032] Rotation period: Based on the ratio of the number of dominant tree species to the technological maturity of the area, combined with a site quality correction factor. The higher the site quality, the smaller the correction factor, and the shorter the rotation period can be; conversely, the lower the site quality, the longer the rotation period.

[0033] Logging intensity: The upper limit is the "stand growth potential threshold of the area". The general principle is to lightly harvest in low site grade areas to fully protect the stand's recovery capacity, and to moderately increase the harvesting intensity in high site grade areas to release growth potential.

[0034] Differentiated implementation method: Sort all site quality zones from high to low according to the "comprehensive site quality score", and record them as level 1 to level n; Growth parameters: The growth parameters of grade 1 are used as the baseline values. The growth parameters of grade i = the baseline values ​​of grade 1 × the relative coefficient of site quality. The relative coefficient of site quality = the comprehensive score of site quality of grade i / the comprehensive score of site quality of grade 1. Rotation period: Rotation period of grade i = Rotation period of grade 1 × (1 + site quality decay coefficient), where site quality decay coefficient = (comprehensive site quality score of grade 1 - comprehensive site quality score of grade i) / score of grade 1; Harvesting intensity: Grade i harvesting intensity = Grade 1 baseline harvesting intensity × (1 - site quality attenuation coefficient), and the harvesting intensity of all site grade areas shall not exceed the upper limit of the management technical regulations for this tree species.

[0035] S4: Construct a multi-objective linear programming model, with objective functions covering maximizing total timber production, minimizing operating costs, and maximizing ecological benefits; Multi-objective linear programming models include: Model for maximizing total timber production: ; Operating cost minimization model: ; Ecological benefit maximization model: ; The constraints include: Logging volume constraints: ; Area balance constraint: ; Spatial constraints: d j ≤1000m; Site adaptability constraints: A h ≤0.6·A suitable ; Among them, V i Let A be the volume of the i-th cubic grade zone. i For the first i Logging area in site grade zone, S i For logging intensity, convert to decimal for calculation, C i For the first i Operating cost per unit area in a site grade zone E i For the first i Ecological benefit value per unit area of ​​site grade zone, dV i / dt represents the volumetric growth rate of the i-th site grade zone, A i,new Let A be the updated area of ​​the i-th site level zone. i,cut Let d be the logging area of ​​the i-th site grade zone. j Let A be the distance from the j-th logging unit to the nearest transport road. h For the area of ​​understory planting, A suitableThe area is suitable for planting under the forest canopy with a canopy closure of 0.6-0.7.

[0036] S5: Linear fitting is performed on the young, middle-aged, and near-mature stages to obtain the corresponding linear equations for each stage. The linear equations of S2 are then embedded into the multi-objective linear programming model as constraints to clearly define the age range of each stage. The linear equations corresponding to each stage are simultaneously embedded into the multi-objective linear programming model as the core component of the growth law constraints: that is, when the age t∈[1,5] (young stage), the volume V≤0.08t+0.02; when t∈[6,10] (middle-aged stage), V≤0.15t-0.35; when t∈[11,+∞) (near-mature stage), V≤0.10t+0.15, ensuring that the model adapts to the growth law of poplar at different growth stages.

[0037] S6: Solve the multi-objective linear programming model to obtain the optimal logging area, rotation period and logging intensity parameters; generate two types of spatial decision maps: logging zoning map and understory economic layout map.

[0038] The model was solved using Lingo or GAMS software. The multi-objective problem was transformed into a single-objective optimization using the weighting method, and the solution accuracy was set to 10. -6 ; The solution results are overlaid with GIS geographic data for analysis, and logging zoning maps and understory planting layout maps are generated using ArcGIS to provide intuitive guidance for on-site management.

[0039] The specific process is as follows: 1. Organize the optimal parameters obtained from the solution (logging intensity, rotation period, and suitability for understory planting in each plot) into an Excel attribute table, which includes fields such as plot number, site grade, optimal logging intensity, rotation period, and suitability for planting; 2. Load the vector plot map (Shapefile format) of the study area into ArcGIS, and use the "Connect" tool to associate the attribute table with the vector map according to the plot number; 3. Load basic geographic layers such as roads and terrain as spatial references; 4. Symbolize the associated data: use different background colors to distinguish different site grades, use different line borders to distinguish different logging intensities, and mark suitable planting areas with special symbols; 5. Add map legend, scale bar, north arrow, map title and other elements, and export as PDF format logging zoning map and understory planting layout map to complete the visualization output.

[0040] A system for optimizing the structure and sustainable management of poplar plantations, comprising: The data acquisition module is used to acquire poplar growth data, site factor data, and operating cost data; The data processing module is communicatively connected to the data acquisition module and is used to perform piecewise linearization processing on the growth model and spatial heterogeneity analysis on the site factor data. The model solving module is communicatively connected to the data processing module and is used to construct and solve a multi-objective linear programming model to generate optimal operating parameters. The decision output module is connected in communication with the model solving module and is used to output logging zoning maps, understory economic layout maps, and management plan reports.

[0041] Taking a poplar plantation demonstration area (1000 hectares) in the Sanggan River region of Shanxi Province as an implementation case, the core parameters are set as follows: After fitting and calibration, the Logistic model has K=25, a=20, and b=0.3, which is suitable for the growth characteristics of poplars in this area; K-means clustering reaches convergence after 50 iterations (convergence threshold 10). -6 The zoning results were verified in the field, with an accuracy of over 85%. Using Lingo software to solve the multi-objective model, the optimal harvesting area was determined to be 320 hectares. This resulted in a 22.5% increase in total timber production compared to the traditional unified management plan, a 13.3% reduction in operating costs, and an 18.1% increase in ecological benefits (carbon sequestration). An alternative implementation method could use GAMS as the solution software, with the rotation period parameter adjusted to 8-15 years based on the growth characteristics of different poplar varieties, and the number of clusters adjusted to 4-5 clusters based on the complexity of the site conditions in the study area.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the structure and sustainable management of poplar plantations, characterized in that, include: S1: Construct a growth model for poplar trees and divide them into three stages: juvenile, middle-aged, and near-mature. S2: Collect site factor data, including soil organic matter content, slope, altitude and annual precipitation; S3: Cluster the site factor data to divide the study area into multiple site grade zones, and configure differentiated growth parameters, rotation period and harvesting intensity for each site grade zone; S4: Construct a multi-objective linear programming model that covers maximizing total timber production, minimizing operating costs, and maximizing ecological benefits; S5: Perform linear fitting on the young, middle-aged and near-mature stages to obtain the corresponding linear equations for each stage. Embed the linear equations as constraints into the multi-objective linear programming model to clearly define the age range of each stage. S6: Solve the multi-objective linear programming model to obtain the optimal logging area, rotation period and logging intensity parameters; Two types of spatial decision maps are generated: logging zoning maps and understory economic layout maps.

2. The method for optimizing the structure and sustainable management of poplar plantations according to claim 1, characterized in that, S1 includes: S11: Construct the growth model, the expression of which is: ,in V t represents the volume of timber per unit area; t represents the age of the forest. K This is the upper limit of volume growth. a , b These are the fitting parameters; S12: Calculate the second derivative of the growth model, set the second derivative to 0, and solve for the growth inflection point; S13: Divided into three stages based on growth inflection points: juvenile stage, middle-aged stage, and near-maturity stage.

3. The method for optimizing the structure and sustainable management of poplar plantations according to claim 1, characterized in that, S2 includes: Soil samples were obtained and their organic matter content was analyzed by field grid sampling, with the sampling grid laid out at a density of 500 meters × 500 meters; Slope and elevation data of the study area were extracted using GIS technology. Long-term observation data from surrounding meteorological stations were collected to calculate the annual precipitation.

4. The method for optimizing the structure and sustainable management of poplar plantations according to claim 1, characterized in that, S3 includes: S31: Determine the number of clusters, which will be the number of site-level zones, n; S32: Perform clustering calculations on the site factor data, divide the study area into n site grade zones, and calculate the comprehensive site quality score for each site grade zone; S33: Based on the number of site grade zones, configure growth parameters, rotation period and harvesting intensity for each site grade zone.

5. The method for optimizing the structure and sustainable management of poplar plantations according to claim 4, characterized in that, S31 includes: Calculate the sum of squared errors (SSE) for cluster numbers k = 1 to 6. C k Let μ be the sample set of the k-th cluster. k Let the center vector of the k-th cluster be denoted as 'k'. Plot the SSE curve as a function of k. The integer approximation of the value of k when the curve shows a clear inflection point is the number of clusters.

6. The method for optimizing the structure and sustainable management of poplar plantations according to claim 4, characterized in that, In S33, the standards for configuring growth parameters, rotation period, and harvesting intensity are as follows: All site quality zones are sorted from highest to lowest according to their "comprehensive site quality score" and designated as level 1 to level n. Growth parameters: The growth parameters of grade 1 are used as the baseline values. The growth parameters of grade i = the baseline values ​​of grade 1 × the relative coefficient of site quality. The relative coefficient of site quality = the comprehensive score of site quality of grade i / the comprehensive score of site quality of grade 1. Rotation period: Rotation period of grade i = Rotation period of grade 1 × (1 + site quality decay coefficient), where site quality decay coefficient = (comprehensive site quality score of grade 1 - comprehensive site quality score of grade i) / score of grade 1; Harvesting intensity: Harvesting intensity of grade i = benchmark harvesting intensity of grade 1 × (1 - site quality attenuation coefficient), and the harvesting intensity of all site grade areas shall not exceed the upper limit of the management technical regulations for this tree species.

7. The method for optimizing the structure and sustainable management of poplar plantations according to claim 1, characterized in that, In S4: Multi-objective linear programming models include: Model for maximizing total timber production: ; Operating cost minimization model: ; Ecological benefit maximization model: ; The constraints include: Logging volume constraints: ; Area balance constraint: ; Spatial constraints: d j ≤1000m; Site adaptability constraints: A h ≤0.6·A suitable ; Among them, V i Let A be the volume of the i-th cubic grade zone. i For the first i Logging area in site grade zone, S i For logging intensity, convert to decimal for calculation, C i For the first i Operating cost per unit area in a site grade zone E i For the first i Ecological benefit value per unit area of ​​site grade zone, dV i / dt represents the volumetric growth rate of the i-th site grade zone, A i,new Let A be the updated area of ​​the i-th site level zone. i,cut Let d be the logging area of ​​the i-th site grade zone. j Let A be the distance from the j-th logging unit to the nearest transport road. h For the area of ​​understory planting, A suitable The area is suitable for planting under the forest canopy with a canopy closure of 0.6-0.

7.

8. The method for optimizing the structure and sustainable management of poplar plantations according to claim 1, characterized in that, In S5, the linear equations corresponding to each stage are synchronously embedded into the multi-objective linear programming model, serving as a core component of the growth law constraint: That is, when the forest age is young, the volume V ≤ 0.08t + 0.02; When the forest age is middle-aged, the timber volume V ≤ 0.15t - 0.35t; When the forest age is near maturity, the volume V ≤ 0.10t + 0.15, where t is the number of years of growth.

9. The method for optimizing the structure and sustainable management of poplar plantations according to claim 1, characterized in that, S6 includes: solving the model using Lingo or GAMS software, transforming multi-objective optimization into single-objective optimization using a weighted method, and setting the solution accuracy to 10%. -6 ; The solution results are overlaid with GIS geographic data for analysis, and logging zoning maps and understory planting layout maps are generated using ArcGIS to provide intuitive guidance for on-site management.

10. A system for optimizing the structure and sustainable management of poplar plantations, used to implement the method for optimizing the structure and sustainable management of poplar plantations according to any one of claims 1-9, characterized in that, The system includes: The data acquisition module is used to acquire poplar growth data, site factor data, and operating cost data; The data processing module is communicatively connected to the data acquisition module and is used to perform piecewise linearization processing on the growth model and spatial heterogeneity analysis on the site factor data. The model solving module is communicatively connected to the data processing module and is used to construct and solve a multi-objective linear programming model to generate optimal operating parameters. The decision output module is connected in communication with the model solving module and is used to output logging zoning maps, understory economic layout maps, and management plan reports.

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