An artificial forest threshold optimization method, device and equipment that trade off ecological services

Abstract

The application relates to the technical field of ecological management, and discloses a reforestation threshold optimization method, device and equipment that balance ecological services. The method comprises the following steps: constructing an ecological hydrological model based on basic geographic information and meteorological and hydrological data of a target basin; setting a reforestation scale parameter, and generating a land use scenario set of different reforestation scales by using a land use evolution model; inputting the scenario set into the ecological hydrological model to calculate corresponding ecological service functions; establishing an optimization model with the three key ecological service functions of basin water yield, carbon sequestration and water source conservation as targets, and solving a reforestation threshold scheme set that reflects the trade-off relationship of ecological services; and obtaining an optimal reforestation area threshold based on a preset weight from the reforestation threshold scheme set. The application realizes full-process automatic processing by coupling the ecological hydrological model and the land use evolution model; the trade-off relationship of ecological services is solved by multi-objective optimization, the optimal reforestation area threshold that takes into account various ecological benefits is scientifically determined, and the planning scientificity is improved.

Description

Technical Field

[0001] This application relates to the field of watershed ecological management technology, specifically to a method, apparatus, and equipment for optimizing the threshold of artificial afforestation that balances ecological services. Background Technology

[0002] Watershed ecological restoration projects are an important means of achieving regional sustainable development. Among these, afforestation, as a key ecological regulation measure, directly impacts the watershed's water production, carbon sequestration, and water conservation services. With the advancement of ecological civilization construction, how to scientifically determine the scale of afforestation to balance the trade-offs among various ecological service functions within limited land resources has become a core issue urgently needing to be addressed in watershed ecological management. Reasonable afforestation threshold planning can not only maximize ecological benefits but also avoid negative ecological effects such as soil drying and water shortages caused by excessive afforestation. Therefore, establishing a scientific method for deciding on afforestation scale has significant practical implications.

[0003] In existing technologies, evaluation methods for afforestation planning mainly rely on scenario simulations using single ecological models or qualitative judgments based on experience. For example, traditional ecological models (such as the InVEST or SWAT models) are often used to assess the ecological services under specific land use patterns. Researchers often compare the merits of different schemes by setting several discrete static scenarios (such as increasing the afforestation rate by 10% or 20%). In addition, some planning methods focus on optimizing a single objective, such as maximizing carbon sinks or prioritizing water protection, lacking quantitative analysis of the competitive relationships between multiple objectives, and usually failing to consider the dynamic evolution of land use spatial distribution with changes in afforestation scale.

[0004] However, the existing methods mentioned above have significant drawbacks: on the one hand, static scenario settings are insufficient to reflect the nonlinear characteristics of ecological responses caused by continuous changes in afforestation scale, making it impossible to capture the critical inflection point of the ecological service trade-off; on the other hand, there is a lack of quantitative tools that combine dynamic land use evolution with ecological process simulation, making it difficult to provide specific afforestation area thresholds or vegetation cover thresholds as a basis for engineering implementation. This often leads to a dilemma in actual decision-making: "qualitative analysis reveals advantages and disadvantages, but quantitative analysis makes it difficult to define boundaries," easily resulting in inefficient allocation of ecological resources or uncontrolled ecological risks. Therefore, there is an urgent need for an artificial afforestation threshold optimization method that can balance multiple ecological service functions. Summary of the Invention

[0005] The lack of dynamic quantitative analysis of the trade-off between ecosystem services in artificial afforestation planning in related technologies makes it impossible to scientifically determine the threshold for afforestation scale that balances multiple ecological benefits.

[0006] In a first aspect, embodiments of this application provide a method for optimizing artificial afforestation thresholds, the method comprising: An eco-hydrological model for simulating various ecosystem services is constructed based on the basic geographic information and meteorological and hydrological data of the target watershed. A afforestation scale variation parameter is set, and a land use evolution model is used to generate land use scenario sets corresponding to different afforestation scales based on the afforestation scale variation parameter. The land use scenario sets are input into the eco-hydrological model to calculate various ecosystem services under each land use scenario. A multi-objective optimization model is established with ecosystem service functions as the optimization objective, and the multi-objective optimization model is solved using a multi-objective optimization algorithm to obtain a set of afforestation threshold schemes reflecting the trade-offs in ecosystem services. The afforestation threshold scheme set is then screened to obtain the optimal afforestation area threshold.

[0007] In conjunction with the first aspect, in one implementation, the step of inputting a set of land use scenarios into an eco-hydrological model to calculate the various ecosystem service functions under each land use scenario includes: Based on the input land use scenario and meteorological and hydrological data, the simulation calculates water production services, carbon sequestration services, and water conservation services.

[0008] In conjunction with the first aspect, in one implementation, the step of simulating and calculating water production services, carbon sequestration services, and water conservation services based on input land use scenarios and meteorological and hydrological data includes: Based on land use scenarios and meteorological and hydrological data, water production, net primary productivity, and water conservation capacity are simulated and calculated, and water production, net primary productivity, and water conservation capacity are respectively used as water production service capacity, carbon sequestration service capacity, and water conservation service capacity.

[0009] In conjunction with the first aspect, in one implementation method, setting the afforestation scale variation parameters includes: The forest area under different afforestation scenarios is calculated based on the forest area in the base year and the step size of forest area change.

[0010] In conjunction with the first aspect, in one implementation, the method of generating land use scenario sets corresponding to different afforestation scales based on afforestation scale change parameters using a land use evolution model includes: Using the forest area of ​​the base year as the initial state, the forest area under different afforestation scale scenarios is calculated sequentially according to the forest area change step size; Using forest area under different afforestation scale scenarios as input, a land use evolution model is used to simulate the spatial distribution map of land use under different afforestation scenarios, so as to generate a continuously changing set of land use scenarios.

[0011] In conjunction with the first aspect, in one implementation, the step of obtaining a set of afforestation threshold schemes reflecting the ecosystem service trade-offs by solving the multi-objective optimization model using a multi-objective optimization algorithm includes: The various ecosystem service functions are normalized and then used as the objective function input into the multi-objective optimization algorithm. A set of afforestation threshold schemes was established by analyzing the objective function curve and the shape of the Pareto front.

[0012] In conjunction with the first aspect, in one implementation, the step of establishing a set of afforestation threshold schemes by analyzing the shape of the objective function curve and the Pareto front includes: The Pareto front is obtained by iteratively searching for the set of non-dominated solutions, and the corresponding set of afforestation threshold schemes is established based on the Pareto front.

[0013] In conjunction with the first aspect, in one implementation, the step of filtering the afforestation threshold scheme set based on preset weights to obtain the optimal afforestation area threshold includes: A weighted comprehensive objective function method is used to assign corresponding preset weight coefficients to each ecosystem service function; The comprehensive score of each scheme in the afforestation threshold scheme set is calculated based on the preset weight coefficient, and the afforestation scale corresponding to the scheme with the highest comprehensive score is determined as the optimal afforestation area threshold.

[0014] Secondly, embodiments of this application provide an artificial afforestation threshold optimization device, the artificial afforestation threshold optimization device comprising: The model building module is used to build eco-hydrological models that simulate various ecosystem service functions based on the basic geographic information and meteorological and hydrological data of the target watershed. The scenario generation module is used to set parameters for changes in afforestation scale and generate land use scenario sets corresponding to different afforestation scales based on the parameters for changes in afforestation scale using a land use evolution model. The calculation module is used to input the land use scenario set into the eco-hydrological model to calculate the various ecosystem service functions under each land use scenario. The optimization solution module is used to establish a multi-objective optimization model with ecosystem service function as the optimization objective, and solve the multi-objective optimization model through a multi-objective optimization algorithm to obtain a set of afforestation threshold schemes that reflect the trade-off relationship of ecosystem services. The threshold decision module is used to filter the afforestation threshold scheme set based on preset weights to obtain the optimal afforestation area threshold.

[0015] Thirdly, embodiments of this application provide an artificial afforestation threshold optimization device, characterized in that the artificial afforestation threshold optimization device includes a processor, a memory, and an artificial afforestation threshold optimization program stored in the memory and executable by the processor, wherein when the artificial afforestation threshold optimization program is executed by the processor, it implements the steps of the artificial afforestation threshold optimization method as described in any of the preceding claims. The beneficial effects of the technical solutions provided in this application include: This invention automates the entire process from data collection to threshold decision-making by coupling an eco-hydrological model and a land use evolution model. By solving the trade-off relationship of ecological services through a multi-objective optimization model, it avoids the one-sidedness of single-objective optimization and can scientifically determine the optimal afforestation area threshold that takes into account multiple ecological benefits, thereby improving the scientificity and rationality of afforestation planning. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an embodiment of the artificial afforestation threshold optimization method proposed in this application. Figure 2 This is a schematic diagram of the hardware structure of the artificial afforestation threshold optimization device involved in the embodiments of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0018] The lack of dynamic quantitative analysis of the trade-off between ecosystem services in artificial afforestation planning in related technologies makes it impossible to scientifically determine the threshold for afforestation scale that balances multiple ecological benefits.

[0019] In a first aspect, embodiments of this application provide a method for optimizing artificial afforestation thresholds, the method comprising: Step S1: Construct an eco-hydrological model based on the basic geographic information and meteorological and hydrological data of the target watershed to simulate various ecosystem services.

[0020] Specifically, basic data such as watershed hydrology, meteorology, topography, land cover, and soil properties are collected and studied, and a multi-ecological service simulation model based on eco-hydrological model is established for the watershed.

[0021] In some optional embodiments, the ecohydrological model simulates ecosystem services including water production services, carbon sequestration services, and water conservation services. In step S1 above, the WaSSI-CN model is used as the ecohydrological model to simulate and evaluate water production, carbon sequestration, and water conservation data under artificial afforestation scenarios. Among these, water production data is crucial for the ecological functions of watershed water cycle and sediment transport, as well as for meeting the water resource needs of economic and social development.

[0022] Furthermore, water yield (R) is calculated by subtracting the changes in evapotranspiration (ET), soil moisture (SM), and snowmelt (SP) from the precipitation (P) within the ecosystem during the calculation period. The formula is as follows:

[0023] Carbon sequestration is characterized by net primary productivity (NPP), which reflects the net carbon sequestration by green plants through photosynthesis. NPP is an important indicator for assessing regional carbon cycling and carbon sequestration capacity, and is calculated using the following formula:

[0024]

[0025] In the formula, GPP is the total primary productivity, UWUE is the potential water use efficiency coefficient, and m, n, and k are preset coefficients (generally empirical coefficients derived from the FLUXNET flux station dataset).

[0026] Furthermore, water conservation characterizes the redistribution of precipitation and the water storage capacity of an ecosystem over a period of time. In this example, based on the simulation of the watershed's water and carbon balance processes, the water balance equation is used to calculate:

[0027] In the above formula, TQ is the water conservation capacity (m3), i is the i-th type of ecosystem in the study area, A is the corresponding ecosystem area, and P, R, and ET are the precipitation, water production, and evapotranspiration within the ecosystem, respectively.

[0028] Step S2: Set parameters for changes in afforestation scale, and use the land use evolution model to generate land use scenario sets corresponding to different afforestation scales based on the parameters for changes in afforestation scale.

[0029] Specifically, the average climate conditions and land use patterns under the current level year are calculated. Based on the historical evolution pattern of land use distribution, the land use evolution model is used to simulate the land use distribution under different afforestation scale scenarios. A set of watershed land use scenarios corresponding to different afforestation scales is constructed, and various ecological service functions of the watershed under different afforestation scenarios are simulated.

[0030] Specifically, step S2 above includes: Step S2a: Calculate the forest area under different afforestation scenarios based on the forest area of ​​the base year and the step size of forest area change.

[0031] Specifically, the forest area under different afforestation scenarios is calculated using the following formula:

[0032] In the formula, A i Let B be the forest area under the i-th scenario, B be the forest area in the base year, and S be the step size for the change in forest area.

[0033] Understandably, this step provides a specific way to set parameters for changes in afforestation scale. Based on the baseline year area and step size calculation, it ensures the regularity and coverage of afforestation scenario generation, providing a reliable data foundation for subsequent multi-scenario simulations.

[0034] Step S2b: Using the forest area of ​​the base year as the initial state, calculate the forest area of ​​different years in sequence according to the forest area change step size.

[0035] Step S2c: Using the forest area under different afforestation scale scenarios as input, the land use evolution model is used to simulate the spatial distribution map of land use under different afforestation scenarios to generate a continuously changing set of land use scenarios.

[0036] In a preferred embodiment, the PLUS model is used to simulate the spatial pattern of watershed land use under different afforestation scenarios, with different types of land use patches such as forests, grasslands, farmland, wetlands, urban land, and bare land as the calculation units.

[0037] In conjunction with the above preferred embodiments, in a specific embodiment, step S2c includes: Step A: Extract the expansion portions of various land uses from the two periods of land use data.

[0038] Step B: Analyze the relationship between the expansion of each land use type and its driving factors using the random forest algorithm in the land use expansion analysis strategy module to obtain the development probability of each land use type.

[0039] Step C: Combine the cellular automata model, random seed generation mechanism, and threshold reduction mechanism to simulate land use spatial patterns under different scenarios.

[0040] Step S3: Input the land use scenario set into the eco-hydrological model to calculate the various ecological service functions under each land use scenario.

[0041] Specifically, watershed land use and corresponding leaf area index in the land use spatial pattern are used as inputs to the eco-hydrological model to simulate and calculate various ecological service functions under different afforestation scenarios, and then generate a continuously changing set of land use scenarios based on the various ecological service functions under different afforestation scenarios.

[0042] In some optional embodiments, as shown in the eco-hydrological model in step S1, water yield, net primary productivity, and water conservation capacity are calculated using watershed land use and the corresponding leaf area index as inputs, and these are then used as water service capacity, carbon sequestration service capacity, and water conservation service capacity, respectively. A continuously changing set of land use scenarios is generated based on the water service capacity, carbon sequestration service capacity, and water conservation service capacity under different afforestation scenarios.

[0043] Understandably, by concretizing the abstract ecological service functions into calculable water production, net primary productivity, and water conservation, a mapping relationship between ecological services and specific physical quantities is established, which improves the operability of model calculations and the quantitative accuracy of results.

[0044] In an optional implementation, step S3 includes: establishing a complete mapping link from macro land use patterns to vegetation structure parameters (leaf area index LAI) and then to key variables of eco-hydrological processes (vegetation evapotranspiration potential PAET), namely, the complete mapping link of "PLUS land use pattern → LAI spatialization → PAET → WaSSI-CN eco-hydrological simulation".

[0045] In conjunction with the above optional implementation methods, step S3 specifically includes: Step S3a, spatialization of leaf area index (LAI).

[0046] Specifically, the land use spatial distribution maps under different afforestation scale scenarios simulated by the PLUS model are spatially matched with the interannual variation curves of leaf area index of typical vegetation types at the same latitude.

[0047] Furthermore, for newly added forest grids, the leaf area index (LAI) of each grid is dynamically calculated based on the afforestation year (forest age), altitude, slope aspect, and soil moisture availability, generating a grid-by-grid spatial distribution map of LAI. For different land use types (farmland, deciduous forest, evergreen forest, mixed forest, grassland, shrubland, wetland, urban area, bare land, and water area), reference ranges for LAI and dynamic adjustment rules are established respectively.

[0048] Step S3b: Vegetation evapotranspiration potential (PAET) estimation.

[0049] Specifically, using the LAI spatial distribution map generated in the previous step as the core driving parameter, and combining meteorological data (precipitation P, potential evapotranspiration PET, temperature, radiation, humidity, etc.), the vegetation evapotranspiration potential (PAET) of different land use types is estimated under conditions that do not consider soil moisture status. The calculation formula is as follows:

[0050] In the formula, PAET represents the evapotranspiration potential considering vegetation effects; P represents precipitation; PET represents potential evapotranspiration; LAI represents the monthly average leaf area index; and a1~a4 are preset parameters.

[0051] It should be noted that this formula reveals the nonlinear coupling mechanism between vegetation evapotranspiration potential and meteorological conditions and vegetation characteristics. Its core innovation lies in using LAI as a key bridge parameter connecting land use patterns and eco-hydrological processes, and capturing the physical mechanism that "when atmospheric evaporation demand increases, vegetation with high LAI has a stronger evaporation response" through the PET\cdot LAI cross term.

[0052] Furthermore, regarding the selection of preset parameters, the parameters in the original WaSSI model are empirical parameters obtained using FLUXNET flux station data. The preset parameters a1, a2, a3, and a4 have values ​​of 0.174, 0.502, 5.310, and 0.022, respectively.

[0053] Understandably, most existing studies use this set of parameters directly, and in actual use, operators can also calculate the preset parameters based on relevant flux observation data of the target watershed.

[0054] Step S3c, Parameter Spatialization and Model-Driven Approach: The calculated PAET, along with the LAI spatial distribution map, is used as the core input parameter of the WaSSI-CN model to drive the model to simulate various ecosystem service functions, including water production, carbon sequestration and water conservation, under different afforestation scenarios.

[0055] Step S4: Establish a multi-objective optimization model with ecosystem service function as the optimization objective, and solve the multi-objective optimization model through a multi-objective optimization algorithm to obtain a set of afforestation threshold schemes that reflect the trade-off relationship of ecosystem services.

[0056] In one specific embodiment, step S4 includes: Step S4a: Normalize each ecological service function and input it as the objective function into the multi-objective optimization algorithm.

[0057] The above steps include: using afforestation area A i Using watershed water production, carbon sequestration, and water conservation as decision variables, and taking these three ecological data as objective functions, the multi-objective optimization problem is expressed as:

[0058] In the formula, FW(A) i ), FC(A i ), FR(A i ) represent the total watershed water production, carbon sequestration, and water conservation services under the i-th afforestation scale scenario, i=1,2,…n.

[0059] Step S4b: Using artificial intelligence algorithms to search and analyze the Pareto front analysis of the comprehensive response characteristics of watershed ecosystem service functions and the trade-off relationship between multiple ecosystem service functions under different afforestation scenarios, a threshold scheme set for afforestation area is established.

[0060] In some alternative implementations, a non-dominated multi-objective optimization algorithm (such as the NSGA-II genetic algorithm) is used to search for the optimal solution set of the three objectives of water production, carbon sequestration and water conservation, to obtain the Pareto front, and to establish the corresponding afforestation threshold scheme set based on the Pareto front.

[0061] It is worth noting that the above implementation method ensures the convergence of the optimization algorithm and the quality of the solution set, guaranteeing that the obtained afforestation threshold scheme set is mathematically optimal. The Pareto front is used to analyze the trade-offs between various objectives, thereby quantitatively characterizing the degree to which increasing the afforestation ratio improves or weakens each service, and clarifying whether there is service synergy or intensified conflict at a certain afforestation level. For example, when forest cover increases from low to high, there may be an inflection point where carbon sequestration and water conservation increase significantly, but water production decreases significantly. By analyzing the comprehensive objective function curve and the shape of the Pareto front, the afforestation scenarios corresponding to these inflection points are identified. Based on the Pareto front, a threshold scheme set for the corresponding afforestation area is established.

[0062] Step S5: Filter the afforestation threshold scheme set based on preset weights to obtain the optimal afforestation area threshold.

[0063] Specifically, step S5 above includes: Step S5a: Using the weighted comprehensive objective function method, assign corresponding preset weight coefficients to each ecosystem service function.

[0064] Specifically, a comprehensive objective function is constructed, taking the three ecological service functions of watershed—water production, carbon sequestration, and water conservation—as optimization objectives:

[0065] in, , , These represent the watershed water production, carbon sequestration, and water conservation services under the i-th afforestation scale scenario, respectively. , , These are the maximum values ​​for each service, used for normalization. , , Each service function is assigned a weight based on its importance, to meet the requirements. .

[0066] Furthermore, the aforementioned importance weights , , All weights are dynamic, requiring real-time calculation of the weights of each objective based on the watershed's ecological security boundaries. Water production weight With the watershed water resources carrying capacity index (The current level of water resource development and utilization relative to the safe threshold) is inversely proportional. When When it falls below a safety threshold (e.g., development utilization rate <30%), Take a lower value (e.g., 0.2); when it approaches or exceeds the warning line (e.g., development utilization rate > 40%), Automatically increase (e.g., 0.6).

[0067] Carbon fixation weight carbon sequestration gap in the basin The carbon sequestration weight is directly proportional to the difference between the target carbon sequestration and the actual carbon sequestration. The larger the carbon sequestration gap, the higher the carbon sequestration weight.

[0068] Water conservation weight With the basin's seasonal drought risk index (Calculated based on historical drought frequency and intensity) It is directly proportional. The higher the drought risk, the greater the weight of water conservation.

[0069] In the first specific embodiment of the dynamic weight allocation in this application, it includes: Scenario 1 (Dry Year): Water resource development and utilization rate reaches 42% (exceeding the 40% warning line), carbon sequestration target completion rate is 90%, and there is no severe drought risk. .

[0070] In a second specific embodiment of the dynamic weight allocation in this application, the scenario includes: Scenario 2 (high water year, large carbon sink gap): water resource development and utilization rate is only 25%, carbon sink gap is 25%, and drought risk is moderate. .

[0071] Step S5b: Calculate the comprehensive score of each scheme in the afforestation threshold scheme set based on the preset weight coefficient, and determine the afforestation scale corresponding to the scheme with the highest comprehensive score as the optimal afforestation area threshold.

[0072] Specifically, based on the actual needs of the study area, different weight combination scenarios are set (i.e., adjustments are made). , , Using the determined dynamic weights, the comprehensive score of each candidate solution in the Pareto front is calculated. The proportion of afforestation area corresponding to the scheme that maximizes the overall score is selected as the final recommended optimal threshold for artificial afforestation.

[0073] In summary, this invention establishes a coupled simulation framework between eco-hydrological processes and land use change, constructing a closed-loop feedback system from "land use evolution" to "eco-hydrological response," and then to "multi-objective intelligent optimization" and "dynamic decision-making." This system scientifically determines the optimal afforestation scale threshold under multiple ecological service objectives, including water production, carbon sequestration, and water conservation, while simultaneously considering the economic feasibility of artificial afforestation and the ecological security boundary. By constructing a Pareto optimal solution set using multi-objective optimization technology, the nonlinear trade-off mechanism among water production, carbon sequestration, and water conservation functions is effectively analyzed, ensuring the maximization of ecological benefits. Through weight assignment and a comprehensive scoring decision model, the optimal afforestation area threshold can be quickly identified from a set of complex solutions, improving the feasibility and adaptability of the planning scheme. Combined with integrated device modules, the entire chain from data processing to decision output is automated, significantly improving the intelligent level of watershed ecological engineering management and providing a scientific quantitative basis and technical path for regional ecological barrier construction and sustainable resource utilization.

[0074] Secondly, embodiments of this application also provide an artificial afforestation threshold optimization device, which includes: a model building module, a scenario generation module, a calculation module, an optimization solution module, and a threshold decision module; wherein, The system comprises the following modules: a model building module for constructing an eco-hydrological model to simulate various ecosystem services based on basic geographic information and meteorological and hydrological data of the target watershed; a scenario generation module for setting parameters for afforestation scale variation and generating land use scenario sets corresponding to different afforestation scales using a land use evolution model; a calculation module for inputting the land use scenario sets into the eco-hydrological model to calculate various ecosystem services under each land use scenario; an optimization solution module for establishing a multi-objective optimization model with ecosystem service functions as the optimization objective, solving the multi-objective optimization model through a multi-objective optimization algorithm to obtain a set of afforestation threshold schemes reflecting the trade-offs in ecosystem services; and a threshold decision module for filtering the set of afforestation threshold schemes based on preset weights to obtain the optimal afforestation area threshold.

[0075] The functions of each module in the aforementioned artificial afforestation threshold optimization device correspond to the steps in the aforementioned artificial afforestation threshold optimization method embodiment, and their functions and implementation processes will not be described in detail here.

[0076] Thirdly, embodiments of this application provide an artificial afforestation threshold optimization device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0077] Reference Figure 2 , Figure 2 This is a schematic diagram of the hardware structure of the artificial afforestation threshold optimization device involved in the embodiments of this application. In the embodiments of this application, the artificial afforestation threshold optimization device may include a processor, a memory, a communication interface, and a communication bus.

[0078] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0079] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the afforestation threshold optimization equipment, as well as interfaces used for interconnecting the afforestation threshold optimization equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0080] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0081] The processor can be a general-purpose processor, which can call the artificial afforestation threshold optimization program stored in memory and execute the artificial afforestation threshold optimization method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the artificial afforestation threshold optimization program is called can refer to the various embodiments of the artificial afforestation threshold optimization method of this application, and will not be repeated here.

[0082] Those skilled in the art will understand that Figure 2The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0083] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0084] The present application has a computer-readable storage medium storing an artificial afforestation threshold optimization program, wherein when the artificial afforestation threshold optimization program is executed by a processor, it implements the steps of the artificial afforestation threshold optimization method as described above.

[0085] The method implemented when the artificial afforestation threshold optimization procedure is executed can be referred to in the various embodiments of the artificial afforestation threshold optimization method of this application, and will not be repeated here.

[0086] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0087] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0088] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0089] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0090] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0092] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for optimizing thresholds in artificial afforestation, characterized in that, The method for optimizing the threshold of artificial afforestation includes: An eco-hydrological model for simulating various ecosystem service functions is constructed based on the basic geographic information and meteorological and hydrological data of the target watershed. Set parameters for changes in afforestation scale, and use a land use evolution model to generate land use scenario sets corresponding to different afforestation scales based on these parameters; The land use scenario set is input into the eco-hydrological model to calculate the various ecological service functions under each land use scenario; A multi-objective optimization model is established with ecosystem service function as the optimization objective. The multi-objective optimization model is solved by a multi-objective optimization algorithm to obtain a set of afforestation threshold schemes that reflect the trade-off relationship of ecosystem services. The afforestation threshold scheme set was screened to obtain the optimal afforestation area threshold.

2. The method for optimizing the threshold of artificial afforestation as described in claim 1, characterized in that, The process of inputting a set of land use scenarios into an eco-hydrological model to calculate various ecosystem service functions under each land use scenario includes: Based on the input land use scenario and meteorological and hydrological data, the simulation calculates water production services, carbon sequestration services, and water conservation services.

3. The method for optimizing the threshold of artificial afforestation as described in claim 2, characterized in that, The process of simulating and calculating water production services, carbon sequestration services, and water conservation services based on input land use scenarios and meteorological and hydrological data includes: Based on land use scenarios and meteorological and hydrological data, water production, net primary productivity, and water conservation capacity are simulated and calculated, and water production, net primary productivity, and water conservation capacity are respectively used as water production service capacity, carbon sequestration service capacity, and water conservation service capacity.

4. The method for optimizing the threshold of artificial afforestation as described in claim 1, characterized in that, The parameters for setting changes in afforestation scale include: The forest area under different afforestation scenarios is calculated based on the forest area in the base year and the step size of forest area change.

5. The method for optimizing the threshold of artificial afforestation as described in claim 4, characterized in that, The land use evolution model generates a set of land use scenarios corresponding to different afforestation scales based on parameters of afforestation scale changes, including: Using the forest area of ​​the base year as the initial state, the forest area under different afforestation scale scenarios is calculated sequentially according to the forest area change step size; Using forest area under different afforestation scale scenarios as input, a land use evolution model is used to simulate the spatial distribution map of land use under different afforestation scenarios, so as to generate a continuously changing set of land use scenarios.

6. The method for optimizing the threshold of artificial afforestation as described in claim 1, characterized in that, The process of solving the multi-objective optimization model using a multi-objective optimization algorithm to obtain a set of afforestation threshold schemes reflecting the trade-offs in ecosystem services includes: The various ecosystem service functions are normalized and then used as the objective function input into the multi-objective optimization algorithm. A set of afforestation threshold schemes was established by analyzing the objective function curve and the shape of the Pareto front.

7. The method for optimizing the threshold of artificial afforestation as described in claim 6, characterized in that, The method of establishing a set of afforestation threshold schemes by analyzing the objective function curve and the shape of the Pareto front includes: The Pareto front is obtained by iteratively searching for the set of non-dominated solutions, and a set of corresponding afforestation threshold schemes is established based on the Pareto front.

8. The method for optimizing the threshold of artificial afforestation as described in claim 1, characterized in that, The process of filtering the afforestation threshold scheme set to obtain the optimal afforestation area threshold includes: A weighted comprehensive objective function method is used to assign corresponding preset weight coefficients to each ecological service function. The comprehensive score of each scheme in the afforestation threshold scheme set is calculated based on the preset weight coefficient, and the afforestation scale corresponding to the scheme with the highest comprehensive score is determined as the optimal afforestation area threshold.

9. A threshold optimization device for artificial afforestation, characterized in that, The artificial afforestation threshold optimization device includes: The model building module is used to build eco-hydrological models that simulate various ecosystem service functions based on the basic geographic information and meteorological and hydrological data of the target watershed. The scenario generation module is used to set parameters for changes in afforestation scale and generate land use scenario sets corresponding to different afforestation scales based on the parameters for changes in afforestation scale using a land use evolution model. The calculation module is used to input the land use scenario set into the eco-hydrological model to calculate the various ecosystem service functions under each land use scenario. The optimization solution module is used to establish a multi-objective optimization model with ecosystem service function as the optimization objective, and solve the multi-objective optimization model through a multi-objective optimization algorithm to obtain a set of afforestation threshold schemes that reflect the trade-off relationship of ecosystem services. The threshold decision module is used to filter the afforestation threshold scheme set based on preset weights to obtain the optimal afforestation area threshold.

10. A threshold optimization device for artificial afforestation, characterized in that, The artificial afforestation threshold optimization device includes a processor, a memory, and an artificial afforestation threshold optimization program stored in the memory and executable by the processor, wherein when the artificial afforestation threshold optimization program is executed by the processor, it implements the steps of the artificial afforestation threshold optimization method as described in any one of claims 1 to 8.

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