A method and apparatus for scenario simulation of water-soil-ecology-economy coupled systems based on system dynamics

Abstract

This invention discloses a method and apparatus for scenario simulation of a water-soil-ecology-economy coupled system based on system dynamics. The method includes: identifying key variables of the water subsystem, soil subsystem, ecological subsystem, and economic subsystem; constructing a causal loop diagram based on the feedback relationships between the key variables of each subsystem; generating a stock-flow diagram based on the causal loop diagram; establishing core equations based on the stock-flow diagram to obtain an ecosystem dynamics model corresponding to the coupled system; inputting scenario parameters of multiple scenarios into the ecosystem dynamics model for scenario simulation, obtaining dynamic change curves of each ecosystem service evaluation index under each scenario within a predetermined time range, and evaluating the ecosystem services of each scenario to determine the optimal scenario. Based on this, the complex feedback relationships between the four subsystems of water, soil, ecology, and economy can be characterized, and the optimal scenario can be determined. The optimal strategy corresponding to the optimal scenario can provide a specific path for the coordinated management and control of regional resources and environment.

Description

Technical Field

[0001] This invention relates to the field of ecological environment management technology, and in particular to a method for simulating scenarios of a water-soil-ecology-economy coupled system based on system dynamics, and a device for simulating scenarios of a water-soil-ecology-economy coupled system based on system dynamics. Background Technology

[0002] Currently, the conflict between the irrational use of water resources, land degradation, ecological space encroachment, and the pursuit of economic growth is becoming increasingly prominent, making the coordination of these four factors a core issue in ecological and environmental management. System dynamics (SD) methods, due to their ability to handle complex high-order, nonlinear, and multi-feedback systems, have been widely applied to the study of single or pairwise related problems such as water resource management, land use change, or eco-economic coordination.

[0003] However, existing technologies still have the following core shortcomings: (1) Limitations in research dimensions Existing technologies mostly focus on single systems (such as water resources) or the relationship between two systems (such as water-economy), failing to construct a closed-loop coupled overall framework for water, soil, ecology, and economy. The feedback links are fragmented, making it impossible to depict the chain reaction of "economic output - resource consumption - ecological protection - system regulation", resulting in simulation results that are difficult to reflect the real evolution law of complex systems.

[0004] (2) The SD model lacks weight-awareness. Existing SD models can only design different scenarios by adjusting variable parameters, but they do not consider the weight differences of each indicator on the overall benefits of the system—that is, different indicators contribute differently to "sustainable development," but treat all indicator changes equally, which makes it impossible to distinguish the policy effects of "optimizing key indicators" and "optimizing secondary indicators," and the simulation results have weak support for decision-making.

[0005] Therefore, a scenario simulation method for a water-soil-ecology-economy coupled system based on system dynamics is needed to solve the problems existing in the above technical solutions. Summary of the Invention

[0006] Therefore, the present invention provides a method and apparatus for simulating scenarios of a water-soil-ecology-economy coupled system based on system dynamics, in order to solve or at least alleviate the problems mentioned above.

[0007] According to one aspect of the present invention, a scenario simulation method for a water-soil-ecology-economy coupled system based on system dynamics is provided, executed in a computing device, comprising: determining key variables of multiple subsystems corresponding to the coupled system, the multiple subsystems including a water subsystem, a soil subsystem, an ecological subsystem, and an economic subsystem; constructing a causal loop diagram based on the feedback relationships between the key variables of each subsystem; generating a stock-flow diagram based on the causal loop diagram; establishing a core equation based on the stock-flow diagram to obtain an ecosystem dynamics model corresponding to the coupled system; inputting scenario parameters of multiple scenarios into the ecosystem dynamics model to perform scenario simulation, obtaining dynamic change curves of each ecosystem service evaluation index under each scenario within a predetermined time range; and evaluating the ecosystem services of each scenario based on the dynamic change curves of each ecosystem service evaluation index under each scenario within the predetermined time range to determine the optimal scenario.

[0008] Optionally, in the water-soil-ecology-economy coupled system scenario simulation method based on system dynamics according to the present invention, the dynamic change curve within the predetermined time range includes the indicator data for each year within the predetermined time range; based on the dynamic change curve of each ecosystem service evaluation indicator under each scenario within the predetermined time range, the ecosystem services of each scenario are evaluated to determine the optimal scenario, including: using the entropy weight method, determining the weight of each ecosystem service evaluation indicator based on the dynamic change curve of each ecosystem service evaluation indicator under each scenario within the predetermined time range; using the improved TOPSIS method, determining the comprehensive ecosystem service benefit score of each scenario based on the dynamic change curve of each ecosystem service evaluation indicator under each scenario within the predetermined time range and the weight of each ecosystem service evaluation indicator, wherein the comprehensive ecosystem service benefit score includes the comprehensive benefit score for each year; and determining the optimal scenario based on the comprehensive ecosystem service benefit score of each scenario.

[0009] Optionally, in the system dynamics-based water-soil-ecology-economy coupled system scenario simulation method according to the present invention, the multiple scenarios include a baseline scenario, an ecological priority scenario, an economic priority scenario, and a water-saving priority scenario; the scenario parameters include the afforestation growth rate, the sewage treatment capacity growth rate, the agricultural total output value growth rate, the industrial total output value growth rate, the agricultural water-saving efficiency coefficient, and the industrial water-saving efficiency coefficient; the ecosystem service evaluation indicators include the area of ​​soil and water conservation, the available water volume, the water resource supply-demand ratio, the ecological land area, and the ecological land type richness, wherein the ecological land type richness represents the sum of the proportions of forest area, grassland area, and wetland area.

[0010] Optionally, in the water-soil-ecology-economy coupled system scenario simulation method based on system dynamics according to the present invention, before inputting the scenario parameters of multiple scenarios into the ecosystem dynamics model for scenario simulation, the method further includes: collecting historical data for each key variable, assigning parameter values ​​to the ecosystem dynamics model based on the historical data, and verifying the rationality and effectiveness of the ecosystem dynamics model based on the historical data.

[0011] Optionally, in the water-soil-ecology-economy coupled system scenario simulation method based on system dynamics according to the present invention, generating a stock-flow diagram based on the causal loop diagram includes: determining stock, flow, and auxiliary variables based on the causal loop diagram; and generating a stock-flow diagram based on the mathematical relationship between stock, flow, and auxiliary variables.

[0012] Optionally, in the system dynamics-based water-soil-ecology-economy coupled system scenario simulation method according to the present invention, the key variables of the water subsystem include total water demand, available water supply, and water resource supply-demand ratio, wherein the total water demand includes ecological water demand, agricultural irrigation water demand, and industrial water demand, the available water supply is related to the wastewater regeneration rate, and the water resource supply-demand ratio is the ratio of total water demand to available water supply; the key variables of the soil subsystem include the area of ​​soil and water conservation, cultivated land area, and area with good soil and water conservation status; the key variables of the ecology subsystem include ecological land area, ecological water demand, and afforestation volume, wherein the ecological land area includes forest area, grassland area, and wetland area; the key variables of the economy subsystem include GDP, total population, total industrial output value, total agricultural output value, fiscal expenditure, and medical expenditure, wherein the total population is determined by the birth rate and death rate.

[0013] Optionally, in the system dynamics-based water-soil-ecology-economy coupled system scenario simulation method according to the present invention, the core equations include the water resource supply-demand ratio formula, the ecological water demand formula, the cultivated land area formula, the available water volume formula, the ecological land area formula, the total water demand formula, the total agricultural output value formula, the total industrial output value formula, the water resource collection formula, and the soil and water conservation area formula.

[0014] According to one aspect of the present invention, a water-soil-ecology-economy coupled system scenario simulation device based on system dynamics is provided, deployed in a computing device, suitable for performing the method described above, the device comprising: The determination module is adapted to determine key variables of multiple subsystems corresponding to the coupled system, the multiple subsystems including a water subsystem, a soil subsystem, an ecological subsystem, and an economic subsystem; The module is suitable for constructing causal loop diagrams based on the feedback relationships between key variables of each subsystem. The generation module is adapted to generate a stock flow diagram based on the causal loop relationship diagram; A module is established to establish core equations based on the stock flow map in order to obtain the ecosystem dynamics model corresponding to the coupled system. The simulation module is suitable for inputting scenario parameters of multiple scenarios into the ecosystem dynamics model to perform scenario simulation, and obtaining the dynamic change curves of each ecosystem service evaluation index under each scenario within a predetermined time range. The assessment module is suitable for evaluating ecosystem services under each scenario based on the dynamic change curves of each ecosystem service evaluation index within a predetermined time range, in order to determine the optimal scenario.

[0015] According to one aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing the water-soil-ecology-economy coupled system scenario simulation method based on system dynamics as described above.

[0016] According to one aspect of the present invention, a computer program product is provided, comprising computer program instructions, wherein the computer program instructions, when executed by a processor, implement the method as described above.

[0017] According to one aspect of the present invention, a readable storage medium storing program instructions is provided, which, when read and executed by a computing device, causes the computing device to perform the water-soil-ecology-economy coupled system scenario simulation method based on system dynamics as described above.

[0018] According to the technical solution of the present invention, a scenario simulation method for a water-soil-ecology-economy coupled system based on system dynamics is provided. By identifying the key variables of the water subsystem, soil subsystem, ecological subsystem, and economic subsystem, a causal loop relationship diagram is constructed based on the feedback relationship between the key variables of each subsystem, and a stock-flow diagram is generated. Based on the stock-flow diagram, the core equation is established to obtain the ecosystem dynamics model corresponding to the coupled system. Then, the scenario parameters of multiple scenarios are input into the ecosystem dynamics model to perform scenario simulation. Finally, the ecosystem services of each scenario are evaluated based on the simulation results to determine the optimal scenario. Based on this, incorporating water, soil, ecology, and economy into a unified SD framework can characterize the complex feedback relationship of "economic growth - resource consumption - ecological protection" among these four subsystems. This overcomes the limitations of existing technologies that rely on a single dimension or pairwise correlations, enabling simulation results to reflect the true evolutionary laws of complex systems. By conducting scenario simulations and ecosystem service assessments, the optimal strategies corresponding to the optimal scenarios can be determined. Based on these optimal strategies, specific paths can be provided for the coordinated management of regional resources and the environment, achieving a synergistic state of efficient resource utilization, continuous environmental improvement, and robust economic development, ultimately enhancing the overall ecosystem services of the region. Furthermore, the ecosystem dynamics model constructed according to this invention can be flexibly adapted to different regions. By adjusting parameters, it can be applied to policy scenario simulations of the water-soil-ecology-economic system in different regions, demonstrating strong promotional value.

[0019] Furthermore, the use of entropy weighting combined with the improved TOPSIS method for ecosystem service assessment not only considers the differences in the weight of each indicator on the overall benefits of ecosystem services, but also avoids subjective bias, enabling objective quantification and comparison of the overall benefits of ecosystem services across multiple scenarios. This improves the scientific rigor of scenario / strategy selection and overcomes the shortcomings of existing models in lacking service assessment.

[0020] Furthermore, by conducting dual tests on ecosystem dynamics models, the accuracy of model-based scenario simulations can be improved, accurately reflecting the evolutionary laws of the system.

[0021] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0022] To achieve the foregoing and related objectives, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings. These aspects indicate various ways in which the principles disclosed herein may be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The foregoing and other objectives, features, and advantages of the invention will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings. Throughout the invention, the same reference numerals generally refer to the same parts or elements.

[0023] Figure 1 A schematic diagram of a computing device 100 provided according to an embodiment of the present invention is shown; Figure 2 A flowchart illustrating a scenario simulation method 200 for a water-soil-ecology-economy coupled system based on system dynamics according to an embodiment of the present invention is shown. Figure 3 An exemplary diagram of a causal loop relationship according to some embodiments of the present invention is shown; Figure 4 An exemplary diagram of stock flow is shown according to some embodiments of the present invention; Figure 5 The diagram illustrates the dynamic changes of the soil and water conservation area under various scenarios in some embodiments of the present invention within a predetermined time range (2019-2030); Figure 6 A schematic diagram illustrating the dynamic variation curves of available water volume over a predetermined time range (2019-2030) under various scenarios according to some embodiments of the present invention is shown. Figure 7 The diagram illustrates the dynamic changes of the water supply-demand ratio under various scenarios in some embodiments of the present invention within a predetermined time range (2019-2030); Figure 8 A schematic diagram illustrating the dynamic change curves of ecological land area under various scenarios in some embodiments of the present invention within a predetermined time range (2019-2030) is shown. Figure 9 A schematic diagram illustrating the dynamic changes in the proportion of ecological land under various scenarios within a predetermined time range (2019-2030) according to some embodiments of the present invention is shown. Figure 10 A schematic diagram of a water-soil-ecology-economy coupled system scenario simulation device 1000 based on system dynamics according to an embodiment of the present invention is shown. Detailed Implementation

[0024] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0025] To address the problems in existing technologies, such as the failure to construct a closed-loop coupled overall framework of water, soil, ecology, and economy, the inability of simulation results to reflect the real evolution law of complex systems, and the failure to consider the weight differences of each indicator on the comprehensive benefits of the system, this invention proposes a scenario simulation method for a water-soil-ecology-economy coupled system based on system dynamics.

[0026] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0027] Figure 1 A schematic diagram of a computing device 100 according to an embodiment of the present invention is shown. Figure 1 As shown, in a basic configuration, computing device 100 includes at least one processing unit 102 and system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 may be implemented as a processor. System memory 104 includes, but is not limited to, volatile memory (e.g., random access memory), non-volatile memory (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, system memory 104 includes an operating system 105.

[0028] According to one aspect, operating system 105 is, for example, suitable for controlling the operation of computing device 100. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other applications, and are not limited to any particular application or system. Figure 1 The basic configuration is illustrated by the components within the dashed lines. According to one aspect, the computing device 100 has additional features or functions. For example, according to one aspect, the computing device 100 includes additional data storage devices (removable and / or non-removable), such as disks, optical discs, or magnetic tapes. This additional storage... Figure 1 The middle part is shown by removable storage device 109 and non-removable storage device 110.

[0029] As stated above, according to one aspect, program module 103 is stored in system memory 104. According to one aspect, program module 103 may include one or more applications. The present invention does not limit the type of application; for example, applications may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slideshow applications, drawing or computer-aided applications, web browser applications, etc.

[0030] According to one aspect, program module 103 may include a plurality of program instructions suitable for executing the system dynamics-based water-soil-ecology-economy coupled system scenario simulation method 200 of the present invention, such that computing device 100 is configured to execute the system dynamics-based water-soil-ecology-economy coupled system scenario simulation method 200 of the present invention.

[0031] According to one aspect, program module 103 may include a system dynamics-based water-soil-ecology-economy coupled system scenario simulation device 1000, which may be configured to perform the system dynamics-based water-soil-ecology-economy coupled system scenario simulation method 200 of the present invention.

[0032] According to one aspect, examples can be practiced on circuits including discrete electronic components, packaged or integrated electronic chips containing logic gates, circuits utilizing microprocessors, or on a single chip containing electronic components or a microprocessor. For example, it can be practiced via wherein... Figure 1 Each or many of the components shown can be implemented as an example by integrating a System-on-a-Chip (SOC) on a single integrated circuit. According to one aspect, such an SOC device may include one or more processing units, graphics units, communication units, system virtualization units, and various application functions, all integrated (or “burned in”) as a single integrated circuit onto a chip substrate. When operating via the SOC, the functions described herein can be operated via dedicated logic integrated on a single integrated circuit (chip) with other components of the computing device 100. Embodiments of the invention can also be implemented using other techniques capable of performing logical operations (e.g., AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. Additionally, embodiments of the invention can be implemented within a general-purpose computer or in any other circuit or system.

[0033] According to one aspect, computing device 100 may also have one or more input devices 112, such as a keyboard, mouse, pen, voice input device, touch input device, etc. It may also include output devices 114, such as a display, speaker, printer, etc. The foregoing devices are examples and other devices may also be used. Computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver and / or transceiver circuitry; Universal Serial Bus (USB), parallel and / or serial ports.

[0034] As used herein, the term computer-readable medium includes computer storage medium. Computer storage medium can include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information (e.g., computer-readable instructions, data structures, or program module 103). System memory 104, removable storage device 109, and non-removable storage device 110 are examples of computer storage media (i.e., memory storage). Computer storage media can include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other article of manufacture that can be used to store information and is accessible by computing device 100. According to one aspect, any such computer storage medium can be part of computing device 100. Computer storage media does not include carrier waves or other transmitted data signals.

[0035] According to one aspect, the communication medium is implemented by computer-readable instructions, data structures, program modules 103, or other data in a modulated data signal (e.g., a carrier wave or other transmission mechanism), and includes any information transmission medium. According to one aspect, the term "modulated data signal" describes a signal having one or more sets of characteristics or altered in a manner that encodes information in the signal. By way of example and not limitation, the communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0036] In an embodiment of the invention, computing device 100 is configured to execute the system dynamics-based water-soil-ecology-economy coupled system scenario simulation method 200 of the invention. Computing device 100 includes one or more processors and one or more readable storage media storing program instructions that, when configured to be executed by the one or more processors, cause the computing device to execute the system dynamics-based water-soil-ecology-economy coupled system scenario simulation method 200 of the present invention.

[0037] Figure 2 A schematic flowchart of a water-soil-ecology-economy coupled system scenario simulation method 200 based on system dynamics according to an embodiment of the present invention is shown. The water-soil-ecology-economy coupled system scenario simulation method 200 based on system dynamics can be executed in a computing device (e.g., the aforementioned computing device 100).

[0038] In embodiments of the present invention, the computing device 100 used to execute the water-soil-ecology-economy coupled system scenario simulation method 200 based on system dynamics of the present invention can be a terminal or a server.

[0039] It should be noted that the coordinated development of water, soil, ecology, and economic systems is the cornerstone of regional sustainable development and ecological environment governance. In this embodiment of the invention, the connotation of "coordinated development of the water-soil-ecology-economy coupled system" is defined as follows: under the constraints of limited resource and environmental carrying capacity, through optimizing water resource allocation, strengthening soil protection, maintaining ecological security, and promoting economic growth, a dynamic balance and positive feedback is achieved to realize a synergistic state of efficient resource utilization, continuous environmental improvement, and steady economic development, ultimately enhancing the overall ecosystem services of the region.

[0040] like Figure 2 As shown, the water-soil-ecology-economy coupled system scenario simulation method 200 based on system dynamics includes the following steps 210~260.

[0041] It should be noted that before performing step 210, the computing device 100 may predetermine the system boundaries, assumptions, and a predetermined time range.

[0042] In some embodiments, to ensure the operability of the model and focus on the core feedback mechanism, the following basic assumptions are set: 1) Assumption of spatial homogeneity: The model simulates the study area as a whole and does not consider its internal spatial heterogeneity. That is, it is assumed that the parameters (such as precipitation and technological progress rate) within the system are uniformly distributed.

[0043] 2) Policy exogeneity assumption: Key policy variables such as the afforestation growth rate, the industrial and agricultural water-saving efficiency coefficient, and the sewage treatment capacity growth rate are used as exogenous scenario parameters to simulate the impact of different policy orientations, rather than endogenizing their decision-making process in the model.

[0044] 3) Key Dominant Path Assumptions: The model focuses on the most important interaction paths between subsystems. For example, in the economic subsystem, the composition of GDP is simplified, and the core paths driven by total industrial output and total agricultural output are mainly considered to highlight the direct constraints and feedback relationships between resources (water, soil) and economic activities.

[0045] 4) Data continuity assumptions: The model is based on the trends and patterns of historical statistical data. It is assumed that during the simulation period (2019-2030), the main structure and feedback relationships of the system will remain relatively stable, and no disruptive technological changes or extreme external shocks will occur.

[0046] In some embodiments, the predetermined time range is, for example, 2019–2030, wherein data from 2019–2024 is used to test the model, and data from 2025–2030 (as the simulation period) is used for simulation analysis. The entire analysis process iterates in years.

[0047] In step 210, the computing device 100 can construct multiple subsystems corresponding to the water-soil-ecology-economy coupled system and determine the key variables of the multiple subsystems. The multiple subsystems specifically include a water subsystem, a soil subsystem, an ecological subsystem, and an economic subsystem.

[0048] Subsequently, in step 220, the computing device 100 can construct a causal loop diagram based on the feedback relationship (influence relationship) between the key variables of each subsystem. Figure 3 A causal loop diagram according to some embodiments of the present invention is illustrated.

[0049] It should be noted that for the water subsystem, the regional water resource supply and demand relationship can be used as the core, and the model can be built around the total water demand and the available water supply. The key variables of the water subsystem include total water demand, available water supply, and the water resource supply-demand ratio. Total water demand is composed of ecological water demand, agricultural irrigation water demand, industrial water demand, and other water demand. In other words, total water demand includes ecological water demand, agricultural irrigation water demand, and industrial water demand. Among them, ecological water demand is related to the area of ​​forest land, grassland, and wetlands; agricultural irrigation water demand is related to the cultivated land area, total agricultural output value, and agricultural water-saving efficiency coefficient; industrial water demand is related to the total industrial output value and industrial water-saving efficiency coefficient. Available water supply mainly comes from surface runoff. At the same time, industrial wastewater, after treatment and regeneration (through the wastewater treatment coefficient and wastewater regeneration path), can supplement the available water supply, forming a positive feedback loop of "increased industrial water demand → increased industrial wastewater discharge → increased wastewater regeneration → increased available water supply", reflecting the gain effect of water resource recycling. It should be understood that the available water supply is related to the wastewater recycling rate.

[0050] The water supply-demand ratio, which is the ratio of total water demand to available water, directly characterizes regional water resource pressure. The key feedback loop of the water subsystem is as follows: Increased industrial output drives up industrial water demand, thus increasing total water demand. If available water is insufficient, the water supply-demand ratio decreases, forming a negative feedback chain: "Industrial output → (+) Industrial water demand → (+) Total water demand → (-) Water supply-demand ratio". The same logic applies to agriculture, forming a negative feedback chain: "Agricultural output → (+) Agricultural irrigation water demand → (+) Total water demand → (-) Water supply-demand ratio," revealing the constraining effect of economic development on the water resource system.

[0051] The soil subsystem focuses on soil erosion control and is linked to land use change and economic development. Key variables in the soil subsystem include the area of ​​soil erosion control, the area of ​​arable land, and the area with good soil and water conservation conditions.

[0052] An increase in the area of ​​soil and water conservation directly increases the area with good soil and water conservation conditions and provides support for the stability and expansion of land types such as forests, grasslands, wetlands, and arable land. Economic development drives the conservation efforts through fiscal investment; that is, the growth of total industrial output can have a positive impact on the area of ​​soil and water conservation.

[0053] As the core carrier of agricultural production, arable land area is directly related to agricultural irrigation water demand and total agricultural output value, thus forming a transmission path of "area of ​​soil and water conservation → (+) arable land area → (+) agricultural irrigation water demand → (+) total water demand". This soil subsystem shows that soil and water conservation provides basic support for agricultural production and ecological protection by reducing the risk of land degradation and ensuring the sustainable use of land resources.

[0054] The ecological subsystem uses the area of ​​soil and water conservation as a hub, connecting various types of ecological land use with ecological water demand. Key variables of the ecological subsystem include ecological land area (including forest area, grassland area, and wetland area), ecological water demand, and afforestation volume. Afforestation and other ecological construction activities directly increase the area of ​​soil and water conservation, thereby promoting the expansion of ecological land areas such as forests, grasslands, and wetlands. This increase in ecological land area will correspondingly increase their respective ecological water demand (forest water demand, grassland water demand, and wetland water demand), which is aggregated into ecological water demand, constituting a significant portion of the region's total water demand.

[0055] The increase in the area of ​​soil and water conservation directly improves the area with good soil and water conservation, thereby reducing the degree of soil erosion and laying the foundation for ecosystem stability. Furthermore, precipitation indirectly affects the health of ecological land by influencing soil moisture retention and ecological water replenishment. The key feedback loops of the ecological subsystem include the transmission chain of "soil and water conservation area → (+) ecological land area → (+) ecological water demand → (+) total water demand," and the negative feedback loop of "soil and water conservation area → (+) area with good soil and water conservation → (-) soil erosion risk," which together maintain the self-regulating function of the ecosystem.

[0056] The economic subsystem, centered on GDP, integrates variables related to population, people's livelihood, and industrial development. Key variables in the economic subsystem include GDP, total population, total industrial output, total agricultural output, fiscal expenditure, and healthcare expenditure. In the population and people's livelihood dimension, the total population is determined by the birth rate and death rate. Increased healthcare expenditure helps reduce the death rate, thus supporting population growth; and healthcare expenditure originates from fiscal expenditure, which in turn is driven by GDP. This forms a positive feedback loop: "GDP → (+) fiscal expenditure → (+) healthcare expenditure → (-) death rate → (+) total population," reflecting the supporting role of economic development in ensuring people's livelihood.

[0057] In terms of industrial development, GDP directly drives the growth of total industrial output. An increase in total industrial output further increases industrial water demand. Total agricultural output is driven by factors such as arable land area and agricultural irrigation water demand. Total agricultural output and total industrial output together constitute the core components of GDP (GDP includes total agricultural output and total industrial output), forming a cyclical transmission chain of "total agricultural output → (+) GDP → (+) total industrial output → (+) industrial water demand." This economic subsystem reflects the deep interconnectedness within the economic system, as well as between the economic system and the water and soil subsystems.

[0058] Next, in step 230, the computing device 100 can generate a stock flow diagram based on the above-described causal loop relationship diagram. Figure 4 An exemplary stock flow diagram is shown according to some embodiments of the present invention.

[0059] Specifically, in step 230, based on the aforementioned causal loop diagram, the stock, flow, and auxiliary variables can first be determined (i.e., the key variables are classified to categorize each key variable as a stock / flow / auxiliary variable). Then, a stock-flow diagram can be generated based on the mathematical relationships between the stock, flow, and auxiliary variables. It can be understood that the stock-flow diagram is used to represent the mathematical relationships between the stock, flow, and auxiliary variables.

[0060] Subsequently, in step 240, the computing device 100 can establish core equations based on the stock flow map to obtain the ecosystem dynamics model corresponding to the coupled system.

[0061] In some embodiments, the core equations include the water resource supply-demand ratio formula, the ecological water demand formula, the arable land area formula, the available water volume formula, the ecological land area formula, the total water demand formula, the total agricultural output value formula, the total industrial output value formula, the water resource collection formula, and the soil and water conservation area formula. See Table 1 below for details.

[0062] Table 1 Core Equations of Ecosystem Dynamics Model

[0063] In some embodiments, after constructing the ecosystem dynamics model corresponding to the coupled system, parameters can be set. Specifically, historical data can be collected for each key variable in the ecosystem dynamics model, for example, historical data of more than 5 years can be collected, and parameters of the ecosystem dynamics model can be assigned based on the historical data. In addition, the grey prediction method can be used to predict the prediction level year based on the prediction year.

[0064] Furthermore, the rationality and effectiveness of the ecosystem dynamics model can be tested based on historical data to determine whether it accurately reflects the system's behavior and change patterns. In some embodiments, the rationality of the model structure and dimensions can be tested by running the "Check Model" and "Unit Check" modules in the Vensim.PLEx32 software. If both modules return "Model is OK," the ecosystem dynamics model is considered rational. Simultaneously, one or more key variables can be selected to test model effectiveness. For example, if the relative error between the simulated and actual values ​​of one or more selected key variables is less than 10%, the ecosystem dynamics model is considered effective and can be used for simulating and predicting the development of the water-soil-ecological-economic system.

[0065] In this embodiment of the invention, to explore the impact of different policy orientations on system evolution and ecosystem services, multiple scenarios (i.e., policy scenarios) with different policy orientations can be designed. Each scenario corresponds to a specific policy, and each scenario is configured with corresponding scenario parameters.

[0066] In step 250, the computing device 100 can input scenario parameters from multiple scenarios into the ecosystem dynamics model to perform scenario simulations, obtaining dynamic change curves of each ecosystem service evaluation index under each scenario within a predetermined time range (e.g., 2019–2030). Here, the dynamic change curves within the predetermined time range include index data for each year within the predetermined time range.

[0067] In some embodiments, four representative scenarios were designed to explore the impact of different policy orientations on system evolution and ecosystem services, as shown in Table 2. These scenarios (four scenarios) include a baseline scenario, an ecological priority scenario, an economic priority scenario, and a water conservation priority scenario. The baseline scenario (S1) continues the current development trend without additional intervention and serves as a baseline for comparison. The ecological priority scenario (S2) focuses on ecological restoration and protection, significantly increasing investment in afforestation and ecological governance. The economic priority scenario (S3) emphasizes economic growth, moderately increasing the growth rate of industrial and agricultural output. The water conservation priority scenario (S4) focuses on the intensive use of water resources, simultaneously improving water conservation efficiency in industry and agriculture and wastewater treatment capacity. Different strategic priorities are characterized by adjusting key policy variables (such as growth rate and efficiency coefficient) in the model.

[0068] In some embodiments, scenario parameters include afforestation growth rate, wastewater treatment capacity growth rate, total agricultural output growth rate, total industrial output growth rate, agricultural water-saving efficiency coefficient, and industrial water-saving efficiency coefficient.

[0069] Table 2 Scenario Design

[0070] In some embodiments, ecosystem service evaluation indicators include the area of ​​soil and water conservation, available water volume, water supply-demand ratio, ecological land area, and ecological land type richness. It should be noted that a smaller area of ​​soil and water conservation indicates a lower degree of soil erosion, indirectly reflecting the effectiveness of soil protection. Available water volume directly reflects supply capacity; a higher water supply-demand ratio indicates a stronger water resource security capacity. A larger ecological land area indicates a stronger regional water interception and storage capacity, indirectly reflecting the level of water resource conservation. Ecological land type richness, also known as the proportion of ecological land, represents the sum of the proportions of forest land area, grassland area, and wetland area. A richer ecological land type indicates a higher proportion of ecological land and more stable biodiversity.

[0071] Figure 5The diagram illustrates the dynamic changes of the soil and water conservation area under various scenarios in some embodiments of the present invention within a predetermined time range (2019-2030).

[0072] like Figure 5 As shown, the area of ​​soil erosion control under all scenarios shows an upward trend, but the growth rate differs from the endpoint. The economic priority scenario (S3) shows the most rapid growth, jumping from approximately 6.1 million hectares in 2019 to approximately 7.9 million hectares in 2030. This may be due to increased fiscal investment resulting from economic growth, supporting larger-scale ecological restoration projects. The area of ​​soil erosion control under the ecological priority scenario (S2) remains stable at approximately 6.2 million hectares during the simulation period, showing the smallest increase, reflecting that its strategic focus is on prevention and protection rather than the expansion of post-event restoration. The soil erosion control areas under the baseline scenario (S1) and the water conservation priority scenario (S4) are at intermediate levels, reaching approximately 6.3 million hectares and approximately 6.7 million hectares respectively by 2030.

[0073] Figure 6 The diagram illustrates the dynamic variation curves of available water volume over a predetermined time range (2019-2030) under various scenarios according to some embodiments of the present invention. Figure 7 The diagram illustrates the dynamic changes of the water supply-demand ratio under various scenarios in some embodiments of the present invention within a predetermined time range (2019-2030).

[0074] like Figure 6 and Figure 7 As shown, the trends in available water volume and the water supply-demand ratio are similar, both exhibiting a fluctuating pattern of "first rising (2019-2020), then falling (2020-2023), and then rising again and stabilizing (after 2023)." In terms of available water volume, the ecological priority scenario (S2) shows a higher available water volume than other scenarios in most years, especially in wet years (such as 2020), indicating that the increase in ecological land use has effectively improved the region's water conservation and regulation capacity. Regarding the water supply-demand ratio, the water conservation priority scenario (S4) shows a significant advantage, with its ratio consistently higher than other scenarios, and exhibiting the strongest recovery ability after reaching a low point in 2023, reaching approximately 13.5 by 2030. This demonstrates that improving water conservation efficiency is the most direct and effective means to alleviate water resource pressure and enhance system resilience. The economic priority scenario (S3), on the other hand, consistently shows the lowest supply-demand ratio, highlighting the enormous pressure that simply pursuing economic growth places on the water resource system.

[0075] Figure 8 The diagram illustrates the dynamic changes of ecological land area over a predetermined time period (2019-2030) under various scenarios according to some embodiments of the present invention. Figure 9The diagram illustrates the dynamic changes of the proportion of ecological land (i.e., the richness of ecological land types) under various scenarios in some embodiments of the present invention within a predetermined time range (2019-2030).

[0076] like Figure 8 and Figure 9 As shown, the changing trends in ecological land area and its proportion reveal the long-term competitive outcomes of land use. Ecological land in all scenarios peaked between 2022 and 2024 and then slowly declined, reflecting the rigid demand for land resources from economic development. The ecological priority scenario (S2) consistently maintained the highest level of ecological land area and proportion, demonstrating that proactive ecological protection policies can effectively mitigate the loss of ecological space. The water conservation priority scenario (S4) performed second best, with its ecological land situation better than the baseline scenario. The economic priority scenario (S3) showed the most significant shrinkage of ecological land, reaching its lowest level in both area and proportion by 2030, indicating that this path has the greatest negative impact on biodiversity conservation services.

[0077] Based on the dynamic change curves of various ecosystem service evaluation indicators under different scenarios within a predetermined time range, it can be seen that different policy orientations can have different impacts on the system evolution path.

[0078] Finally, in step 260, the computing device 100 can evaluate the ecosystem services (comprehensive benefits) of each scenario based on the dynamic change curves (indicator data for each year) of each ecosystem service evaluation index within a predetermined time range under each scenario, so as to determine the optimal scenario.

[0079] It should be noted that the various ecosystem service evaluation indicators (area of ​​soil and water conservation, available water volume, water supply-demand ratio, ecological land area, and ecological land type richness) in some embodiments of the present invention involve multiple ecosystem service types, such as water resource conservation services, soil protection services, and biodiversity maintenance services (i.e., multi-dimensional indicators). Ecosystem services are the core manifestation of the functions of the water-soil-ecology-economy coupled system. Therefore, ecosystem services in various scenarios can be evaluated based on various ecosystem service evaluation indicators related to multiple ecosystem service types.

[0080] Water conservation services refer to the ability of an ecosystem to regulate the water cycle and store water resources through processes such as interception, infiltration, and storage. This is crucial for maintaining regional water supply and demand balance and ecological security. Key variables in the ecosystem dynamics model corresponding to water conservation services include forest area, grassland area, and wetland area. Forest land, grassland, and wetlands are ecological land types with strong water conservation functions. In the ecosystem dynamics model constructed in this invention, the areas of these three types are core stock variables, and their changes directly characterize the regulatory capacity of ecological land on hydrology. A larger total area of ​​forest land, grassland, and wetlands (i.e., ecological land area) generally indicates a stronger surface runoff interception capacity and groundwater recharge capacity, indirectly reflecting an improvement in the level of water conservation services. The ecosystem dynamics model can output dynamic data on the areas of these three land types, providing support for the quantification of this service.

[0081] Soil conservation services refer to the functions of ecosystems in retaining soil and mitigating erosion through vegetation canopy, litter, and root systems. They are fundamental to ensuring land productivity and ecosystem stability. The key variable in ecosystem dynamics models corresponding to soil conservation services is soil erosion area. Soil erosion area is a flow variable characterizing the degree of soil erosion in ecosystem dynamics models and directly reflects the state of soil conservation services. The smaller this area, the lower the intensity of soil erosion, the better the preservation of soil resources, and the better the soil conservation service effect. By simulating changes in soil erosion area under different scenarios using ecosystem dynamics models, dynamic assessment of the effectiveness of soil conservation services can be achieved.

[0082] Biodiversity conservation services refer to the functions of ecosystems in providing habitats for various organisms and maintaining species coexistence and reproduction; they are a core indicator of ecosystem complexity and stability. The key variable corresponding to biodiversity conservation services in ecosystem dynamics models is ecological land use richness (ecological land area). Ecological land use richness is a comprehensive index calculated based on forest, grassland, and wetland area data from ecosystem dynamics models, used to characterize habitat diversity. Generally, the richer and more complex the ecological land use types, the more diverse the habitats for species with different ecological niches, and the stronger the biodiversity conservation capacity. Obtaining basic land use area data through ecosystem dynamics models can indirectly quantify the dynamic level of this service.

[0083] Therefore, water conservation services are related to available water volume and the water supply-demand ratio. Soil protection services are related to the area of ​​soil erosion control. Biodiversity conservation services are related to the richness of ecological land use types.

[0084] Based on this, the present invention constructs an ecosystem service evaluation system, as shown in Table 3.

[0085] Table 3 Ecosystem Service Evaluation System

[0086] In some embodiments of the present invention, since the various ecosystem service evaluation indicators (area of ​​soil and water conservation, available water volume, water supply-demand ratio, ecological land area, and ecological land type richness) involve multiple ecosystem service types (multi-dimensional indicators), and it is necessary to avoid bias caused by subjective weighting and objectively quantify the comprehensive benefits under different policy scenarios, in some embodiments of the present invention, in step 260, a combination of "entropy weighting + improved TOPSIS method" can be used to calculate the comprehensive score to evaluate the ecosystem services of the coupled system. The entropy weighting method can assign weights based on the objective laws of the indicator data, eliminating subjective interference; the improved TOPSIS method can solve the inverse problem of traditional TOPSIS (ideal solution / weight changes causing ranking distortion), adapting to the needs of comprehensive evaluation of multi-objective and multi-attribute ecosystem services.

[0087] Specifically, in step 260, the entropy weight method can first be used to determine the weight of each ecosystem service evaluation indicator based on the dynamic change curves (indicator data for each year) of each ecosystem service evaluation indicator within a predetermined time range under each scenario. Then, the improved TOPSIS method can be used to determine the comprehensive ecosystem service benefit score (including the comprehensive benefit score for each year) for each scenario based on the dynamic change curves of each ecosystem service evaluation indicator within a predetermined time range under each scenario and the weight of each ecosystem service evaluation indicator. Based on the comprehensive ecosystem service benefit score for each scenario, the optimal scenario can be determined.

[0088] It should be noted that indicator weighting mainly includes subjective and objective weighting methods. Subjective weighting methods include the analytic hierarchy process (AHP), expert scoring, ordinal relation method, and matter-element analysis. Objective weighting methods include entropy weighting, principal component analysis, and coefficient of variation method. Objective weighting methods can assign weights to indicators based on the objective laws of data, objectively reflecting the relative importance of a particular indicator in the entire evaluation system. Among them, entropy weighting is an objective weighting method based on the concept of information entropy in information theory. Its core is to automatically calculate weights based on the degree of variation of indicator data, relying entirely on the data itself without subjective judgment, thus eliminating subjectivity in the weighting process. Entropy weighting is widely used in the comprehensive evaluation of multiple objectives and multiple indicators, and is suitable for determining the weight of indicators in any evaluation problem. Entropy is a measure of uncertain information; the smaller the entropy, the greater the information content of the indicator data, and the higher the corresponding weight.

[0089] In some embodiments of the present invention, the specific method for determining the weights of each ecosystem service evaluation index using the entropy weight method is as follows.

[0090] First, assuming there are m scenarios and n ecosystem service evaluation indicators, a standardized judgment matrix X* is constructed by standardizing the dynamic change curves (indicator data for each year) of each ecosystem service evaluation indicator within a predetermined time range under each scenario, as follows: (1) Subsequently, the information entropy of each ecosystem service evaluation index can be calculated based on the standardized judgment matrix according to the following formulas (2)-(4): (2) (3) Furthermore, the weights of each ecosystem service evaluation indicator can be determined based on its information entropy, as shown in the following formula: (4) in, .

[0091] The traditional TOPSIS method calculates the proximity of an evaluated object to the ideal solution by approximating the positive and negative ideal solutions. It considers the optimal result to be the time closest to the positive ideal solution and the time furthest from the negative ideal solution, and then ranks the evaluated objects based on this. However, in multi-objective, multi-attribute comprehensive evaluations, the traditional TOPSIS method often suffers from a reverse problem: changes in the ideal solution or indicator weights can lead to changes in the ranking results, and even affect the correctness of future decisions.

[0092] Therefore, in some embodiments of the present invention, the improved TOPSIS method is used to comprehensively evaluate the ecosystem services of the coupled system, which can eliminate the inverse problem and provide a scientific and reasonable reference for the development of ecosystem services.

[0093] The improved TOPSIS method redefines positive and negative ideal solutions, assuming that both positive and negative ideal solutions have their own absolute states, and the object being evaluated is always in a state between the absolute positive and negative ideal solutions.

[0094] In some embodiments of the present invention, the specific process of the improved TOPSIS method is as follows.

[0095] Using the improved TOPSIS method, firstly, a weighted judgment matrix can be constructed based on the dynamic change curves of each ecosystem service evaluation index within a predetermined time range under each scenario, as well as the weights of each ecosystem service evaluation index, as shown in the following formula: (5) (6) Subsequently, the absolute positive ideal point and the absolute negative ideal point can be determined based on the weighted judgment matrix.

[0096] The absolutely positive ideal point is shown in the following formula: (7) The absolute negative ideal point is shown in the following equation: (8) Since the dynamic change curves (indicator data for each year) of each ecosystem service evaluation indicator under each scenario within the predetermined time range have been standardized, the absolute positive ideal point and the absolute negative ideal point can usually be set as follows: (9) (10) Furthermore, based on the dynamic change curves (after standardization) of each ecosystem service evaluation index within a predetermined time range under each scenario, and the weights of each ecosystem service evaluation index, the Euclidean distance between each scenario and the absolute positive ideal point and the absolute negative ideal point can be calculated.

[0097] The Euclidean distance between the scenario and the absolute positive ideal point is as follows: (11) The Euclidean distance between the scenario and the absolute negative ideal point is as follows: (12) For each scenario, the relative proximity of the scenario can be calculated based on the Euclidean distance between the scenario and the absolute positive ideal point and the absolute negative ideal point, as shown in the following formula (13), and the relative proximity of the scenario is used as the comprehensive benefit score of the ecosystem service of the scenario (the comprehensive benefit score of each year).

[0098] (13) In the formula, Ci represents the overall ecosystem service benefit score for the i-th scenario. The larger the Ci value, the better the overall ecosystem service benefit of the corresponding scenario.

[0099] Furthermore, the optimal scenario can be determined based on the comprehensive ecosystem service benefit score of each scenario. Specifically, the scenarios can be ranked based on their comprehensive ecosystem service benefit scores. For example, the scenarios can be ranked based on the average comprehensive benefit score of each scenario over each year (specifically, each year of the simulation period) (as the long-term comprehensive benefit score) to determine the scenario with the highest long-term comprehensive benefit score, and the scenario with the highest long-term comprehensive benefit score is selected as the optimal scenario.

[0100] In some embodiments, in step 250, the dynamic changes of the evaluation indicators of each ecosystem service corresponding to the four ecosystem services can be quantitatively simulated based on the ecosystem dynamics model and ecosystem service evaluation system constructed in this invention, so as to obtain the dynamic change curves of each ecosystem service evaluation indicator within a predetermined time range (2019-2030) (indicator data for each year), including the predicted indicator data of each ecosystem service evaluation indicator for each year from 2025 to 2030.

[0101] In some embodiments, under the baseline scenario, the annual indicator data (based on the simulation results of the ecosystem dynamics model) for each ecosystem service evaluation indicator from 2019 to 2030 are shown in Table 4. The standardized results are shown in Table 5.

[0102] Table 4. Annual data for each ecosystem service indicator under the baseline scenario.

[0103] Table 5 Standardized results of annual data for each ecosystem service indicator under the baseline scenario.

[0104] Taking the 2030 indicator data as an example, the weights of each ecosystem service evaluation indicator calculated according to formulas (1)-(4) are 0.16, 0.02, 0.02, 0.42 and 0.38, respectively.

[0105] According to equations (5)-(13), the comprehensive benefit scores for each year of the baseline scenario can be calculated as follows: 0.2102, 0.2468, 0.2102, 0.1826, 0.1405, 0.1977, 0.2091, 0.2103, 0.2122, 0.2137, 0.2144, and 0.2154. The scores are then sorted as shown in Table 6.

[0106] Table 6. Ranking of Overall Benefit Scores for Each Year in the Baseline Scenario

[0107] As shown in Table 6, the comprehensive benefit score (Ci=0.2468) was highest in 2020 under the baseline scenario. This was directly due to its peak performance in the highly weighted indicators of available water (1332.02) and water supply-demand ratio (13.62) during the simulation period. Conversely, the score in 2023 (Ci=0.1405) was the lowest, precisely because its performance in the two key indicators (available water 658.89, supply-demand ratio 6.98) was at its worst level during the simulation period. This objectively indicates that the dynamics of water resources are the main source of fluctuations in comprehensive benefits. From the temporal changes in the comprehensive benefit score, the ecosystem service status showed a clear "V"-shaped evolution trend during the simulation period. Comprehensive benefits plummeted from the high point in 2020 to the trough in 2023, and then steadily recovered from 2024 until 2030 when it returned to a relatively high level (ranked second). This remarkable fluctuation trajectory closely matches the dynamic changes in the water supply and demand relationship simulated in the ecosystem dynamics model, revealing the instability within the system.

[0108] In some embodiments, to select the optimal strategy that maximizes the comprehensive benefits of regional ecosystem services from a global perspective, a long-term comprehensive benefit evaluation method can be adopted. Specifically, the average comprehensive benefit score of each scenario in each year of the simulation period (2025-2030) (which better reflects the sustained and stable effects of the policy) can be used as the long-term comprehensive benefit score of each scenario. The scenarios are then ranked based on their long-term comprehensive benefit scores to determine the scenario with the highest long-term comprehensive benefit score as the optimal scenario.

[0109] The long-term comprehensive benefit scores and rankings for each scenario are shown in Table 7.

[0110] Table 7 Scenario Ranking Based on Long-Term Comprehensive Benefits

[0111] Although the ecological priority scenario (S2) leads in several ecological indicators, the water conservation priority scenario (S4) achieves the highest long-term overall benefit score (0.209) by a narrow margin. The fundamental reason for this is that S4 outperforms the water resources dimension (supply-demand ratio and available water quantity, with a combined weight of 0.8), which has the highest weight. This indicates that in the water-soil-ecology-economy coupled system, water resources are the weakest link and the key lever restricting the overall ecosystem service function.

[0112] Therefore, the water-saving priority scenario with the highest long-term comprehensive benefit score can be identified as the optimal scenario (optimal strategy). This optimal strategy effectively alleviates the system's core resource constraints by significantly improving industrial and agricultural water use efficiency and strengthening wastewater resource utilization, thereby creating more favorable conditions for soil protection and ecological space maintenance, and achieving a higher-level synergistic enhancement of multiple ecosystem services. This strategy not only has the best comprehensive benefits, but its simulated path also shows a consistently high and stable water supply-demand ratio, indicating that it can provide stronger system resilience and sustainability.

[0113] Figure 10 A schematic diagram of a water-soil-ecology-economy coupled system scenario simulation device 1000 based on system dynamics according to an embodiment of the present invention is shown. The water-soil-ecology-economy coupled system scenario simulation device 1000 based on system dynamics can be deployed in a computing device 100, and the water-soil-ecology-economy coupled system scenario simulation device 1000 based on system dynamics is configured to execute the water-soil-ecology-economy coupled system scenario simulation method 200 based on system dynamics of the present invention.

[0114] like Figure 10 As shown, in an embodiment of the present invention, the water-soil-ecology-economy coupled system scenario simulation device 1000 based on system dynamics includes a determination module 1100, a construction module 1200, a generation module 1300, a establishment module 1400, a simulation module 1500, and an evaluation module 1600 that are sequentially connected in communication.

[0115] The determination module 1100 is used to determine the key variables of multiple subsystems corresponding to the coupled system. These subsystems include a water subsystem, a soil subsystem, an ecological subsystem, and an economic subsystem.

[0116] Module 1200 is used to construct a causal loop diagram based on the feedback relationships between key variables of each subsystem; The generation module 1300 is used to generate a stock flow diagram based on the causal loop relationship diagram.

[0117] Module 1400 is used to establish core equations based on stock flow maps in order to obtain the ecosystem dynamics model corresponding to the coupled system.

[0118] The simulation module 1500 is used to input scenario parameters of multiple scenarios into the ecosystem dynamics model to perform scenario simulation and obtain the dynamic change curves of each ecosystem service evaluation index under each scenario within a predetermined time range.

[0119] The evaluation module 1600 is used to evaluate the ecosystem services of each scenario based on the dynamic change curves of each ecosystem service evaluation index within a predetermined time range under each scenario, so as to determine the optimal scenario.

[0120] It should be noted that the determination module 1100, construction module 1200, generation module 1300, establishment module 1400, simulation module 1500, and evaluation module 1600 are respectively used to execute the aforementioned steps 210 to 260. Here, the specific execution logic of each unit can be found in the description of steps 210 to 260 in the previous method 200, and will not be repeated here.

[0121] According to the water-soil-ecology-economy coupled system scenario simulation method 200 based on system dynamics in the embodiments of the present invention, the key variables of the water subsystem, soil subsystem, ecological subsystem, and economic subsystem are determined. A causal loop relationship diagram is constructed based on the feedback relationship between the key variables of each subsystem, and a stock-flow diagram is generated. The core equation is established based on the stock-flow diagram to obtain the ecosystem dynamics model corresponding to the coupled system. Then, the scenario parameters of multiple scenarios are input into the ecosystem dynamics model to perform scenario simulation. Finally, the ecosystem services of each scenario are evaluated based on the simulation results to determine the optimal scenario. Based on this, incorporating water, soil, ecology, and economy into a unified SD framework can characterize the complex feedback relationship of "economic growth - resource consumption - ecological protection" among these four subsystems. This overcomes the limitations of existing technologies that rely on a single dimension or pairwise correlations, enabling simulation results to reflect the true evolutionary laws of complex systems. By conducting scenario simulations and ecosystem service assessments, the optimal strategies corresponding to the optimal scenarios can be determined. Based on these optimal strategies, specific paths can be provided for the coordinated management of regional resources and the environment, achieving a synergistic state of efficient resource utilization, continuous environmental improvement, and robust economic development, ultimately enhancing the overall ecosystem services of the region. Furthermore, the ecosystem dynamics model constructed according to this invention can be flexibly adapted to different regions. By adjusting parameters, it can be applied to policy scenario simulations of the water-soil-ecology-economic system in different regions, demonstrating strong promotional value.

[0122] Furthermore, the use of entropy weighting combined with the improved TOPSIS method for ecosystem service assessment not only considers the differences in the weight of each indicator on the overall benefits of ecosystem services, but also avoids subjective bias, enabling objective quantification and comparison of the overall benefits of ecosystem services across multiple scenarios. This improves the scientific rigor of scenario / strategy selection and overcomes the shortcomings of existing models in lacking service assessment.

[0123] Furthermore, by conducting dual tests on ecosystem dynamics models, the accuracy of model-based scenario simulations can be improved, accurately reflecting the evolutionary laws of the system.

[0124] The various techniques described herein can be implemented in combination with hardware or software, or a combination thereof. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, USB flash drive, floppy disk, CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into and executed by a machine such as a computer, the machine becomes an apparatus for practicing the present invention.

[0125] When the program code is executed on a programmable computer, the mobile terminal generally includes a processor, a processor-readable storage medium (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store program code; the processor is configured to execute the water-soil-ecology-economy coupled system scenario simulation method of the present invention based on system dynamics, according to instructions in the program code stored in the memory.

[0126] By way of example, and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer-readable instructions, data structures, program modules, or other data. Communication media generally embodies computer-readable instructions, data structures, program modules, or other data in the form of modulated data signals such as carrier waves or other transmission mechanisms, and includes any information delivery medium. Any combination of the above is also included within the scope of readable media.

[0127] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with the examples of this invention. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0128] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0129] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof.

[0130] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0131] Unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

Claims

1. A scenario simulation method for a water-soil-ecology-economy coupled system based on system dynamics, executed on a computing device, comprising: Identify the key variables of multiple subsystems corresponding to the coupled system, including a water subsystem, a soil subsystem, an ecological subsystem, and an economic subsystem; Based on the feedback relationships between key variables of each subsystem, a causal loop diagram is constructed. Based on the aforementioned causal loop relationship diagram, a stock flow diagram is generated; Based on the stock flow map, a core equation is established to obtain the ecosystem dynamics model corresponding to the coupled system; The scenario parameters of multiple scenarios are input into the ecosystem dynamics model to perform scenario simulation, and the dynamic change curves of each ecosystem service evaluation index under each scenario within a predetermined time range are obtained. Based on the dynamic change curves of each ecosystem service evaluation index under each scenario within a predetermined time range, the ecosystem services of each scenario are evaluated to determine the optimal scenario.

2. The method as described in claim 1, wherein, The dynamic change curves within the predetermined time range include indicator data for each year within the predetermined time range; based on the dynamic change curves of each ecosystem service evaluation indicator under each scenario within the predetermined time range, the ecosystem services of each scenario are evaluated to determine the optimal scenario, including: Using the entropy weight method, the weights of each ecosystem service evaluation index are determined based on the dynamic change curves of each ecosystem service evaluation index within a predetermined time range under each scenario. Using the improved TOPSIS method, the comprehensive benefit score of ecosystem services for each scenario is determined based on the dynamic change curves of each ecosystem service evaluation index within a predetermined time range under each scenario and the weights of each ecosystem service evaluation index. The comprehensive benefit score of ecosystem services includes the comprehensive benefit score for each year. The optimal scenario is determined based on the comprehensive benefit score of ecosystem services for each scenario.

3. The method as described in claim 1, wherein, The multiple scenarios include a baseline scenario, an ecological priority scenario, an economic priority scenario, and a water conservation priority scenario; The scenario parameters include the afforestation growth rate, the sewage treatment capacity growth rate, the total agricultural output value growth rate, the total industrial output value growth rate, the agricultural water-saving efficiency coefficient, and the industrial water-saving efficiency coefficient. The ecosystem service evaluation indicators include the area of ​​soil and water conservation, available water volume, water supply-demand ratio, ecological land area, and ecological land type richness. The ecological land type richness represents the sum of the proportions of forest area, grassland area, and wetland area.

4. The method according to any one of claims 1-3, wherein, Before inputting scenario parameters from multiple scenarios into the ecosystem dynamics model for scenario simulation, the following steps are also included: Historical data is collected for each key variable, and parameter values ​​are assigned to the ecosystem dynamics model based on the historical data; Based on the historical data, the rationality and effectiveness of the ecosystem dynamics model are tested.

5. The method according to any one of claims 1-3, wherein, Based on the aforementioned causal loop diagram, a stock flow diagram is generated, including: Based on the aforementioned causal loop diagram, the stock, flow, and auxiliary variables are determined; Based on the mathematical relationships between stock, flow, and auxiliary variables, a stock-flow diagram is generated.

6. The method according to any one of claims 1-3, wherein, The key variables of the water subsystem include total water demand, available water supply, and water supply-demand ratio. The total water demand includes ecological water demand, agricultural irrigation water demand, and industrial water demand. The available water supply is related to the wastewater recycling rate. The water supply-demand ratio is the ratio of total water demand to available water supply. The key variables of the soil subsystem include the area of ​​soil erosion control, the area of ​​cultivated land, and the area with good soil and water conservation status. The key variables of the ecological subsystem include ecological land area, ecological water demand, and afforestation volume, wherein the ecological land area includes forest area, grassland area, and wetland area. The key variables of the economic subsystem include GDP, total population, total industrial output, total agricultural output, fiscal expenditure, and medical expenditure, wherein the total population is determined by the birth rate and the death rate.

7. The method according to any one of claims 1-3, wherein, The core equations include the water resource supply-demand ratio formula, the ecological water demand formula, the arable land area formula, the available water volume formula, the ecological land area formula, the total water demand formula, the total agricultural output value formula, the total industrial output value formula, the water resource collection formula, and the soil and water conservation area formula.

8. A scenario simulation device for a water-soil-ecology-economy coupled system based on system dynamics, deployed in a computing device, suitable for performing the method as described in any one of claims 1-7, the device comprising: The determination module is adapted to determine key variables of multiple subsystems corresponding to the coupled system, the multiple subsystems including a water subsystem, a soil subsystem, an ecological subsystem, and an economic subsystem; The module is suitable for constructing causal loop diagrams based on the feedback relationships between key variables of each subsystem. The generation module is adapted to generate a stock flow diagram based on the causal loop relationship diagram; A module is established to establish core equations based on the stock flow map in order to obtain the ecosystem dynamics model corresponding to the coupled system. The simulation module is suitable for inputting scenario parameters of multiple scenarios into the ecosystem dynamics model to perform scenario simulation, and obtaining the dynamic change curves of each ecosystem service evaluation index under each scenario within a predetermined time range. The assessment module is suitable for evaluating ecosystem services under each scenario based on the dynamic change curves of each ecosystem service evaluation index within a predetermined time range, in order to determine the optimal scenario.

9. A computing device, comprising: At least one processor; and A memory storing program instructions, wherein the program instructions are configured to be processed by the at least one processor, the program instructions including instructions for processing the method as described in any one of claims 1-7.

10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method as described in any one of claims 1-7.

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