Ecological restoration measure carbon sink effect quantification simulation prediction method and system based on digital modeling

Abstract

The application relates to the technical field of computer simulation, and provides a method and system for quantitatively simulating and predicting carbon sink effects of ecological restoration measures based on digital modeling. The method comprises the following steps: collecting ecological element information of a target river basin and extracting a multi-dimensional spatial distribution feature set, combining digital baseline data to establish an ecological environment response simulation model under engineering disturbance; constructing a carbon sink effect quantification model based on construction and operation scenario information, embedding the model into a related node of the ecological environment response simulation model to realize coupling; simulating the dynamic influence of engineering construction and operation on the carbon sink capacity of the river basin based on the coupled model, generating a multi-objective optimization decision matrix and outputting a pre-optimization decision suggestion, realizing the collaborative simulation of ecological environment response and carbon sink effect quantification, and improving the accuracy of the forward-looking assessment of the carbon sink influence of a hydropower project and the scientificity of decision suggestions.

Description

Technical Field

[0001] This application belongs to the field of computer simulation technology, specifically relating to a method and system for quantitative simulation and prediction of the carbon sink effect of ecological restoration measures based on digital modeling. Background Technology

[0002] Hydropower projects, as an important means of clean energy development, have multi-dimensional impacts on the ecological environment of watersheds during their construction and operation. Changes in carbon sequestration capacity are a key indicator for assessing the ecological effects of these projects. Current technologies for assessing the ecological and environmental impacts of hydropower projects often focus on monitoring and simulating single ecological elements (such as vegetation cover and water quality changes). This involves acquiring information on ecological elements through methods like plot surveys and remote sensing, and then combining this information with ecological models to analyze the impact of project disturbances on ecosystem structure and function. Regarding carbon sequestration effect assessment, static estimation methods based on plot measurement data are frequently used, or simple empirical models are employed to quantify changes in carbon sequestration capacity caused by project construction and operation. Furthermore, current technologies often focus on adjusting parameters and connecting data from a single model, lacking in-depth analysis and collaborative adaptation mechanisms for the correlation nodes between ecological and environmental responses and carbon sequestration effect quantification models.

[0003] However, the existing technology for simulating ecological and environmental responses is disconnected from the process of quantifying carbon sink effects, making it impossible to systematically assess the dynamic impact of engineering disturbances on carbon sink capacity. Furthermore, the model coupling lacks a collaborative adaptation mechanism, making it difficult to accurately reflect the correlation between changes in ecological elements and changes in carbon sink capacity. In addition, decision-making recommendations often lack multi-objective optimization support, failing to provide comprehensive pre-optimization solutions for hydropower projects. Summary of the Invention

[0004] This application provides a method and system for quantitative simulation and prediction of the carbon sink effect of ecological restoration measures based on digital modeling.

[0005] In a first aspect, embodiments of this application provide a method for quantitative simulation and prediction of the carbon sink effect of ecological restoration measures based on digital modeling, applied to a carbon sink effect quantitative simulation and prediction system, the method comprising:

[0006] Ecological element information of the target watershed is collected, and spatial correlation analysis and distribution feature extraction are performed on the ecological element information to obtain a multidimensional ecological element spatial distribution feature set.

[0007] By combining the spatial distribution feature set of the multidimensional ecological elements with the digital baseline data of the target watershed, an ecological environment response simulation model under engineering disturbance is established through the correlation mapping between ecological processes and environmental responses. The ecological environment response simulation model reflects the correlation impact of engineering disturbance on changes in ecological elements.

[0008] Extract construction and operation context information of hydropower projects in the target watershed and construct a carbon sink effect quantification model. The carbon sink effect quantification model quantifies the changes in carbon sink capacity caused by the hydropower projects based on the construction and operation context information.

[0009] The carbon sink effect quantification model is embedded into the associated nodes of the ecological environment response simulation model. Model coupling is achieved through model parameter collaborative adaptation and data interaction channel construction. Based on the coupled model, the dynamic impact of the construction and operation of the hydropower project on the carbon sink capacity of the target watershed is simulated, and carbon sink simulation prediction results are obtained.

[0010] Based on the carbon sink simulation and prediction results, a multi-objective optimization decision matrix is ​​generated through the correlation and adaptation of multi-objective optimization criteria. The multi-objective optimization decision matrix is ​​then used to output the preliminary optimization decision suggestions for the hydropower project under different engineering schemes.

[0011] Secondly, embodiments of this application provide a carbon sink effect quantitative simulation and prediction system, which includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the above method.

[0012] Thirdly, embodiments of this application provide a computer-readable storage medium including a computer program, which, when run on a carbon sink effect quantification simulation and prediction system, causes the carbon sink effect quantification simulation and prediction system to perform the steps of the above-described method.

[0013] This application embodiment collects ecological element information of the target watershed and extracts a multi-dimensional spatial distribution feature set. Combined with digital baseline data, it establishes an ecological environment response simulation model under engineering disturbance. Simultaneously, it constructs a carbon sink effect quantification model based on construction and operation context information. The carbon sink effect quantification model is embedded into the associated nodes of the ecological environment response simulation model to achieve coupling. Finally, based on the carbon sink simulation prediction results of the coupled model, a multi-objective optimization decision matrix is ​​generated and a pre-optimization decision suggestion is output. This technology constructs a complete chain technology of "ecological elements-environmental response-carbon sink quantification-model coupling-decision optimization". It realizes accurate simulation of the dynamic impact of hydropower project construction and operation on watershed carbon sink capacity and pre-optimization decision of multiple schemes. It breaks through the limitations of traditional technology, such as the separation of ecological environment response and carbon sink effect quantification and the lack of synergistic adaptation between models. It improves the systematicness of the carbon sink impact assessment of hydropower projects and the foresight of decision suggestions, and provides technical support for the eco-friendly construction and operation of hydropower projects. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a method for quantitative simulation and prediction of the carbon sink effect of ecological restoration measures based on digital modeling, as provided in an embodiment of this application.

[0015] Figure 2 This is a schematic diagram of the structure of a carbon sink effect quantitative simulation and prediction system provided in an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this application. Obviously, the described embodiments are only some embodiments of the technical solutions of this application, and not all embodiments. Based on the embodiments recorded in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the technical solutions of this application.

[0017] See Figure 1 This application provides a method for quantitative simulation and prediction of the carbon sink effect of ecological restoration measures based on digital modeling. This method can be applied to a carbon sink effect quantitative simulation and prediction system. The specific process is as follows: Steps 110-150.

[0018] Step 110: Collect ecological element information of the target watershed, perform spatial correlation analysis and distribution feature extraction on the ecological element information, and obtain a multidimensional ecological element spatial distribution feature set.

[0019] In this embodiment, the target watershed is a typical mountainous watershed containing planned hydropower projects. The carbon sink effect quantitative simulation and prediction system first collects ecological element information of the target watershed through a deployed multi-source sensor network. This ecological element information includes vegetation type, vegetation cover, soil organic matter content, soil moisture content, river water level, and flow velocity. Next, the system performs spatial correlation analysis on the collected ecological element information. For example, it uses spatial autocorrelation analysis to identify spatial clusters of vegetation cover and soil moisture content. Simultaneously, it extracts the distribution characteristics of each ecological element through feature extraction algorithms, such as the spatial gradient variation characteristics of vegetation cover and the spatial variation characteristics of soil organic matter content. Finally, it integrates these features to obtain a multi-dimensional ecological element spatial distribution feature set. This feature set contains spatial distribution information of ecological elements in multiple dimensions, such as vegetation dimension distribution characteristics, soil dimension distribution characteristics, and hydrological dimension distribution characteristics.

[0020] Step 120: Combining the spatial distribution feature set of the multidimensional ecological elements with the digital baseline data of the target watershed, an ecological environment response simulation model under engineering disturbance is established through the correlation mapping between ecological processes and environmental responses. The ecological environment response simulation model reflects the correlation impact of engineering disturbance on changes in ecological elements.

[0021] In this embodiment, the digital baseline data is the ecological element benchmark data of the target watershed before it is disturbed by hydropower projects, including historical vegetation growth data, original soil physicochemical properties data, and hydrological natural cycle data. The system integrates the spatial distribution feature set of multidimensional ecological elements with the digital baseline data, and then constructs a correlation mapping relationship between ecological processes and environmental responses. For example, it establishes a correlation mapping between vegetation growth processes and soil moisture and nutrient supply, and a correlation mapping between hydrological cycle processes and topography and vegetation cover. Based on the above correlation mapping relationships, an ecological environment response simulation model is constructed. This model is a distributed eco-hydrological model, and the model parameter configuration includes vegetation growth parameters (such as growth rate, maximum biomass, etc.), soil hydrological parameters (such as saturated hydraulic conductivity, field capacity, etc.), and topographic parameters (such as slope, aspect, etc.). The model can simulate the impact of engineering disturbances on changes in ecological elements during the construction and operation of hydropower projects, such as changes in the inundated area caused by reservoir impoundment and downstream flow regulation, for example, simulating the degradation process of vegetation in the inundated area and the change process of soil moisture content in the downstream river channel.

[0022] Step 130: Extract the construction and operation context information of hydropower projects in the target watershed and construct a carbon sink effect quantification model. The carbon sink effect quantification model quantifies the changes in carbon sink capacity caused by the hydropower projects based on the construction and operation context information.

[0023] Step 131: Extract construction and operation context information related to carbon sinks from the construction planning documents and operation record data of hydropower projects in the target watershed.

[0024] In this embodiment of the application, the system processes the construction planning documents (such as feasibility study reports, preliminary design reports, etc.) and operation record data (such as historical water level data, discharge data, etc.) of hydropower projects through a text parsing algorithm, identifies and extracts information related to carbon sinks, such as the reservoir inundation range recorded in the construction planning documents affecting the change of vegetation carbon sinks, and the water level scheduling plan in the operation record data affecting the change of wetland carbon sinks, etc., and separates the above information to form a construction and operation context information set.

[0025] Step 132: Perform feature analysis on the construction and operation context information to obtain the static and dynamic features in the construction and operation context information. Transform the static features into basic parameters of the carbon sink effect quantification model and transform the dynamic features into dynamic input variables of the model. The basic parameters are used to set the initial calculation boundary of the model, and the dynamic input variables are used to reflect the context changes in different operating periods.

[0026] In this embodiment, the system employs a feature parsing algorithm to process construction and operation context information. For example, information that does not change over time, such as dam height and installed capacity, is identified as static features, while information that changes over time, such as annual water level scheduling plans and discharge methods, is identified as dynamic features. Then, the static features are transformed into basic parameters for a carbon sink effect quantification model. For instance, the inundation range parameter corresponding to dam height sets the calculation boundary of the model, and the power generation efficiency parameter corresponding to installed capacity indirectly affects carbon sink changes. The dynamic features are transformed into dynamic input variables for the model. For example, the water level time series corresponding to the annual water level scheduling plan serves as a dynamic input variable, reflecting the impact of water level changes during different operating periods on the carbon sink process.

[0027] Step 133: Based on the correlation mechanism between ecological restoration measures and carbon sink generation, a model structure for a carbon sink effect quantification model is built. The model structure includes a context feature mapping layer, a carbon sink process simulation layer, and a quantification result output layer.

[0028] In this embodiment, the correlation mechanism between ecological restoration measures and carbon sink generation includes vegetation restoration measures (such as afforestation and reforestation) increasing the vegetation carbon pool, and wetland restoration measures increasing the wetland carbon pool. The system builds a model structure based on these correlation mechanisms. The context feature mapping layer uses a neural network structure to transform construction and operation context information into feature vectors. The carbon sink process simulation layer combines process models and machine learning models to simulate the generation (such as carbon fixation by vegetation photosynthesis and soil microbial carbon fixation), transfer (such as the transfer of vegetation litter to the soil carbon pool and river carbon transport), and storage (such as vegetation biomass carbon storage and soil organic carbon storage) processes of carbon sinks. The quantitative result output layer uses a fully connected layer structure to output quantitative data on changes in carbon sink capacity.

[0029] Step 134: Initialize the context feature mapping layer with the basic parameters corresponding to the static features, input the dynamic input variables into the context feature mapping layer for feature transformation, and generate intermediate features related to the carbon sink process.

[0030] In this embodiment, the system first initializes the weight parameters of the context feature mapping layer based on the basic parameters corresponding to static features (such as the inundation range parameter corresponding to dam height, the power generation efficiency parameter corresponding to installed capacity, etc.). Then, dynamic input variables (such as the water level time series corresponding to the annual water level scheduling plan, the flow time series corresponding to the discharge mode, etc.) are input into the context feature mapping layer. The context feature mapping layer extracts and transforms features from the dynamic input variables through the forward propagation process of the neural network, generating intermediate features related to the carbon sink process, such as wetland carbon sink potential features corresponding to water level changes, and river carbon transport features corresponding to flow changes, etc.

[0031] Step 135: The intermediate features are passed into the carbon sink process simulation layer. Through the process correlation simulation of carbon sink generation, transfer and storage, quantitative data on the change of carbon sink capacity are obtained.

[0032] In this embodiment, after receiving intermediate features, the carbon sink process simulation layer first simulates the carbon sink generation process. For example, it simulates the carbon sequestration amount of vegetation photosynthesis based on the vegetation growth status characteristics in the intermediate features, and the carbon sequestration amount of soil microorganisms based on the soil physicochemical properties. Next, it simulates the carbon sink transfer process. For example, it simulates the transfer amount of litter to the soil carbon pool based on the characteristics of vegetation litter, and the amount of river carbon transport based on the characteristics of river flow. Then, it simulates the carbon sink storage process. For example, it simulates the storage amount of vegetation carbon pool based on the characteristics of vegetation biomass, and the storage amount of soil carbon pool based on the characteristics of soil organic carbon. Through the correlation simulation of the above processes, the carbon sink process simulation layer outputs quantitative data on changes in carbon sink capacity, such as the carbon sink increment and carbon sink decrement at different time periods.

[0033] Step 136: The quantified data is input into the quantification result output layer for standardization processing to generate a carbon sink effect quantification model that reflects the changes in carbon sink capacity under different scenarios.

[0034] In this embodiment of the application, after receiving the quantitative data on changes in carbon sequestration capacity, the quantitative result output layer processes the quantitative data using a standardization algorithm (such as Z-score standardization, Min-Max standardization, etc.) to eliminate the influence of different dimensions, and then outputs the standardized data to form a carbon sequestration effect quantitative model. This model can output the corresponding quantitative data on changes in carbon sequestration capacity based on the input construction and operation context information.

[0035] Step 140: Embed the carbon sink effect quantification model into the associated node of the ecological environment response simulation model, and couple the model through model parameter coordination and adaptation and data interaction channel construction. Based on the coupled model, simulate the dynamic impact of the construction and operation of the hydropower project on the carbon sink capacity of the target watershed to obtain carbon sink simulation prediction results.

[0036] Step 141: Analyze the output node type and data transmission format of the ecological environment response simulation model, and identify the associated nodes related to carbon sink changes. The associated nodes are the output ports that reflect the impact of changes in ecological elements on carbon sinks.

[0037] Step 1411: Obtain the structural description document and output variable list of the ecological environment response simulation model, and extract the node identifier, output data type and data transmission format of all output nodes.

[0038] In this embodiment, the system uses a document parsing tool to read the structural specification document of the ecological environment response simulation model. This document details the model's output node settings, including the unique identifier of each output node (e.g., "NDVI_change" corresponding to the normalized vegetation index change), the type of output data (e.g., continuous numerical values, categorical labels), and the encoding format used for data transmission (e.g., XML format, binary stream format). Simultaneously, the system exports a list of output variables from the model's configuration file. This list specifies the node identifier for each output variable, data precision requirements (e.g., retaining three decimal places), and other information. After integrating this information, the system forms a dataset containing all output node attributes, providing a foundation for subsequent identification of related nodes.

[0039] Step 1412: Based on the core influencing factors of carbon sink generation, determine the types of ecological elements related to carbon sink changes. The types of ecological elements cover vegetation cover, soil physicochemical properties, and hydrological cycle status.

[0040] In this embodiment, the system, based on the fundamental theory of the carbon cycle, identifies key influencing factors for carbon sink formation, including vegetation photosynthetic efficiency, soil organic carbon fixation capacity, and the regulatory role of hydrological processes on carbon migration. Corresponding to ecological element types, vegetation cover (e.g., vegetation type, cover, growth stage) directly affects carbon uptake; soil physicochemical properties (e.g., soil pH, organic matter content, texture) determine soil carbon pool capacity; and hydrological cycle conditions (e.g., surface runoff intensity, groundwater depth) influence carbon migration pathways and storage stability. The system verifies the rationality of these ecological element types through an expert knowledge base, ensuring comprehensive coverage of key driving factors for carbon sink changes.

[0041] Step 1413: Associate and match the output data type of the output node with the determined ecological element type, and filter out the output nodes whose output data type corresponds to the ecological element type.

[0042] In this embodiment, the system establishes a mapping table between output data types and ecological element types. For example, a node with an output data type of "percentage of vegetation cover" corresponds to the ecological element type of "vegetation cover status," a node with an output data type of "soil organic carbon content (g / kg)" corresponds to the ecological element type of "soil physicochemical properties," and a node with an output data type of "monthly average runoff (m³ / s)" corresponds to the ecological element type of "hydrological cycle status." The system traverses all output nodes, matches them according to the mapping table, and filters out output nodes that meet the conditions, forming a preliminary list of candidate associated nodes.

[0043] Step 1414: By simulating typical engineering disturbance data, the selected output nodes are functionally verified to determine whether the output data of the output nodes reflects the changing trend of ecological elements. Output nodes that reflect the changing trend of ecological elements are retained, and output nodes that cannot reflect the changing trend of ecological elements are removed.

[0044] In this embodiment, the system selects typical engineering disturbance scenarios, such as "reservoir impoundment leading to expansion of the inundation area" and "reduction of dam discharge flow," and inputs the parameters corresponding to these scenarios into the ecological environment response simulation model. After the model runs, the system collects the output data of the selected candidate related nodes and analyzes whether the data change trend with the intensity of disturbance conforms to ecological laws. For example, when the disturbance data of "expansion of the inundation area" is input, the output node corresponding to "change in vegetation cover" should output data showing a trend of decreasing cover. If the output data of a node does not change significantly or the change trend is opposite to the expectation, it is determined that the node cannot effectively reflect the change of ecological elements and is removed from the candidate list.

[0045] Step 1415: Mark the retained output nodes as associated nodes related to carbon sink changes, and record the node identifier, output data type and data transmission format of the associated nodes to form an associated node list.

[0046] In this embodiment, the system uniformly marks the output nodes retained after functional verification, for example, by adding the prefix "Carbon_Related_" before the node identifier. It also records the output data type (e.g., "floating-point" or "integer") and transmission format (e.g., "JSON key-value pairs" or "CSV column") for each node. The system organizes this information into a structured list of associated nodes, which also includes the ecological element type corresponding to each node, facilitating parameter mapping during subsequent model coupling.

[0047] Step 1416: Based on the information in the list of associated nodes, determine the ecological element change dimension corresponding to each associated node.

[0048] In this embodiment of the application, the system analyzes the specific dimensions of ecological element changes reflected in the output data of each associated node.

[0049] For example, a node labeled "Carbon_Related_Vegetation" outputs "vegetation cover change rate," with the corresponding ecological element change dimension being "temporal dynamic change of vegetation cover"; a node labeled "Carbon_Related_Soil" outputs "spatial distribution of soil organic carbon content," with the corresponding ecological element change dimension being "spatial heterogeneity change of soil carbon pool"; and a node labeled "Carbon_Related_Hydrology" outputs "river water level fluctuation amplitude," with the corresponding ecological element change dimension being "short-term impact of hydrological conditions on carbon migration." The system supplements the list of associated nodes with the above dimensional information, clarifying the role of each node in carbon sink simulation.

[0050] Step 142: Extract the input parameter types and output data formats of the carbon sink effect quantification model, match the input ports of the carbon sink effect quantification model with the associated nodes of the ecological environment response simulation model, and determine the parameter adaptation relationship.

[0051] In this embodiment, the system first extracts the input parameter types (such as vegetation cover change rate, soil organic matter content change rate, river water level change rate, etc.) and output data formats (such as CSV format, NetCDF format, etc.) of the carbon sink effect quantification model. Then, it matches the input ports of the carbon sink effect quantification model (such as input ports receiving vegetation cover change rate, input ports receiving soil organic matter content change rate, etc.) with the associated nodes of the ecological environment response simulation model (such as nodes outputting vegetation cover change, nodes outputting soil organic matter content change, etc.). For example, it matches the input port of the carbon sink effect quantification model that receives vegetation cover change rate with the associated node of the ecological environment response simulation model that outputs vegetation cover change to determine the parameter matching relationship. That is, the vegetation cover change data output by the associated node of the ecological environment response simulation model is used as the input data of the input port of the carbon sink effect quantification model.

[0052] Step 143: Adjust the parameter dimensions and data format of the ecological environment response simulation model and the carbon sink effect quantification model based on the parameter adaptation relationship, so that the input port of the carbon sink effect quantification model can directly receive the output data of the associated nodes of the ecological environment response simulation model.

[0053] In this embodiment, the system first analyzes whether the parameter dimensions (such as spatial resolution and temporal resolution) of the output data from the associated nodes of the ecological environment response simulation model are consistent with the parameter dimensions of the data received by the input port of the carbon sink effect quantification model, based on parameter adaptation relationships. For example, the spatial resolution of the vegetation cover change data output by the associated nodes of the ecological environment response simulation model is 1km×1km, and the temporal resolution is monthly, while the spatial resolution of the data received by the input port of the carbon sink effect quantification model is 500m×500m, and the temporal resolution is weekly. If they are inconsistent, the system resamples the output data of the ecological environment response simulation model, adjusting the spatial resolution to 500m×500m and the temporal resolution to weekly. Simultaneously, the input port parameters of the carbon sink effect quantification model are adjusted to ensure it can receive the adjusted parameter dimension data. Furthermore, the system adjusts the data format of the two models, for example, converting the JSON format data output by the associated nodes of the ecological environment response simulation model into the CSV format data received by the input port of the carbon sink effect quantification model, ensuring that the input port of the carbon sink effect quantification model can directly receive the output data from the associated nodes of the ecological environment response simulation model.

[0054] Step 144: Establish a data interaction channel between the ecological environment response simulation model and the carbon sink effect quantification model; the data interaction channel is a two-way data transmission channel, through which the output data of the associated nodes of the ecological environment response simulation model is transmitted to the carbon sink effect quantification model, and through which the intermediate calculation data of the carbon sink effect quantification model is fed back to the ecological environment response simulation model.

[0055] In this embodiment, the system uses message queue middleware (such as RabbitMQ, Kafka, etc.) to build a data interaction channel. First, a data sender is set up at the associated nodes of the ecological environment response simulation model. The output data of the associated nodes is encapsulated into messages according to a pre-defined data format (such as JSON) and sent to the message queue. Then, a data receiver is set up at the input port of the carbon sink effect quantification model. It receives messages sent by the ecological environment response simulation model from the message queue and parses out the output data, using it as input data for the carbon sink effect quantification model. Simultaneously, a data sender is set up at the intermediate calculation module of the carbon sink effect quantification model. Intermediate calculation data (such as carbon sink generation rate, carbon transfer amount, etc.) is encapsulated into messages and sent to the message queue. A data receiver is set up at the parameter adjustment module of the ecological environment response simulation model. It receives intermediate calculation data sent by the carbon sink effect quantification model from the message queue, using it as the basis for adjusting the simulation results of ecological element changes. In this way, bidirectional data transmission between the two models is achieved.

[0056] Step 145: Set the coupling iteration conditions, whereby the difference in carbon sink change between two coupling calculations reaches a stable range, initialize the calculation parameters of the coupling model, and input the construction and operation scenario information into the coupling model.

[0057] Step 1451: Based on the calculation accuracy requirements of the carbon sink effect quantification model, and combined with the fluctuation range of the carbon sink background value of the target watershed, determine the stable range of the carbon sink change difference.

[0058] In this embodiment, the system first obtains the design accuracy index of the carbon sink effect quantification model, such as requiring the model to have a prediction relative error of no more than 5% for carbon sink amount. Simultaneously, the system uses historical monitoring data to statistically analyze the fluctuation range of the carbon sink background value in the target watershed; for example, the background value is M tons of carbon equivalent per year, and the fluctuation range is ±N tons of carbon equivalent. The system sets the stable range to 1 / 10 of the background value fluctuation range, i.e., ±N / 10 tons of carbon equivalent, ensuring that the difference in carbon sink change at the end of the iteration is within the acceptable range of model accuracy and does not exceed the reasonable range of natural fluctuations.

[0059] Step 1452: The absolute difference between the carbon sequestration capacity change data obtained from two adjacent coupled iterations is less than the stable range, which is set as the termination condition for the coupled iteration, and the upper limit of the number of iterations is set simultaneously.

[0060] In this embodiment, the system specifies two conditions for iteration termination: first, the absolute difference between the carbon sequestration capacity change data output from two adjacent iterations is less than the stable range determined in step 1451; second, the number of iterations does not exceed a preset upper limit (e.g., 30 times). The iteration process terminates when either condition is met. Setting an upper limit on the number of iterations is to avoid wasting computational resources due to slow model convergence and to ensure the efficiency of coupled computation.

[0061] Step 1453: Extract the basic calculation parameters necessary for model operation from the initial parameter configuration files of the carbon sink effect quantification model and the ecological environment response simulation model. The basic calculation parameters include model structure parameters, initial state parameters and constraint condition parameters.

[0062] In this embodiment, the system extracts model structure parameters (such as the number of hidden layers in the neural network and the depth of the decision tree), initial state parameters (such as initial carbon pool storage and baseline carbon sequestration rate), and constraint parameters (such as upper and lower thresholds for carbon sink capacity) from the configuration file of the carbon sink effect quantification model; and extracts model structure parameters (such as grid cell size and computational step size of the hydrological module), initial state parameters (such as initial vegetation cover and initial soil moisture content), and constraint parameters (such as minimum requirements for ecological flow) from the configuration file of the ecological environment response simulation model. The system categorizes and stores these parameters, establishing a parameter index table to facilitate subsequent verification and assignment.

[0063] Step 1454: Perform joint verification based on the parameter definitions, unit expressions, and value range matching relationships of different models, and assign the basic calculation parameters that have completed joint verification to the corresponding models to complete the initialization of the calculation parameters of the coupled models.

[0064] In this embodiment, the system first verifies the consistency of parameter definitions across different models. For example, it confirms whether the "vegetation carbon sequestration rate" in the carbon sink effect quantification model and the "net primary productivity (NPP)" in the ecological environment response simulation model refer to the same physical quantity. Next, the system standardizes the unit representation of the parameters, for example, converting the "soil organic carbon content (%)" in the carbon sink effect quantification model to the "g / kg" unit consistent with the ecological environment response simulation model. Then, the system checks whether the parameter value ranges match, for example, ensuring that the value range of "carbon pool turnover time" in the carbon sink effect quantification model includes the possible values ​​output by the ecological environment response simulation model. After completing all verifications, the system writes the parameters into the runtime configuration files of the two models respectively, completing the initialization.

[0065] Step 1455: Input the construction and operation scenario information into the coupling model according to the input interface specification of the coupling model.

[0066] In this embodiment, the system converts construction and operation scenario information into standard format input data according to the input interface requirements of the coupled model. For example, the scenario information of "expanding the reservoir inundation area by 10%" is converted into spatial raster data recognizable by the ecological environment response simulation model, and the scenario information of "adjusting the discharge mode to seasonal regulation" is converted into time series parameters receivable by the carbon sink effect quantification model. The system verifies the correctness of the input data format through interface testing tools to ensure that the scenario information can be accurately parsed by the coupled model.

[0067] Step 146: The ecological environment response simulation model is invoked to simulate changes in ecological elements based on the input construction and operation scenario information. The ecological element change data is output through the associated nodes and transmitted to the carbon sink effect quantification model through the data interaction channel.

[0068] In this embodiment, the system first invokes the ecological environment response simulation model, inputting construction and operation scenario information (such as information on the expansion of reservoir inundation area and annual water level scheduling plan) into the model. Based on the input construction and operation scenario information, the ecological environment response simulation model simulates the changes in ecological elements, such as the decrease in vegetation cover and increase in soil moisture content caused by the expansion of reservoir inundation area, and the changes in river flow and river water level caused by the annual water level scheduling plan. Then, the ecological environment response simulation model outputs the above ecological element change data through associated nodes (such as nodes outputting changes in vegetation cover, soil moisture content, and river water level), in a pre-defined JSON format. Finally, the above ecological element change data is transmitted to the input port of the carbon sink effect quantification model through a data interaction channel (such as a message queue).

[0069] Step 147: The carbon sink effect quantification model is invoked to calculate the carbon sink capacity change data based on the ecological element change data and its own parameters. The data is then fed back to the ecological environment response simulation model through the data interaction channel. Based on the ecological environment response simulation model, the simulation results of ecological element change are adjusted in combination with the feedback data.

[0070] In this embodiment, the system invokes a carbon sink effect quantification model, inputting ecological element change data (such as vegetation cover change data, soil moisture content change data, river water level change data, etc.) passed through a data interaction channel into the model. Simultaneously, it combines the model's own parameters (such as vegetation carbon sequestration rate parameters, soil carbon pool turnover parameters, etc.) for calculation. The carbon sink effect quantification model first simulates the carbon sink generation process, such as calculating changes in vegetation photosynthetic carbon sequestration based on vegetation cover change data, and changes in soil microbial carbon sequestration based on soil moisture content change data; then it simulates the carbon sink transfer process, such as calculating the amount transferred to the soil carbon pool based on changes in vegetation litter amount, and changes in river carbon transport based on river water level change data; next, it simulates the carbon sink storage process, such as calculating changes in vegetation biomass carbon storage based on changes in vegetation biomass, and changes in soil organic carbon storage based on changes in soil organic carbon content, etc. Through the above calculations, data on changes in carbon sink capacity (such as annual carbon sink increment, annual carbon sink decrease, etc.) are obtained. Subsequently, the carbon sink effect quantification model feeds back the above data on changes in carbon sink capacity to the ecological environment response simulation model through a data interaction channel. After receiving the feedback data, the ecological environment response simulation model adjusts the simulation results of changes in ecological elements based on the above data. For example, if the data on changes in carbon sink capacity shows a significant decrease in vegetation carbon sink, the ecological environment response simulation model adjusts the vegetation growth simulation parameters, such as reducing the vegetation growth rate, to more accurately simulate changes in ecological elements.

[0071] Step 148: Repeat the data interaction and simulation calculation steps until the carbon sink change difference meets the coupling iteration condition, stop the iteration and extract the final carbon sink capacity change data to obtain the carbon sink simulation prediction result.

[0072] In this embodiment, the system first records the carbon sink capacity change data obtained from the current coupled iterative calculation, and then compares it with the carbon sink capacity change data obtained from the previous coupled iterative calculation, calculating the absolute difference between the two. Next, it determines whether the absolute difference is less than a set stable range, and simultaneously determines whether the number of iterations has reached its upper limit. If the absolute difference is less than the stable range and the number of iterations has not reached its upper limit, the next coupled iterative calculation is executed, that is, the ecological environment response simulation model is called again to simulate changes in ecological elements, and the output ecological element change data is input into the carbon sink effect quantification model. The carbon sink effect quantification model calculates the carbon sink capacity change data and feeds it back to the ecological environment response simulation model, which adjusts the simulation results. If the absolute difference is greater than or equal to the stable range and the number of iterations has not reached its upper limit, the iterative calculation continues; if the number of iterations reaches its upper limit, the iteration stops and a warning message is output. If the absolute difference is less than the stable range, the iteration is stopped and the final carbon sink capacity change data is extracted. This data includes the carbon sink capacity changes in different time periods (such as during the construction period, the initial operation period, and the stable operation period), such as annual carbon sink volume, carbon sink increment, and carbon sink decrement. The above data are integrated to obtain the carbon sink simulation prediction results.

[0073] Step 150: Based on the carbon sink simulation and prediction results, a multi-objective optimization decision matrix is ​​generated through the correlation and adaptation of multi-objective optimization criteria. The multi-objective optimization decision matrix is ​​then used to output the pre-optimization decision suggestions for the hydropower project under different engineering schemes.

[0074] Step 151: Use a digital model to perform feature analysis on the carbon sink simulation prediction results and extract the core features from the carbon sink simulation prediction results. The core features cover the dynamic change features of carbon sink, the spatial correlation features of carbon sink, and the temporal evolution features. The core features are all generated based on the coupled output data of the carbon sink effect quantification model and the ecological environment response simulation model.

[0075] In this embodiment, the digital model is a feature analysis model based on machine learning. The system first inputs the carbon sink simulation prediction results into the digital model, which then extracts features from the carbon sink simulation prediction results. For example, it extracts dynamic change features of carbon sink through time series analysis, including the trend of carbon sink amount change (e.g., rising, falling, stabilizing) and rate of change (e.g., annual change rate) in different time periods (e.g., during construction, in the initial stage of operation, and during the stable operation period); it extracts spatial correlation features of carbon sink through spatial correlation analysis, including the correlation between the spatial distribution of carbon sink amount and the distribution of ecological elements (e.g., the degree of overlap between high carbon sink value areas and high vegetation cover value areas), and the spatial aggregation characteristics of carbon sink amount (e.g., the aggregation range of high carbon sink value areas); and it extracts temporal evolution features of carbon sink through time series analysis, including the interannual variation pattern of carbon sink amount (e.g., periodic changes, trend changes), and prediction stability features (e.g., changes in prediction error in different prediction periods). The above core features are all generated based on the coupled output data of the carbon sink effect quantification model and the ecological environment response simulation model. For example, the carbon sink dynamic change feature is generated based on the carbon sink amount data of different time periods output by the coupled model, the carbon sink spatial correlation feature is generated based on the spatial distribution data of carbon sink amount and the spatial distribution data of ecological elements output by the coupled model, and the carbon sink temporal evolution feature is generated based on the temporal data of carbon sink amount output by the coupled model.

[0076] Step 152: Establish a mapping relationship between multi-objective optimization criteria and core features of carbon sinks through a feature association learning mechanism. The multi-objective optimization criteria focus on carbon sink enhancement, ecological synergy, and model prediction adaptation. The setting of the multi-objective optimization criteria is based on the output logic of the digital model and the feature analysis results.

[0077] Step 1521: Extract the original feature data of the generated carbon sink dynamic change features, carbon sink spatial correlation features, and time series evolution features through the feature output interface of the carbon sink effect quantification model. The original feature data retains the complete feature dimensions calculated by the carbon sink effect quantification model.

[0078] In this embodiment, the system uses the API interface of the carbon sink effect quantification model to call the feature output function and obtain the original feature data. This data is stored in the form of a multi-dimensional array, with each dimension corresponding to a feature indicator. For example, the dimensions of dynamic carbon sink change features include "monthly average carbon sink increment," "quarterly carbon sink fluctuation range," and "interannual carbon sink growth rate," etc.; the dimensions of spatial correlation features include "the percentage of overlapping area between high-value carbon sink areas and nature reserves," "the spatial autocorrelation coefficient of carbon sink amount," and "the percentage of carbon sink contribution from different land use types," etc.; the dimensions of temporal evolution features include "the trend term coefficient of carbon sink amount," "the amplitude of periodic fluctuations," and "the root mean square of prediction error," etc. The system ensures that the original feature data is not subjected to any dimensionality reduction or simplification processing, preserving all the details of the model calculations.

[0079] Step 1522: The extracted original feature data is deredundant and feature-enhanced through a feature preprocessing procedure to obtain enhanced feature data.

[0080] In this embodiment, the system first performs redundancy removal by calculating the correlation between features using the Pearson correlation coefficient method. Any feature in a feature pair with a correlation coefficient greater than 0.9 is removed to reduce information redundancy. Then, the system performs feature enhancement by generating new features through feature cross-combination. For example, multiplying the "monthly average carbon sink increment" by the "quarterly carbon sink fluctuation range" yields a "carbon sink growth stability index," and adding the "overlapping area ratio between high-carbon sink areas and nature reserves" to the "spatial autocorrelation coefficient of carbon sink volume" yields an "eco-friendly carbon sink distribution index." The enhanced feature data has richer dimensions and can more comprehensively reflect the complex characteristics of carbon sink changes.

[0081] Step 1523: Construct a feature association mapping network. The feature association mapping network is used to receive enhanced feature data. The hidden layer is used to mine features through a multi-layer perceptual structure. The output layer corresponds to the potential association dimension of the multi-objective optimization criterion.

[0082] In this embodiment, the feature association mapping network adopts a three-layer fully connected neural network structure: the number of neurons in the input layer is equal to the dimension of the enhanced feature data; two hidden layers are set, with the number of neurons in the first layer being twice that of the input layer and the number of neurons in the second layer being 1.5 times that of the input layer, and the activation function is the ReLU function to realize non-linear mapping and deep mining of features; the output layer is set with three neurons, which correspond to the association strength of the three criteria of carbon sink enhancement guidance, ecological synergy guidance, and model prediction adaptation guidance, respectively, and the activation function is the Sigmoid function, which maps the output value to the 0-1 interval, representing the degree of association between features and criteria.

[0083] Step 1524: Based on the output logic of the digital model, set the training objective of the feature association mapping network, train the feature association mapping network by matching historical carbon sink simulation data with the corresponding optimization criteria, and dynamically adjust the network parameters during the training process to improve the accuracy of the mapping relationship.

[0084] In this embodiment, the system sets the training objective as minimizing the mean squared error between the correlation strength of the network output and the actual adaptation result. The system uses historical carbon sink simulation data as input samples and the corresponding optimization criterion adaptation results (annotated by domain experts) as label samples. The training process employs mini-batch gradient descent with a batch size of 32, an initial learning rate of 0.001, and a learning rate decay strategy (multiplying the learning rate by 0.9 every 10 epochs). The system monitors model performance using a validation set; when the validation set error no longer decreases for five consecutive epochs, training stops, and the optimal model parameters are saved.

[0085] Step 1525: After training is completed, the enhanced feature data is input, and the correlation strength between each feature in the enhanced feature data and the carbon sink enhancement guidance, ecological synergy guidance and model prediction adaptation guidance is output through the feature association mapping network.

[0086] In this embodiment, the system inputs the enhanced feature data obtained in step 1522 into the trained feature association mapping network. The network outputs three values ​​between 0 and 1, representing the association strength between the input feature and the three criteria. The closer the value is to 1, the stronger the association between the feature and the criterion. For example, if the carbon sink growth stability index corresponds to a carbon sink improvement-oriented association strength of 0.85, it indicates that this feature has a significant impact on the carbon sink improvement effect; if the eco-friendly carbon sink distribution index corresponds to an eco-coordination-oriented association strength of 0.92, it indicates that this feature can well reflect the eco-coordination effect.

[0087] Step 1526: Based on the ranking results of the correlation strength, the core features of carbon sinks associated with each criterion are selected, a one-to-one mapping relationship table is established, and the expression logic of the multi-objective optimization criterion is optimized in combination with the mapping relationship table. The update of the mapping relationship table synchronously responds to the output changes of the digital model.

[0088] In this embodiment, the system sorts the correlation strength of each criterion in descending order and selects the top three features as core features. For example, the core features of the carbon sink enhancement guidance criterion are "monthly average carbon sink increment," "interannual carbon sink growth rate," and "carbon sink growth stability index"; the core features of the ecological synergy guidance criterion are "eco-friendly carbon sink distribution index," "overlapping area ratio of high carbon sink areas and nature reserves," and "carbon sink contribution ratio of different land use types"; the core features of the model prediction adaptation guidance criterion are "root mean square of prediction error," "trend term coefficient of carbon sink," and "amplitude of periodic fluctuations." The system establishes a mapping relationship table, clarifies the core features corresponding to each criterion, and optimizes the description of the criterion according to the meaning of the core features. For example, the carbon sink enhancement guidance criterion is described as "the effect of engineering schemes on the sustainable growth capacity of carbon sinks," making it more consistent with the connotation of the core features. When the output features of the digital model change, the system automatically retrains the feature association mapping network and updates the mapping relationship table.

[0089] Step 153: The multi-objective optimization criteria are transformed into quantitatively calculated feature evaluation dimensions through a criterion quantification adaptation mechanism. Each evaluation dimension forms a one-to-one adaptation relationship with the core features of carbon sink.

[0090] In this embodiment, the criterion quantification and adaptation mechanism combines fuzzy mathematics and machine learning methods. The system first analyzes the multi-objective optimization criteria, clarifying the connotation and evaluation objective of each criterion. For example, the evaluation objective of the carbon sink enhancement-oriented criterion is the degree to which the engineering scheme enhances carbon sink capacity; the evaluation objective of the ecological synergy-oriented criterion is the degree of synergy between the engineering scheme and other functions of the ecosystem; and the evaluation objective of the model prediction adaptation-oriented criterion is the degree of adaptation between the engineering scheme and the carbon sink simulation prediction model. Then, based on the mapping relationship between the core characteristics of carbon sinks and the multi-objective optimization criteria, each criterion is transformed into a quantitatively calculated feature evaluation dimension. For example, the carbon sink enhancement-oriented criterion is transformed into a "carbon sink increment evaluation dimension," which corresponds to the carbon sink increment characteristic in the dynamic change characteristics of carbon sinks; the ecological synergy-oriented criterion is transformed into a "carbon sink evaluation dimension for biodiversity conservation areas," which corresponds to the carbon sink characteristics of biodiversity conservation areas in the spatial correlation characteristics of carbon sinks; and the model prediction adaptation-oriented criterion is transformed into a "prediction stability evaluation dimension," which corresponds to the prediction stability characteristic in the temporal evolution characteristics of carbon sinks. Each evaluation dimension is matched one-to-one with the core characteristics of carbon sinks, ensuring that the evaluation dimensions accurately reflect the requirements of the criteria.

[0091] Step 154: Use the feature normalization processing logic of the digital model to perform dimensional unification processing on the evaluation data corresponding to the core features of carbon sink.

[0092] In this embodiment, the feature normalization processing logic of the digital model adopts the Min-Max standardization method. The system first determines the minimum and maximum values ​​for each evaluation dimension. For example, the minimum value for the carbon sink increment evaluation dimension is a negative number (representing carbon sink reduction), and the maximum value is a positive number (representing carbon sink increment). The minimum value for the carbon sink evaluation dimension of biodiversity conservation areas is 0, and the maximum value is the maximum carbon sink amount of the biodiversity conservation area. The minimum value for the prediction stability evaluation dimension is 0, and the maximum value is 1 (representing complete prediction stability). Then, the evaluation data for each evaluation dimension is processed using Min-Max standardization, converting the evaluation data into dimensionless values ​​between 0 and 1. For example, for a certain evaluation data x in the carbon sink increment evaluation dimension, the standardized value is (x - minimum value) / (maximum value - minimum value). This processing eliminates the dimensional differences between different evaluation dimensions, making the evaluation data comparable.

[0093] Step 155: By using a matrix-based feature recombination method, a multi-objective optimization decision matrix is ​​generated, with the digital model input parameters corresponding to different engineering schemes as row identifiers, multi-objective optimization criteria as column identifiers, and normalized evaluation data as matrix elements.

[0094] Step 1551: Extract the input parameter set corresponding to different engineering schemes through the input parameter management module of the digital model. The input parameter set covers all situational feature data of hydropower engineering construction and operation, and serves as the row identifier basis for the multi-objective optimization decision matrix.

[0095] In this embodiment, the system's input parameter management module stores parameter information for all candidate engineering schemes. Each scheme's input parameter set includes engineering design parameters (such as dam height, reservoir capacity, and installed capacity), construction sequence parameters (such as start date, construction period, and phased construction plan), operation scheduling parameters (such as water level control range, discharge rules, and ecological flow guarantee measures), and ecological restoration parameters (such as vegetation restoration area, soil improvement measures, and habitat protection schemes). The system assigns a unique identifier (such as scheme ID) to each scheme and associates the scheme ID with the corresponding input parameter set, using it as the row identifier for the decision matrix.

[0096] Step 1552: Arrange the carbon sink enhancement guidance, ecological synergy guidance and model prediction adaptation guidance criteria in order of importance of feature analysis, and use them as column labels for the multi-objective optimization decision matrix. The column label order should be consistent with the feature correlation strength of the criteria.

[0097] In this embodiment, the system determines the importance order of criteria by the correlation strength output by the feature association mapping network. For example, the carbon sink enhancement guidance criterion has the highest correlation strength (average correlation strength of 0.88), followed by the ecological synergy guidance criterion (0.75), and the model prediction adaptation guidance criterion has the lowest (0.62). Therefore, the column labeling order of the decision matrix is ​​"carbon sink enhancement guidance", "ecological synergy guidance", and "model prediction adaptation guidance", ensuring that the column arrangement is consistent with the degree of influence of the criteria on carbon sink changes.

[0098] Step 1553: Extract the corresponding evaluation data of each engineering scheme input parameter set under each optimization criterion from the normalization processing result of the digital model. The evaluation data has eliminated dimensional differences through feature processing.

[0099] In this embodiment, the system extracts evaluation data for each engineering scheme under three criteria from the output database of the digital model. These data are dimensionless values ​​(range 0-1) after Min-Max standardization. For example, Scheme A has an evaluation score of 0.82 under the carbon sequestration enhancement criterion, 0.78 under the ecological synergy criterion, and 0.65 under the model prediction adaptation criterion; the corresponding data for Scheme B are 0.75, 0.85, and 0.70; and the corresponding data for Scheme C are 0.90, 0.68, and 0.72. The system stores this data categorized by scheme ID, establishing a correlation between the evaluation data and the scheme.

[0100] Step 1554: Start the matrix construction process and set the filling rules for matrix elements. The filling rules are formulated based on the mapping relationship between the core characteristics and criteria of carbon sinks.

[0101] In this embodiment, the system sets the filling rule as follows: for each engineering scheme (row identifier), its evaluation data under each criterion (column identifier) ​​is directly filled into the corresponding matrix cell. The filling rule also stipulates that if evaluation data for a scheme under a certain criterion is missing, it is filled with the average of the evaluation data of all schemes under that criterion to ensure the integrity of the matrix. Furthermore, the filling rule requires data format validation, such as ensuring that the data are floating-point numbers between 0 and 1, to avoid invalid data entering the matrix.

[0102] Step 1555: Fill the normalized evaluation data into the corresponding positions of the matrix one by one according to the correspondence between row identifiers and column identifiers. During the filling process, the rationality and suitability of the data are verified in real time.

[0103] In this embodiment, the system iterates through the evaluation data of each engineering scheme, locates the corresponding matrix cell based on the row identifier (scheme ID) and column identifier (criterion name), and fills in the data. During the filling process, the system checks in real time whether the data conforms to the filling rules. For example, if the evaluation data of scheme D under the carbon sequestration enhancement guiding criterion is 1.2 (outside the 0-1 range), the system automatically corrects it to 1.0 and records the correction log. At the same time, the system checks the adaptability of the data to the actual situation of the scheme. For example, if the ecological restoration parameters of scheme E show that the vegetation restoration area is very small, but the evaluation data under the ecological synergy guiding criterion is very high, the system issues an early warning and requires manual review.

[0104] Step 1556: Dynamically correct the filled initial matrix using the feature interaction data of the digital model; wherein, the correction logic is based on the coupling output deviation adjustment of the carbon sink effect quantification model and the ecological environment response simulation model.

[0105] In this embodiment, the system acquires feature interaction data from the digital model. This data records the output deviation between the two models during the coupling calculation process, such as the deviation between the carbon sink amount predicted by the carbon sink effect quantification model and the theoretical carbon sink amount corresponding to the changes in ecological elements output by the ecological environment response simulation model. The system adjusts the evaluation data of the corresponding scheme according to the magnitude of the deviation. For example, if the coupling output deviation of scheme F is positive (the predicted carbon sink amount is higher than the theoretical value), the evaluation data under its carbon sink enhancement guidance criterion is lowered by 5%; if it is negative, it is raised by 5%. The correction logic ensures that the evaluation data can reflect the actual effect of model coupling and improves the reliability of the decision matrix.

[0106] Step 1557: Generate a multi-objective optimization decision matrix based on the corrected matrix.

[0107] In this embodiment, the system converts the corrected matrix into a standard tabular format, where rows represent engineering solutions, columns represent optimization criteria, and the values ​​in the cells are normalized and corrected evaluation data. The system stores the decision matrix as a CSV file for easy subsequent weight allocation and solution ranking.

[0108] Step 156: Combining the importance ranking results of the core features, assign dynamic weight values ​​to each optimization criterion through weighted feature fusion. The dynamic weight values ​​are automatically adjusted based on the feature sensitivity analysis of the carbon sink simulation prediction results.

[0109] In this embodiment, the system first determines the importance ranking of core features using the analytic hierarchy process (AHP). For example, the importance weight of the dynamic change feature of carbon sinks is 0.4, the importance weight of the spatial correlation feature of carbon sinks is 0.3, and the importance weight of the temporal evolution feature of carbon sinks is 0.3. Then, based on the importance ranking of core features, an initial weight value is assigned to each optimization criterion through weighted feature fusion. For example, the carbon sink enhancement guidance criterion corresponds to the dynamic change feature of carbon sinks, with an initial weight value of 0.4; the ecological synergy guidance criterion corresponds to the spatial correlation feature of carbon sinks, with an initial weight value of 0.3; and the model prediction adaptation guidance criterion corresponds to the temporal evolution feature of carbon sinks, with an initial weight value of 0.3. Next, the system performs feature sensitivity analysis on the carbon sink simulation prediction results. For example, by changing a certain parameter of the engineering scheme (such as the reservoir inundation range), the system observes the degree of change of the core features in the carbon sink simulation prediction results. The greater the degree of change, the higher the sensitivity of the core feature to that parameter. Based on the results of feature sensitivity analysis, the dynamic weight values ​​are automatically adjusted. For example, if the dynamic changes in carbon sinks are highly sensitive to the reservoir inundation range parameter, the weight value of the carbon sink enhancement guidance criterion is increased; if the spatial correlation characteristics of carbon sinks are highly sensitive to the operation and scheduling scheme parameters, the weight value of the ecological synergy guidance criterion is increased. The adjustment range of the dynamic weight values ​​is ± a certain percentage of the initial weight values ​​to ensure the rationality of the weight values.

[0110] Step 157: Based on the prediction confidence of the carbon sink effect quantification model and the output stability of the ecological environment response simulation model, the comprehensive evaluation scores of different engineering schemes are ranked based on ranking feature adaptation to obtain the ranking results.

[0111] In this embodiment, the system first calculates the prediction confidence of the carbon sink effect quantification model. The prediction confidence is determined based on factors such as the model's prediction error, the amount of training data, and the accuracy of the validation data. For example, the smaller the prediction error, the larger the amount of training data, and the higher the accuracy of the validation data, the higher the prediction confidence. Next, the system calculates the output stability of the ecological environment response simulation model. Output stability is determined based on factors such as the fluctuation range and consistency of the model's output data. For example, the smaller the fluctuation range and the higher the consistency of the output data, the higher the output stability. Then, the system calculates the comprehensive evaluation score for each engineering scheme based on the evaluation data and dynamic weight values ​​in the multi-objective optimization decision matrix. The comprehensive evaluation score is calculated by multiplying the evaluation data of each evaluation dimension by the corresponding dynamic weight value and then summing the results. For example, the comprehensive evaluation score for engineering scheme 1 is (carbon sink enhancement-oriented evaluation data × carbon sink enhancement-oriented weight) + (ecological synergy-oriented evaluation data × ecological synergy-oriented weight) + (model prediction adaptation-oriented evaluation data × model prediction adaptation-oriented weight). Subsequently, the system adjusts the comprehensive evaluation score based on the prediction confidence of the carbon sink effect quantification model and the output stability of the ecological environment response simulation model. For example, if the prediction confidence and output stability are both high, the weight of the comprehensive evaluation score is increased; if the prediction confidence or output stability is low, the weight of the comprehensive evaluation score is decreased. Finally, based on the adjusted comprehensive evaluation score, different engineering schemes are ranked according to ranking feature adaptation to obtain the ranking results. For example, the engineering scheme with the highest score is ranked from high to low, and the scheme with the highest score is the optimal scheme.

[0112] Step 158: Discover the differences in the core features among different engineering schemes, generate adjustment logic based on the ranking results, and generate preliminary optimization decision suggestions based on the digital model and feature analysis according to the adjustment logic.

[0113] Step 1581: Input the comprehensive evaluation scores of different engineering schemes and the corresponding core carbon sink feature data into the difference feature mining model. The difference feature mining model sets the analysis dimensions based on the output logic of the digital model.

[0114] In this embodiment, the difference feature mining model adopts an architecture combining k-means clustering and decision tree classification algorithms. The model's analysis dimensions include numerical differences in core carbon sink features, differences in feature change trends, and differences in feature combination patterns. All these dimensions are consistent with the output logic of the digital model, ensuring that the mining results reflect the model's response patterns to engineering solutions. The system inputs the ranked solution scores and corresponding core feature data into the model. For example, the comprehensive score of solution A (0.80) and its core feature data (carbon sink increment 0.85, eco-friendly carbon sink distribution 0.78, prediction stability 0.65) are input into the model to provide basic data for difference analysis.

[0115] Step 1582: Based on the similarity of the dynamic change characteristics, spatial correlation characteristics, and time-series evolution characteristics of carbon sinks, the core characteristic data of carbon sinks of each engineering scheme are clustered through feature clustering grouping processing to divide the scheme clusters with similar characteristics.

[0116] In this embodiment, the system employs the k-means clustering algorithm, using the numerical values ​​of carbon sink dynamic change characteristics, spatial correlation characteristics, and temporal evolution characteristics as clustering variables, and setting the number of clusters to 3 (preferred cluster, unoptimized cluster, and unrecommended cluster). The algorithm determines similarity by calculating the Euclidean distance between features, grouping closely spaced schemes into the same cluster. For example, schemes A, C, and E all have carbon sink increments greater than 0.8 and eco-friendly carbon sink distributions greater than 0.75, and are grouped into the preferred cluster; schemes B and D have carbon sink increments between 0.7 and 0.8 and eco-friendly carbon sink distributions between 0.7 and 0.75, and are grouped into the unoptimized cluster; schemes F and G have carbon sink increments less than 0.7 and eco-friendly carbon sink distributions less than 0.7, and are grouped into the unrecommended cluster. The system assigns a label to each cluster for subsequent analysis.

[0117] Step 1583: For each cluster of schemes, use a feature comparison analysis algorithm to discover the differences between the schemes in the cluster and the optimal scheme in terms of the core features of carbon sink.

[0118] In this embodiment, the system first determines the optimal solution (the solution with the highest overall score) for each cluster. For example, the optimal solution for the recommended cluster is solution C (overall score 0.85), the optimal solution for the cluster to be optimized is solution B (0.78), and the optimal solution for the cluster not recommended is solution F (0.65). Then, the system uses a decision tree classification algorithm to identify the differences between other solutions within the cluster and the optimal solution in core features, based on the optimal solution. For example, the differences between solution D and the optimal solution B in the cluster to be optimized are "lower carbon sink increment by 0.05", "lower eco-friendly carbon sink distribution by 0.03", and "higher prediction stability by 0.02"; the differences between solution G and the optimal solution F in the cluster not recommended are "lower carbon sink increment by 0.08", "lower eco-friendly carbon sink distribution by 0.06", and "lower prediction stability by 0.01". The system quantifies these differences into specific numerical gaps, clarifying the direction for solution improvement.

[0119] Step 1584: Combine the mapping relationship of the multi-objective optimization criteria to analyze the degree of influence of the difference points on the evaluation results of each criterion, and determine the core difference points with the highest influence weight. The core difference points are the key targets for scheme optimization.

[0120] In this embodiment, the system analyzes the impact of differences on the evaluation results of the criteria based on the mapping relationship between multi-objective optimization criteria and core features. For example, "low carbon sink increment by 0.05" corresponds to the carbon sink enhancement guidance criterion, with an impact weight of 0.4; "low eco-friendly carbon sink distribution by 0.03" corresponds to the ecological synergy guidance criterion, with an impact weight of 0.3; and "high prediction stability by 0.02" corresponds to the model prediction adaptation guidance criterion, with an impact weight of 0.2. The system calculates the weighted impact value of each difference point (difference point value × corresponding criterion weight). For example, the weighted impact value of "low carbon sink increment by 0.05" is 0.05 × 0.4, "low eco-friendly carbon sink distribution by 0.03" is 0.03 × 0.3, and "high prediction stability by 0.02" is 0.02 × 0.2. The difference point with the highest weighted impact value is the core difference point. For example, "low carbon sink increment by 0.05" is the core difference point of scheme D, and "low carbon sink increment by 0.08" is the core difference point of scheme G.

[0121] Step 1585: Based on the parameter adjustment logic of the digital model, corresponding adjustment directions are generated for the core differences. The adjustment directions include the optimization logic of the engineering scheme input parameters, the parameter adaptation logic of the digital model, and the rule adjustment logic of feature processing.

[0122] In this embodiment, the system generates adjustment directions for the core difference point "carbon sequestration increment is 0.05 lower": the optimization logic for the engineering scheme input parameters is "increase vegetation restoration area by 10%" and "optimize soil improvement measures (such as adding organic fertilizer)"; the parameter adaptation logic for the digital model is "increase the parameter value of vegetation carbon sequestration rate in the carbon sequestration effect quantification model by 5%"; and the rule adjustment logic for feature processing is "add cross features of carbon sequestration increment and vegetation restoration area in the feature preprocessing stage". All of the above adjustment directions are based on the parameter sensitivity analysis results of the digital model, ensuring that the adjustment can effectively improve the carbon sequestration increment.

[0123] Step 1586: For the priority recommendation scheme cluster, generate adjustment logic to enhance core advantages; for the scheme cluster to be optimized, formulate adjustment strategies based on core differences, including the adjustment range of digital model input parameters and the optimization path of feature processing; for the non-recommended scheme cluster, recommend suitable schemes in the optimal scheme cluster through a scheme adaptation recommendation mechanism, and mark the core logic of scheme replacement and the migration rules of digital model parameters.

[0124] In this embodiment, the system's adjustment logic for priority recommendation clusters is "strengthening vegetation restoration measures (such as using native tree species and increasing irrigation frequency) to maintain the carbon sink increment advantage" and "optimizing ecological flow scheduling to improve the stability of eco-friendly carbon sink distribution"; the adjustment strategy for clusters to be optimized is "adjusting the vegetation restoration area to 200-300 hectares (the original area is 150 hectares)" and "adding the cross-feature of carbon sink increment and soil improvement measures in the feature processing stage"; the scheme adaptation recommendation mechanism for non-recommended clusters is "recommending scheme C in the priority recommendation cluster as the adaptation scheme", the core logic of labeling and replacement is "the carbon sink increment of scheme C is 0.15 higher than that of scheme G, and the eco-friendly carbon sink distribution is 0.12 higher", and the migration rule of digital model parameters is "replacing the engineering design parameters (dam height, reservoir capacity) of scheme G with the corresponding parameters of scheme C, and retaining the operation scheduling parameters of scheme G".

[0125] Step 1587: Combining the adjustment logic, optimization strategies, and replacement rules of all types of scheme clusters, sort them according to the priority of feature analysis, and generate the aforementioned pre-optimization decision suggestions.

[0126] In this embodiment, the system prioritizes the adjustment logic, optimization strategies, and replacement rules according to the priority of feature analysis (carbon sequestration enhancement > ecological synergy > model prediction adaptation), generating final decision recommendations. The recommendations include: Option C is given priority, suggesting strengthened vegetation restoration measures and ecological flow scheduling; Options B and D, to be optimized, should adjust the vegetation restoration area to 200-300 hectares and optimize the feature processing rules; Options F and G, not recommended, should be replaced with Option C, migrating relevant parameters. The system outputs the recommendations in a structured report, clearly defining the optimization direction, specific measures, and expected effects of each option, providing direct evidence for engineering decisions.

[0127] In an independent embodiment, the method further includes: comparing the carbon sink simulation prediction results with the actual monitored carbon sink data at the feature level through a result comparison module of the digital model to identify the distribution of differences in core features; performing feature decomposition and correlation analysis on the difference distribution data to locate the sources of prediction bias in the carbon sink effect quantification model, including model feature mapping bias, parameter adaptation bias, and feature processing bias; constructing a bias correction factor system based on the feature attributes of the bias sources, where each correction factor corresponds to a bias source, and the correction factor includes feature adjustment logic and model adaptation rules; selecting the optimal combination of correction factors from the bias correction factor system according to the influence range and intensity of the bias sources through a factor screening and adaptation mechanism, the combination logic being formulated based on the output stability requirements of the digital model; and embedding the selected correction factor combination into the carbon sink effect quantification model. The feature processing flow of the carbon sink effect quantification model should be quantified, and the feature mapping rules, parameter calculation logic, and feature interaction mechanism of the carbon sink effect quantification model should be adjusted to correct the bias of the carbon sink effect quantification model. The corrected carbon sink effect quantification model should be recoupled with the ecological environment response simulation model, and the complete process of model coupling, feature interaction, and simulation prediction should be repeated to generate the corrected carbon sink simulation prediction results. The deviation value between the corrected carbon sink simulation prediction results and the actual monitoring data should be calculated. If the deviation value does not reach the preset accuracy threshold, the steps of deviation tracing, factor screening, and model adjustment should be repeated until the deviation value meets the requirements. Based on the final corrected carbon sink simulation prediction results, the evaluation data and weight allocation of the multi-objective optimization decision matrix should be updated, and the comprehensive evaluation score and ranking results of each engineering scheme should be recalculated. Combining the updated ranking results and model correction logic, the content of the pre-optimization decision suggestions should be optimized.

[0128] This application focuses on the precise iteration of carbon sink effect quantification models and the dynamic optimization of engineering solutions. First, by combining feature matching algorithms with statistical analysis, the carbon sink simulation prediction results are compared with actual monitoring data (covering multiple dimensions such as vegetation biomass carbon, soil organic carbon, and river carbon transport). Differences are identified from three core dimensions: dynamic changes (e.g., the trend of carbon sink increments during the construction period), spatial correlations (e.g., carbon sink distribution in the biodiversity core area), and temporal evolution (e.g., interannual variation patterns), providing targeted evidence for tracing the source of deviations. Next, the difference data undergoes feature decomposition (e.g., separating incremental differences and rate differences) and correlation analysis to accurately locate the source of deviations: if the difference has a high correlation with the input feature mapping, it is determined to be a feature mapping deviation; if it is strongly correlated with model parameters (e.g., vegetation carbon sequestration rate), it is a parameter adaptation deviation; if it is related to the feature processing process (e.g., normalization), it is a feature processing deviation.

[0129] Furthermore, a correction factor system is constructed based on deviation attributes. Extraction and transformation factors are designed for feature mapping deviation, value and dimensionality factors are designed for parameter adaptation deviation, and normalization and combination factors are designed for feature processing deviation. Each factor includes feature adjustment logic and model adaptation rules. Subsequently, combining the scope of deviation impact (whole basin / local) and intensity (high / medium / low), the optimal combination of correction factors is selected using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method to ensure the stability of the corrected model output. The correction factors are embedded into the model feature processing flow, adjusting the feature mapping rules, parameter calculation logic, and interaction mechanism. After model correction, the ecological environment response simulation model is recoupled to generate correction results. If the deviation does not reach the preset accuracy threshold (e.g., 5%), the correction process is repeated. Finally, the correction results are used to update the evaluation data and weight allocation of the multi-objective optimization decision matrix, recalculate the scheme scores and ranking, and combine the ranking and correction logic to optimize decision recommendations.

[0130] In this way, precise closed-loop correction of model bias is achieved, improving the accuracy of carbon sink prediction; by dynamically updating the decision matrix, the evaluation of engineering schemes is made more in line with the actual carbon sink effect, thereby improving the scientific nature and operability of decision recommendations.

[0131] In one independent embodiment, the method further includes: collecting historical ecological element data and corresponding carbon sink data of the target watershed to establish an ecological element-carbon sink time-series correlation database; analyzing the historical data in the correlation database through a time-series feature mining mechanism to mine the correlation patterns between ecological element evolution and carbon sink changes, wherein the correlation patterns characterize the coupling logic of feature time-series changes; transforming the mined time-series correlation patterns into time-series adjustment rules for an ecological environment response simulation model, wherein the adjustment rules cover the model's time step optimization, feature dynamic update logic, and parameter time-series adaptation mechanism; optimizing the structure of the ecological environment response simulation model based on the time-series adjustment rules to enhance the model's ability to simulate the evolution of ecological elements, wherein the optimized model is used to reproduce the historical carbon sink change process; acquiring potential scenario prediction data of the target watershed, and performing scenario feature transformation on the potential scenario prediction data. The process involves: 1) Analyzing the potential input scenario characteristics of the ecological environment response simulation model, including time-series characteristic change trends; 2) Coupling the optimized ecological environment response simulation model with a carbon sink effect quantification model to initiate the simulation prediction process, simulating the impact of hydropower projects on carbon sink capacity under different potential scenarios, and generating potential carbon sink simulation prediction results; 3) Expanding the evaluation dimensions of the multi-objective optimization decision matrix based on the feature requirements of the potential carbon sink simulation prediction results through a multi-objective dimension expansion mechanism, resulting in expanded criteria; 4) Transforming the potential carbon sink simulation prediction results into evaluation data corresponding to the expanded criteria, and recalculating the comprehensive evaluation score of each engineering scheme; 5) Optimizing the periodic guidance content of the pre-optimization decision recommendations based on the comprehensive evaluation score and the characteristic analysis of the periodic carbon sink impact, outputting pre-optimization decision recommendations that balance the transient carbon sink improvement effect with the periodic ecological stability requirements.

[0132] This application's embodiments construct a forward-looking mechanism of "historical data-driven - model optimization - scenario prediction - decision upgrading": First, historical ecological elements (vegetation type, soil moisture content, river water level, etc.) and corresponding carbon sink data of the target watershed are collected to establish a time-series correlation database. Each record contains ecological data and carbon sink data at a certain moment, providing a data foundation for pattern mining. After preprocessing the data through time series analysis methods such as ARIMA and LSTM, the time-series coupling patterns between ecological elements and carbon sinks are mined (such as the leading effect of vegetation cover change on carbon sinks, and the synchronous relationship between soil organic matter and carbon sinks). The patterns are then transformed into time-series adjustment rules for the ecological environment response simulation model: optimizing the time step to match the leading and lagging relationship of features, designing dynamic feature update logic to synchronously update carbon sink data, and establishing a parameter time-series adaptation mechanism (such as adjusting river carbon transport parameters based on preceding water levels).

[0133] Furthermore, based on rule-based model structure optimization, modules for dynamic feature updates and parameter time-series adaptation are added to enhance the model's ability to simulate ecological evolution. The accuracy is verified by reproducing historical carbon sequestration processes using the optimized model. Subsequently, potential scenario data (climate change, land use, hydropower operation, etc.) are acquired and transformed into input features containing time-series change trends (such as time-series data on temperature rise and water level scheduling adjustments). The model is then coupled to simulate the carbon sequestration impact under different scenarios, generating potential carbon sequestration prediction results. Based on the requirements of result features, the evaluation dimensions of the multi-objective decision matrix are expanded, adding criteria such as carbon sequestration climate sensitivity, land use sensitivity, and operation scheduling sensitivity. The potential results are transformed into expanded-dimensional data, and the comprehensive score of the scheme is recalculated. Combining the score with periodic carbon sequestration impact analysis (such as interannual / seasonal variation patterns), the periodic guidance content of the decision recommendations is optimized.

[0134] This design can enhance the ability to simulate ecological evolution, thereby reproducing historical carbon sequestration processes; through potential scenario prediction and dimensional expansion, it enables decision-making to take into account both transient carbon sequestration enhancement and long-term ecological stability, providing forward-looking support for hydropower projects to cope with future uncertainties.

[0135] This application embodiment collects ecological element information of the target watershed and extracts a multi-dimensional spatial distribution feature set. Combined with digital baseline data, it establishes an ecological environment response simulation model under engineering disturbance. Simultaneously, it constructs a carbon sink effect quantification model based on construction and operation context information. The carbon sink effect quantification model is embedded into the associated nodes of the ecological environment response simulation model to achieve coupling. Finally, based on the carbon sink simulation prediction results of the coupled model, a multi-objective optimization decision matrix is ​​generated and a pre-optimization decision suggestion is output. This technology constructs a complete chain technology of "ecological elements-environmental response-carbon sink quantification-model coupling-decision optimization". It realizes accurate simulation of the dynamic impact of hydropower project construction and operation on watershed carbon sink capacity and pre-optimization decision of multiple schemes. It breaks through the limitations of traditional technology, such as the separation of ecological environment response and carbon sink effect quantification and the lack of synergistic adaptation between models. It improves the systematicness of the carbon sink impact assessment of hydropower projects and the foresight of decision suggestions, and provides technical support for the eco-friendly construction and operation of hydropower projects.

[0136] Based on the same inventive concept, embodiments of this application also provide a quantitative simulation and prediction system for carbon sink effects. See also... Figure 2 As shown, it is a schematic diagram of the structure of a possible carbon sink effect quantitative simulation and prediction system provided in the embodiments of this application. Figure 2 In the carbon sink effect quantification simulation and prediction system 200, there are a processor 210 and a memory 220. The memory 220 stores computer programs that can be executed by the processor 210. By executing the instructions stored in the memory 220, the processor 210 can perform the steps of the above-mentioned method for quantifying and predicting the carbon sink effect of ecological restoration measures based on digital modeling.

[0137] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium including a computer program. When the computer program is run on a carbon sink effect quantification simulation and prediction system, the computer program is used to cause the carbon sink effect quantification simulation and prediction system to perform the steps of the aforementioned method for quantifying and predicting the carbon sink effect of ecological restoration measures based on digital modeling. In some possible embodiments, various aspects of the method for quantifying and predicting the carbon sink effect of ecological restoration measures based on digital modeling provided in this application can also be implemented in the form of a program product, including a computer program. When the program product is run on a carbon sink effect quantification simulation and prediction system, the computer program is used to cause the carbon sink effect quantification simulation and prediction system to perform the steps of the aforementioned method for quantifying and predicting the carbon sink effect of ecological restoration measures based on digital modeling. For example, the carbon sink effect quantification simulation and prediction system can perform, for example, ... Figure 1 The steps shown above are merely preferred exemplary embodiments of this application and are not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concepts and spirit of this application.

Claims

1. A method for quantitative simulation and prediction of the carbon sink effect of ecological restoration measures based on digital modeling, characterized in that, The method includes: Ecological element information of the target watershed is collected, and spatial correlation analysis and distribution feature extraction are performed on the ecological element information to obtain a multidimensional ecological element spatial distribution feature set. By combining the spatial distribution feature set of the multidimensional ecological elements with the digital baseline data of the target watershed, an ecological environment response simulation model under engineering disturbance is established through the correlation mapping between ecological processes and environmental responses. The ecological environment response simulation model reflects the correlation impact of engineering disturbance on changes in ecological elements. Extract construction and operation context information of hydropower projects in the target watershed and construct a carbon sink effect quantification model. The carbon sink effect quantification model quantifies the changes in carbon sink capacity caused by the hydropower projects based on the construction and operation context information. The carbon sink effect quantification model is embedded into the associated nodes of the ecological environment response simulation model. Model coupling is achieved through model parameter collaborative adaptation and data interaction channel construction. Based on the coupled model, the dynamic impact of the construction and operation of the hydropower project on the carbon sink capacity of the target watershed is simulated, and carbon sink simulation prediction results are obtained. Based on the carbon sink simulation and prediction results, a multi-objective optimization decision matrix is ​​generated through the correlation and adaptation of multi-objective optimization criteria. The multi-objective optimization decision matrix is ​​then used to output the pre-optimization decision suggestions for the hydropower project under different engineering schemes. The process of extracting construction and operation context information of hydropower projects in the target watershed and constructing a quantitative model of carbon sink effects includes: Separate carbon sink-related construction and operation context information from the construction planning documents and operation record data of hydropower projects in the target watershed; The construction and operation context information is subjected to feature analysis to obtain static and dynamic features. The static features are transformed into basic parameters of the carbon sink effect quantification model, and the dynamic features are transformed into dynamic input variables of the model. The basic parameters are used to set the initial calculation boundary of the model, and the dynamic input variables are used to reflect the context changes in different operating stages. Based on the correlation mechanism between ecological restoration measures and carbon sink generation, a model structure for a carbon sink effect quantification model is constructed. The model structure includes a context feature mapping layer, a carbon sink process simulation layer, and a quantification result output layer. The context feature mapping layer is initialized by the basic parameters corresponding to the static features, and the dynamic input variables are input into the context feature mapping layer for feature transformation to generate intermediate features related to the carbon sink process. The intermediate features are passed into the carbon sink process simulation layer, and quantitative data on changes in carbon sink capacity are obtained through process correlation simulation of carbon sink generation, transfer and storage. The quantified data is fed into the quantification result output layer for standardization processing to generate a quantification model of carbon sink effect that reflects changes in carbon sink capacity under different scenarios.

2. The method as described in claim 1, characterized in that, The carbon sink effect quantification model is embedded into the associated nodes of the ecological environment response simulation model. Model coupling is achieved through model parameter collaborative adaptation and data interaction channel construction. Based on the coupled model, the dynamic impact of the construction and operation of the hydropower project on the carbon sink capacity of the target watershed is simulated to obtain carbon sink simulation prediction results, including: The output node type and data transmission format of the ecological environment response simulation model are analyzed, and the associated nodes related to carbon sink changes are identified. The associated nodes are the output ports that reflect the impact of changes in ecological elements on carbon sinks. Extract the input parameter types and output data formats of the carbon sink effect quantification model, match the input ports of the carbon sink effect quantification model with the associated nodes of the ecological environment response simulation model, and determine the parameter adaptation relationship; Based on the parameter adaptation relationship, the parameter dimensions and data format of the ecological environment response simulation model and the carbon sink effect quantification model are adjusted so that the input port of the carbon sink effect quantification model can directly receive the output data of the associated nodes of the ecological environment response simulation model. A data interaction channel is established between the ecological environment response simulation model and the carbon sink effect quantification model. The data interaction channel is a two-way data transmission channel. The output data of the associated nodes of the ecological environment response simulation model is transmitted to the carbon sink effect quantification model through the data interaction channel, and the intermediate calculation data of the carbon sink effect quantification model is fed back to the ecological environment response simulation model through the data interaction channel. Set coupling iteration conditions, wherein the carbon sink change difference between two coupling calculations reaches a stable range, initialize the calculation parameters of the coupling model, and input the construction and operation scenario information into the coupling model; The ecological environment response simulation model is invoked to simulate changes in ecological elements based on the input construction and operation scenario information. The ecological element change data is output through the associated nodes and transmitted to the carbon sink effect quantification model through the data interaction channel. The carbon sink effect quantification model is invoked to calculate the carbon sink capacity change data based on the ecological element change data and its own parameters. The data is then fed back to the ecological environment response simulation model through the data interaction channel. Based on the ecological environment response simulation model and the feedback data, the simulation results of ecological element change are adjusted. The data interaction and simulation calculation steps are executed repeatedly until the carbon sink change difference meets the coupling iteration condition. Then, the iteration stops and the final carbon sink capacity change data is extracted to obtain the carbon sink simulation prediction result.

3. The method as described in claim 2, characterized in that, The process involves analyzing the output node types and data transmission formats of the ecological environment response simulation model to identify associated nodes related to carbon sink changes, including: Obtain the structural specification document and output variable list of the ecological environment response simulation model, and extract the node identifier, output data type and data transmission format of all output nodes; Based on the core influencing factors of carbon sink generation, the types of ecological elements related to carbon sink changes are identified, including vegetation cover, soil physicochemical properties, and hydrological cycle status. The output data type of the output node is associated and matched with the determined ecological element type, and the output nodes corresponding to the ecological element type of the output data type are filtered out. By inputting simulated typical engineering disturbance data, the selected output nodes are functionally verified to determine whether the output data of the output nodes reflects the changing trend of ecological elements. Output nodes that reflect the changing trend of ecological elements are retained, and output nodes that cannot reflect the changing trend of ecological elements are removed. The retained output nodes are marked as associated nodes related to carbon sink changes, and the node identifier, output data type and data transmission format of the associated nodes are recorded to form an associated node list; Based on the information in the list of associated nodes, determine the dimensions of ecological element changes corresponding to each associated node.

4. The method as described in claim 2, characterized in that, The setting of coupling iteration conditions, wherein the difference in carbon sink change between two coupling calculations reaches a stable range, initializing the calculation parameters of the coupling model, and inputting the construction and operation scenario information into the coupling model, includes: Based on the calculation accuracy requirements of the carbon sink effect quantification model, and combined with the fluctuation range of the carbon sink background value of the target watershed, the stable range of the carbon sink change difference is determined. The absolute difference between the carbon sequestration capacity change data obtained from two adjacent coupled iterations is set to be less than the stable range as the termination condition for coupled iteration, and an upper limit for the number of iterations is set simultaneously. From the initial parameter configuration files of the carbon sink effect quantification model and the ecological environment response simulation model, the basic calculation parameters necessary for model operation are extracted. The basic calculation parameters include model structure parameters, initial state parameters and constraint condition parameters. Based on the parameter definitions, unit expressions, and value range matching relationships of different models, joint verification is performed, and the basic calculation parameters that have completed joint verification are assigned to the corresponding models to complete the initialization of the calculation parameters of the coupled models. According to the input interface specification of the coupling model, the construction and operation scenario information is transmitted to the designated input port of the coupling model to start the coupling calculation process.

5. The method as described in claim 1, characterized in that, Based on the carbon sink simulation and prediction results, a multi-objective optimization decision matrix is ​​generated through the correlation and adaptation of multi-objective optimization criteria. Using this multi-objective optimization decision matrix, preliminary optimization decision suggestions for the hydropower project under different engineering schemes are output, including: The carbon sink simulation prediction results are analyzed using a digital model to extract the core features. These core features include the dynamic change features of carbon sinks, the spatial correlation features of carbon sinks, and the temporal evolution features. All core features are generated based on the coupled output data of the carbon sink effect quantification model and the ecological environment response simulation model. A mapping relationship between multi-objective optimization criteria and core features of carbon sinks is established through a feature association learning mechanism. The multi-objective optimization criteria focus on carbon sink enhancement, ecological synergy, and model prediction adaptation. The setting of the multi-objective optimization criteria is based on the output logic of the digital model and the feature analysis results. The multi-objective optimization criteria are transformed into quantitatively calculated feature evaluation dimensions through a criterion quantification adaptation mechanism. Each evaluation dimension forms a one-to-one adaptation relationship with the core features of carbon sink. The feature normalization processing logic of the digital model is used to perform dimensional unification processing on the evaluation data corresponding to the core features of carbon sinks. By using a matrix-based feature recombination method, a multi-objective optimization decision matrix is ​​generated, with the digital model input parameters corresponding to different engineering schemes as row identifiers, the multi-objective optimization criteria as column identifiers, and the normalized evaluation data as matrix elements. Based on the importance ranking results of the core features, a dynamic weight value is assigned to each optimization criterion through weighted feature fusion. The dynamic weight value is automatically adjusted based on the feature sensitivity analysis of the carbon sink simulation prediction results. Based on the prediction confidence of the carbon sink effect quantification model and the output stability of the ecological environment response simulation model, the comprehensive evaluation scores of different engineering schemes are ranked based on ranking feature adaptation to obtain the ranking results. The differences between different engineering schemes in the core features are explored, and adjustment logic is generated based on the ranking results. Based on the adjustment logic, preliminary optimization decision suggestions are generated based on the digital model and feature analysis.

6. The method as described in claim 5, characterized in that, The establishment of a mapping relationship between multi-objective optimization criteria and core features of carbon sinks through a feature association learning mechanism includes: The original feature data of the generated carbon sink dynamic change features, carbon sink spatial correlation features, and time series evolution features are extracted through the feature output interface of the carbon sink effect quantification model. The original feature data retains the complete feature dimensions calculated by the carbon sink effect quantification model. The extracted raw feature data is deredundant and enhanced through a feature preprocessing procedure to obtain enhanced feature data. A feature association mapping network is constructed, which is used to receive enhanced feature data, the hidden layer is used to realize feature mining through a multi-layer perceptual structure, and the output layer corresponds to the potential association dimension of the multi-objective optimization criterion. The training objective of the feature association mapping network is set based on the output logic of the digital model. The feature association mapping network is trained by adapting historical carbon sink simulation data with the corresponding optimization criteria, and the network parameters are dynamically adjusted during the training process to improve the accuracy of the mapping relationship. After training, the enhanced feature data is input, and the feature association mapping network outputs the correlation strength between each feature in the enhanced feature data and the carbon sink enhancement guidance, ecological synergy guidance, and model prediction adaptation guidance. Based on the ranking results of correlation strength, the core features of carbon sinks associated with each criterion are selected, and a one-to-one mapping relationship table is established. The expression logic of the multi-objective optimization criteria is optimized by combining the mapping relationship table, and the update of the mapping relationship table synchronously responds to the output changes of the digital model.

7. The method as described in claim 5, characterized in that, The method of generating a multi-objective optimization decision matrix through matrix feature recombination, using the digital model input parameters corresponding to different engineering schemes as row identifiers, multi-objective optimization criteria as column identifiers, and normalized evaluation data as matrix elements, includes: The input parameter management module of the digital model extracts the input parameter sets corresponding to different engineering schemes. The input parameter sets cover all situational feature data of hydropower engineering construction and operation, and serve as the row identifier basis of the multi-objective optimization decision matrix. The criteria for carbon sink enhancement, ecological synergy, and model prediction adaptation are arranged in order of importance of feature analysis and used as column labels for the multi-objective optimization decision matrix. The order of column labels is consistent with the feature correlation strength of the criteria. The evaluation data of each engineering scheme input parameter set under each optimization criterion is extracted from the normalization processing result of the digital model. The evaluation data has eliminated the dimensional differences through feature processing. Initiate the matrix construction process and set the filling rules for matrix elements. The filling rules are formulated based on the mapping relationship between the core characteristics and criteria of carbon sinks. The normalized evaluation data are filled into the corresponding positions of the matrix one by one according to the correspondence between row and column labels, and the rationality and suitability of the data are verified in real time during the filling process. The initial matrix after filling is dynamically corrected using the feature interaction data of the digital model; wherein, the correction logic is based on the adjustment of the coupling output deviation between the carbon sink effect quantification model and the ecological environment response simulation model; Based on the corrected matrix, a multi-objective optimization decision matrix is ​​generated.

8. The method as described in claim 5, characterized in that, The process involves identifying the differences in core features among different engineering solutions, generating adjustment logic based on the ranking results, and then generating preliminary optimization decision suggestions based on the digital model and feature analysis, including: The comprehensive evaluation scores of different engineering schemes and the corresponding core carbon sink feature data are input into the differential feature mining model, which sets the analysis dimensions based on the output logic of the digital model. Based on the similarity of the dynamic change characteristics, spatial correlation characteristics and temporal evolution characteristics of carbon sinks, the core characteristic data of carbon sinks of each engineering scheme are clustered through feature clustering grouping processing to divide the scheme clusters with similar characteristics. For each cluster of schemes, the feature comparison analysis algorithm is used to discover the differences between the schemes in the cluster and the optimal scheme in the core features of carbon sequestration; By combining the mapping relationship of multi-objective optimization criteria, the influence of the difference points on the evaluation results of each criterion is analyzed, and the core difference points with the highest influence weight are determined. These core difference points are the key targets for scheme optimization. The parameter adjustment logic based on the digital model generates corresponding adjustment directions for the core differences. The adjustment directions include the optimization logic of the engineering scheme input parameters, the parameter adaptation logic of the digital model, and the rule adjustment logic of feature processing. For the priority recommendation scheme cluster, an adjustment logic is generated to enhance the core advantages and features; for the scheme cluster to be optimized, an adjustment strategy is formulated based on the core differences, the adjustment strategy including the adjustment range of the digital model input parameters and the optimization path of feature processing; For clusters of unrecommended solutions, an optimal solution cluster is recommended through a solution adaptation recommendation mechanism, and the core logic of solution replacement and the migration rules of digital model parameters are marked. By combining the adjustment logic, optimization strategies, and replacement rules of all types of solution clusters, and sorting them according to the priority of feature analysis, the aforementioned pre-optimization decision suggestions are generated.

9. A quantitative simulation and prediction system for carbon sink effects, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of any one of the methods described in claims 1 to 8.

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