Ecological system service space-time evaluation and compensation decision-making method based on multi-model integration

Abstract

The invention discloses an ecological system service space-time evaluation and compensation decision method based on multi-model integration, and belongs to the technical field of ecological environment evaluation and decision support. According to the method, firstly, multiple key ecological services such as water yield and carbon sequestration are dynamically quantified through an ecological system service model; the core of the method is that key dominant factors influencing various services are accurately identified through machine learning, and geographical differences of action effects of the factors are further quantified, so that accurate positioning of dominant effects is realized. In addition, complex causal conduction paths among the factors are analyzed by using a structural equation model. According to the method, through deep integration and hierarchical progressive analysis of multiple models, the crossing from phenomenon evaluation to cause diagnosis is realized, and an accurate scientific decision basis is provided for formulating regionalized and differentiated ecological compensation policies and sustainable management strategies.

Description

Technical Field

[0001] This invention relates to the fields of data processing and intelligent decision-making technology, and in particular to a method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model integration. Background Technology

[0002] This invention relates to the fields of data processing and intelligent decision-making technology, and in particular to an intelligent system applied to the spatiotemporal assessment and compensation decision-making methods of ecosystem services. Currently, quantitative assessment of ecosystem services is fundamental to formulating ecological compensation policies and conducting environmental management. Existing technologies mostly employ GIS combined with professional models to assess the functional quantities and value of ecosystem services, aiming to provide a scientific basis for macro-level environmental protection.

[0003] Patent CN202410249443.8 discloses a quantitative evaluation method for watershed ecological compensation standards based on GEP accounting. This technology uses Gross Ecosystem Product (GEP) as its core, summing the functional and value quantities of ecosystem service products to form a comprehensive ecological value index. This index provides a macro-level quantitative basis for determining the total amount of ecological compensation, representing the forefront of this field towards comprehensive and standardized value accounting. However, macro-value aggregation methods, as exemplified by the aforementioned patent, suffer from fundamental technical defects that fail to meet the needs of precise and intelligent decision-making. First, there is a lack of transparency in the diagnostic process. While the core of this method is to calculate the total value, it cannot explain the underlying reasons for changes in value. It lacks a mechanism to identify key driving factors and their spatial heterogeneous impacts, meaning it cannot quantify the differences in the impact of the same factor at different geographical locations. Second, there is a static nature in decision support. The output compensation standard is an assessment result based on historical data, failing to respond to user-inputted future planning scenarios and conduct forward-looking, dynamic ecological impact assessments.

[0004] Based on the deficiencies revealed in the aforementioned prior art documents, the key technical problems to be solved by this invention can be summarized as follows: how to construct a transparent diagnostic model that can not only assess value but also accurately identify the key factors that lead to changes in value and their differences in impact at different geographical locations; and how to upgrade the static assessment results into a dynamic and interactive decision simulator to predict the ecological impact of different future planning schemes in real time.

[0005] How can we create an intelligent decision engine that can automatically generate an optimal investment plan that includes specific locations, measures, and funding, while meeting budget constraints? Summary of the Invention

[0006] In view of the aforementioned existing problems, the present invention is proposed.

[0007] Therefore, this invention provides a spatiotemporal assessment and compensation decision-making method for ecosystem services based on multi-model integration and a yield improvement system to solve the contradiction between the continuous risk of thermal damage and the narrow process window. That is, in order to obtain an effective self-alignment driving force, it is necessary to raise the local temperature to the melting temperature that may damage the components, and it is difficult to balance the two.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] This invention provides a method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble, which includes the following steps:

[0010] Step 1: Ecological asset archiving and digitization. Construct a digital ledger system that refines geographical areas into management units and tracks the evolution of the ecological asset value of each unit over time. The system grids the target area and integrates multi-source heterogeneous data layers, such as remote sensing images, meteorological and hydrological data, and socio-economic data, into each grid management unit in a time-series manner.

[0011] Step 2: Benchmark quantification of intrinsic ecological value. For each management unit in the digital ledger system, a benchmark service value vector is calculated and generated to quantify its intrinsic service supply capacity through the ecosystem service quantification model. This vector contains multiple preset dimensions for quantifying different ecosystem service capabilities.

[0012] Step 3: Global analysis of value-driven laws. Based on machine learning models, the benchmark service value vector is analyzed to identify the key driving factors affecting its changes. Model interpretation technology is used to generate a global nonlinear dependency curve that reveals the response of ecosystem service value to changes in each key factor.

[0013] Step 4: Localized calibration of impact effectiveness. A spatial variability analysis model is run for each management unit. The spatial variability analysis model performs localized calibration of the global nonlinear dependency curve and calculates a unique geographic value multiplier for each key driver to quantify the specific impact effectiveness of the key driver at a specific geographic location.

[0014] Step 5: Comprehensive valuation and mapping of ecological assets. Based on the benchmark service value vector and the geographic value multiplier, calculate and generate a dynamic and comprehensive ecological asset reference value for each management unit, and store the ecological asset reference value in the digital ledger system to form an ecological asset value map that is dynamically updated with time and space.

[0015] Step 6: Scenario-based decision-making and intelligent solution generation. Based on the future land use planning scenario input by the user, the system automatically executes the following decision-making process: First, it accurately calculates the overall ecological impact assessment value generated in the land use planning scenario from the ecological asset value map; then, based on the calculation result, it intelligently matches and generates an optimal ecological compensation investment plan, which clearly defines the combination of geographical units used for compensation, the types of intervention measures matched with them, and the corresponding resource allocation strategies.

[0016] As a preferred embodiment of the multi-model integration-based spatiotemporal assessment and compensation decision-making method for ecosystem services described in this invention, the specific implementation of ecological asset archiving and digitization in step 1 includes: establishing a standardized geospatial benchmark framework, and dividing the target area into an array of management units coded by unique spatial units based on the geospatial benchmark framework.

[0017] Subsequently, the multi-source heterogeneous data is standardized in terms of spatial dimension and synchronized in terms of temporal dimension. The combination of the spatial unit code and the year is used as a unique index for structured integration to form a multi-dimensional ecological asset database that supports dynamic query and analysis.

[0018] As a preferred embodiment of the spatiotemporal assessment and compensation decision-making method for ecosystem services based on multi-model integration described in this invention, the benchmark quantification of intrinsic ecological value in step 2 specifically includes: processing the multidimensional attribute data of the management unit and generating multiple single-dimensional ecological capacity indices through a series of specialized assessment algorithms; and combining the ecological capacity indices into a standardized, multidimensional benchmark service value vector.

[0019] As a preferred embodiment of the spatiotemporal assessment and compensation decision-making method for ecosystem services based on multi-model integration described in this invention, the global analysis of the value-driven law in step 3 specifically includes: taking each dimension of the benchmark service value vector as the analysis target and taking all other data layers as potential influencing factors, and constructing a machine learning regression model for learning complex nonlinear relationships.

[0020] The machine learning regression model is used to automatically evaluate the influence weight of each potential influencing factor in order to screen out a set of key driving factors that have a decisive impact on the analysis target;

[0021] Based on the trained machine learning regression model, a model interpretation technique is applied to quantify and generate a global nonlinear dependency curve that reveals how the value of analyzed ecosystem services responds to changes in the key driving factors by isolating and simulating the independent changes of each key driving factor.

[0022] As a preferred embodiment of the multi-model ensemble-based ecosystem service spatiotemporal assessment and compensation decision-making method of the present invention, the localized calibration of the impact effectiveness in step 4 specifically includes: combining the global nonlinear dependency with the unique geographic context information of each management unit through a spatial statistical regression model that can quantify spatial heterogeneity, thereby calculating a unique geographic value multiplier for each key driving factor.

[0023] As a preferred embodiment of the multi-model fusion-based spatiotemporal assessment and compensation decision-making method for ecosystem services described in this invention, the comprehensive valuation and mapping of ecological assets in step 5 is further characterized by being implemented through a multi-criteria value aggregation algorithm. This algorithm weights and fuses the benchmark service value vector reflecting the quantity of ecosystem service supply with the geographic value multiplier reflecting the importance and scarcity of the service in a specific geographic location to generate a comprehensive ecological asset reference value. Subsequently, the ecological asset reference value is written back to the digital ledger system to dynamically update and generate a visualized ecological asset value map.

[0024] As a preferred embodiment of the spatiotemporal assessment and compensation decision-making method for ecosystem services based on multi-model integration described in this invention, the scenario-based decision-making in step 6 is further characterized by being implemented through a dynamic assessment process: receiving a user-defined future land use planning scenario through a human-computer interaction interface, and extracting a clear geographical scope and change type from it;

[0025] Based on the analyzed geographical scope and change type, the system retrieves and aggregates data from the ecological asset value map to accurately calculate the overall ecological impact assessment value of the scenario.

[0026] As a preferred embodiment of the spatiotemporal assessment and compensation decision-making method for ecosystem services based on multi-model integration described in this invention, the generation of the intelligent scheme in step 6 is further characterized by being implemented through a spatial decision optimization engine. This engine uses the overall ecological impact assessment value calculated in the dynamic assessment process as the resource constraint boundary, and through a heuristic global search algorithm, automatically searches for and generates an optimal compensation investment scheme that maximizes the net gain of regional ecosystem services among all management units that meet the preset conditions.

[0027] As a preferred embodiment of the multi-model ensemble-based spatiotemporal assessment and compensation decision-making method for ecosystem services described in this invention, the method further includes a multi-scale ecosystem service relationship diagnosis step, which specifically includes: after step 2, deploying a spatial correlation analysis engine, which performs statistical correlation analysis on each dimension of the benchmark service value vector at multiple spatial scales to quantify and output the interaction relationships and scale dependencies between different ecosystem services.

[0028] As a preferred embodiment of the spatiotemporal assessment and compensation decision-making method for ecosystem services based on multi-model integration described in this invention, the method further includes a deep analysis step of the ecosystem action mechanism, which specifically includes: after step 3, deploying a causal path analysis engine, which quantifies and outputs the direct and indirect influence paths between the key driving factors and between the key driving factors and ecosystem services by constructing and fitting a structural equation model.

[0029] The beneficial effects of this invention are as follows: First, this invention achieves a leap from opaque value assessment to transparent causal diagnosis. It can accurately identify key driving factors and the geographical differences in their effectiveness, providing core mechanistic support for formulating precise management policies tailored to local conditions.

[0030] Secondly, this invention realizes the transformation from static summarization to dynamic decision-making. It can respond to the user's input of future land use planning scenarios and instantly calculate its overall ecological impact assessment value, greatly improving the scientific nature and foresight of the decision-making.

[0031] Finally, this invention achieves an upgrade from macro-level guidance to precise investment. Under budget constraints, it can automatically generate an optimal compensation investment plan through intelligent optimization algorithms, clearly defining the combination of geographical units, intervention measures, and resource allocation, fundamentally improving the efficiency of ecological compensation resource allocation. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of the spatiotemporal assessment and compensation decision-making process for ecosystem services based on multi-model integration.

[0034] Figure 2 This is an internal workflow diagram for the scenario-based decision-making and intelligent solution generation module.

[0035] Figure 3 This is a schematic diagram of the internal workflow of the multi-scale ecosystem service relationship diagnosis module. Detailed Implementation

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0037] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0038] Secondly, the term "one embodiment" or "example" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the invention. The appearance of an embodiment in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0039] Example 1

[0040] Reference Figure 1 This is the first embodiment of the present invention, which proposes a spatiotemporal assessment and compensation decision-making method for ecosystem services based on multi-model ensemble. Its overall workflow is characterized by the following steps:

[0041] Step 1: Ecological Asset Archiving and Digitization. This involves constructing a digital ledger system that refines geographical areas into management units and tracks the evolution of ecological asset value over time for each unit. The system grids the target area and integrates multi-source heterogeneous data layers, including remote sensing imagery, meteorological and hydrological data, and socioeconomic data, for each grid management unit. Step 2: Benchmark Quantification of Intrinsic Ecological Value. For each management unit in the digital ledger system, an ecosystem service quantification model is used to calculate and generate a benchmark service value vector that quantifies its intrinsic service supply capacity. This vector includes multiple pre-defined dimensions used to quantify different ecosystem service capacities.

[0042] Step 3: Global analysis of value-driven laws. Based on machine learning models, the benchmark service value vector is analyzed to identify the key driving factors affecting its changes. Model interpretation technology is used to generate global nonlinear dependency curves that reveal the response of ecosystem service value to changes in each key factor.

[0043] Step 4: Localized calibration of impact effectiveness. A spatial variability analysis model is run for each management unit. The spatial variability analysis model localizes the global nonlinear dependency curve and calculates a unique geographic value multiplier for each key driver to quantify the specific impact effectiveness of the key driver at a specific geographic location.

[0044] Step 5: Comprehensive valuation and mapping of ecological assets. Based on the benchmark service value vector and geographic value multiplier, calculate and generate a dynamic and comprehensive ecological asset reference value for each management unit, and store the ecological asset reference value in the digital ledger system to form an ecological asset value map that is dynamically updated with time and space.

[0045] Step 6: Scenario-based decision-making and intelligent solution generation. Based on the future land use planning scenario input by the user, the system automatically executes the following decision-making process: First, it accurately calculates the overall ecological impact assessment value generated in the land use planning scenario from the ecological asset value map; then, based on the calculation result, it intelligently matches and generates an optimal ecological compensation investment plan, which clearly defines the combination of geographical units used for compensation, the types of intervention measures matched with them, and the corresponding resource allocation strategies.

[0046] This methodology involves several key steps. First, by standardizing and aligning multi-source heterogeneous data in a spatiotemporal manner, a dynamic and accurate digital ledger system for ecological assets is constructed, serving as the data foundation for the entire approach. Second, a professional ecosystem service quantification model is used to transform raw geographic data into quantifiable, multi-dimensional ecosystem service value vectors, enabling a benchmark assessment of the intrinsic value of ecological assets. Third, machine learning and model interpretation techniques are introduced to identify key drivers influencing changes in ecological value and their universal response patterns from complex data relationships. Furthermore, a spatial variability analysis model is used to localize and calibrate these universal patterns, precisely quantifying the specific impact of each driver at different geographical locations. Finally, based on the benchmark value of ecosystem services and the localized impact, a comprehensive valuation model is used to calculate the comprehensive ecological asset reference value for each geographic unit, generating a visualized ecological asset value map. Ultimately, the system can respond to user-inputted future planning scenarios, calculate their ecological impact in real time, and automatically generate an ecological compensation investment plan that achieves an optimal balance between economic and ecological benefits through intelligent optimization algorithms, thus forming a complete closed loop from data integration, value assessment, mechanism diagnosis to intelligent decision-making.

[0047] Example 2

[0048] Reference Figure 2This is the second embodiment of the present invention. This embodiment proposes a spatiotemporal assessment and compensation decision-making method for ecosystem services based on multi-model ensemble. The specific implementation steps of this embodiment are as follows:

[0049] The specific implementation of ecological asset archiving and digitization in step 1 includes: establishing a standardized geospatial benchmark framework, and dividing the target area into an array of management units coded by unique spatial units based on the geospatial benchmark framework; then standardizing the spatial dimension and synchronizing the temporal dimension of the multi-source heterogeneous data, and using the combination of spatial unit code and year as a unique index for structured integration to form a multi-dimensional ecological asset database that supports dynamic query and analysis.

[0050] The specific implementation of the benchmark quantification of intrinsic ecological value in step 2 includes: processing the multi-dimensional attribute data of the management unit and generating multiple single-dimensional ecological capability indices through a series of specialized evaluation algorithms; and combining the ecological capability indices into a standardized, multi-dimensional benchmark service value vector.

[0051] The global analysis of value-driven patterns in step 3 specifically includes: taking each dimension of the benchmark service value vector as the analysis target and all other data layers as potential influencing factors, constructing a machine learning regression model for learning complex nonlinear relationships; using the machine learning regression model to automatically evaluate the influence weight of each potential influencing factor to screen out a set of key driving factors that have a decisive impact on the analysis target; based on the trained machine learning regression model, applying a model interpretation technique to quantify and generate a global nonlinear dependency curve that reveals how the value of the analyzed ecosystem services responds to changes in key driving factors by isolating and simulating the independent changes of each key driving factor.

[0052] The localized calibration that affects effectiveness in step 4 is further characterized by combining the global nonlinear dependency with the unique geographic context information of each management unit through a spatial statistical regression model that can quantify spatial heterogeneity, thereby calculating a unique geographic value multiplier for each key driver.

[0053] Step 5, the comprehensive valuation and mapping of ecological assets, is further characterized by the use of a multi-criteria value aggregation algorithm. This algorithm weights and merges a benchmark service value vector reflecting the quantity of ecological service supply with a geographical value multiplier reflecting the importance and scarcity of the service in a specific geographical location to generate a comprehensive ecological asset reference value. Subsequently, the ecological asset reference value is written back to the digital ledger system to dynamically update and generate a visualized ecological asset value map.

[0054] The scenario-based decision-making in step 6 is further characterized by being achieved through a dynamic evaluation process: receiving user-defined future land use planning scenarios through a human-computer interaction interface, and extracting clear geographical scope and change types from them;

[0055] Based on the analyzed geographical scope and type of change, the system retrieves and aggregates data from the ecological asset value map to accurately calculate the overall ecological impact assessment value of the scenario.

[0056] The generation of the intelligent solution in step 6 is further characterized by being achieved through a spatial decision optimization engine. This engine uses the overall ecological impact assessment value calculated in the dynamic assessment process as the resource constraint boundary, and through a heuristic global search algorithm, automatically searches for and generates an optimal compensation investment plan that maximizes the net gain of regional ecosystem services among all management units that meet the preset conditions.

[0057] In step 1, the archiving and digitization of ecological assets, this embodiment uses the Yellow River Basin as the study area, with a time span from 2000 to 2020. First, a unified geospatial reference framework is established in the computer system. Specifically, a universal transverse Mercator projection coordinate system based on the 1984 World Geodetic Coordinate System is adopted, and the entire Yellow River Basin is standardized into a grid with a spatial resolution of 1 km x 1 km. Each grid management unit is assigned a unique spatial unit code. Subsequently, spatial standardization and precise registration are performed on multi-source heterogeneous data, including remote sensing imagery, meteorological observations, and socio-economic statistics. Specifically, land use and land cover data, NDVI vegetation index data, DEM elevation data, and GDP spatial distribution data from the Resource and Environmental Science Data Center, population density data from WorldPop, and soil data from the National Qinghai-Tibet Plateau Data Center are all reprojected and resampled using bilinear interpolation or the nearest neighbor method to ensure their spatial resolution and pixel boundaries are fully aligned with the 1 km grid framework. Next, temporal synchronization processing is performed to unify all data to an annual time step. For example, daily meteorological data from 109 meteorological stations obtained from the China Meteorological Data Network are spatially interpolated and temporally aggregated using ANUSPLIN software to generate annual precipitation and temperature raster layers. For land use data with only snapshots from 2000, 2010, and 2020, a forward-filling strategy is used, meaning data from 2001-2009 uses the 2000 values, and so on. Finally, all data that has undergone spatiotemporal alignment is stored in a multidimensional ecological asset database, using spatial unit codes and years as unique indexes, forming a digital ledger system.

[0058] In step 2, the benchmark quantification of intrinsic ecological value, based on the records of each management unit in the digital ledger system for each year, is implemented in this embodiment by deploying an integrated InVEST (Integrated Assessment Model of Ecosystem Services and Trade-offs) model suite to calculate the benchmark service value vector. This suite serves as a concrete implementation of the ecosystem service quantification engine. In this embodiment, the multiple preset dimensions for quantifying different ecosystem service capabilities mentioned in claim 1 are preferably set as five key dimensions: water yield, carbon sequestration, food production, habitat quality, and soil conservation. The ecosystem service quantification engine automatically calls the attribute data of each management unit in the database for the corresponding year as input parameters for each module of the InVEST model, and sequentially runs the five core modules of water yield, carbon sequestration, crop production, habitat quality, and soil conservation. After each module runs, it outputs a quantitative indicator, such as annual water yield depth (mm), total carbon storage (tons / hectare), annual food yield (tons), habitat quality index (0-1), and average annual soil conservation (tons / hectare). The system combines these five specific values ​​into a five-dimensional benchmark service value vector and writes it back to the database.

[0059] In step 3, the global analysis of value-driven patterns uses a machine learning regression model to identify key driving factors. In a preferred approach, this model is a random forest regression model. Taking the habitat quality index as an example, the system uses it as the dependent variable and all other relevant variables in the database, such as GDP, population density, elevation, slope, and distance from roads, as independent variables to train a random forest regression model. After training, the feature importance assessment mechanism built into the random forest regression model is used to rank the influence weights of all independent variables, selecting the top few factors with a cumulative contribution exceeding 85% as key driving factors. In this embodiment, the feature importance assessment mechanism built into the random forest regression model can be calculated based on Gini impurity or out-of-bag error. The Gini impurity-based method quantifies the average reduction in impurity a feature brings to the data when it acts as a splitting node in all decision trees of the model; the greater the reduction, the more important the feature. The out-of-bag error-based approach assesses the importance of a feature by measuring the decrease in its prediction accuracy on the out-of-bag test set after randomly rearranging its values; the more significant the decrease in accuracy, the more important the feature. Subsequently, to reveal the specific response patterns of habitat quality to these key factors, this embodiment applies partial dependency analysis to generate a series of global nonlinear dependency curves on the pre-trained random forest regression model. Each curve visually demonstrates how, while controlling for other factors, changes in a single key driver (such as GDP) from low to high independently and nonlinearly affect changes in the habitat quality index.

[0060] In step 4, the localization calibration of impact effectiveness aims to quantify the differences in the influence of key driving factors across different geographical locations. This embodiment employs a geographically weighted regression model as the spatial variability analysis model. Taking habitat quality as an example, the geographically weighted regression model uses the habitat quality index as the dependent variable and key driving factors (such as GDP and population density) selected by the random forest as independent variables to perform local regression analysis on a per-management-unit basis across the entire study area. For each 1 km × 1 km management unit, the model independently calculates a unique set of local regression coefficients. For instance, in urban centers, the local regression coefficient for GDP might be a large negative value, while in ecotourism areas, it might be close to zero or even positive. These dynamically changing local regression coefficients constitute the geographical value multiplier in this invention, accurately quantifying the specific impact effectiveness of each driving factor at a particular geographical location.

[0061] Step 5: Comprehensive valuation and mapping of ecological assets.

[0062] This embodiment uses a multi-criteria value aggregation function to calculate the comprehensive ecological asset reference value of each management unit. In a preferred implementation, this aggregation function can be expressed as:

[0063]

[0064] in, It is a management unit In the The ecological asset reference value of the year; This management unit is in the Year The benchmark service value of an ecosystem service; It is the geographic value multiplier corresponding to the service; It is the first The weighting coefficient of each service can be preset by expert scoring or analytic hierarchy process.

[0065] The core logic of this function lies in the weighted fusion of two types of key information: one is the benchmark service value vector calculated in step 2, reflecting the quantity of ecosystem service supply; the other is the geographic value multiplier calculated in step 4, reflecting the importance and scarcity of the service in a specific geographical location. For example, a unit with high water production will have its final comprehensive value significantly amplified if its corresponding rainfall-driven geographic value multiplier is also large (indicating that the region is extremely sensitive to rainfall and water resources are scarce). The system calculates such a single, standardized ecological asset reference value for each management unit in each year and stores it in a digital ledger system, thereby generating an ecological asset reference value map that can be dynamically queried and visualized.

[0066] In step 6, scenario-based decision-making and intelligent solution generation are implemented. This embodiment provides a human-computer interaction interface based on a web browser-based geographic information interaction platform. Users can input a future land use planning scenario through this interface, for example, defining a geographical area planned for an industrial park by drawing polygons online. Upon receiving the scenario, the system first automatically retrieves the reference ecological asset values ​​of all affected management units from the ecological asset value map based on the geographical area. Then, based on the type of change (e.g., from forest land to construction land), it calculates the value reduction and sums them to obtain the overall ecological impact assessment value (i.e., ecological liability) generated by the planning scenario. Then, based on this calculation result, the system launches a spatial decision optimization engine with a genetic algorithm at its core. In this embodiment, the multi-objective optimization function solved by the optimization engine can be mathematically expressed as:

[0067]

[0068] Constraints: ,

[0069] This function aims to maximize the number of selected management units undertaking ecological restoration. The net gain in the reference value of ecological assets ( ), while ensuring that the total cost of all remedial measures does not exceed the pre-set total budget ( ), where decision variables It is a 0-1 variable, representing whether to select this unit for repair.

[0070] Using ecological liability as a budget constraint and maximizing the net gain in regional ecosystem services as the optimization objective, the engine performs a global iterative search within a pre-defined pool of candidate solutions (such as all suitable afforestation wastelands) to find the optimal combination of management units. Ultimately, the engine outputs an optimal ecological compensation investment plan, which, in the form of a report and map, clearly defines the specific plots (geographical unit combinations) used for compensation, the matching ecological restoration measures (such as afforestation at a cost of X yuan / hectare), and the corresponding funding allocation strategy. This provides decision-makers with a data-driven, scientific, precise, and actionable blueprint.

[0071] Example 3

[0072] Reference Figure 2This is the third embodiment of the present invention. This embodiment provides a specific implementation method for multi-scale ecosystem service relationship diagnosis, which includes: after step 2, deploying a spatial correlation analysis engine. This engine uses the baseline service value vectors of all management units in the digital ledger system for a specific year as the initial dataset. Subsequently, based on preset multi-level geographical boundaries such as sub-basins, prefecture-level cities, and provinces, spatial aggregation statistics are performed on the ecosystem service values ​​of these management units to generate multiple datasets at different spatial scales. Next, the engine independently applies Pearson correlation analysis to each scale dataset to calculate the correlation coefficients between all pairs of ecosystem services and organizes the results into a correlation coefficient matrix.

[0073] A correlation coefficient matrix is ​​a standardized numerical table used to quantify the strength and direction of the pairwise relationships between multiple variables. In this matrix, each value (i.e., the correlation coefficient) ranges from -1 to +1: positive numbers close to +1 indicate a significant synergistic relationship between the two ecosystem services, meaning that an improvement in one service will accompany an improvement in the other; negative numbers close to -1 indicate a significant trade-off between the two services, meaning that an improvement in one service may come at the expense of the other; and values ​​close to 0 indicate that the two services are independent and have a weak correlation.

[0074] Finally, the spatial correlation analysis engine integrates the correlation coefficient matrices at all scales and outputs a multi-scale comparative analysis report. This report clearly quantifies the synergistic and trade-off relationships among ecosystem services and their specific trends as observed at different scales by comparing the changes in matrix values ​​at different scales.

[0075] Example 4

[0076] Reference Figure 3 This is the fourth embodiment of the present invention. In this embodiment, a specific implementation method for in-depth analysis of ecosystem action mechanism is provided. The specific implementation includes: after step 3, deploying a causal path analysis engine. The engine quantifies and outputs the direct and indirect influence paths between the key driving factors and between the key driving factors and ecosystem services by constructing and fitting a structural equation model.

[0077] The causal path analysis engine extracts long-term panel data from the digital ledger system based on a pre-defined theoretical path model. For example, it constructs a theoretical model that hypothesizes that socioeconomic development (consisting of GDP and population density) indirectly affects habitat quality by influencing land use intensity (consisting of the proportion of construction land, etc.). Next, the engine applies a structural equation model based on partial least squares to fit the extracted data to this theoretical model. Finally, the engine quantifies and outputs a structured path analysis report, which includes the standardized path coefficients, significance levels, and decomposition values ​​of all direct and indirect effects for each influencing path in the model, revealing the complex transmission mechanisms by which driving factors influence ecosystem services.

[0078] In summary, this invention represents a leap from opaque value assessment to transparent causal diagnosis. It can accurately identify key driving factors and the geographical differences in their effectiveness, providing core mechanistic support for formulating precise, site-specific management policies.

[0079] Secondly, this invention realizes the transformation from static summarization to dynamic decision-making. It can respond to the user's input of future land use planning scenarios and instantly calculate its overall ecological impact assessment value, greatly improving the scientific nature and foresight of the decision-making.

[0080] Finally, this invention achieves an upgrade from macro-level guidance to precise investment. Under budget constraints, it can automatically generate an optimal compensation investment plan through intelligent optimization algorithms, clearly defining the combination of geographical units, intervention measures, and resource allocation, fundamentally improving the efficiency of ecological compensation resource allocation.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble, characterized in that, This method is executed by a computer system and includes the following steps: Step 1: Ecological asset archiving and digitization. Construct a digital ledger system that refines geographical areas into management units and tracks the evolution of the ecological asset value of each unit over time. The system grids the target area and integrates multi-source heterogeneous data layers, such as remote sensing images, meteorological and hydrological data, and socio-economic data, into each grid management unit in a time-series manner. Step 2: Benchmark quantification of intrinsic ecological value. For each management unit in the digital ledger system, a benchmark service value vector is calculated and generated to quantify its intrinsic service supply capacity through the ecosystem service quantification model. This vector contains multiple preset dimensions for quantifying different ecosystem service capabilities. Step 3: Global analysis of value-driven laws. Based on machine learning models, the benchmark service value vector is analyzed to identify the key driving factors affecting its changes. Model interpretation technology is used to generate a global nonlinear dependency curve that reveals the response of ecosystem service value to changes in each key factor. Step 4: Localized calibration of impact effectiveness. A spatial variability analysis model is run for each management unit. The spatial variability analysis model performs localized calibration of the global nonlinear dependency curve and calculates a unique geographic value multiplier for each key driver to quantify the specific impact effectiveness of the key driver at a specific geographic location. Step 5: Comprehensive valuation and mapping of ecological assets. Based on the benchmark service value vector and the geographic value multiplier, calculate and generate a dynamic and comprehensive ecological asset reference value for each management unit, and store the ecological asset reference value in the digital ledger system to form an ecological asset value map that is dynamically updated with time and space. Step 6: Scenario-based decision-making and intelligent solution generation. Based on the future land use planning scenario input by the user, the system automatically executes the following decision-making process: First, it accurately calculates the overall ecological impact assessment value generated in the land use planning scenario from the ecological asset value map; then, based on the calculation result, it intelligently matches and generates an optimal ecological compensation investment plan, which clearly defines the combination of geographical units used for compensation, the types of intervention measures matched with them, and the corresponding resource allocation strategies.

2. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The specific implementation of ecological asset archiving and digitization in step 1 includes: establishing a standardized geospatial benchmark framework, and dividing the target area into an array of management units coded by unique spatial units based on the geospatial benchmark framework; Subsequently, the multi-source heterogeneous data is standardized in terms of spatial dimension and synchronized in terms of temporal dimension. The combination of the spatial unit code and the year is used as a unique index for structured integration to form a multi-dimensional ecological asset database that supports dynamic query and analysis.

3. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The specific implementation of the benchmark quantification of intrinsic ecological value in step 2 includes: processing the multidimensional attribute data of the management unit through a series of specialized evaluation algorithms and generating multiple single-dimensional ecological capability indices; and combining the ecological capability indices into a standardized, multidimensional benchmark service value vector.

4. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The global analysis of the value-driven laws in step 3 specifically includes: taking each dimension of the benchmark service value vector as the analysis target and taking all other data layers as potential influencing factors, and constructing a machine learning regression model for learning complex nonlinear relationships. The machine learning regression model is used to automatically evaluate the influence weight of each potential influencing factor in order to screen out a set of key driving factors that have a decisive impact on the analysis target; Based on the trained machine learning regression model, a model interpretation technique is applied to quantify and generate a global nonlinear dependency curve that reveals how the value of analyzed ecosystem services responds to changes in the key driving factors by isolating and simulating the independent changes of each key driving factor.

5. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The localized calibration of the effect in step 4 is further characterized by combining the global nonlinear dependency with the unique geographic context information of each management unit through a spatial statistical regression model that can quantify spatial heterogeneity, thereby calculating a unique geographic value multiplier for each key driver.

6. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The comprehensive valuation and mapping of ecological assets in step 5 is further characterized by being implemented through a multi-criteria value aggregation algorithm. This algorithm weights and fuses the benchmark service value vector, which reflects the quantity of ecological service supply, with the geographic value multiplier, which reflects the importance and scarcity of the service in a specific geographic location, to generate a comprehensive ecological asset reference value. Subsequently, the ecological asset reference value is written back to the digital ledger system to dynamically update and generate a visualized ecological asset value map.

7. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The scenario-based decision-making in step 6 is further characterized by being achieved through a dynamic evaluation process: receiving user-defined future land use planning scenarios through a human-computer interaction interface, and extracting a clear geographical scope and change type from them. Based on the analyzed geographical scope and change type, the system retrieves and aggregates data from the ecological asset value map to accurately calculate the overall ecological impact assessment value of the scenario.

8. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The generation of the intelligent solution in step 6 is further characterized by being achieved through a spatial decision optimization engine. This engine uses the overall ecological impact assessment value calculated in the dynamic assessment process as the resource constraint boundary, and through a heuristic global search algorithm, automatically searches for and generates an optimal compensation investment scheme that maximizes the net gain of regional ecosystem services among all management units that meet the preset conditions.

9. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The method further includes a multi-scale ecosystem service relationship diagnosis step, which is specifically implemented by: after step 2, deploying a spatial correlation analysis engine, which performs statistical correlation analysis on each dimension of the benchmark service value vector at multiple spatial scales to quantify and output the interaction relationship and scale dependence between different ecosystem services.

10. The method for spatiotemporal assessment and compensation decision-making of ecosystem services based on multi-model ensemble as described in claim 1, characterized in that, The method further includes a deep analysis step of ecosystem action mechanism, which is specifically implemented by: after step 3, deploying a causal path analysis engine, which quantifies and outputs the direct and indirect influence paths between the key drivers and between the key drivers and ecosystem services by constructing and fitting a structural equation model.

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