Method and system for constructing river and lake water ecological product value accounting system

Abstract

The present invention discloses a method and system for constructing a river and lake water ecological product value accounting system, constructing an ecological product classification system and a value calculation method system; sequentially analyzing the spatiotemporal scale characteristics of the formation and function of each ecological product, and the spatiotemporal scales of the corresponding natural and socio-economic data, and using a coupling model to generate various standardized spatiotemporal data products supporting ecological product accounting; constructing a multi-criteria accounting model, and evaluating the weight of each criterion indicator in the multi-criteria accounting model; obtaining the basin-scale ecological product value accounting results and conducting sensitivity analysis and spatiotemporal distribution difference analysis, outputting and storing the basin-scale river and lake water ecological product value accounting model; integrating multi-source heterogeneous data to improve the comprehensiveness and accuracy of accounting.

Description

Technical Field

[0001] The present invention relates to hydrological and water ecological technology, and in particular to a method and system for constructing a value accounting system for river and lake water ecological products. Background Art

[0002] Accurately assessing the contribution of river and lake ecosystems and converting them into quantifiable and tradable ecological products has become the key to promoting ecological compensation and optimizing resource allocation. River and lake water ecological products include not only traditional tangible products such as water resources supply and aquatic product supply, but also intangible services such as water purification, climate regulation, and biodiversity maintenance. Scientifically and rationally calculating the value of these ecological products is of great significance for formulating watershed protection policies, implementing ecological compensation mechanisms, and promoting the marketization of ecological products. At the same time, it provides quantitative indicators for assessing the health of river and lake ecosystems and monitoring the effects of ecological restoration, and provides a decision-making basis for balancing the relationship between economic development and ecological protection.

[0003] At present, research on the value accounting of river and lake water ecological products at home and abroad has made certain progress. In terms of theoretical framework, scholars have constructed a multi-dimensional evaluation system including supply, regulation, culture and support services based on the theory of ecosystem service assessment. In terms of accounting methods, traditional methods such as market value method, replacement cost method, and travel cost method are widely used, and new methods such as biophysical models and spatial econometrics are also being introduced. In terms of data acquisition, the application of advanced technologies such as remote sensing technology, the Internet of Things, and big data has significantly improved the temporal and spatial resolution and accuracy of data. Some regions have carried out ecological product value accounting practices at the basin scale, providing support for policy making. However, current research still has many limitations and it is difficult to meet the needs of refined management and market-oriented operations.

[0004] Specifically, the existing research mainly has the following technical problems: the ecological product classification system lacks systematicity and consistency, which makes it difficult to compare and integrate accounting results, and is greatly affected by human subjective factors. The inconsistent spatiotemporal scales of multi-source heterogeneous data increase the uncertainty of accounting results. The lack of systematic quantification of the uncertainty of accounting results reduces the credibility and practicality of the results. The interactions and trade-offs between ecological products have not been fully revealed, which affects the assessment of the overall value of the ecosystem. The lack of spatiotemporal analysis of the dynamic changes in the value of ecological products makes it difficult to support long-term ecological management decisions. In short, the accounting methods of existing technologies are greatly affected by subjective human factors, are not accurate, and need to be improved.

[0005] Hence the need for research and innovation. Summary of the invention

[0006] The purpose of the invention is to provide a method and system for constructing a river and lake water ecological product value accounting system to solve one of the above-mentioned problems existing in the prior art.

[0007] Technical solution, according to one aspect of the present application, provides a method for constructing a river and lake water ecological product value accounting system, comprising the following steps:

[0008] Step S1: Collect research data, sort out river and lake water ecological products in the research data, form an ecological product library, and build an ecological product classification system and value calculation method system;

[0009] Step S2: sequentially analyze the spatiotemporal scale characteristics of the formation and function of each ecological product, as well as the spatiotemporal scales of the corresponding natural and socioeconomic data, and use a coupling model to generate various standardized spatiotemporal data products that support ecological product accounting;

[0010] Step S3: Based on the ecological product classification system, value calculation method system and standardized spatiotemporal data products, a multi-criteria accounting model is constructed using the analytic hierarchy process, and the weight of each criterion indicator in the multi-criteria accounting model is evaluated;

[0011] Step S4, determine the scope of the accounting sample area, and collect the accounting sample area data, calculate and extrapolate the value of various ecological products in the accounting sample area, obtain the basin-scale ecological product value accounting results and conduct sensitivity analysis and spatiotemporal distribution difference analysis, output and store the basin-scale river and lake water ecological product value accounting model.

[0012] According to one aspect of the present application, step S5 is also included. When in use, basic data of the area to be evaluated is collected, and the river and lake water ecological product value accounting model is called to give a calculation result.

[0013] According to another aspect of the present application, a river and lake water ecological product value accounting system based on multi-source data mining is also provided, including:

[0014] at least one processor; and,

[0015] a memory communicatively connected to at least one of the processors; wherein,

[0016] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the method for constructing a river and lake water ecological product value accounting system as described in any of the above technical solutions.

[0017] Beneficial effects: by adjusting parameters through scenario simulation and sample plots, the problem of subjectively setting ecological product weight parameters is avoided. At the same time, multi-source heterogeneous data is integrated to improve the comprehensiveness and accuracy of accounting; machine learning methods such as random forests are used to enhance the prediction ability and generalization of the model; the time and space scale deduction method is introduced to solve the connection problem of value accounting between different scales; combined with quantitative analysis and expert knowledge, the credibility and practicality of accounting results are improved. It effectively solves the technical problems of single data source, weak model prediction ability, difficult scale conversion, and low credibility of results in traditional accounting methods, and provides a new technical path for the accurate quantification of the value of river and lake water ecological products.

[0018] In step 1, the literature data is mined through software, and an ecological product library is built on this basis, avoiding the subjective arbitrariness of experts in setting ecological products, identifying the internal connections between different categories of ecological products, and building an ecological product network relationship diagram to solve the problem that subjectively set products are prone to overlap or omission. In step 2, the obtained ecological products are placed in the spatiotemporal coupling model, and the rationality of the ecological product library under different simulation scenarios is analyzed. At the same time, standardized spatiotemporal data products used to support ecological product accounting in different scenarios are obtained, and a mapping relationship between typical scenarios and ecological product accounting systems is established.

[0019] In step S3, based on the data of the ecological accounting system in different typical scenarios obtained in step S2, verification is performed in specific scenarios, that is, verification is performed based on the rationality of the data system in real scenarios. Finally, extrapolation and expansion are performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the present invention.

[0021] Figure 2 It is a flow chart of step S1 of the present invention.

[0022] Figure 3 It is a flow chart of step S2 of the present invention.

[0023] Figure 4 It is a flow chart of step S3 of the present invention.

[0024] Figure 5 It is a flow chart of step S4 of the present invention. DETAILED DESCRIPTION

[0025] In this application, the value of river and lake water ecological products mainly includes water resource supply, provision of aquatic products, inland shipping, hydropower generation, other material products, flood regulation, water conservation, water purification, climate regulation, carbon fixation capacity, tourism and recreation, landscape value, and species conservation.

[0026] According to one aspect of the present application, a method for constructing a river and lake water ecological product value accounting system is provided, comprising the following steps:

[0027] Step S1: Collect research data, sort out river and lake water ecological products in the research data, form an ecological product library, and build an ecological product classification system and value calculation method system;

[0028] Step S2: sequentially analyze the spatiotemporal scale characteristics of the formation and function of each ecological product, as well as the spatiotemporal scales of the corresponding natural and socioeconomic data, and use a coupling model to generate various standardized spatiotemporal data products that support ecological product accounting;

[0029] Step S3: Based on the ecological product classification system, value calculation method system and standardized spatiotemporal data products, a multi-criteria accounting model is constructed using the analytic hierarchy process, and the weight of each criterion indicator in the multi-criteria accounting model is evaluated;

[0030] Step S4, determine the scope of the accounting sample area, and collect the accounting sample area data, calculate and extrapolate the value of various ecological products in the accounting sample area, obtain the basin-scale ecological product value accounting results and conduct sensitivity analysis and spatiotemporal distribution difference analysis, output and store the basin-scale river and lake water ecological product value accounting model.

[0031] Step S5: When in use, collect basic data of the area to be evaluated, and call the river and lake water ecological product value accounting model to give the calculation result.

[0032] By building an ecological product library and classification system, the problems of unclear definition and chaotic classification of ecological products in existing research have been solved; the coupling model is used to generate standardized spatiotemporal data products, overcoming the problem of inconsistent spatiotemporal scales of multi-source data; the introduction of a multi-criteria accounting model has improved the comprehensiveness and scientificity of the accounting; by extrapolating the sample area accounting to the watershed scale, the technical bottleneck of large-scale ecological product value assessment has been solved; combined with sensitivity analysis and expert evaluation, the reliability and practicality of the accounting results have been enhanced. It effectively addresses key technical problems such as data heterogeneity, scale conversion, and model construction in the ecological product value accounting, and provides a systematic solution to improve the accuracy and credibility of the accounting.

[0033] First, the ecological product classification system and calculation method system are screened and constructed. Then, in step S2, a simulation scenario set is generated according to the constructed model. Based on the data of the scenario set, the weights of the multi-criteria accounting model are evaluated. Then, field verification is carried out in the sample area and promoted, thus solving the problem of subjective weight setting by humans.

[0034] According to one aspect of the present application, step S1 specifically comprises:

[0035] Step S11, obtaining research literature to build a research literature library, extracting research data from the research literature library, and sorting out river and lake water ecological products in the research data to form an ecological product library;

[0036] Step S12: Based on the ecosystem service classification system, classify the ecological product library from the perspectives of supply, regulation, support and culture;

[0037] Step S13: Analyze and identify the internal connections between different types of ecological products, and construct an ecological product network relationship diagram;

[0038] Step S14, calculating the connectivity of each node in the ecological product network relationship diagram, identifying key nodes, obtaining key ecological products and optimizing them;

[0039] Step S15: Systematically classify the selected ecological products, and build an ecological product classification system and value calculation method system with logical consistency and no repeated calculations.

[0040] Through literature mining and data analysis, the ecological products of rivers and lakes are systematically identified and classified, solving the problems of unclear definition and inconsistent classification standards of ecological products in existing research; the network analysis method is introduced to construct the ecological product relationship diagram, revealing the complex interactions between ecological products, overcoming the limitation of traditional classification methods that ignore the connection between ecological products; the key node identification and optimization strategy is adopted to ensure the focus and efficiency of accounting; a classification system with logical consistency and no repeated calculation is constructed to solve the problem of repeated calculation in existing accounting. It lays a solid foundation for the scientific classification and value accounting of river and lake ecological products, and helps to improve the systematicness and accuracy of accounting.

[0041] In this embodiment, in step S11, the process of document mining is specifically as follows:

[0042] Download research literature to the database, use SQLite and other tools to screen the literature, extract keywords of river and lake water ecological products, screen and sort river and lake ecological products according to the keywords, and form an ecological product library.

[0043] According to one aspect of the present application, step S2 is specifically:

[0044] Step S21, reading the ecological product classification system constructed in step S15, and analyzing the spatiotemporal scale characteristics of the formation and function of each ecological product;

[0045] Step S22: for each ecological product, based on its spatiotemporal scale characteristics, match the spatiotemporal scale of the natural and socioeconomic data of the ecological product;

[0046] Step S23: Using the GDC method, organize and integrate multi-source data including ecological products, natural and socio-economic data at each spatiotemporal scale to form a data cube;

[0047] Step S24: construct a spatiotemporal coupling model of river and lake hydrology, water resources, water environment and water ecological processes, and generate standardized spatiotemporal data products for supporting ecological product accounting through data cubes and spatiotemporal coupling models.

[0048] Hydrological model: Use distributed hydrological models (such as SWAT) to simulate basin hydrological processes;

[0049] Water resource model: Based on the principle of water balance, a water resource allocation model is constructed;

[0050] Water environment model: Use water quality models (such as WASP) to simulate the migration and transformation of pollutants;

[0051] Water ecology model: Combines the niche model and population dynamics model to simulate the distribution and population changes of key species.

[0052] Through the data exchange interface between models, the above four models are dynamically coupled to capture the spatiotemporal dynamics of the value of ecological products;

[0053] By analyzing the spatiotemporal scale characteristics of ecological products, the precise connection between data needs and ecological processes is achieved, solving the problem of arbitrary data selection in traditional methods; the GDC method is used to organize and integrate multi-source data, overcoming the technical difficulties of integrating data from different sources and scales; the spatiotemporal coupling model of hydrology, water resources, water environment and water ecological processes is constructed, and the dynamic simulation of multi-dimensional ecological processes is realized, which improves the scientific nature of accounting; standardized spatiotemporal data products are generated, providing a high-quality data foundation for subsequent accounting, solving the problem of uneven data quality in traditional accounting. It effectively responds to key technical problems such as data heterogeneity and inconsistent spatiotemporal scales in ecological product value accounting, and provides data guarantee for improving the accuracy and reliability of accounting.

[0054] In another embodiment of the present application, a standardized spatiotemporal data product for supporting ecological product accounting is generated through a data cube and a spatiotemporal coupling model. In this process, the following method is used to solve the problem of inconsistent spatiotemporal scales of multi-source data:

[0055] (1) Time scale unification: For data with low time resolution, time interpolation methods (such as linear interpolation or spline interpolation) are used for time upsampling; for data with high time resolution, time aggregation methods (such as average or cumulative value) are used for downsampling;

[0056] (2) Spatial scale unification: For data with low spatial resolution, spatial interpolation methods (such as Kriging interpolation) are used for spatial upsampling; for data with high spatial resolution, spatial aggregation methods (such as regional statistics) are used for downsampling.

[0057] According to one aspect of the present application, step S3 is specifically:

[0058] Step S31, selecting ecological indicators that can characterize the supply and demand of each type of ecological products, social indicators that characterize the beneficiaries of each type of ecological products, and economic indicators that characterize the realization of ecological product value, and constructing an ecological product accounting indicator library;

[0059] Step S32: compare and select the indicators in the ecological product accounting indicator library to obtain an indicator quantification method that is operable and explainable;

[0060] Step S33: for the selected ecological product accounting indicators, a multi-criteria evaluation model for the value accounting of various ecological products is constructed by using the hierarchical analysis method, and the internal action mechanism and weight between different criterion indicators are analyzed through the multi-criteria evaluation model;

[0061] Step S34: construct a scenario parameter set, simulate the spatiotemporal pattern of supply and demand of ecological products under various scenarios through a spatiotemporal coupling model; input the scenario simulation results into a multi-criteria comprehensive evaluation model, calculate the value of ecological products under different scenarios, and obtain the calculation results;

[0062] Step S35: Use sensitivity analysis method to measure the relative contribution of different accounting criteria results to the total value; use extreme condition analysis method to measure the response of total value to changes in key accounting parameters; and modify the weights of criterion indicators based on the sensitivity and extreme condition analysis results.

[0063] By building a comprehensive accounting indicator library, the problem of one-sided and unsystematic indicator selection in traditional methods has been solved; the hierarchical analysis method has been used to build a multi-criteria evaluation model, which has achieved scientific quantification of the value of different types of ecological products and overcome the limitations of single indicator evaluation; the scenario analysis method has been introduced to reveal the changing laws of ecological product value under different conditions, and enhance the foresight and adaptability of accounting results; through sensitivity analysis and extreme condition analysis, key influencing factors have been identified, and the pertinence and accuracy of accounting have been improved; the weights of criterion indicators have been dynamically adjusted to achieve adaptive optimization of the model, solving the problem that fixed weight models are difficult to adapt to changes in complex ecosystems. It has effectively dealt with technical difficulties such as multi-dimensional evaluation, dynamic adaptation, and uncertainty analysis in the accounting of ecological product value, and provided methodological support for improving the scientificity and reliability of accounting.

[0064] According to one aspect of the present application, step S4 is specifically:

[0065] Step S41, determine the scope of the accounting sample area, and according to the scale of the accounting sample area, use a multi-criteria comprehensive evaluation model to calculate the value of each type of ecological product in the accounting sample area, obtain the sample area accounting results, record them as the value of the ecological products in the sample area, and conduct expert evaluation, and adjust the parameters of the multi-criteria comprehensive evaluation model according to the evaluation results;

[0066] Step S42, analyzing and calculating the quantitative relationship between the value of ecological products at the sample area scale and the driving factors;

[0067] Step S43: Use random forest to construct a scale deduction model, and generalize the value of ecological products to the watershed scale through the calculation results of the sample area;

[0068] Step S44, obtain the basin-scale ecological product value accounting results and conduct sensitivity analysis and spatiotemporal distribution difference analysis, output and store the basin-scale river and lake water ecological product value accounting model.

[0069] By combining sample area accounting with expert evaluation, the accuracy and credibility of the accounting results are improved, and the problem of lack of field verification in traditional methods is solved; the quantitative relationship between the value of ecological products in the sample area and driving factors is analyzed, revealing the internal mechanism of value formation, overcoming the limitations of the "black box" model that is difficult to explain; the use of random forests to construct a scale deduction model has achieved scientific extrapolation from point to surface, solving the technical bottleneck of large-scale ecological product value assessment; combined with sensitivity analysis and spatiotemporal distribution difference analysis, the reliability and spatial heterogeneity of the accounting results are comprehensively evaluated, and the application value of the results is improved. It effectively responds to key technical problems such as scale conversion, mechanism analysis, and uncertainty assessment in ecological product value accounting, and provides a systematic solution for ecological product value accounting at the watershed scale.

[0070] According to one aspect of the present application, step S13 is specifically:

[0071] Step S131, sequentially retrieve the supply data of each ecological product within a predetermined time period, and construct a standardized time series data set;

[0072] Step S132: For each pair of ecological products, calculate the mutual information under different time lags, determine the optimal time lag between each pair of ecological products based on the normalized maximum mutual information criterion, and determine whether there is a causal influence between the ecological products. If so, construct an ecological product network relationship directed graph, where the nodes are ecological products, the direction of the edge represents the causal relationship, and the edge weight is the normalized maximum mutual information criterion value;

[0073] Step S133: obtaining the pre-stored prior knowledge of the causal relationship between ecological products and converting it into the constraint conditions of the directed graph of the ecological product network relationship, using a constrained structural learning algorithm, integrating data-driven causal inference results and expert knowledge, and obtaining a revised causal graph;

[0074] Step S134: construct a dynamic Bayesian network model based on the corrected causal graph, verify the prediction performance of the dynamic Bayesian network model, and adjust the network structure of the ecological product network relationship directed graph according to the prediction results;

[0075] Step S135, calculating the topological parameters of the adjusted network structure, including node in-degree and out-degree, betweenness centrality, PageRank value and clustering coefficient.

[0076] Through time series analysis and mutual information calculation, the dynamic relationship between ecological products is objectively quantified, solving the problem of strong subjectivity in relationship identification in traditional methods; the introduction of dynamic Bayesian networks captures the complex temporal dependencies between ecological products, overcoming the limitation of static network models that ignore the time dimension; the integration of expert knowledge and data-driven causal inference improves the scientificity and interpretability of network construction, and balances the contribution of prior knowledge and empirical data; by calculating a variety of network topology parameters, the importance of ecological products in the system is comprehensively evaluated, providing a scientific basis for determining the focus of accounting. It effectively addresses technical difficulties such as system complexity, dynamics, and uncertainty in the accounting of ecological product value, and provides a new analytical framework for in-depth understanding of the interaction mechanism between ecological products.

[0077] According to one aspect of the present application, step S42 is specifically:

[0078] Step S421, retrieve the multidimensional data of the sampling area and perform standardization and outlier detection to obtain standardized multidimensional data; perform Z-score standardization on continuous variables; use the local outlier factor (LOF) algorithm to perform outlier detection; and use multiple interpolation to fill in missing values.

[0079] Step S422, extracting original driving factors and nonlinear features from the standardized multidimensional data, visualizing the extracted nonlinear features using the t-SNE method, and preliminarily obtaining potential data structures and clusters;

[0080] Step S423: Perform multi-scale entropy analysis and calculation on the time series of each driving factor and ecological product value, calculate the sample entropy at different time scales, and preliminarily screen driving factors and ecological product value pairs with similar complexity based on the entropy analysis results;

[0081] Step S424: Apply the dynamic time warping algorithm to the selected time series to quantify the similarity of the time series; construct a time series causal network based on the DTW distance, and use the convergent cross-mapping method to quantify the causal relationship strength to obtain the driving factors that are most relevant to the value of the ecological product and most likely to have a causal relationship;

[0082] Step S425, construct and use a generalized additive model to fit the nonlinear relationship between the value of ecological products and key driving factors; automatically select smoothing parameters for the generalized additive model through thin plate regression splines; evaluate the contribution of each driving factor through ANOVA decomposition, and obtain the quantitative relationship between the value of ecological products and driving factors at the sample area scale.

[0083] Through multi-scale entropy analysis, the complexity characteristics of the value of ecological products and driving factors are revealed, solving the problem that traditional linear analysis ignores system complexity; the dynamic time warping algorithm is used to objectively quantify the similarity of time series, overcoming the limitation that traditional correlation analysis is difficult to handle asynchronous data; the convergence cross-mapping method is introduced to identify the true causal relationship and avoid misjudgment caused by false correlation; the generalized additive model is used to capture the nonlinear relationship between value and driving factors, improving the flexibility and fitting accuracy of the model. It effectively copes with technical difficulties such as nonlinear relationship, time lag, causal inference, etc. in the value accounting of ecological products, and provides a powerful analytical tool for in-depth understanding of the value formation mechanism of ecological products.

[0084] According to one aspect of the present application, step S43 is specifically:

[0085] Step S431, obtaining the ecological product value data and the corresponding driving factor data of the sample area; collecting and preprocessing the spatial variable data and economic and social data at the watershed scale;

[0086] Step S432: extracting geographic spatial features, and using a moving window method to calculate feature statistics at different spatial scales; extracting time series statistical features and calculating lag features; and generating interactive features based on geographic spatial features and time series features;

[0087] Step S433, call the pre-built random forest to build a scale deduction model to generalize the value of ecological products to the watershed scale through the accounting results of the sample area, and obtain the watershed scale ecological product value accounting results.

[0088] Through multi-scale feature extraction, the spatial heterogeneity and scale dependence of the value of ecological products are fully captured, solving the problem of traditional methods ignoring spatial features; combining geographic spatial features and time series features, a rich set of prediction variables is constructed, improving the prediction ability and generalization of the model; using the random forest algorithm, high-dimensional features and nonlinear relationships are effectively processed, overcoming the limitations of traditional regression models; scientific extrapolation from sample areas to watershed scales is achieved, solving the technical bottleneck of large-scale ecological product value assessment. It effectively responds to key technical problems such as scale conversion, spatial heterogeneity, and nonlinear modeling in ecological product value accounting, providing a new technical path for improving the accuracy and reliability of large-scale ecological product value accounting.

[0089] According to one aspect of the present application, step S44 is specifically:

[0090] Step S441: Based on the random forest model, select the top N driving factors in importance as key parameters; N is a natural number greater than 3;

[0091] Step S442: For each key parameter, calculate the sensitivity index, and sort the parameters by the absolute value of the sensitivity index to identify the most sensitive local parameter;

[0092] Step S443, using the Sobol method to generate parameter combinations for Latin hypercube sampling, calculating the first-order sensitivity index, the total effect index and the second-order interaction sensitivity index, and analyzing and screening the parameter pairs of interaction effects;

[0093] Step S444: collect experts' data on the calculation results, extract key correction suggestions and score them, and calculate Kendall's W coefficient to evaluate the consistency of expert opinions;

[0094] Step S445: Compare the sensitivity analysis results with the expert judgment results to identify areas where there are significant differences between the quantitative analysis and the expert opinions;

[0095] Step S446: Calculate the correction coefficient for each key parameter, apply the correction coefficient to adjust the original calculation result, and calculate the confidence interval of the corrected result.

[0096] Step S447, calculate the global Moran's I index of the corrected ecological product value, evaluate the overall spatial aggregation degree, calculate the local Moran's I index, identify high-value clusters and low-value clusters, and generate a LISA cluster diagram;

[0097] Step S448, use the LOESS method to extract long-term trends, calculate trend slopes, identify rising or falling areas, and use STL to decompose seasonal components; detect change points in the time series, map the detected change points to space, and analyze the spatial distribution pattern of the change points.

[0098] By combining local and global sensitivity analysis, the importance and interaction effects of model parameters were comprehensively evaluated, solving the problem that traditional methods are difficult to identify key parameters; the introduction of expert evaluation and Kendall's W coefficient analysis improved the credibility and practicality of the accounting results, overcoming the limitation of pure quantitative analysis lacking empirical judgment; spatial autocorrelation analysis and hot spot analysis were used to reveal the spatial aggregation characteristics of ecological product value, providing a scientific basis for regional differentiated management; through time series decomposition and change point detection, the dynamic change law of ecological product value was captured, and the time dimension explanatory power of accounting results was enhanced. It effectively dealt with technical difficulties such as uncertainty quantification, result verification, and spatiotemporal pattern recognition in ecological product value accounting, and provided a systematic analysis framework for improving the reliability and application value of accounting results.

[0099] In another embodiment of the present application, it also includes:

[0100] Step S449: Model verification and update:

[0101] Regularly collect new observational data for model validation;

[0102] Calculate the error between the model prediction value and the actual observation value to evaluate the model performance;

[0103] If the model performance deteriorates, retrain the model and update the model parameters;

[0104] Conduct a comprehensive model assessment every year to consider introducing new drivers or adjusting the model structure;

[0105] Establish a model version control system to record the content and performance changes of each update.

[0106] Step S4410: Result visualization and report generation:

[0107] Develop an interactive Web GIS platform to allow users to browse and query accounting results;

[0108] Generate a comprehensive report including accounting results, sensitivity analysis, spatiotemporal distribution characteristics, etc.;

[0109] Design decision-support dashboards to visualize key metrics and trends.

[0110] In summary, in this embodiment, based on the ecosystem service classification system, classification is carried out from the perspectives of supply, regulation, support and culture, and the internal connection between products is identified through network analysis methods, and finally a classification system with logical consistency and no repeated calculation is constructed. The systematic and consistent classification is guaranteed, which is conducive to the comparison and integration of accounting results. The geographic data cube (GDC) method is used to organize and integrate multi-source data, and the spatiotemporal scales of different data sources are unified through time interpolation, spatial interpolation and other methods. This method effectively reduces the uncertainty caused by inconsistent data. Methods that can handle nonlinear relationships are used in multiple steps. For example, the generalized additive model (GAM) is used in S425 to fit the nonlinear relationship between the value of ecological products and driving factors, and the random forest model is used in S433 for scale deduction. These methods can effectively capture complex nonlinear ecological processes and improve accounting accuracy. Characteristic statistics of different spatial scales are extracted, and the random forest model is used for scale deduction. These steps take into account spatial heterogeneity and scale effects. Spatial autocorrelation analysis and hot spot analysis also help to reveal spatial heterogeneity. Expert opinions were collected through the Delphi method, and the expert evaluation results were combined with the quantitative analysis results, effectively integrating expert knowledge. By constructing an ecological product network relationship diagram, especially in S132-S135, the interactions and trade-offs between ecological products were deeply revealed by calculating mutual information and constructing dynamic Bayesian networks. In S43, a random forest was used to construct a scale deduction model, which achieved scientific extrapolation from the sample area to the watershed scale. This method takes into account a variety of spatial and temporal characteristics and can effectively deal with scale conversion problems. Time series decomposition and change point detection methods were used to analyze the long-term trend and seasonal changes in the value of ecological products. Spatial autocorrelation analysis and hotspot analysis also revealed spatial distribution characteristics. These analyses provide support for long-term ecological management decisions.

[0111] According to another aspect of the present application, a river and lake water ecological product value accounting system based on multi-source data mining is also provided, including:

[0112] at least one processor; and,

[0113] a memory communicatively connected to at least one of the processors; wherein,

[0114] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the method for constructing a river and lake water ecological product value accounting system as described in any of the above technical solutions.

[0115] In one case, the specific process is as follows:

[0116] S1. Eco-product screening and classification

[0117] S11. Comprehensively sort out various river and lake water ecological products involved in the literature to form an ecological product database.

[0118] The Chinese and English literature databases were systematically searched to construct a literature dataset of river and lake water ecological products. Text mining and analysis were performed on the literature dataset, and ecological product keywords were extracted through software. Synonym merging and hyponymy of the keywords were performed to form a standardized ecological product catalog.

[0119] S12. Refer to the ecosystem service classification system and classify the ecological product library from the perspectives of supply, regulation, support, and culture. Refer to the classic ecosystem service classification systems such as MA, TEEB, and CICES, and extract and summarize the advantages and disadvantages of each system by using software to extract keywords. Starting from the formation mechanism of ecological products, determine the classification principles applicable to river and lake water ecological products. Follow the classification principles and classify the product types in the ecological product catalog from the bottom up.

[0120] S13. Identify the intrinsic connections between different categories of ecological products and construct an ecological product network relationship diagram.

[0121] Conduct a literature review on the interaction between ecological products and determine the direction of interaction between different ecological products (positive, negative or neutral). Conduct a causal loop analysis on river and lake water ecological processes to reveal the process mechanism of the direction of interaction between ecological products. Apply complex network theory to construct a directed ecological product network with ecological products as nodes and interaction directions as edges. Calculate the topological parameters such as degree and betweenness of network nodes to measure the importance of each ecological product node in the network.

[0122] S14. Based on the network relationship diagram, select ecological products that have key contributions and can be reasonably quantified. Calculate the connectivity of each node in the network relationship diagram and identify key nodes (ecological products). Construct a coupling relationship matrix between ecological products and economic, social and ecological indicators to measure the contribution of each product to the economic, social and ecological environment. Conduct quantifiability assessments on each ecological product to determine the availability of product data and the measurability of indicators. Optimize key network nodes and ecological product types.

[0123] S15 Systematically classify the selected ecological products and build an ecological product classification system with logical consistency and no duplication. Refer to the classification principles determined in S12 to systematically sort out the selected ecological products. Identify the logical relationship between different categories and build a classification system with clear vertical (supply-regulation-support-culture) and horizontal (within the same category) structures. Conduct expert consultation to judge the logical consistency of the classification system and make corrections and improvements based on the opinions.

[0124] S2. Spatial and temporal scale matching and standardization

[0125] S21 Analyze the spatiotemporal scale characteristics of the formation and function of each ecological product.

[0126] Based on existing theories and observation cases, summarize the time scale characteristics of various ecological products (formation rate, duration of action, etc.). Based on existing theories and observation cases, summarize the spatial scale characteristics of various ecological products (key distribution areas, diffusion and transmission distance, etc.).

[0127] S22 Based on the spatiotemporal scale characteristics of ecological products, match the spatiotemporal scale of natural and socioeconomic data. Summarize natural background data such as basin hydrology, water environment, land use, and remote sensing, and analyze their spatiotemporal resolution. Summarize socioeconomic data such as basin population, economy, and industry, and analyze their spatiotemporal resolution. Calculate the matching degree between the spatiotemporal resolution of natural and socioeconomic data and the spatiotemporal scale of ecological products. Screen natural and socioeconomic data with high matching degree to form a basic data set to support ecological product accounting.

[0128] S23. Use the Geospatial Data Cube method to achieve efficient organization and integration of multi-source heterogeneous data. Construct a regular spatiotemporal reference frame at the watershed scale to form the basic grid of the data cube. Map the spatiotemporal attribute information of the basic data set to the spatiotemporal dimension of the reference frame. Organize the attribute information of the basic data set into the attribute dimension of the reference frame. Establish semantic associations, coordinate associations, topological associations and other associations between data sets. Based on the association relationships, realize the flexible calling of different slices and views of the data cube.

[0129] S24. Construct a spatiotemporal coupling model of river and lake hydrology, water resources, water environment, and water ecological processes at the basin scale. Based on the basin hydrological model, simulate the spatiotemporal process of rainfall-runoff-sediment-groundwater. Based on the basin water resources allocation model, simulate the spatiotemporal balance of water supply and use in the basin. Based on the basin water environment model, simulate the spatiotemporal changes of indicators such as river and lake water quality and water temperature. Based on the water ecological model, simulate the growth succession process of water ecological elements such as aquatic vegetation and plankton. Through the data exchange interface between models, realize the dynamic coupling between different process models.

[0130] S25. Generate various standardized spatiotemporal data products that support ecological product accounting using data cubes and coupling models. Perform time series analysis on data cubes to generate temporally continuous ecological product accounting data sets. Perform spatial interpolation and extrapolation on data cubes to generate spatially continuous ecological product accounting data sets. Perform scenario simulation on coupling models to generate spatiotemporal distribution data of ecological product supply and demand under different scenarios. Evaluate the accuracy of data products and form accuracy metadata for data products.

[0131] S3. Construction of multi-criteria accounting model

[0132] S31. Establish an ecological product accounting indicator database covering ecology, society and economy. Select ecological indicators that can quantitatively represent the supply and demand of various ecological products. Select social indicators that can quantitatively represent the beneficiaries of various ecological products. Select economic indicators that can quantitatively represent the value realization of various ecological products.

[0133] S32. Compare and select the quantitative methods of various indicators, and select the ones that are intuitive, operational and explainable. Based on literature research, sort out the existing quantitative methods of various indicators. Evaluate the theoretical basis of the quantitative methods from an economic perspective. Evaluate the scale applicability of the quantitative methods from a geographical perspective. Evaluate the interpretability of the quantitative methods from a management perspective. Based on the comprehensive evaluation results, select the quantitative methods of various indicators.

[0134] S33. Use the analytic hierarchy process to construct a multi-criteria evaluation model for the value accounting of various ecological products. Invite experts in the fields of ecology, economics, geography, and management to conduct brainstorming to identify the criterion layer that affects the value of ecological products. Conduct causal mechanism analysis to identify the indicator layer corresponding to the criterion layer. Use the 9-measure scale method to design a questionnaire to investigate experts' judgments on the importance of different criteria. Based on the questionnaire survey results, calculate the maximum eigenvalue of the judgment matrix and perform a consistency test to determine the criterion layer weights. Use the weighted summation method to construct a multi-criteria comprehensive evaluation model for the value of various ecological products.

[0135] S34. Use methods such as structural equation models to analyze the internal mechanisms and weights between different criteria indicators. Based on literature research and expert interviews, construct theoretical hypothesis paths between criteria indicators. Based on standardized spatiotemporal data products, calculate the quantitative values ​​of the criteria indicators of each evaluation unit. Use structural equation models to fit and optimize the theoretical hypothesis paths to reveal the causal relationship between the criteria indicators. Use path analysis methods to measure the direct and indirect effects of each criterion indicator on the formation of ecological product value. Comprehensively consider the results of hierarchical analysis and structural equation analysis to determine the comprehensive weight of the multi-criteria evaluation model.

[0136] S35. Conduct multi-scenario analysis to evaluate the contribution of various accounting results to the total value under different scenarios. Set different scenarios for ecological protection and industrial development and construct scenario parameter sets. Input scenario parameters into the coupling model constructed in S24 to simulate the spatiotemporal pattern of supply and demand of ecological products under different scenarios. Input scenario simulation results into the multi-criteria comprehensive evaluation model to calculate the value of ecological products under different scenarios. Use sensitivity analysis methods to measure the relative contribution of different accounting criteria results to the total value. Use extreme condition analysis methods to measure the response of total value to changes in key accounting parameters.

[0137] S4. Scaling and extrapolation

[0138] S41. At the sample plot scale, use the multi-criteria accounting model constructed in S3 to calculate the value of various ecological products. Select several representative sample plots (townships, villages) in the basin and obtain the sample plot boundary vector data. Clip the basin standardized spatiotemporal data products generated by S25 to the sample plot boundaries to form an ecological, social, and economic data set at the sample plot scale. Input the sample plot data set into the multi-criteria accounting model constructed in S33 to calculate the indicator values ​​of each criterion layer. Substitute the criterion layer indicator values ​​into the weighted summation formula constructed in S33 to obtain the value of various ecological products at the sample plot scale. Statistical analysis is performed on the accounting results to reveal the spatial differentiation characteristics of the value of ecological products at the sample plot scale.

[0139] S42. Analyze the quantitative relationship between the value of ecological products and driving factors (such as area, output, landscape quality, etc.) at the sample area scale. Perform landscape ecological analysis on the acquired sample area boundary data, and extract data such as landscape components and landscape indices of the sample area. Perform spatial statistical analysis on the generated watershed standardized spatiotemporal data products, and extract data such as supply, consumption, and environmental impact of ecological products at the sample area scale. Perform correlation analysis on the calculated sample area ecological product value data to screen key driving factors. Use regression analysis, principal component analysis and other methods to quantitatively characterize the functional relationship between the value of ecological products in the sample area and key driving factors. Use path analysis to evaluate the direct and indirect effects of each driving factor on the value of ecological products.

[0140] S43. Use machine learning methods such as random forests to construct a scale extrapolation model to achieve the extrapolation of the value of ecological products at the watershed scale. Randomly divide the ecological product value and driving factor data at the sample area scale into training sets and validation sets according to a certain proportion. Train the random forest regression model with the ecological product value as the dependent variable and the driving factor as the independent variable. Use the validation set data to evaluate the fitting accuracy of the random forest regression model and adjust the model parameters. Input the standardized spatiotemporal data products of the watershed into the trained and optimized random forest model, and extrapolate to obtain the spatial distribution map of the ecological product value at the watershed scale. Evaluate the uncertainty of the extrapolated results and generate a spatial uncertainty distribution map of the ecological product value at the watershed scale.

[0141] S44. Optimize the results of ecological product value accounting at the watershed scale in a multi-scenario and multi-objective manner, and balance the demands of ecological protection and economic development. Set different scenario targets such as ecological protection intensity, industrial structure, and resource utilization efficiency. Use the watershed process coupling model constructed by S24 to simulate the spatiotemporal pattern of supply and demand of ecological products in the watershed under different scenarios. Perform multi-objective optimization on the simulation results of different scenarios to generate a spatial configuration plan for the value of watershed ecological products that meets the goals of ecosystem integrity and balance of supply and demand of ecological products. Evaluate the economic, ecological, and social benefits of different spatial configuration plans for ecological product value, and balance the demands of different stakeholders. Visualize the spatial configuration plan for the value of watershed ecological products after the balance, and form a spatial decision-making plan for watershed management.

[0142] S45. Conduct sensitivity analysis to evaluate the response of accounting results to changes in key parameters, and correct the accounting results through expert knowledge. Conduct sensitivity analysis on the key parameters of the S43 deduction model to evaluate the impact of parameter changes on the accounting results. Conduct sensitivity analysis on the target weights of the S44 optimization model to evaluate the impact of weight changes on the spatial configuration plan. Invite experts in the fields of watershed management, ecological protection, and resource utilization to evaluate the rationality of the accounting results. Based on expert feedback, calibrate the sensitivity parameters to form a revised accounting result of the value of watershed ecological products. Compare and analyze the accounting results before and after correction, and quantitatively evaluate the effect of expert knowledge on reducing the uncertainty of the accounting results.

[0143] Based on the results of the ecological product value accounting at the watershed scale, the spatiotemporal differentiation characteristics of various ecological product values ​​are analyzed. The spatial autocorrelation analysis of the generated spatial distribution map of the ecological product value of the watershed is carried out to characterize the spatial agglomeration characteristics of the ecological product value. The generated spatial distribution map of the ecological product value of the watershed is spatially segmented to identify the hot and cold areas of the ecological product value. The time series analysis of the generated standardized spatiotemporal data products of the watershed is carried out to characterize the annual fluctuation law of the ecological product value of the watershed. The constructed watershed process coupling model is used to analyze the impact of climate change, land use and other factors on the spatiotemporal pattern of ecological product value. The spatial statistical method is used to analyze the scale characteristics of the variation of ecological product value in different regions of the watershed and determine the optimal management scale. The trade-off and synergy relationship between the values ​​of different categories of ecological products is explored, and the key trade-off coercion factors are identified. The spatial superposition analysis of the generated ecological product value accounting results is carried out to characterize the spatial correlation of the values ​​of different categories of ecological products. The Mahalanobis distance of different ecological product value combinations of each evaluation unit is calculated to measure the similarity of the values ​​of different categories of ecological products. The niche overlap analysis method is used to measure the niche differentiation and niche overlap of different ecological product values. Combined with the simulation results, the impact of changes in external stress factors on the ecological niche of the ecosystem and the trade-off of ecological product value is analyzed. A multi-objective decision-making method is used to optimize the watershed management strategy under the conflict of ecological product value trade-off. Based on the ecological product trade-off relationship, a multi-objective optimization model for watershed ecological product management is constructed.

[0144] Maintaining key ecological processes, promoting synergistic gains of ecological products, and controlling ecological stress factors are taken as the objective functions of watershed ecological product management. Land use control, industrial structure optimization, and resource utilization efficiency improvement are taken as decision variables for watershed ecological product management. The analysis results are converted into constraints of the objective function. Intelligent optimization algorithms such as multi-objective genetic algorithms are used to solve the model and generate the optimal decision set for watershed ecological product management. Based on the game analysis method, the acceptance of management decisions by different stakeholders is judged and appropriate adjustments are made. Phased goals for watershed ecological product management are formulated, and the goal achievement paths under different management scenarios are simulated.

[0145] On the basis of constraints, differentiated ecological product management target systems are set for different management stages. The phased target system is input into the multi-objective optimization model constructed by S53 to generate the optimal decision set for different management stages. Using the scenario analysis method, different external drive combinations are designed to simulate the target realization path of the optimal decision set under different scenarios. The economic cost, ecological performance, and social impact of the target realization path under different scenarios are evaluated, and a comparison and trade-off is made.

[0146] Form a time decision-making plan for watershed ecological product management based on phased goals and management scenarios.

[0147] Incorporate the accounting of ecological product value into decision-making applications such as watershed ecological compensation and ecological asset accounting to improve the management and application level of accounting results. Use the generated watershed ecological product value accounting results as the basic basis for horizontal ecological compensation between upstream and downstream of the watershed. Incorporate the generated watershed ecological product value accounting results into the compilation of the watershed ecological balance sheet to assess the level of sustainable development of the watershed. Use the generated watershed ecological product value accounting results as an important indicator for the assessment of the effectiveness of water ecological protection in the watershed. Embed the spatial configuration plan and time decision-making plan for watershed ecological product management into the compilation of the watershed national land space planning. Incorporate the ecological product value accounting method into the business operations such as watershed ecological environment monitoring and early warning, and ecological security assessment.

[0148] In short, in order to improve the accuracy and objectivity of ecological product value accounting, we first obtain river and lake ecological product data through computer literature mining, then conduct causal analysis and screening, and after screening, simulate river and lake scenarios through coupling models to obtain river and lake ecological product systems under different scenarios, give a reasonable range of weights, and then verify through measured data from real scenarios to further improve accuracy, and finally perform extrapolation or use integrated models for calculations.

[0149] It should be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

Claims

1. A method for constructing a river and lake water ecological product value accounting system, characterized in that: The steps include: Step S1: Collect research data, sort out river and lake water ecological products in the research data, form an ecological product library, and build an ecological product classification system and value calculation method system; Step S2: sequentially analyze the spatiotemporal scale characteristics of the formation and function of each ecological product, as well as the spatiotemporal scales of the corresponding natural and socioeconomic data, and use a coupling model to generate various standardized spatiotemporal data products that support ecological product accounting; Step S3: Based on the ecological product classification system, value calculation method system and standardized spatiotemporal data products, a multi-criteria accounting model is constructed using the analytic hierarchy process, and the weight of each criterion indicator in the multi-criteria accounting model is evaluated; Step S4, determine the scope of the accounting sample area, collect the accounting sample area data, calculate and extrapolate the value of various ecological products in the accounting sample area, obtain the basin-scale ecological product value accounting results and perform sensitivity analysis and spatiotemporal distribution difference analysis, and output and store the basin-scale river and lake water ecological product value accounting model; The step S1 is specifically as follows: Step S11, obtaining research literature to build a research literature library, extracting research data from the research literature library, and sorting out river and lake water ecological products in the research data to form an ecological product library; Step S12: Based on the ecosystem service classification system, classify the ecological product library from the perspectives of supply, regulation, support and culture; Step S13: Analyze and identify the internal connections between different types of ecological products, and construct an ecological product network relationship diagram; Step S14, calculating the connectivity of each node in the ecological product network relationship diagram, identifying key nodes, obtaining key ecological products and optimizing them; Step S15: systematically classify the selected ecological products, and construct an ecological product classification system and value calculation method system with logical consistency and no repeated calculation; The step S2 is specifically as follows: Step S21, read the ecological product classification system, and analyze the spatiotemporal scale characteristics of the formation and function of each ecological product; Step S22: for each ecological product, based on its spatiotemporal scale characteristics, matching the spatiotemporal scales of the natural and socioeconomic data of the ecological product; Step S23: Using the GDC method, organize and integrate multi-source data including ecological products, natural and socio-economic data at each spatiotemporal scale to form a data cube; Step S24: construct a spatiotemporal coupling model of river and lake hydrology, water resources, water environment and water ecological processes, and generate standardized spatiotemporal data products for supporting ecological product accounting through data cubes and spatiotemporal coupling models; The step S3 is specifically as follows: Step S31, selecting ecological indicators that can characterize the supply and demand of each type of ecological product, social indicators that characterize the beneficiaries of each type of ecological product, and economic indicators that characterize the realization of the value of ecological products, and constructing an ecological product accounting indicator library; Step S32: compare and select the indicators in the ecological product accounting indicator library to obtain an indicator quantification method that is operable and explainable; Step S33: for the selected ecological product accounting indicators, a multi-criteria evaluation model for the value accounting of various ecological products is constructed by using the hierarchical analysis method, and the internal action mechanism and weight between different criterion indicators are analyzed through the multi-criteria evaluation model; Step S34: construct a scenario parameter set, simulate the spatiotemporal pattern of supply and demand of ecological products under various scenarios through a spatiotemporal coupling model; input the scenario simulation results into a multi-criteria comprehensive evaluation model, calculate the value of ecological products under different scenarios, and obtain the calculation results; Step S35: Use sensitivity analysis to measure the relative contribution of different accounting criteria results to the total value; use extreme condition analysis to measure the total value response to changes in key accounting parameters; and modify the weights of criteria indicators based on sensitivity and extreme condition analysis results. The step S4 is specifically as follows: Step S41, determine the scope of the accounting sample area, and according to the scale of the accounting sample area, use a multi-criteria comprehensive evaluation model to calculate the value of each type of ecological product in the accounting sample area, obtain the sample area accounting results, record them as the value of the ecological products in the sample area, and conduct expert evaluation, and adjust the parameters of the multi-criteria comprehensive evaluation model according to the evaluation results; Step S42, analyzing and calculating the quantitative relationship between the value of ecological products at the sample area scale and the driving factors; Step S43: Use random forest to construct a scale deduction model, and generalize the value of ecological products to the watershed scale through the calculation results of the sample area; Step S44, obtaining the basin-scale ecological product value accounting results and performing sensitivity analysis and spatiotemporal distribution difference analysis, and outputting and storing the basin-scale river and lake water ecological product value accounting model; The step S13 is specifically as follows: Step S131, sequentially retrieve the supply data of each ecological product within a predetermined time period, and construct a standardized time series data set; Step S132: For each pair of ecological products, calculate the mutual information under different time lags, determine the optimal time lag between each pair of ecological products based on the normalized maximum mutual information criterion, and determine whether there is a causal influence between the ecological products. If so, construct an ecological product network relationship directed graph, where the nodes are ecological products, the direction of the edge represents the causal relationship, and the edge weight is the normalized maximum mutual information criterion value; Step S133: obtaining the pre-stored prior knowledge of the causal relationship between ecological products and converting it into the constraint conditions of the directed graph of the ecological product network relationship, using a constrained structural learning algorithm, integrating data-driven causal inference results and expert knowledge, and obtaining a revised causal graph; Step S134: construct a dynamic Bayesian network model based on the corrected causal graph, verify the prediction performance of the dynamic Bayesian network model, and adjust the network structure of the ecological product network relationship directed graph according to the prediction results; Step S135, calculating the topological parameters of the adjusted network structure, including node in-degree and out-degree, betweenness centrality, PageRank value and clustering coefficient; The step S42 is specifically as follows: Step S421, retrieve the multidimensional data of the sampling area and perform standardization and outlier detection to obtain standardized multidimensional data; Step S422, extracting original driving factors and nonlinear features from the standardized multidimensional data, visualizing the extracted nonlinear features using the t-SNE method, and preliminarily obtaining potential data structures and clusters; Step S423: Perform multi-scale entropy analysis and calculation on the time series of each driving factor and ecological product value, calculate the sample entropy at different time scales, and preliminarily screen driving factors and ecological product value pairs with similar complexity based on the entropy analysis results; Step S424: Apply the dynamic time warping algorithm to the selected time series to quantify the similarity of the time series; construct a time series causal network based on the DTW distance, and use the convergent cross-mapping method to quantify the causal relationship strength to obtain the driving factors that are most relevant to the value of the ecological product and most likely to have a causal relationship; Step S425: construct and use a generalized additive model to fit the nonlinear relationship between the value of ecological products and key driving factors; select automatic smoothing parameters for the generalized additive model through thin plate regression splines; evaluate the contribution of each driving factor through ANOVA decomposition to obtain the quantitative relationship between the value of ecological products and driving factors at the sample area scale; The step S43 is specifically as follows: Step S431, obtaining the ecological product value data and the corresponding driving factor data of the sample area; collecting and preprocessing the spatial variable data and economic and social data at the watershed scale; Step S432: extracting geographic spatial features, and using a moving window method to calculate feature statistics at different spatial scales; extracting time series statistical features and calculating lag features; and generating interactive features based on geographic spatial features and time series features; Step S433, calling the pre-built random forest to build a scale deduction model to generalize the ecological product value to the watershed scale through the accounting results of the sample area, and obtaining the watershed scale ecological product value accounting results; S44 is specifically: Step S441: Based on the random forest model, select the top N driving factors in importance as key parameters; N is a natural number greater than 3; Step S442: For each key parameter, calculate the sensitivity index, and sort the parameters by the absolute value of the sensitivity index to identify the most sensitive local parameter; Step S443, using the Sobol method to generate parameter combinations for Latin hypercube sampling, calculating the first-order sensitivity index, the total effect index and the second-order interaction sensitivity index, and analyzing and screening the parameter pairs of interaction effects; Step S444: collect experts' data on the calculation results, extract key correction suggestions and score them, and calculate Kendall's W coefficient to evaluate the consistency of expert opinions; Step S445: Compare the sensitivity analysis results with the expert judgment results to identify areas where there are significant differences between the quantitative analysis and the expert opinions; Step S446: Calculate the correction coefficient for each key parameter, apply the correction coefficient to adjust the original calculation result, and calculate the confidence interval of the corrected result. Step S447, calculate the global Moran's I index of the corrected ecological product value, evaluate the overall spatial aggregation degree, calculate the local Moran's I index, identify high-value clusters and low-value clusters, and generate a LISA cluster diagram; Step S448, using the LOESS method to extract long-term trends, calculate trend slopes, identify rising or falling areas, and use STL to decompose seasonal components; detecting change points in the time series, mapping the detected change points to space, and analyzing the spatial distribution pattern of the change points; Among them, the hydrological model adopts SWAT; based on the principle of water balance, a water resource allocation model is constructed; the water environment model adopts WASP; the water ecological model: combines the niche model and the population dynamics model to simulate the distribution and quantity changes of key species; through the data exchange interface between the models, the dynamic coupling of the above four models is realized; Steps S22 to S24 are further as follows: Based on the spatiotemporal scale characteristics of ecological products, match the spatiotemporal scale of natural and socioeconomic data; summarize natural background data including basin hydrology, water environment, land use, and remote sensing, and analyze their spatiotemporal resolution; summarize socioeconomic data including basin population, economy, and industry, and analyze their spatiotemporal resolution; calculate the matching degree between the spatiotemporal resolution of natural and socioeconomic data and the spatiotemporal scale of ecological products; select natural and socioeconomic data with high matching degree to form a basic data set to support ecological product accounting; Use the geoscientific data cube method to achieve efficient organization and integration of multi-source heterogeneous data; construct a regular spatiotemporal reference frame at the watershed scale to form the basic grid of the data cube; map the spatiotemporal attribute information of the basic data set to the spatiotemporal dimension of the reference frame; organize the attribute information of the basic data set into the attribute dimension of the reference frame; establish associations between data sets, including semantic associations, coordinate associations, and topological associations; realize flexible calling of different slices and views of the data cube based on associations; At the basin scale, a spatiotemporal coupling model of river and lake hydrology, water resources, water environment, and water ecological processes is constructed; based on the basin hydrological model, the spatiotemporal processes of rainfall-runoff-sediment-groundwater are simulated; based on the basin water resources allocation model, the spatiotemporal balance of water supply and use in the basin is simulated; based on the basin water environment model, the spatiotemporal changes of river and lake water quality and water temperature are simulated; based on the water ecological model, the growth and succession processes of water ecological elements including aquatic vegetation and plankton are simulated; and dynamic coupling between different process models is achieved through the data exchange interface between models.

2. A river and lake water ecological product value accounting system based on multi-source data mining, characterized in that: include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the method for constructing a river and lake water ecological product value accounting system as described in claim 1.

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