Eod project financial feasibility assessment and resource balancing decision support system and method

Abstract

This invention discloses a decision support system and method for financial feasibility assessment and resource balancing of EOD projects, belonging to the field of intelligent decision-making technology for engineering project management. The invention includes: a case library construction module, a data preprocessing module, an indicator weight determination module, a similar case retrieval module, a case correction module, a case reuse and solution generation module, an indicator prediction module, and a result output and interaction module. Through a combination of case structuring, similarity retrieval, interpretable weight learning, threshold-triggered iterative correction, and constraint embedding mechanisms, this invention achieves standardization, quantification, and interpretability of financial feasibility assessment and resource balancing solution generation during the EOD project initiation phase. This significantly improves the efficiency of argumentation, enhances cross-regional reuse capabilities, and reduces the risk of fund mismatch and debt repayment during the implementation phase.

Description

Technical Field

[0001] This invention relates to the field of intelligent decision-making technology for engineering project management, specifically to a decision support system and method for financial feasibility assessment and resource balancing of EOD projects. Background Technology

[0002] EOD projects, based on ecological environment governance and supported by industrial introduction and operation, transform ecological value into economic value through integrated governance, restoration, development, and operation, thereby forming a sustainable investment and financing loop. These projects often involve multiple types of governance engineering and multi-industry combinations, with funding sources, construction sequence, and revenue recovery highly coupled in space and time, exhibiting characteristics such as large investment scale, long payback period, diversified revenue structure, and strong cash flow uncertainty. Simultaneously, EOD projects are typically subject to multiple policy constraints, including ecological red lines, land use compliance, industry access, fiscal affordability, and financing supervision. The project initiation stage requires not only assessing overall financial feasibility but also coordinating resource allocation and capital structure to avoid risks such as mismatch between returns and inputs, disconnect between engineering and industry, and imbalance between funding and schedule, which could affect the project's approval rate and subsequent implementation performance.

[0003] In current practice, the construction and evaluation of EOD projects still rely primarily on experience-based judgment, expert consultation, and one-off calculations. Common practices involve drawing conclusions based on investment estimates, return projections, and sensitivity analyses. However, there is a lack of standardized expressions and interpretable mechanisms for how to reuse experience from similar projects, how to quantify and correct differences, and why one solution is superior to another. Especially when promoting across regions, differences in resource endowments, industrial foundations, governance objectives, and policy interpretations make it difficult to directly transfer existing solutions. This leads to repetitive modeling and parameter adjustments in the comparison process, resulting in low efficiency and insufficient consistency in conclusions. Simultaneously, the lack of a reusable case library and quantifiable similarity retrieval methods makes it difficult to systematically mine and attribute the contribution of key indicators in historical projects. This further limits the transition of financial feasibility assessments and resource balancing decisions from case-by-case analysis to interpretable and reusable engineering applications.

[0004] To this end, this invention proposes a decision support system and method for financial feasibility assessment and resource balancing of EOD projects. Summary of the Invention

[0005] The purpose of this invention is to provide a financial feasibility assessment and resource balancing decision support system and method for EOD projects, which can realize experience reuse and data-driven collaborative decision-making in various environmental governance and industrial configuration scenarios, and improve the objectivity, interpretability and transferability of the assessment.

[0006] According to a first aspect of the present invention, in order to achieve the above-mentioned objective, the present invention provides the following technical solution: an EOD project financial feasibility assessment and resource balancing decision support system, comprising: The case library construction module is used to collect historical EOD project cases and standardize the representation of each case using a four-tuple structure to form case data. The four-tuple structure includes the case identifier and basic attribute dimension ID, the environmental governance problem type dimension P, the resource balance and industry combination dimension S, and the financial and risk assessment dimension E. The data preprocessing module is used to receive the four-tuple structured case data output by the case library construction module, divide the fields of ID, P, S and E dimensions in the case data into string type, symbol type, numeric type and Boolean type, and perform text cleaning, category encoding, normalization and binarization processing respectively, perform statistical imputation or rule repair on missing data, and write the processed standardized data back to the case library. The indicator weight determination module is used to construct an input feature vector based on the standardized data output by the data preprocessing module, using fields in the ID, P, and S dimensions, and using the E dimension financial and risk assessment indicators as supervision labels to train a gradient boosting decision tree model. The gradient boosting decision tree model is used to quantify the contribution of each input feature to the input indicator by statistically analyzing the loss reduction brought by each input feature in all decision tree split nodes. After normalization, the indicator weight vector W is output. The similar case retrieval module receives target project data, calculates the local similarity between the target project and historical cases based on the weight vector W output by the indicator weight determination module according to the field type differences, and combines them into a global similarity, outputting the ranking result of similar cases; wherein, the edit distance is used for string fields, the complete consistency judgment is used for symbolic fields, the state consistency judgment is used for boolean fields, and the local similarity is calculated by the algorithm for numerical fields respectively. The case correction module receives the global similarity score output by the similar case retrieval module and compares the global similarity score with preset dual thresholds. If the value is greater than or equal to the first threshold, case reuse is triggered directly. When the resource balance parameters are between the first threshold and the second threshold, the resource balance parameters are iteratively adjusted. The adjustment process recalculates the global similarity and feasibility constraints after each iteration until the first threshold is met or the maximum number of iterations is reached. The resource balance parameters include the industry mix ratio, the financing structure ratio, the phased investment plan, and the phased financing plan. When the value is less than the second threshold, output a message indicating that there are no similar cases. The case reuse and solution generation module is used to extract governance path, industry introduction structure, revenue formation mechanism, financing tool combination and phased timing elements from the solution S dimension of highly similar cases when the global similarity meets the first threshold. It also combines the differences between the target project and similar cases to generate a set of candidate reference solutions for the target project by calling the mapping rule library. The indicator prediction module is used to predict the target project based on the candidate solutions output by the case reuse and solution generation module. It uses a gradient boosting decision tree model to predict the indicators and output the predicted indicators. Based on the predicted indicators, a comprehensive scoring function is constructed to quantitatively compare and select the candidate solutions and output the recommended solution. The predicted indicators include financial internal rate of return, financial net present value, investment payback period, return on investment and debt service coverage ratio. The results output and interaction module receives the output results from the similar case retrieval module, the indicator weight determination module, and the indicator prediction module. It displays the list of similar cases, weight explanations, prediction results, and decision suggestions in a visual interface and supports user interaction adjustments.

[0007] Furthermore, the quadruple structure is represented as follows: The ID includes the project number, project name, implementing entity, project location, and project financing information; the P dimension describes the type of environmental governance problem; the S dimension describes the balance of resource categories and industry combination patterns; and the E dimension describes financial and risk assessment indicators.

[0008] Furthermore, in the indicator weight determination module, the gradient boosting decision tree model quantifies the contribution of the input indicators and outputs a normalized indicator weight vector W, as follows: Gradient boosting decision tree models use a stepwise addition model to construct prediction functions. In the m-th iteration, the negative gradient is used as the pseudo-residual: Training regression tree Fitting Updated with a step size v=0.05: ; Where u represents the round number, u=200; L is the mean squared error loss function. For the input feature vector, For output metrics; In terms of weight calculation, the gradient boosting decision tree model measures the contribution by statistically analyzing the loss reduction (Gain) caused by each feature's participation in node splitting across all trees: Let Split(m) be the set of splitting nodes of feature m in all trees. The gain is Gain n Then the feature importance is: Therefore, the normalization yields the following weights: In the formula, Indicates the first One input feature; This represents the total number of input features; This indicates that among all regression trees in the GBDT model, the first one is selected. The set of all split nodes whose features serve as the basis for partitioning; Represents the set of split nodes A split node in the process; Indicates the first The decrease in loss function or gain value of each split node after feature partitioning is used to characterize the contribution of the node split to model optimization. Indicates the first The importance of each input feature is equal to the sum of the gain values ​​of that feature at all relevant split nodes; Indicates the first The importance of each input feature; Represents the input feature index, where .

[0009] Furthermore, the similar case retrieval module calculates local similarity for string fields using edit distance, and the local similarity... satisfy: in For target string With historical strings Edit distance between; The similar case retrieval module calculates local similarity for symbolic fields using consistency judgment. Satisfy: When hour ,otherwise ; The similar case retrieval module first performs min-max normalization on numerical fields. Then calculate the local similarity. ; The similar case retrieval module calculates local similarity for Boolean fields using consistency criteria. Satisfy: When hour ,otherwise .

[0010] Furthermore, the global similarity The weighted sum of the local similarities of each field satisfies: in The normalized weights output by the module determine the indicator weights.

[0011] Furthermore, in the case correction module, during the iterative adjustment of resource balance parameters, the feasibility constraints for verification are calculated and checked simultaneously, specifically including: Resource balance constraint: At any point t, the cumulative funds must not be less than the cumulative investment. And meet the requirements at the end of the period. ; Financial feasibility constraints include: Non-negative net present value: At the same time, the debt service coverage ratio meets the requirements. Where CF is net cash flow, OCF is operating cash flow, and DS is current principal and interest payment. r is the discount rate.

[0012] Furthermore, the case reuse and solution generation module includes: Extract governance path, industrial introduction structure, revenue formation mechanism, financing tool combination and phased timing elements from the S dimension of similar cases; Combining the difference vector between the target project and similar cases A set of candidate solutions is generated through the Map function, which is driven by the rule base and parameter templates. The details are as follows: In the formula, This is the set of candidate solutions.

[0013] Furthermore, based on the predicted indicators output by the indicator prediction module, a comprehensive scoring function is constructed to quantitatively compare and select candidate solutions, and a recommended solution is output, as follows: The indicator prediction module takes the feature vectors and correction parameters of candidate solutions as input, and outputs the predicted financial internal rate of return (FIRR), financial net present value (FNPV), payback period, return on investment, and debt service coverage ratio (DSCR). It also constructs a comprehensive scoring function for comparing multiple solutions. in To measure the deviation of investment funds, The weighting coefficients and .

[0014] According to a second aspect of the present invention, the present invention provides a method for financial feasibility assessment and resource balancing decision support for EOD projects, employing the EOD project financial feasibility assessment and resource balancing decision support system described in the first aspect, comprising the following steps: S1: Receive historical EOD project case data and use a four-tuple structure to standardize the representation of each case; the four-tuple structure includes case identifier and basic attribute dimension ID, environmental governance problem type dimension P, resource balance and industry combination dimension S, and financial and risk assessment dimension E; S2: Perform missing data repair, data type identification and standardization on the case data, and write the processed standardized data back to the case library; S3: Based on supervised learning, train the weight recognition model, calculate the feature contribution of each input indicator and normalize it to obtain the indicator weight vector W; S4: Input the target project data, calculate the local similarity of each field according to the field type, combine the weight vector W of the index to form the global similarity, and output the ranking result of similar cases; S5: Determine whether the global similarity has reached the preset threshold: If it has, proceed to step S6; if it has not, start the dynamic correction engine to iteratively adjust the resource balance parameters, and return to step S4 to recalculate the global similarity until the preset threshold or the maximum number of iterations is reached. S6: Reuse the experience of highly similar cases to extract key points of financing structure, industry combination and risk control, and generate a set of reference solutions for the target project; S7: Use the GBDT model to predict the target project's internal rate of return, net present value, payback period, return on investment, and debt service coverage ratio, and output the prediction results and decision recommendations.

[0015] According to a third aspect of the present invention, the present invention provides a terminal device, comprising: At least one processor and a memory; the memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the EOD project financial feasibility assessment and resource balancing decision support method as described in the second aspect is implemented.

[0016] This invention has at least the following beneficial effects: 1. Enable reusable transfer of EOD project knowledge and improve cross-regional adaptability. This invention represents historical projects in a unified manner using quadruples and aligns basic attributes, governance issues, resource balance, and evaluation results at the field level. This allows similar projects to be uniformly coded, retrieved, and reused across different regions, implementing entities, and industry combinations. Because cases are stored as computable feature vectors, compared to traditional textual evidence, it significantly reduces the subjectivity of cross-project comparisons, improves the operability of cross-regional migration and reuse, and thus enhances the adaptability of EOD project solutions under different resource endowments and policy interpretations.

[0017] 2. Provides similarity metrics by field type to improve the accuracy and stability of matching similar items. This invention designs local similarity calculation methods for string, category, Boolean, and numerical fields respectively, and performs global similarity synthesis based on normalization. This avoids the problems of dimensional confusion and accumulated bias caused by simply weighting different types of indicators or manual scoring in existing methods. Through the joint calculation of local and global similarity, the system can stably output the ranking results of similar cases and provide a calculable basis for subsequent scheme reuse, thereby improving the accuracy, repeatability, and engineering stability of similar item retrieval.

[0018] 3. Use GBDT learning weights and feature importance to form an interpretable chain of evidence. Existing EOD (Effective Distance) project assessment methods often only provide NPV (Net Product Value) and IRR (Internal Rate of Return) results, making it difficult to explain the factors contributing to changes in key indicators. This invention obtains feature weights through supervised learning and outputs field-level contributions in a quantifiable form, allowing for clear traceability of similarity. This mechanism provides an auditable chain of evidence for project approval, expert review, and regulatory oversight, reducing reliance on personal experience and improving the transparency and consistency of conclusions.

[0019] 4. Feasible solutions are generated through threshold triggering and iterative correction, reducing trial-and-error costs. When the retrieved similar cases are insufficient for direct reuse, this invention employs a tiered threshold strategy to trigger dynamic correction. During the iteration process, key parameters such as industry mix, financing structure, and phased funding sequence are updated directionally. Simultaneously, global similarity and constraint satisfaction are checked in each round. Compared to traditional methods that involve repeated manual adjustments of assumptions, repetitive calculations, and multiple rounds of discussion, this invention can automatically approximate feasible solutions within a framework of rules and constraints, thereby significantly reducing trial-and-error costs and improving solution generation efficiency and convergence stability.

[0020] 5. Explicitly consider constraints such as resource balance, capital ratio, and debt repayment coverage. Traditional financial feasibility assessments often reveal issues such as seemingly feasible overall indicators but mismatched phased funding, concentrated debt repayment pressure, and insufficient capital ratios to meet regulatory requirements. This invention uses constraints such as phased funding matching with investment, capital ratio, DSCR lower limit, and non-negative NPV as hard constraints or penalty conditions for correction and solution generation. This ensures that the output solution naturally meets key compliance constraints and cash flow safety boundaries, improving the feasibility and robustness of the solution.

[0021] 6. Establish a multi-scheme quantitative comparison and comprehensive scoring mechanism. This invention predicts and verifies the financial and risk indicators of candidate solutions, and uses a comprehensive scoring function to perform unified quantitative comparisons of NPV, IRR, DSCR, risk level, and funding matching deviation. It can output recommended solutions and ranking results within the same evaluation framework. Compared to traditional multi-solution comparisons that rely on manual interpretation and difficult-to-reproduce comprehensive judgments, this invention enables rapid comparison under a unified standard, improves decision-making efficiency, and ensures consistency of results across different evaluation batches.

[0022] 7. Supports continuous iterative updates to improve the long-term effectiveness of the system. As new projects are continuously added to the case library, the system can continuously update the feature distribution and model parameters, enabling similarity retrieval and indicator prediction capabilities to improve with data accumulation. This approach gives the system continuous learning capabilities, allowing it to better adapt to distribution drift caused by policy constraints, changes in the financing environment, and shifts in industry revenue structures, thereby enhancing the stability and effectiveness of the system in long-term applications.

[0023] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0024] Figure 1 This is a logic block diagram of the system described in this invention; Figure 2 This is a comparison chart of the prediction performance of the GBDT algorithm of this invention with other algorithms; Figure 3 This is a schematic diagram of the CBR result display interface of the system in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the system project decision suggestion reference interface in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the case prediction process in Embodiment 1 of the present invention; Figure 6 This is a schematic diagram of the system case information input interface in Embodiment 1 of the present invention; Figure 7 This is a schematic diagram illustrating the prediction of financial and risk indicators in Embodiment 1 of the present invention; Figure 8This is a schematic diagram of the sensitivity analysis of total investment in Embodiment 1 of the present invention, wherein (a) shows the sensitivity of the financial internal rate of return when the total investment changes, (b) shows the sensitivity of the financial net present value when the total investment changes, (c) shows the sensitivity of the investment payback period when the total investment changes, (d) shows the sensitivity of the investment profit rate when the total investment changes, and (e) shows the sensitivity of the debt service coverage ratio when the total investment changes. Figure 9 This is a schematic diagram of the optimal solution distribution for each output item in Embodiment 1 of the present invention, where (a) is the optimal solution for GDP and total investment in terms of financial internal rate of return, (b) is the optimal solution for GDP and total investment in terms of financial net present value, (c) is the optimal solution for GDP and total investment in terms of investment payback period, (d) is the optimal solution for GDP and total investment in terms of investment profit rate, (e) is the optimal solution for GDP and total investment in terms of debt service coverage ratio, and (f) is the optimal solution for GDP and total investment in terms of comprehensive score. Figure 10 This is a flowchart illustrating the method described in Embodiment 2 of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0026] This invention constructs a complete, closed-loop, and interpretable decision support system encompassing data governance, knowledge representation, intelligent reasoning, and quantitative prediction. Its core contributions compared to existing technologies are as follows: First, this invention constructs a structured and scalable EOD project full-element case library and a standardized knowledge representation system, realizing a paradigm shift in project experience from implicit and scattered to explicit and systematic.

[0027] Traditional EOD project evaluation relies heavily on the unstructured experience of individual experts, lacking systematic historical data support. This invention is the first to design and implement a standardized case knowledge representation framework for the EOD field. Specifically, it innovatively proposes a four-tuple structure model of "Case Identifier (ID) - Problem Description (P) - Solution (S) - Effect Evaluation (E)". This model deconstructs each EOD project into computable data units: the ID dimension integrates the project's basic attributes and regional fiscal constraints; the P dimension standardizes the classification of nine categories of environmental issues according to the "Guidelines for Entry into the Ecological and Environmental Protection Financial Support Project Reserve"; the S dimension systematically defines seven resource balance paths, including ecological agriculture, cultural tourism, and industrial support; and the E dimension anchors core performance indicators such as the internal rate of return and debt service coverage ratio. Based on this structure, this patent systematically collects, cleans, and encodes 126 valid cases covering national and provincial pilot projects, forming the first structured EOD case library in China oriented towards decision support. More importantly, the system is designed with a companion data update mechanism. As new projects are implemented and added to the database, the case library can be dynamically expanded and improved, enabling the decision knowledge base to continuously evolve. This lays a solid and dynamic data foundation for subsequent intelligent applications and completely changes the current situation of knowledge "isolation" in this field.

[0028] Secondly, this invention deeply adapts the Case-Based Reasoning (CBR) method to complex EOD decision-making scenarios and designs an interpretable similarity measurement mechanism based on machine learning, realizing a revolutionary decision-making model that shifts from qualitative "experience-based decision-making" to quantitative "case-based evidence-based decision-making".

[0029] This invention does not mechanically apply traditional CBR (Content-Based Similarity) but proposes a hybrid retrieval model of "differentiated local similarity calculation - interpretable global weight synthesis" to address the multi-source heterogeneous nature (numerical, categorical, Boolean, and textual) of EOD (Environmental Distress Object) project data. At the local level, a normalized interpolation algorithm is used for numerical fields (such as investment amount), and semantic consistency judgment is used for categorical fields (such as environmental problem type), solving the measurement challenge of direct comparison of mixed data types. At the global synthesis level, its core innovation lies in abandoning subjective weighting methods and introducing machine learning methods for weighting: using a gradient boosting decision tree (GBDT) model, with historical case inputs and financial performance labels, it is trained to automatically quantify the impact weight of each feature on the financial outcome. During retrieval, the global similarity is synthesized using this data-driven weighted average. This mechanism makes "case similarity" no longer a black box concept; its measurement process has a traceable and auditable chain of evidence, and the decision-making basis changes from "I think it's similar" to "data proves it's similar," significantly improving the objectivity, accuracy, and persuasiveness of case matching.

[0030] Third, this invention constructs an integrated decision-making process of "retrieval-correction-prediction" and realizes forward-looking prediction of financial indicators based on machine learning, forming a systematic intelligent decision support capability.

[0031] This invention embeds the aforementioned data foundation and retrieval mechanism into a complete decision-making workflow with feedback adjustment capabilities, ultimately focusing on quantitative prediction. This invention proposes a "dual-threshold triggered dynamic correction" mechanism: when the retrieval similarity is insufficient, the correction engine is automatically activated. Under the dual hard constraints of resource balance and financial feasibility, project parameters are iteratively adjusted to intelligently generate feasible solutions. The most forward-looking original feature is reflected in the "case prediction" module: the system reuses a pre-trained GBDT model as the prediction engine. When the target project features are input, the model can directly output predicted values ​​for key indicators such as internal rate of return, net present value, and debt service coverage ratio. This means that decision-makers can obtain a quantitative feasibility preview based on historical big data patterns at the early stages of project planning, replacing the traditional model that relies on rough estimations or entirely post-hoc verification.

[0032] In summary, this invention provides a systematic integrated innovation, which for the first time in the field of EOD project management constructs a full-chain, closed-loop decision support solution that includes "standardized knowledge base construction → data-driven case evidence retrieval → constraint-guided intelligent solution generation → machine learning-based financial indicator prediction".

[0033] Example 1: This invention discloses a decision support system and method for financial feasibility assessment and resource balancing of EOD (Eco-environment-oriented Development) projects. Based on machine learning and case-based reasoning, it achieves experience reuse and data-driven collaborative decision-making. The system includes a case library construction module, a data preprocessing module, an indicator system and weight determination module, a similar case retrieval module, a case correction module, a case reuse and solution generation module, a financial and risk indicator prediction module, and a result output and interaction module. The case library uses a four-tuple structure to standardize project representation. The data preprocessing module performs missing data repair, type identification, and normalization on multi-source heterogeneous data. The similar case retrieval module calculates local similarity based on edit distance (string similarity), discrete symbol consistency similarity, normalized difference similarity, and Boolean consistency similarity according to field data type, and combines this with indicator weights obtained from machine learning to form a global similarity. The case correction module triggers a dynamic correction engine based on the global similarity threshold, outputting feasible resource allocation adjustment schemes. The prediction module predicts indicators such as financial internal rate of return, financial net present value, investment payback period, return on investment, and debt service coverage ratio based on the GBDT model.

[0034] Please see Figure 1 This invention provides a technical solution: an EOD project financial feasibility assessment and resource balancing decision support system, comprising a case library construction module, a data preprocessing module, an indicator weight determination module, a similar case retrieval module, a case correction module, a case reuse and solution generation module, an indicator prediction module, and a result output and interaction module, wherein: The case library building module is used to create standardized cases from historical EOD projects and store them in the library. Each case uses a four-tuple structure. The definition of the project structure is as follows: ID represents the case identifier and basic attributes, including at least the project number, project name, implementing entity, project location, and financing information. It can also automatically associate fiscal constraint information such as regional GDP, fiscal revenue, government debt, and debt ratio based on the region. P represents the problem description dimension, used to characterize the type of ecological and environmental problem the project aims to solve, covering at least categories such as air pollution prevention and control, water ecological environment protection, comprehensive management of key marine areas, soil pollution prevention and control, agricultural and rural pollution control, solid waste treatment and disposal and comprehensive resource utilization, ecological protection and restoration, and other environmental governance. S represents the solution dimension, used to characterize the balanced combination of resources for ecological governance and industrial development, covering at least balanced resource types such as ecological agriculture, cultural tourism, ecological health and wellness, resource utilization, ecological industry, industrial support, and regional development, and including a phased investment plan. With installment financing plan E represents the performance evaluation dimension, which includes at least the following indicators: Financial Internal Rate of Return (FIRR), Financial Net Present Value (FNPV), Investment Recovery Period, Return on Investment, and Debt Service Coverage Ratio (DSCR). The data processing module is used to classify and standardize the mixed-type data in the case quadruple, at least dividing the fields into string, symbolic, numeric, and boolean types, and converting them into a unified computable feature vector. For numeric fields x Minimum-maximum normalization is used: Encoding mapping is used for category fields, True / False normalization is used for boolean fields, and statistical imputation or rule-based repair is used for missing values ​​to ensure consistency between similarity calculation and machine learning training input. Specifically, Table 1 presents the data type classification of the core fields in the EOD project case library. It can be seen that string fields are mainly used to record text information such as project number, project name, and implementing entity; symbolic fields mainly represent discrete location attributes such as the province and prefecture-level city where the project is located; numerical fields include continuous variables such as total investment, capital, loan funds, loan term, regional GDP, fiscal revenue, government debt, government debt ratio, and related financial indicators; and Boolean fields are used to indicate whether the environmental problem type and resource balance type exist. This classification reflects the mixed nature of information composition in EOD project cases and lays the foundation for adopting differentiated processing methods for different types of fields.

[0035] The indicator weight determination module, for a dataset of 127 cases, employs five-fold cross-validation and utilizes the Gradient Boosting Decision Tree (GBDT) algorithm. The GBDT model learns the indicator weights and field importance to achieve objectivity in similarity synthesis and indicator prediction. GBDT uses a stepwise addition model to construct the prediction function. In the m In each iteration, the negative gradient is used as the pseudo-residual: Training regression tree Fitting Updated with a step size v=0.05: Where L is the loss function, The input feature vector specifically includes "Total Investment", "Capital", "Loan Funds", "Loan Term", "Gross Regional Product", "Fiscal Revenue", "Government Debt", "Government Debt Ratio", "Air Pollution Prevention and Control", "Water Ecological Environment Protection", "Comprehensive Management of Key Marine Areas", "Soil Pollution Prevention and Control", "Agricultural and Rural Pollution Control", "Solid Waste Treatment and Disposal and Comprehensive Resource Utilization", "Ecological Protection and Restoration", "Other Environmental Governance", "Ecological Agriculture", "Cultural Tourism", "Ecological Health and Wellness", "Resource Utilization", "Ecological Industry", "Industrial Support", and "Regional Development". The output indicators specifically include "Financial Internal Rate of Return (FIRR)", "Financial Net Present Value (FNPV)", "Investment Recovery Period (Pt)", "Return on Investment", and "Debt Service Coverage Ratio". The model continuously corrects the residuals by iteratively generating a weak learner, enabling it to characterize the multi-factor nonlinear relationships in EOD financial evaluation. u represents the round, u=200. In terms of weight calculation, GBDT measures the contribution by statistically analyzing the loss reduction (Gain) caused by each feature's participation in node splitting across all trees. Let Split(m) be the set of splitting nodes for feature m across all trees. If the gain is Gainn, then the feature importance is: The weights are then normalized to obtain the following: In the formula, Indicates the first One input feature; This represents the total number of input features; This indicates that among all regression trees in the GBDT model, the first one is selected. The set of all split nodes whose features serve as the basis for partitioning; Represents the set of split nodes A split node in the process; Indicates the first The decrease in loss function or gain value of each split node after feature partitioning is used to characterize the contribution of the node split to model optimization. Indicates the first The importance of each input feature is equal to the sum of the gain values ​​of that feature at all relevant split nodes; Indicates the first The importance of each input feature; Represents the input feature index, where ; Weights are used for global similarity synthesis and field contribution interpretation; In other embodiments, methods such as Support Vector Machine (SVM), Random Forest (RF), and Linear Regression (LR) are also selected, using project performance-related indicators as output variables to identify the importance of different input features. Figure 2 This paper compares the training performance of four types of models—Support Vector Machine (SVM), Random Forest (RF), Linear Regression (LR), and Gradient Boosting Decision Tree (GBDT)—on different output metrics. It can be seen that the Linear Regression model struggles to effectively characterize the complex nonlinear relationships in EOD projects; while SVM and RF demonstrate high fitting ability on some metrics, their stability across metrics is relatively insufficient; in contrast, GBDT shows more stable overall performance across all output metrics, exhibiting superior fitting accuracy and robustness. Therefore, this embodiment ultimately selects GBDT as the core model for metric weight identification. This choice not only helps improve the reliability of feature importance identification but also enhances the data support capability for subsequent case matching. Based on this, this embodiment utilizes the feature importance analysis results of the GBDT model to obtain the relative weights of each evaluation indicator. The results show that variables such as capital, loan funds, regional development, and government debt ratio have high weights, indicating that financing capacity, resource development models, and local fiscal constraints are key factors affecting the financial feasibility of EOD projects. In contrast, some environmental problem types and industrial models have relatively low weights, reflecting more of an indirect impact on the project's revenue structure and risk level. Therefore, similarity calculation in case reasoning no longer relies on subjective weighting but is based on data-driven weight identification, thereby improving the objectivity and scientific rigor of case matching.

[0036] Table 2 presents the weights and rankings of the evaluation indicators identified by the GBDT model. It can be further seen that high-weight variables are mainly concentrated in financing investment, regional fiscal constraints, and resource development models. This aligns with the actual characteristic that the financial feasibility of EOD projects highly depends on funding capacity, revenue compensation pathways, and local carrying capacity. Overall, the data processing and indicator weight identification completed in this section provide a unified data foundation and weight support for subsequent global similarity calculations in case retrieval and for predicting financial and risk indicators.

[0037] The similar case search module is used to search for target projects. Historical Cases Calculate local similarities and combine them into a global similarity, then output the ranking results of similar cases: This embodiment adopts a two-stage approach: "local similarity calculation – global similarity synthesis". Local similarity is calculated using a differential algorithm based on field type; for string fields, Levenshtein distance is used to calculate similarity. Symbolic category fields are judged based on complete consistency. ,when ;otherwise Numerical fields are similarized using the proportion of differences after normalization. Boolean fields use state consistency determination: ,when ;otherwise ; Global similarity is used to comprehensively reflect the overall matching degree between the target case and historical cases. Assume there are M valid fields in total, and the... m The local similarity of each field is , No. m The weight of each field is Then the global similarity is: ; The system converts the Sim data into percentage format and outputs the top K similar cases, along with field-level contribution scores. To form an interpretable chain of evidence; Specifically, compared with the traditional equal-weighted case retrieval method, the case retrieval method used in this embodiment can more fully reflect the actual strength of different features in financial feasibility assessment, thereby improving the accuracy and relevance of similar case screening. Figure 3 The case retrieval results display interface of the CBR module is shown. It can be seen that the system can sort historical cases according to global similarity and output high similarity cases and their core attribute information, providing a basis for subsequent case reuse. The case study correction module is used to adjust the parameters of the solution in a constraint-driven manner when there are differences between similar cases and the target project. These parameters include at least the industry mix ratio, financing structure ratio, and phased investment. With installment financing The parameters are adjusted, and the global similarity and feasibility constraints are recalculated after each iteration until the preset threshold is met or the maximum number of iterations is reached. Feasibility constraints include at least resource balance constraints and financial feasibility constraints: Resource balance constraints are at any point in time t The cumulative funds must not be less than the cumulative investment: And meet the requirements at the end of the period. Financial feasibility constraints include the requirement that net present value (NPV) be non-negative. And the internal rate of return equation: At the same time, the debt service coverage ratio meets the requirements. Where CF is net cash flow, OCF is operating cash flow, and DS is current principal and interest payment. r The discount rate; Specifically, considering that the target project and historical cases do not always achieve a perfect match, this embodiment further sets up a case correction mechanism to enhance the method's adaptability to new situations. Based on the global similarity, the search results are divided into three categories: when the global similarity is higher than 85%, it indicates that the target case and historical cases are highly consistent in core features, and can directly enter the case reuse stage; when the global similarity is between 60% and 85%, the system activates a dynamic correction mechanism to appropriately adjust key parameters such as environmental problem type and industry combination mode, and recalculates the similarity to obtain better matching results; when the global similarity is lower than 60%, it is considered that there are no effective cases available for reference in the case library, and the system prompts the user to re-examine the input parameters or postpone the decision. Through this mechanism, the case reasoning process is no longer limited to passively searching for historical experience, but can achieve active adaptation within a certain range. The case reuse and solution generation module extracts elements such as governance path and industry introduction structure, revenue formation mechanism, financing tool combination and phased timing from the solution dimension S of similar cases, and combines them with the target project's difference vector. Perform mapping to generate a set of candidate solutions The mapping function satisfies , where Map is the mapping function driven by the rule base and parameter templates; Specifically, in the case reuse phase, this embodiment extracts core experiences such as industry portfolio models, financing structure design, application of policy tools, and key points of risk control from historical cases with high similarity rankings, generating reference solutions for the target project. When multiple highly similar cases are retrieved, the system further cross-compares the commonalities and differences between these cases to condense transferable experience rules, and conducts adaptation analysis based on the actual context of the target project. Figure 4 A decision-making suggestion interface for target projects is provided, demonstrating that the system not only offers similar case information but also generates actionable solution suggestions based on this information. Therefore, the case-based reasoning process goes beyond simply "finding similar cases" and further transforms from "experience sharing" to "decision support." The indicator prediction module uses the GBDT regression model to predict and verify the financial and risk indicators of candidate solutions. Taking the feature vectors and correction parameters of the candidate solutions as input, it outputs predicted indicators such as FIRR, FNPV, payback period, return on investment, and DSCR. A comprehensive scoring function is also constructed for comparing multiple solutions. ,in To measure the deviation of investment funds, The weighting coefficients and ; Specifically, in addition to case retrieval and case reuse, this embodiment further introduces machine learning methods to predict key financial and risk indicators of the target project, thereby enhancing the quantitative support capability of decision-making results. This embodiment uses 23 input, environmental governance and industrial allocation indicators in the case library as input features, and indicators such as financial internal rate of return, financial net present value, investment payback period, investment profit rate and debt service coverage ratio as output variables to construct a GBDT prediction model. This model uses multiple regression trees to gradually fit the residuals and continuously correct the prediction error, thereby achieving a high-precision approximation of complex nonlinear relationships. Figure 5The case prediction process constructed in this embodiment is presented. It can be seen that after the target project completes the standardized feature expression, it first enters the trained GBDT model to predict financial and risk indicators. Then it is linked with the case retrieval and case reuse stages, and finally jointly serve the feasibility judgment and scheme comparison of the project. Thus, financial indicator prediction is not an isolated calculation module, but a key component embedded in the entire decision support framework. During the prediction process, the indicator weights identified above are not simply manually assigned and directly input into the GBDT model. Instead, they are implicitly reflected in the prediction path through the node splitting and feature contribution mechanism within GBDT. In other words, high-weight indicators such as capital, loan funds, regional development, and government debt ratio participate more frequently in the key node division during the tree model construction process, thus having a more significant impact on the final prediction results. Meanwhile, indicators with relatively lower weights play a supplementary and corrective role in local intervals. In this way, the model can learn the complex mapping relationship between input features and financial performance based on historical cases and transfer this pattern to the predictive analysis of new projects. Once the standardized feature data of the target project is input into the trained GBDT model, the system can output the corresponding financial and risk indicator prediction results, which will form a collaborative support with the case retrieval and solution generation stages. On the one hand, the case retrieval results can provide the target project with historical experience and implementation paths that can be referenced; on the other hand, the indicator prediction results can provide a quantitative basis for judging the project's return level, investment recovery characteristics and debt repayment ability. The two complement each other, enabling the decision support process constructed in this embodiment to achieve a closed-loop connection from "similar case reference" to "quantitative result verification". Overall, this process leverages the advantages of case-based reasoning in reusing experiential knowledge and utilizes machine learning to enhance the quantitative capabilities of financial and risk analysis, thereby providing more systematic, interpretable, and operational technical support for the financial feasibility assessment, resource balance analysis, and scheme optimization of EOD projects. The results output and interaction module receives the output results from the similar case retrieval module, the indicator weight determination module, and the indicator prediction module. It displays the list of similar cases, weight explanations, prediction results, and decision suggestions in a visual interface and supports user interaction adjustments. Furthermore, the results output and interaction module outputs a list of candidate solutions, recommended solutions and their evidence chains. The evidence chains include at least field-level similarity contribution, GBDT feature importance ranking and key constraint satisfaction, thereby supporting the interpretability and auditability of the project approval and review process.

[0038] The technical solution of this invention will be further elaborated below with specific examples: I. Model-Based Simulation and Application of Decision Processes 1. Standardized representation and data processing of target cases According to the case quadruple structure C=(ID,P,S,E) constructed in this embodiment, the project can be standardized as information in four levels: In the case identification dimension, the project name is "A Certain Canal EOD Project," the implementing entity is a certain city investment and development company, the project location is a certain city in a southern province, the total investment is 479,301,900 yuan, the capital is 97,301,900 yuan, the loan amount is 382,000,000 yuan, and the loan term is 23 years; it also relates to regional fiscal indicators such as GDP, fiscal revenue, government debt, and government debt ratio. In the problem description dimension, the core environmental problems of the project include river regulation, dredging, bank protection, sewage outlet remediation along the river, and riverside ecological restoration, which can be categorized as "water ecological environment protection." In the solution dimension, the project introduces the construction of a green and low-carbon industrial park on the basis of ecological environment restoration, using the benefits of industrial upgrading to subsidize ecological governance investment; its resource balancing can be categorized into "ecological industry" and "industrial support." In terms of performance evaluation, the project's existing financial calculations include an internal rate of return of 4.87%, a net present value of -255.93 million yuan, a payback period of 15.9 years, a return on investment of 0.584%, and a debt service coverage ratio of 132.8%. This information forms the basis for subsequent case retrieval, financial forecasting, and model effectiveness analysis.

[0039] Table 3 presents the standardized case representation results of a canal EOD project. It can be seen that this case has a relatively complete data foundation in four dimensions: case identification, problem description, solution, and effect evaluation, and can well meet the input requirements for similar case retrieval and financial indicator prediction. In particular, this project is highly typical in terms of financing structure, regional fiscal conditions, types of environmental problems, and resource balancing models, providing a good sample for verifying the applicability of this case in complex EOD project scenarios.

[0040] 2. Case retrieval and similarity matching results The preprocessed target case feature vector is input into the model, and case retrieval is carried out using a two-step method of "local similarity calculation → global similarity synthesis" to achieve accurate matching between the target case and 126 historical cases in the case database. Figure 6An input interface for target case information is provided, demonstrating the organization of standardized input data before case retrieval. It can be seen that before entering the case retrieval module, the target project has already completed a structured expression according to dimensions such as case identifier, problem description, solution, and effect evaluation. This provides a unified data foundation for subsequent local and global similarity calculations. Therefore, the case retrieval process no longer relies on unstructured empirical judgments but is built upon standardized case input.

[0041] In the local similarity calculation stage, for the three core fields (numerical, Boolean, and symbolic) formed after the standardization of the target case, the single-dimensional matching degree between the target case and each historical case is calculated field by field according to the proposed differentiated local similarity algorithm. Specifically: for the four numerical fields such as total investment, capital, loan funds, and loan term, the difference in dimensions is first eliminated by Min-Max normalization, and then the improved Euclidean distance method is used to calculate the similarity. The basic principle is that the smaller the numerical difference, the closer the local similarity is to 1. For Boolean fields, which mainly cover classification indicators such as environmental issues (e.g., water ecological environment protection) and resource balance (e.g., ecological industry, industrial support), a binary matching method is used. That is, the local similarity is 1 when the field attributes are completely consistent, and 0 otherwise. In this case, the Boolean feature vector of the target case is [Water Ecological Environment Protection = 1, Ecological Industry = 1, Industrial Support = 1]. When the corresponding dimension features of historical cases are completely consistent with this, the local similarity of this type of field is 1. For symbolic fields, which mainly include the province and prefecture-level city where the project is located, the exact matching method is used for measurement. The local similarity is 1 when the regional attributes are completely consistent, and 0 otherwise. Through the above method, the local similarity between the target case and 126 historical cases on 26 effective core fields is calculated, and a local similarity matrix with a dimension of 126×26 is constructed, laying the data foundation for subsequent global similarity synthesis. The weight percentages of each type of field are as follows: String type (2) accounts for 3.21%, Symbol type (2) accounts for 3.21%, Numeric type (7) accounts for 60.41%, and Boolean type (15) accounts for 33.17%.

[0042] In the global similarity calculation stage, based on the weight system of 23 indicators obtained through GBDT model training (including capital weight 0.387893, loan funds 0.099531, area development weight 0.094942, government debt ratio 0.064133, etc.), the local similarity matrix is ​​weighted and summed to finally obtain the global similarity between the target case and each historical case.

[0043] Through the two-step calculation method described above, the system selected the top three cases with the highest global similarity to a certain canal EOD project from 126 historical cases. The global similarity of the top three historical cases with high similarity all exceeded 90%, reaching 95.22%, 94.24%, and 93.39% respectively. This indicates that the constructed case representation and weighted matching method can accurately identify reference cases that are highly similar to the characteristics of the target project. The core information of the three reference cases covers the case number, core environmental issues, balanced resources, key parameters of financing structure, and global similarity, as shown in Table 4.

[0044] The matching results of the top three highly similar cases retrieved with a certain canal EOD project show significant business logic rationality and practical reference value. The two are highly consistent in core feature dimensions, mainly reflected in the following three aspects.

[0045] First, regarding the nature of the ecological issues, the core environmental problems in these three similar cases all focus on water ecological environment protection, which is completely consistent with the core governance needs of the target case, such as canal channel improvement, watershed sewage treatment, and riverside ecological restoration. All three belong to the category of EOD projects in watershed water ecological governance, showing a high degree of consistency in governance technology approaches and problem-solving orientations. Second, regarding the value realization logic, the first three similar cases all follow the typical logical framework of EOD projects: "based on ecological governance and centered on industrial feedback," which is highly consistent with the core idea of ​​the target case: "achieving revenue feedback for governance through the introduction of ecological industries and industrial feedback after water ecological restoration." Third, regarding the characteristics of the financing structure, the loan terms in these three similar cases are in the same long-term range as the target case, all adopting a "capital + long-term loan" financing model, primarily driven by policy banks or government-bank cooperation loans. Among these, the high-weight indicators identified by the GBDT model, such as capital and loan funds, are highly matched with the characteristics of the target case. It is worth noting that Case 1 and the target case are both projects within the same city area, and the dual fit of regional attributes and industrial logic makes them the most similar reference cases.

[0046] In summary, Table 4 presents the top three historical cases with the highest similarity to the target project, along with their core attributes. It can be seen that these three cases exhibit high consistency with the target project in terms of environmental problem types, resource balancing models, and key parameters of financing structures. The case with the highest similarity not only shares a high degree of similarity with the target project in terms of environmental governance types and industrial support paths, but also shares the same urban region, thus possessing stronger contextual comparability and reference value. Although the other two cases are located in different urban regions, they also show high consistency with the target project in terms of water ecological environment protection orientation, industrial support logic, and long-term loan arrangements. The search results fully validate the accuracy and effectiveness of the CBR-ML hybrid decision-making model constructed in this invention in EOD project case matching. The selected highly similar cases provide high-quality historical reference samples for subsequent case reuse, financial indicator prediction, and resource balancing strategy formulation.

[0047] 3. Case Correction Mechanism Trigger Analysis Based on the established case similarity threshold judgment rules, a global similarity of ≥85% indicates a high degree of overlap in the core attributes of the cases, allowing them to directly enter the reuse stage; a global similarity of 60% ≤ global similarity < 85% requires the activation of a dynamic correction engine to adjust relevant parameters; and a global similarity of <60% is considered as no matching case, suggesting a postponement of project decisions. Calculations show that the top three highly similar cases retrieved this time have global similarities of 95.22%, 94.24%, and 93.39% with a certain canal EOD project, respectively, all significantly higher than the 85% threshold, meeting the conditions for direct reuse. Therefore, there is no need to activate the dynamic correction engine to adjust and optimize core parameters such as environmental problem type, industry combination model, and resource allocation ratio; the case can directly enter the case reuse stage, extracting practical experience from historical cases to provide decision-making references for the target project.

[0048] 4. Case reuse and solution generation For historical cases with high similarity obtained from retrieval, the model extracts reusable experiential knowledge through a combination of multi-dimensional feature anchoring, common experience acquisition, and personalized adaptation. Through cross-analysis of the top three similarity cases, common feasible experiences are condensed, and regionally adaptable implementation strategies are extracted from cases with high similarity as core references, forming a reusable experience system. Based on the resource endowment, environmental governance needs, and industrial positioning of a certain canal EOD project, the model determines that the project is financially feasible and recommends referencing case

[00002] for implementation, focusing on learning from its implementation model and financing structure and adapting it to local conditions. In terms of implementation model, a "ecological restoration first, industrial phased introduction" strategy is adopted, prioritizing the completion of ecological projects such as river dredging and bank protection, and then promoting the construction of a green and low-carbon industrial park in stages to achieve complementary values ​​between ecology and industry; in terms of financing structure, the "government-bank cooperation, long-term credit" model is adopted, relying on local government credit to build a financing platform, connecting with policy banks to obtain long-term funds, rationally allocating capital and loan ratios, and exploring pathways for social capital participation.

[0049] 5. Financial and Risk Indicator Forecasts After completing case retrieval and matching, the standardized final feature vector of a certain canal EOD project was input into a GBDT prediction model trained with historical cases to predict financial and risk indicators. This model uses 23 core features of the EOD project, including total investment, capital, loan funds, loan term, regional fiscal indicators, environmental problem type, and resource balancing model, as well as environmental governance and industrial allocation indicators, as input features. The output variables are financial internal rate of return, net present value, investment payback period, and debt service coverage ratio. Through iterative residual fitting of multiple regression trees, a high-precision approximation of complex nonlinear relationships is achieved. In this prediction process, based on the indicator weighting system obtained from GBDT feature importance analysis, the model assigns higher predictive contributions to high-weight indicators such as capital, loan funds, and regional development. Combined with the target project's financing structure, environmental governance type, industrial combination model, and regional fiscal characteristics, after multiple rounds of iterative calculation and residual correction, the model outputs the predicted results of the core financial and risk indicators of the canal EOD project, as detailed below. Figure 7 As shown, the projected financial internal rate of return (FIRR) is 6.58%; the projected financial net present value (FNPV) is -209.052 million yuan; the projected payback period (Pt) is 16.59 years; the projected return on investment is 3.71%; and the projected debt service coverage ratio is 116.84%. These five projected indicators quantitatively characterize the financial feasibility and risk level of a certain canal EOD project from multiple dimensions, including profitability, investment recovery, and debt repayment. This not only provides a quantitative basis for early-stage project decision-making but also lays the foundation for subsequent comparison and verification with actual project operation data.

[0050] Based on the predicted characteristics, the target project exhibits typical EOD project attributes from a financial perspective. First, the negative net present value indicates that the project retains strong public welfare and long-term investment attributes, and market-based returns alone cannot fully cover the initial governance investments. Second, the relatively long payback period suggests a significant lag in the realization of project benefits, consistent with the common characteristic of ecological governance projects: high upfront investment and slow return release. Third, the high debt service coverage ratio indicates that, under existing financing arrangements and benefit compensation mechanisms, the project still possesses a certain degree of debt security. Overall, the model results effectively reveal the fundamental characteristic of EOD projects: "limited returns but with certain financial balance potential."

[0051] Based on the indicator predictions, the system further integrates the financing models, industry introduction pace, and risk control experience of the aforementioned highly similar cases to generate decision-making reference schemes for the target projects. Specifically, the system suggests adopting the "ecological restoration first, industry phased introduction" implementation path embodied in the most similar case. This means prioritizing the completion of river remediation and ecological restoration projects, followed by phased advancement of the green and low-carbon industrial park construction, to mitigate the risks arising from the asynchronous release of large-scale initial investment and subsequent benefits. Regarding financing arrangements, the system recommends continuing the basic model of "government guidance + long-term credit," and while maintaining a relatively stable capital-to-loan ratio, further exploring pathways for social capital participation to enhance the project's funding capacity and long-term operational resilience. Therefore, the financial indicator prediction results are not isolated outputs, but rather form a decision support chain together with case reuse and scheme generation.

[0052] The results show that the constructed GBDT prediction model can comprehensively characterize the target project's profitability, investment recovery characteristics, and debt repayment capacity, and effectively links with the case retrieval and experience reuse stages. On the one hand, the quantitative prediction results provide data support for judging the project's financial feasibility; on the other hand, the proposed solutions based on similar cases enhance the interpretability and practical operability of the results. These two aspects complement each other, enabling the method in this embodiment to not only answer the question "Is the project feasible?" during the EOD project demonstration process, but also further support the decision-making needs of "How to optimize the implementation of the project?" II. Model Validity Analysis and Discussion 1. Verification of Case Matching Accuracy To further evaluate the predictive capability of the constructed model, this embodiment compares the output of the GBDT model with the actual financial calculation data of the target project item by item, thereby analyzing its predictive accuracy on key financial and risk indicators. Evaluation indicators include the financial internal rate of return, financial net present value, payback period, return on investment, and debt service coverage ratio, reflecting the degree of deviation between the model output and the actual values ​​from both absolute and relative error perspectives. This comparison further determines the applicability and reliability of the proposed method in the financial feasibility assessment of EOD projects.

[0053] 2. Verification of the accuracy of financial indicator forecasts To quantify and verify the accuracy of the model's predictions of financial and risk indicators for EOD projects, the predicted values ​​of the GBDT model were compared item by item with the actual data from the project's operation. The absolute error and relative error rate were calculated and compared with the error rate of the traditional feasibility study report estimation method to verify the prediction accuracy of this model, as shown in Table 5. According to the error analysis results, this model has the highest accuracy in predicting the investment payback period, with a relative error rate of only 4.34%. The relative error rates for predicting the debt service coverage ratio and the financial internal rate of return are 12.02% and 35.11%, respectively.

[0054] Despite the relatively high error of some indicators, the solution in this embodiment still demonstrates good effectiveness from the perspective of project decision support. First, at the case matching level, the system can quickly identify reference cases highly similar to the target project from 126 historical cases, and the matching results show strong business consistency in terms of environmental problem attributes, industrial feedback logic, and financing structure. This indicates that the case representation and retrieval mechanism constructed in this embodiment has good adaptability. Second, at the financial forecasting level, the model has relatively stable predictive performance for key indicators such as investment payback period, debt service coverage ratio, and net present value, which can provide valuable quantitative references for project selection, scheme demonstration, and risk identification. Third, at the comprehensive decision-making level, the solution in this embodiment does not only output a single predicted value, but can generate an operational reference scheme by combining the experience of highly similar cases. This makes up for the shortcomings of a single prediction model in terms of interpretability and implementation guidance.

[0055] The results show that this embodiment can effectively achieve synergistic support among historical case matching, key financial indicator prediction, and reference solution generation. Its advantage lies not in achieving high-precision fitting of all financial indicators, but in providing a comprehensive decision-making basis that combines similar case reference, quantitative result analysis, and solution support capabilities in complex project scenarios. Therefore, the proposed method shows good application potential in terms of case matching accuracy, financial analysis effectiveness, and practical support capabilities.

[0056] In summary, this case study uses a national-level EOD pilot project—a canal EOD project—as the empirical research object. It strictly follows the six-stage CBR-ML hybrid model architecture constructed in this study: "case representation → data processing → case retrieval → case correction → case reuse → case prediction." It completes end-to-end empirical testing from target project standardization, high-similarity case retrieval, decision-making scheme generation to financial and risk indicator prediction. Furthermore, it conducts a systematic analysis of model effectiveness from four dimensions: case matching accuracy, financial indicator prediction accuracy, practical guidance value for decision support, and decision-making efficiency.

[0057] Empirical results demonstrate that the CBR-ML hybrid decision-making model constructed in this embodiment exhibits excellent application performance: First, standardized case representation and case library construction allow the previously scattered practical experience of EOD projects to be transformed into structured knowledge resources, thereby improving the retrieval and reusability of historical cases. Second, GBDT-based indicator weight identification enhances the objectivity of similarity calculation and financial forecasting, reducing the reliance on manual weight setting in the case matching process. Third, a linkage mechanism is formed between case retrieval, case correction, case reuse, and financial forecasting, shifting the decision-making process from static judgment to dynamic support. Compared with traditional experience-based judgment, this method has significant advantages in transparency, repeatability, and quantification; compared with single prediction models, it retains the ability to transfer experience and learn from solutions, making it more suitable for supporting decision-making in complex project scenarios.

[0058] 3. Sensitivity Analysis and Discussion of Multi-Objective Optimization Building upon the aforementioned methodological verification, this embodiment further combines sensitivity analysis and multi-objective optimization results to discuss the relationship between the financial feasibility of EOD projects and resource allocation. Compared to simply judging project performance based on point value predictions, sensitivity analysis and multi-objective optimization can further reveal the impact of changes in key decision variables on project performance, thus providing supplementary basis for project parameter adjustment, resource allocation optimization, and risk boundary identification. For complex projects like EOD projects that combine public welfare and commercial aspects, this analysis helps to understand the coupling relationship between investment scale, regional carrying capacity, and overall performance at a higher level.

[0059] Figure 8 The results of the sensitivity analysis of total investment are presented, where (a) shows the sensitivity of the financial internal rate of return (IRR) to changes in total investment, (b) shows the sensitivity of the financial net present value (NPV) to changes in total investment, (c) shows the sensitivity of the payback period to changes in total investment, (d) shows the sensitivity of the return on investment (ROI) to changes in total investment, and (e) shows the sensitivity of the debt service coverage ratio (DSR) to changes in total investment. It can be seen that changes in total investment have a significant impact on the project's financial performance, but this impact is not linear and continuous; rather, it exhibits obvious step-like and threshold-like characteristics. Specifically, the financial IRR, NPV, payback period, ROI, and DSR all fluctuate in segments with changes in total investment, and the direction of change for different indicators varies across different ranges. This indicates that for EOD projects, total investment is not a simple monotonic optimization variable, but rather has an appropriate range and critical boundaries: too low an investment may lead to insufficient project profitability, while too high an investment may significantly increase the financing burden and pressure to realize profits.

[0060] From a resource allocation perspective, the above results indicate that the financial performance of EOD projects is highly sensitive to investment scale, but this sensitivity does not mean that "increasing investment will improve performance." On the contrary, different financial and risk indicators show significant heterogeneity in their responses to changes in total investment, reflecting a multi-objective trade-off between profitability, investment recovery, debt repayment security, and resource allocation efficiency within the project. In other words, the determination of project investment scale should not be based solely on the optimal value of a single indicator, but should comprehensively consider the coordination and robustness among different objectives, thereby avoiding an overall performance decline while improving local indicators.

[0061] Figure 9 The optimal solution distributions for each output item are presented, where (a) is the optimal solution for GDP and total investment in terms of internal rate of return, (b) is the optimal solution for GDP and total investment in terms of net present value, (c) is the optimal solution for GDP and total investment in terms of investment payback period, (d) is the optimal solution for GDP and total investment in terms of return on investment, (e) is the optimal solution for GDP and total investment in terms of debt service coverage ratio, and (f) is the optimal solution for GDP and total investment in terms of comprehensive score. It can be seen that the comprehensive performance of EOD projects is influenced by both regional economic carrying capacity and investment scale, and there is a clear nonlinear coupling relationship between the two. Under the condition that total investment and regional GDP are the key decision variables, the five financial and risk indicators and the comprehensive target score all exhibit segmented variation characteristics. Overall, an increase in regional GDP usually helps improve the overall financial performance and comprehensive feasibility of the project, while the role of total investment is more complex: in the low to medium investment range, the comprehensive performance of the project fluctuates significantly with changes in investment; when the investment scale exceeds certain thresholds, the comprehensive score does not continue to improve, but may even decline. Therefore, simply expanding the scale of investment cannot guarantee optimal overall performance. A more reasonable approach is to control the scale of investment within a moderate range and match it with the regional economic carrying capacity.

[0062] From a project management perspective, this result offers important insights. Traditional project feasibility studies often focus on single core indicators such as the internal rate of return (IRR) or net present value (NPV). However, the results of this example demonstrate that the feasibility of EOD projects must be comprehensively balanced among profitability, investment recovery, debt repayment security, and resource allocation efficiency. This is especially true for ecological governance projects, which inherently possess strong public welfare attributes. Decisions based solely on a single revenue indicator may underestimate their overall implementation value; conversely, ignoring financial boundaries and financing constraints could lead to increased operational risks. Therefore, in the EOD project decision-making process, it is necessary to use sensitivity analysis and multi-objective optimization as auxiliary tools to identify key influencing factors, determine parameter threshold ranges, and seek a more robust balance among multiple objectives.

[0063] This section further illustrates that the financial feasibility and resource balance of EOD projects are not determined by a single variable, but rather by the combined effect of multiple factors. Sensitivity analysis reveals the local response patterns to changes in key decision variables, while multi-objective optimization further demonstrates the coupling relationships and optimal distribution characteristics among different objectives. The combination of these two approaches not only expands the explanatory depth of the project decision-making process using the method in this embodiment, but also provides a more targeted analytical basis for subsequent optimization and parameter adjustment of EOD projects.

[0064] Example 2: like Figure 8 As shown, this embodiment provides a method for EOD project financial feasibility assessment and resource balancing decision support, using the EOD project financial feasibility assessment and resource balancing decision support system described in Embodiment 1, including the following steps: S1: Receive historical EOD project case data and use a four-tuple structure to standardize the representation of each case; the four-tuple structure includes case identifier and basic attribute dimension ID, environmental governance problem type dimension P, resource balance and industry combination dimension S, and financial and risk assessment dimension E; S2: Perform missing data repair, data type identification and standardization on the case data, and write the processed standardized data back to the case library; S3: Based on supervised learning, train the weight recognition model, calculate the feature contribution of each input indicator and normalize it to obtain the indicator weight vector W; S4: Input the target project data, calculate the local similarity of each field according to the field type, combine the weight vector W of the index to form the global similarity, and output the ranking result of similar cases; S5: Determine whether the global similarity has reached the preset threshold: If it has, proceed to step S6; if it has not, start the dynamic correction engine to iteratively adjust the resource balance parameters, and return to step S4 to recalculate the global similarity until the preset threshold or the maximum number of iterations is reached. S6: Reuse the experience of highly similar cases to extract key points of financing structure, industry combination and risk control, and generate a set of reference solutions for the target project; S7: Use the GBDT model to predict the target project's internal rate of return, net present value, payback period, return on investment, and debt service coverage ratio, and output the prediction results and decision recommendations.

[0065] In summary, this invention achieves standardization, quantification, and interpretability of financial feasibility assessment and resource balancing scheme generation during the EOD project initiation stage through a combination of case structuring, similarity retrieval, interpretable weight learning, threshold-triggered iterative correction, and constraint embedding mechanisms. This significantly improves the efficiency of argumentation, enhances cross-regional reuse capabilities, and reduces the risk of fund mismatch and debt repayment during the implementation stage.

[0066] Example 3: This embodiment provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it adopts the EOD project financial feasibility assessment and resource balancing decision support method described in Embodiment 2.

[0067] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.

[0068] Furthermore, the processor can be a central processing unit (CPU). Of course, depending on the actual use, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it in this regard.

[0069] It should be noted that, in this embodiment, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0070] For those skilled in the art, the specific meanings of the above terms in this invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on," "mounted on," "fixed to," or "set on" another element, it may be directly on the other element or there may be an intermediate element present. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used in this embodiment are for illustrative purposes only and do not represent the only possible implementation.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0072] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

Claims

1. An EOD project financial feasibility assessment and resource balancing decision support system, characterized in that, include: The case library construction module is used to collect historical EOD project cases and use a four-tuple structure to standardize the representation of each case to form case data; The four-tuple structure includes the case identifier and basic attribute dimension ID, the environmental governance problem type dimension P, the resource balance and industry combination dimension S, and the financial and risk assessment dimension E; The data preprocessing module is used to receive the four-tuple structured case data output by the case library construction module, divide the fields of ID, P, S and E dimensions in the case data into string type, symbol type, numeric type and Boolean type, and perform text cleaning, category encoding, normalization and binarization processing respectively, perform statistical imputation or rule repair on missing data, and write the processed standardized data back to the case library. The indicator weight determination module is used to construct an input feature vector based on the standardized data output by the data preprocessing module, using fields in the ID, P, and S dimensions, and using the E dimension financial and risk assessment indicators as supervision labels to train a gradient boosting decision tree model. The gradient boosting decision tree model is used to quantify the contribution of each input feature to the input indicator by statistically analyzing the loss reduction brought by each input feature in all decision tree split nodes. After normalization, the indicator weight vector W is output. The similar case retrieval module receives target project data, calculates the local similarity between the target project and historical cases based on the weight vector W output by the indicator weight determination module according to the field type differences, and combines them into a global similarity, outputting the ranking result of similar cases; wherein, the edit distance is used for string fields, the complete consistency judgment is used for symbolic fields, the state consistency judgment is used for boolean fields, and the local similarity is calculated by the algorithm for numerical fields respectively. The case correction module receives the global similarity score output by the similar case retrieval module and compares the global similarity score with preset dual thresholds. If the value is greater than or equal to the first threshold, case reuse is triggered directly. When the resource balance parameters are between the first threshold and the second threshold, the resource balance parameters are iteratively adjusted. The adjustment process recalculates the global similarity and feasibility constraints after each iteration until the first threshold is met or the maximum number of iterations is reached. The resource balance parameters include the industry mix ratio, the financing structure ratio, the phased investment plan, and the phased financing plan. When the value is less than the second threshold, output a message indicating that there are no similar cases. The case reuse and solution generation module is used to extract governance path, industry introduction structure, revenue formation mechanism, financing tool combination and phased timing elements from the solution S dimension of highly similar cases when the global similarity meets the first threshold. It also combines the differences between the target project and similar cases to generate a set of candidate reference solutions for the target project by calling the mapping rule library. The indicator prediction module is used to predict the target project based on the candidate solutions output by the case reuse and solution generation module. It uses a gradient boosting decision tree model to predict the indicators and output the predicted indicators. Based on the predicted indicators, a comprehensive scoring function is constructed to quantitatively compare and select the candidate solutions and output the recommended solution. The predicted indicators include financial internal rate of return, financial net present value, investment payback period, return on investment and debt service coverage ratio. The results output and interaction module receives the output results from the similar case retrieval module, the indicator weight determination module, and the indicator prediction module. It displays the list of similar cases, weight explanations, prediction results, and decision suggestions in a visual interface and supports user interaction adjustments.

2. The EOD project financial feasibility assessment and resource balancing decision support system according to claim 1, characterized in that, The quadruple structure is represented as follows: The ID includes the project number, project name, implementing entity, project location, and project financing information; the P dimension describes the type of environmental governance problem; the S dimension describes the balance of resource categories and industry combination patterns; and the E dimension describes financial and risk assessment indicators.

3. The EOD project financial feasibility assessment and resource balancing decision support system according to claim 2, characterized in that, In the indicator weight determination module, the gradient boosting decision tree model quantifies the contribution of the input indicators and outputs a normalized indicator weight vector W, as follows: Gradient boosting decision tree models use a stepwise addition model to construct prediction functions. In the m-th iteration, the negative gradient is used as the pseudo-residual: Training regression tree Fitting Updated with a step size v=0.05: ; Where u represents the round number, u=200; L is the mean squared error loss function. For the input feature vector, For output metrics; In terms of weight calculation, the gradient boosting decision tree model measures the contribution by statistically analyzing the loss reduction (Gain) caused by each feature's participation in node splitting across all trees: Let Split(m) be the set of split nodes of feature m in all trees, and Gain be the gain of node n. n Then the feature importance is: Therefore, the normalization yields the following weights: In the formula, Indicates the first One input feature; This represents the total number of input features; This indicates that among all regression trees in the GBDT model, the first one is selected. The set of all split nodes whose features serve as the basis for partitioning; Represents the set of split nodes A split node in the process; Indicates the first The decrease in loss function or gain value of each split node after feature partitioning is used to characterize the contribution of the node split to model optimization. Indicates the first The importance of each input feature is equal to the sum of the gain values ​​of that feature at all relevant split nodes; Indicates the first The importance of each input feature; Represents the input feature index, where .

4. The EOD project financial feasibility assessment and resource balancing decision support system according to claim 3, characterized in that, The similar case retrieval module calculates local similarity for string fields using edit distance. satisfy: in For target string With historical strings Edit distance between; The similar case retrieval module calculates local similarity for symbolic fields using consistency judgment. Satisfy: When hour ,otherwise ; The similar case retrieval module first performs min-max normalization on numerical fields. Then calculate the local similarity. ; The similar case retrieval module calculates local similarity for Boolean fields using consistency criteria. Satisfy: When hour ,otherwise .

5. The EOD project financial feasibility assessment and resource balancing decision support system according to claim 4, characterized in that, global similarity The weighted sum of the local similarities of each field satisfies: in The normalized weights output by the module determine the indicator weights.

6. The EOD project financial feasibility assessment and resource balancing decision support system according to claim 5, characterized in that, In the case correction module, during the iterative adjustment of resource balance parameters, the feasibility constraints for verification are calculated and checked simultaneously, specifically including: Resource balance constraint: At any point t, the cumulative funds must not be less than the cumulative investment. and meet the requirements at the end of the period. ; Financial feasibility constraints include: Non-negative net present value: At the same time, the debt service coverage ratio meets the requirements. Where CF is net cash flow, OCF is operating cash flow, and DS is current principal and interest payment. r is the discount rate.

7. The EOD project financial feasibility assessment and resource balancing decision support system according to claim 6, characterized in that, The case reuse and solution generation module includes: Extract governance path, industrial introduction structure, revenue formation mechanism, financing tool combination and phased timing elements from the S dimension of similar cases; Combining the difference vector between the target project and similar cases A set of candidate solutions is generated through the Map function, which is driven by the rule base and parameter templates. The details are as follows: In the formula, This is the set of candidate solutions.

8. The EOD project financial feasibility assessment and resource balancing decision support system according to claim 7, characterized in that, Based on the predicted indicators output by the indicator prediction module, a comprehensive scoring function is constructed to quantitatively compare and select candidate solutions, and a recommended solution is output, as follows: The indicator prediction module takes the feature vectors and correction parameters of candidate solutions as input, and outputs the predicted financial internal rate of return (FIRR), financial net present value (FNPV), payback period, return on investment, and debt service coverage ratio (DSCR). It also constructs a comprehensive scoring function for comparing multiple solutions. in To measure the deviation of investment funds, The weighting coefficients and .

9. A method for financial feasibility assessment and resource balancing decision support for EOD projects, employing the EOD project financial feasibility assessment and resource balancing decision support system as described in any one of claims 1 to 8, characterized in that, Includes the following steps: S1: Receive historical EOD project case data and use a four-tuple structure to standardize the representation of each case; the four-tuple structure includes case identifier and basic attribute dimension ID, environmental governance problem type dimension P, resource balance and industry combination dimension S, and financial and risk assessment dimension E; S2: Perform missing data repair, data type identification and standardization on the case data, and write the processed standardized data back to the case library; S3: Based on supervised learning, train the weight recognition model, calculate the feature contribution of each input indicator and normalize it to obtain the indicator weight vector W; S4: Input the target project data, calculate the local similarity of each field according to the field type, combine the weight vector W of the index to form the global similarity, and output the ranking result of similar cases; S5: Determine whether the global similarity has reached the preset threshold: If it has, proceed to step S6; if it has not, start the dynamic correction engine to iteratively adjust the resource balance parameters, and return to step S4 to recalculate the global similarity until the preset threshold or the maximum number of iterations is reached. S6: Reuse the experience of highly similar cases to extract key points of financing structure, industry combination and risk control, and generate a set of reference solutions for the target project; S7: Use the GBDT model to predict the target project's internal rate of return, net present value, payback period, return on investment, and debt service coverage ratio, and output the prediction results and decision recommendations.

10. A terminal device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the EOD project financial feasibility assessment and resource balancing decision support method as described in claim 9 is implemented.

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