Methods for constructing ecological credit evaluation models

Abstract

This invention provides a method for constructing an ecological credit evaluation model, comprising: collecting green finance activity data of the evaluation subject; parsing the green finance activity data to obtain a subject profile feature library, which includes the basic information of the evaluation subject, its behavioral pattern characteristics and preference characteristics in the field of green finance; assigning a multi-dimensional identity identifier set to the evaluation subject; constructing an evaluation subject profile based on the subject profile feature library and the multi-dimensional identity identifier set; based on the constructed causal inference model, mining the internal and external factors that influence the green finance behavior and ecological credit of the evaluation subject according to the evaluation subject profile; training a pre-trained neural network model according to the internal and external factors until the training is completed, thereby obtaining an ecological credit evaluation model, which significantly improves the scientificity, accuracy and practicality of the ecological credit evaluation results.

Description

Technical Field

[0001] This invention relates to the field of intelligent processing technology, and specifically to a method for constructing an ecological credit evaluation model. Background Technology

[0002] With increasing global emphasis on ecological and environmental protection and the rapid development of the green economy, green finance, as an important tool for promoting sustainable ecological development, plays a crucial role in resource allocation and risk prevention. Ecological credit rating, as a core component of the green finance system, aims to scientifically assess the creditworthiness of market participants in green finance activities, providing a basis for financial institutions' decision-making, policy formulation, and industry supervision. This effectively reduces transaction risks, optimizes resource allocation efficiency, and promotes the healthy development of green industries.

[0003] Currently, traditional credit rating methods mainly rely on structured data such as financial indicators and historical transaction records, and conduct credit assessments by constructing fixed-weight scoring models or simple machine learning algorithms. These methods have several limitations: on the one hand, they overemphasize financial data, making it difficult to comprehensively capture the behavioral characteristics and ecological impact of market participants in green finance activities, such as insufficient consideration of non-financial factors like the effectiveness of green project implementation and environmental risk response capabilities; on the other hand, fixed evaluation models lack adaptability to dynamic market environments and innovative business scenarios, and cannot promptly reflect the impact of external factors such as policy changes and industry fluctuations on ecological credit.

[0004] In recent years, some studies have attempted to apply deep learning technology to the field of credit rating, automatically extracting data features through neural networks. However, existing technologies still have significant shortcomings: at the data processing level, most do not fully integrate multi-source heterogeneous data, and make insufficient use of unstructured data (such as industry news, policy texts, and environmental monitoring reports), resulting in incomplete information mining; in terms of model construction, there is a lack of in-depth analysis of the causal relationships between credit-influencing factors, and predictions are made solely based on data correlation, making it difficult to accurately reveal the intrinsic mechanism of ecological credit formation, thus affecting the accuracy and reliability of the evaluation results; in addition, existing models generally lack targeted processing of key factors such as spatiotemporal characteristics and subject differences in ecological credit rating scenarios, and cannot meet the innovative and ever-changing needs of green finance business.

[0005] Therefore, there is an urgent need for an ecological credit evaluation model construction method that can fully integrate multi-source data, deeply explore causal relationships, and adapt to the characteristics of green finance business, so as to improve the scientificity, accuracy and practicality of ecological credit evaluation and promote the high-quality development of the green finance industry. Summary of the Invention

[0006] To address the aforementioned technical problems, this application provides a method for constructing an ecological credit evaluation model, which at least solves or alleviates the problems existing in the prior art.

[0007] To achieve the above objectives, according to one aspect of this application, a method for constructing an ecological credit evaluation model is provided, comprising:

[0008] Collect data on the green finance activities of the evaluation entities;

[0009] The green finance activity data is analyzed to obtain a subject profile feature library of the evaluation subject, which includes the basic information of the evaluation subject, its behavioral pattern characteristics and preference characteristics in the field of green finance;

[0010] Assign a multi-dimensional identity set to the evaluation subject, the multi-dimensional identity set including a composite subject ID consisting of a timestamp, a region identifier, a subject type identifier, and a unique serial number;

[0011] Based on the subject profile feature library of the evaluation subject and the multi-dimensional identity identifier set, an evaluation subject profile is constructed;

[0012] Based on the constructed causal inference model, and according to the profile of the evaluation subject, the internal and external factors influencing the green finance behavior and ecological credit of the evaluation subject are explored.

[0013] The pre-trained neural network model is trained based on the internal and external factors until the training is completed, resulting in an ecological credit evaluation model.

[0014] The technical solution in this application has at least the following technical advantages:

[0015] ① By collecting data on the green finance activities of the evaluated entities and analyzing it to obtain a feature library of entity profiles containing basic information, behavioral pattern characteristics, and preference characteristics, this method overcomes the limitations of traditional methods that rely solely on structured financial data. By combining a multi-dimensional identity set to construct an evaluation entity profile, it can integrate multi-source heterogeneous data, comprehensively covering various types of information about the entity in green finance activities, including unstructured behavioral preference data, effectively solving the problem of incomplete information mining in existing technologies and more accurately depicting the characteristics of the evaluated entities.

[0016] ② Utilizing the constructed causal inference model, this method mines the characteristics of internal and external factors influencing ecological credit based on the profile of the evaluation subject. Unlike existing methods that rely solely on data correlation for prediction, this approach can deeply analyze the intrinsic mechanism of ecological credit formation, revealing the impact of various factors on ecological credit from a causal logic perspective. This avoids misjudgments caused by spurious correlations and significantly improves the scientific rigor and accuracy of ecological credit evaluation results.

[0017] ③ The pre-trained neural network model is trained based on the identified internal and external factors. The dynamic learning capability of the neural network can adapt to the innovative and ever-changing nature of green finance business. Compared with traditional fixed-weight scoring models, this method can learn in real time external factors such as policy changes and industry fluctuations, as well as internal factors such as changes in the subject's own behavioral patterns. This allows the model to better adapt to different time and space and business scenarios, solving the problem of poor adaptability of existing models to dynamic environments and improving the practicality and reliability of ecological credit evaluation. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for constructing an ecological credit evaluation model according to an embodiment of this application. Detailed Implementation

[0019] like Figure 1 As shown, an embodiment of this application provides a method for constructing an ecological credit evaluation model, which includes:

[0020] Collect data on the green finance activities of the evaluation entities;

[0021] The green finance activity data is analyzed to obtain a subject profile feature library of the evaluation subject, which includes the basic information of the evaluation subject, its behavioral pattern characteristics and preference characteristics in the field of green finance;

[0022] Assign a multi-dimensional identity set to the evaluation subject, the multi-dimensional identity set including a composite subject ID consisting of a timestamp, a region identifier, a subject type identifier, and a unique serial number;

[0023] Based on the subject profile feature library of the evaluation subject and the multi-dimensional identity identifier set, an evaluation subject profile is constructed;

[0024] Based on the constructed causal inference model, and according to the profile of the evaluation subject, the internal and external factors influencing the green finance behavior and ecological credit of the evaluation subject are explored.

[0025] The pre-trained neural network model is trained based on the internal and external factors until the training is completed, resulting in an ecological credit evaluation model.

[0026] Preferably, in a specific application scenario, the integration of technologies such as quantum computing, graph theory, and higher-order tensor operations is implemented in an alternative or preferred manner, as detailed below:

[0027] 1. Data collection and preprocessing for green finance activities

[0028] Suppose the collected green finance activity data constitutes a tensor Where n is the number of evaluation subjects, m is the data feature dimension, k is the time series length, and l is the number of data source channels. Tensor decomposition technology is used to denoise and enhance the features of the original data, specifically through Higher-Order Singular Value Decomposition (HOSVD): in, For the core tensor, U 1 U 2 U 3 U 4 These are the orthogonal basis matrices for their respective dimensions. The core tensor is then modified by setting a threshold. The elements are filtered, key feature components are retained, and the preprocessed data tensor is reconstructed.

[0029] 2. Construction of Subject Portrait Feature Database

[0030] 2.1 Basic Information Extraction

[0031] Basic information is extracted based on graph convolutional networks (GCNs), and the relationships between subjects are constructed as a graph structure. Among the nodes For each evaluation subject, edge ε represents the relationship between subjects. The feature vector update formula for the i-th node is: in, Let i be the feature vector of node i in the l-th layer. Let c be the set of neighboring nodes of node i. ij W is the normalized coefficient between nodes i and j. (l) Let b be the weight matrix of the l-th layer. (l) σ is the bias vector, and σ is the activation function (such as LeakyReLU). After multiple iterations, the basic information feature vector b is output. i .

[0032] 2.2 Behavioral Pattern Feature Extraction

[0033] A behavioral pattern feature extraction model is constructed using the quantum superposition principle. The quantum state vector |ψ| is defined. i >Indicates the behavioral pattern of the i-th evaluator: Where, |φ j >Based on the fundamental behavioral pattern quantum state, α ij Let be the quantum state superposition coefficient, satisfying The quantum state is transformed by the quantum gate operation U: |ψ i′ >=U|ψ i Measuring the transformed quantum state yields the behavioral pattern feature vector a. i .

[0034] 2.3 Preference Feature Extraction

[0035] Based on the attention mechanism and reinforcement learning-based preference feature extraction model, the preference feature extraction function is defined as: p i =RL-Attention(d i (R) where R is the reward function matrix, which is continuously optimized through reinforcement learning algorithms to improve the ability to capture key preference features. The formula for calculating the attention mechanism is: Where Q, K, and V are the query, key, and value matrices, respectively, and d k For key vector dimensions. Subject portrait feature library F i Still expressed as

[0036] 3. Multi-dimensional identity set and subject profile construction

[0037] Multi-dimensional identity set I i =[t i ,r i ,s i ,u i ] T Remain unchanged. When constructing the main subject profile, tensor fusion technology is introduced to integrate the main subject profile feature library F. i With multi-dimensional identity set I i Merge into tensors

[0038] 4. Causal inference models uncover the characteristics of internal and external factors.

[0039] Based on a dual framework of structural causal modeling (SCM) and causal effect estimation, the structural equation is defined as: X = f(U,E) + g(Z), where X is an endogenous variable (green finance behavior and ecological credit indicators), U is an exogenous variable (internal and external factors), E is the error term, and Z is an instrumental variable. Causal effect estimation methods (such as dual machine learning) are used to calculate the causal effect τ of each factor on ecological credit. Here, Y(1) and Y(0) represent the outcome variables when the intervention occurred and when it did not, respectively. By screening for factors with significant causal effects, the internal factor characteristic vector was obtained. and external factor eigenvectors

[0040] 5. Training the pre-trained neural network model

[0041] A hybrid deep neural network architecture is employed, combining Transformer and Graph Neural Network (GNN). The input layer receives feature vectors from internal and external factors. Processed by the Transformer encoding layer: The data is then input into the graph neural network layer, and the graph convolution update formula is: The output layer employs an adaptive weighted fusion mechanism to obtain the ecological credit evaluation results. Where, ω l For adaptive weights, joint optimization is achieved through backpropagation and a meta-learning algorithm. The loss function is a composite loss function: L = λ₁L MSE +λ2L CE +λ3L TV Among them, L MSE For mean square error loss, L CE For cross-entropy loss, L TV λ1, λ2, and λ3 are the total variation regularization terms, and their optimal values ​​are determined using a Bayesian optimization algorithm.

[0042] Therefore, the above solution has the following technical advantages:

[0043] I. Traditional techniques typically perform only simple numerical statistics or linear transformations on structured financial data and transaction records, making it difficult to handle multi-source heterogeneous data. For example, when faced with textual policy information and visual environmental monitoring data from green finance activities, traditional methods often fail to effectively extract key features. This solution introduces Higher-Order Tensor Decomposition (HOSVD) to process the tensors of green finance activity data. It can simultaneously perform feature extraction and noise reduction from multiple dimensions, including subject, features, time, and data source. This is achieved through the core tensor. The screening and reconstruction can accurately retain key information related to ecological credit, such as the green finance behavior characteristics of entities under different time points and data channels, effectively solving the problem of insufficient data utilization in traditional methods and improving the comprehensiveness and depth of data processing.

[0044] II. Traditional behavioral pattern and preference feature extraction often relies on fixed rules or simple clustering algorithms, such as K-means clustering to segment behavioral patterns. This depends on manually setting thresholds and rules to mine preferences, failing to capture innovative nonlinear relationships and dynamically changing features. In this scheme, behavioral pattern feature extraction employs the quantum state superposition principle to construct a quantum state vector |ψ i By employing quantum gate operations to achieve nonlinear transformations of behavioral patterns, this approach can simulate the diversity and uncertainty of agent behavior, overcoming the limitations of traditional methods that only provide static and linear descriptions of behavioral patterns. Preference feature extraction, combining attention mechanisms and reinforcement learning, dynamically optimizes the reward function matrix R, adaptively focusing on key preference features. Compared to traditional fixed-rule mining methods, this approach can more accurately capture the dynamic changes in agents' preferences during green finance activities.

[0045] Third, traditional credit rating models are mostly based on data correlation for prediction, which cannot accurately distinguish between causal relationships and false associations. For example, they may incorrectly associate occasional market fluctuations with changes in the creditworthiness of the main body.

[0046] The causal inference model constructed in this scheme combines structural causal modeling (SCM) with a dual machine learning approach, by introducing the instrumental variable Z and calculating the causal effect. It can rigorously distinguish the causal impact of various factors on ecological credit. In green finance scenarios, it can accurately determine the true causal effect of factors such as policy and regulatory changes and internal management adjustments on ecological credit, avoiding evaluation bias caused by spurious correlations in traditional methods, and significantly improving the scientificity and reliability of evaluation results.

[0047] Fourth, traditional neural network models have a simple structure and often use fixed loss functions and optimization algorithms, making them difficult to adapt to the innovation and dynamism of green finance business. They also have poor generalization ability when dealing with ecological credit evaluations of different entities and in different scenarios.

[0048] This scheme employs a hybrid architecture combining Transformer and Graph Neural Network (GNN). The Transformer encoding layer effectively captures long-distance dependencies between factors, while the GNN layer uncovers graph-structured relationships between ecological asset elements. The combination of these two approaches comprehensively addresses innovative relationships in ecological credit evaluation. Simultaneously, the composite loss function L = λ1L... MSE +λ2L CE +λ3L TV By combining Bayesian optimization to adjust the weight coefficients, not only prediction error and classification accuracy are considered, but the model stability is also improved through the total variation regularization term. Compared with the traditional single loss function, it can optimize the model more accurately, making the model more adaptable and generalizable in the innovative and ever-changing scenarios of green finance.

[0049] Optionally, the pre-trained neural network model is trained based on the internal and external factor characteristics until training is complete, resulting in an ecological credit evaluation model, including:

[0050] Based on the characteristics of the internal and external factors, a network structure with ecological asset elements as related branches is constructed to form an ecological asset relationship map, and a unique element association ID is assigned to each ecological asset element.

[0051] Obtain descriptions of green finance business scenarios to build a scenario simulation module;

[0052] Based on the scenario simulation module, the scenario interaction score between the evaluation subject and the ecological asset relationship map is evaluated to form a multi-dimensional scenario association matrix;

[0053] Based on the multi-dimensional scenario association matrix, the pre-trained neural network model is trained until the training is completed, resulting in an ecological credit evaluation model.

[0054] Preferably, in a specific application scenario, the above solution is described in an alternative or preferred manner.

[0055] 1. Construct an ecological asset relationship map

[0056] Let the set of internal and external factors be . Where N represents the total number of factor characteristics. The set of ecological asset elements is denoted as... M represents the quantity of ecological asset elements. Define the correlation matrix. Among them, element g ij Indicates the characteristic of factor f i With ecological asset element a j The correlation strength is calculated as follows: in, and These are the factor characteristics f i and ecological asset elements a j The feature vectors are obtained through a pre-trained word vector model (such as Word2Vec) or a feature extraction network; cosine(·,·) is the cosine similarity function, used to measure the similarity between two vectors.

[0057] Based on the correlation matrix G, construct an ecological asset relationship map. The node set is Edges (a) in edge set ε i ,a j The weight w ij Determined by the following formula: For each ecological asset element a j Assign a unique element association ID using a hash function h(a j ID generation j =h(a j This ensures that each element can be uniquely identified during data processing and model training.

[0058] 2. Construct a scenario simulation module

[0059] Let the set of text describing green finance business scenarios be denoted as . S represents the number of scene description texts. A pre-trained language model (such as BERT) is used to process each text t. s Encode to obtain feature vectors Where D is the dimension of the feature vector.

[0060] When constructing the scenario simulation module, a variational autoencoder (VAE) model is introduced. Encoder q φ (z|h s ) Text features h s Mapping to latent space K is the dimension of the latent space, and its calculation process is as follows: z s =μs +σ s ⊙∈where, For random noise following a standard normal distribution, ⊙ denotes element-wise multiplication. Decoder p θ (h s |z) Reconstruct text features based on latent variable z, and train a VAE model by minimizing reconstruction loss and KL divergence loss to achieve effective modeling and simulation of green finance business scenarios.

[0061] 3. Form a multi-dimensional scenario association matrix

[0062] For the evaluation subject u, under scenario s, and ecological asset element a j The interaction score (score(u,s,j)) is calculated using a reinforcement learning method. The policy network π is defined. θ (a|s,u) represents the probability of selecting ecological asset element a under scenario s and subject u. Rewards are obtained through interaction with the ecological asset relationship graph, based on the reward function R(u,s,a). The reward function can be set according to the green finance business objectives, such as improving ecological benefits and reducing risk. A deep Q-network (DQN) algorithm is used to optimize the policy network, with the Q-value function Q... θ (s,u,a) represents the expected cumulative reward for choosing action a under scenario s and subject u, and its update formula is: Q θ (s,u,a)←Q θ (s,u,a)+α[r+γmax a′ Q θ′ (s′,u,a′)-Q θ [(s,u,a)], where α is the learning rate, γ is the discount factor, r is the immediate reward, (s′,a′) represents the next scenario and action, and θ′ represents the target network parameters. Finally, a multi-dimensional scenario association matrix is ​​generated. Where element m usj = score(u,s,j), where U is the number of evaluators.

[0063] 4. Training the pre-trained neural network model

[0064] Let the pre-trained neural network model be The input is a multi-dimensional contextual association matrix M, which is processed through multiple layers of a neural network. The loss function L is defined as: L = λ1L regression +λ2L classification +λ3L entropy Among them, L regression The regression loss, used to measure the difference between the predicted ecological credit score and the actual score, is calculated using the mean squared error (MSE). For the ecological credit score predicted by the model, y us This is a genuine rating.classification The classification loss, used to measure the difference between the predicted and actual ecological credit ratings, is calculated using cross-entropy loss. C represents the number of categories in the ecological credit rating system. and These represent the probability distributions of the actual and predicted ecological credit ratings, respectively. L entropy This is the entropy regularization term for the policy network, used to increase policy diversity. It is calculated as follows: λ1, λ2, and λ3 are weight coefficients, whose optimal values ​​are determined using cross-validation. The gradient of the loss function with respect to the model parameters ω is calculated using the backpropagation algorithm. Update model parameters using an adaptive learning rate optimization algorithm (such as Adam): Where β is the learning rate, the training is iterated continuously until the loss function converges, and the final ecological credit evaluation model is obtained.

[0065] Therefore, the above solution has the following technical advantages:

[0066] First, traditional technologies typically use manually set rules or simple weighting methods to determine the relationships between ecological asset elements. For example, setting fixed weights for financial indicators and ecological credit based on experience makes it difficult to adapt to innovative and ever-changing green finance scenarios, and lacks quantitative analysis of unstructured factors.

[0067] This scheme calculates the correlation strength g between factor characteristics and ecological asset elements using the correlation matrix G. ij By utilizing cosine similarity combined with pre-trained word vector models or feature extraction networks, internal and external factor features and ecological asset elements are transformed into vector forms for quantitative calculation, effectively processing the information contained in unstructured data such as text and images. (Edge weight w) ij The calculation comprehensively considers the influence of all factors and characteristics, making the ecological asset relationship map more accurately reflect the actual relationship. For example, it can dynamically capture the impact of policy changes on different ecological asset elements, which greatly improves the accuracy and dynamic adaptability of relationship modeling compared with traditional methods.

[0068] Second, traditional scenario simulation relies on expert experience or simple rule matching, which makes it difficult to simulate innovative green finance business scenarios and cannot handle semantic information and uncertainty in scenario descriptions. For example, classifying business scenarios based solely on fixed templates cannot reflect the dynamic changes and individual differences in scenarios.

[0069] This solution introduces a variational autoencoder (VAE) and utilizes a pre-trained language model, BERT, to encode text representing green finance business scenarios. The encoder maps text features to a latent space, and the decoder reconstructs these features. By minimizing the reconstruction loss and KL divergence loss during training, probabilistic modeling of scenario features is achieved, generating diverse scenario representations that conform to real-world distributions. For example, different business scenario simulations can be generated based on policy adjustments, providing richer and more realistic input for ecological credit evaluation and addressing the limitations of traditional methods in terms of singular scenario modeling and lack of flexibility.

[0070] Third, traditional interactive scoring calculations are mostly based on fixed scoring rules, such as simply scoring based on transaction amount and frequency. This cannot adapt to the innovative behaviors of different subjects in diverse scenarios, and it is difficult to dynamically adjust the evaluation strategy, resulting in a lack of accuracy and real-time performance in the evaluation results.

[0071] This scheme employs reinforcement learning, using a policy network π. θ The interaction score calculation is optimized using (a|s,u) and Deep Q-Network (DQN). The policy network dynamically generates the probability of selecting ecological asset elements based on the scenario and the subject. The DQN learns the optimal policy through interaction with the ecological asset relationship graph and dynamically adjusts the behavior according to the reward function R(u,s,a). For example, during market fluctuations, the model can quickly adjust the evaluation strategy for different ecological asset elements, enabling the multi-dimensional scenario association matrix to reflect the actual interaction of the subject in innovative scenarios in real time. Compared with traditional fixed strategies, this significantly improves the dynamism and accuracy of the evaluation.

[0072] Fourth, traditional neural network training often uses a single loss function (such as mean squared error), which only focuses on the difference between the predicted value and the true value, ignoring the model's generalization ability and strategy diversity, which can easily lead to overfitting and poor performance when facing new scenarios.

[0073] This scheme adopts a composite loss function L = λ1L regression +λ2L classification +λ3L entropy The model simultaneously considers regression loss, classification loss, and entropy regularization of the policy network. Regression and classification losses ensure prediction accuracy, while entropy regularization increases policy diversity and prevents the model from getting trapped in local optima. Weight coefficients λ1, λ2, and λ3 are determined through cross-validation, and the model parameters are updated using an adaptive learning rate optimization algorithm. This ensures that the model maintains both prediction accuracy and good generalization ability in innovative green finance scenarios, overcoming the limitations of traditional training methods.

[0074] Optionally, the pre-trained neural network model is trained based on a multi-dimensional scenario association matrix until training is complete, resulting in an ecological credit evaluation model, including:

[0075] Obtain the real-time performance evaluation value of the evaluation subject in green finance activities and the actual contribution evaluation value of ecological asset elements to green finance business;

[0076] Based on the real-time performance evaluation value and the actual contribution evaluation value, dynamic weights are calculated for ecological asset elements to calibrate the multi-dimensional scenario correlation matrix.

[0077] Based on the calibrated multi-dimensional scenario association matrix, the pre-trained neural network model is trained until the training is completed, resulting in an ecological credit evaluation model.

[0078] Preferably, in a specific application scenario, the above solution is described in an alternative or preferred manner.

[0079] 1. Obtain real-time performance evaluation values ​​and actual contribution evaluation values.

[0080] Let the set of evaluation subjects be . Where U represents the number of evaluation subjects; the set of ecological asset elements is... M represents the quantity of ecological asset elements.

[0081] 1.1 Calculation of Real-Time Performance Evaluation Value

[0082] Construct a real-time performance evaluation index system N represents the number of indicators. For the evaluation subject u... i In indicator I j The real-time data below is denoted as x. ij Principal component analysis (PCA) combined with the entropy weight method was used to determine the weights w of each indicator. j First, PCA is used to analyze the original data matrix X = [x ij ] U×N Dimensionality reduction yields the principal component matrix PC and eigenvalue vector λ. The entropy weight method calculates the index weights using the following formula: in, e j For indicator I j The information entropy. Then the indicator weights. Evaluation subject u i Real-time performance evaluation value R i for:

[0083] 1.2 Calculation of Actual Contribution Assessment Value

[0084] For ecological asset element a k Its actual contribution to green finance business is assessed at C. k Data Envelopment Analysis (DEA) combined with grey relational analysis was used for calculation. Let I be the input vector of the decision-making unit (ecological asset element). k =[ik1 i k2 ,…,i kP The output vector is O. k =[o k1 ,o k2 ,…,o kQ [P represents the quantity of input indicators, and Q represents the quantity of output indicators.] The efficiency value θ of ecological asset elements is calculated using the DEA model. k : Where λ=[λ1,λ2,…,λ M ] T This is the weight vector.

[0085] Then, grey relational analysis is used to calculate the correlation γ between ecological asset elements and green finance business objectives. k The final actual contribution assessment value C k =αθ k +(1-α)γ k α is the weighting adjustment coefficient.

[0086] 2. Calculate dynamic weights and calibrate the multi-dimensional scenario association matrix.

[0087] Let the multi-dimensional scenario association matrix be M = [m ijs ] U×M×S Where S is the number of scenarios, m ijs Indicates the evaluation subject u i Under scenario s and ecological asset element a j Interaction scores. A dynamic weight calculation model is constructed, and an Adaptive Weighted Neural Network (AWNN) is used to calculate the dynamic weights w = [w1, w2, ..., w...]. M ] T The input layer receives a real-time performance evaluation vector R = [R1, R2, ..., R...]. U ] T And the actual contribution evaluation value vector C = [C1, C2, ..., C M ] T The hidden layer activation function is σ(·) (e.g., LeakyReLU), and the output layer outputs dynamic weights. The network forward propagation formula is: h l =σ(W l h l-1 +b l Among them, h l W is the output vector of the l-th layer. l Let b be the weight matrix of the l-th layer. l Let h0 be the bias vector, and h0 = [R] T C T ] T The calibrated multidimensional scenario association matrix The calculation is as follows:

[0088] 3. Training a pre-trained neural network model based on the calibration matrix

[0089] Let the pre-trained neural network model be The input is the calibrated multi-dimensional contextual association matrix M. Training is performed using an improved hybrid loss function L: L = λ₁L MSE +λ2L Focal +λ3L Contrastive , where L MSE Mean squared error loss is used to measure the difference between the predicted and the true values. y is the model's predicted value. ijs This represents the true ecological credit score. L Focal Focus loss is used to address the imbalanced sample problem. in, γ is the adjustment parameter for predicting probabilities. Contrastive To enhance the model's feature representation capabilities by contrasting learning losses: Among them, z ijs The feature vector output by the model. z is the feature vector of the positive sample. iks Let be the negative sample feature vector, sim(·,·) be the similarity function, and τ be the temperature parameter.

[0090] Calculate the gradient using the backpropagation algorithm. The AdamW optimization algorithm is used to update the model parameters ω: Where β is the learning rate, λ wd This represents the weight decay coefficient. The model is iteratively trained until the loss function converges, yielding the final ecological credit evaluation model.

[0091] Therefore, the above-mentioned solution, compared with traditional techniques, focuses on dynamic weight calibration and model optimization in the training of the ecological credit evaluation model. Compared with traditional techniques, it offers the following advantages in terms of data evaluation, weight calculation, and model training:

[0092] 1. Traditional ecological credit rating systems often employ single indicators or simple weighted averages when assessing the real-time performance of the evaluated entity and its contribution to ecological assets. For example, assessing an enterprise's ecological credit solely based on its green project investment amount ignores crucial factors such as project implementation efficiency and environmental benefits. Furthermore, when evaluating the contribution of ecological assets, they lack a systematic analysis of input and output, making it difficult to accurately measure their actual value. This approach is highly subjective, uses single indicators, and fails to comprehensively reflect the true situation of the evaluated entity and its ecological assets.

[0093] In this application, the real-time performance evaluation value is calculated using Principal Component Analysis (PCA) combined with the entropy weight method. PCA can reduce the dimensionality of multidimensional raw data, remove data redundancy, and extract key information; the entropy weight method objectively determines the indicator weights based on the degree of variation of the data itself, avoiding interference from human factors. For example, in green finance scenarios, multiple indicators such as the company's cash flow, project progress, and environmental impact can be comprehensively considered to accurately evaluate its real-time performance. The actual contribution evaluation value is calculated by combining Data Envelopment Analysis (DEA) and Grey Relational Analysis. DEA is used to evaluate the input-output efficiency of ecological asset elements, while Grey Relational Analysis measures the degree of correlation between elements and green finance business objectives. The combination of the two can comprehensively and accurately evaluate the actual contribution of ecological asset elements.

[0094] 2. Traditional methods of determining weights are often fixed, making it difficult to dynamically adjust them based on actual circumstances once set. For example, in credit rating models, the weights of each evaluation indicator remain unchanged over a long period, failing to adapt to changes in the green finance market environment, policy adjustments, and the impact of the evaluation entity's own development. This static weighting makes the evaluation results lack flexibility and timeliness.

[0095] This solution employs an Adaptive Weighted Neural Network (AWNN) to calculate the dynamic weights of ecological asset elements. AWNN takes real-time performance evaluation vectors and actual contribution evaluation vectors as input. Through multi-layer neural network learning and training, it can automatically capture the dynamic changes in the real-time performance of the evaluation subject and the actual contribution of ecological asset elements, thereby adjusting the weights of each element. As green finance business scenarios constantly evolve, such as a surge in market demand for a certain ecological asset, AWNN can promptly increase the weight of that asset element, making the multi-dimensional scenario correlation matrix more closely reflect the actual situation and enhancing the adaptability and accuracy of the evaluation model.

[0096] 3. Traditional neural network model training typically uses a single loss function, such as mean squared error loss, which only focuses on the difference between predicted and true values. This makes it difficult to address the problem of imbalanced samples and fails to effectively enhance the model's feature representation capabilities. In ecological credit evaluation, there are situations where the number of samples for certain ecological credit levels is extremely small. A single loss function will lead to poor learning performance of the model on a small number of samples, reducing the overall evaluation accuracy.

[0097] This application employs an improved hybrid loss function, which includes mean squared error loss (L... MSE ), focus loss (L) Focal ) and contrastive learning loss (L Contrastive L MSE To ensure a basic fit between the predicted and actual values; L Focal By adjusting the parameter γ, the weight of easily classified samples is reduced, and the focus is placed on difficult-to-classify samples, effectively solving the problem of imbalanced samples; L ContrastiveBy comparing the feature vectors of positive and negative samples, the model's ability to distinguish different ecological credit characteristics is enhanced, thus improving its feature representation capabilities. The synergistic effect of multiple loss functions allows for more comprehensive optimization during training, improving the accuracy of ecological credit evaluation and the model's generalization ability.

[0098] Optionally, the pre-trained neural network model is trained based on the calibrated multi-dimensional scenario association matrix until training is complete, resulting in an ecological credit evaluation model, including:

[0099] Based on the calibrated multi-dimensional scenario association matrix, the risks and benefits brought by ecological asset elements are quantitatively encoded to obtain risk coding vectors and benefit coding vectors.

[0100] The risk encoding vector and the benefit encoding vector are input into a pre-trained neural network model for training until the training is completed, thus obtaining the ecological credit evaluation model.

[0101] Preferably, in a specific application scenario, the above solution is described in an alternative or preferred manner.

[0102] 1. Quantitative coding of ecological asset elements' risks and returns

[0103] Let the calibrated multidimensional scenario association matrix be... Where U represents the number of evaluation subjects, M represents the number of ecological asset elements, and S represents the number of scenarios. Indicates the evaluation subject u i Under scenario s and ecological asset element a j The calibrated interactive score.

[0104] 1.1 Risk Quantification Coding

[0105] Construct a risk assessment function Risk(a) j ) Regarding ecological asset element a j To quantify the risks, a hybrid model based on fuzzy comprehensive evaluation and Bayesian networks is adopted. First, the set of risk assessment indicators is determined. N represents the number of indicators and the corresponding weight vector w. r =[w r1 ,w r2 ,…,w rN ] T The weights are determined using the Analytic Hierarchy Process (AHP) combined with the entropy weight method. For each evaluation index R... k Establish fuzzy membership functions Membership degree used to map the original index value x to the interval [0,1]. Ecological asset element a j In the index R k The fuzzy membership vector below is Where i = 1, 2, ..., U, s = 1, 2, ..., S. The preliminary risk value Risk is calculated using fuzzy transformation. pre (a j ): Then, construct the Bayesian network. Among the nodes It includes risk assessment indicators and the risk status of ecological asset elements, with edges ε representing probabilistic dependencies between nodes. The conditional probability table (CPT) of the Bayesian network is learned based on historical data. The initial risk value is then corrected using Bayesian inference formulas. Where X is the observed value vector of the risk assessment indicator. The risk values ​​of all ecological asset elements are normalized and encoded to obtain the risk coding vector R = [R1, R2, ..., R...]. M ] T The encoding function is R all This is the set of risk values ​​for all ecological asset elements.

[0106] 1.2 Profit Quantification Coding

[0107] Construct a return evaluation function Return(a) j This study employs a combination of random forest regression and Markov chain Monte Carlo (MCMC) simulation. The random forest regression model (RF) is used to analyze the ecological asset element a. j Historical return data Y j =[y j1 ,y j2 ,…,y jT [T] represents the number of time periods, and the corresponding feature vector X. j =[x j1 ,x j2 ,…,x jT The training process involves using feature vectors that incorporate factors related to the returns of ecological asset elements, such as market demand and policy support. The random forest regression prediction function is: Where B is the number of decision trees in the random forest, f b (x jt Let x be the pair of decision trees in the b-th tree. jt The predicted values ​​are obtained. Uncertainty analysis is performed on the prediction results of random forest regression using MCMC simulation to estimate the probability distribution of returns. Let the Markov chain state transition probability matrix be P, and the initial state probability vector be π0. After L iterations, the probability distribution vector π of the returns is obtained. L The payoff is the expected value of the probability distribution: The revenue values ​​of all ecological asset elements are normalized and encoded to obtain the revenue encoding vector V = [V1, V2, ..., V]. M ] T The encoding function is V all It is the set of revenue values ​​for all ecological asset elements.

[0108] 2. Training of pre-trained neural network models based on encoding vectors

[0109] Let the pre-trained neural network model be The input layer receives the risk encoding vector R and the profit encoding vector V, and concatenates them into the input vector I = [R]. T V T ] T A network structure combining a deep residual network (ResNet) with an attention mechanism is adopted. In the residual block, let the input of the l-th layer be h. l The output is h l+1 The residual connection formula is: h l+1 =h l +F(h l W l Where F(·) is the residual function, W l Let W be the weight matrix of the l-th layer. The attention mechanism is calculated as follows: Q = W q I,K=W k I,V=W v I Where Q, K, and V are the query, key, and value matrices, respectively, and W... q W k W v Let d be the weight matrix. k The key vector dimension is used. The output layer employs a multi-task learning loss function L, which includes an ecological credit score regression loss L. regression And credit rating classification loss L classification : L=λ1L regression +λ2L classification in, For the predicted ecological credit score, y i This represents the actual credit score; C represents the number of credit rating categories, and y represents the actual credit score. ic and These represent the probability distributions of the actual and predicted credit ratings, respectively, with λ1 and λ2 being weighting coefficients determined through cross-validation. The gradient is calculated using the backpropagation algorithm. The model parameter ω is updated using the AdaGrad optimization algorithm: Where η is the learning rate, T is the number of training rounds, and ∈ is a very small positive number to prevent the denominator from being zero. The training is iterated continuously until the loss function converges, resulting in the final ecological credit evaluation model.

[0110] Therefore, in the context of ecological credit evaluation, traditional technologies have many limitations in risk-return quantification and model training. This application integrates cutting-edge technologies such as fuzzy comprehensive evaluation, Bayesian networks, and random forests, achieving breakthroughs from multiple dimensions and possessing the following significant advantages:

[0111] First, traditional ecological credit assessments often rely on fixed thresholds or simple linear weighted models for risk quantification. For example, they often classify risk levels based on only a few indicators such as the number of corporate defaults and debt-to-equity ratios, lacking dynamic assessment of innovative risk factors and handling of uncertainties. This approach is ill-suited to the non-linear risks inherent in green finance, such as policy changes and environmental fluctuations, and is prone to misjudgment.

[0112] This application employs a hybrid model combining fuzzy comprehensive evaluation and Bayesian networks. Fuzzy comprehensive evaluation, by establishing fuzzy membership functions, maps multi-dimensional risk assessment indicators (such as policy compliance and environmental impact) into fuzzy membership degrees, effectively addressing the fuzziness and uncertainty of evaluation indicators. For example, when assessing the risks faced by enterprises due to changes in environmental policies, fuzzy functions can transform fuzzy factors such as policy implementation strength and enterprise adaptability into quantifiable membership degrees. Bayesian networks, based on historical data, learn the probabilistic dependencies between nodes and use Bayesian inference to correct initial risk values, dynamically updating risk assessment results. When new environmental policies emerge, Bayesian networks can quickly adjust the risk values ​​of ecological asset elements based on the correlation probability between policies and risk indicators, providing a more accurate and dynamic characterization of risk compared to traditional methods.

[0113] Second, traditional revenue assessment often uses deterministic models, such as simple linear regression or empirical formulas, which only consider a few key variables and ignore the impact of uncertain factors such as market fluctuations and unforeseen events. Therefore, it cannot accurately predict the revenue of ecological asset elements in an innovative environment.

[0114] This application employs a method combining random forest regression and Markov chain Monte Carlo (MCMC) simulation. Random forest regression, by integrating multiple decision trees, can handle high-dimensional, nonlinear data and effectively capture innovative relationships between ecological asset returns and multiple factors such as market demand and policy support. For example, when predicting the returns of new energy projects, factors such as market electricity price fluctuations and changes in government subsidies can be comprehensively considered. MCMC simulation performs uncertainty analysis on the prediction results of the random forest, estimating the probability distribution of returns by constructing a Markov chain and calculating the expected value. This makes the evaluation result not only a deterministic value but also includes uncertainty information, enabling a more comprehensive reflection of the true situation of ecological asset returns, and significantly improving accuracy and reliability compared to traditional methods.

[0115] Third, traditional neural network models have simple structures and often use a single loss function, such as mean squared error, which only focuses on the difference between the predicted value and the true value. When dealing with multi-objective tasks of ecological credit evaluation (such as score prediction and grade classification), it is difficult to balance the importance of different tasks, and the model has weak generalization ability.

[0116] This application employs a deep residual network (ResNet) combined with an attention mechanism and multi-task learning. ResNet addresses the vanishing gradient problem in deep network training through residual connections, enabling the construction of deeper network structures and the extraction of more innovative features. The attention mechanism allows the model to focus on key risk and reward encoding features, improving feature representation capabilities. The loss function of multi-task learning includes both regression and classification losses, with weight coefficients determined through cross-validation, effectively balancing ecological credit score prediction and credit rating classification tasks. For example, during training, λ1 and λ2 can be adjusted according to actual needs to achieve better model performance on different tasks. Compared to traditional models, this combination exhibits stronger adaptability and higher prediction accuracy in innovative ecological credit evaluation tasks.

[0117] Optionally, based on the calibrated multi-dimensional scenario correlation matrix, the risks and benefits brought by ecological asset elements are quantified and encoded to obtain risk encoding vectors and benefit encoding vectors, including:

[0118] The calibrated multi-dimensional scenario association matrix is ​​processed by extracting structured and unstructured data, separating the structured data in the matrix, and collecting unstructured data from green finance activities to obtain a set of data to be fused.

[0119] Feature extraction is performed on text data in unstructured data to obtain text feature vectors;

[0120] Feature fusion is performed on structured data and text feature vectors to obtain enhanced context-related data;

[0121] Data integration and processing are performed on the timestamp and regional identification information of the enhanced scenario-related data and the multi-dimensional identity identifiers of the evaluation subjects. The timestamp and regional identification information are then associated with the enhanced scenario-related data to obtain spatiotemporal related data.

[0122] Based on spatiotemporal correlation data, we can mine the probability of the impact of factors on risk and return in order to screen out the core factors.

[0123] The selected core factors are mapped using quantum computing principles, and the core factors are mapped into qubits that simultaneously represent multiple risk and return states, thus obtaining the quantized factor representation.

[0124] The quantum entanglement phenomenon is simulated on the quantized factor representation. Based on the correlation between factors, an innovative nonlinear correlation between factors is established, and a quantum heuristic risk-return quantification model is constructed.

[0125] Quantum solutions are used to solve the quantum-inspired risk-return quantification model in order to calculate the quantitative values ​​of the risks and returns brought to ecological asset elements.

[0126] The quantified values ​​are input into the constructed risk encoding generator and profit encoding generator to generate the initial risk encoding vector and profit encoding vector;

[0127] The generated risk encoding vector and profit encoding vector are processed by a discriminator to obtain the risk encoding vector and profit encoding vector respectively.

[0128] Preferably, in a specific application scenario, the above solution is described in an alternative or preferred manner.

[0129] 1. Extraction and processing of structured and unstructured data

[0130] Let the calibrated multidimensional scenario association matrix be... Where n represents the number of evaluators, m represents the feature dimension, and s represents the number of scenarios. Tensor decomposition is used to decompose it into core tensors. and multiple factor matrices Right now Among them, × i This represents the tensor product operation along the i-th dimension, where r1, r2, and r3 are the ranks of the corresponding dimensions. (Structured data tensor) By preserving the core tensor The structured data is obtained by slicing the dimensions related to the structured features, while the unstructured data information is obtained by mapping the factor matrix to a specific mapping function f. unstr From the original matrix Extract from, forming an unstructured data set.

[0131] 2. Feature Extraction from Unstructured Text Data

[0132] For unstructured data sets The text data is encoded using a pre-trained deep language model (such as BERT-Large). Let the text sequence be w1, w2, ..., w T The hidden state tensor is obtained after model encoding. Where d h The hidden layer dimension is used. Feature vectors are calculated using a multi-head attention mechanism.

[0133] Among them, Wq W k W v Let d be the projection matrix. k Let h be the dimension of the key vector and h be the number of attention heads, ultimately resulting in the text feature tensor. d f The feature dimensions after fusion.

[0134] 3. Feature fusion

[0135] Structured data tensors Transform into matrix S mat Then, with the text feature tensor To achieve fusion, a gating mechanism is introduced, and a gating vector is defined. Where σ is the Sigmoid function. W represents matrix concatenation. g and b g These are learnable parameters. The fused enhanced contextual association data ε is: Here, ⊙ represents element-wise multiplication.

[0136] 4. Data integration and processing

[0137] Timestamp information of the evaluation subject and area identification information All are represented in tensor form. Through tensor concatenation operations, they are integrated with the enhanced contextual association data ε into a spatiotemporal association data tensor. And perform High-Order Singular Value Decomposition (HOSVD) on it: in, V1 is the core tensor, and V2, V3 are factor matrices.

[0138] 5. Core Factor Screening

[0139] Based on spatiotemporal correlation data tensors Building dynamic Bayesian networks in Let ε be the set of nodes and ε be the set of edges. Let X be a set of time slices. Define the conditional probability distribution P(X). i |Pa(X i ),t), where For node variables, Pa(X) i ) represents the set of its parent nodes. Inference is performed using a particle filter algorithm to calculate the dynamic probability of each factor's impact on risk R and return G. and in For observation information at time t. According to the information gain criterion IG(X)... i )=H(R)+H(G)-H(R,G|Xi ) Screen the core factors, where H(·) is the Shannon entropy.

[0140] 6. Quantum computing principle mapping processing

[0141] For the selected set of core factors Map it to a quantum state. Let the number of core elements be c, and construct a quantum register. Each qubit |ψ i The status is determined by the core factor value x. i Perform parameterization: Where β and γ are adjustment parameters, and atan2 is the arctangent function in the four quadrants.

[0142] 7. Simulation of quantum entanglement phenomenon

[0143] Based on the correlation matrix among core factors Quantum entanglement is constructed using controlled quantum gates. For λ in the matrix ij For elements ≠ 0, a controlled phase gate (CPHASE) is applied between the i-th and j-th qubits: Through a series of quantum gate operations, the quantum state |Φ> of the quantum heuristic risk-return quantization model is obtained.

[0144] 8. Quantum Solving

[0145] The quantum Monte Carlo method is used to solve for the quantum state |Φ>. The Hamiltonian is defined. Its eigenstates |∈ n >and eigenvalues ​​∈ n satisfy The sample set is obtained through quantum sampling. Calculate the quantitative values ​​of risk and return: in, and These are the Hamiltonian components related to risk and return, respectively.

[0146] 9. Encoding Generation

[0147] A deep generative adversarial network (GAN) is constructed as the encoding generator. The generator G receives quantified values ​​of risk and reward R. q and G q and random noise Output the initial risk encoding vector r and the return encoding vector g: r, g = G([R q G q The discriminator D discriminates the generated encoded vectors and outputs the probability p = D([r;g]). The parameters of the generator and discriminator are optimized through adversarial training.

[0148] 10. Discriminator Judgment Processing

[0149] A reinforcement learning mechanism is introduced to optimize the discriminator. A reward function R(r,g) is defined, which assigns a reward based on the difference between the discrimination result and the true data distribution. A deep Q-network (DQN) is used to learn the optimal policy π. * The discriminator parameters θ are updated iteratively. D : Where γ is the discount factor and α is the learning rate, the final reliable risk encoding vector and return encoding vector are obtained through continuous training.

[0150] Therefore, the above-mentioned solution integrates cutting-edge technologies such as tensor analysis, quantum computing, and deep learning, and has the following significant advantages in data processing, feature extraction, and quantitative modeling compared with traditional ecological asset risk and return assessment technologies.

[0151] 1. Traditional methods typically treat data as a two-dimensional matrix, which can only handle structured numerical data and is difficult to effectively integrate unstructured data such as text and images. For example, in ecological asset risk assessment, traditional methods rely solely on structured data such as financial statements to calculate risk indicators, ignoring the risk information contained in unstructured texts such as policy documents and market news, resulting in a single assessment dimension.

[0152] This application employs tensor decomposition techniques to process high-dimensional matrices. pass Separate structured and unstructured data features. Simultaneously, utilize a pre-trained language model (such as BERT-Large) combined with a multi-head attention mechanism to process unstructured text, transforming text sequences into feature tensors. This approach can not only handle multi-source heterogeneous data, but also preserve multi-dimensional correlation information of the data through tensor operations. For example, when analyzing regional ecological asset risks, it can simultaneously integrate structured transaction data from different times and different entities with unstructured policy texts to comprehensively capture risk signals.

[0153] 2. Traditional feature fusion methods often employ simple concatenation or weighted summation, failing to consider the dynamic relationships and differences in importance between features. For example, in credit assessment, directly adding features such as a company's asset size and transaction frequency fails to reflect the weight changes of different features in different scenarios.

[0154] This application introduces a gating mechanism. Dynamically adjust structured data S using the Sigmoid function mat Text features The fusion ratio. In green finance scenarios, when policies change, the gating vector automatically enhances the weight of policy-related text features and weakens the influence of other features, so that the fused enhanced scenario-related data ε can more accurately reflect the key factors of current ecological asset risk and return.

[0155] 3. Traditional methods rely on correlation analysis or fixed rules to screen factors, which cannot handle dynamically changing causal relationships. For example, risk indicators are determined solely based on the correlation of historical data, and these indicators become ineffective when the market environment changes.

[0156] This application constructs a dynamic Bayesian network. Calculating the dynamic influence probability using the particle filter algorithm And through the information gain criterion IG(X) i This method filters core factors. In ecological asset assessment, it can update the probability of factors' impact on risk and return in real time. For example, when a sudden environmental event occurs in a region, dynamic Bayesian networks can quickly identify the impact of event-related factors on ecological asset risk, making it more timely and causally explanatory than traditional methods.

[0157] Traditional quantitative models, based on classical probability or linear regression, are difficult to handle innovative systems with high dimensions and nonlinearity. For example, when using linear models to predict the returns of ecological assets, they cannot accurately depict the coupling effect between market fluctuations and policy interventions.

[0158] This application maps core factors to quantum states |ψ>, constructs quantum entanglement through controlled phase gates, and then uses the quantum Monte Carlo method to solve for the quantitative values ​​of risk and return. Simultaneously, it combines generative adversarial networks (GANs) and deep Q-networks (DQNs) for encoding generation and discriminator optimization. This interdisciplinary integration enables the model to simultaneously represent the superposition of multiple risk-return states. For example, when evaluating new energy projects, the quantum heuristic model can simulate the risk-return distribution under the simultaneous changes of multiple factors such as policy subsidies and technological breakthroughs, and compared to traditional models, it can more realistically reflect the uncertainty and innovation of ecological assets.

[0159] Optionally, the risk encoding vector and the benefit encoding vector are input into a pre-trained neural network model for training until the training is completed, thus obtaining an ecological credit evaluation model.

[0160] The risk encoding vector and the profit encoding vector are fused using a self-attention mechanism to generate a set of feature-enhanced encoding vectors.

[0161] The enhanced coding vector set is subjected to stratified sampling. Based on the risk level and return potential data of ecological asset elements, the enhanced coding vector set is divided into different levels to obtain the risk-return coding vector layer.

[0162] The risk-return encoding vector layer is input into the embedded dynamic routing module in the pre-trained neural network model to extract key risk-return features of ecological asset elements.

[0163] By inputting the key risk and return characteristics of ecological asset elements into the graph neural network module of the pre-trained neural network model, the propagation pattern of risk and return encoding vector layers at different levels in the relation graph is explored, and semantic features among ecological credit evaluation factors are generated.

[0164] The semantic features between ecological credit evaluation factors are input into the transformer module in the pre-trained neural network model to capture the distance dependence features between ecological credit evaluation factors.

[0165] The semantic features and distance dependence features among ecological credit evaluation factors are fused into a feature vector for evaluation, and then input into the output layer of a pre-trained neural network model to generate ecological credit evaluation prediction results and compare them with real ecological credit labels to calculate the loss value.

[0166] Based on the loss value, the model parameters of the pre-trained neural network model are optimized through backpropagation until the loss value reaches the preset model convergence condition, at which point the training ends to obtain the ecological credit evaluation model.

[0167] Preferably, in a specific application scenario, the above solution is described in an alternative or preferred manner.

[0168] 1. Self-attention mechanism fusion processing

[0169] Let the risk encoding vector be The revenue encoding vector is Concatenate the two to form the initial encoding vector. Introducing a multi-head self-attention mechanism, for the h-th attention head, the query matrix Q is calculated. h Key matrix K h Sum matrix V h : in, Let d be a learnable weight matrix. k Let d be the dimension of the key vector. v The dimension of the value vector.

[0170] Calculate the attention score matrix A h : Obtain the output z of the h-th attention head h :z h =A h V h The outputs of all attention heads are concatenated and subjected to a linear transformation to obtain the feature-enhanced encoding vector z: z = W o [z1;z2;…;z H ]in, The output weight matrix is ​​H, where H is the number of attention heads and d is the weight matrix. outThis is the output dimension. The above operation is repeated for the encoded vectors of multiple samples to obtain the feature-enhanced encoded vector set. N is the number of samples.

[0171] 2. Stratified sampling

[0172] Let the risk level data of ecological asset elements be: Earnings potential data is Normalize it: Define hierarchical indicators ω1 and ω2 are weighting coefficients, determined through cross-validation.

[0173] Based on the hierarchical index I, the feature-enhanced encoding vector set is... Divided into L levels, denoted as A risk-return encoding vector layer is formed. The specific partitioning method is as follows: L-1 thresholds {τ1, τ2, ..., τ} are set. L-1}, when τ l-1 <I i ≤τ l At that time, z i Classified to level l (where τ0=-∞, τ L =+∞).

[0174] 3. Feature extraction from embedded dynamic routing module

[0175] For each risk-return encoding vector layer Convert it to tensor form N l Let be the number of samples in layer l. The embedded dynamic routing module consists of multiple dynamic routing layers. In the t-th dynamic routing layer, for each encoding vector in layer l... Calculate its coupling coefficient b with the next capsule layer. lij : in, Let d be a learnable weight matrix. c For capsule dimensions, Let be the output vector of the j-th capsule in layer t. The final coupling coefficients are obtained by iteratively updating the coupling coefficients. Then, the input vector of the j-th capsule in the t-th layer is calculated. The capsule output vector is obtained after applying an activation function (such as the squash function). After passing through multiple dynamic routing layers, the key risk and return characteristics of ecological asset elements are extracted. C represents the total number of capsules.

[0176] 4. Mining the propagation patterns of graph neural network modules

[0177] Representing the ecological asset relationship map as a graph structure Among the nodes Corresponding to ecological asset elements, edges ε represent the relationships between elements. The key characteristics of risk and return of ecological asset elements are then considered. As node features, construct a node feature matrix.

[0178] The graph neural network module uses a graph convolutional network (GCN). In the l-th layer, the feature update formula for node i is: in, Let i be the feature vector of node i in the l-th layer. Let c be the set of neighboring nodes of node i. ij The normalized coefficients between nodes i and j Let be the weight matrix of the l-th layer. Here, σ is the bias vector, and σ is the activation function (such as ReLU). After passing through a multi-layer graph convolutional network, the final feature vectors of each node are obtained. This process uncovers the propagation patterns of risk-return encoding vector layers at different levels within the relationship graph, generating semantic features among ecological credit evaluation factors. d s This is the semantic feature dimension.

[0179] 5. Distance dependency capture in the Transformer module

[0180] The semantic features S among the ecological credit evaluation factors are input into the Transformer module. In the encoder layer of the Transformer, for each semantic feature vector s... i ∈S, calculate its position code p i This yields an input vector x containing location information. i =s i +p i .

[0181] After passing through a multi-head attention mechanism and a feedforward neural network layer, the attention score matrix A is calculated: Where Q = W q X, K = W k X, V = W v X represents the query matrix, key matrix, and value matrix, respectively. The attention output Z: Z = AV is obtained and processed through a feedforward neural network layer and layer normalization operations to capture the distance dependence features among ecological credit evaluation factors. d d For distance-dependent feature dimensions.

[0182] 6. Feature Fusion and Model Training

[0183] The semantic features S and distance dependency features D among the ecological credit evaluation factors are fused to obtain the feature evaluation feature vector F: F = W f [S T ;D T ] T in, To fuse the weight matrix, d f This represents the fused feature dimension. The feature evaluation feature vector F is input into the output layer of the pre-trained neural network model to output the ecological credit evaluation prediction result. C label Let L be the number of ecological credit label categories. Define the loss function L as a weighted sum of cross-entropy loss and mean squared error loss: L = λ₁L CE +λ2L MSE in, λ1 and λ2 are weighting coefficients, representing genuine ecological credit labels. The gradient is calculated using the backpropagation algorithm based on the loss value L. The AdamW optimization algorithm is used to update the parameters θ of the pre-trained neural network model: Where β is the learning rate, λ wd This is the weight decay coefficient. The model is iterated and trained continuously until the loss value reaches the preset model convergence condition (e.g., L < ∈, where ∈ is the convergence threshold), resulting in the final ecological credit evaluation model.

[0184] Therefore, the above-mentioned solution integrates cutting-edge technologies such as multi-head self-attention, dynamic routing, and graph neural networks, and has the following technical advantages over traditional technologies in terms of feature processing, relationship modeling, and model optimization:

[0185] 1. Traditional feature fusion methods often employ linear combinations with fixed weights. For example, in ecological credit evaluation, risk and return indicators are simply added together with empirical weights, which fails to adapt to the dynamic changes in feature importance across different samples and scenarios. This approach neglects the innovative interactive relationships between features, resulting in low information utilization efficiency.

[0186] This application introduces a multi-head self-attention mechanism by calculating the query matrix Q. h Key matrix K h Sum matrix V h Dynamically generate attention score matrix A h In ecological asset risk assessment, this mechanism can automatically capture the correlation strength between the risk encoding vector r and the return encoding vector g across different dimensions. For example, when market volatility is high, the model will increase the weight of risk-related features and weaken the interference of return features. The multi-head design can also extract feature information from multiple perspectives, and compared to traditional methods, it can more comprehensively and flexibly integrate risk and return features, improving the accuracy of feature representation.

[0187] 2. Traditional stratification methods rely on human experience to classify data into strata. For example, ecological assets are simply divided into high, medium and low risk levels based on fixed risk thresholds. This cannot reflect the dynamic coupling relationship between return potential and risk level, and it is difficult to adapt to changes in data distribution.

[0188] This application is based on risk level data R. level and revenue potential data G level This method achieves data-driven dynamic stratification by normalizing and weighting the stratification index I. In green finance scenarios, when the potential return of an ecological asset increases due to policy support, this method can adjust its stratification level in real time, making the risk-return encoding vector layer more closely reflect the actual situation. Cross-validation determines the weight coefficients ω1 and ω2, further optimizing the stratification strategy. Compared with traditional methods, this method can more accurately characterize the risk-return distribution of ecological assets, providing a higher-quality data structure for subsequent analysis.

[0189] 3. Traditional neural networks extract features through fully connected layers or simple convolutional layers, lacking effective modeling of feature hierarchy and spatial relationships. For example, when dealing with innovative features of ecological assets, it is difficult to distinguish the importance and interaction of different factors.

[0190] In this application, the embedded dynamic routing module utilizes the capsule network principle to iteratively update the coupling coefficient. This module enables dynamic information transfer between encoded vectors and capsules. In extracting key features of ecological asset risk and return, it captures the hierarchical structure of features, such as separating macro-level policy impacts from micro-level enterprise operational characteristics to avoid information aliasing. The squash function ensures that the direction and length of the capsule output vector have practical meaning, and compared to traditional methods, it can more effectively extract key features with structured information, enhancing the model's ability to express the characteristics of innovative ecological assets.

[0191] 4. Traditional machine learning models often assume that data samples are independent of each other, which makes it difficult to effectively handle innovative relationships between ecological assets. For example, when assessing regional ecological credit, they ignore the impact of cooperation and competition between enterprises on credit.

[0192] In this application, the graph neural network module uses an ecological asset relationship graph. Based on this, node features are updated using a Graph Convolutional Network (GCN). In ecological credit evaluation, the feature update formula for node i considers neighboring nodes. This information can be used to uncover the propagation patterns of different ecological assets within a relationship graph. For example, when a company defaults on its credit, the model can automatically propagate the impact of this information on the credit of its related companies through graph convolution operations. Compared to traditional methods, this approach can more realistically simulate the interactions between ecological assets, improving the accuracy and interpretability of credit assessments.

[0193] 5. Traditional sequence modeling methods (such as recurrent neural networks) are difficult to capture long-distance dependencies and lack effective modeling of the synergistic effects of different types of features when fusing features, resulting in insufficient comprehensive analysis of innovative factors in ecological credit evaluation.

[0194] In this application, the Transformer module, through a multi-head attention mechanism and positional encoding, can capture the long-distance dependency characteristics among ecological credit evaluation factors. In ecological asset risk assessment, it can analyze the impact of ecological policies at different times and in different regions on the current asset credit. Combining a fusion strategy of semantic features S and distance dependency features D, and using a weighted matrix W... f By achieving multi-feature synergy, compared with traditional methods, it can more comprehensively consider various factors and their interrelationships in ecological credit evaluation, optimize model prediction performance, and improve the reliability and stability of ecological credit evaluation.

[0195] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for constructing an ecological credit evaluation model, characterized in that, include: Collect data on the green finance activities of the evaluation entities; Data on green finance activities is analyzed to obtain a profile feature library of the evaluation subjects, which includes the basic information of the evaluation subjects, their behavioral patterns and preference characteristics in the field of green finance; Assign a multi-dimensional identity set to the evaluation subject, the multi-dimensional identity set including a composite subject ID consisting of a timestamp, a region identifier, a subject type identifier, and a unique serial number; Based on the subject profile feature library and multi-dimensional identity identifier set of the evaluation subject, construct the evaluation subject profile; Based on the constructed causal inference model, and according to the profile of the evaluation subject, we explore the internal and external factors that influence the green finance behavior and ecological credit of the evaluation subject; among which, the internal factors include changes in the subject's own behavior patterns. The pre-trained neural network model is trained based on internal and external factor characteristics until training is complete, resulting in an ecological credit evaluation model, including: Based on the characteristics of internal and external factors, a network structure with ecological asset elements as related branches is constructed to form an ecological asset relationship map, and a unique element association ID is assigned to each ecological asset element. Obtain descriptions of green finance business scenarios to build a scenario simulation module; Based on the scenario simulation module, the scenario interaction scores of the evaluation subject and the ecological asset relationship map are assessed to form a multi-dimensional scenario association matrix; Based on the multi-dimensional scenario association matrix, the pre-trained neural network model is trained until training is complete, resulting in an ecological credit evaluation model, including: Calibrate the multi-dimensional scenario association matrix; Based on the calibrated multi-dimensional scenario association matrix, the risks and returns brought by ecological asset elements are quantified and encoded to obtain risk and return coding vectors. This includes: extracting structured and unstructured data from the calibrated multi-dimensional scenario association matrix, separating the structured data from the matrix, and collecting unstructured data from green finance activities to obtain a dataset to be integrated; extracting features from the text data in the unstructured data to obtain text feature vectors; fusing the structured data and text feature vectors to obtain enhanced scenario association data; integrating the enhanced scenario association data with the timestamps and regional identifiers in the multi-dimensional identity set of the evaluation subjects, and associating the timestamps and regional identifiers with the enhanced scenario association data to obtain spatiotemporal association data; and mining the probability of the impact of factors on risks and returns based on the spatiotemporal association data to screen out core factors, thereby obtaining risk and return coding vectors. The ecological credit evaluation model is obtained by inputting risk encoding vectors and benefit encoding vectors into a pre-trained neural network model for training until the training is completed.

2. The method for constructing an ecological credit evaluation model according to claim 1, characterized in that, Calibrate the multi-dimensional scenario association matrix, including: Obtain the real-time performance evaluation value of the evaluation subject in green finance activities and the actual contribution evaluation value of ecological asset elements to green finance business; Based on the real-time performance evaluation value and the actual contribution evaluation value, dynamic weights are calculated for ecological asset elements to calibrate the multi-dimensional scenario correlation matrix.

3. The method for constructing an ecological credit evaluation model according to claim 1, characterized in that, Based on spatiotemporal correlation data, the probability of factors affecting risk and return is mined to identify core factors, thereby obtaining risk and return encoding vectors, including: The selected core factors are mapped using quantum computing principles, and the core factors are mapped into qubits that simultaneously represent multiple risk and return states, thus obtaining the quantized factor representation. The quantum entanglement phenomenon is simulated on the quantized factor representation. Based on the correlation between factors, an innovative nonlinear correlation between factors is established, and a quantum heuristic risk-return quantification model is constructed. Quantum solutions are used to solve the quantum-inspired risk-return quantification model in order to calculate the quantitative values ​​of the risks and returns brought to ecological asset elements. The quantified values ​​are input into the constructed risk encoding generator and profit encoding generator to generate the initial risk encoding vector and profit encoding vector; The initial risk encoding vector and profit encoding vector are processed by a discriminator to obtain the risk encoding vector and profit encoding vector.

4. The method for constructing an ecological credit evaluation model according to claim 1, characterized in that, The risk encoding vector and the benefit encoding vector are input into the pre-trained neural network model for training until the training is completed, thus obtaining the ecological credit evaluation model. The risk encoding vector and the profit encoding vector are fused using a self-attention mechanism to generate a set of feature-enhanced encoding vectors. The enhanced coding vector set is subjected to stratified sampling. Based on the risk level and return potential data of ecological asset elements, the enhanced coding vector set is divided into different levels to obtain the risk-return coding vector layer. The risk-return encoding vector layer is input into the embedded dynamic routing module in the pre-trained neural network model to extract key risk-return features of ecological asset elements. By inputting the key risk and return characteristics of ecological asset elements into the graph neural network module of the pre-trained neural network model, the propagation pattern of risk and return encoding vector layers at different levels in the relation graph is explored, and semantic features among ecological credit evaluation factors are generated. The semantic features between ecological credit evaluation factors are input into the transformer module in the pre-trained neural network model to capture the distance dependence features between ecological credit evaluation factors. The semantic features and distance dependence features among ecological credit evaluation factors are fused into a feature vector for evaluation, and then input into the output layer of a pre-trained neural network model to generate ecological credit evaluation prediction results and compare them with real ecological credit labels to calculate the loss value. Based on the loss value, the model parameters of the pre-trained neural network model are optimized through backpropagation until the loss value reaches the preset model convergence condition, at which point the training ends to obtain the ecological credit evaluation model.

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