Establishment method of ecological product data element organization model

Abstract

The invention provides a method for establishing an ecological product data element organization model, and the method comprises the steps: obtaining natural ecological basic data which comprises at least one of forest description metadata, river description metadata, lake description metadata, grassland description metadata and cultivated land description metadata; quantifying and indexing the natural ecology basic data to generate natural ecology basic data elements; based on the constructed value evaluation model, performing ecological value evaluation on the natural ecological basic data elements to obtain a corresponding ecological contribution estimated value; based on the constructed economic and social association model, performing economic and social association assessment on the natural ecological basic data elements to obtain a relative regional economic development contribution assessment value; and constructing an ecological product data element organization model based on the ecological contribution estimation value and the regional economic development contribution estimation value. According to the method, comprehensive analysis and mastering of the overall condition of the ecological product are facilitated, and a reliable basis is provided for ecological protection and reasonable utilization of resources.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent processing, and particularly relates to a method for establishing an ecological product data element organization model. Background Art

[0002] With the continuous increase in global attention to ecological environment protection and sustainable development, the importance of ecological products in economic and social development has become increasingly prominent. As the output of natural ecosystems, ecological products have a wide range, covering various material products and services provided by multiple ecological systems such as forests, rivers, lakes, grasslands, and cultivated lands.

[0003] In the current field of ecological product data management, a series of severe challenges are faced. On the one hand, the sources of ecological product data are numerous and scattered, and the data of different ecological systems lack effective integration and unified management. The ecological data collected by various departments and regions have different formats and lack standardization, which makes it difficult for data to be interconnected and shared, greatly limiting the comprehensive analysis and understanding of the overall situation of ecological products. On the other hand, there are serious deficiencies in the in-depth processing and utilization of data. A large amount of original ecological data only stays at the simple recording stage, without scientific quantification and indexing, and cannot be effectively transformed into data elements for decision-making reference. For example, in the ecological value assessment link, due to the lack of a mature and widely recognized assessment system, the results obtained by different assessment methods vary greatly, making it difficult to accurately reflect the true value of ecological products. In terms of economic and social relevance, existing research and practices are difficult to accurately quantify the contributions of ecological products to regional economic growth, social well-being improvement, etc. This current situation makes it difficult to give full play to the role of ecological products in actual economic and social activities, is not conducive to formulating scientific and reasonable coordinated strategies for ecological protection and economic development, and also hinders the effective transformation of ecological resources into economic value. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a method for establishing an ecological product data element organization model to at least solve or alleviate the problems existing in the above prior art.

[0005] To achieve the above object, according to one aspect of the present application, there is provided a method for establishing an ecological product data element organization model, which includes:

[0006] Obtain natural ecological basic data, where the natural ecological basic data includes at least one of forest description metadata, river description metadata, lake description metadata, grassland description metadata, and cultivated land description metadata;

[0007] Quantify and index the natural ecological basic data to generate natural ecological basic data elements;

[0008] Based on the constructed value evaluation model, conduct an ecological value evaluation on the natural ecological basic data elements to obtain the corresponding ecological contribution valuation;

[0009] Based on the constructed economic and social correlation model, conduct an economic and social correlation evaluation on the natural ecological basic data elements to obtain the relative regional economic development contribution valuation;

[0010] Based on the ecological contribution valuation and the regional economic development contribution valuation, construct an ecological product data element organization model.

[0011] The technical solutions in this application have at least the following technical advantages:

[0012] ① By obtaining various natural ecological basic data such as forests, rivers, lakes, grasslands, and cultivated lands, it changes the previous situation where data sources are numerous and scattered, laying a foundation for subsequent unified management. Quantify and index these data to generate natural ecological basic data elements, enabling ecological data in different formats to be converted into a unified standard, solving the problem of difficult interconnection, sharing, and interoperability between data, and helping to comprehensively analyze and grasp the overall situation of ecological products.

[0013] ② With the help of the constructed value evaluation model, conduct an ecological value evaluation on the natural ecological basic data elements. Compared with the previous situation where the lack of a mature and widely recognized evaluation system led to huge differences in results, this solution can obtain a relatively accurate ecological contribution valuation, more truly reflect the ecological value of ecological products, and provide a reliable basis for ecological protection and rational utilization of resources.

[0014] ③ Use the constructed economic and social correlation model to conduct an economic and social correlation evaluation on the natural ecological basic data elements, and be able to obtain the relative regional economic development contribution valuation, solving the problem that existing research and practice are difficult to accurately quantify the contribution of ecological products to regional economic growth and social well-being improvement, and helping to deeply understand the mechanism of action of ecological products in economic and social activities.

[0015] ④ Based on the ecological contribution valuation and the regional economic development contribution valuation, construct an ecological product data element organization model, closely linking ecological value with economic and social development, which is conducive to formulating scientific and reasonable collaborative strategies for ecological protection and economic development, and promoting the effective conversion of ecological resources into economic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flowchart of the method for establishing an ecological product data element organization model according to an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Figure 1 It is a schematic flowchart of the method for establishing an ecological product data element organization model according to an embodiment of this application. AsFigure 1 As shown, it includes:

[0018] Obtain natural ecological basic data, where the natural ecological basic data includes at least one of forest description metadata, river description metadata, lake description metadata, grassland description metadata, and cultivated land description metadata;

[0019] Quantify and index the natural ecological basic data to generate natural ecological basic data elements;

[0020] Based on the constructed value evaluation model, conduct an ecological value evaluation on the natural ecological basic data elements to obtain the corresponding ecological contribution valuation;

[0021] Based on the constructed economic and social association model, conduct an economic and social relevance evaluation on the natural ecological basic data elements to obtain the relative regional economic development contribution valuation;

[0022] Based on the ecological contribution valuation and the regional economic development contribution valuation, construct an ecological product data element organization model.

[0023] Preferably, in a specific application scenario, during the process of obtaining natural ecological basic data, assume that the natural ecological basic data obtained from different data sources is represented by the set N = {n1, n2,..., n m}, where n i (1 ≤ i ≤ m) represents a certain type of natural ecological basic data (such as forest description metadata, river description metadata, etc.), and m is the number of data types.

[0024] For each type of data n i , it may be affected by multiple factors and can be obtained through a complex weighted integral function. Assume that the set of influencing factors is F = {f1, f2,..., f k}, and each influencing factor f j has a corresponding weight function w j (t) (t represents time), then the acquisition formula for n i is: where g ij is a mapping function about n i and f j , which describes the action mode of the jth influencing factor on the ith type of natural ecological basic data. t0 and t1 are the time intervals for data collection.

[0025] Preferably, in a specific application scenario, during the process of quantifying and indexing the natural ecological basic data, in order to convert the natural ecological basic data into computable elements, quantification and indexing are required. Assume that the quantification function is Q, and for the natural ecological basic data n i , the quantified value is q i= Q(n i ). Here, Q is a multi - layer perceptron (MLP) whose input is the feature vector of n i , and the output is the quantized scalar value.

[0026] Q(n i ) = σ(W L ·σ(W L-1 …σ(W1·n i + b1)…)+b L ),

[0027] where W l and b l are the weight matrix and bias vector of the l - th layer of the MLP respectively, σ is the activation function (such as the ReLU function σ(x)=max(0, x)), and L is the number of layers of the MLP.

[0028] Indexing is to convert the quantized value into the natural ecological basic data element e i . Let the indexing function be I, which is a function based on fuzzy logic. Suppose there is a set of fuzzy rules R = {r1, r2, …, r s}, each rule r p corresponds to a membership function μ p (q i ) and an output value o p , then:

[0029] Preferably, in a specific application scenario, during the ecological value evaluation of natural ecological basic data elements based on the constructed value evaluation model, let the value evaluation model be V, which is a model optimized by support vector regression (SVR) and genetic algorithm. The natural ecological basic data element vector E=(e1, e2, …, e m ), and the ecological contribution estimate v can be calculated through the following steps:

[0030] First, use the genetic algorithm to optimize the parameters C (penalty factor) and γ (kernel function coefficient) of the SVR. Let the fitness function Fitness(C, γ) be the reciprocal of the mean square error of the SVR model on the training set:

[0031] where y i is the true ecological value in the training set, is the predicted value of the SVR model under the parameters C and γ, and N is the number of samples in the training set.

[0032] After genetic algorithm iteration, the optimal parameters C * and γ *Then, use the SVR model with optimal parameters to predict E, and obtain the ecological contribution estimate v: where α i is the Lagrange multiplier of the SVR model, K is the kernel function (such as the radial basis kernel function K(x, y) = exp(-γ * ‖x - y‖ 2 ²)), E i is the sample vector in the training set, and b is the bias of the SVR model.

[0033] Preferably, in a specific application scenario, during the process of evaluating the economic and social relevance of natural ecological basic data elements based on the constructed economic and social association model, let the economic and social association model be S, which is a model based on a dynamic Bayesian network (DBN). The natural ecological basic data element vector E serves as the input node of the DBN, and the relative regional economic development contribution estimate r is the output node of the DBN.

[0034] The state transition probability matrix T of the DBN describes the change of node states over time, and the observation probability matrix O describes the relationship between the input node and the output node. For discrete time step t, the joint probability distribution of the DBN is:

[0035]

[0036] where X i is the hidden state vector at the i-th time step, and Y i is the observation vector at the i-th time step (i.e., E).

[0037] Infer the DBN through the forward-backward algorithm or the particle filter algorithm to obtain the relative regional economic development contribution estimate r:

[0038]

[0039] where P(r|E,X) is the conditional probability of outputting r given the input E and the hidden state X, and P(X|E) is the posterior probability of the hidden state X given the input E.

[0040] Preferably, in a specific application scenario, during the process of constructing an ecological product data element organization model based on the ecological contribution estimate and the regional economic development contribution estimate, let the ecological product data element organization model be M, which is a model based on a graph neural network (GNN). Use the ecological contribution estimate v and the relative regional economic development contribution estimate r as node features to construct a graph G = (V, E), where V is the set of nodes and E is the set of edges.

[0041] The message passing mechanism of the GNN is defined as:

[0042]

[0043] Among them, is the feature vector of node i at the l-th layer, is the set of neighbor nodes of node i, d i and d j are the degrees of nodes i and j respectively, W (l) and b (l) are the weight matrix and bias vector of the l-th layer, and σ is the activation function.

[0044] After message passing through L layers, the feature vectors of all nodes are aggregated to obtain the output M of the ecological product data element organization model:

[0045] Among them, Aggregate can be an aggregation function such as summation, averaging, etc.

[0046] Preferably, the technical solution provided by the above embodiment has the following technical advantages:

[0047] 1. In the link of obtaining natural ecological basic data, this application uses a weighted integral function to obtain data, comprehensively considering various influencing factors and their weights changing over time, and establishing a connection between the influencing factors and natural ecological basic data through a mapping function. Compared with traditional technologies: Traditional data acquisition methods are mostly simple data collection, may only focus on single or a few factors, and do not consider the dynamic changes of factors over time. For example, when traditional methods obtain forest description metadata, they may only record static data such as forest area, ignoring the impacts of dynamic factors such as climate and pests on forest ecology. In contrast, by considering various dynamic factors, the data obtained by this application can better reflect the true state of the ecosystem. Taking the acquisition of river description metadata as an example, it can comprehensively consider factors such as water flow velocity, water quality changes, and surrounding vegetation cover changes to accurately obtain river ecological basic data, providing a richer and more reliable data basis for subsequent analysis.

[0048] 2. In the quantification and indexing of natural ecological basic data, in the quantification process of this application, a multi-layer perceptron (MLP) is used to transform the feature vector of natural ecological basic data into a quantified scalar value through multi-layer non-linear transformation; for indexing, a function based on fuzzy logic is utilized to determine the elements of natural ecological basic data according to fuzzy rules and membership functions. Traditional quantification adopts simple linear transformation and cannot fully explore the complex features of data; traditional indexing may lack effective handling of data ambiguity and uncertainty. For example, when traditional quantification of ecological data is carried out, the data is simply scaled according to a fixed ratio, making it difficult to reflect the internal complex relationships of the data. Therefore, in this application, the multi-layer non-linear transformation of MLP can explore the complex non-linear features in natural ecological basic data and improve the accuracy of quantification. The indexing method based on fuzzy logic can effectively handle the ambiguity and uncertainty of ecological data and generate natural ecological basic data elements that are more in line with the actual ecological situation. When quantifying and indexing the description metadata of grasslands, it can accurately reflect the characteristics of grassland ecology in different fuzzy states (such as the fuzzy definition of grassland degradation degree), providing more practical quantification indicators for subsequent evaluation.

[0049] 3. In the ecological value assessment process, this application is based on a value assessment model optimized by support vector regression (SVR) and genetic algorithm. The genetic algorithm is used to find the optimal parameters of SVR to improve the prediction accuracy of the model, and then the ecological value of the natural ecological basic data element vector is predicted through the SVR model. Traditional ecological value assessment models adopt simple linear regression or empirical formulas, cannot adapt to the complexity of the ecosystem, and the parameters are often fixed, lacking an optimization mechanism. For example, when traditional assessment of forest ecological value is carried out, it only estimates based on the forest area and a fixed value per unit area, without considering complex factors such as the diversity of the forest ecosystem. Therefore, the genetic algorithm in this application optimizes the SVR parameters, enabling the model to better adapt to the complex features of ecological data and improving the accuracy of ecological value assessment. When assessing the ecological value of lakes, various factors such as lake area, water depth, and biodiversity can be comprehensively considered to accurately evaluate the contribution value of the lake ecosystem, providing a more scientific decision-making basis for ecological protection and resource management.

[0050] 4. In the economic and social relevance assessment section, based on the economic and social correlation model of the dynamic Bayesian network (DBN), this application describes the node state changes and input-output relationships through the state transition probability matrix and the observation probability matrix, and uses the forward-backward algorithm or the particle filter algorithm for inference to obtain the relative regional economic development contribution estimate. Traditional relevance assessments use static statistical analysis methods and cannot consider the dynamic relationship between ecological data and economic and social factors. For example, when analyzing the traditional ecological and economic correlation, only the statistical data of a certain period is used for simple correlation analysis, which cannot reflect the changes over time. This application can effectively handle the dynamic relationship between ecological data and economic and social factors through the DBN model, taking into account the evolution of the ecosystem and the economic and social system over time. When analyzing the correlation between arable land descriptive metadata and regional economic development, it can dynamically reflect the impact of changes in arable land use methods on the regional economy at different times, providing dynamic and accurate references for formulating sustainable regional development strategies.

[0051] 5. In the section of constructing the ecological product data element organization model, this application constructs the ecological product data element organization model based on the graph neural network (GNN). The node features are updated through the message passing mechanism, and the final model output is obtained through the aggregation function. Traditional model construction uses simple data splicing or rule-based methods and cannot effectively mine the complex correlation relationships between data. For example, when constructing the traditional ecological product data model, various types of data are simply listed, and the internal connections between the data cannot be reflected. In this application, the message passing mechanism of the GNN can fully mine the complex correlation relationships between data such as ecological contribution estimates and regional economic development contribution estimates, and generate a more comprehensive and representative ecological product data element organization model through the aggregation function. When integrating various ecological product data, it can clearly present the interaction relationships between different ecological products and economic and social development, providing strong model support for the comprehensive management and development and utilization of ecological products.

[0052] Optionally, the obtaining of the natural ecological basic data includes: based on the constructed multi-source data collection rules, parsing the carriers carrying different types of natural ecological basic data to obtain the natural ecological basic data therefrom.

[0053] Preferably, in a specific application scenario, a deep learning network enhanced by quantum entanglement is used to construct the multi-source data collection rules. Let be the multi-source data collection rule function, which is expressed as the output of a multi-layer quantum entanglement neural network.

[0054] In the quantum entanglement neural network layer, for the l-th layer of the quantum entanglement neural network, its input is x (l-1) , and the output is x (l) , which can be calculated by the following formula:

[0055] Among them: σ is the activation function, such as the Quantum Rectified Linear Unit (QuantumReLU), defined as σ(z) = max(0, z), which can better preserve the quantum characteristics of data in the quantum computing environment. is the quantum entanglement unitary matrix of the l-th layer, which describes the quantum entanglement relationship between the neurons in this layer. In the natural ecological data scenario, this entanglement relationship can simulate the complex associations between different types of ecological data, such as the potential connections between tree growth and soil moisture, light intensity in the forest. b (l) is the bias vector of the l-th layer, used to adjust the activation threshold of the neuron. In ecological data processing, it is adjusted according to different ecological environmental factors. For example, when collecting data in different climate regions, the bias vector can compensate for the impact of environmental differences on the data.

[0056] Multi-source data acquisition rule function can be expressed as: where X is the initial input matrix of all data sources, and L is the total number of layers of the quantum entanglement neural network.

[0057] Let be the carrier set carrying different types of natural ecological basic data, and each carrier can be expressed as a high-dimensional tensor T c . To parse useful data from the carriers, this application uses methods based on fractional calculus and topological data analysis.

[0058] First, perform fractional calculus operations on the carrier tensor T c . Let α be the fractional order, and the fractional derivative can be calculated through the Grünwald-Letnikov definition:

[0059]

[0060] where: a is the starting time point, and t is the current time point. is the fractional binomial coefficient, and Γ is the gamma function.

[0061] In natural ecological data, fractional calculus can capture some non-integer-order dynamic changes in the ecosystem, such as the slow growth or decay process of biological population numbers.

[0062] Then, use the topological data analysis (TDA) method to extract the topological features of the carrier tensor. The persistent homology group of the carrier tensor can be calculated by constructing the Vietoris-Rips complex. Let ∈ be the parameter of the Vietoris-Rips complex, which controls the connection threshold between vertices in the complex.

[0063] The persistent homology group H k (T c , ∈) describes the k-dimensional topological features of the carrier tensor at different scales. For example, in ecological data, the 0-dimensional persistent homology group can represent the connected components in an ecosystem, and the 1-dimensional persistent homology group can represent the holes or cyclic structures in an ecosystem.

[0064] After constructing the multi-source data collection rules and parsing the carrier, the natural ecological basic data can be obtained from the carrier. Let D be the matrix of the finally obtained natural ecological basic data, which is calculated by the following formula: where: ⊙ is the element-wise multiplication operator. M is a mask matrix used to filter out some invalid or noisy data. In natural ecological data, the mask matrix can be set according to the data quality assessment results, such as removing some abnormal measurement values or missing values.

[0065] Therefore, based on the constructed multi-source data collection rules, the process of parsing the carrier carrying different types of natural ecological basic data to obtain the natural ecological basic data is as follows:

[0066] Read the parameters of the multi-source data collection rules (such as the quantum entanglement unitary matrix and the bias vector b (l) ), the carrier tensor T c and the mask matrix M.

[0067] Parse the carrier tensor through fractional calculus and topological data analysis to identify the useful features in it.

[0068] Input the parsed carrier tensor into the multi-source data collection rule function for processing to obtain the preliminary data result.

[0069] Multiply the preliminary data result element-wise with the mask matrix to obtain the final natural ecological basic data matrix D and output it.

[0070] Preferably, the above technical processing process for obtaining the natural ecological basic data has the following technical advantages:

[0071] (1) The quantum entanglement unitary matrix describes the quantum entanglement relationship between neurons. In the natural ecological data scenario, it can simulate the complex correlations between different types of ecological data. The natural ecological system is a highly complex and interconnected system. For example, the growth of trees in a forest has potential connections with various factors such as soil humidity, light intensity, and temperature. Traditional deep learning networks may be difficult to fully capture these complex non-linear correlations, while the characteristics of quantum entanglement enable the network to learn these correlations in a way that transcends classical logic, thus more accurately constructing the multi-source data collection rules.

[0072] (2) Bias vector b (l) It can be adjusted according to different ecological environment factors. In different climate regions and geographical environments, the characteristics and distributions of ecological data may vary greatly. By adjusting the bias vector, the network can compensate for the impact of these environmental differences on the data, making the multi-source data acquisition rules more adaptable and robust. For example, the numerical ranges and distributions of soil moisture data collected in arid and humid regions may be completely different. The adjustment of the bias vector can help the network better handle these differences and improve the accuracy of data acquisition.

[0073] (3) The Quantum Rectified Linear Unit (QuantumReLU) as an activation function can better preserve the quantum characteristics of data in a quantum computing environment. This is of great significance for processing ecological data that may have quantum characteristics (such as quantum effects in some microscopic ecosystems) or leveraging the advantages of quantum computing to accelerate the computing process. Preserving the quantum characteristics of data can make the network more accurate in processing data and avoid errors caused by information loss.

[0074] (4) Fractional calculus calculates the fractional derivative through the Grünwald-Letnikov definition and can capture some non-integer-order dynamic changes in the ecosystem. Many processes in natural ecosystems, such as the slow growth or decay of biological populations and the evolution of ecosystems, often do not conform to the description of traditional integer-order derivatives. Fractional calculus can more accurately describe the dynamic characteristics of these processes, thereby providing richer information for the analysis of ecological data. For example, when studying the population change of an endangered species, fractional calculus can reveal the subtle trends of population change and help scientists better formulate conservation strategies.

[0075] (5) Topological data analysis (TDA) calculates the persistent homology group of the carrier tensor by constructing the Vietoris-Rips complex. The persistent homology group can describe the topological characteristics of the carrier tensor at different scales, such as connected components, holes, or cyclic structures in the ecosystem. These topological characteristics reflect the internal structure and organization of the ecosystem and are of great significance for understanding the stability, function, and evolution of the ecosystem. For example, in the analysis of a forest ecosystem, the 0-dimensional persistent homology group can represent the connectivity of different tree communities in the forest, and the 1-dimensional persistent homology group can represent the ecological corridors or cyclic paths in the forest. By mining these topological characteristics, potential laws and key nodes in the ecosystem can be discovered, providing a decision-making basis for ecological protection and management.

[0076] (6) The mask matrix M is used to filter out some invalid or noisy data. During the process of natural ecological data collection, due to factors such as sensor errors and environmental interference, some abnormal measurement values or missing values may be generated. These invalid data will affect the subsequent data analysis and processing results. By using the mask matrix, these invalid data can be removed from the final result, improving the quality and reliability of the data. For example, when monitoring lake water quality data, abnormal values caused by sensor failures may occur, and the mask matrix can filter out these abnormal values, making the analysis results more accurate.

[0077] (7) By inputting the parsed carrier tensor into the multi-source data collection rule function for processing and multiplying it element-wise with the mask matrix, the effective integration of multi-source data is achieved. Natural ecological data usually comes from multiple different data sources, such as satellite remote sensing, ground monitoring stations, etc. The data formats, features, and accuracies of these data sources may vary. Through the above method, these multi-source data can be uniformly processed and integrated to extract useful information from them, providing comprehensive and accurate data support for ecological research and decision-making.

[0078] Optionally, the quantification and indexing of the natural ecological basic data to generate natural ecological basic data elements include:

[0079] Based on the constructed professional knowledge base in the ecological field, perform mapping processing on the natural ecological basic data to convert the natural ecological basic data into ecological semantic expression vectors;

[0080] Match the ecological semantic expression vectors with the semantic feature ranges corresponding to different ecological environment quality levels to obtain the ecological semantic feature quantification values of the corresponding natural ecological basic data;

[0081] Input the ecological semantic expression vectors into the index calculation model to calculate the ecological index values of the natural ecological basic data;

[0082] Perform weighted synthesis on the ecological semantic feature quantification values and ecological index values to generate natural ecological basic data elements.

[0083] Preferably, in a specific application scenario, during the mapping process based on the professional knowledge base in the ecological field, assume that the professional knowledge base in the ecological field is a high-dimensional knowledge tensor with a dimension of It integrates various ecological knowledge structures and relationships. The set of natural ecological basic data is denoted as D = {d1, d2, …, d n}, where d i is the i-th natural ecological basic data, and d i can be represented as a multi-modal vector Represents information in spatial, temporal and environmental dimensions respectively.

[0084] Defining the mapping function This function is based on quantum entanglement enhanced knowledge graph embedding technology. Let H be a quantum entanglement hypergraph, whose nodes represent concepts in the knowledge base and edges represent quantum entanglement relationships between concepts. The mapping process can be expressed as: Among them, QEKG-Embed(c j ,H) is to convert the concept c in the knowledge base j The vector representation obtained by the quantum entanglement knowledge graph embedding algorithm, α ij is the weight calculated by the attention mechanism: Sim(d i ,c j ) is a semantic similarity measure based on the fractional kernel function:

[0085] Among them, K α (t) is the fractional order kernel function, α is the fractional order parameter, It is concept j The initial vector representation of .

[0086] Preferably, in a specific useful scenario, in the process of matching the ecological semantic expression vector with the environmental quality level, the set of different ecological environment quality levels is set to Each quality level q k Corresponding to a semantic feature range based on fractal geometry

[0087] Defining the matching function It combines topological data analysis and fuzzy logic reasoning. First, the ecological semantic expression vector v is calculated i To each The topological distance δ ik : Among them, TD-Dist is a topological distance metric based on persistent homology. Then, through the fuzzy membership function μ k (δi k ) Calculate vi to belong to quality level q k Membership degree:

[0088] Among them, σ k and β k is related to the quality level q k Finally, the quantized value of ecological semantic feature qi is: where r k is the quality grade q k The corresponding quantization coefficients.

[0089] Preferably, in a specific application scenario, assume that the index calculation model is a neural network model optimized based on deep reinforcement learning and quantum genetic algorithm. The network includes an input layer, multiple hidden layers, and an output layer. The input of the l-th layer is z l , and the output is z l+1 , then:

[0090] where and are the weight matrix and bias vector optimized by the quantum genetic algorithm (QGA). QGA searches for the optimal network parameters through qubit encoding and quantum gate operations.

[0091] During the training process, use the deep reinforcement learning algorithm (such as the deep deterministic policy gradient algorithm, DDPG) to optimize the network. Assume that the state s is the ecological semantic expression vector v i , the action a is the output adjustment of the network, and the reward r is based on the accuracy evaluation of the ecological index. Then the policy network π(s) is used to generate actions, and the value network Q(s,a) is used to evaluate the value of actions.

[0092] Preferably, in a specific application scenario, during the process of weighted synthesis to generate natural ecological basic data elements, assume that the weight of the ecological semantic feature quantization value q i is ω1, and the weight of the ecological index value I i is ω2, and ω1 + ω2 = 1. The weights ω1 and ω2 here are dynamically changing and are determined by the adaptive weight adjustment algorithm: ω2 = 1 - ω1, where Var(q i ) and Var(I i ) are the variances of the ecological semantic feature quantization value and the ecological index value respectively. The natural ecological basic data element E i is: E i = ω1q i + ω2I i .

[0093] Preferably, in a specific application scenario, the process of obtaining the set of natural ecological basic data elements is as follows:

[0094] Read the professional knowledge base in the ecological field The natural ecological basic data set D, the semantic feature range of different ecological environment quality levels The parameters of the index calculation model (such as those optimized by QGA and ) and the relevant statistical information (such as the data required for variance calculation).

[0095] Extract and preprocess the multi-modal features of the natural ecological basic data, and convert it into the d i form, and perform necessary encoding and embedding operations.

[0096] Apply the mapping function to map the natural ecological basic data into an ecological semantic expression vector.

[0097] Use the matching function to calculate the quantization value of the ecological semantic features.

[0098] Calculate the ecological index value through the index calculation model Calculate the ecological index value.

[0099] Use the adaptive weight adjustment algorithm to determine the weights ω1 and ω2, and perform weighted synthesis to obtain the natural ecological basic data elements.

[0100] Output the final set of natural ecological basic data elements {E1, E2, …, E n}.

[0101] Preferably, the above technical solution provided in a specific application scenario has the following technical advantages:

[0102] Compared with the traditional natural ecological basic data processing method, the above complex formula system integrating multiple cutting-edge technologies shows significant advantages in terms of processing efficiency, accuracy, adaptability, and the ability to mine complex ecological relationships.

[0103] (1) Traditional mapping based on knowledge graphs mostly relies on classical semantic similarity algorithms, such as those based on term frequency-inverse document frequency (TF-IDF) or simple semantic distance calculations. These methods often establish the connection between data and knowledge base concepts only from the literal meaning of the text or simple knowledge associations, and have limited ability to mine complex potential semantic relationships in the field of natural ecology. For example, when analyzing forest ecological data, traditional methods may only be able to identify the direct associations between tree species and common ecological descriptions, and it is difficult to capture the indirect but close connections such as tree growth and soil microorganisms, the surrounding water area ecology, etc. This solution adopts the knowledge graph embedding technology enhanced by quantum entanglement, and uses the quantum entanglement hypergraph H to describe the relationships between concepts. The characteristics of quantum entanglement enable different concepts to generate complex associations beyond classical logic at the quantum level, and can deeply mine the delicate and deep semantic connections between various factors in the natural ecological system. For example, when studying forest ecology, it reveals the potential semantic associations between seemingly unrelated factors such as tree growth and soil microbial communities, local microclimate, etc., so that the mapped ecological semantic expression vector can more comprehensively and accurately reflect the semantic information of the natural ecological basic data.

[0104] (2) In traditional mapping processing, weight assignment is often static or adjusted only based on simple rules. For natural ecological basic data from different sources and with different characteristics, it is impossible to dynamically adjust the matching weights with the concepts in the knowledge base according to their characteristics, lacking flexibility. For example, for grassland ecological data collected in different seasons, it is difficult for traditional methods to dynamically adjust the attention to related concepts such as vegetation growth and animal migration according to seasonal changes. This solution calculates the weight α with the help of the attention mechanism ij , enabling the mapping process to be dynamically adjusted according to the semantic similarity between the natural ecological basic data d i and the concept c j in the knowledge base. Ecological data is diverse and dynamic, and different data has different degrees of association with the concepts in the knowledge base in different situations. The attention mechanism can make the mapping function focus on the concepts most relevant to the current data, improving the accuracy and flexibility of the mapping. For example, when processing lake ecological data in different seasons, it can automatically adjust the attention to related concepts such as water temperature and plankton quantity according to seasonal characteristics, making the mapping results more in line with the actual situation.

[0105] (3) Traditional semantic similarity calculations are usually based on integer-order measurement methods and cannot well describe many processes with non-integer-order dynamic characteristics in natural ecological systems. When calculating the similarity between ecological data and the concepts in the knowledge base, it has insufficient ability to capture the subtle differences and complex patterns in data changes. For example, when analyzing the changes in the number of biological populations, traditional methods are difficult to accurately describe their non-integer-order characteristics such as slow fluctuations and gradual evolution. This application's semantic similarity metric Sim(d i , c j ) based on the fractional-order kernel function considers the fractional-order parameter α and can more precisely describe the similarity between data and concepts. Many processes in natural ecological systems have non-integer-order dynamic characteristics, and the fractional-order kernel function can capture these subtle differences and complex patterns, such as the slow fluctuations in the number of biological populations and the gradual evolution of ecological systems, thereby improving the accuracy of semantic similarity calculations and providing a basis for accurate mapping.

[0106] (4) Traditional matching methods mostly judge the matching relationship between ecological semantic expression vectors and the semantic feature ranges of environmental quality grades based on simple threshold judgments or conventional metrics such as Euclidean distance, and cannot deeply understand the internal topological structure of ecological systems. For example, when evaluating the ecological environment quality of rivers, traditional methods are difficult to accurately judge the relationship between data such as water quality and biodiversity and environmental quality grades from topological perspectives such as the connectivity of ecological networks and the hierarchy of ecological communities. This application uses the topological distance metric TD-Dist based on persistent homology to calculate the ecological semantic expression vector v i to the semantic feature ranges of each environmental quality grade Distance. Natural ecosystems have an inherent topological structure, such as the connectivity of ecological networks, the hierarchy of ecological communities, etc. Topological data analysis can extract stable topological features from these complex data, regardless of the specific form of the data and noise. By calculating the topological distance, the matching degree between the ecological semantic expression vector and different environmental quality levels can be judged more accurately, potential laws and structural information in ecological data can be discovered, and a more reliable basis for environmental quality assessment can be provided.

[0107] (5) The traditional matching process is weak in dealing with the uncertainty of ecological data. It usually adopts simple binary judgment, that is, the data either belongs to a certain environmental quality level or does not, and it cannot effectively handle the widespread fuzzy boundary situations in the ecosystem. For example, when classifying the wetland ecological environment quality level, it is difficult for the traditional method to give a reasonable assessment for some data in the critical state. This application introduces the fuzzy membership function μ k (δ ik ) to handle the uncertainty of ecological data. The ecosystem itself has uncertainty and fuzziness. Fuzzy logic reasoning can calculate the membership degree of the ecological semantic expression vector belonging to each quality level according to the topological distance δ ik in a more flexible and reasonable way to handle this uncertainty. By comprehensively considering the membership degree, a more realistic quantification value of ecological semantic features can be obtained, avoiding the limitations of traditional binary logic judgment.

[0108] (6) When traditional neural networks calculate ecological index values, parameter optimization mostly uses classical algorithms such as gradient descent. These algorithms are prone to falling into local optimal solutions. Especially when facing the complex non-linear relationships of natural ecological data, it is difficult to find the globally optimal network parameters, resulting in limited model performance. For example, when predicting forest carbon storage indicators, traditional optimization algorithms may not be able to fully explore the complex relationships among multiple factors such as forest area, tree species composition, and growth years. This application uses the quantum genetic algorithm (QGA) to optimize the weight matrix and bias vector of the neural network. Traditional neural network parameter optimization methods are prone to falling into local optimal solutions, while the quantum genetic algorithm uses qubit encoding and quantum gate operations, and has stronger global search ability. In the calculation of ecological indicators, the complexity and non-linearity of the data make it crucial to find the optimal network parameters. QGA can search for the optimal solution in a larger search space, improve the performance and generalization ability of the neural network, and thus calculate the ecological index value more accurately.

[0109] (7) Traditional index calculation models are usually trained based on fixed datasets. Once the ecological environment changes, it is difficult for the models to quickly adapt to the new data distribution and characteristics. For example, when a forest is invaded by new pests and diseases or encounters extreme climate events, it is very difficult for traditional models to adjust the index calculation results in a timely manner according to the changes in the ecosystem. This application trains an index calculation model based on deep reinforcement learning algorithms (such as DDPG). The ecosystem is a dynamically changing system, and environmental factors, biological communities, etc. are constantly evolving. Deep reinforcement learning can adapt to the dynamic changes of the ecosystem by interacting with the environment and continuously adjusting the output of the network according to the reward signal. For example, when evaluating the forest ecological health index, as the forest grows, is affected by natural disasters or human interventions, etc., the model can adjust the index calculation results in a timely manner according to the new data and feedback information, ensuring the timeliness and accuracy of the index.

[0110] (8) In traditional weighted synthesis methods, the weights are often set based on experience or simple fixed ratios and cannot be dynamically adjusted according to the actual characteristics of ecological data and the importance at different stages. For example, when calculating the comprehensive ecological index, the weight allocation for the quantified values of ecological semantic features and ecological index values may not reflect their true contributions in different ecological scenarios. This application uses an adaptive weight adjustment algorithm to determine the weights ω1 and ω2 according to the variances of the quantified value q i of the ecological semantic features and the ecological index value I i . In different ecological scenarios and data conditions, the stability and importance of the quantified values of ecological semantic features and ecological index values may be different. Adaptive weight adjustment can dynamically adjust the weights of the two according to the statistical characteristics of the data, making the natural ecological basic data elements obtained by weighted synthesis better reflect the actual situation of the data. For example, when the variance of the quantified value of ecological semantic features is large, it indicates that its data fluctuates greatly. At this time, its weight is appropriately reduced to reduce the impact of unstable factors on the final element and improve the quality and reliability of the element.

[0111] Optionally, based on the constructed value evaluation model, the ecological value of natural ecological basic data elements is evaluated to obtain the corresponding ecological contribution estimate, including:

[0112] Mapping the natural ecological basic data elements into the feature space where the value evaluation model is located to obtain an ecological feature dimension vector;

[0113] Inputting the ecological feature dimension vector into the value evaluation model for forward propagation to determine the ecological contribution estimate of the natural ecological basic data elements.

[0114] Optionally, based on the constructed economic and social association model, an economic and social relevance assessment is performed on the natural ecological basic data elements to obtain a relative regional economic development contribution valuation, including: classifying and coding the natural ecological basic data elements to construct a natural ecological coding matrix; inputting the natural ecological coding into the economic and social association model for forward propagation calculation to obtain the regional economic development contribution valuation.

[0115] Preferably, in a specific application scenario, when performing ecological value assessment based on a value assessment model, let the set of natural ecological basic data elements be E = {E1, E2, …, E n}, where E i represents the i-th natural ecological basic data element. The feature space where the value assessment model is located is and its dimension is m. Use a function based on quantum entanglement mapping to complete the mapping. This mapping function takes into account the quantum entanglement relationship between data elements to capture the complex interactions in the natural ecological system.

[0116]

[0117] where: x i is the ecological feature dimension vector obtained after mapping the i-th natural ecological basic data element, which is an m-dimensional vector. α ij is the weight calculated through the quantum entanglement attention mechanism and is used to measure the influence degree of E j on the mapping result of E i . Its calculation method is as follows:

[0118] Sim q (E i , E j ) is a measure based on quantum state similarity, which takes into account the feature similarity of natural ecological basic data elements at the quantum level. Assume that the natural ecological basic data elements can be represented by quantum states and , then:

[0119] QE-Embed(E j ) is the m-dimensional vector representation obtained by encoding the natural ecological basic data element E j through the quantum entanglement embedding algorithm. This algorithm utilizes the entanglement characteristics of quantum bits to encode data elements into the feature space to better preserve the internal structure and relationships of the data.

[0120] Preferably, in a useful scenario, when the ecological feature dimension vector is input into the value evaluation model for forward propagation, assume that the value evaluation model is a deep neural network composed of L layers, and the weight matrix of the l-th layer is W l , and the bias vector is b l , and the activation function is σ l .

[0121] For the input ecological feature dimension vector x i , the forward propagation process can be expressed as: where: z l is the output vector of the l-th layer. is the ecological contribution valuation of the i-th natural ecological basic data element.

[0122] Preferably, in a specific embodiment, during the economic and social relevance evaluation based on the economic and social association model, when classifying and coding the natural ecological basic data elements and constructing the natural ecological coding matrix, assume that the set of natural ecological basic data elements is E = {E1, E2,..., E n}}, and the classification coding function is which maps each natural ecological basic data element to a k-dimensional coding vector.

[0123] where c i is the coding vector of the i-th natural ecological basic data element.

[0124] The natural ecological coding matrix C is expressed as:

[0125]

[0126] The classification coding function is based on a method based on fractal coding, considering the self-similarity and hierarchy of natural ecological data elements. For example, for different types of ecosystems (such as forests, grasslands, wetlands, etc.), they can be coded according to the fractal characteristics of their ecological structures.

[0127] Preferably, during the process of inputting the natural ecological coding into the economic and social association model for forward propagation calculation, assume that the economic and social association model is also a deep neural network composed of M layers, and the weight matrix of the m-th layer is W m′ , and the bias vector is b m′ , and the activation function is σ m′ .

[0128] For the input natural ecological coding matrix C, it can be input row by row into the model for forward propagation. For the i-th row coding vector c i , the forward propagation process is as follows:

[0129] h0 = c i

[0130] h m = σ m′ (W m′ h m-1 + b m′ ), m = 1, 2, …, M

[0131]

[0132] Where: h m is the output vector of the m-th layer. is the estimated value of the contribution of the i-th natural ecological basic data element to regional economic development.

[0133] Preferably, in a specific application scenario, the aggregation implementation process of the above solution is as follows:

[0134] Read the set E of natural ecological basic data elements, the parameters of the value evaluation model (weight matrix W l , bias vector b l ), and the parameters of the economic and social correlation model (weight matrix W m′ , bias vector b m′ ).

[0135] Preprocess the natural ecological basic data elements for mapping and encoding operations.

[0136] Apply the quantum entanglement mapping function to map the natural ecological basic data elements to the feature space of the value evaluation model to obtain the ecological feature dimension vector.

[0137] Input the ecological feature dimension vector into the value evaluation model for forward propagation to calculate the ecological contribution estimate.

[0138] Classify and encode the natural ecological basic data elements to construct the natural ecological coding matrix.

[0139] Input the natural ecological coding matrix row by row into the economic and social correlation model for forward propagation to calculate the estimated value of the contribution to regional economic development.

[0140] Output the ecological contribution estimate of each natural ecological basic data element and the estimated value of the contribution to regional economic development

[0141] Preferably, based on the technical solution provided in the above embodiments, the following technical benefits are achieved:

[0142] (1) Traditional natural ecological data mapping methods are mostly based on simple statistical features or empirical rules to map data into the feature space. When facing the complex interaction relationships in natural ecosystems, this approach is difficult to capture comprehensively. For example, when evaluating the ecological value of a forest, traditional methods may only consider surface features such as the number and species of trees, and cannot deeply explore the impact of the potential connections between trees and soil microorganisms, and the surrounding water ecosystem on the ecological value. This application adopts a quantum entanglement mapping function Calculate the weight α through the quantum entanglement attention mechanism ij , and use the quantum entanglement embedding algorithm QE-Embed. The characteristics of quantum entanglement enable complex correlations to occur among different natural ecological basic data elements at the quantum level, and can discover deep relationships that are difficult to find by traditional methods. In the forest scenario, it can reveal the internal connections between tree growth and factors such as soil microbial communities and local microclimates, making the mapped ecological feature dimension vectors more comprehensive and accurate in reflecting the actual situation of the ecosystem, and providing richer and more accurate information for subsequent value evaluation.

[0143] (2) Traditional mapping functions lack consideration of the internal structure and quantum characteristics of ecological data, which may result in the loss of important information and limited mapping accuracy. Taking the mapping of wetland ecological data as an example, traditional methods may not be able to accurately reflect the true role of wetland water quality, biodiversity and other characteristics in ecological value assessment. This application is based on the measure of quantum state similarity Sim q Considering the feature similarity of natural ecological basic data elements at the quantum level, it can more finely describe the relationship between data. The QE-Embed algorithm encoded through the entanglement characteristics of quantum bits better preserves the internal structure of the data, significantly improving the mapping accuracy from data elements to the feature space, enabling subsequent value evaluation models to perform operations based on more accurate data features and enhancing the evaluation accuracy.

[0144] (3) Traditional value evaluation models are mostly neural networks with fixed structures and parameters, which are difficult to adapt to the dynamic changes and diversity of natural ecosystems. When the ecosystem changes due to factors such as climate change and human activities, traditional models cannot adjust the evaluation strategy in a timely manner. This application adopts a multi-layer deep neural network as the value evaluation model, and performs calculations through a complex forward propagation process. The model structure is flexible, and the parameters can be optimized through a large amount of data training, and it can better adapt to the dynamic characteristics of natural ecosystems. For example, when the forest ecosystem changes due to pests or fires, the model can accurately adjust the ecological contribution valuation through forward propagation according to the new ecological feature dimension vector, improving the timeliness and reliability of the evaluation.

[0145] (4) Traditional classification and coding methods are mostly based on simple category divisions. For example, classifying ecosystems into several categories such as forests and grasslands cannot fully utilize the rich information of natural ecological data. When analyzing the contribution of grassland ecology to regional economic development, traditional coding may only focus on limited information such as grassland area and main livestock species, ignoring the complex structure and changes within the grassland ecosystem. This application uses a method based on fractal coding to construct a natural ecological coding matrix. Fractal coding takes into account the self-similarity and hierarchy of natural ecological data elements and can describe ecosystem characteristics more comprehensively. Taking the grassland as an example, the grassland ecological structure can be coded more meticulously according to the fractal characteristics of grassland vegetation distribution, providing richer and more accurate input information for the economic and social correlation model and helping to deeply analyze the internal relationship between grassland ecology and regional economic development.

[0146] (5) Traditional economic and social correlation models handle ecological data relatively simply and are difficult to uncover the complex non-linear relationship between the ecosystem and regional economic development. For example, when evaluating the economic contribution of wetland ecology to the surrounding tourism industry, traditional models may only consider the simple correlation between wetland area and the number of tourists and cannot analyze the comprehensive impact of factors such as wetland ecological diversity and landscape uniqueness on tourism economy. This application uses a deep neural network as the economic and social correlation model and calculates the valuation of regional economic development contribution through multi-layer forward propagation. The powerful non-linear fitting ability of the deep neural network can uncover the ecological-economic relationships hidden in the natural ecological coding matrix. In the wetland scenario, the model can comprehensively consider various factors such as wetland water quality, biodiversity, and landscape aesthetics to accurately evaluate the contribution of wetland ecology to regional economic development and provide a more scientific basis for ecological protection and economic development decision-making.

[0147] (6) Due to the limitations of traditional coding and models, the evaluation results are often not comprehensive and accurate enough to reflect the true contribution of natural ecosystems to regional economic and social development. Through a creative coding and model calculation process, this application comprehensively considers various characteristics of natural ecosystems and their interrelationships and can more accurately evaluate the contribution of natural ecological basic data elements to regional economic development. It not only considers the direct economic impact but also can analyze the contribution of the ecosystem to indirect aspects such as social well-being and ecological service functions, providing a more comprehensive and accurate reference for formulating scientific and reasonable ecological and economic policies.

[0148] Optionally, based on the ecological contribution valuation and the regional economic development contribution valuation, construct an ecological product data element organization model, including:

[0149] Perform data mapping between the ecological contribution valuation and the regional economic development contribution valuation to build a causal logic chain between the ecological contribution valuation and the regional economic development contribution valuation;

[0150] Conduct dynamic correlation analysis on the causal logic chain to obtain a dynamic interaction evolution relationship tree between the ecological contribution valuation and the regional economic development contribution valuation;

[0151] Based on the dynamic interaction evolution relationship tree, conduct abstraction of ecological product elements and classification of economic contribution elements;

[0152] Based on the classification results of the abstracted ecological product elements and economic contribution elements, construct an ecological product data element organization model.

[0153] Preferably, in a specific embodiment, during the process of data mapping and causal logic chain construction, let the ecological contribution valuation set be ε = {e1, e2, …, e n}, where e i represents the ecological contribution valuation corresponding to the i-th natural ecological basic data element; the regional economic development contribution valuation set is r i is the regional economic development contribution valuation corresponding to the i-th natural ecological basic data element. Build a causal logic chain through a function based on quantum causal relationship mapping. This function takes into account the causal correlation of quantum states to capture the complex and subtle causal connection between ecological and economic contribution valuations.

[0154]

[0155] Where: represents the strength of the causal logic chain from the ecological contribution valuation e i to the regional economic development contribution valuation r j , which is a quantified value used to measure the tightness of the causal relationship between the two. β ijk is the weight calculated through the quantum causal attention mechanism, used to adjust the influence of different quantum causal paths on the causal logic chain strength. Its calculation method is as follows: Here, Q-Causal-Sim(e i , r j , k) is a measure based on quantum causal similarity, which takes into account the similarity degree of the ecological contribution valuation e i and the regional economic development contribution valuation r j on the k-th quantum causal path. Assume that the ecological contribution valuation and the regional economic development contribution valuation can be represented by quantum states and , and there is a specific quantum operation on the k-th quantum causal path, then:

[0156] Q-Causal-Link(e i , r j, k) is the correlation value between e and r on the k-th quantum causal path obtained through the quantum causal model. This model is based on the law of causality in quantum mechanics and describes the causal relationship between ecological and economic contribution valuations by modeling the evolution and interaction of quantum states. For example, in a forest ecosystem, e i may represent the ecological contribution valuation of forest carbon sequestration, and r j represents the contribution valuation of regional economic development brought by forest tourism. Through the quantum causal model, the causal path by which forest carbon sequestration affects the tourism experience and ultimately contributes to regional economic development by improving the ecological environment can be analyzed. i may represent the ecological contribution valuation of forest carbon sequestration, and r j represents the contribution valuation of regional economic development brought by forest tourism. Through the quantum causal model, the causal path by which forest carbon sequestration affects the tourism experience and ultimately contributes to regional economic development by improving the ecological environment can be analyzed.

[0157] Preferably, in a specific application scenario, when performing dynamic correlation analysis and constructing a dynamic interaction evolution relationship tree, when performing dynamic correlation analysis on the causal logic chain, time series factors are considered. Let the time series be t = 1, 2, …, T. A method based on time-varying topological data analysis and dynamic Bayesian network is used to construct a dynamic interaction evolution relationship tree. First, a time-varying topological distance function is defined to measure the topological distance between two causal logic chains and at time t. This function is based on persistent homology theory and measures the difference between causal logic chains by calculating the characteristic changes of causal logic chains in the topological space.

[0158] Where: and are the topological spaces corresponding to the causal logic chains and at time t, respectively, obtained by representing the causal logic chains as topological graphs and calculating their persistent homology groups. Dist(x, y) is the distance metric between two points x and y in the topological space, which can be based on geodesic distance or other topological distance metric methods.

[0159] Then, based on the dynamic Bayesian network, the conditional probability is defined to represent the probability distribution of the causal logic chain at time t + 1 given the causal logic chain at time t.

[0160]

[0161] Where: γ k is the weight parameter used to adjust the importance of different causal logic chains in conditional probability calculation. For example, when analyzing the dynamic impact of a wetland ecosystem on regional economic development, a larger γ kvalue, because water resource regulation has an important impact on regional economy (such as agricultural irrigation, hydropower generation, etc.).

[0162] By continuously iteratively calculating the above conditional probabilities, a dynamic interaction evolution relationship tree is constructed. The nodes in the tree represent the causal logic chains at different time points, and the edges represent the dynamic associations between the causal logic chains, and their weights are determined by the conditional probabilities.

[0163] Preferably, in a specific application scenario, when abstracting ecological product elements and classifying economic contribution elements, based on the dynamic interaction evolution relationship tree, ecological product element abstraction and economic contribution element classification are carried out. Let the set of ecological product elements be The set of economic contribution element classifications is Define the ecological product element abstraction function And the economic contribution element classification function

[0164] p k represents the k-th type of ecological product element, which is obtained by processing the set of relevant causal logic chains belonging to the k-th type of ecological product element through the ecological product element abstraction function Here, the set of causal logic chains is is the strength of the causal logic chain from the ecological contribution valuation e i to the regional economic development contribution valuation r j c l represents the l-th type of economic contribution element, which is obtained by operating on the set of relevant causal logic chains belonging to the l-th type of economic contribution element .

[0165] Among them, the ecological product element abstraction function For example, it is obtained by clustering and feature extraction methods based on deep learning. By clustering and analyzing the causal logic chains belonging to the same type of ecological product element, extracting their common features, and forming ecological product elements. For example, for ecological product elements such as wood production, eco-tourism, and carbon sequestration in forest ecosystems, by analyzing the ecological processes, resource utilization methods, etc. involved in the relevant causal logic chains, abstraction and definition are carried out. The economic contribution element classification function For example, it is obtained based on the Analytic Hierarchy Process (AHP) and the method of fuzzy comprehensive evaluation. First, determine multiple factors affecting economic contribution, such as industrial type, market demand, policy support, etc., and determine the weights of these factors through the AHP method. Then, for each causal logic chain, determine the category of economic contribution elements to which it belongs through the fuzzy comprehensive evaluation method according to its influence degree on different economic contribution factors. For example, when analyzing the contribution of the grassland ecosystem to regional economic development, for the causal logic chain related to livestock breeding, consider its influence on factors such as livestock production value, employment, and industrial chain extension, and determine that it belongs to the livestock industry contribution category in the direct economic contribution elements through the fuzzy comprehensive evaluation.

[0166] Preferably, in a specific application scenario, when constructing the ecological product data element organization model, based on the abstracted ecological product elements and the classification results of economic contribution elements, construct the ecological product data element organization model. Let the ecological product data element organization model be Expressed as a directed graph Where is the node set, including ecological product elements and economic contribution elements, and ε is the edge set, representing the association between ecological product elements and economic contribution elements.

[0167]

[0168] ε = {(p k , c l ) | There is a causal logic chain connecting the corresponding ecological product element and economic contribution element}

[0169] When constructing the model, assign weights to each edge, and the weights are based on the strength of the causal logic chain and the information in the dynamic interaction evolution relationship tree. Let the weight of the edge (p k , c l ) be w kl , then:

[0170] Where: λ ij is the adjustment factor, used to consider the relative importance of different causal logic chains in the model. For example, for the causal logic chain that has a significant impact on regional economic development, a larger λi j value is given. Tree-Weight(i,j) is the weight information related to the causal logic chain in the dynamic interaction evolution relationship tree, reflecting the importance of this causal logic chain in the dynamic evolution process. For example, in the time series, if a certain causal logic chain plays a key role in the association between ecological product elements and economic contribution elements at multiple time points, then its corresponding Tree-Weight(i,j) value is larger.

[0171] Preferably, in a specific application scenario, the process of generating the ecological product data element organization model includes:

[0172] Read the ecological contribution valuation set ε, the regional economic development contribution valuation set Quantum causal model parameters (such as quantum operations etc.), time-varying topological data analysis related parameters (such as topological space construction parameters, distance metric parameters, etc.), dynamic Bayesian network parameters (such as conditional probability tables, etc.), ecological product element abstraction and economic contribution element classification related parameters (such as clustering algorithm parameters, AHP weight vectors, fuzzy comprehensive evaluation membership function parameters, etc.).

[0173] Preprocess the ecological contribution valuation and the regional economic development contribution valuation, and convert them into a data format suitable for quantum causal relationship mapping and dynamic correlation analysis.

[0174] Apply the quantum causal relationship mapping function Build a causal logic chain.

[0175] Construct a dynamic interaction evolution relationship tree through time-varying topological data analysis and dynamic Bayesian network.

[0176] Use the ecological product element abstraction function and the economic contribution element classification function Perform element abstraction and classification.

[0177] Construct an ecological product data element organization model according to the abstraction and classification results, and calculate the weights of the edges in the model.

[0178] Output the ecological product data element organization model including the node set and the edge set ε and its weight information, which can be used for further applications such as ecological product value assessment and economic development planning.

[0179] Preferably, in a specific application scenario, the solution provided by the embodiments of the present application has the following technical advantages:

[0180] (1) When traditional methods construct the causal logic chain of ecological and economic contribution valuations, they mostly rely on simple linear regression or experience-based qualitative analysis. For example, when analyzing the relationship between forest ecology and regional economy, only the direct linear correlation between forest area and the output value of the timber industry may be considered, and it is difficult to explore the complex indirect causal relationships between ecological functions such as forest carbon sequestration and climate regulation and multiple industries such as tourism and agriculture. This solution uses a function based on quantum causal relationship mapping to build a causal logic chain. Calculate the weight β through the quantum causal attention mechanism ijk, considering the causal correlation of quantum states, can capture the complex and subtle causal paths between ecological and economic contribution valuations. For example, in the forest scenario, it is possible to deeply analyze the complex causal chain in which forest carbon sequestration promotes the growth of tourism consumption by improving air quality and enhancing tourism comfort, making the strength of the causal logic chain more comprehensively and accurately reflect the ecological-economic relationship.

[0181] (2) Traditional quantification methods measure the ecological-economic correlation rather roughly and cannot accurately depict the contribution degrees of different causal paths. When evaluating the contribution of grassland ecology to regional economy, the quantitative analysis of the impact of various grassland ecological factors on livestock breeding is not fine enough. This application is based on the quantum causal similarity metric Q-Causal-Sim(e i ,r j ,k), and through the inner product operation of quantum states can accurately quantify the similarity degree between the ecological contribution valuation e i and the regional economic development contribution valuation r j on each quantum causal path, providing support for accurately calculating the strength of the causal logic chain and greatly improving the quantification accuracy of causal correlation.

[0182] (3) Traditional dynamic correlation analysis is mostly based on simple time series trend analysis and is difficult to comprehensively reflect the complex evolution of the ecological-economic relationship over time. For example, when analyzing the dynamic impact of wetland ecology on regional economy, it is impossible to timely capture the sudden changes in the ecological-economic relationship caused by factors such as climate change and policy adjustments. This application uses time-varying topological data analysis and dynamic Bayesian networks to construct a dynamic interaction evolution relationship tree. The time-varying topological distance function is based on the persistent homology theory and can sensitively capture the characteristic changes of the causal logic chain in the topological space, reflecting the dynamic evolution of the ecological-economic relationship. The dynamic Bayesian network considers the dynamic dependencies between multiple causal logic chains through conditional probabilities and can timely adapt to the dynamic changes of the ecological and economic systems, comprehensively presenting their interactive evolution process.

[0183] (4) Traditional methods are difficult to comprehensively consider the dynamic interactive impacts of multiple factors when analyzing the ecological-economic relationship. When studying the forest ecology and regional economic development, the comprehensive analysis of various ecological processes within the forest ecosystem and external factors such as policies and markets is insufficient. This application can flexibly adjust the importance of different causal logic chains in conditional probability calculation through the weight parameter γ k in the dynamic Bayesian network. For example, in the wetland ecosystem, weights are set according to the actual impact degrees of different causal logic chains such as water resource regulation and biodiversity protection on regional economy (agricultural irrigation, ecological tourism, etc.), and the impacts of multiple factors on the ecological-economic dynamic relationship can be comprehensively considered.

[0184] (5) The abstraction of traditional ecological product elements is mostly based on simple classification criteria and cannot fully explore the inherent complex characteristics of the ecosystem. For example, when defining forest ecological product elements, only surface functions such as wood and tourism are classified, ignoring the complex ecological processes and interactions within the ecosystem. The ecological product element abstraction function of this application Based on deep learning clustering and feature extraction methods, it conducts in-depth clustering analysis on the causal logic chains belonging to the same type of ecological product elements, and can extract deep common features such as ecological processes and resource utilization methods. For example, in a forest ecosystem, the unique features of ecological product elements such as wood production, eco-tourism, and carbon sequestration can be accurately abstracted, providing more powerful support for the scientific definition and management of ecological products.

[0185] (6) The traditional classification method of economic contribution elements lacks a comprehensive analysis of economic contribution factors and has strong classification subjectivity. When classifying grassland ecological economic contribution elements, it may only conduct simple classification based on the main industries, without fully considering the comprehensive impacts of factors such as market demand and policy support. The economic contribution element classification function of this application Based on the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation methods, it first determines the weights of multiple factors such as industrial type, market demand, and policy support through AHP, and then uses fuzzy comprehensive evaluation to determine the economic contribution element categories according to the influence degree of each factor based on the causal logic chain. For example, when analyzing the causal logic chain of grassland livestock breeding, it can comprehensively consider its impacts on aspects such as livestock industry output value, employment, and industrial chain extension, making the classification of economic contribution elements more scientific and reasonable.

[0186] (7) The traditional ecological product data element organization model constructed is relatively simple and cannot fully reflect the complex correlation between ecological product elements and economic contribution elements. When constructing a forest ecological-economic model, it may only present a few direct correlations, missing a large number of indirect but important connections. The ecological product data element organization model constructed in this application is presented in the form of a directed graph, with nodes covering ecological product elements and economic contribution elements, and the edges and their weights integrating the strength of the causal logic chain and the information in the dynamic interaction evolution relationship tree. By adjusting the factor λ ij and the tree weight Tree-Weight(i,j), it can comprehensively and accurately reflect the correlation between ecological and economic elements, providing a more complete and accurate model support for ecological product value assessment and economic development planning.

[0187] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the present application that have similar functions.

Claims

1. A method for establishing an ecological product data element organization model, characterized in that, Including: Obtaining natural ecological basic data, where the natural ecological basic data includes at least one of forest description metadata, river description metadata, lake description metadata, grassland description metadata, and cultivated land description metadata; Quantifying and indexing the natural ecological basic data to generate natural ecological basic data elements; Based on the constructed value evaluation model, conducting an ecological value evaluation on the natural ecological basic data elements to obtain corresponding ecological contribution valuations; Based on the constructed economic and social correlation model, conducting an economic and social correlation evaluation on the natural ecological basic data elements to obtain relative regional economic development contribution valuations; Based on the ecological contribution valuations and regional economic development contribution valuations, constructing an ecological product data element organization model.

2. The method according to claim 1, wherein The obtaining of the natural ecological basic data includes: based on the constructed multi-source data collection rules, parsing carriers carrying different types of natural ecological basic data to obtain the natural ecological basic data therefrom.

3. The method according to claim 1, wherein The quantifying and indexing of the natural ecological basic data to generate natural ecological basic data elements includes: Based on the constructed professional knowledge base in the ecological field, conducting mapping processing on the natural ecological basic data to convert the natural ecological basic data into ecological semantic expression vectors; Matching the ecological semantic expression vectors with the semantic feature ranges corresponding to different ecological environment quality levels to obtain the ecological semantic feature quantization values corresponding to the natural ecological basic data; Inputting the ecological semantic expression vectors into an index calculation model to calculate the ecological index values of the natural ecological basic data; Conducting weighted synthesis on the ecological semantic feature quantization values and ecological index values to generate natural ecological basic data elements.

4. The method according to claim 1, wherein The conducting of an ecological value evaluation on the natural ecological basic data elements based on the constructed value evaluation model to obtain corresponding ecological contribution valuations includes: Mapping the natural ecological basic data elements into the feature space where the value evaluation model is located to obtain ecological feature dimension vectors; Inputting the ecological feature dimension vectors into the value evaluation model for forward propagation to determine the ecological contribution valuations of the natural ecological basic data elements.

5. The method according to claim 1, wherein The conducting of an economic and social correlation evaluation on the natural ecological basic data elements based on the constructed economic and social correlation model to obtain relative regional economic development contribution valuations includes: classifying and coding the natural ecological basic data elements to construct a natural ecological coding matrix; inputting the natural ecological coding into the economic and social correlation model for forward propagation calculation to obtain the regional economic development contribution valuations.

6. The method according to claim 1, wherein Constructing an ecological product data element organization model based on the ecological contribution valuations and regional economic development contribution valuations includes: Conducting data mapping between the ecological contribution valuations and regional economic development contribution valuations to build a causal logic chain between the ecological contribution valuations and regional economic development contribution valuations; Conducting dynamic correlation analysis on the causal logic chain to obtain a dynamic interaction evolution relationship tree between the ecological contribution valuations and regional economic development contribution valuations; Based on the dynamic interaction evolution relationship tree, conducting ecological product element abstraction and economic contribution element classification; Based on the classification results of the abstracted ecological product elements and economic contribution elements, construct an organizational model for ecological product data elements.

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