A method for establishing an ecological product data element organization model

Abstract

This invention provides a method for establishing an organizational model of ecological product data elements. The method involves acquiring basic natural ecological data, including at least one of forest descriptive metadata, river descriptive metadata, lake descriptive metadata, grassland descriptive metadata, and cultivated land descriptive metadata. The basic natural ecological data is quantified and indexed to generate basic natural ecological data elements. Based on a constructed value assessment model, the ecological value of these data elements is assessed to obtain corresponding ecological contribution estimates. Based on a constructed economic and social correlation model, the economic and social correlation of these data elements is assessed to obtain relative regional economic development contribution estimates. Based on the ecological contribution estimates and regional economic development contribution estimates, an organizational model of ecological product data elements is constructed. This application helps to comprehensively analyze and understand the overall situation of ecological products, providing a reliable basis for ecological protection and rational resource utilization.

Description

Technical Field

[0001] This invention relates to the field of intelligent processing technology, specifically to a method for establishing an organizational model of ecological product data elements. Background Technology

[0002] With increasing global attention to ecological environmental protection and sustainable development, the importance of ecological products in economic and social development is becoming increasingly prominent. As the output of natural ecosystems, ecological products have a wide range, encompassing various material products and services provided by multiple ecosystems such as forests, rivers, lakes, grasslands, and arable land.

[0003] The current field of ecological product data management faces a series of severe challenges. On the one hand, ecological product data comes from numerous and scattered sources, and data from different ecosystems lacks effective integration and unified management. Ecological data collected by various departments and regions varies in format and lacks standardization, making interconnection and sharing difficult and severely 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 raw ecological data remains at the simple recording stage, without scientific quantification and indexation, and cannot be effectively transformed into data elements for decision-making reference. For example, in the ecological value assessment stage, due to the lack of a mature and widely accepted assessment system, the results obtained from different assessment methods vary greatly, making it difficult to accurately reflect the true value of ecological products. Regarding economic and social relevance, existing research and practice struggle to accurately quantify the contributions of ecological products to regional economic growth and social welfare improvement. This situation makes it difficult for ecological products to fully play their role in actual economic and social activities, hindering the formulation of scientific and reasonable strategies for coordinated ecological protection and economic development, and impeding the effective transformation of ecological resources into economic value. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method for establishing an organizational model for ecological product data elements, thereby at least solving or mitigating the problems existing in the prior art.

[0005] To achieve the above objectives, according to one aspect of this application, a method for establishing an organization model of ecological product data elements is provided, comprising:

[0006] Acquire basic natural ecological data, which 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 basic natural ecological data to generate basic natural ecological data elements;

[0008] Based on the constructed value assessment model, ecological value assessment is carried out on basic natural ecological data elements to obtain corresponding ecological contribution estimates.

[0009] Based on the constructed economic and social linkage model, the economic and social linkage of basic natural ecological data elements is assessed to obtain an estimate of their relative contribution to regional economic development.

[0010] Based on the valuation of ecological contribution and the valuation of contribution to regional economic development, an organization model for ecological product data elements is constructed.

[0011] The technical solution in this application has at least the following technical advantages:

[0012] ① By acquiring basic natural ecological data from various sources such as forests, rivers, lakes, grasslands, and cultivated land, the previous situation of numerous and scattered data sources has been changed, laying the foundation for subsequent unified management. Quantifying and indexing this data generates basic natural ecological data elements, enabling ecological data in different formats to be transformed into a unified standard. This solves the problem of difficulty in interconnecting and sharing data, and helps to comprehensively analyze and understand the overall situation of ecological products.

[0013] ② By using the constructed value assessment model to conduct ecological value assessment of basic natural ecological data elements, compared with the previous situation where the lack of a mature and widely recognized assessment system led to huge differences in results, this scheme can obtain a relatively accurate ecological contribution valuation, more realistically reflect the ecological value of ecological products, and provide a reliable basis for ecological protection and rational use of resources.

[0014] ③ By using the constructed economic and social linkage model to assess the economic and social linkage of basic natural ecological data elements, a relative estimate of their contribution to regional economic development can be obtained. This solves the problem that existing research and practice cannot accurately quantify the contribution of ecological products to regional economic growth and social well-being, and helps to gain a deeper understanding of the role mechanism of ecological products in economic and social activities.

[0015] ④ Based on the valuation of ecological contribution and the valuation of contribution to regional economic development, an ecological product data element organization model is constructed, which closely links ecological value with economic and social development. This is conducive to formulating scientific and reasonable strategies for the coordinated development of ecological protection and economic development, and promoting the effective transformation of ecological resources into economic value. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a method for establishing an ecological product data element organization model according to an embodiment of this application. Detailed Implementation

[0017] Figure 1 This is a schematic diagram illustrating a method for establishing an organizational model of ecological product data elements according to an embodiment of this application. Figure 1 As shown, it includes:

[0018] Acquire basic natural ecological data, which 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 basic natural ecological data to generate basic natural ecological data elements;

[0020] Based on the constructed value assessment model, ecological value assessment is carried out on basic natural ecological data elements to obtain corresponding ecological contribution estimates.

[0021] Based on the constructed economic and social linkage model, the economic and social linkage of basic natural ecological data elements is assessed to obtain an estimate of their relative contribution to regional economic development.

[0022] Based on the valuation of ecological contribution and the valuation of contribution to regional economic development, an organization model for ecological product data elements is constructed.

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

[0024] For each data n i It may be influenced by multiple factors and can be obtained through a complex weighted integral function. Assume the set of influencing factors is F = {f1, f2, ..., f...} k}, each influencing factor f j There is a corresponding weight function w j (t)(t represents time), then n i The formula for obtaining it is: Among them, g ij It's about n i and f j The mapping function describes how the j-th influencing factor affects the i-th type of basic natural ecological data. t0 and t1 are the time intervals for data collection.

[0025] Preferably, in the process of quantifying and indexing basic natural ecological data in specific application scenarios, quantification and indexing are necessary to transform the basic natural ecological data into computable elements. Let the quantization function be Q, and for basic natural ecological data n... i The quantized value is q i=Q(n) i Here, Q is a multilayer perceptron (MLP) with input n. i The feature vectors are quantized scalar values.

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

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

[0028] Indication, on the other hand, transforms the quantified values ​​into basic data elements of the natural ecosystem. i Let the indexing function be I, which is a function based on fuzzy logic. Assume there exists a set of fuzzy rules R = {r1, r2, ..., r...} s}, each rule r p Corresponding to a membership function μ p (q i ) and an output value o p ,but:

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

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

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

[0032] The optimal parameter C is obtained through iterative genetic algorithm. * and γ *Then, the SVR model with optimal parameters is used to predict E, yielding the ecological contribution estimate v: Where, α i It is the Lagrange multiplier of the SVR model, and K is the kernel function (such as the radial basis function K(x,y)=exp(-γ). * ||xy|| 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 assessment of the economic and social relevance of basic natural ecological data elements based on the constructed economic and social relevance model, let the economic and social relevance model be S, which is a model based on a dynamic Bayesian network (DBN). The vector E of basic natural ecological data elements serves as the input node of the DBN, and the relative contribution estimate r to regional economic development is the output node of the DBN.

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

[0035]

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

[0037] The relative contribution to regional economic development, r, is obtained by inferring the DBN using either a forward-backward algorithm or a particle filter algorithm.

[0038]

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

[0040] Preferably, in a specific application scenario, during the process of constructing an ecological product data element organization model based on ecological contribution valuation and regional economic development contribution valuation, the ecological product data element organization model is denoted as M, which is a graph neural network (GNN) based model. The ecological contribution valuation v and the relative regional economic development contribution valuation r are used 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 GNN is defined as follows:

[0042]

[0043] in, It is the feature vector of node i in the l-th layer. Let d be the set of neighboring nodes of node i. i and d j These are the degrees of nodes i and j, respectively, and W. (l) and b (l) σ is the weight matrix and bias vector of the l-th layer, and σ is the activation function.

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

[0045] Aggregate can be an aggregation function such as summation or averaging.

[0046] Preferably, the technical solution provided in the above embodiments has the following technical advantages:

[0047] 1. In the acquisition of basic natural ecological data, this application employs a weighted integral function to obtain data, comprehensively considering multiple influencing factors and their weights over time. A mapping function is used to establish a link between these influencing factors and the basic natural ecological data. Compared to traditional techniques, traditional data acquisition methods often involve simple data collection, potentially focusing only on a single or a few factors without considering their dynamic changes over time. For example, traditional methods for acquiring forest descriptive metadata may only record static data such as forest area, ignoring the impact of dynamic factors like climate and pests on forest ecology. This application, by considering multiple dynamic factors, enables the acquired data to better reflect the true state of the ecosystem. Taking river descriptive metadata acquisition as an example, it comprehensively considers factors such as water flow velocity, water quality changes, and changes in surrounding vegetation cover to accurately acquire basic river ecological data, providing a richer and more reliable data foundation for subsequent analysis.

[0048] 2. In the quantification and indexing of basic natural ecological data, this application uses a multilayer perceptron (MLP) for quantification. Through multilayer nonlinear transformation, the feature vectors of basic natural ecological data are converted into quantified scalar values. Indexing utilizes fuzzy logic-based functions, determining the elements of basic natural ecological data according to fuzzy rules and membership functions. Traditional quantification employs simple linear transformations, which cannot fully explore the complex characteristics of the data; traditional indexing may lack effective handling of data fuzziness and uncertainty. For example, traditional ecological data quantification simply scales the data according to a fixed ratio, failing to reflect the inherent complex relationships within the data. Therefore, in this application, the multilayer nonlinear transformation of MLP can uncover the complex nonlinear characteristics in basic natural ecological data, improving the accuracy of quantification. The fuzzy logic-based indexing method can effectively handle the fuzziness and uncertainty of ecological data, generating basic natural ecological data elements that better reflect the actual ecological situation. When quantifying and indexing grassland descriptive metadata, it can accurately reflect the characteristics of grassland ecology under different fuzzy states (such as the fuzzy definition of grassland degradation), providing more realistic quantitative indicators for subsequent assessments.

[0049] 3. In the ecological value assessment stage, this application uses a value assessment model optimized by Support Vector Regression (SVR) and a genetic algorithm. The genetic algorithm is used to find the optimal parameters of the SVR to improve the model's predictive accuracy. Then, the SVR model is used to predict the ecological value of the vector elements of basic natural ecological data. Traditional ecological value assessment models use simple linear regression or empirical formulas, which cannot adapt to the complexity of ecosystems, and the parameters are often fixed, lacking optimization mechanisms. For example, traditional forest ecological value assessments rely solely on forest area and fixed unit area value, without considering complex factors such as the diversity of forest ecosystems. Therefore, the genetic algorithm in this application optimizes the SVR parameters, enabling the model to better adapt to the complex characteristics of ecological data and improve the accuracy of ecological value assessment. When assessing the ecological value of lakes, multiple factors such as lake area, water depth, and biodiversity can be comprehensively considered to accurately assess the contribution of the lake ecosystem, providing a more scientific basis for decision-making in ecological protection and resource management.

[0050] 4. In the economic and social correlation assessment stage, this application uses a dynamic Bayesian network (DBN) model to describe node state changes and input-output relationships through state transition probability matrices and observation probability matrices. It then uses a forward-backward algorithm or particle filter algorithm for reasoning to obtain an estimate of the relative contribution to regional economic development. Traditional correlation assessments employ static statistical analysis methods, which cannot consider the dynamic relationship between ecological data and economic and social factors. For example, traditional analyses of ecological and economic correlations rely solely on simple correlation analysis based on statistical data from a specific period, failing to reflect changes over time. This application, through the DBN model, effectively handles the dynamic relationship between ecological data and economic and social factors, taking into account the evolution of ecosystems and socio-economic systems over time. When analyzing the correlation between cultivated land descriptive metadata and regional economic development, it can dynamically reflect the impact of changes in cultivated land use patterns on the regional economy at different times, providing a dynamic and accurate reference for formulating sustainable regional development strategies.

[0051] 5. In the construction of the ecological product data element organization model, this application constructs an ecological product data element organization model based on a graph neural network (GNN). Node features are updated through a message passing mechanism, and the final model output is obtained through an aggregation function. Traditional model construction uses simple data concatenation or rule-based methods, which cannot effectively uncover the complex relationships between data. For example, traditional ecological product data model construction simply lists various types of data without reflecting the inherent connections between them. In this application, the message passing mechanism of GNN can fully uncover the complex relationships between data such as ecological contribution valuation and regional economic development contribution valuation, generating a more comprehensive and representative ecological product data element organization model through an aggregation function. When integrating multiple ecological product data, it can clearly present the interaction 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 acquisition of basic natural ecological data includes: based on the constructed multi-source data acquisition rules, parsing the carriers carrying different types of basic natural ecological data to obtain the basic natural ecological data from them.

[0053] Preferably, in a specific application scenario, a deep learning network based on quantum entanglement enhancement is used to construct multi-source data acquisition rules. Let... The rule function for multi-source data acquisition is represented as the output of a multi-layer quantum entangled neural network.

[0054] In a quantum entangled neural network layer, for the l-th layer, the input is x. (l-1) The output is x (l) It can be calculated using the following formula:

[0055] Where σ is the activation function, such as the Quantum ReLU, defined as σ(z) = max(0,z), which can better preserve the quantum properties of data in a quantum computing environment. is the quantum entanglement unitary matrix of the l-th layer, which describes the quantum entanglement relationship between neurons in this layer. In natural ecological data scenarios, this entanglement relationship can simulate the complex correlations between different types of ecological data, such as the potential relationship between tree growth in a forest and soil moisture and light intensity. (l) This is the bias vector of the l-th layer, used to adjust the activation threshold of neurons. In ecological data processing, it is adjusted according to different ecological and 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 It can be represented as: Where X is the initial input matrix of all data sources, and L is the total number of layers in the quantum entangled neural network.

[0057] set up It is a collection of carriers containing different types of basic natural ecological data, each carrier It can be represented as a high-dimensional tensor T c In order to extract useful data from the carrier, this application uses a method based on fractional calculus and topological data analysis.

[0058] First, regarding the carrier tensor T c Perform fractional calculus. Let α be of fractional order; the fractional derivative can be calculated using the Grünwald-Letnikov definition:

[0059]

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

[0061] In natural ecological data, fractional calculus can capture some non-integer order dynamic changes in ecosystems, such as the slow growth or decline of biological populations.

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

[0063] 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, 0-maintained long homology groups can represent connected components in an ecosystem, while 1-maintained long homology groups can represent holes or loop structures in an ecosystem.

[0064] After constructing multi-source data collection rules and parsing the data carrier, basic natural ecological data can be obtained from the carrier. Let D be the final obtained basic natural ecological data matrix, calculated using the following formula: Where: ⊙ is the element-wise multiplication operator. M is a mask matrix used to filter out 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 measurements or missing values.

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

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

[0067] The carrier tensor is analyzed using fractional calculus and topological data analysis to identify useful features.

[0068] The parsed carrier tensor is input into the multi-source data acquisition rule function. The data is processed to obtain preliminary results.

[0069] The preliminary data results are multiplied element by element with the mask matrix to obtain the final natural ecological basic data matrix D, which is then output.

[0070] Preferably, the above-described technical processing procedure for acquiring basic natural ecological data has the following technical advantages:

[0071] (1) Quantum entanglement unitary matrix This describes the quantum entanglement between neurons, enabling the simulation of complex correlations between different types of ecological data in natural ecological data scenarios. Natural ecosystems are highly complex and interconnected systems; for example, the growth of trees in a forest is potentially linked to various factors such as soil moisture, light intensity, and temperature. Traditional deep learning networks may struggle to fully capture these complex nonlinear correlations, while the properties of quantum entanglement allow the network to learn these correlations in a way that transcends classical logic, thus more accurately constructing rules for multi-source data collection.

[0072] (2) Bias vector b (l) The bias vector can be adjusted based on different ecological and environmental factors. In different climatic regions and geographical environments, the characteristics and distribution of ecological data can vary significantly. By adjusting the bias vector, the network can compensate for the impact of these environmental differences on the data, making the multi-source data collection rules more adaptable and robust. For example, soil moisture data collected in arid and humid regions may have drastically different numerical ranges and distributions; adjusting the bias vector can help the network better handle these differences and improve the accuracy of data collection.

[0073] (3) Quantum ReLU, as an activation function, can better preserve the quantum properties of data in a quantum computing environment. This is of great significance for processing ecological data that may have quantum properties (such as quantum effects in certain micro-ecosystems) or for leveraging the advantages of quantum computing to accelerate the computation process. Preserving the quantum properties of data allows the network to process data more accurately and avoids errors caused by information loss.

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

[0075] (5) Topological Data Analysis (TDA) calculates persistent homology groups of carrier tensors by constructing Vietoris-Rips complexes. Persistent homology groups describe the topological characteristics of carrier tensors at different scales, such as connected components, pores, or cyclic structures in an ecosystem. These topological features reflect the intrinsic structure and organization of the ecosystem, and are crucial for understanding its stability, function, and evolution. For example, in analyzing forest ecosystems, a 0-persistent homology group can represent the connectivity of different tree communities in the forest, while a 1-persistent homology group can represent ecological corridors or cyclic paths within the forest. By mining these topological features, potential patterns and key nodes in the ecosystem can be discovered, providing a basis for decision-making in ecological protection and management.

[0076] (6) The mask matrix M is used to filter out invalid or noisy data. During the natural ecological data collection process, some abnormal or missing values ​​may be generated due to factors such as sensor errors and environmental interference. 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 results, improving the quality and reliability of the data. For example, when monitoring lake water quality data, there may be outliers caused by sensor malfunctions. The mask matrix can filter out these outliers, making the analysis results more accurate.

[0077] (7) By inputting the parsed carrier tensor into the multi-source data acquisition rule function The data is processed and multiplied element-wise with the mask matrix, achieving effective integration of multi-source data. Natural ecological data typically comes from multiple different data sources, such as satellite remote sensing and ground monitoring stations. The data formats, characteristics, and accuracy of these data sources may vary. The above method allows for unified processing and integration of this multi-source data, extracting useful information and providing comprehensive and accurate data support for ecological research and decision-making.

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

[0079] Based on the constructed professional knowledge base in the ecological field, the basic natural ecological data is mapped and processed to transform the basic natural ecological data into ecological semantic expression vectors;

[0080] The ecological semantic expression vector is matched with the semantic feature range corresponding to different ecological environment quality levels to obtain the ecological semantic feature quantification value of the corresponding natural ecological basic data.

[0081] The ecological semantic expression vector is input into the index calculation model to calculate the ecological index values ​​of the natural ecological basic data;

[0082] The quantitative values ​​of ecological semantic features and ecological index values ​​are weighted and synthesized to generate basic data elements of natural ecology.

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

[0084] Define mapping function This function is based on a knowledge graph embedding technique enhanced by quantum entanglement. Let H be a quantum entangled hypergraph, where nodes represent concepts in the knowledge base and edges represent the quantum entanglement relationships between concepts. The mapping process can be represented as: Among them, QEKG-Embed(c j H) is the concept c in the knowledge base j The vector representation obtained through the quantum entanglement knowledge graph embedding algorithm, α ij The weights are calculated using the attention mechanism. Sim(d i ,c j () is a semantic similarity measure based on fractional kernel functions:

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

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

[0087] Define matching function It combines topological data analysis and fuzzy logic reasoning. First, it calculates the ecological semantic representation vector v. i To each Topological distance δ ik : TD-Dist is a topological distance metric based on persistent cohomology. Then, it uses the fuzzy membership function μ... k (δi k ) Calculate the quality level q that vi belongs to. k Membership degree:

[0088] Where, σ k and β k It is related to quality grade q k The relevant parameters. Finally, the quantized value of the ecological semantic feature qi is: Where r k It is quality grade q k The corresponding quantization coefficient.

[0089] Preferably, in a specific application scenario, an index calculation model is set up. This is a neural network model optimized based on deep reinforcement learning and quantum genetic algorithms. The network consists of an input layer, multiple hidden layers, and an output layer. The input to the l-th layer is z. l The output is z l+1 ,but:

[0090] in, and These 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 training, deep reinforcement learning algorithms (such as Deep Deterministic Policy Gradient Algorithm, DDPG) are used to optimize the network. Let state s be the ecological semantic representation vector v. i Action 'a' is used to adjust the network's output, and reward 'r' is based on the accuracy assessment of ecological indicators. The policy network π(s) is used to generate actions, and the value network Q(s,a) is used to evaluate the value of those actions.

[0092] Preferably, in a specific application scenario, during the weighted synthesis process of generating basic natural ecological data elements, a quantitative value q for ecological semantic features is set. i The weight is ω1, and the ecological index value is I. i The weight is ω2, and ω1 + ω2 = 1. Here, the weights ω1 and ω2 are dynamically changed and determined through an adaptive weight adjustment algorithm. ω2=1-ω1, where Var(q) i ) and Var(I i These represent the variances of the quantified values ​​of ecological semantic features and the values ​​of ecological indicators, respectively. Natural ecological basic data element E i For: E i =ω1q i +ω2I i .

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

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

[0095] Multimodal feature extraction and preprocessing of basic natural ecological data are performed to convert them into d i The format is determined, and the necessary encoding and embedding operations are performed.

[0096] Using mapping functions Mapping basic natural ecological data into ecological semantic expression vectors.

[0097] Use matching functions Calculate the quantitative value of ecological semantic features.

[0098] Through index calculation model Calculate ecological indicator values.

[0099] The weights ω1 and ω2 are determined by an adaptive weight adjustment algorithm, and then weighted and synthesized to obtain the basic data elements of the natural ecology.

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

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

[0102] Compared with traditional methods of processing basic natural ecological data, the complex formula system that integrates multiple cutting-edge technologies exhibits significant advantages in terms of processing efficiency, accuracy, adaptability, and the ability to uncover complex ecological relationships.

[0103] (1) Traditional knowledge graph-based mapping processing often relies on classical semantic similarity algorithms, such as term frequency-inverse document frequency (TF-IDF) or simple semantic distance calculation. These methods often establish connections between data and knowledge base concepts based solely on the literal meaning of the text or simple knowledge associations, and have limited ability to mine complex latent semantic relationships in the natural ecology field. For example, when analyzing forest ecological data, traditional methods may only be able to identify direct associations between tree species and common ecological descriptions, and it is difficult to capture indirect but close connections between tree growth and soil microorganisms, surrounding aquatic ecosystems, etc. This scheme adopts quantum entanglement-enhanced knowledge graph embedding technology, using a quantum entangled 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 subtle and deep semantic connections between various factors in the natural ecosystem. For example, when studying forest ecology, it reveals the latent semantic connections between tree growth and seemingly unrelated factors such as soil microbial communities and local microclimates, so that the ecological semantic expression vector obtained by mapping can more comprehensively and accurately reflect the semantic information of the basic natural ecological data.

[0104] (2) In traditional mapping processes, weight allocation is often static or based solely on simple rule adjustments. For basic natural ecological data from different sources and with varying characteristics, it is impossible to dynamically adjust the matching weights with knowledge base concepts according to their specific features, lacking flexibility. For example, for grassland ecological data collected in different seasons, traditional methods struggle to dynamically adjust the attention given to concepts such as vegetation growth and animal migration based on seasonal changes. This approach utilizes an attention mechanism to calculate the weight α. ij This enables the mapping process to be based on basic natural ecological data d i With concept c in the knowledge base j The semantic similarity is dynamically adjusted. Ecological data is diverse and dynamic; different data have varying degrees of relevance to knowledge base concepts in different contexts. Attention mechanisms allow mapping functions to 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 from different seasons, the attention given to related concepts such as water temperature and plankton abundance can be automatically adjusted according to seasonal characteristics, making the mapping results more consistent with reality.

[0105] (3) Traditional semantic similarity calculations are usually based on integer-order metrics, which cannot adequately describe many processes in natural ecosystems that exhibit non-integer-order dynamic characteristics. When calculating the similarity between ecological data and knowledge base concepts, they are insufficient in capturing subtle differences and complex patterns in data changes. For example, when analyzing changes in biological population sizes, traditional methods struggle to accurately characterize their slow fluctuations, gradual evolution, and other non-integer-order features. This application proposes a semantic similarity metric based on a fractional-order kernel function, Sim(d i ,c j The fractional-order parameter α is considered, which allows for a more detailed characterization of the similarity between data and concepts. Many processes in natural ecosystems have non-integer-order dynamic characteristics. Fractional-order kernel functions can capture these subtle differences and complex patterns, such as the slow fluctuations in biological populations and the gradual evolution of ecosystems, thereby improving the accuracy of semantic similarity calculations and providing a foundation for accurate mapping.

[0106] (4) Traditional matching methods often rely on simple threshold judgments or conventional metrics such as Euclidean distance to determine the matching relationship between ecological semantic expression vectors and the semantic feature range of environmental quality levels, failing to provide a deep understanding of the intrinsic topological structure of ecosystems. For example, when assessing the ecological environment quality of rivers, traditional methods struggle to accurately determine the relationship between data such as water quality and biodiversity and environmental quality levels from a topological perspective, considering factors such as the connectivity of ecological networks and the hierarchy of ecological communities. This application utilizes the persistent cohomology-based topological distance metric TD-Dist to calculate the ecological semantic expression vector v. i To the semantic feature range of each environmental quality level The distance between ecological data and environmental quality levels is crucial. Natural ecosystems possess inherent topological structures, such as the connectivity of ecological networks and the hierarchy of ecological communities. Topological data analysis can extract stable topological features from these complex data, unaffected by the specific form of the data or noise. By calculating topological distance, we can more accurately determine the degree of matching between ecological semantic expression vectors and different environmental quality levels, discover potential patterns and structural information in ecological data, and provide a more reliable basis for environmental quality assessment.

[0107] (5) Traditional matching processes are relatively weak in handling the uncertainty of ecological data. They typically employ simple binary judgments, meaning the data either belongs to a certain environmental quality level or does not, which cannot effectively address the widespread fuzzy boundary situations in ecosystems. For example, when classifying the ecological environment quality levels of wetlands, traditional methods struggle to provide reasonable assessments for some data in critical states. This application introduces a fuzzy membership function μ. k (δ ik Addressing the uncertainty of ecological data. Ecosystems themselves possess uncertainty and fuzziness; fuzzy logic reasoning can address this uncertainty based on topological distance δ. ik Calculating the membership degree of the ecological semantic expression vector to each quality level allows for a more flexible and reasonable handling of this uncertainty. By comprehensively considering the membership degree, we can obtain ecological semantic feature quantification values ​​that better reflect reality, avoiding the limitations of traditional binary logic judgments.

[0108] (6) Traditional neural networks often employ classical algorithms such as gradient descent for parameter optimization when calculating ecological indicators. These algorithms are prone to getting trapped in local optima, especially when dealing with complex nonlinear relationships in natural ecological data, making it difficult to find globally optimal network parameters and thus limiting model performance. For example, when predicting forest carbon storage, traditional optimization algorithms may not be able to fully exploit the complex relationships between multiple factors such as forest area, tree species composition, and age. This application uses a quantum genetic algorithm (QGA) to optimize the weight matrix of the neural network. and bias vector Traditional neural network parameter optimization methods are prone to getting trapped in local optima, while quantum genetic algorithms, utilizing qubit encoding and quantum gate operations, possess stronger global search capabilities. In ecological indicator calculations, the complexity and nonlinearity of the data make finding the optimal network parameters crucial. QGA can find optimal solutions in a larger search space, improving the performance and generalization ability of neural networks, thereby calculating ecological indicator values ​​more accurately.

[0109] (7) Traditional indicator calculation models are usually trained on fixed datasets. Once the ecological environment changes, the model struggles to adapt quickly to new data distributions and characteristics. For example, when forests are attacked by new pests or diseases or encounter extreme weather events, traditional models struggle to adjust the indicator calculation results in a timely manner according to changes in the ecosystem. This application trains the indicator calculation model based on deep reinforcement learning algorithms (such as DDPG). An ecosystem is a dynamically changing system, with environmental factors and biological communities constantly evolving. Deep reinforcement learning, through interaction with the environment, continuously adjusts the network output based on reward signals, enabling it to adapt to the dynamic changes in the ecosystem. For example, when assessing forest ecological health indicators, as forests grow, are affected by natural disasters, or undergo human intervention, the model can adjust the indicator calculation results in a timely manner based on new data and feedback information, ensuring the timeliness and accuracy of the indicators.

[0110] (8) In traditional weighted composite methods, weights are often based on experience or simple fixed ratios, and cannot be dynamically adjusted according to the actual characteristics of ecological data and the importance of 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 the true contributions of the two under different ecological scenarios. This application adopts an adaptive weight adjustment algorithm based on the quantified value q of the ecological semantic features. i and ecological index value I i The variance determines the weights ω1 and ω2. Under different ecological scenarios and data conditions, the stability and importance of the quantified values ​​of ecological semantic features and ecological indicators may vary. Adaptive weight adjustment can dynamically adjust the weights of both based on the statistical characteristics of the data, making the weighted composite natural ecological basic data elements more reflective of the actual situation. For example, when the variance of the quantified values ​​of ecological semantic features is large, it indicates that the data fluctuates significantly. In this case, appropriately reducing their weights can reduce the impact of unstable factors on the final elements and improve the quality and reliability of the elements.

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

[0112] By mapping the basic data elements of the natural ecology to the feature space where the value assessment model is located, an ecological feature dimension vector is obtained;

[0113] The ecological characteristic dimension vector is input into the value assessment model for forward propagation to determine the ecological contribution estimate of the basic natural ecological data elements.

[0114] Optionally, the assessment of the economic and social relevance of basic natural ecological data elements based on the constructed economic and social linkage model to obtain a relative contribution estimate to regional economic development includes: classifying and coding basic natural ecological data elements to construct a natural ecological coding matrix; inputting the natural ecological codes into the economic and social linkage model and performing forward propagation calculations to obtain a regional economic development contribution estimate.

[0115] Preferably, in a specific application scenario, when conducting ecological value assessment based on a value assessment model, the set of basic natural ecological data elements is set as E = {E1, E2, ..., E...}. n}, where E i Let represent the i-th basic natural ecological data element. The feature space in which the value assessment model resides is . Its dimension is m. A function based on quantum entanglement mapping is used. The mapping is performed by a function that takes into account the quantum entanglement between data elements to capture the complex interactions in natural ecosystems.

[0116]

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

[0118] Sim q (E i E j This is a measure based on quantum state similarity, which considers the similarity of characteristics of basic natural ecological data elements at the quantum level. It assumes that basic natural ecological data elements can be represented by quantum states. and If it means:

[0119] QE-Embed(E j ) is the basic data element of natural ecology E j The m-dimensional vector representation obtained through the quantum entanglement embedding algorithm. This algorithm utilizes the entanglement properties of qubits to encode data elements into a feature space, thereby better preserving the inherent structure and relationships of the data.

[0120] Preferably, in a useful scenario, when the ecological feature dimension vector is input into the value assessment model for forward propagation, the value assessment model is assumed to be a deep neural network consisting of L layers, with the weight matrix of the l-th layer being W. l The bias vector is b l The activation function is σ. l .

[0121] For the input ecological feature dimension vector x i The forward propagation process can be represented as: Where: z l It is the output vector of the l-th layer. It is the ecological contribution estimate of the i-th basic natural ecological data element.

[0122] Preferably, in a specific embodiment, during the economic and social correlation assessment based on the economic and social correlation model, when classifying and coding the basic natural ecological data elements and constructing the natural ecological coding matrix, the set of basic natural ecological data elements is set as E = {E1, E2, ..., E...}. n The classification coding function is: It maps each basic natural ecological data element to a k-dimensional encoded vector.

[0123] Where c i It is the encoding vector of the i-th basic natural ecological data element.

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

[0125]

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

[0127] Preferably, in the process of inputting the natural ecological code into the economic and social relationship model for forward propagation calculation, the economic and social relationship model is also assumed to be a deep neural network consisting of M layers, with the weight matrix of the m-th layer being W. m′ The bias vector is b m′ The activation function is σ m′ .

[0128] Given the input natural ecology coding matrix C, it can be fed into the model row by row for forward propagation. For the coding vector c in the i-th row... 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 It is the output vector of the m-th layer. It is the estimated contribution of the i-th basic natural ecological data element to regional economic development.

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

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

[0135] Preprocessing of basic natural ecological data elements is performed to facilitate mapping and encoding operations.

[0136] Using quantum entanglement mapping function By mapping the basic data elements of the natural ecology to the feature space of the value assessment model, an ecological feature dimension vector is obtained.

[0137] The ecological characteristic dimension vector is input into the value assessment model for forward propagation to calculate the ecological contribution estimate.

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

[0139] The natural ecology coding matrix is ​​input row by row into the economic and social linkage model for forward propagation to calculate the estimated contribution of regional economic development.

[0140] Output the ecological contribution estimate for each basic natural ecological data element. Valuation of contribution to regional economic development

[0141] Preferably, the technical solution provided by the above embodiments has the following technical advantages:

[0142] (1) Traditional natural ecological data mapping methods are mostly based on simple statistical features or empirical rules, mapping data to a feature space. When faced with the complex interactions within natural ecosystems, this approach struggles to capture all aspects. For example, when assessing the ecological value of forests, traditional methods may only consider surface characteristics such as the number and species of trees, failing to delve into the potential connections between trees and soil microorganisms, as well as the surrounding aquatic ecosystems, and their impact on ecological value. This application employs a quantum entanglement mapping function. The weight α is calculated using the quantum entanglement attention mechanism. ij Furthermore, the quantum entanglement embedding algorithm QE-Embed is utilized. The properties of quantum entanglement enable complex correlations between different basic natural ecological data elements at the quantum level, revealing deep-seated relationships that are difficult to discover using traditional methods. In forest scenarios, this can reveal the intrinsic connections between tree growth and factors such as soil microbial communities and local microclimates, allowing the mapped ecological feature dimension vector to more comprehensively and accurately reflect the actual situation of the ecosystem, providing richer and more accurate information for subsequent value assessment.

[0143] (2) Traditional mapping functions lack consideration for the intrinsic structure and quantum properties of ecological data, which may lead to the loss of important information and limited mapping accuracy. Taking wetland ecological data mapping as an example, traditional methods may not accurately reflect the true role of wetland water quality, biodiversity, and other characteristics in ecological value assessment. This application uses the quantum state similarity metric Sim... q By considering the similarity of characteristics among fundamental natural ecological data elements at the quantum level, the relationships between data can be characterized more meticulously. The QE-Embed algorithm, which encodes data using the entanglement properties of qubits, better preserves the inherent structure of the data, significantly improves the mapping accuracy from data elements to the feature space, and enables subsequent value assessment models to perform calculations based on more accurate data features, thereby enhancing assessment accuracy.

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

[0145] (4) Traditional classification and coding methods are mostly based on simple category divisions, such as dividing ecosystems into forests, grasslands, etc., which cannot fully utilize the rich information in 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 fractal coding-based method to construct a natural ecological coding matrix. Fractal coding considers the self-similarity and hierarchy of natural ecological data elements, and can more comprehensively describe the characteristics of the ecosystem. Taking grassland as an example, the grassland ecological structure can be encoded in more detail based on the fractal characteristics of grassland vegetation distribution, providing richer and more accurate input information for economic and social linkage models, and helping to deeply analyze the intrinsic connection between grassland ecology and regional economic development.

[0146] (5) Traditional economic and social linkage models have relatively simple processing of ecological data, making it difficult to uncover the complex nonlinear relationships between ecosystems and regional economic development. For example, when assessing the economic contribution of wetland ecosystems to the surrounding tourism industry, traditional models may only consider the simple correlation between wetland area and the number of tourists, failing to analyze the comprehensive impact of factors such as wetland biodiversity and landscape uniqueness on the tourism economy. This application uses deep neural networks as the economic and social linkage model, calculating the estimated contribution of regional economic development through multi-layer forward propagation. The powerful nonlinear fitting ability of deep neural networks can uncover the hidden ecological-economic relationships in the natural ecological coding matrix. In the wetland scenario, the model can comprehensively consider multiple factors such as wetland water quality, biodiversity, and landscape aesthetics, accurately assessing the contribution of wetland ecosystems to regional economic development, and providing a more scientific basis for ecological protection and economic development decisions.

[0147] (6) Due to the limitations of traditional coding and models, the assessment results are often not comprehensive or accurate enough, failing to reflect the true contribution of natural ecosystems to regional economic and social development. This application, through a creative coding and model calculation process, comprehensively considers the various characteristics of natural ecosystems and their interrelationships, enabling a more accurate assessment of the contribution of basic natural ecological data elements to regional economic development. It not only considers direct economic impacts but also analyzes the indirect contributions of ecosystems to social well-being and ecosystem service functions, providing a more comprehensive and precise reference for formulating scientific and rational ecological economic policies.

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

[0149] Data mapping is performed between the ecological contribution valuation and the regional economic development contribution valuation to establish a causal logic chain between the ecological contribution valuation and the regional economic development contribution valuation.

[0150] Dynamic correlation analysis of causal logic chains yields a dynamic interaction evolution relationship tree between the ecological contribution estimate and the regional economic development contribution estimate;

[0151] Based on a dynamic interactive evolutionary relationship tree, ecological product elements are abstracted and economic contribution elements are classified.

[0152] Based on the abstracted classification results of ecological product elements and economic contribution elements, an organizational model for ecological product data elements is constructed.

[0153] Preferably, in a specific embodiment, during the data mapping and causal logic chain construction process, the ecological contribution valuation set is set as ε={e1,e2,…,e n}, where e i The ecological contribution estimate represents the value corresponding to the i-th basic natural ecological data element; the set of regional economic development contribution estimates is as follows: r i This is the estimated contribution of the i-th basic natural ecological data element to regional economic development. It is calculated using a function based on quantum causality mapping. This function constructs a causal logic chain. It considers the causal relationships of quantum states to capture the complex and subtle causal connections between ecological and economic contribution estimates.

[0154]

[0155] in: Indicates the ecological contribution valuation e i Contribution to regional economic development (valuation r) j The strength of the causal logical chain is a quantifiable value used to measure the tightness of the causal relationship between two things. β ijk These weights, calculated using a quantum causal attention mechanism, are used to adjust the influence of different quantum causal paths on the strength of the causal logic chain. The 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 ecological contribution estimate e. i And the estimated contribution of regional economic development r j The degree of similarity along the k-th quantum causal path. Assume that the ecological contribution estimate and the regional economic development contribution estimate can be represented by quantum states. and This indicates that a specific quantum operation exists on the k-th quantum causal path. but:

[0156] Q-Causal-Link(e i ,r j,k) is obtained through the quantum causal model on the k-th quantum causal path e i With r j The correlation value. This model, based on causality in quantum mechanics, describes the causal relationship between estimated ecological and economic contributions by modeling the evolution and interaction of quantum states. For example, in a forest ecosystem, e i This may represent an estimate of the ecological contribution of forest carbon sequestration, r j The contribution of forest tourism to regional economic development can be estimated. Through quantum causal models, we can analyze the causal path of how forest carbon sequestration improves the ecological environment, thereby affecting the tourism experience and ultimately contributing to regional economic development.

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

[0158] in: and These are causal logic chains. and The topological space corresponding to time t is obtained by representing the causal logic chain as a topological graph and calculating its persistent homology group. 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 metrics.

[0159] Then, based on the dynamic Bayesian network, conditional probabilities are defined. This indicates that the causal logical chain is known at time t. In this case, causal logic chain The probability distribution at time t+1.

[0160]

[0161] Wherein: γ k This is a weighting parameter used to adjust the importance of different causal logistic chains in conditional probability calculations. For example, when analyzing the dynamic impact of wetland ecosystems on regional economic development, causal logistic chains related to water resource regulation might be given a larger γ value. kThis is valuable because water resource regulation has a significant impact on regional economies (such as agricultural irrigation and hydropower generation).

[0162] By iteratively calculating the aforementioned conditional probabilities, a dynamic interactive evolutionary relationship tree is constructed. Nodes in the tree represent causal logical chains at different points in time, and edges represent the dynamic connections between causal logical chains, with their weights determined by the conditional probabilities.

[0163] Preferably, in a specific application scenario, when abstracting ecological product elements and classifying economic contribution elements, a dynamic interactive evolutionary relationship tree is used to perform both the abstraction of ecological product elements and the classification of economic contribution elements. Let the set of ecological product elements be... The economic contribution factor classification set is as follows Define abstract functions for ecological product elements Classification function of economic contribution factors

[0164] p k This represents the k-th type of ecological product element, which is represented by an abstract function of ecological product elements. This is obtained by processing the set of relevant causal logic chains belonging to the k-th type of ecological product element. The set of causal logic chains here is... Based on the ecological contribution valuation e i Contribution to regional economic development (valuation r) j The strength of the causal logic chain. l This represents the l-th type of economic contribution factor, defined by the economic contribution factor classification function. The set of relevant causal logic chains belonging to the l-th type of economic contribution factors The results were obtained through the operation.

[0165] Among them, the abstract function of ecological product elements For example, clustering and feature extraction methods based on deep learning can be used. By clustering causal chains belonging to the same category of ecological product elements, their common features are extracted to form ecological product elements. For instance, for ecological product elements in forest ecosystems such as timber production, ecotourism, and carbon sequestration, abstraction and definition are performed by analyzing the ecological processes and resource utilization patterns involved in the relevant causal chains. Economic contribution element classification function. For example, this can be achieved using the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation methods. First, multiple factors influencing economic contribution are identified, such as industry type, market demand, and policy support. The weights of these factors are then determined using the AHP method. Next, for each causal chain, based on its influence on different economic contribution factors, the category of economic contribution element to which it belongs is determined using the fuzzy comprehensive evaluation method. For instance, when analyzing the contribution of grassland ecosystems to regional economic development, for causal chains related to livestock farming, considering their impact on livestock output, employment, and industrial chain extension, fuzzy comprehensive evaluation determines that it belongs to the livestock industry contribution category among the direct economic contribution elements.

[0166] Preferably, in a specific application scenario, when constructing the ecological product data element organization model, the ecological product data element organization model is constructed based on the abstracted classification results of ecological product elements and economic contribution elements. Let the ecological product data element organization model be... Represented as a directed graph in ε is a set of nodes containing ecological product elements and economic contribution elements, and ε is a set of edges representing the relationships between ecological product elements and economic contribution elements.

[0167]

[0168] ε={(p k ,c l There exists a causal logical chain connecting the corresponding ecological product elements and economic contribution elements.

[0169] When constructing the model, each edge is assigned a weight based on the strength of the causal logical chain and information from the dynamic interaction evolution relationship tree. Let edge (p) k ,c l The weight of ) is w kl ,but:

[0170] Where: λ ij This is an adjustment factor used to consider the relative importance of different causal logic chains in the model. For example, a larger λi is given to causal logic chains that have a significant impact on regional economic development. j The value is Tree-Weight(i,j), which represents the causal logical chain within the dynamically evolving relational tree. The relevant weight information reflects the importance of the causal chain in the dynamic evolution process. For example, in a time series, if a causal chain plays a key role in the correlation between ecological product factors and economic contribution factors at multiple time points, then its corresponding Tree-Weight(i,j) value is relatively large.

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

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

[0173] The ecological contribution estimates and regional economic development contribution estimates are preprocessed and converted into a data format suitable for quantum causal mapping and dynamic correlation analysis.

[0174] Using quantum causal mapping function Establish a causal logic chain.

[0175] A dynamic interactive evolution relationship tree is constructed through time-varying topological data analysis and dynamic Bayesian networks.

[0176] Using abstract functions of ecological product elements Classification function of economic contribution factors Perform element abstraction and classification.

[0177] Based on the abstraction and classification results, an organization model for ecological product data elements is constructed, and the weights of the edges in the model are calculated.

[0178] Output ecological product data element organization model Includes a set of nodes The edge set ε and its weight information 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 in this application embodiment has the following technical advantages:

[0180] (1) Traditional methods often rely on simple linear regression or empirical qualitative analysis when constructing causal chains for estimating ecological and economic contributions. For example, when analyzing the relationship between forest ecology and regional economy, they may only consider the direct linear correlation between forest area and timber industry output, making it difficult to uncover the complex indirect causal relationships between forest carbon sequestration, climate regulation, and other ecological functions and multiple industries such as tourism and agriculture. This scheme adopts a function based on quantum causal mapping. Construct a causal logic chain. Calculate the weight β using a quantum causal attention mechanism. ijkBy considering quantum state causal relationships, it can capture the complex and subtle causal paths between ecological and economic contribution estimates. For example, in forest scenarios, it can delve into the complex causal chain where forest carbon sequestration improves air quality, enhances tourist comfort, and thus promotes tourism consumption growth, thereby increasing the strength of the causal logic chain. To more comprehensively and accurately reflect the ecological-economic relationship.

[0181] (2) Traditional quantitative methods for measuring the relationship between ecology and economy are relatively coarse and cannot accurately characterize the contribution of different causal pathways. When assessing the contribution of grassland ecology to regional economy, the quantitative analysis of the influence of multiple grassland ecological factors on livestock farming is not precise enough. This application is based on the quantum causal similarity measure Q-Causal-Sim(e i ,r j ,k), through the inner product operation of quantum states It can accurately quantify the valuation of ecological contribution e i And the estimated contribution of regional economic development r j The degree of similarity on each quantum causal path provides support for accurately calculating the strength of causal logical chains, greatly improving the accuracy of causal correlation quantification.

[0182] (3) Traditional dynamic correlation analysis is mostly based on simple time series trend analysis, which is difficult to fully reflect the complex evolution of ecological and economic relationships over time. For example, when analyzing the dynamic impact of wetland ecology on regional economy, it is impossible to capture abrupt changes in ecological-economic relationships 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 interactive evolution relationship tree. Time-varying topological distance function Based on persistent cohomology theory, dynamic Bayesian networks can sensitively capture the characteristic changes of causal logical chains in topological space, reflecting the dynamic evolution of ecological-economic relationships. They utilize conditional probability... It takes into account the dynamic dependencies between multiple causal logical chains, and can adapt to the dynamic changes of ecological and economic systems in a timely manner, fully presenting their interactive evolution process.

[0183] (4) Traditional methods struggle to comprehensively consider the dynamic interactions of multiple factors when analyzing the relationship between ecology and economy. In studies of forest ecology and regional economic development, there is insufficient comprehensive analysis of various ecological processes within the forest ecosystem, as well as external factors such as policies and markets. This application utilizes the weight parameter γ in a dynamic Bayesian network... k It allows for flexible adjustment of the importance of different causal logic chains in conditional probability calculations. For example, in wetland ecosystems, weights can be set for different causal logic chains such as water resource regulation and biodiversity conservation based on their actual impact on the regional economy (agricultural irrigation, ecotourism, etc.), enabling a comprehensive consideration of the impact of multiple factors on the dynamic relationship between ecology and economy.

[0184] (5) Traditional abstraction of ecological product elements is often based on simple classification criteria, failing to fully explore the inherent complex characteristics of ecosystems. For example, when defining forest ecological product elements, classification is based solely on surface functions such as timber and tourism, neglecting the complex ecological processes and interactions within the ecosystem. This application's ecological product element abstraction function... Deep learning-based clustering and feature extraction methods can be used to perform in-depth clustering analysis on causal logic chains belonging to the same category of ecological product elements, extracting deep-seated common features such as ecological processes and resource utilization patterns. For example, in forest ecosystems, the unique characteristics of ecological product elements such as timber production, ecotourism, and carbon sequestration can be accurately abstracted, providing stronger support for the scientific definition and management of ecological products.

[0185] (6) Traditional methods for classifying economic contribution factors are not comprehensive enough in their analysis of these factors and are highly subjective. When classifying grassland ecological economic contribution factors, they may only be based on a simple classification of major industries, without fully considering the combined effects of market demand, policy support, and other factors. This application's economic contribution factor classification function... Based on the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation, this method first uses AHP to determine the weights of multiple factors such as industry type, market demand, and policy support. Then, fuzzy comprehensive evaluation is used to determine the category of economic contribution factors based on the degree of influence of each factor on the causal logic chain. For example, when analyzing the causal logic chain of grassland animal husbandry, it can comprehensively consider its impact on animal husbandry output value, employment, and industrial chain extension, making the classification of economic contribution factors more scientific and reasonable.

[0186] (7) Traditionally constructed ecological product data element organization models are relatively simple and cannot fully reflect the complex relationships between ecological product elements and economic contribution elements. When constructing a forest ecology-economy model, only a few direct relationships may be presented, while many indirect but important connections are omitted. The ecological product data element organization model constructed in this application... Presented in the form of a directed graph, the nodes encompass ecological product elements and economic contribution elements, and the edges and their weights integrate the strength of the causal logical chain. Information within the dynamic, interactive evolutionary relationship tree. This is achieved by adjusting the factor λ. ij The Tree-Weight(i,j) model can comprehensively and accurately reflect the relationship between ecological and economic factors, providing more complete and accurate model support for ecological product value assessment and economic development planning.

[0187] 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 establishing an organizational model of ecological product data elements, characterized in that, include: Acquire basic natural ecological data, which includes at least one of forest description metadata, river description metadata, lake description metadata, grassland description metadata, and cultivated land description metadata. The basic natural ecological data is acquired using a weighted integral function. The basic data of natural ecology are quantified and indexed to generate basic data elements of natural ecology. Quantification uses a multilayer perceptron (MLP), and indexing utilizes a function based on fuzzy logic. Based on the constructed value assessment model, ecological value assessment is carried out on the basic data elements of natural ecology to obtain the corresponding ecological contribution estimate. The ecological value assessment is based on the value assessment model optimized by support vector regression (SVR) and genetic algorithm. Based on the constructed economic and social linkage model, the economic and social linkage of basic natural ecological data elements is assessed to obtain an estimate of their relative contribution to regional economic development. The economic and social linkage assessment is based on a dynamic Bayesian network (DBN) model. Based on the valuation of ecological contribution and the valuation of contribution to regional economic development, an organization model of ecological product data elements is constructed. The organization model of ecological product data elements is based on a graph neural network (GNN). The quantification and indexing of basic natural ecological data to generate basic natural ecological data elements includes: Based on the constructed professional knowledge base in the ecological field, the basic natural ecological data is mapped and processed to transform the basic natural ecological data into ecological semantic expression vectors; The ecological semantic expression vector is matched with the semantic feature range corresponding to different ecological environment quality levels to obtain the ecological semantic feature quantification value of the corresponding natural ecological basic data. The ecological semantic expression vector is input into the index calculation model to calculate the ecological index values ​​of the natural ecological basic data; The quantitative values ​​of ecological semantic features and ecological index values ​​are weighted and synthesized to generate basic data elements of natural ecology; Based on ecological contribution valuation and regional economic development contribution valuation, an ecological product data element organization model is constructed, including: Data mapping is performed between the ecological contribution valuation and the regional economic development contribution valuation to establish a causal logic chain between the ecological contribution valuation and the regional economic development contribution valuation. Dynamic correlation analysis of causal logic chains yields a dynamic interaction evolution relationship tree between the ecological contribution estimate and the regional economic development contribution estimate; Based on a dynamic interactive evolutionary relationship tree, ecological product elements are abstracted and economic contribution elements are classified. Based on the abstracted classification results of ecological product elements and economic contribution elements, an organizational model for ecological product data elements is constructed.

2. The method according to claim 1, characterized in that, The acquisition of basic natural ecological data includes: based on the constructed multi-source data collection rules, parsing carriers carrying different types of basic natural ecological data to obtain the basic natural ecological data from them.

3. The method according to claim 1, characterized in that, The constructed value assessment model is used to assess the ecological value of basic natural ecological data elements, resulting in corresponding ecological contribution estimates, including: By mapping the basic data elements of the natural ecology to the feature space where the value assessment model is located, an ecological feature dimension vector is obtained; The ecological characteristic dimension vector is input into the value assessment model for forward propagation to determine the ecological contribution estimate of the basic natural ecological data elements.

4. The method according to claim 1, characterized in that, The aforementioned economic and social linkage model assesses the economic and social linkages of basic natural ecological data elements to obtain an estimated contribution to regional economic development. This includes: classifying and coding basic natural ecological data elements to construct a natural ecological coding matrix; inputting the natural ecological codes into the economic and social linkage model for forward propagation calculation to obtain an estimated contribution to regional economic development.

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