Method for predicting the development risks of ecological products

Abstract

The application provides a method for predicting development risks of ecological products, comprising the following steps: acquiring multi-source heterogeneous data of ecological products; performing stream processing on the multi-source heterogeneous data to form a multi-source heterogeneous data stream; performing multi-modal feature engineering construction on the multi-source heterogeneous data stream to form physical layer feature data, ecological layer feature data and social and economic layer feature data; performing multi-modal feature fusion on the physical layer feature data, the ecological layer feature data and the social and economic layer feature data to generate ERIFM ecological development risk comprehensive feature data; performing vectorization on the ERIFM ecological development risk comprehensive feature data to obtain an ecological development risk feature vector; and inputting the ecological development risk feature vector into a development risk quantification model to calculate an ecological development risk score. The application can more accurately predict risks, can help in-depth analysis of the essential characteristics of ecological product development risks, and can make risk assessment more comprehensive and accurate.

Description

Technical Field

[0001] This invention relates to the field of intelligent processing technology, and more specifically to a method for predicting the development risks of ecological products. Background Technology

[0002] In today's society, the development of ecological products is of vital importance for achieving sustainable economic development and environmental protection. Ecological products not only include material products provided by natural ecosystems, such as timber and water resources, but also encompass ecosystem services, such as soil and water conservation and climate regulation. With the increasing demands for ecological environment quality and the rapid development of the ecological economy, ecological product development projects are proliferating, involving multiple fields such as ecological agriculture, ecotourism, and ecological restoration.

[0003] However, the development of ecological products faces numerous uncertainties and risks. From the perspective of the natural environment, ecosystems are highly complex and dynamic, and their stability is easily affected by factors such as natural disasters, climate change, and environmental pollution. For example, extreme weather events may damage ecological agriculture production facilities and crops, leading to a decline in agricultural output; water pollution may affect the water quality and landscape of ecotourism areas, reducing their attractiveness to tourists.

[0004] At the socio-economic level, fluctuations in market demand, changes in policies and regulations, and the pace of technological innovation can also pose risks to the development of eco-products. Market demand for eco-products may be influenced by factors such as consumer preferences and the economic situation. If the developed eco-products fail to meet market demand, it may lead to unsold products and economic losses. Adjustments to policies and regulations may impose new requirements on the development standards and approval processes for eco-products, increasing development costs and difficulties. Furthermore, the continuous emergence of new technologies brings both opportunities and challenges to the development of eco-products. Failure to adopt appropriate technologies in a timely manner may put development projects at a disadvantage in market competition.

[0005] Currently, a comprehensive and effective risk prediction method is lacking in the development of ecological products. Existing risk assessment methods often only consider single or a few factors, failing to comprehensively consider the complex factors involving ecology, environment, society, and economy. This results in inaccurate and unreliable predictions of risks in ecological product development, failing to provide developers with a scientific basis for decision-making, and thus affecting the success rate and sustainability of ecological product development projects. Therefore, there is an urgent need for a method that can accurately predict the risks of ecological product development to ensure the smooth progress of ecological product development and the healthy development of the ecological economy. Summary of the Invention

[0006] To address the aforementioned technical problems, this application provides a method for predicting the development risks of ecological products, in order to at least solve or mitigate the problems existing in the prior art.

[0007] To achieve the above objectives, according to one aspect of this application, a method for predicting the development risks of ecological products is provided, comprising:

[0008] Acquire multi-source heterogeneous data on ecological products, including at least one of the following: environmental physical data, ecological remote sensing data, biodiversity data, and socio-economic data;

[0009] Streaming processing is performed on multi-source heterogeneous data to form a multi-source heterogeneous data stream;

[0010] Multimodal feature engineering is performed on the multi-source heterogeneous data streams to form physical layer feature data, ecological layer feature data, and socio-economic layer feature data;

[0011] Multimodal feature fusion is performed on the physical layer feature data, ecological layer feature data, and socio-economic layer feature data to generate ERIFM comprehensive feature data on ecological development risks.

[0012] Vectorize the comprehensive characteristic data of ERIFM ecological development risks to obtain the ecological development risk characteristic vector;

[0013] The ecological development risk feature vector is input into the development risk quantification model to calculate the ecological development risk score.

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

[0015] ① This approach acquires multi-source heterogeneous data on ecological products, encompassing environmental physical data, ecological remote sensing data, biodiversity data, and socioeconomic data. This comprehensive approach considers various factors, including natural environment and socioeconomic factors, during the development of ecological products. Compared to existing risk assessment methods that only consider a single or limited number of factors, this comprehensive data acquisition method captures more information influencing the risks of ecological product development, thus enabling more accurate risk prediction. For example, by combining environmental physical data and socioeconomic data, the impact of natural environmental changes and market demand fluctuations on ecological product development can be considered simultaneously, avoiding inaccurate risk predictions due to the neglect of certain important factors.

[0016] ② Streaming processing of multi-source heterogeneous data creates a multi-source heterogeneous data stream. Multimodal feature engineering then constructs physical, ecological, and socioeconomic layer feature data. This series of operations enables effective processing and feature extraction of massive and complex data. Streaming processing allows for real-time and efficient data processing, ensuring data timeliness; multimodal feature engineering uncovers potential information from different perspectives, enabling subsequent risk analysis to be based on more valuable feature data. This helps to deeply analyze the essential characteristics of risks in the development of eco-products and improves risk analysis capabilities.

[0017] ③ Multimodal feature fusion is performed on the physical, ecological, and socioeconomic layer characteristic data to generate ERIFM comprehensive ecological development risk characteristic data. This fusion method integrates characteristic information from different levels to form a more comprehensive and representative comprehensive feature that reflects the overall situation of ecological product development risks. It overcomes the limitations of existing methods that cannot comprehensively consider multiple factors, making risk assessment more comprehensive and accurate.

[0018] ④ The ERIFM comprehensive feature data of ecological development risks is vectorized to obtain ecological development risk feature vectors, which are then input into the development risk quantification model to calculate the ecological development risk score. Vectorization allows the data to exist in a form more suitable for model processing, facilitating the quantification model's calculations. The quantification model can accurately calculate the ecological development risk score based on the feature vectors, providing developers with a specific and intuitive risk quantification indicator. This provides a scientific basis for ecological product development decisions, improving the success rate and sustainability of ecological product development projects. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of a method for predicting the development risks of an ecological product according to an embodiment of this application. Detailed Implementation

[0020] Figure 1 This is a schematic diagram of a method for predicting the development risks of an ecological product according to an embodiment of this application. Figure 1As shown, it includes: acquiring multi-source heterogeneous data of ecological products, including at least one of the following: environmental physical data, ecological remote sensing data, biodiversity data, and socio-economic data; performing streaming processing on the multi-source heterogeneous data to form a multi-source heterogeneous data stream; constructing multi-modal feature engineering on the multi-source heterogeneous data stream to form physical layer feature data, ecological layer feature data, and socio-economic layer feature data; performing multi-modal feature fusion on the physical layer feature data, ecological layer feature data, and socio-economic layer feature data to generate ERIFM comprehensive ecological development risk feature data; vectorizing the ERIFM comprehensive ecological development risk feature data to obtain an ecological development risk feature vector; and inputting the ecological development risk feature vector into a development risk quantification model to calculate an ecological development risk score.

[0021] Preferably, in a specific application scenario, when acquiring multi-source heterogeneous data on ecological products, the environmental physical data set is set as follows: Ecological remote sensing data set is Biodiversity dataset Socioeconomic data set A multi-source heterogeneous data set D can be represented as a power set combination of them: Where i ep i er i bd i se ∈{0,1}, when i=0 it means that this type of data is not selected, and when i=1 it means that this type of data is selected.

[0022] The physical functions of each parameter in the above scheme are as follows: The kth data item in environmental physical data may be a specific measurement value such as temperature or humidity at a certain time and place. The k-th data item in ecological remote sensing data, such as a pixel value in a remote sensing image or a processed feature value. The k-th data item in biodiversity data, such as the population size and distribution range of a certain species. The k-th data item in socioeconomic data, such as the GDP value or population density of a region. ep ,n er ,n bd ,n se : These represent the quantities of environmental physical data, ecological remote sensing data, biodiversity data, and socioeconomic data, respectively.

[0023] Preferably, in a specific application scenario, when streaming multi-source heterogeneous data to form a multi-source heterogeneous data stream, it is assumed that the data is streamed according to a time series t = 1, 2, ..., T. For each time step t, different types of data may require different preprocessing functions. Let the preprocessing function for environmental physical data be f. ep The ecological remote sensing data preprocessing function is f er The biodiversity data preprocessing function is f bd The socioeconomic data preprocessing function is f se .

[0024] At time step t, the processed data are as follows: S ep,t =f ep (D ep ,t), S er,t =f er (D er ,t), S bd,t =f bd (D bd ,t), S se,t =f se (D se ,t).

[0025] Multi-source heterogeneous data stream S t It can be obtained through weighted concatenation:

[0026]

[0027] Where w i It is a weighting coefficient, and These are the components of the corresponding data in the i-th dimension.

[0028] The parameters in the above scheme are explained as follows: f ep ,f er ,f bd ,f se These are preprocessing functions for different types of data, which may include operations such as data cleaning, normalization, and feature extraction. i Weighting coefficients are used to adjust the importance of different types of data in multi-source heterogeneous data streams.

[0029] Preferably, in a specific application scenario, when constructing multimodal feature engineering for multi-source heterogeneous data streams, linear regression analysis is performed on the multi-source heterogeneous data stream S. Let S be an m×n matrix (m is the number of time steps, and n is the data dimension), and the linear regression model is as follows: Where i = 1, ..., m, j = 1, ..., n, x ik It is the independent variable (e.g., time), ∈ij This is the error term.

[0030] Ridge regression is used to solve for the regression coefficient β, with the objective function being: Where λ is the regularization parameter. The regression coefficients are obtained by minimizing J(β). This leads to the trend term T.

[0031] Perform a Discrete Fourier Transform (DFT) on the trend term T: Where N is the data length, and u = 0, 1, ..., N-1.

[0032] Preferably, in a specific application scenario, when generating ecological layer characteristic data, spectral features F are extracted from the multi-source heterogeneous data stream S. s Texture features F t Shape characteristics F sh A convolutional neural network (CNN) can be used. Let S be the feature map X obtained after passing through l layers of a CNN. l ,but: Where f is the activation function, W is the convolution kernel, b is the bias, K is the number of convolution kernels, and M×N is the kernel size. Therefore, the ecological remote sensing features F are obtained. erf =[F s ,F t ,F sh ].

[0033] Extract species richness features F from S. sr Species distribution characteristics F sd Ecological relationship characteristics F er Graph Neural Networks (GNNs) can be used. Let the graph G = (V, E) have a node feature matrix H. After passing through t layers of a GNN, a new node feature matrix H is obtained. t :

[0034] Where N(i) is the set of neighboring nodes of node i, d i Let be the degree of node i, W be the weight matrix, b be the bias, and σ be the activation function. Therefore, the biodiversity feature F is obtained. bdf =[F sr ,F sd ,F er ].

[0035] The ecological remote sensing features and biodiversity features are fused using an attention mechanism: in e i It is the attention score, which can be calculated through a fully connected layer.

[0036] Preferably, in a specific application scenario, when generating socioeconomic layer characteristic data, demographic features F are extracted from the multi-source heterogeneous data stream S. pd Economic Development Characteristics F ed Policy and regulatory characteristics F pr Then, spatial autocorrelation analysis (such as Moran's I index) is used for spatial feature mining. Let x i W is the feature value of the i-th region. ij If the weight matrix is ​​a spatial matrix, then Moran's I exponent is: Where n is the number of regions. The characteristic data F of the socioeconomic stratum were obtained through spatial autocorrelation analysis. se .

[0037] The parameters in the above scheme are explained as follows: β kj λ: Regression coefficients in a linear regression model. λ: Regularization parameter in ridge regression. Convolutional kernel weights in a CNN. l Bias in CNNs. t : The weight matrix in a GNN. b t Bias in GNNs. α i Attention weights. W ij Spatial weight matrix.

[0038] Preferably, in a specific application scenario, when performing multimodal feature fusion on physical layer, ecological layer, and socioeconomic layer feature data,

[0039] Extracting physical layer feature data F based on 1D convolutional capsule layers p Local pattern features in, let F p It is a vector of length L, which undergoes a 1D convolution operation: Where K is the kernel size. It is the convolution kernel weight, b i It is a bias, resulting in physical capsule C. p .

[0040] Ecological layer feature data F extracted from 2D convolutional capsule layers e Spatial hierarchical features in, let F e It is an M×N matrix that has undergone a 2D convolution operation: Where K1×K2 is the convolution kernel size. It is the convolution kernel weight, b mn It is a bias, resulting in ecological capsule C. e .

[0041] Based on graph capsule network, feature data of socioeconomic layer F is extracted. seSpatial dependency adjustment in graph capsule network: Let graph G = (V, E) and node feature matrix H.

[0042] Therefore, the economic capsule C was obtained. se .

[0043] Preferably, in a specific application scenario, when calculating the fusion weight matrix, a Gaussian kernel function is used to calculate the global development risk correlation of the physical capsule, ecological capsule, and economic capsule, generating the fusion weight matrix W: Where i,j∈{p,e,se}, and σ is the Gaussian kernel bandwidth.

[0044] Preferably, in a specific application scenario, during multimodal feature fusion, the physical layer, ecological layer, and socioeconomic layer feature data are fused based on the fusion weight matrix W to generate ERIFM comprehensive feature data F of ecological development risk. erifm : The parameters in the above scheme are explained as follows: Kernel weights in a 1D convolutional capsule layer. i : Bias in 1D convolutional capsule layers. Kernel weights in a 2D convolutional capsule layer. mn σ: Bias in a 2D convolutional capsule layer. σ: Gaussian kernel bandwidth.

[0045] Preferably, in a specific application scenario, when vectorizing the comprehensive feature data of ERIFM ecosystem development risks, the comprehensive feature data of ERIFM ecosystem development risks is capsule-grouped according to modality, resulting in several modal capsule groups G1, G2, ..., G m .

[0046] Dynamic pooling is applied to the modal capsule set using adaptive max pooling. Let the modal capsule set G be... k Given a P×Q matrix, after adaptive max pooling, we obtain a matrix G of size P′×Q′. k′ : Where Ω pq This corresponds to the pooling region. Calculate the information entropy H between modal capsule groups. Assume there are modal capsule groups G. k There are n k There are n elements, and their probability distribution is: The information entropy is then: The information entropy of all modal capsule groups is H = [H1, H2, ..., H...]. m ].

[0047] Preferably, in a specific application scenario, during norm processing, norm processing is performed on each modal capsule group based on the information entropy H, using L...p Norm:

[0048] in

[0049] Preferably, in a specific application scenario, semantic enhancement is performed on the capsule norm vector N using a Long Short-Term Memory (LSTM) network. Let N be a sequence of length T, processed by LSTM units:

[0050] i t =σ(W ii N t +W hi h t-1 +b i ), f t =σ(W if N t +W hf h t-1 +b f ), o t =σ(W io N t +W ho h t-1 +b o ), g t =tanh(W ig N t +W hg h t-1 +b g ), c t =f t ⊙c t-1 +i t ⊙g t h t =o t ⊙tanh(c t ), where i t ,f t ,o t These are the input gate, forget gate, and output gate, respectively. t It is a candidate memory unit, c t It is a memory unit, h t Here, W is the hidden state, W is the weight matrix, b is the bias, and ⊙ represents element-wise multiplication. The final result is the ecological development risk feature vector V = h. T .

[0051] The parameters in the above scheme are explained as follows: P, Q: Original size of the modal capsule group. P′, Q′: Size after adaptive max pooling. p: L p The parameter of the norm. W ii W hi ,…:The weight matrix in LSTM. bi ,b f ,…:Bias in LSTM.

[0052] Preferably, in a specific application scenario, when the ecological development risk feature vector is input into the development risk quantification model, let the ecological development risk feature vector be V, and the development risk quantification model can be represented as a combination of a series of functions:

[0053] Based on the input layer, the ecological development risk feature vector V is imputed using Lagrange interpolation. Let the position of the missing value in V be x0, and the positions of the known values ​​be x1, x2, ..., xn. n The corresponding values ​​are y1, y2, ..., y n The interpolation formula is: in We obtain the continuous standardized feature tensor T1.

[0054] Preferably, in a specific application scenario, during local feature extraction, local feature patterns are extracted from the continuously normalized feature tensor T1 based on a dynamic feature enhancement layer, using depthwise separable convolution. Let T1 be a C×H×W tensor, after which the depthwise separable convolution operation is performed: The local feature vector V1 is obtained.

[0055] Preferably, in a specific application scenario, during spatial dependency extraction, spatial dependency extraction is performed on the local feature vector V1 based on the feature relationship layer, using a Graph Attention Network (GAT). Let the graph G = (V, E), and the node feature matrix be H. After passing through the GAT layer:

[0056] Where a is the parameter vector of the attention mechanism, W is the weight matrix, and the spatially enhanced feature vector V2 is obtained.

[0057] Preferably, in a specific application scenario, based on the multimodal attention fusion layer, multi-head attention fusion is performed on the spatial augmented feature vector V2. Let the number of heads be h, then: V3 = Concat(head1, head2, ..., head h W O , where head i =Attention(Q) i ,K i V i ), Q i =V2W i Q K i =V2W i K V i =V2Wi V , d k It is the dimension of the key vector.

[0058] Preferably, in a specific application scenario, during context feature generation, the fused feature vector V3 is reshaped into a sequence form based on the global context modeling layer, using the Transformer encoder layer. Let V3 be a d-dimensional vector, which passes through a multi-head attention and feedforward network:

[0059] V3′=LayerNorm(V3+MultiHeadAttention(V3)), C=LayerNorm(V3′+FeedForward(V 3′ MultiHeadAttention is a multi-head attention mechanism, and FeedForward is a feedforward network.

[0060] Preferably, in a specific application scenario, during residual processing, the context feature C is processed based on the residual network. Let the residual block be R, then: A = C + R(C).

[0061] Preferably, in a specific application scenario, during probability distribution prediction, based on the probability distribution prediction layer, the abstract development risk feature A is mapped to the development risk level space using a Gaussian Mixture Model (GMM). Let A be a d-dimensional vector, and the probability density function of the GMM is:

[0062] Where π k It is the mixing coefficient. It is a Gaussian distribution, μ k It is the mean vector, ∑ k It is the covariance matrix. The parameters are estimated using the Expectation-Maximization (EM) algorithm to obtain the development risk probability vector P.

[0063] Preferably, in a specific application scenario, when determining confidence level, based on the decision output layer and the confusion matrix M, a confidence level assessment is performed on the development risk probability vector P, and the ecosystem development risk score R is calculated:

[0064] The parameters in the above scheme are explained as follows: x0, x1, ..., x n : Position in Lagrange interpolation. y0,y1,…,y n : Values ​​in Lagrange interpolation. Separable convolution kernel weights in convolution. a: Attention parameter vector in a graph attention network. W i Q W i KW i V W O The weight matrix in a multi-head attention mechanism. π k ,μ k ,∑ k Parameters in a Gaussian mixture model. M ij : Confusion matrix elements.

[0065] Preferably, in a specific application scenario, compared with traditional technologies, the above technical solution has the following technical advantages:

[0066] 1. When acquiring multi-source heterogeneous data of ecological products, the power set combination method is used to represent the multi-source heterogeneous data set. This approach comprehensively considers all possible combinations of different data types, flexibly selecting data according to actual needs. Traditional techniques focus only on a single data type or simply piece together several data sets, lacking a comprehensive consideration of the possibilities for data combinations. In contrast, the power set combination method in this application can accurately select the required data type based on the characteristics of different ecosystem product development projects, avoiding data loss or redundancy, improving the relevance and effectiveness of the data, and thus providing more accurate foundational data for subsequent risk prediction.

[0067] 2. This application uses a weighted splicing method in streaming processing. Obtain multi-source heterogeneous data stream S t And through different preprocessing functions f ep ,f er ,f bd ,f se Different types of data are processed. Simultaneously, a weighting coefficient w is introduced during the calculation process. i This approach adjusts the importance of different data types. Traditional streaming processing may simply concatenate data sequentially without considering the differences in importance between different data types. However, the weighted concatenation method in this application assigns different weights to data based on their impact on ecological development risks, highlighting the role of key data and enabling multi-source heterogeneous data streams to better reflect the essential characteristics of ecological development risks. Furthermore, different preprocessing functions can be customized to address the characteristics of different data types, improving the accuracy and efficiency of data processing.

[0068] 3. In the physical layer feature data extraction, this application uses ridge regression to solve the linear regression coefficients and performs a discrete Fourier transform (DFT) on the trend term. Ridge regression addresses the overfitting problem in linear regression by introducing a regularization parameter λ, while DFT converts time-domain data into frequency-domain data to extract frequency features. Traditional linear regression methods are susceptible to data noise and multicollinearity, leading to inaccurate regression coefficient estimates. The regularization effect of ridge regression effectively reduces these effects, improving the stability and generalization ability of the regression model. DFT can capture periodic changes in the data, providing richer physical layer feature information for ecological development risk prediction and helping to discover potential risk factors.

[0069] 4. In the extraction of ecological layer feature data, this application uses Convolutional Neural Networks (CNNs) to extract spectral, texture, and shape features, and Graph Neural Networks (GNNs) to extract biodiversity features, and fuses these features through an attention mechanism. CNNs can automatically extract local features from image data, while GNNs are suitable for processing data with graph structures. The attention mechanism can assign weights based on the importance of features. Traditional feature extraction methods may require manual feature design, which is not only inefficient but also difficult to capture complex feature information. CNNs and GNNs can automatically learn features from the data, improving the accuracy and efficiency of feature extraction. The introduction of the attention mechanism makes the fused features more prominent in terms of important information, enhancing the ability of ecological layer feature data to represent the risks of ecological development.

[0070] 5. In the extraction of socioeconomic feature data, this application uses spatial autocorrelation analysis (such as Moran's I index) for spatial feature mining. This index can measure the spatial correlation of data and discover the spatial distribution patterns of the data. Traditional methods ignore the spatial correlation of socioeconomic data and cannot fully mine the potential information in the data. Spatial autocorrelation analysis can reveal the spatial interaction and influence of socioeconomic factors, providing more comprehensive socioeconomic information for ecological development risk prediction and helping to identify risk differences between different regions.

[0071] 6. In multimodal feature fusion, this application uses capsule networks to extract features from different levels and generates a fusion weight matrix by calculating the correlation through a Gaussian kernel function. Then, weighted fusion is performed. Capsule networks can better capture the hierarchical structure and spatial relationships of data, while the Gaussian kernel function can measure the similarity between different features. Traditional feature fusion methods simply splice features from different levels together without considering the correlation and hierarchical structure between features. The combination of capsule networks and the Gaussian kernel function can more effectively fuse features from different levels, highlight the role of important features, reduce the interference of redundant information, and generate more representative ERIFM ecosystem development risk comprehensive feature data.

[0072] 7. In the feature vectorization process, this application uses adaptive max pooling for dynamic pooling, through L... p Norm processing is performed, and semantic enhancement is achieved using a Long Short-Term Memory (LSTM) network. Adaptive max pooling automatically adjusts the pooling region based on the characteristics of the data. p Norms can standardize features, and LSTM can process sequential data and capture its temporal dependencies. Traditional pooling methods have a fixed pooling region, making them unable to adapt to variations in data. Adaptive max pooling, on the other hand, can flexibly adjust the pooling region based on the actual data, retaining more important information. p The use of norms can improve the comparability and stability of feature vectors. LSTM can process time-series information in ecological development risk data, mine the latent semantics of the data, and improve the quality of ecological development risk feature vectors.

[0073] 8. In the risk quantification model developed in this application, Lagrange interpolation is used for imputation, separable convolutions are used to extract local features, graph attention networks (GAT) are used to extract spatial dependencies, multi-head attention fusion and Transformer encoder layers are used for context modeling, Gaussian mixture models (GMM) are used for probability distribution prediction, and finally, confidence is determined based on the confusion matrix. Traditional risk quantification models use simple interpolation methods, which cannot accurately fill in missing values. Separable convolutions can reduce computational cost and improve model training efficiency compared to traditional convolutions. Graph attention networks can better capture the spatial dependencies of data, and multi-head attention fusion and Transformer encoder layers can fully mine the contextual information of the data. Gaussian mixture models can more flexibly fit the probability distribution of data, improving the accuracy of risk prediction. The use of the confusion matrix can determine the confidence of the prediction results, providing a more reliable basis for decision-making.

[0074] Optionally, the method further includes: performing time window interpolation, anomaly detection, and feature derivation processing on environmental physical data to obtain EnvironmentalTime-series FeatureData; performing radiometric correction, spatial registration, and semantic segmentation on ecological remote sensing data to obtain Radiometric Semantic Imagery (RC_Semantic_Imagery); performing species standardization, spatiotemporal annotation, and abundance estimation on biodiversity data to obtain Bio-TS_Data; and performing text structuring, spatial mapping, and indicator normalization on socioeconomic data to obtain GridEco_Indicators.

[0075] Preferably, in a specific application scenario, when processing environmental physical data, the environmental physical data set is set as EPD = {e1, e2, ..., e...} n}, where e i This represents the i-th environmental physical data point, such as the measured value of temperature, humidity, etc., as they change over time.

[0076] For time window interpolation processing with a time window size of w, let t be the time index. At time point t, the data within the time window is e. t-w+1 ,e t-w+2 ,…,e t Time window difference d t The calculation is as follows:

[0077] w: The size of the time window determines the amount of historical data used in calculating the difference. For example, in the development of eco-products, if the focus is on the impact of short-term environmental changes on the product, a smaller w value can be set; if long-term trends are considered, w should be increased. e i d: Environmental physical data measured at time point i, such as the temperature value at a certain moment. t The time window difference at time point t reflects the degree of deviation between the current data and the average data within the time window.

[0078] Preferably, in a specific application scenario, anomaly detection is performed using the statistically based 3σ principle. First, the mean μ of the data within the time window is calculated. t and standard deviation σ t : If |e t -μ t |>3σ t Then determine e t This is an outlier.

[0079] μ t σ represents the mean of environmental physical data within a time window, signifying the average level of the data during that period.t The standard deviation of environmental physical data within a time window measures the dispersion of the data. In the context of eco-product development, a larger standard deviation may indicate greater environmental fluctuations, impacting product development risks. The 3σ rule uses an empirical threshold of 3 to determine data anomalies. When a data point deviates from the mean by more than three times the standard deviation, it is considered potentially anomaly, possibly due to measurement errors or sudden environmental events. This is crucial for predicting risks in eco-product development, as abnormal environmental data can directly affect product quality or feasibility.

[0080] Preferably, in a specific application scenario, during feature derivation processing, the existing environmental physical data features are denoted as f1, f2, ..., f m New features f can be obtained through feature derivation. new For example, the rate of change of data at adjacent time points can be used as a new feature:

[0081] (when e t-1 (≠0 o'clock), f new,t New characteristics arising at time point t, such as the rate of change of environmental physical quantities. In the development of eco-products, the rate of change of environmental factors may reflect potential risks better than absolute values; for example, rapid temperature changes may affect the growth cycle of eco-agricultural products. t and e t-1 : Environmental physical data measurements at adjacent time points.

[0082] Preferably, in a specific application scenario, when processing ecological remote sensing data, the ecological remote sensing image data is denoted as ERI, which can be represented as a three-dimensional matrix ERI(x,y,b), where x and y are spatial coordinates and b is a band index.

[0083] Preferably, in a specific application scenario, during radiometric correction, the correction is used to eliminate the influence of sensor errors and external factors such as the atmosphere on the radiation amount. A correction method based on the radiative transfer model is adopted, where the corrected radiance L is... c (x,y,b) is:

[0084]

[0085] Where L m (x,y,b) is the measured radiance, L0(b) is the offset of band b, and G(b) is the gain of band b.

[0086] L m(x,y,b): The measured radiance of the ecological remote sensing image at spatial location (x,y) and band b, which is the data acquired by the original sensor. L0(b): The offset of band b, used to correct sensor zero-point errors, etc. In ecological product development, accurate zero-point calibration ensures accurate perception of the ecological environment. For example, for remote sensing images monitoring vegetation growth, correct radiometric calibration helps to accurately determine the health status of vegetation. G(b): The gain of band b, reflecting the sensor's amplification capability for different bands of radiation. The gain settings for different bands affect the image's sensitivity to different ground features, which is crucial for identifying specific ecological elements in ecological product development. L c (x,y,b): Corrected radiance, providing more accurate basic data for subsequent analysis.

[0087] Preferably, in a specific application scenario, during spatial registration, assuming a reference image RI(x,y), the goal of spatial registration is to find a transformation function T that maps the pixels in the ecological remote sensing image ERI to the coordinate system of the reference image. A feature-matching-based registration method is adopted, which calculates the correspondence between feature points and uses an affine transformation model: Where (x,y) are the pixel coordinates in the original image, (x′,y′) are the pixel coordinates in the registered image, and a ij The parameter t is the transformation matrix. x and t y It is the translation parameter.

[0088] (x,y): The original coordinates of the pixels in the ecological remote sensing image. In the context of ecological product development, these coordinates correspond to their actual geographic locations. (x′,y′): The coordinates of the pixels in the reference coordinate system after spatial registration, enabling comparison and analysis of remote sensing images from different sources or at different times within the same geographic coordinate system, which helps monitor the dynamic changes in the ecological product development area. ij and t x ,t y Affine transformation parameters, calculated using a feature matching algorithm, are used to describe image rotation, scaling, and translation transformations to achieve accurate spatial registration.

[0089] Preferably, in a specific application scenario, semantic segmentation is performed using a fully convolutional neural network (FCN) from deep learning. Let the input radiometrically corrected and spatially registered image be I. After a series of convolution, pooling, and deconvolution operations, the output is a probability map P(c|x,y) indicating the semantic category of each pixel, where c represents the semantic category (e.g., vegetation, water, bare land). P(c|x,y) = softmax(F... FCN (I)(x,y)), where F FCNIt is a fully convolutional neural network function.

[0090] I: Ecological remote sensing image data after radiometric correction and spatial registration, used as input to the semantic segmentation model. P(c|x,y): The probability that a pixel at spatial location (x,y) belongs to semantic category c. In ecological product development, semantic segmentation can quickly identify the distribution of different ecological elements, such as determining vegetation cover areas suitable for ecotourism development or cultivated land areas for ecological agriculture. Softmax function: Converts the raw scores output by the neural network into a probability distribution, used to determine the most likely semantic category for each pixel. F FCN Fully convolutional neural networks can automatically extract image features and perform semantic classification by learning from a large amount of labeled remote sensing image data.

[0091] Preferably, in a specific application scenario, when forming biodiversity data processing, the biodiversity dataset is denoted as BBD, which contains various information about species.

[0092] Let the list of species be S = {s1, s2, ..., s} k}, for each species s i The observed data in different samples is b ij (j = 1, 2, ..., n). Species standardization unifies data from different species to a standardized scale, using the Z-score standardization method: Where μ i It is species s i The mean of the observed data, σ i That is the standard deviation. ij : species s i The observational data in the j-th sample, such as the number of individuals of a certain species in a certain region. Standardized species data makes data from different species comparable. In ecoproduct development, this helps to comprehensively assess the impact of different species on ecosystem stability and product development. For example, when assessing the impact of ecotourism development on biodiversity, standardized data can more intuitively reflect changes in different species. i : species s i The mean of the observed data represents the average level of that species in the sample. σ i : species s i The standard deviation of observed data measures the degree of dispersion of the data.

[0093] Preferably, in a specific application scenario, during spatiotemporal annotation, the time series is set as T = {t1, t2, ..., t...} m The spatial region is divided into R = {r1, r2, ..., r}. l For each species si Data b ij Mark the corresponding time t p and spatial region r q To form spatiotemporal labeled data points (s i ,b ij ,t p ,r q ). t p The time point corresponding to biodiversity observation data is crucial for analyzing the changing trends of biodiversity over time in the development of ecological products. For example, the seasonal changes of certain species may affect the production cycle of ecological products. q Spatial labeling of biodiversity observation data helps to understand the distribution of different species within ecological product development areas, providing a basis for rational planning of development activities and avoiding damage to biodiversity hotspots.

[0094] Preferably, in a specific application scenario, when estimating abundance, a model-based abundance estimation method is used, assuming species s i The abundance of the environmental variable E = {e1, e2, ..., e o A linear relationship exists: Where A i It is species s i The estimated abundance, β ji It is the regression coefficient, ∈ i This is the error term. The regression coefficient β is solved using the least squares method.

[0095] A i : species s i β-species abundance estimation is crucial for assessing biodiversity. In the development of biodiversity products, accurate species abundance estimation helps evaluate the health and stability of ecosystems. For example, high species abundance may indicate a more resilient ecosystem, positively impacting the sustainability of biodiversity product development. ji The regression coefficient reflects the environmental variable e. j For species s i The degree of influence of abundance. In practical applications, these coefficients can help identify which environmental factors play a key role in biodiversity, thereby enabling targeted protection and management during the development of ecological products. i Error term: Reflects the portion of variation that the model cannot explain.

[0096] Preferably, in a specific application scenario, when forming socio-economic data processing, the socio-economic data set is denoted as SED, which includes data in text form, geospatial related data, and various economic indicator data.

[0097] For socioeconomic data in text format (such as policy documents, news reports, etc.), natural language processing techniques are used for structuring. Assuming the text data is TXT, named entity recognition (NER) technology is used to identify entities E = {e1, e2, ..., e...} in the text. u}, such as personal names, place names, organization names, economic indicator names, etc. Then, the relationship between entities R = {(e i ,r ij ,e j )}, where r ij Represents entity e i and e j The relationship between them.

[0098] TXT: Raw textual socioeconomic data. In the context of ecoproduct development, this text may contain information related to policies, regulations, market dynamics, etc., which is valuable for risk prediction. E: The set of entities identified from the text, such as the names of economic support policies in policy documents and related beneficiary regions. This entity information forms the basis for subsequent analysis. R: The set of relationships between entities, such as the relationship between policies and beneficiary regions. This helps to understand the interaction between socioeconomic factors and provides more in-depth information for ecoproduct development risk assessment.

[0099] Preferably, in a specific application scenario, during spatial mapping, the geographic spatial region is divided into grids G = {g1, g2, ..., g...} v For spatially relevant information in socioeconomic data (such as population distribution, industry distribution, etc.), it is mapped to the corresponding grid. Let the spatial location be (x,y), and calculate its grid index k: k = gridIndex(x,y), where gridIndex is a function defined according to the grid partitioning rules.

[0100] G: The set of geospatial grids. In the development of ecological products, spatially gridding socioeconomic data helps to integrate it with ecological geographic information for comprehensive analysis. For example, it allows analysis of the impact of population density and economic development level in different grid areas on the market demand for ecological products. (x,y): The geospatial coordinates corresponding to the socioeconomic data, such as the geographical coordinates of a company. k: The grid index of the spatial location (x,y). Spatial mapping establishes a connection between socioeconomic data and geospatial space, facilitating subsequent spatial analysis and modeling.

[0101] Preferably, in a specific application scenario, when normalizing indicators, let the socioeconomic indicator data be I = {i1, i2, ..., i w The index data is normalized to the [0,1] interval using the max-min normalization method.

[0102] I: The original set of socioeconomic indicator data, such as GDP and per capita income in different regions. In the risk prediction of eco-product development, these indicators reflect the state of the socioeconomic environment and have a significant impact on the market prospects and development feasibility of the products. j : The j-th socioeconomic indicator data. Normalized socioeconomic indicator data are easier to compare and analyze on the same scale, which helps to comprehensively assess the impact of different indicators on the risks of ecological product development.

[0103] Therefore, the above technical solution has the following technical advantages:

[0104] 1. By calculating the difference between current data and the average data within a time window, short-term fluctuations in environmental physical quantities can be captured. The core of this method lies in using a sliding time window to dynamically analyze data changes and reflect the real-time dynamic characteristics of the environment. Traditional methods only focus on the absolute values ​​of the data or simple trend analysis, ignoring short-term fluctuation information. For example, when monitoring the temperature in an ecological product development area, traditional methods may only record the daily average temperature, failing to detect sudden rises or falls in temperature in a timely manner, which has a significant impact on some temperature-sensitive ecological products (such as the cultivation of specific flowers).

[0105] This application can accurately reveal short-term abnormal changes in environmental data, providing more timely risk warnings for the development of ecological products. For example, in ecological fisheries, rapid changes in water temperature may affect fish growth; by using time window differences, such risks can be detected in a timely manner, allowing for adjustments to aquaculture strategies.

[0106] 2. This application is based on the statistical 3σ principle, identifying outliers by calculating the mean and standard deviation of data and setting reasonable thresholds. This principle utilizes the statistical distribution characteristics of data to effectively distinguish between normal and abnormal data. Traditional anomaly detection methods rely on simple rules or human experience, which are highly subjective and prone to overlooking complex anomalies. For example, when monitoring air quality data, traditional methods may judge anomalies solely based on whether the concentration of a single pollutant exceeds a fixed standard, ignoring anomalies caused by the synergistic effects of multiple pollutants. This application can objectively and accurately identify outliers in environmental physical data, improving data quality. In ecotourism development, if environmental monitoring data shows anomalies (such as abnormally high noise levels), it may indicate the presence of disturbance sources in the surrounding area (such as construction activities), affecting the tourist experience. Timely detection allows for proactive countermeasures.

[0107] 3. This application generates new features from existing data features through mathematical operations, uncovering potential relationships and trends between data points and enriching the dimensions of data features. Traditional methods are often limited to using raw data features, failing to fully extract the hidden information behind the data. For example, when analyzing the impact of soil moisture on ecological agricultural products, traditional methods only focus on the absolute value of moisture, ignoring important derived features such as the rate of change of moisture, making it difficult to comprehensively assess the combined impact of soil moisture conditions on crop growth. This application increases the richness of data features, providing more effective information for risk prediction models. Taking ecological forestry development as an example, the derived feature of the rate of change of tree growth rate can more accurately reflect the health status of trees and changes in the growth environment, helping to predict forestry development risks in advance.

[0108] 4. This application, based on a radiative transfer model, corrects the measured radiance by offset and gain, eliminating the influence of sensor and atmospheric factors to restore the true radiative information of ground features. Traditional radiometric correction methods are not precise enough and cannot fully account for complex atmospheric environments and sensor characteristic variations. For example, in early ecological remote sensing applications, simple radiometric correction models could not adapt to differences in atmospheric composition and thickness in different seasons and regions, leading to deviations in the corrected image's reflection of ecological elements. This application enables ecological remote sensing images to more accurately reflect the true radiative characteristics of ground features, improving the accuracy of identifying and analyzing ecological and environmental elements. When assessing the impact of ecological product development on vegetation cover, accurate radiometrically corrected images can clearly distinguish different vegetation types and their growth status, providing a reliable basis for rationally planning development activities.

[0109] 5. This application utilizes an affine transformation model to find the correspondence between different images through feature matching, thereby mapping ecological remote sensing images to a unified coordinate system. Traditional registration methods are ineffective in complex terrain or when image features are not obvious, resulting in low registration accuracy. For example, in mountainous areas and other areas with complex terrain, traditional methods struggle to accurately match image feature points, leading to significant registration errors and affecting the accurate delineation and dynamic monitoring of ecological product development areas. This application ensures that ecological remote sensing images from different sources and at different times can be accurately compared and analyzed under the same coordinate system, facilitating accurate monitoring of the dynamic changes in ecological product development areas. In ecological restoration projects, by comparing registered remote sensing images from different periods, the vegetation restoration status can be clearly observed, and the restoration effect and development risks can be assessed.

[0110] 6. This application utilizes a fully convolutional neural network based on deep learning to perform end-to-end learning on remote sensing images, automatically extracting image features and classifying each pixel into its corresponding semantic category. Traditional semantic segmentation methods rely on manually designed features, resulting in low efficiency and poor accuracy, making them unsuitable for complex and diverse ecological scenarios. For example, when identifying different land features in a wetland ecosystem, traditional methods require a large number of manually defined features and struggle to accurately distinguish subtle differences in land features. This application can quickly and accurately perform semantic segmentation on ecological remote sensing images, automatically identifying different ecological elements. In ecological product development planning, semantic segmentation can quickly obtain information such as land use type and vegetation cover, providing support for the rational layout of development projects and reducing development risks caused by misjudgment of ecological elements.

[0111] 7. This application employs the Z-score standardization method to unify observational data from different species to the same scale, eliminating differences in data dimensions and variability among species and ensuring data comparability. Traditional methods fail to effectively standardize species data, leading to uneven impacts from species with significant data differences on the results when comprehensively analyzing biodiversity. For example, in assessing the impact of ecoproduct development on biodiversity, unstandardized data on large and small species may cause changes in small species to be overlooked. This application enables the analysis of data from different species at the same scale, more fairly reflecting the status and changes of each species in the ecosystem. In ecotourism development, standardized biodiversity data allows for a more comprehensive assessment of the impact of development activities on various species, leading to the formulation of more scientific protection measures.

[0112] 8. This application adds time and spatial labels to biodiversity observation data, clearly defining the corresponding time points and spatial regions, and constructing a spatiotemporal information framework for biodiversity. Traditional methods lack a systematic integration of spatiotemporal information in biodiversity data, making it difficult to analyze the spatiotemporal dynamics of biodiversity. For example, previous records of species distribution may not have been precisely linked to time information, making it impossible to understand the migration and evolution of species over time, which is detrimental to long-term planning for ecological product development. This application comprehensively records the spatiotemporal information of biodiversity, providing a foundation for in-depth analysis of the spatiotemporal relationship between biodiversity and ecological product development. In ecological agriculture development, spatiotemporally labeled data can be used to analyze the occurrence patterns of crop pests and diseases in different seasons and regions, enabling early risk prevention and ensuring the quality of agricultural products.

[0113] 9. This application estimates species abundance based on a linear relationship model between environmental variables and species abundance, using the least squares method to solve for regression coefficients. This principle considers the impact of environmental factors on species abundance and quantifies this relationship through a mathematical model. Traditional abundance estimation methods are too simplistic and do not fully consider the combined effects of environmental factors. For example, estimating abundance solely based on directly observed species numbers ignores the impact of environmental changes (such as climate change and habitat destruction) on species survival and reproduction, leading to inaccurate abundance estimates. This application estimates species abundance more accurately, providing crucial data for assessing ecosystem stability and the impact of ecological product development on biodiversity. In the process of ecological product development, accurate species abundance information helps determine the carrying capacity of the ecosystem, rationally plan the scale of development, and avoid irreversible damage to biodiversity caused by over-exploitation.

[0114] 10. This application utilizes named entity recognition and relation extraction techniques from natural language processing to extract valuable entity and relation information from unstructured text, transforming text data into a structured form. Traditional methods rely on manual reading and organization of text information, which is extremely inefficient and prone to errors. For example, when analyzing the impact of numerous policy documents on the development of eco-products, manual information sifting is time-consuming and laborious, and important information may be missed due to subjective factors. This application efficiently extracts key information from text data, providing rich socio-economic background data for predicting risks in eco-product development. In eco-product market analysis, by structuring news reports, industry reports, and other texts, information such as market demand and policy guidance can be quickly obtained, assisting decision-making and reducing market risks.

[0115] 11. This application maps spatially relevant information from socioeconomic data to corresponding grids based on geospatial grid division rules, achieving the integration of socioeconomic data and geospatial data. Traditional methods cannot effectively link socioeconomic data with geospatial data, making it difficult to conduct comprehensive analysis based on spatial location. For example, when studying the relationship between economic development and the ecological environment in ecological product development areas, traditional methods cannot intuitively display the spatial distribution relationship between economic indicators and ecological elements in different regions, which is not conducive to formulating targeted development strategies. This application spatializes socioeconomic data, facilitating comprehensive analysis in conjunction with ecological geographic information. In ecological product development planning, the spatially mapped socioeconomic data provides a direct understanding of the matching between population, economic distribution, and ecological resources in different regions, optimizing development layout and reducing development risks caused by unreasonable spatial planning.

[0116] 12. This application employs a maximum-minimum normalization method to unify socioeconomic indicator data of different magnitudes and dimensions into the [0,1] interval, eliminating the impact of data differences on the analysis results. Traditional analysis methods do not normalize socioeconomic indicators, leading to the dominance of larger-value indicators in the comprehensive assessment of ecological product development risks, while some important smaller-value indicators are overlooked. For example, in assessing the feasibility of ecological product development projects, without indicator normalization, aggregate indicators such as GDP may mask the impact of important indicators such as per capita income and industrial structure. This application enables the comparison and analysis of different socioeconomic indicators on the same scale, improving the accuracy and scientific rigor of risk assessment. In the ecological product development risk assessment model, normalized indicator data can more reasonably reflect the contribution of each factor to risk, providing a more reliable basis for decision-making.

[0117] Optionally, the streaming processing of multi-source heterogeneous data to form a multi-source heterogeneous data stream includes: dividing environmental temporal feature data into two-dimensional blocks according to a set time window and spatial region to obtain spatiotemporal block data; vectorizing radiometric semantic images to obtain a vector feature set, and establishing a spatial index on the vector feature set to obtain spatially indexed vector data; performing relationship modeling on biological spatiotemporal data to obtain an ecological relationship map, and establishing a path index on the ecological relationship map to obtain indexed ecological relationship data; performing dimensional modeling on gridded economic indicator data to obtain a star pattern dataset, and establishing a dimensional index on the star pattern dataset to obtain indexed economic indicator data; and further processing the spatiotemporal data... The data blocks, spatially indexed vector data, indexed ecological relationship data, and indexed economic indicator data are aligned. The aligned spatiotemporal block data, spatially indexed vector data, indexed ecological relationship data, and indexed economic indicator data are then transformed to obtain environmental feature vector groups, spatial feature vector groups, ecological feature vector groups, and economic feature vector groups, respectively. These vector groups are then fused to obtain a fused feature vector. Based on a set sliding window, the fused feature vector is processed according to a set time step to convert the data within the sliding window into a byte stream, thus forming a multi-source heterogeneous data stream.

[0118] Preferably, in a specific application scenario, when performing two-dimensional segmentation of environmental time-series feature data, the environmental time-series feature data is denoted as ETFD, which is a three-dimensional array ETFD(t,s,f), where t represents the time dimension, s represents the spatial dimension, and f represents the feature dimension. The time window size is set to w. t The size of the spatial region is w s .

[0119] In the time dimension, starting from t=1, with a step size of w tPerform block division; in the spatial dimension, starting from s=1, with a step size of w s Divide into blocks.

[0120] For the i-th time block and the j-th spatial block, the spatiotemporal block data TSD ij For: TSD ij (k,l,m)=ETFD((i-1)w t +k,(j-1)w s +l,m), where k=1,…,w t , l=1,…,w s m = 1, ..., F (F is the number of characteristics).

[0121] ETFD: Environmental Time Series Feature Data, which includes environmental physical data processed through time window interpolation, anomaly detection, and feature derivation to obtain datasets with temporal and spatial dimensions and multiple features. For example, in the development of eco-products, it may include the changing characteristics of environmental factors such as temperature, humidity, and light at different times and locations. t: Time dimension index, representing a point in time. s: Spatial dimension index, representing a spatial location. f: Feature dimension index, such as temperature features, humidity features, etc. w_t: The set size of the time window, determining the number of time steps contained in each time block. For example, if the focus is on the impact of short-term environmental changes on eco-products, a smaller w can be set. t If long-term trends are considered, then increase w. t w s The size of the designated spatial area determines the spatial range contained in each spatial block. When the ecological product development area is large, a suitable w s The space can be divided into reasonable sub-regions for analysis. (TSD) ij Spatiotemporal block data consisting of the i-th time block and the j-th spatial block is a subset of environmental temporal characteristic data within a specific time and spatial range.

[0122] Preferably, in a specific application scenario, when vectorizing and establishing a spatial index for the radiometric semantic image, the radiometric semantic image RC_Semantic_Imagery is a two-dimensional array RC_Semantic_Imagery(x,y), where x and y are the spatial coordinates of the image.

[0123] Vectorization algorithms such as edge detection and region growing are used to convert semantic objects in the image into vector features. Let the set of vector features obtained after vectorization be VF, where each vector feature v∈VF can be represented as a polygon, consisting of a series of vertex coordinates (x1, y1), (x2, y2), ..., (x...). n ,y nThe system consists of four nodes. A quadtree spatial indexing algorithm is used. The root node is N0, which covers the entire image area. For node N, if the number of vector features it contains exceeds a threshold T, it is divided into four child nodes N1, N2, N3, and N4, each covering one-quarter of the original node's area.

[0124] Let the coverage area of ​​node N be a rectangle [x min ,x max ,y min ,y max For a vector feature v, the method to determine whether it belongs to node N is as follows: And y min ≤y i ≤y max For i = 1, ..., n

[0125] After spatial indexing is constructed, spatially indexed vector data (SIVD) is obtained.

[0126] RC_Semantic_Imagery: Ecological remote sensing imagery data after radiometric correction and semantic segmentation, containing image content with semantic information, such as different land cover categories (vegetation, water bodies, buildings, etc.). x,y: Spatial coordinates of the image, used to locate each pixel in the image. VF: Vectorized vector feature set, converting semantic objects in the image into vector form for easier subsequent spatial analysis and processing. For example, vectorizing a forest area in the image into a polygonal vector feature. v: Vector feature, the basic unit after vectorization, whose shape is described by a series of vertex coordinates. T: Quadtree node partitioning threshold; when the number of vector features contained in a node exceeds this threshold, the node is partitioned to improve the efficiency of spatial indexing. N: Quadtree node; each node represents a spatial region containing a certain number of vector features. SIVD: Spatial indexed vector data; by establishing a quadtree spatial index, spatial querying and retrieval of vector features becomes more efficient. In ecological product development, it allows for quick querying of ecological features within a specific area (such as the distribution range of a specific vegetation type).

[0127] Preferably, in a specific application scenario, when performing relationship modeling and path indexing on biological spatiotemporal data, the biological spatiotemporal data Bio_TS_Data is assumed to contain species information S = {s1, s2, ..., s...}. n Time information T = {t1, t2, ..., t} m Spatial information R = {r1, r2, ..., r} l}, and information on relationships between species R species (e.g., predator-prey relationships, symbiotic relationships, etc.)

[0128] Construct an ecological relationship graph (ERM), where nodes represent species s. i The edges represent the relationships between species, r. ij ∈R species The weight w of the edge ij It indicates the strength of the relationship (e.g., the degree of dependence of a predator on its prey in a predator-prey relationship).

[0129] An ecological relationship graph can be represented by an adjacency matrix A, where A ij for:

[0130]

[0131] Preferably, in a specific application scenario, Dijkstra's algorithm is used to create path indexes for the ecological relationship graph. Let there be two nodes s in the graph. a and s b From s a to s b Shortest path length d(s) a ,s b )for:

[0132]

[0133] Where P(s) a ,s b ) is from s a to s b The set of all paths.

[0134] By calculating the shortest path length between all node pairs, an indexed ecological relationship data IERD is established.

[0135] Bio_TS_Data: A biodiversity dataset containing information on species standardization, spatiotemporal annotation, abundance estimation, and other aspects. In ecological product development, it involves the distribution of different species at different times and spaces, as well as their interrelationships. S: Species set, containing various species within the ecosystem. T: Temporal set, recording the time points corresponding to the biodiversity data. R: Spatial set, representing the spatial region corresponding to the biodiversity data. R species: Set of relationships between species, such as predation and symbiosis, which have a significant impact on ecosystem stability and ecological product development. ERM: Ecological Relationship Graph, visually representing the relationships between species in a graphical way, aiding in understanding the structure and function of the ecosystem. A: Adjacency matrix, used to mathematically represent the ecological relationship graph, facilitating graph operations and analysis. w_{ij}: Edge weights, reflecting the weights of the species s. i and s jThe strength of the relationship between species can be used to assess the extent to which interspecies interactions affect ecological products in ecological product development. d(s a ,s b ): From species s a to s b The shortest path length is obtained, and the path index allows for quick querying of indirect relationships and influence paths between species. IERD: Indexed Ecological Relationship Data. By establishing a path index, it improves the efficiency of querying and analyzing species relationships in ecological relationship maps, providing a more convenient way to obtain information for risk assessment of ecological product development.

[0136] Preferably, in a specific application scenario, when performing dimensional modeling and establishing dimensional indexes for grid economic indicator data, the grid economic indicator data Grid_Eco_Indicators is assumed to contain multiple economic indicators I = {i1, i2, ..., i...}. k}, and the corresponding spatial grid information G={g1,g2,…,g v Construct a star schema dataset SMD, using spatial grids as the fact table and economic indicators as the dimension table. The fact table F contains a field g (spatial grid identifier) ​​and corresponding measures m1, m2, ..., m for each economic indicator. k Dimension table D i (i = 1, ..., k) includes economic indicator i i Detailed attribute information. For example, a record in fact table F can be represented as (g j ,m 1j ,m 2j ,…,m kj ), where m ij It is the measure of the i-th economic indicator in the j-th spatial grid.

[0137] Preferably, in a specific application scenario, when creating a dimension index, for each dimension table D... i Create an index based on economic indicator values. Let dimension table D. i The economic indicator value in the middle is x il (l=1,…,n i n i For dimension table D i The number of records (in the dimension table D) can be used as the index structure, employing a balanced binary search tree (AVL tree). For the query value q, in the dimension table D... i Search for x that meets the condition in the index. il The time complexity of the record with =q is O(logn). i After constructing the dimensional index, the indexed economic indicator data IEMD is obtained.

[0138] Grid_Eco_Indicators: Socioeconomic data processed through text structuring, spatial mapping, and indicator normalization, recording various economic indicator information in grid units. Used in eco-product development to analyze the impact of different regional economic conditions on product development. I: A set of economic indicators, such as GDP, per capita income, and industrial structure, reflecting different aspects of the socio-economic environment. G: A set of spatial grids, dividing geographic space into multiple grids for easy association and analysis with economic indicator data. SMD: Star schema dataset, a commonly used data warehouse modeling method, facilitating data analysis and querying through fact tables and dimension tables. Used in eco-product development risk assessment for multi-dimensional analysis of economic indicators in different spatial regions. F: Fact table, storing the correspondence between spatial grids and economic indicator measurements. Di: Dimension table, storing detailed attribute information of economic indicators for deeper analysis and querying. m_{ij}: The measurement value of the i-th economic indicator in the j-th spatial grid, key data for analyzing the relationship between the economy and eco-product development. x il Dimension table D i The economic indicator value of the l-th record. q: Query value, used to find records in the dimensional index that meet a specific economic indicator value. IEMD: Indexed economic indicator data. By establishing a dimensional index, the efficiency of querying and analyzing economic indicator data is improved, providing faster data support for ecological product development decisions.

[0139] Preferably, in a specific application scenario, during data alignment and transformation, the data is categorized as spatiotemporal block data (TSD), spatially indexed vector data (SIVD), indexed ecological relationship data (IERD), and indexed economic indicator data (IEMD).

[0140] The four types of data are aligned using spatial and temporal information. Let the aligned spatiotemporal block data be TSD. aligned Spatial indexed vector data is SIVD aligned Indexed ecological relationship data is IERD aligned Indexed economic indicator data is IEMD aligned .

[0141] Spatially, data is matched based on spatial grids or geographic coordinates; temporally, data is matched based on points in time or time ranges.

[0142] Let the transformation function f env f spatial f eco f econ These are used to convert aligned spatiotemporal block data, spatially indexed vector data, indexed ecological relationship data, and indexed economic indicator data into feature vector groups, respectively.

[0143] The environmental feature vector set EVG is: EVG = f env (TSD aligned The spatial feature vector set SVG is: SVG = f spatial (SIVD aligned The ecological feature vector group ECVG is: ECVG = f eco (IERD aligned The economic feature vector set EEVG is: EEVG = f econ (IEMD aligned TSD aligned SIVD aligned IERD aligned IEMD aligned Alignment ensures spatial and temporal consistency among different data types, facilitating subsequent joint analysis. env f spatial f eco f econ Transformation functions are functions that convert different types of data into feature vector sets based on the characteristics of the data and the needs of the analysis. For example, f env It is possible to convert environmental features such as temperature and humidity in spatiotemporal segmented data into a feature vector, where each dimension of the vector represents a different environmental feature or a combination of features. -EVG, SVG, ECVG, EEVG: These are environmental feature vector groups, spatial feature vector groups, ecological feature vector groups, and economic feature vector groups, respectively. They are the converted data formats, which facilitate vector group fusion and subsequent analysis and processing.

[0144] Preferably, in a specific application scenario, when constructing vector group fusion, let the environmental feature vector group EVG = {e1, e2, ..., e p}, Spatial feature vector set SVG={s1,s2,…,s q}, Ecological Feature Vector Group ECVG={c1,c2,…,c r}, the economic feature vector set EEVG={g1,g2,…,g s The fused feature vector FV is obtained by weighted fusion.

[0145] Where w ei w sj w ck w gl It is a weighting coefficient, and e i s j c k g lThese are the feature vectors in the environmental feature vector group, spatial feature vector group, ecological feature vector group, and economic feature vector group, respectively. Weighting coefficients are used to adjust the contribution of different feature vector groups in the fused feature vector. In the risk assessment of ecological product development, different weights can be set according to the degree of influence of different factors on the risk. For example, if ecological factors have a greater impact on the risk, the weights can be appropriately increased. The value of FV: Fusion Feature Vector, which integrates feature information from multiple aspects such as environment, space, ecology and economy, provides a more comprehensive data foundation for the subsequent generation of multi-source heterogeneous data streams.

[0146] Preferably, in a specific application scenario, when constructing a multi-source heterogeneous data stream, the fused feature vector FV is set, and the sliding window size is set to w. slide The time step is Δt. Starting from t=1, within each time step Δt, a sliding window process is applied to the fused feature vector. Let the fused feature vector within the nth sliding window be FV. n Convert it to a byte stream BS n FV n =[FV((n-1)Δt+1),FV((n-1)Δt+2),…,FV((n-1)Δt+w slide The conversion of byte streams can be done using binary serialization. Assuming each element in FV is a floating-point number, the floating-point number is converted to binary representation according to the IEEE 754 standard. Then, the binary bits of all elements within the sliding window are concatenated sequentially to form the byte stream BS. n For example, for a 32-bit floating-point number x, its IEEE 754 representation consists of a sign bit s (1 bit), an exponent bit e (8 bits), and a mantissa bit m (23 bits). The conversion process is: x = (-1) s ×1.m×2 e-127 Convert and concatenate all FV elements within the sliding window in this manner to obtain the byte stream BS. n As the time step progresses, BS1, BS2, ... are generated sequentially; these byte streams together constitute the multi-source heterogeneous data stream MSDS. slide The sliding window size determines the number of fused feature vectors included in each sliding window operation. In the scenario of risk prediction for ecosystem product development, if the focus is on the combined changes of multiple factors over a short period of time, a smaller w can be set. slide To analyze trends over a longer time span, increase w. slide Δt: Time step, which is the time interval between each sliding window operation. Shorter time steps can capture more subtle real-time changes and are suitable for ecosystem product development scenarios that are sensitive to changes in risk; longer time steps are more suitable for macro trend analysis. FV nThe subset of fused feature vectors within the nth sliding window contains multi-source feature information within a specific time window. BS n The byte stream obtained by converting the fused feature vector within the nth sliding window is the basic building block of multi-source heterogeneous data streams. Converting the fused feature vector into a byte stream facilitates efficient storage, transmission, and subsequent processing within the computer system. MSDS: Multi-source heterogeneous data stream, consisting of a series of byte streams generated in chronological order. n It integrates the characteristic information of multi-source heterogeneous data such as environment, space, ecology and economy, and provides a unified data input format for the risk prediction model of ecological product development.

[0147] In the specific application scenario of risk prediction for ecological product development, the above-mentioned technical solution offers the following advantages over traditional technologies in terms of data processing, analysis, and utilization:

[0148] 1. This application uses a two-dimensional block approach to divide environmental temporal characteristic data by setting time windows and spatial regions. This discretizes continuous spatiotemporal data, allowing for a finer-grained observation and analysis of environmental data variations across different spatiotemporal ranges. Traditional techniques only perform simple time-series analysis or overall statistics of spatial regions on environmental data, failing to simultaneously consider changes in both time and space. For example, when analyzing temperature changes in a specific ecological region, traditional methods may only focus on the overall average temperature change over time, ignoring local differences at different locations within the region at the same time or at different times within the same location. The reduced data size after block division in this application allows the processor to process and analyze each block more efficiently, reducing computational resource consumption. Furthermore, it enables more precise capture of local characteristics and anomalous changes in environmental data within specific spatiotemporal ranges, providing more detailed environmental risk information for ecological product development. For instance, in ecological agriculture development, it allows for timely detection of temperature anomalies in a specific plot within a specific time period, enabling proactive measures to protect crops.

[0149] 2. This application vectorizes radiometric semantic images and establishes a spatial index, converting image data into vector features. The spatial index structure (such as a quadtree) efficiently organizes and manages these vector features, facilitating rapid spatial querying and analysis. Traditional image processing techniques typically analyze images pixel-by-pixel, resulting in low efficiency for complex feature identification and spatial queries. For example, when searching for vegetation distribution within a specific ecological area, traditional methods may require traversing all pixels of the image, which is time-consuming and prone to errors. The spatial indexing in this application enables rapid location of relevant vector features during spatial queries (such as range queries and adjacency queries), significantly improving the efficiency of spatial analysis. Furthermore, vectorization converts features in the image into explicit vector features, facilitating more accurate identification and analysis of different ecological elements and providing a more reliable basis for planning and decision-making in ecological product development. For example, in ecotourism development, it can accurately determine the distribution of natural landscapes and ecological resources within a scenic area.

[0150] 3. This application models the relationships between biological spatiotemporal data, constructing an ecological relationship map to represent the interrelationships between species. It also establishes an efficient query mechanism for the map using path indexing (such as the Dijkstra algorithm), facilitating the analysis of indirect relationships and impact paths between species. Traditional biodiversity analysis methods often focus on individual species characteristics and population statistics, neglecting the complex interrelationships between species. When analyzing the impact of ecosystems on the development of ecological products, it is difficult to comprehensively consider the interactions and chain reactions between species. The ecological relationship map in this application can intuitively display the network of relationships between species, helping developers to fully understand the structure and function of ecosystems and better assess the potential impact of ecological product development on ecosystems. Furthermore, path indexing makes querying relationships between species and impact paths more efficient, enabling the timely detection of potential ecological risks. For example, in ecological aquaculture, it allows for rapid understanding of the impact of changes in one species on other related species, allowing for proactive measures to maintain ecological balance.

[0151] 4. This application employs a star schema for dimensional modeling of grid-based economic indicator data, organizing the data into fact tables and dimension tables, and improving the efficiency of querying and analyzing economic indicator data through dimensional indexes (such as AVL trees). Traditional economic data processing methods use simple tabular formats to store and analyze data, which have limited capabilities for complex multidimensional queries and analyses. When analyzing the relationship between ecological product development and economic factors, it is difficult to quickly obtain and integrate economic information from different dimensions. The star schema dimensional modeling of this application facilitates multidimensional data analysis, enabling in-depth mining of economic data from different perspectives (such as time, space, and economic indicator types), providing more comprehensive economic information for ecological product development decisions. Moreover, the establishment of dimensional indexes allows for rapid location of relevant data when querying specific economic indicators or performing range queries, reducing query time and improving decision-making efficiency. For example, when evaluating the economic benefits of ecological product development projects, economic indicator data from different regions and time periods can be quickly obtained for comparative analysis.

[0152] 5. This application aligns different types of data using spatial and temporal information to ensure data consistency across the spatiotemporal dimensions. The aligned data is then converted into feature vector sets, facilitating subsequent fusion and analysis. Traditional techniques often struggle to address spatiotemporal inconsistencies when processing multi-source heterogeneous data, hindering effective data integration and utilization. Data from different sources may differ in temporal scale, spatial resolution, and other aspects, making unified analysis difficult. This application's data alignment enables the integration of different types of data within the same spatiotemporal framework, eliminating spatiotemporal differences and providing a foundation for subsequent joint analysis. Furthermore, converting the data into feature vector sets unifies the data representation, facilitating vector operations and fusion, and improving the versatility and flexibility of data processing.

[0153] 6. This application employs a weighted fusion approach to combine feature vectors from different types, such as environmental, spatial, ecological, and economic data, into a single fused feature vector, comprehensively considering the impact of various factors on the risks of ecological product development. Traditional methods focus only on a single type of data or simply superimpose different types of data, failing to fully consider the interactions and weighting relationships between different factors. When assessing the risks of ecological product development, the influence of certain important factors may be overlooked. The fused feature vector of this application can comprehensively reflect information from multiple aspects, including environment, space, ecology, and economy, providing a more comprehensive assessment of the risks of ecological product development. By adjusting the weighting coefficients, the importance of different factors in risk assessment can be flexibly adjusted according to actual circumstances. The fused feature vector of this application, integrating multi-source data, provides richer information, helping to improve the accuracy and reliability of risk prediction models and providing a more scientific basis for ecological product development decisions.

[0154] 7. This application uses a sliding window to process the fused feature vectors and converts the data within the sliding window into a byte stream, forming a multi-source heterogeneous data stream, which facilitates data storage, transmission, and real-time processing. Traditional data processing methods cannot effectively handle the real-time and streaming characteristics of multi-source heterogeneous data, making it difficult to meet the real-time data requirements for ecological product development risk prediction. Data storage and transmission may also suffer from inefficiency. The multi-source heterogeneous data stream of this application can reflect the dynamic changes of environmental, ecological, and economic factors in real time, providing a data foundation for real-time risk prediction. The processor can perform real-time analysis and processing of the data stream, promptly identifying potential risks and taking corresponding measures. This application converts data into a byte stream format, reducing data storage space and transmission bandwidth requirements, and improving the efficiency of data storage and transmission. At the same time, the byte stream format facilitates exchange and sharing between different systems, enhancing the universality and scalability of the data.

[0155] Optionally, the step of constructing multimodal feature engineering on the multi-source heterogeneous data stream to form physical layer feature data, ecological layer feature data, and socio-economic layer feature data includes: performing linear regression analysis on the multi-source heterogeneous data stream to obtain trend terms, and performing Fourier transform on the trend terms to form physical layer feature data; extracting spectral features, texture features, and shape features from the multi-source heterogeneous data stream to obtain ecological remote sensing features; extracting species richness features, species distribution features, and ecological relationship features from the multi-source heterogeneous data stream to obtain biodiversity features; fusing the ecological remote sensing features and biodiversity features to form ecological layer feature data; and extracting demographic features, economic development features, and policy and regulatory features from the multi-source heterogeneous data stream, and performing spatial feature mining on the demographic features, economic development features, and policy and regulatory features to form socio-economic layer feature data.

[0156] Preferably, in a specific application scenario, when constructing physical layer feature data, the multi-source heterogeneous data stream is... Where N is the number of data points, x i This is the data stream observation at time point i. A multiple linear regression model is used. To fit the data, where t i β is the time index of the i-th time point. j (j=0,1,…,M) are regression coefficients, ∈ i It is an error term, and ∈ i ~N(0,σ 2 ).

[0157] To minimize the sum of squared errors using the least squares method

[0158] For Q(β) with respect to β k(k = 0, 1, ..., M) Taking the partial derivatives and setting them to 0, we obtain the normal equation system:

[0159]

[0160] Represented in matrix form, let...

[0161] The normal system of equations can then be expressed as X T Xβ=X T y, the solution is Trend Item in X: Multi-source heterogeneous data stream. In the context of ecological product development, this may be a combination of environmental indicators (such as temperature and humidity), ecological indicators (such as changes in species abundance), and economic indicators (such as market demand) over a period of time. N: The number of data points, reflecting the time duration or number of observations. M: The order of the polynomial, controlling the complexity of the regression model and adjustable according to data trends. β j : Regression coefficients, β0 is the intercept term, β j (j>0) represents the coefficients of time terms of different orders, reflecting the changing patterns of data over time. T: Trend term, which describes the long-term changing trend of multi-source heterogeneous data streams and helps to analyze the overall trend of the data.

[0162] Preferably, in a specific application scenario, when the Fourier transform is used to form physical layer feature data, a Discrete Fourier Transform (DFT) is performed on the trend term T. The formula for the Discrete Fourier Transform is:

[0163] To improve computational efficiency, the Fast Fourier Transform (FFT) algorithm is typically used. Physical layer feature data. in F k The discrete Fourier transform of the trend term T converts the time-domain data to the frequency domain, where k represents the index of the frequency component. P k Physical layer characteristic data is the modulus of the Fourier transform result, reflecting the intensity of different frequency components. It can be used to analyze the periodic changes in data, such as the seasonal fluctuations of ecosystems.

[0164] Preferably, in a specific application scenario, when constructing ecological layer feature data, spectral features S and texture features T are extracted from multi-source heterogeneous data streams. ex Shape features Sh. Let the ecological remote sensing image data be I(x,y), where (x,y) are the pixel coordinates of the image.

[0165] Spectral features S: For multispectral imagery, spectral features can be obtained by calculating statistics for different bands. For example, the mean value of the b-th band. variance Where H and W are the height and width of the image, respectively, and I b (x, y) is the pixel value of the b-th band at (x, y). Spectral feature vector B represents the number of bands. Texture feature T ex Texture features are extracted using the Gray-Level Co-occurrence Matrix (GLCM). Let d be the distance between pixel pairs and θ be the direction of the pixel pair. The GLCM G(i,j; d,θ) represents the number of times pixel pairs with gray values ​​i and j appear at distance d and direction θ. Various texture features, such as contrast, can be calculated based on GLCM. Correlation Where L is the number of gray levels, μ i ,μ j σ is the mean of i and j. i ,σ j It represents the standard deviation of i and j. Texture feature vector T ex ={C,R,…}. Shape features: For a target object in an image, shape features can be obtained by calculating its geometric parameters. Let the set of boundary points of the target object be... perimeter Area A can be obtained through integration or pixel counting; circularity Shape feature vector Sh={L,A,C r}

[0166] I(x,y): Ecological remote sensing image data, representing the ecologically relevant portion of a multi-source heterogeneous data stream, used to acquire spectral and textural information of ground features. B: Number of spectral bands; different bands correspond to different spectral ranges and can be used to identify different ground feature types. d and θ: Distance and orientation parameters of the gray-level co-occurrence matrix, used to describe the spatial relationship between pixel pairs. L: Number of gray levels, affecting the accuracy of texture feature calculation. S: Spectral feature vector, reflecting the reflectance characteristics of ground features in different spectral bands, and can be used to distinguish different ecological elements such as vegetation and water bodies. T ex : Texture feature vector, describes the texture information of the ground surface, which helps to identify different types of ecological landscapes. Sh: Shape feature vector, reflects the geometric shape characteristics of the ground object, and can be used to identify specific ecological objects.

[0167] Preferably, in a specific application scenario, when extracting biodiversity features, species richness feature R, species distribution feature D, and ecological relationship feature E are extracted from multi-source heterogeneous data streams. Let the species observation data be... Where s i It is the name of the species, l i It is the observation location, t i It is the observation time.

[0168] Species richness characteristic R: in a specific region A and time interval [t] start ,t end Within [the range], species richness R = |{s] i |l i ∈A,t start ≤t i ≤t end |, that is, the number of different species observed in that region and time period. Species distribution characteristics D: The spatial distribution of species can be described using kernel density estimation (KDE). Let K(x) be the kernel function (such as a Gaussian kernel). Where x is a spatial vector, d is the spatial dimension, and σ is the bandwidth parameter, the density estimate of species s at position y is: Where N s x is the number of observations of species s. i is the observation location of species s, and h is the bandwidth. Species distribution feature vector. Where S is the set of species, and Y is the set of locations within the study area. Ecological relationship characteristics E: Constructing an ecological relationship map G = (V, E) g ), where V is the set of species nodes, E g It is a set of edges, and the weight w of each edge is... ij This represents the strength of the relationship between species i and j (e.g., predation, symbiosis, etc.). Ecological relationships can be represented using graph features (e.g., node degree, clustering coefficient, shortest path length, etc.). For example, the degree of node i... Clustering coefficient Where E i This represents the number of edges between node i and its neighboring nodes. (Ecological relationship feature vector)

[0169] O: Species observation data, recording observation information of different species at different times and locations. A: Study area, used to define the spatial range of species richness and distribution. [t] start ,t end []: Time interval, used to analyze species changes within a specific time period. K(x): Kernel function, used for kernel density estimation, describing the spatial distribution of species. σ and h: Bandwidth parameters of the kernel function, affecting the smoothness of the density estimate. G: Ecological relationship map, visually displaying the relationships between species. R: Species richness feature, reflecting the degree of species diversity in the ecosystem. D: Species distribution feature, showing the spatial distribution of species. E: Ecological relationship feature, reflecting the interactions between species and the structure of the ecosystem.

[0170] Preferably, in a specific application scenario, when feature fusion is used to form ecological layer feature data, a weighted fusion method is used to fuse ecological remote sensing features and biodiversity features. Let the ecological remote sensing feature vector V...eco-remote =[S;T ex ;Sh], biodiversity feature vector V bio-diversity = [R; D; E], the fused ecological layer feature data E layer For: E layer =w1V eco-remote +w2V bio-diversity

[0171] w1 and w2 are weight coefficients, and w1 + w2 = 1. The optimal weights can be determined through methods such as cross-validation.

[0172] V eco-remote Ecological remote sensing feature vectors integrate spectral, texture, and shape features. V bio-diversity : Biodiversity feature vector, containing features such as species richness, distribution, and ecological relationships. w1 and w2: Weighting coefficients used to adjust the importance of ecological remote sensing features and biodiversity features in the fusion process. E layer Ecosystem feature data integrates information from both ecological remote sensing and biodiversity, providing a comprehensive basis for assessing the state of ecosystems and the ecological impacts of ecological product development.

[0173] Preferably, in a specific application scenario, when constructing socioeconomic layer feature data, demographic features P are extracted from multi-source heterogeneous data streams. demo Economic Development Characteristics E dev Policy and regulatory characteristics P policy .

[0174] Let the population statistics be Where p i It refers to an individual in the population, a i It's age, g i It's about gender, l i It refers to the place of residence; economic development data is... Where r i It is a region, t i It is time, v i These are economic indicators (such as GDP, industrial output, etc.); policy and regulatory text data are... Demographic characteristics P demo It can calculate the population N. pop =|P data Age distribution Where n k The age range is [a] k ,a k+1 Population size and sex ratio within the area population density Where A region It represents the area of ​​the study region. The demographic eigenvector Pdemo =[N pop A dist G ratio ;D pop Economic Development Characteristics E dev For each region r and time t, calculate the mean of the economic indicators. growth rate Economic development feature vector E dev ={μ r,t ,g r,t} r∈R,t∈T Where R is the set of regions and T is the set of times. Policy and regulatory characteristics P policy This involves using natural language processing techniques (such as the bag-of-words model and TF-IDF) to extract features from policy and regulatory texts. A bag-of-words model is then used. Policy and regulatory texts word frequency vectors in It is the word w k In the text Frequency of occurrence in policy and regulation feature vectors. in

[0175] P data Demographic data records individual information about a population. data Economic development data, including economic indicator values ​​from different regions and time periods. policy Policy and regulatory text data, used to analyze the impact of policies on the development of ecoproducts. pop Population size reflects the population size of the study area. A dist Age distribution helps in understanding the age structure of a population. ratio The gender ratio has an impact on the labor market and the consumer market. pop Population density reflects the spatial distribution of the population. (μ) r,t : The average economic indicators of region r over time t, reflecting the region's level of economic development. g r,t The economic growth rate of region r at time t reflects the trend of economic development. word w k In policy and regulatory texts The TF-IDF value in the text is used to measure the importance of the word in the text. demo : Demographic feature vector, which integrates information such as population size, age, gender, and distribution. E dev The economic development feature vector shows the level and trend of economic development in different regions and at different times. policy : Policy and regulation feature vectors, which extract key information from policy and regulation texts.

[0176] Preferably, in a specific application scenario, when spatial feature mining is used to form socioeconomic layer feature data, spatial feature mining is performed on demographic features, economic development features, and policy and regulatory features. Let P demo E dev P policy Corresponding spatial distribution data Spatial autocorrelation analysis (such as Moran's I index) is used to uncover spatial correlations. For spatial variables Z(x) (such as population density, economic indicators, etc.), Moran's I index is defined as: Where n is the number of spatial units, w ij These are elements of the spatial weight matrix, representing the spatial relationship (such as adjacency) between spatial units i and j. Missing values ​​in spatial data are filled using spatial interpolation (such as kriging interpolation). Given known data points... The point to be predicted is x0, and the Kriging interpolation formula is: Where λ i These are the weighting coefficients, obtained by solving the semivariance function and the Kriging equations. Socioeconomic layer characteristic data S layer It is a vector that integrates spatial features such as spatial correlation and interpolation results.

[0177] These are spatial distribution data representing demographic characteristics, economic development characteristics, and policy and regulatory characteristics. Z(x): Spatial variables, such as population density and economic indicators. ij : Elements of the spatial weight matrix, describing the adjacency or distance relationships between spatial units. I: Moran's I index, used to measure the autocorrelation of spatial variables; a value greater than 0 indicates positive correlation, less than 0 indicates negative correlation, and close to 0 indicates random distribution. Kriging interpolation results are used to predict spatial variable values ​​at unknown locations. layer Socioeconomic layer characteristic data integrates spatial information on socioeconomic characteristics, which helps to analyze the impact of socioeconomic differences in different regions on the development of ecological products.

[0178] Preferably, in the specific scenario of risk prediction for ecological product development, the above-mentioned technology has the following technical advantages compared with traditional technologies:

[0179] 1. This application utilizes multiple linear regression to fit multi-source heterogeneous data streams, determining regression coefficients by minimizing the sum of squared errors to capture long-term data trends. Subsequently, discrete Fourier transform is used to convert the time-domain trend term to the frequency domain, analyzing the periodic changes in the data. Traditional methods often employ simple linear regression, which can only describe a single trend in the data and cannot handle complex changes. In frequency domain analysis, there may be a lack of precise transformation tools, making it difficult to deeply explore the periodic characteristics of the data. For example, when analyzing environmental temperature data in the development of ecological products, traditional simple regression cannot accurately present complex temperature change trends, and the analysis of seasonal and other periodic changes is also relatively coarse. The multiple linear regression in this application can adapt to more complex data changes. By adjusting the polynomial order, it more accurately fits the trends of multi-source heterogeneous data streams, providing a more realistic basis for risk prediction. Fourier transform clearly shows the components of the data at different frequencies, accurately revealing the periodic fluctuations of the ecosystem, such as the seasonal influence on ecological products, which helps in the early planning of production and sales.

[0180] 2. The spectral features of this application are obtained by calculating the mean, variance, and other statistical quantities of different bands in multispectral images, reflecting the response of ground features to different spectra. Texture features are extracted based on the spatial relationship of pixel pairs using a gray-level co-occurrence matrix. Shape features are determined by calculating geometric parameters such as the perimeter, area, and roundness of the target object. Traditional spectral feature extraction only focuses on a few bands, making it difficult to comprehensively reflect the characteristics of ground features. Texture feature extraction methods are limited and cannot fully explore complex texture information. Shape feature calculation may not be accurate enough, and its ability to describe irregular shapes is insufficient. For example, when identifying vegetation types related to ecological products, traditional methods do not comprehensively analyze the spectral features of vegetation, which can easily lead to misclassification of vegetation types. The comprehensive spectral feature extraction of this application can more accurately distinguish different ecological elements, providing accurate information for resource assessment in ecological product development. Texture feature extraction based on a gray-level co-occurrence matrix can distinguish different ecological landscapes in detail, assisting in the precise planning of ecological product development areas. Accurate shape feature calculation helps to identify specific ecological objects and improves the accuracy of ecological environment assessment in ecological product development.

[0181] 3. This application determines species richness by statistically analyzing the number of species in a specific region and time period; species distribution is estimated using kernel density, which describes the spatial distribution density of species using kernel functions; ecological relationships are represented by constructing an ecological relationship map, using the characteristics of the map to reflect the interactions between species. Traditional species richness statistics lack clear spatiotemporal boundaries, affecting data accuracy. Species distribution analysis methods are simplistic and cannot accurately present complex distribution situations. Ecological relationship studies often rely on simple qualitative descriptions and lack quantitative analysis. When assessing the impact of ecological product development on biodiversity, traditional methods cannot accurately grasp the dynamic changes of species in space and the complex relationships between species. This application provides precise data for assessing the ecological impact of ecological product development through clearly defined spatiotemporal species richness statistics. Kernel density estimation can intuitively display species distribution, providing a scientific basis for the spatial planning of ecological product development. The quantitative ecological relationship map characteristics comprehensively reveal the ecosystem structure, helping developers better understand the potential impact of ecological product development on ecological balance and formulate reasonable development strategies.

[0182] 4. This application employs a weighted fusion method, combining ecological remote sensing features and biodiversity features with different weights to comprehensively reflect multifaceted information about the ecosystem. Traditional fusion methods often involve simple overlay, failing to consider the differences in importance between different features and making it difficult to fully leverage the advantages of multi-source data. In ecological product development risk prediction, the inability to effectively integrate ecological remote sensing and biodiversity information leads to incomplete risk assessment. The weighted fusion method in this application can flexibly adjust feature weights according to the ecological product development scenario, highlighting key information and enabling ecological layer feature data to more comprehensively and accurately reflect the ecosystem state, thereby improving the reliability of ecological product development risk prediction.

[0183] 5. This application obtains demographic characteristics by calculating indicators such as population size, age distribution, gender ratio, and population density; economic development characteristics are reflected by calculating the mean and growth rate of economic indicators; and policy and regulatory characteristics are extracted from policy texts using techniques such as the bag-of-words model and TF-IDF in natural language processing. Traditional demographic characteristic analysis lacks in-depth exploration of details such as age distribution. Economic development characteristic analysis focuses more on total volume and pays insufficient attention to dynamic indicators such as growth rate. Policy and regulatory characteristic extraction relies on manual reading, which is inefficient and highly subjective. When analyzing the relationship between ecological product development and socio-economic factors, traditional methods cannot comprehensively and accurately grasp the impact of socio-economic factors. This application's comprehensive demographic characteristic analysis provides detailed basis for assessing market demand and labor resources for ecological product development. Attention to dynamic economic development indicators helps to grasp market trends and optimize investment strategies for ecological product development. Natural language processing technology efficiently and objectively extracts key information from policies and regulations, providing policy guidance for ecological product development decisions.

[0184] 6. This application utilizes spatial autocorrelation analysis (such as Moran's I index) to measure the correlation of spatial variables and fills in missing values ​​in spatial data through spatial interpolation (such as Kriging interpolation), thus synthesizing these spatial characteristics to form socioeconomic layer characteristic data. Traditional spatial analysis lacks effective means to measure spatial correlation, and the methods for handling missing values ​​in spatial data are simplistic, leading to inaccurate and incomplete spatial characteristic analysis. In the regional planning of ecological product development, it is impossible to accurately analyze the spatial differences of socioeconomic factors. The spatial autocorrelation analysis in this application clearly demonstrates the spatial distribution patterns of socioeconomic factors, providing a reference for the regional layout of ecological product development. Spatial interpolation accurately fills in missing values, improving the quality of spatial data and enabling the socioeconomic layer characteristic data to more comprehensively and accurately reflect regional differences, thus contributing to the scientific planning of ecological product development.

[0185] Optionally, the step of performing multimodal feature fusion on the physical layer feature data, ecological layer feature data, and socioeconomic layer feature data to generate ERIFM comprehensive ecological development risk feature data includes: extracting local pattern features from the physical layer feature data based on 1D convolutional capsule layers to generate physical capsules; extracting spatial hierarchical features from the ecological layer feature data based on 2D convolutional capsule layers to generate ecological capsules; extracting spatial dependency adjustments from the socioeconomic layer feature data based on graph capsule networks to generate economic capsules; calculating the global development risk correlation of physical capsules, ecological capsules, and economic capsules to generate a fusion weight matrix; and performing multimodal feature fusion on the physical layer feature data, ecological layer feature data, and socioeconomic layer feature data based on the fusion weight matrix to generate ERIFM comprehensive ecological development risk feature data.

[0186] Preferably, in a specific application scenario, when generating physical capsules by extracting local pattern features from physical layer feature data based on 1D convolutional capsule layers, the physical layer feature data is set as follows: Where N is the number of features. A 1D convolution kernel is used. Perform a convolution operation with a stride of s and padding of p.

[0187] Convolution output C l The calculation is as follows: Where l satisfies 1 ≤ l ≤ N - M + 1 + 2p, and l increases with a step size s. Preferably, in a specific application scenario, when the dynamic routing algorithm generates physical capsules, let the convolution output C = {C l}, and input it into the dynamic routing algorithm. Assume there exists n p A low-level capsule (convolution output unit) and m p A high-level capsule (physical capsule). Initial route weight b ij =0, where i = 1, ..., n p j = 1, ..., m pThe following steps are performed iteratively r times: Calculate the coupling coefficient. Calculate the input of the advanced capsule For s j Apply the squeeze function Get the physical capsule v j Update route weight b ij =b ij +v j ·C i Ultimately, a physical capsule set is obtained. P: Physical layer feature data, containing frequency domain features obtained after linear regression and Fourier transform of multi-source heterogeneous data streams, reflecting the periodic changes and trends of physical environmental factors during the development of ecological products. K: 1D convolution kernel, used to extract local patterns in the physical layer feature data; different kernels can capture different types of local features. M: Kernel length, determining the size of the local area considered by the convolution operation. s: Convolution stride, controlling the interval at which the convolution operation moves across the feature data. p: Padding size, used to handle boundary cases, ensuring that the convolution operation can cover the entire feature data. n p : Number of low-level capsules, corresponding to the number of convolutional output units. m p : Number of advanced capsules, i.e., the number of physical capsules generated. b ij : Routing weight, used to measure the connection strength between lower-level capsules and higher-level capsules. ij The coupling coefficient reflects the proportion of contribution of the lower-level capsule to the higher-level capsule. j The input to a higher-level capsule is a weighted sum of the outputs of lower-level capsules. j Physical capsules represent local pattern features in physical layer feature data.

[0188] Preferably, in a specific application scenario, when generating ecological capsules by extracting spatial hierarchical features from ecological layer feature data based on 2D convolutional capsule layers, let the ecological layer feature data be... Where H and W represent the height and width of the feature data, respectively. A 2D convolution kernel is used. Perform a convolution operation with a stride of (s) h ,s w ), fill with (p h ,p w Convolution output The calculation is as follows: Where l satisfies 1≤l≤H-M+1+2p h k satisfies 1≤k≤W-N+1+2p w And l and k are respectively in step size s h and s wIncremental. Preferably, in a specific application scenario, when the dynamic routing algorithm generates ecological capsules, similar to generating physical capsules, the 2D convolutional output is input into the dynamic routing algorithm. Assume there exists n e A low-level capsule (2D convolutional output unit) and m e One premium capsule (ecological capsule).

[0189] After r iterations of dynamic routing, a set of ecological capsules is obtained. E: Ecological layer characteristic data, which integrates ecological remote sensing features and biodiversity features, and has spatial distribution characteristics. K 2D : 2D convolution kernel, used to extract spatial hierarchical features from ecological layer feature data. M and N: The height and width of the 2D convolution kernel, determining the size of the spatial range considered by the convolution operation. (s h ,s w ): Convolution stride, which controls the interval of movement in the height and width directions of the convolution operation, respectively. (p h ,p w ): Padding size, used to handle boundary cases and ensure that the convolution operation can cover the entire ecological layer feature data. e : Number of low-level capsules, corresponding to the number of 2D convolution output units. m e : Number of advanced capsules, i.e., the number of eco-capsules generated. V e Ecological capsules represent the spatial hierarchical features in ecological layer feature data.

[0190] Preferably, in a specific application scenario, when generating economic capsules by adjusting the spatial dependencies in socioeconomic layer feature data based on graph capsule networks, let the socioeconomic layer feature data be S, and construct a graph G = (V, E), where V is the set of nodes, each node corresponds to a socioeconomic feature region, and E is the set of edges, with edge weights w. ij This represents the spatial dependency between nodes i and j (e.g., determined by spatial distance, economic ties, etc.). A graph convolution operation is performed on graph G, and the feature vector of node i is denoted as x. i The output h of the convolutional layer in the figure i The calculation is as follows:

[0191] Where N(i) is the set of neighboring nodes of node i, d i σ is the degree of node i, and σ is the activation function (such as ReLU). Preferably, in a specific application scenario, when the dynamic routing algorithm generates economic capsules, the graph convolution output is input into the dynamic routing algorithm. Assume there are n_{s} low-level capsules (graph convolution output units) and m... s One premium capsule (economic capsule).

[0192] S: Socioeconomic layer characteristic data, including spatial distribution information of demographic characteristics, economic development characteristics, and policy and regulatory characteristics. G: Graph, used to represent the spatial dependencies between regions exhibiting socioeconomic characteristics. w ij The edge weight reflects the strength of the spatial dependency between nodes i and j. i : The feature vector of node i, representing the socioeconomic characteristics of the region. h i The output of the graph convolutional layer fuses the feature information of a node and its neighboring nodes. s : Number of low-level capsules, corresponding to the number of graph convolution output units. m s : Number of advanced capsules, i.e., the number of economic capsules generated. V s The economic capsule represents the spatial dependence adjustment features in the socioeconomic layer characteristic data.

[0193] Preferably, in a specific application scenario, when calculating the global development risk correlation of the physical capsule, ecological capsule, and economic capsule to generate a fusion weight matrix, the physical capsule is set as follows: Ecological capsules Economic Capsule

[0194] Calculate the correlation between pairs of capsules, such as physical capsules. He Ecological Capsules correlation It can be calculated using cosine similarity: Similarly, calculation (Correlation between physical capsules and economic capsules) and (Correlation between ecological capsules and economic capsules). These correlations are combined into a matrix R, and then normalized to obtain the fusion weight matrix W, for example:

[0195] V p V e V s These are collections of physical capsules, ecological capsules, and economic capsules, respectively. These represent the correlations between physical capsules and ecological capsules, physical capsules and economic capsules, and ecological capsules and economic capsules, respectively. R: Correlation matrix, recording the correlation information between different types of capsules. W: Fusion weight matrix, used to determine the weights of different types of feature data in multimodal feature fusion.

[0196] Preferably, in a specific application scenario, when multimodal feature fusion of physical layer feature data, ecological layer feature data, and socioeconomic layer feature data based on a fusion weight matrix to generate ERIFM comprehensive ecological development risk feature data, let the physical layer feature data P, ecological layer feature data E, and socioeconomic layer feature data S be converted into vector forms P, E, and S, respectively. The fused ERIFM comprehensive ecological development risk feature data F is calculated as follows: F = W1P + W2E + W3S, where W1, W2, and W3 are the weight submatrices in the fusion weight matrix W corresponding to the physical layer, ecological layer, and socioeconomic layer, respectively. P, E, and S: vector representations of the physical layer, ecological layer, and socioeconomic layer feature data, respectively. W1, W2, and W3: weight submatrices in the fusion weight matrix W corresponding to the feature data of each layer. F: ERIFM comprehensive ecological development risk feature data, which integrates feature information from multiple aspects such as physical, ecological, and socioeconomic aspects, and can be used for ecological product development risk prediction.

[0197] In the specific scenario of risk prediction for ecological product development, the technologies mentioned above offer the following advantages compared to traditional technologies:

[0198] 1. The above-mentioned scheme of this application utilizes 1D convolutional capsule layers, 2D convolutional capsule layers, and graph capsule networks to extract features from physical, ecological, and socioeconomic layer feature data, respectively. The capsule network, through a dynamic routing algorithm, can better capture local patterns, spatial hierarchy, and spatial dependencies in the data. For example, the dynamic routing algorithm adaptively adjusts the routing weights based on the correlation between low-level and high-level capsules, enabling high-level capsules to focus on more meaningful features. Traditional convolutional neural networks (CNNs) only focus on the presence or absence of features when processing features, ignoring the orientation and spatial relationships between features. For example, when identifying the topographic features of ecological product development areas, traditional CNNs may not be able to accurately distinguish the spatial hierarchy of different topographic features. The capsule network of this application can retain the orientation and spatial information of features, and the generated physical, ecological, and economic capsules can more accurately represent the features at their respective levels. In ecological layer feature extraction, it can more accurately identify the spatial hierarchy relationships of different elements in the ecosystem, such as the distribution structure of forests, rivers, and farmland. This application has better adaptability to changes in data pose and occlusion. In actual ecological product development, there may be situations where some data is obscured or missing. Capsule networks can still effectively extract key features, improving the stability of feature extraction.

[0199] 2. This application employs different feature extraction methods based on the different characteristics of the physical, ecological, and socioeconomic layer data. For example, 1D convolutional capsule layers are used for the physical layer, suitable for processing one-dimensional time-series features; 2D convolutional capsule layers are used for the ecological layer, effectively extracting two-dimensional spatial features; and graph capsule networks are used for the socioeconomic layer, capable of processing graph-structured data with spatial dependencies. Traditional techniques use a uniform approach to process all types of data, failing to fully exploit the inherent characteristics of different data types. For instance, using image processing methods to process socioeconomic data ignores the spatial dependencies within it. This application's dedicated methods for different data types fully leverage the characteristics of the data, improving the efficiency and quality of feature extraction. When processing socioeconomic layer data, graph capsule networks better capture the economic connections and policy impacts between different regions, providing more valuable information for risk prediction. This application can extract features from multiple dimensions, covering physical, ecological, and socioeconomic aspects, providing a more comprehensive information foundation for subsequent risk prediction.

[0200] 3. This application generates a fusion weight matrix by calculating the global development risk correlation among physical, ecological, and economic capsules. This method considers the interrelationships between features at different levels, making the fusion process more reasonable. For example, calculating the correlation between different capsules using cosine similarity can quantify their degree of association in risk prediction. Traditional feature fusion methods typically use fixed weights for fusion, without considering the intrinsic connections between different features. For example, simply adding features from different levels with equal weights may lead to the underestimation of the role of some important features and the overemphasis on unimportant features. The fusion weight matrix of this application can dynamically adjust the weights according to the correlation between different features, so that features with high correlation to risk prediction receive more weight during the fusion process, improving the quality of the fused features. In ecological product development risk prediction, when the correlation between ecological layer features and risk is high, the weight of ecological capsules in the fusion will increase accordingly. This application can organically combine features from different levels, avoiding information loss and redundancy. Through reasonable weight allocation, the fused features can more comprehensively and accurately reflect the risk situation of ecological product development.

[0201] 4. This application uses a fusion weight matrix to fuse multimodal features of physical, ecological, and socioeconomic layer characteristic data to generate ERIFM comprehensive feature data on ecological development risks. This fusion method integrates information from multiple levels, enabling a more comprehensive reflection of various risk factors in the development of ecological products. Traditional risk prediction methods only consider factors at one or a few levels, ignoring the interactions between factors at different levels. For example, considering only ecological and environmental factors while ignoring the impact of socioeconomic factors on ecological product development leads to inaccurate risk prediction results. The comprehensive feature data in this application includes information from multiple aspects such as physical, ecological, and socioeconomic factors, enabling a more comprehensive assessment of the risks that may be faced in the development of ecological products. For example, when considering market demand for ecological products, combining demographic and economic development characteristics of the socioeconomic layer with the resource status of the ecological layer can more accurately predict the sales risks of products. The more accurate risk prediction in this application can provide early warnings for ecological product development, helping developers adjust their development strategies in a timely manner and make more informed decisions. For example, when a high ecological and environmental risk is predicted for a certain area, developers can take measures in advance for ecological protection or adjust their development plans.

[0202] Optionally, the step of vectorizing the ERIFM comprehensive feature data on ecological development risks to obtain an ecological development risk feature vector includes: grouping the ERIFM comprehensive feature data on ecological development risks into capsules according to modalities to obtain several modal capsule groups; performing dynamic pooling on the modal capsule groups to calculate the information entropy between the modal capsule groups; performing norm processing on each modal capsule group based on the information entropy to obtain a capsule norm vector; and performing semantic enhancement on the capsule norm vector to generate an ecological development risk feature vector.

[0203] Preferably, in a specific application scenario, when the ERIFM ecosystem development risk comprehensive characteristic data is capsule-grouped according to modality to obtain several modal capsule groups, let the ERIFM ecosystem development risk comprehensive characteristic data be... Where N is the total number of features. Based on modality, it is divided into M modal capsule groups. Here I m It is the set of feature indices belonging to the m-th modality, and In the scenario of predicting risks in ecological product development, M is typically 3, corresponding to the physical, ecological, and socio-economic layers, respectively. F: ERIFM comprehensive feature data on ecological development risks, a complex set of features integrating information from different levels, comprehensively reflecting potential risk factors in the ecological product development process. N: The total number of features in the comprehensive feature data. G mThe m-th modal capsule contains relevant features for a specific mode. For example, the physical layer modal capsule G1 contains physical environmental features such as temperature and humidity; the ecological layer modal capsule G2 contains ecologically relevant features such as species richness and ecological landscape texture; and the socioeconomic layer modal capsule G3 contains socioeconomic features such as population density and economic growth rate. m : The feature index set belonging to the m-th modality, used to accurately classify features of different modalities.

[0204] Preferably, in a specific application scenario, when performing dynamic pooling on modal capsule groups to calculate the information entropy between modal capsule groups, for each modal capsule group... Where K m It represents the number of capsules in the m-th modality capsule group. A dynamic pooling method based on an attention mechanism is employed.

[0205] Define attention weights Among them W m It is a learnable weight matrix, b m It is the bias vector, q m This is the query vector. Pooling result.

[0206] Preferably, in a specific application scenario, when calculating information entropy, let the vectors of the M modal capsules obtained after pooling be h1, h2, ..., h... M First, calculate the joint probability distribution P(h1,h2,…,h). M This can be obtained through kernel density estimation methods:

[0207] Where n is the number of samples, K is the kernel function (such as Gaussian kernel), and h m It is the bandwidth parameter of the m-th mode.

[0208] Information entropy In practical calculations, approximate calculations can be performed using the Monte Carlo sampling method.

[0209] G m : The m-th modal capsule group. K m : The number of capsules in the m-th modal capsule group. The attention weight of the j-th capsule in the m-th modal capsule group is used to measure the importance of that capsule in the pooling process. m b m q m These are the learnable weight matrix, bias vector, and query vector in the attention mechanism, used to adaptively allocate attention. m: The vector of the m-th modal capsule after dynamic pooling. n: The number of samples, used for sample statistics in kernel density estimation. K: The kernel function, used to estimate the joint probability distribution. h m : The bandwidth parameter of the m-th mode, controlling the smoothness of the kernel function. H: Information entropy, reflecting the degree of uncertainty and information redundancy between different modal capsules.

[0210] Preferably, in a specific application scenario, when performing norm processing on each modal capsule group based on information entropy to obtain the capsule norm vector, for each modal capsule group G m First, calculate the conditional information entropy H associated with this mode. m =H| -m (That is, the information entropy of this mode while keeping other modes fixed).

[0211] Define weights Where β is the adjustment parameter. For G m Each capsule Calculate its p-norm Where D is the dimension of the capsule. Capsule norm vector.

[0212] H m ω represents the conditional information entropy of the m-th mode, reflecting the uncertainty of that mode within the overall information. m : The weight of the m-th mode, adjusted according to the conditional information entropy, with β controlling the degree of weight adjustment. p: The order of the norm, commonly p=1 or p=2. The p-norm of the j-th capsule in the m-th modal capsule group measures the characteristic strength of the capsule. m : The capsule norm vector of the m-th mode, which integrates the norm information of all capsules in the mode and takes into account the weight of the mode.

[0213] Preferably, in a specific application scenario, when semantically enhancing the capsule norm vectors to generate ecological development risk feature vectors, the M capsule norm vectors n1, n2, ..., n are used. M Concatenate them into a vector v = [n1; n2; ...; n M Semantic augmentation is performed using a deep residual network (ResNet). Assume the ResNet has L residual blocks, and the input to the l-th residual block is x. l The output is x l+1 x l+1 =x l +F(x l W l ), where F(x) l W l ) is the residual function, for example σ is the activation function (such as ReLU). It is a weight matrix. This is the bias vector. After processing through L residual blocks, the final output vector r is obtained, which is the ecological development risk feature vector. v: The concatenated vector, containing capsule norm information of different modalities. L: The number of residual blocks in ResNet, controlling the network depth. F(x l W l ): Residual function, used to learn residual information of input features, helping network training and avoiding the gradient vanishing problem. r: Ecosystem development risk feature vector, after semantic enhancement, can more accurately represent the risk characteristics in the ecosystem product development process, and can be used in subsequent risk prediction models.

[0214] In the scenario of risk prediction for ecological product development, the technological innovations mentioned above have the following technical advantages over traditional technologies in terms of feature grouping, feature pooling, norm processing, and semantic enhancement:

[0215] 1. The above scheme precisely groups the comprehensive characteristic data of ERIFM ecosystem development risks based on modality, through a clearly defined index set I. m This application ensures accurate segmentation of different modal features, with each modal feature set being mutually exclusive, guaranteeing the rigor of the grouping. Traditional techniques often lack clear modal distinctions when grouping features, simply dividing them according to data source or general category. This results in insufficient grouping precision, confusion between different modal features, and an inability to accurately reflect risk information at different levels in ecological product development. For example, when distinguishing between physical and ecological layer features, traditional methods may misclassify some indirect features of the physical environment's impact on the ecosystem, affecting subsequent analysis. The precise modal grouping in this application clearly separates features from different levels such as the physical, ecological, and socio-economic layers, making the features contained in each modal capsule highly homogeneous, providing a foundation for subsequent specialized processing for different modalities. For instance, when analyzing market risks in ecological product development, relevant features can be directly obtained from the socio-economic layer modal capsule, avoiding interference from features at different levels. The clear grouping in this application helps improve the interpretability of features, enabling developers and decision-makers to more clearly understand the risk factors represented by each modality. For example, through physical layer modal capsules, one can intuitively understand the potential impact of physical environmental factors such as temperature and humidity on the development of eco-products.

[0216] 2. This application employs a dynamic pooling method based on an attention mechanism, using a learnable weight matrix W. m Bias vector b m and query vector q m Adaptively assign attention weights α m jThis paper highlights the role of key capsules. When calculating information entropy, a kernel density estimation method is used to estimate the joint probability distribution, more accurately reflecting the relationships between different modal capsule groups. Traditional pooling methods (such as max pooling and average pooling) typically use fixed rules for pooling, neglecting the importance differences between capsules and potentially losing crucial information. When calculating information entropy, traditional methods may use simple frequency statistics, which have poor fitting ability for complex data distributions and cannot accurately reflect the uncertainty and information redundancy between different modalities. The dynamic pooling of this application's attention mechanism can adaptively allocate weights according to the importance of capsules, enabling focus on key information during pooling and improving the quality of feature representation. For example, in the ecological layer modal capsule group, if some capsules represent key species information in the ecosystem, dynamic pooling will give these capsules higher weights, more accurately capturing ecological risk information. The kernel density estimation method in this application can better fit complex data distributions, thus calculating information entropy more accurately. Accurate information entropy helps developers understand the degree of correlation and information redundancy between different modalities, providing a more reliable basis for subsequent feature fusion and risk prediction.

[0217] 3. This application calculates the weight ω of each mode based on information entropy. m The degree of weight adjustment is controlled by adjusting the parameter β. The p-norm is calculated for each capsule, considering the impact of different orders of norms on feature strength, and a comprehensive capsule norm vector n is obtained. m Traditional norm processing typically employs fixed weights or simple normalization methods, neglecting the differences in information entropy between different modalities and failing to adaptively adjust based on the uncertainty and importance of different modalities. Furthermore, traditional methods may only use a single norm order, failing to comprehensively reflect the feature strength of the capsule. This application dynamically adjusts the modal weights based on information entropy, assigning higher weights to modalities with higher uncertainty in ecoproduct development risk prediction, thus more rationally integrating information from different modalities. For example, if the socioeconomic layer has high information entropy, indicating greater uncertainty in that modality, it will be given higher weight in norm processing to highlight its impact on risk prediction. This application uses the p-norm, allowing for the selection of appropriate orders based on different application scenarios, measuring the feature strength of the capsule from multiple dimensions. For example, the p=1 norm emphasizes feature sparsity, while the p=2 norm emphasizes overall feature strength; by selecting different p values, the feature information of the capsule can be more comprehensively reflected.

[0218] 4. This application uses a deep residual network (ResNet) to semantically enhance the concatenated vectors. By learning residual information of input features through residual blocks F(\mathbf{x}_l,\mathbf{W}_l}), it avoids the vanishing gradient problem and improves the network's training performance. Traditional semantic enhancement methods may employ simple multilayer perceptrons (MLPs), which are prone to gradient vanishing or exploding problems as network depth increases, leading to training difficulties and hindering the effective learning of complex semantic information. This application's ResNet can learn deeper feature information through multiple residual blocks, uncovering potential semantic relationships between features of different modalities. In ecological product development risk prediction, it can discover complex interactions between physical, ecological, and socioeconomic features, improving the accuracy of risk prediction. The introduction of residual blocks in this application allows the network to update parameters more stably during training, avoiding the vanishing gradient problem and improving the network's training efficiency and generalization ability. It can converge to the optimal solution faster and has better adaptability to different datasets and tasks.

[0219] Optionally, the step of inputting the ecological development risk feature vector into the development risk quantification model to calculate the ecological development risk score includes: based on the input layer, performing interpolation padding on the ecological development risk feature vector to obtain a continuous standardized feature tensor; based on the dynamic feature enhancement layer, extracting local feature patterns from the continuous standardized feature tensor to obtain a local feature vector; based on the feature relation layer, extracting spatial dependencies from the local feature vector to obtain a spatially enhanced feature vector; based on the multimodal attention fusion layer, performing multi-head attention fusion on the spatially enhanced feature vector to obtain a fused feature vector; based on the global context modeling layer, reshaping the fused feature vector into a sequence form to obtain context features; based on the residual network, performing residual processing on the context features to obtain abstract development risk features; based on the probability distribution prediction layer, mapping the abstract development risk features to the development risk level space to output a development risk probability vector; and based on the decision output layer, judging the confidence level of the development risk probability vector based on the confusion matrix to calculate the ecological development risk score.

[0220] Preferably, in a specific application scenario, when the ecological development risk feature vector is imputed to obtain a continuous standardized feature tensor based on the input layer, let the ecological development risk feature vector be v = [v1, v2, ..., v n ], where n is the vector dimension. When there are missing values ​​in the vector, a linear interpolation imputation method is used. Assume the value at position i is missing, and its preceding and following non-missing values ​​are v and v, respectively. i-k and v i+l (k,l≥1), then fill value for: After padding, the feature vector is standardized to obtain the standardized feature vector v. std The standardized formula is: in The mean, The standard deviation is denoted as .

[0221] Finally, the standardized feature vector is reshaped into a continuous standardized feature tensor T, assuming it is reshaped into a tensor of shape (c, h, w) satisfying c × h × w = n. v: Ecological development risk feature vector, containing multifaceted feature information about ecological development risk obtained in previous steps. n: Dimension of the feature vector, reflecting the number of features. The values ​​obtained after linear interpolation to fill in missing values ​​ensure data integrity. std : Standardized feature vectors, eliminating the influence of different dimensions between features. μ: Mean of the feature vectors, reflecting the average level of the data. σ: Standard deviation of the feature vectors, reflecting the dispersion of the data. T: Continuously standardized feature tensor, facilitating processing in subsequent layers; different dimensions may represent different feature categories or spatial structures of features.

[0222] Preferably, in a specific application scenario, when extracting local feature patterns from a continuously normalized feature tensor based on a dynamic feature enhancement layer to obtain a local feature vector, a deformable convolutional neural network (DCN) is used to extract the local feature patterns. Let the input continuously normalized feature tensor be T, the deformable convolutional kernel be K, and the offset Δp be generated through an additional convolutional layer.

[0223] The output y(p) of deformable convolution is: Where p is the position on the output feature map, K is the number of convolutional kernels, and p k This represents the offset position of the convolution kernel. The output feature map is then subjected to global average pooling to obtain the local feature vector f. local : Where H and W are the height and width of the feature map. T: Continuously normalized feature tensor, used as input to deformable convolution. K: Deformable convolution kernel, used to extract local feature patterns. Δp: Offset, allowing the convolution kernel to adaptively focus on features at different locations, enhancing the ability to extract local features. y(p): Output of deformable convolution, reflecting the response of local features. K: Number of convolution kernels, determining the types of local features extracted. p k : The fixed offset position of the convolution kernel. f local Local feature vectors are compressed into one-dimensional vectors by global average pooling, which facilitates subsequent processing.

[0224] Preferably, in a specific application scenario, when extracting spatial dependencies from local feature vectors based on a feature relationship layer to obtain spatially enhanced feature vectors, a graph neural network (GNN) is used to extract the spatial dependencies between local feature vectors. A graph G = (V, E) is constructed, where nodes V are elements in the local feature vectors, edges E represent the relationships between nodes, and the edge weights w... ij It can be calculated using cosine similarity: The propagation rules of GNN are as follows: in Let d be the feature representation of node i at level l, N(i) be the set of neighboring nodes of node i, and d i Let W be the degree of node i. (l) Let b be the weight matrix of the l-th layer. (l) σ is the bias vector, and σ is the activation function (such as ReLU). After propagation through L layers of GNN, the feature representations of all nodes are concatenated to obtain the spatially augmented feature vector f. space G = (V, E): The constructed graph used to represent the relationships between local eigenvectors. ij The edge weight reflects the similarity between nodes i and j. The feature representation of node i in layer l gradually incorporates information from its neighboring nodes as the GNN layers increase. N(i): The set of neighboring nodes of node i, defining the scope of information propagation. i : The degree of node i, used to normalize edge weights. W (l) : The weight matrix of the l-th layer, used to learn the relationships between nodes. b (l) : The bias vector of the l-th layer, used to help adjust the output result. f space Spatial augmented feature vectors contain the spatial dependencies between local feature vectors.

[0225] Preferably, in a specific application scenario, when performing multi-head attention fusion on the spatial augmented feature vector based on the multimodal attention fusion layer to obtain the fused feature vector, a multi-head attention mechanism is used to fuse the spatial augmented feature vector. Let the spatial augmented feature vector be f. space Project them onto the query (Q), key (K), and value (V) matrices respectively: Q = f space W Q K = f space W K V = f space W V W Q W K and W V Let Q, K, and V be the learnable projection matrix. Divide Q, K, and V into h heads, and calculate the attention for each head as follows: Where d kLet f be the dimension of the key. The attention results from h heads are concatenated, and then a linear transformation is applied to obtain the fused feature vector f. fusion :f fusion =Concat(Attention1,Attention2,…,Attention h W O W O This is the output projection matrix. space : Spatial augmented feature vectors, used as input for the multi-head attention mechanism. Q, K, V: Query, key, and value matrices, which map the input vectors to different spaces through projection operations. W Q W K W V : The learnable projection matrix used to adjust the direction and scale of the projection. h: The number of heads; different heads can focus on different feature subspaces. d k The dimension of the key is used to scale the attention score and avoid gradient vanishing or exploding. Attention i The attention result for the i-th head reflects the degree of attention given to different feature subspaces. W O Output projection matrix, which linearly combines the results of multi-head attention. fusion : The fused feature vectors integrate information from different feature subspaces.

[0226] Preferably, in a specific application scenario, when reshaping the fused feature vector into a sequence form to obtain context features in the global context modeling layer, the fused feature vector f is... fusion Reshape into a sequence of shape (T,d), where T is the sequence length and d is the feature dimension of each time step.

[0227] The sequence is modeled using a Long Short-Term Memory (LSTM) network. The LSTM cell state update formula is as follows:

[0228] i t =σ(W ii x t +W hi h t-1 +b i ), f t =σ(W if x t +W hf h t-1 +b f ), g t =tanh(W ig x t +W hg h t-1 +b g ), o t=σ(W io x t +W ho h t-1 +b o ), C t =f t ⊙C t-1 +i t ⊙g t h t =o t ⊙tanh(C t ). Where x t h is the input at time step t. t-1 C is the hidden state from the previous moment. t-1 i represents the cell state at the previous time step. t f t o t These are the input gate, forget gate, and output gate, respectively. t Let h be the candidate unit state, ⊙ be the element-wise multiplication, W be the weight matrix, b be the bias vector, σ be the sigmoid function, and tanh be the hyperbolic tangent function. Finally, let h be the hidden state at the last time step. T As context feature f context f fusion : The feature vectors are fused and reshaped into a sequence form as input to the LSTM. T: Sequence length, determining the number of time steps processed by the LSTM. d: Feature dimension at each time step. x t : The input at time step t, a sequence representation from the fused feature vectors. h t-1 The hidden state from the previous moment, used to pass on historical information. (C) t-1 The previous unit state, used for long-term memory information. t f t o t Input gate, forget gate, and output gate control the inflow, retention, and output of information. t : Candidate unit state, used to update the unit state. W: Weight matrix, used to learn patterns and relationships in the sequence. b: Bias vector, used to adjust the output. f context Contextual features contain global contextual information of the fused feature vectors.

[0229] Preferably, in a specific application scenario, when obtaining abstract development risk features by performing residual processing on context features based on a residual network, a residual network (ResNet) is used to process the context features f. contextProcessing is then performed. Let the input of the residual block be x, and the output be y. The structure of the residual block is: y = x + F(x), where F(x) is the residual function, for example, F(x) = σ(W2σ(W1x + b1) + b2), W1 and W2 are weight matrices, b1 and b2 are bias vectors, and σ is the activation function (such as ReLU). After L... res After processing each residual block, the abstract development risk characteristic f is obtained. abstract .

[0230] f context : Contextual features, used as input to the residual network. x: Input to the residual block, initially the contextual features. y: Output of the residual block, retaining input information through residual connections. F(x): Residual function, used to learn the residual information of the input features. W1, W2: Weight matrices, used to learn the abstract representation of the features. b1, b2: Bias vectors, used to adjust the output results. L res The number of residual blocks controls the depth of the network. abstract Abstract development risk characteristics, and after processing with residual networks, higher-level abstract features are extracted.

[0231] Preferably, in a specific application scenario, when mapping abstract development risk features to a development risk level space in the probability distribution-based prediction layer to output a development risk probability vector, a fully connected layer is used to map the abstract development risk features f abstract Mapping to the development risk level space. Assume there are m development risk levels, and the output of the fully connected layer is: z = f abstract W out +b out W out To output the weight matrix, b out This is the output bias vector. The softmax function is used to transform z into a probability distribution, yielding the development risk probability vector p:

[0232] f abstract : Abstract development risk characteristics, used as input to the fully connected layer. m: The number of development risk levels, representing different degrees of risk. z: The output of the fully connected layer, the unnormalized score of the development risk level. W out Output a weight matrix used to map abstract features to a development risk level space. out : Output bias vector to help adjust the output results. p: Develop risk probability vector, where each element represents the probability of the corresponding risk level.

[0233] Preferably, in a specific application scenario, when calculating the ecological development risk score by judging the confidence level of the development risk probability vector based on the confusion matrix at the decision output layer, let the confusion matrix be M, and its elements M ijThis represents the number of samples where the true risk level is i but is predicted to be risk level j.

[0234] First, calculate the confidence level (conf) for each risk level. j : Then, the ecosystem development risk score S is calculated based on the development risk probability vector p and the confidence level conf: Where r j represents the weight of the j-th risk level, reflecting the importance of that risk level. M: Confusion matrix, used to evaluate the model's predictive performance. ij The elements of the confusion matrix record the predictions for different risk levels. j : The confidence level of the j-th risk level, reflecting the reliability of the prediction for that risk level. p: Developed risk probability vector, providing the predicted probability for each risk level. r j : The weight of the j-th risk level, the importance of different risk levels is determined based on the actual application scenario. S: Ecosystem development risk score, which comprehensively considers prediction probability, confidence level and risk level weight.

[0235] The above-mentioned scheme for calculating the ecological development risk score has the following technical advantages:

[0236] 1. This application employs a series of innovative feature processing methods, such as deformable convolution, graph neural networks, and multi-head attention mechanisms, to process and enhance the feature vectors of ecological development risks at multiple levels. Traditional techniques, using simple convolution operations and single fully connected layers, cannot fully explore the complex relationships and spatial dependencies between features. For example, traditional convolution operations can only extract features at fixed locations and scales, making it difficult to capture subtle changes and potential patterns in the complex and ever-changing risk features of ecological development. The deformable convolution of this application can adaptively adjust the position of the convolution kernel, capturing local feature patterns more flexibly, and extracting features more accurately for irregularly distributed risk factors in the ecological environment (such as local ecological damage areas). Graph neural networks can explore the spatial dependencies between features, considering the mutual influence between different elements in the ecosystem, such as the correlation between ecological indicators in different regions, thus providing more comprehensive risk information. The multi-head attention mechanism of this application can focus on features from multiple perspectives, fusing information from different feature subspaces and improving the expressive power of features. In ecological development risk prediction, it can more accurately reflect the weights and interactions between different risk factors, providing a more reliable basis for subsequent risk scoring.

[0237] 2. This application uses a Long Short-Term Memory (LSTM) network to perform global context modeling on the fused feature vectors, considering the sequence information and temporal dependencies of the features. Traditional methods ignore the time-series characteristics of features, treating data as independent samples. In ecological development, risk factors often have temporal continuity and cumulative effects, which traditional methods cannot effectively capture. The LSTM in this application can process long-sequence data, effectively preserving and transmitting historical information through forgetting gates, input gates, and output gates. In ecological development risk prediction, the impact of changes in the ecological environment over a period of time on current risks can be considered, such as the cumulative effect of climate change on ecosystem stability over the past few years, thus more accurately predicting future risks. This application utilizes contextual information for modeling, which can reduce the impact of short-term fluctuations or noise, improving the stability and reliability of risk prediction. In the process of developing ecological products, long-term risk trends can be assessed more accurately, providing more stable support for decision-making.

[0238] 3. This application uses a confusion matrix to assess the confidence level of the development risk probability vector and calculates an ecological development risk score by combining risk level weights, comprehensively considering both the accuracy of the prediction and the importance of different risk levels. Traditional methods simply determine the risk level based on the predicted probability, ignoring the reliability of the prediction and the differences between different risk levels. For example, for some high-risk situations with low prediction accuracy, traditional methods may not be able to accurately assess the actual risk. This application, by introducing confidence levels and risk level weights, can more comprehensively assess ecological development risks. Higher weights are given to risk levels with high prediction accuracy, while considering the differences in importance between different risk levels, making the risk score more consistent with reality. In ecological product development decisions, high-risk areas and key risk factors can be identified more accurately, allowing for corresponding preventative and response measures. Based on accurate risk scores, decision-makers can formulate ecological development plans more scientifically, allocate resources rationally, and reduce risks during the development process. For example, for projects with high risk scores, monitoring and protection measures can be increased to ensure the sustainable development of the ecological environment.

[0239] 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 of predicting the risk of developing an ecological product, characterized in that, The method comprises the following steps: acquiring multi-source heterogeneous data of ecological products, which comprises at least one of the following: environmental physical data, ecological remote sensing data, biodiversity data, social and economic data; stream processing the multi-source heterogeneous data to form a multi-source heterogeneous data stream; multi-modal feature engineering construction is performed on the multi-source heterogeneous data stream to form physical layer feature data, ecological layer feature data and social and economic layer feature data, which comprises the following steps: linear regression analysis is performed on the multi-source heterogeneous data stream to obtain a trend item, and Fourier transform is performed on the trend item to form the physical layer feature data; ecological remote sensing features are obtained by extracting spectral features, texture features and shape features from the multi-source heterogeneous data stream; biodiversity features are obtained by extracting species richness features, species distribution features and ecological relationship features from the multi-source heterogeneous data stream; the ecological remote sensing features and the biodiversity features are fused to form the ecological layer feature data; population statistics features, economic development features and policy and regulation features are extracted from the multi-source heterogeneous data stream, and spatial feature mining is performed on the population statistics features, the economic development features and the policy and regulation features to form the social and economic layer feature data; multi-modal feature fusion is performed on the physical layer feature data, the ecological layer feature data and the social and economic layer feature data to generate ERIFM ecological development risk comprehensive feature data, which comprises the following steps: local mode features in the physical layer feature data are extracted based on a 1D convolution capsule layer to generate physical capsules; spatial hierarchical features in the ecological layer feature data are extracted based on a 2D convolution capsule layer to generate ecological capsules; spatial dependence adjustment in the social and economic layer feature data is extracted based on a graph capsule network to generate economic capsules; global development risk correlation of the physical capsules, the ecological capsules and the economic capsules is calculated to generate a fusion weight matrix; multi-modal feature fusion is performed on the physical layer feature data, the ecological layer feature data and the social and economic layer feature data based on the fusion weight matrix to generate the ERIFM ecological development risk comprehensive feature data; vectorization is performed on the ERIFM ecological development risk comprehensive feature data to obtain an ecological development risk feature vector; the ecological development risk feature vector is input into a development risk quantification model to calculate an ecological development risk score.

2. The method of claim 1, wherein the ecological product development risk prediction method is characterized by, The method further comprises the following steps: time window difference, anomaly detection and feature derivation processing are performed on the environmental physical data to obtain environmental time-series feature data; radiation correction, spatial registration and semantic segmentation are performed on the ecological remote sensing data to obtain RC_Semantic_Imagery radiometrically corrected semantic images; species standardization, spatio-temporal annotation and abundance estimation are performed on the biodiversity data to obtain Bio_TS_Data biological spatio-temporal data; text structuring, spatial mapping and index normalization are performed on the social and economic data to obtain Grid_Eco_Indicators grid economic indicator data.

3. The method of claim 1, wherein the ecological product development risk prediction method is characterized by, The stream processing of the multi-source heterogeneous data to form a multi-source heterogeneous data stream comprises the following steps: The environment time sequence feature data is two-dimensionally blocked according to a set time window and a space region to obtain space-time blocked data; The vectorization is performed on the radiometric semantic image to obtain a vector element set, and a space index is established on the vector element set to obtain space-indexed vector data; The relationship modeling is performed on the biological space-time data to obtain an ecological relationship graph, and a path index is established on the ecological relationship graph to obtain indexed ecological relationship data; The dimension modeling is performed on the grid economic index data to obtain a star mode data set, and a dimension index is established on the star mode data set to obtain indexed economic index data; The space-time blocked data, the space-indexed vector data, the indexed ecological relationship data and the indexed economic index data are aligned, and the aligned space-time blocked data, the space-indexed vector data, the indexed ecological relationship data and the indexed economic index data are respectively converted to obtain an environment feature vector group, a space feature vector group, an ecological feature vector group and an economic feature vector group; The environment feature vector group, the space feature vector group, the ecological feature vector group and the economic feature vector group are fused to obtain a fused feature vector. The ecological development risk feature vector is obtained by vectorizing the ERIFM ecological development risk comprehensive feature data, including:

4. The method of claim 1, wherein the ecological product development risk prediction method is characterized by, The ERIFM ecological development risk comprehensive feature data is capsule grouped according to the mode to obtain a plurality of mode capsule groups; the information entropy between the mode capsule groups is calculated by dynamic pooling of the mode capsule groups; Based on the information entropy, the norm of each mode capsule group is processed to obtain a capsule norm vector; The capsule norm vector is semantically enhanced to generate an ecological development risk feature vector. The ecological development risk feature vector is input into the development risk quantification model to calculate the ecological development risk score, including:

5. The method of claim 1, wherein the ecological product development risk prediction method is characterized by, Based on the input layer, the ecological development risk feature vector is differentially filled to obtain a continuous standardized feature tensor; Based on the dynamic feature enhancement layer, the local feature mode is extracted from the continuous standardized feature tensor to obtain a local feature vector; Based on the feature relationship layer, the spatial dependence of the local feature vector is extracted to obtain a spatial enhanced feature vector; Based on the multi-modal attention fusion layer, the spatial enhanced feature vector is multi-head attention fused to obtain a fused feature vector; Based on the global context modeling layer, the fused feature vector is reshaped into a sequence form to obtain a context feature; Based on the residual network, the context feature is residual processed to obtain an abstract development risk feature; Based on the probability distribution prediction layer, the abstract development risk feature is mapped into a development risk level space to output a development risk probability vector; Based on the decision output layer, the confidence of the development risk probability vector is judged based on the confusion matrix to calculate the ecological development risk score. ​

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