Ecological product development risk prediction method

Abstract

The invention provides a development risk prediction method of an ecological product. The method comprises the following steps: acquiring multi-source heterogeneous data of the ecological product; performing streaming 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 economic layer feature data; performing multi-modal feature fusion on the physical layer feature data, the ecological layer feature data and the social economic layer feature data to generate ERIFM ecological development risk comprehensive feature data; vectorizing 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. According to the scheme, the risk can be predicted more accurately, the essential characteristics of the ecological product development risk can be deeply analyzed, and the risk assessment is more comprehensive and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent processing, and particularly to a method for predicting the development risks of ecological products. Background Art

[0002] In today's society, the development of ecological products is of crucial significance for achieving economic sustainable development and ecological environment protection. Ecological products not only include material products provided by natural ecosystems, such as wood, water resources, etc., but also cover ecological service functions, such as soil and water conservation, climate regulation, etc. With the continuous improvement of people's requirements for the quality of the ecological environment and the rapid development of the ecological economy, the development projects of ecological products are increasing day by day, covering multiple fields such as ecological agriculture, ecological tourism, and ecological restoration.

[0003] However, the development of ecological products faces many uncertainties and risks. From the perspective of the natural environment, ecological systems 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 climate events may damage the production facilities and crops of ecological agriculture, resulting in a decrease in agricultural product yields; water pollution may affect the water quality and landscape of ecological tourism scenic spots, reducing the attraction of tourists.

[0004] At the social and economic level, fluctuations in market demand, changes in policies and regulations, and the speed of technological innovation will also bring risks to the development of ecological products. The market demand for ecological products may be affected by factors such as consumer preferences and economic situations. If the developed ecological products cannot meet the market demand, it may lead to product overstock and economic losses. Adjustments in policies and regulations may put forward new requirements for the development standards, approval processes, etc. of ecological products, increasing the development costs and difficulties. In addition, the continuous emergence of new technologies brings both opportunities and challenges to the development of ecological products. If appropriate technologies cannot be adopted in a timely manner, the development projects may be at a disadvantage in the market competition.

[0005] At present, in the process of ecological product development, there is a lack of a comprehensive and effective risk prediction means. Existing risk assessment methods often only consider single factors or a few factors, and it is difficult to comprehensively consider complex factors in multiple aspects such as ecology, environment, society, and economy, resulting in inaccurate and unreliable prediction of the development risks of ecological products, and unable to provide scientific decision-making basis for developers, 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 development risks of ecological products to ensure the smooth progress of ecological product development and the healthy development of the ecological economy. Summary of the Invention

[0006] To solve the above technical problems, the present application provides a method for predicting the development risk of ecological products, so as to at least solve or alleviate the problems existing in the above prior art.

[0007] To achieve the above object, according to one aspect of the present application, there is provided a method for predicting the development risk of ecological products, which includes:

[0008] Obtain multi-source heterogeneous data of ecological products, which includes at least one of the following: environmental physical data, ecological remote sensing data, biodiversity data, and social economic data;

[0009] Perform streaming processing on the multi-source heterogeneous data to form a multi-source heterogeneous data stream;

[0010] Perform multi-modal feature engineering construction on the multi-source heterogeneous data stream to form physical layer feature data, ecological layer feature data, and social economic layer feature data;

[0011] Perform multi-modal feature fusion on the physical layer feature data, ecological layer feature data, and social economic layer feature data to generate ERIFM comprehensive ecological development risk feature data;

[0012] Vectorize the ERIFM comprehensive ecological development risk feature data to obtain an ecological development risk feature vector;

[0013] Input the ecological development risk feature vector into a development risk quantification model to calculate an ecological development risk score.

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

[0015] ① By obtaining multi-source heterogeneous data of ecological products, covering environmental physical data, ecological remote sensing data, biodiversity data, social economic data, etc., this solution can comprehensively consider various factors such as the natural environment and social economy in the process of ecological product development. Compared with the existing risk assessment methods that only consider single or a few factors, this comprehensive data acquisition method can capture more information affecting the development risk of ecological products, thus predicting risks more accurately. For example, by combining environmental physical data and social economic data, the impacts of natural environmental changes and market demand fluctuations on the development of ecological products can be considered simultaneously, avoiding inaccurate risk prediction due to neglecting some important factors.

[0016] ② Streaming multi-source heterogeneous data to form multi-source heterogeneous data streams, and then construct multimodal feature engineering to obtain physical layer, ecological layer and socio-economic layer feature data. This series of operations can effectively process and extract features from massive and complex data. Streaming processing can process data in real time and efficiently to ensure the timeliness of data; multimodal feature engineering can mine the potential information of data from different angles, so that subsequent risk analysis can be based on more valuable feature data. This helps to deeply analyze the essential characteristics of ecological product development risks and improve risk analysis capabilities.

[0017] ③ Multimodal feature fusion of the characteristic data of the physical layer, ecological layer and socio-economic layer is performed to generate ERIFM ecological development risk comprehensive feature data. This fusion method can integrate the characteristic information at different levels to form a more comprehensive and representative comprehensive feature, reflecting the overall situation of ecological product development risks. It overcomes the limitation of existing methods that cannot comprehensively consider multiple factors, making risk assessment more comprehensive and accurate.

[0018] ④ Vectorize the comprehensive characteristic data of ERIFM ecological development risk to obtain the ecological development risk characteristic vector, and input it into the development risk quantification model to calculate the ecological development risk score. Vectorization allows the data to exist in a form that is more suitable for model processing, which is convenient for the quantitative model to calculate. The quantitative model can accurately calculate the ecological development risk score based on the characteristic vector, providing developers with a specific and intuitive risk quantification indicator, thereby providing a scientific basis for ecological product development decision-making and improving the success rate and sustainability of ecological product development projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a method for predicting development risks of an ecological product according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] Figure 1 This is a flow chart of a method for predicting the development risk of an ecological product according to an embodiment of the present application. Figure 1As shown in the figure, it includes: obtaining multi-source heterogeneous data of ecological products, which includes at least one of the following: environmental physical data, ecological remote sensing data, biodiversity data, and socioeconomic 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 socioeconomic layer feature data; performing multi-modal feature fusion on the physical layer feature data, ecological layer feature data, and socioeconomic layer feature data to generate comprehensive ERIFM ecological development risk feature data; vectorizing the comprehensive ERIFM 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 obtaining multi-source heterogeneous data of ecological products, let the environmental physical data set be the ecological remote sensing data set be the biodiversity data set be the socioeconomic data set be The multi-source heterogeneous data set D can be represented as the combination of their power sets: where i ep , i er , i bd , i se ∈ {0, 1}. When i = 0, it means not selecting this type of data, and when i = 1, it means selecting this type of data.

[0022] For each parameter in the above solution, its physical function is as follows: The k-th data item in the environmental physical data may be specific measurement values such as temperature and humidity at a certain location and time. The k-th data item in the 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 the biodiversity data, such as the population quantity and distribution range of a certain species. The k-th data item in the socioeconomic data, such as the GDP value and population density of a certain region. n ep , n er , n bd , n se : respectively represent the quantities of environmental physical data, ecological remote sensing data, biodiversity data, and socioeconomic data.

[0023] Preferably, in a specific application scenario, when performing streaming processing on multi-source heterogeneous data to form a multi-source heterogeneous data stream, it is assumed that the data is processed in a time series of 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 preprocessing function for ecological remote sensing data be f er , the preprocessing function for biodiversity data be f bd , and the preprocessing function for socioeconomic data be f se .

[0024] At time step t, the processed data of each type are: 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] The multi-source heterogeneous data stream S t can be obtained by weighted concatenation:

[0026]

[0027] where w i is the weight coefficient, and are the components of the corresponding data in the i-th dimension, respectively.

[0028] In the above solution, the descriptions of each parameter are as follows: f ep , f er , f bd , f se : They are the preprocessing functions for different types of data, which may include operations such as data cleaning, normalization, and feature extraction. w i : The weight coefficient, which is used to adjust the importance of different types of data in the multi-source heterogeneous data stream.

[0029] Preferably, in a specific application scenario, when performing multi-modal feature engineering construction on the multi-source heterogeneous data stream, a 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 where i = 1, …, m, j = 1, …, n, x ik is the independent variable (such as time, etc.), ∈ij is the error term.

[0030] Use Ridge Regression to solve the regression coefficient β, and the objective function is: where λ is the regularization parameter. The regression coefficient is obtained by minimizing J(β) and then the trend term T is obtained.

[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 forming the ecological layer feature data, extract the spectral feature F from the multi-source heterogeneous data stream S s , the texture feature F t , the shape feature F sh A convolutional neural network (CNN) can be used. Let the feature map X be obtained after passing S through l layers of CNN l , then: where f is the activation function, W is the convolutional kernel, b is the bias, K is the number of convolutional kernels, and M×N is the size of the convolutional kernel. Thus, the ecological remote sensing feature F is obtained erf = [F s , F t , F sh .

[0033] Extract the species richness feature F from S sr , the species distribution feature F sd , the ecological relationship feature F er A graph neural network (GNN) can be used. Let the graph G = (V, E), the node feature matrix be H, and the new node feature matrix H be obtained after passing through t layers of GNN t :

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

[0035] Fuse the ecological remote sensing feature and the biodiversity feature, and use the attention mechanism: where e i is the attention score and can be calculated through a fully connected layer.

[0036] Preferably, in a specific application scenario, when forming the socio-economic layer feature data, the demographic features F are extracted from the multi-source heterogeneous data stream S pd , economic development features F ed , policy and regulation features F pr . After that, spatial autocorrelation analysis (such as Moran's I index) is used for spatial feature mining. Let x i be the feature value of the i-th region, and W ij be the spatial weight matrix. Then Moran's I index is: where n is the number of regions, and the socio-economic layer feature data F is obtained through spatial autocorrelation analysis se .

[0037] In the above solution, the descriptions of each parameter are as follows: β kj : The regression coefficient in the linear regression model. λ: The regularization parameter of ridge regression. The convolution kernel weights in the CNN. b l : The bias in the CNN. W t : The weight matrix in the GNN. b t : The bias in the GNN. α i : The attention weight. W ij : The spatial weight matrix.

[0038] Preferably, in a specific application scenario, when performing multi-modal feature fusion on the physical layer, ecological layer, and socio-economic layer feature data,

[0039] based on the 1D convolutional capsule layer, the local pattern features in the physical layer feature data F p are extracted. Let F p be a vector of length L. After 1D convolution operation: where K is the convolution kernel size, is the convolution kernel weight, and b i is the bias, and the physical capsule C p is obtained.

[0040] Based on the 2D convolutional capsule layer, the spatial hierarchical features in the ecological layer feature data F e are extracted. Let F e be an M×N matrix. After 2D convolution operation: where K1×K2 is the convolution kernel size, is the convolution kernel weight, and b mn is the bias, and the ecological capsule C e is obtained.

[0041] Based on the graph capsule network, the socio-economic layer feature data F seFor the spatial dependence adjustment in it, let the graph G=(V, E), the node feature matrix be H, and after the graph capsule network operation:

[0042] Thus, the economic capsule C is obtained se .

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

[0044] Preferably, in a specific application scenario, during multi-modal feature fusion, based on the fusion weight matrix W, the feature data of the physical layer, ecological layer, and socio-economic layer are subjected to multi-modal feature fusion to generate the comprehensive ecological development risk feature data F of ERIFM erifm : The descriptions of the various parameters in the above scheme are as follows: The convolution kernel weight in the 1D convolutional capsule layer. b i : The bias in the 1D convolutional capsule layer. The convolution kernel weight in the 2D convolutional capsule layer. b mn : The bias in the 2D convolutional capsule layer. σ: The Gaussian kernel bandwidth.

[0045] Preferably, in a specific application scenario, when vectorizing the comprehensive ecological development risk feature data of ERIFM, the comprehensive ecological development risk feature data of ERIFM is grouped into capsule groups according to the modality, and several modality capsule groups G1, G2,... are obtained m .

[0046] Perform dynamic pooling on the modality capsule groups, using Adaptive MaxPooling. Let the modality capsule group G k be a matrix of P×Q, and after adaptive max pooling, a matrix G of size P′×Q′ is obtained k′ : where Ω pq is the corresponding pooling area. Calculate the information entropy H between the modality capsule groups. Assume that the modality capsule group G k has n k elements, and its probability distribution is Then the information entropy is: The information entropy of all modality capsule groups is H = [H1, H2,..., H m .

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

[0048] Among them

[0049] Preferably, in a specific application scenario, when semantic enhancement is performed, the capsule norm vector N is semantically enhanced using a long short-term memory network (LSTM). Let N be a sequence of length T, passing through the LSTM unit:

[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 are the input gate, forget gate, and output gate respectively, g t is the candidate memory unit, c t is the memory unit, h t is the hidden state, W is the weight matrix, b is the bias, and ⊙ is element-wise multiplication. Finally, the ecological development risk feature vector V = h T .

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

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

[0053] Based on the input layer, perform difference filling on the ecological development risk feature vector V using Lagrange interpolation. Let the positions of the missing values in V be x0, and the positions of the known values be x1, x2, …, x n , and the corresponding values be y1, y2, …, y n , then the interpolation formula is: where Obtain the continuous standardized feature tensor T1.

[0054] Preferably, in a specific application scenario, during local feature extraction, based on the dynamic feature enhancement layer, extract local feature patterns from the continuous standardized feature tensor T1 using depthwise separable convolution. Let T1 be a tensor of C×H×W, and after the depthwise separable convolution operation: Obtain the local feature vector V1.

[0055] Preferably, in a specific application scenario, during spatial dependence extraction, based on the feature relationship layer, perform spatial dependence extraction on the local feature vector V1 using the graph attention network (GAT). Let the graph G = (V, E), the node feature matrix be H, and after passing through the GAT layer:

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

[0057] Preferably, in a specific application scenario, based on the multi-modal attention fusion layer, perform multi-head attention fusion on the spatially enhanced 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 is the dimension of the key vector.

[0058] Preferably, in a specific application scenario, when generating context features, based on the global context modeling layer, the fused feature vector V3 is reshaped into a sequence form and the encoder layer of the Transformer is used. Let V3 be a d-dimensional vector, and through the multi-head attention and the feed-forward network:

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

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

[0061] Preferably, in a specific application scenario, when predicting the probability distribution, based on the probability distribution prediction layer, the abstract development risk feature A is mapped into the development risk level space, and the Gaussian mixture model (GMM) is used. Let A be a d-dimensional vector, and the probability density function of GMM is:

[0062] where π k is the mixing coefficient, is the Gaussian distribution, μ k is the mean vector, and ∑ k is the covariance matrix. The parameters are estimated by the expectation maximization (EM) algorithm to obtain the development risk probability vector P.

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

[0064] The descriptions of the various parameters in the above solutions are as follows: x0, x1,..., x n : The positions in the Lagrangian interpolation. y0, y1,..., y n : The values in the Lagrangian interpolation. The convolution kernel weights in the separable convolution. a: The attention parameter vector in the graph attention network. W i Q , W i K,W i V ,W O : The weight matrix in the multi-head attention mechanism. π k ,μ k ,∑ k : The parameters in the Gaussian mixture model. M ij : The elements in the confusion matrix.

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

[0066] 1. When obtaining multi-source heterogeneous data of ecological products, the power set combination method is used to represent the multi-source heterogeneous data set This method can comprehensively consider all possible combinations of different types of data and flexibly select data according to actual needs. The traditional technology only focuses on single-type data or simply concatenates several types of data together, lacking a comprehensive consideration of the possibility of data combination. In this application, the power set combination method can accurately select the required data types according to the characteristics of different ecological product development projects, avoiding data missing or redundancy, improving the pertinence and effectiveness of data, and thus providing more accurate basic data for subsequent risk prediction.

[0067] 2. In this application, in the streaming processing, the weighted concatenation method is used to obtain the multi-source heterogeneous data stream S t , and different preprocessing functions f ep ,f er ,f bd ,f se are used to process different types of data. At the same time, a weight coefficient w i is introduced in the calculation process to adjust the importance of different types of data. Traditional streaming processing may simply concatenate data in sequence without considering the importance difference of different types of data. In this application, the weighted concatenation method can assign different weights according to the impact degree of data on ecological development risks, highlighting the role of key data and making the multi-source heterogeneous data stream better reflect the essential characteristics of ecological development risks. In addition, different preprocessing functions can perform personalized processing according to the characteristics of different types of data, improving the accuracy and efficiency of data processing.

[0068] 3. In the extraction of physical layer feature data in this application, ridge regression is used to solve the linear regression coefficients, and the discrete Fourier transform (DFT) is performed on the trend term. Ridge regression solves the overfitting problem in linear regression by introducing a regularization parameter λ, and DFT can convert time-domain data into frequency-domain data to extract the frequency characteristics of the data. Traditional linear regression methods are easily affected by data noise and multicollinearity, resulting in inaccurate estimation of regression coefficients. The regularization effect of ridge regression can effectively reduce these effects and improve the stability and generalization ability of the regression model. DFT can capture the 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 in this application, a convolutional neural network (CNN) is used to extract spectral, texture, and shape features, a graph neural network (GNN) is used to extract biodiversity features, and feature fusion is performed through an attention mechanism. CNN can automatically extract local features in image data, GNN is suitable for processing data with a graph structure, and the attention mechanism can assign weights according to 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. CNN and GNN can automatically learn the features in the data, improving the accuracy and efficiency of feature extraction. The introduction of the attention mechanism makes the fused features more capable of highlighting important information and enhances the representation ability of ecological layer feature data for ecological development risks.

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

[0071] 6. In the multi-modal feature fusion of this application, a capsule network is used to extract features at different levels, and a Gaussian kernel function is used to calculate the correlation to generate a fusion weight matrix And perform weighted fusion. Capsule networks can better capture the hierarchical structure and spatial relationships of data, and the Gaussian kernel function can measure the similarity between different features. Traditional feature fusion methods simply concatenate features from different levels 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 comprehensive feature data of ERIFM ecological development risks.

[0072] 7. In the feature vectorization process of this application, adaptive max pooling is used for dynamic pooling, L p -norm processing is performed through the L p -norm, and a long short-term memory network (LSTM) is used for semantic enhancement. Adaptive max pooling can automatically adjust the pooling area according to the characteristics of the data. The L p -norm can standardize the features, and the LSTM can process sequential data and capture the temporal dependence of the data. Traditional pooling methods have a fixed pooling area and cannot adapt to the changes of different data. Adaptive max pooling can flexibly adjust the pooling area according to the actual situation of the data and retain more important information. The use of the L

[0073] 8. In the development risk quantification model of this application, Lagrange interpolation is used for difference filling, separable convolution is used to extract local features, a graph attention network (GAT) is used to extract spatial dependencies, multi-head attention fusion and Transformer encoder layers are used for context modeling, and a Gaussian mixture model (GMM) is used for probability distribution prediction. Finally, confidence judgment is performed based on the confusion matrix. Traditional risk quantification models use simple interpolation methods and cannot accurately fill missing values. Separable convolution can reduce the computational amount compared to traditional convolution and improve the training efficiency of the model. The graph attention network can better capture the spatial dependence of the data, and multi-head attention fusion and Transformer encoder layers can fully mine the context information of the data. The Gaussian mixture model can more flexibly fit the probability distribution of the data and improve the accuracy of risk prediction. The use of the confusion matrix can perform confidence judgment on the prediction results and provide a more reliable basis for decision-making.

[0074] Optionally, the method further includes: performing time window difference, anomaly detection, and feature derivation processing on environmental physical data to obtain environmental time-series feature data; performing radiometric calibration, spatial registration, and semantic segmentation on ecological remote sensing data to obtain radiometrically calibrated semantic imagery; performing species standardization, spatio-temporal annotation, and abundance estimation on biodiversity data to obtain bio-temporal data; and performing text structuring, spatial mapping, and index normalization on social and economic data to obtain grid economic indicator data.

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

[0076] For the time window difference 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 . The time window difference d t is calculated as follows:

[0077] w: The time window size, which determines the number of historical data participating in the difference calculation. For example, in ecological product development, if short-term environmental changes on the product are concerned, a smaller w value can be set; if long-term trends are considered, w is increased. e i : The measured value of environmental physical data at time point i, such as the air temperature value at a certain moment. d t : The time window difference at time point t, which reflects the degree of deviation of the current data from the average data within the time window.

[0078] Preferably, in a specific application scenario, when performing anomaly detection, the 3σ principle based on statistics is used for anomaly detection. First, calculate the mean μ t and standard deviation σ t of the data within the time window: If |e t - μ t | > 3σ t , then e t is determined as an outlier.

[0079] μ t : The mean of environmental physical data within the time window, representing the average level of the data during this time period. σt : The standard deviation of environmental physical data within a time window measures the degree of data dispersion. In the scenario of ecological product development, a larger standard deviation may imply greater environmental fluctuations, which can affect the product development risk. The 3 in the 3σ principle is an empirical threshold used to judge whether the data is abnormal. When the deviation of a data point from the mean exceeds 3 times the standard deviation, that data point is considered potentially abnormal, possibly due to measurement errors or sudden environmental events. This is important for predicting the risk of ecological product development because abnormal environmental data may directly impact the quality or feasibility of the product.

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

[0081] (when e t-1 ≠0), f new,t : The new feature derived at time point t, such as the change rate of environmental physical quantities. In ecological product development, the change rate of environmental factors may reflect potential risks more than absolute values. For example, too rapid temperature changes may affect the growth cycle of ecological agricultural products. e t and e t-1 : Measured values of environmental physical data at adjacent time points.

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

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

[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 the spatial position (x, y) and band b, which is the data collected by the original sensor. L0(b): The offset of band b, used to correct the zero error of the sensor, etc. In the development of ecological products, accurate zero correction can ensure accurate perception of the ecological environment. For example, for remote sensing images monitoring vegetation growth, correct radiometric correction helps accurately judge the health status of vegetation. G(b): The gain of band b, which reflects the amplification ability of the sensor for radiation in different bands. The gain settings for different bands affect the sensitivity of the image to different ground object features and are crucial for identifying specific ecological elements in the development of ecological products. L c (x, y, b): The corrected radiance, providing more accurate basic data for subsequent analysis.

[0087] Preferably, in a specific application scenario, during spatial registration, assume there is 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. Using a feature - based matching registration method, by calculating the corresponding relationships between feature points, an affine transformation model is used: where (x, y) are the pixel coordinates in the original image, (x′, y′) are the pixel coordinates in the registered image, a ij is a parameter of the transformation matrix, t x and t y are translation parameters.

[0088] (x, y): The original coordinates of the pixels in the ecological remote sensing image. In the scenario of ecological product development, these coordinates correspond to positions in the actual geographical space. (x′, y′): The coordinates of the pixels in the reference coordinate system after spatial registration, enabling remote sensing images from different sources or at different times to be compared and analyzed in the same geographical coordinate system, which helps monitor the dynamic changes in the ecological product development area. a ij and t x , t y : Affine transformation parameters, calculated through the feature - matching algorithm, used to describe transformations such as rotation, scaling, and translation of the image to achieve accurate spatial registration.

[0089] Preferably, in a specific application scenario, during semantic segmentation, a fully convolutional neural network (FCN) in deep learning is used for semantic segmentation. Assume the input image after radiometric correction and spatial registration is I. After a series of convolution, pooling, and de - convolution operations, the probability map P(c|x, y) of each pixel belonging to different semantic classes is output, where c represents the semantic class (such as vegetation, water body, bare land, etc.). P(c|x, y) = softmax(F FCN (I)(x, y)), where F FCNIt is a fully convolutional neural network function.

[0090] I: The ecological remote sensing image data after radiometric correction and spatial registration, which is used as the input of the semantic segmentation model. P(c|x,y): The probability that the pixel at the spatial position (x,y) belongs to the semantic category c. In the development of ecological products, through semantic segmentation, the distribution of different ecological elements can be quickly identified, such as determining the vegetation coverage area suitable for eco-tourism development or the cultivated land range for ecological agriculture. Softmax function: Converts the raw scores output by the neural network into a probability distribution, which is used to determine the semantic category that each pixel is most likely to belong to. F FCN : A fully convolutional neural network that can automatically extract image features and perform semantic classification by learning a large amount of labeled remote sensing image data.

[0091] Preferably, in a specific application scenario, when forming biodiversity data processing, let the biodiversity data set be BBD, which contains various information about species.

[0092] Let the species list 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 is to unify the data of different species to a standard scale, using the Z-score standardization method: where μ i is the mean of the observed data of species s i , and σ i is the standard deviation. b ij : The observed data of species s i in the jth sample, such as the number of individuals of a certain species in a certain area. The standardized species data makes the data of different species comparable. In the development of ecological products, this helps to comprehensively evaluate the impact of different species on the stability of the ecosystem and product development. For example, when evaluating the impact of eco-tourism development on biodiversity, the standardized data can more intuitively reflect the changes of different species. μ i : The mean of the observed data of species s i , representing the average level of the species in the sample. σ i : The standard deviation of the observed data of species s i , measuring the degree of dispersion of the data.

[0093] Preferably, in a specific application scenario, during spatio-temporal annotation, let the time series be T = {t1, t2, …, t m}, and the spatial region is divided into R = {r1, r2, …, r l}. For each species si Data b ij , mark its corresponding time t p and spatial region r q , to form spatio-temporal annotation data points (s i , b ij , t p , r q ). t p : The time point corresponding to the biodiversity observation data. In the development of ecological products, time information is important for analyzing the change trend of biodiversity over time. For example, the seasonal changes of certain species may affect the production cycle of ecological products. r q : The spatial region corresponding to the biodiversity observation data. Spatial annotation helps to understand the distribution of different species within the ecological product development area, provides a basis for reasonably planning development activities, and avoids damaging biodiversity hotspots.

[0094] Preferably, in a specific application scenario, when estimating abundance, a model-based abundance estimation method is adopted. Assume that the abundance of species s i has a linear relationship with the environmental variables E = {e1, e2,..., e o}: where A i is the estimated abundance of species s i , β ji is the regression coefficient, and ∈ i is the error term. The regression coefficient β is solved by the least squares method.

[0095] A i : The estimated abundance of species s i . Abundance is an important indicator to measure biodiversity. In the development of ecological products, accurately estimating species abundance helps to evaluate the health and stability of the ecosystem. For example, a high abundance of species may mean that the ecosystem has stronger anti-interference ability, which has a positive impact on the sustainability of ecological product development. β ji : The regression coefficient, which reflects the influence degree of environmental variable e j on the abundance of species s i . In practical applications, these coefficients can help determine which environmental factors play a key role in biodiversity, so as to carry out targeted protection and management during the ecological product development process. ∈ i : The error term, which reflects the part of variation that the model cannot explain.

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

[0097] For socio - economic data in text form (such as policy documents, news reports, etc.), natural language processing techniques are used for structuring. Let the text data be TXT. Through named - entity recognition (NER) technology, entities E = {e1, e2, …, e u} in the text are recognized, such as names of people, places, organizations, names of economic indicators, etc. Then, through relation extraction technology, the relationships R = {(e i , r ij , e j )} between entities are determined, where r ij represents the relationship between entity e i and e j .

[0098] TXT: The original socio - economic data in text form. In the scenario of ecological product development, this text may contain information related to policies and regulations, market dynamics, etc., which is valuable for risk prediction. E: The set of entities recognized from the text. For example, entities such as the names of economic support policies in the recognized policy documents and related beneficiary regions. This entity information is the basis for subsequent analysis. R: The set of relationships between entities. For example, the association relationship between policies and beneficiary regions helps to understand the interaction between socio - economic factors and provides more in - depth information for the risk assessment of ecological product development.

[0099] Preferably, in a specific application scenario, during spatial mapping, let the geographical space region be divided into grids G = {g1, g2, …, g v}. For the spatial - related information (such as population distribution, industrial distribution, etc.) in socio - economic data, it is mapped to the corresponding grids. Let the spatial position be (x, y), and its grid index k is calculated: k = gridIndex(x, y), where gridIndex is a function defined according to the grid division rules.

[0100] G: The set of divided geographical - space grids. In ecological product development, spatial gridding of socio - economic data helps in comprehensive analysis combined with ecological geographical information. For example, analyzing the impact of population density and economic development level in different grid regions on the market demand for ecological products. (x, y): The geographical - space coordinates corresponding to socio - economic data. For example, the geographical location coordinates of a certain enterprise. k: The grid index where the spatial position (x, y) is located. Establishing a connection between socio - economic data and geographical space through spatial mapping facilitates subsequent spatial analysis and modeling.

[0101] Preferably, in a specific application scenario, during indicator normalization, let the socio - economic indicator data be I = {i1, i2, …, i w}, and the maximum - minimum normalization method is used to normalize the indicator data to the interval [0, 1]:

[0102] I: The original set of socio - economic indicator data, such as indicators like GDP and per capita income in different regions. In the risk prediction of ecological product development, these indicators reflect the status of the socio - economic environment and have an important impact on the market prospect and development feasibility of the product. i j : The j - th socio - economic indicator data. The normalized socio - economic indicator data. The normalized data is convenient for comparison and analysis on the same scale, and helps to comprehensively evaluate the influence degree of different indicators on the risk of ecological product development.

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

[0104] 1. By calculating the difference between the current data and the average data within the time window, the short - term fluctuations of environmental physical quantities are captured. The core 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 data or simple trend analysis, ignoring short - term fluctuation information. For example, when monitoring the temperature in the ecological product development area, the traditional method may only record the daily average temperature and cannot 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 the short - term abnormal changes in environmental data and provide more timely risk warnings for ecological product development. For example, in ecological fisheries, rapid changes in water temperature may affect fish growth, and such risks can be detected in a timely manner through the difference within the time window to adjust the breeding strategy.

[0106] 2. Based on the 3σ principle of statistics, this application calculates the mean and standard deviation of the data and sets reasonable thresholds to identify outliers. This principle utilizes the statistical distribution characteristics of the data to effectively distinguish normal and abnormal data. Traditional outlier detection methods rely on simple rules or manual experience judgment, which is highly subjective and prone to missing complex abnormal situations. For example, when monitoring air quality data, the traditional method may only judge whether a single pollutant concentration exceeds a fixed standard to determine abnormality, ignoring abnormal situations caused by the synergistic effects between multiple pollutants. This application can objectively and accurately identify outliers in environmental physical data and improve data quality. In ecological tourism development, if abnormal environmental monitoring data (such as a sudden increase in noise) appears, it may indicate the presence of interference sources (such as construction activities) in the vicinity, affecting the tourism experience, and timely detection can enable early adoption of countermeasures.

[0107] 3. This application generates new features through mathematical operations from existing data features, explores the potential relationships and changing trends among data, and enriches the data feature dimensions. Traditional methods are often limited to using the original data features and cannot fully explore the information hidden 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 and ignore important derivative features such as the moisture change rate, making it difficult to comprehensively evaluate the comprehensive impact of soil moisture conditions on crop growth. This application increases the richness of data features and provides more effective information for the risk prediction model. Taking ecological forestry development as an example, the derivative feature of the change rate of tree growth speed can more accurately reflect the health status of trees and the changes in the growth environment, helping to predict forestry development risks in advance.

[0108] 4. This application, based on the radiation transfer model, eliminates the influence of factors such as sensors and the atmosphere by performing offset and gain corrections on the measured radiance, and restores the true surface radiation information. Traditional radiation correction methods are not precise enough and cannot fully consider the complex atmospheric environment and changes in sensor characteristics. For example, in early ecological remote sensing applications, simple radiation correction models could not adapt to the differences in atmospheric composition and thickness in different seasons and regions, resulting in biases in the corrected images' reflection of ecological elements. This application enables ecological remote sensing images to more accurately reflect the true radiation characteristics of surface objects, improving the accuracy of identifying and analyzing ecological environment elements. When evaluating the impact of ecological product development on vegetation cover, accurate radiation-corrected images can clearly distinguish different vegetation types and their growth conditions, providing a reliable basis for reasonably planning development activities.

[0109] 5. This application uses the affine transformation model to find the corresponding relationships between different images through feature matching, and realizes mapping ecological remote sensing images to a unified coordinate system. Traditional registration methods have poor effects and low registration accuracy in complex terrains or when image features are not obvious. For example, in mountainous areas with complex terrains, traditional methods are difficult to accurately match image feature points, resulting in large registration errors and affecting the precise definition 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 in the same coordinate system, helping to accurately monitor the dynamic changes in ecological product development areas. In ecological restoration projects, by comparing remotely sensed images registered at different times, the vegetation restoration situation can be clearly observed, and the restoration effect and development risks can be evaluated.

[0110] 6. This application uses a fully convolutional neural network in deep learning to perform end-to-end learning on remote sensing images, automatically extract image features, and classify each pixel into corresponding semantic categories. Traditional semantic segmentation methods rely on manually designed features, with low efficiency and poor accuracy, and are difficult to handle complex and diverse ecological scenarios. For example, when identifying different ground objects in a wetland ecosystem, traditional methods require a large number of manually defined features and are difficult to accurately distinguish some subtle differences in ground objects. This application can quickly and accurately perform semantic segmentation on ecological remote sensing images and automatically identify different ecological elements. In the development planning of ecological products, information such as land use types and vegetation coverage can be quickly obtained through semantic segmentation, providing support for the reasonable layout of development projects and reducing development risks caused by misjudgment of ecological elements.

[0111] 7. This application uses the Z-score normalization method to unify the observation data of different species to the same scale, eliminate the differences in data dimensions and variation degrees between species, and make the data comparable. Traditional methods have not effectively normalized species data, resulting in uneven impacts of species with large data differences on the results when comprehensively analyzing biodiversity. For example, when evaluating the impact of ecological product development on biodiversity, the data of large and small species are not normalized, which may cause the changes of small species to be ignored. This application enables the analysis of different species data at the same scale, more fairly reflecting the status and changes of each species in the ecosystem. In the development of eco-tourism, through the normalized biodiversity data, the impacts of development activities on various species can be more comprehensively evaluated, and more scientific protection measures can be formulated.

[0112] 8. This application adds time and space tags to biodiversity observation data, clarifies the time points and spatial regions corresponding to the data, and constructs a spatio-temporal information framework for biodiversity. The traditional method lacks systematic integration of the spatio-temporal information of biodiversity data and is difficult to analyze the spatio-temporal dynamic changes of biodiversity. For example, previous records of species distribution may not be accurately associated with time information, making it impossible to understand the migration and evolution of species over time, which is not conducive to the long-term planning of ecological product development. This application comprehensively records the spatio-temporal information of biodiversity, providing a basis for in-depth analysis of the spatio-temporal relationship between biodiversity and ecological product development. In the development of ecological agriculture, the occurrence patterns of crop pests and diseases in different seasons and regions can be analyzed through spatio-temporal labeled data, and risks can be prevented in advance to ensure the quality of agricultural products.

[0113] 9. Based on the linear relationship model between environmental variables and species abundance, this application uses the least squares method to solve the regression coefficients to estimate species abundance. This principle takes into account the impact of environmental factors on species abundance and quantifies this relationship through a mathematical model. Traditional abundance estimation methods are too simple and do not fully consider the combined effects of environmental factors. For example, estimating abundance only based on the directly observed number of species ignores the impact of environmental changes (such as climate change and habitat destruction) on the survival and reproduction of species, resulting in inaccurate abundance estimation. This application can estimate species abundance more accurately, providing key data for evaluating the stability of the ecosystem and the impact of ecological product development on biodiversity. During the process of ecological product development, accurate species abundance information helps to judge the carrying capacity of the ecosystem, reasonably plan the development scale, and avoid irreversible damage to biodiversity caused by overdevelopment.

[0114] 10. This application uses named entity recognition and relation extraction techniques in natural language processing to extract valuable entity and relation information from unstructured text and convert the text data into a structured form. Traditional methods rely on manual reading and sorting of text information, which is extremely inefficient and error-prone. For example, when analyzing the impact of a large number of policy documents on ecological product development, manual screening of information is time-consuming and laborious, and important information may be missed due to subjective factors. This application can efficiently extract key information from text data, providing rich socioeconomic background materials for ecological product development risk prediction. In the analysis of the ecological product market, through the structured processing of texts such as news reports and industry reports, information such as market demand and policy orientation can be quickly obtained to assist decision-making and reduce market risks.

[0115] 11. According to the geographical space grid division rules, this application maps the spatial-related information in socioeconomic data to the corresponding grids to achieve the integration of socioeconomic data and geographical space. Traditional methods cannot effectively associate socioeconomic data with geographical space and are difficult to conduct comprehensive analysis based on spatial location. For example, when studying the relationship between economic development and ecological environment in the ecological product development area, 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 realizes the spatialization of socioeconomic data, facilitating comprehensive analysis in combination with ecological geographical information. In the ecological product development plan, through the spatially mapped socioeconomic data, the matching situation of population, economic distribution and ecological resources in different regions can be intuitively understood, the development layout can be optimized, and the development risks caused by unreasonable spatial planning can be reduced.

[0116] 12. This application adopts the maximum-minimum normalization method to unify the socio-economic indicator data of different magnitudes and dimensions into the interval [0, 1], eliminating the influence of data differences on the analysis results. Traditional analysis methods do not normalize socio-economic indicators, resulting in larger numerical indicators dominating the analysis results when comprehensively evaluating the development risks of ecological products, while some important small-magnitude indicators are ignored. For example, when evaluating the feasibility of an ecological product development project, without normalizing the indicators, total quantity indicators such as GDP may obscure the influence of important indicators such as per capita income and industrial structure. This application enables different socio-economic indicators to be compared and analyzed on the same scale, improving the accuracy and scientific nature of risk assessment. In the ecological product development risk assessment model, the normalized indicator data can more reasonably reflect the contribution of each factor to the risk, providing a more reliable basis for decision-making.

[0117] Optionally, the streaming processing of the multi-source heterogeneous data to form a multi-source heterogeneous data stream includes: two-dimensionally partitioning the environmental time-series feature data according to a set time window and spatial region to obtain spatio-temporal partitioned data; vectorizing the radiometric calibration semantic image to obtain a vector feature set, and establishing a spatial index for the vector feature set to obtain spatially indexed vector data; performing relationship modeling on the biological spatio-temporal data to obtain an ecological relationship map, and establishing a path index for the ecological relationship map to obtain indexed ecological relationship data; performing dimensional modeling on the grid economic indicator data to obtain a star schema data set, and establishing a dimensional index for the star schema data set to obtain indexed economic indicator data; aligning the spatio-temporal partitioned data, spatially indexed vector data, indexed ecological relationship data, and indexed economic indicator data, and respectively converting the aligned spatio-temporal partitioned data, spatially indexed vector data, indexed ecological relationship data, and indexed economic indicator data to obtain an environmental feature vector group, a spatial feature vector group, an ecological feature vector group, and an economic feature vector group; fusing the environmental feature vector group, spatial feature vector group, ecological feature vector group, and economic feature vector group to obtain a fused feature vector; based on a set sliding window, performing a sliding window process on the fused feature vector according to a set time step to convert the data within the sliding window into a byte stream to form a multi-source heterogeneous data stream.

[0118] Preferably, in a specific application scenario, when two-dimensionally partitioning the environmental time-series feature data, let the environmental time-series feature data be 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. Set the time window size to w t , and the spatial region size to w s .

[0119] In the time dimension, starting from t = 1, with a step size of w tPerform chunking; in the spatial dimension, starting from s = 1, with a step size of w s Perform chunking.

[0120] For the i-th time chunk and the j-th spatial chunk, the spatio-temporal chunk data TSD ij is: 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 features).

[0121] ETFD: Environmental time-series feature data, which contains a dataset with time and space dimensions and multiple features obtained by processing environmental physical data through time-window difference, anomaly detection, and feature derivation. For example, in the development of ecological products, it may include the change characteristics of environmental factors such as temperature, humidity, and light at different times and locations. t: Time dimension index, representing a time point. s: Spatial dimension index, representing a spatial position. f: Feature dimension index, such as temperature feature, humidity feature, etc. w_t: Set time window size, which determines the number of time steps included in each time chunk. For example, if you are concerned about the impact of short-term environmental changes on ecological products, you can set a smaller w t ; if considering long-term trends, increase w t . w s : Set spatial region size, which determines the spatial range included in each spatial chunk. When the ecological product development area is large, an appropriate w s can divide the space into reasonable sub-regions for analysis. TSD ij : Spatio-temporal chunk data composed of the i-th time chunk and the j-th spatial chunk, which is a subset of the environmental time-series feature data within a specific time and space range.

[0122] Preferably, in a specific application scenario, when vectorizing the radiometric calibration semantic image and establishing a spatial index, assume that the radiometric calibration 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] Through vectorization algorithms such as edge detection and region growing, convert the semantic objects in the image into vector features. Assume that the vector feature set obtained after vectorization is 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 n) It consists of... The quadtree spatial indexing algorithm is adopted. Let the root node be N0, which covers the entire image area. For node N, if the number of vector features it contains exceeds the threshold T, it is divided into four child nodes N1, N2, N3, and N4, which respectively cover one - quarter of the original node area.

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

[0125] After the spatial index is constructed, the spatially indexed vector data SIVD is obtained.

[0126] RC_Semantic_Imagery: Ecological remote - sensing image data after radiometric correction and semantic segmentation, which contains image content with semantic information, such as different land - cover classes (vegetation, water body, buildings, etc.). x, y: The spatial coordinates of the image, used to locate each pixel in the image. VF: The set of vectorized vector features, which converts the semantic objects in the image into vector form for subsequent spatial analysis and processing. For example, a forest area in the image is vectorized into a polygon vector feature. v: Vector feature, which is the basic unit after vectorization and is described by a series of vertex coordinates. T: The threshold for quadtree node division. When the number of vector features contained in a node exceeds this threshold, node division is performed to improve the efficiency of spatial indexing. N: Quadtree node, each node represents a spatial area and contains a certain number of vector features. SIVD: Spatially indexed vector data. By establishing a quadtree spatial index, the spatial query and retrieval of vector features are made more efficient. In the development of ecological products, the ecological elements in a certain area (such as the distribution range of a specific vegetation type) can be quickly queried.

[0127] Preferably, in a specific application scenario, when modeling the relationships of biological spatio - temporal data and establishing a path index, let the biological spatio - temporal data Bio_TS_Data contain species information S = {s1, s2,..., s n}}, time information T = {t1, t2,..., t m}}, spatial information R = {r1, r2,..., r l}}, and the relationship information R species (such as predation relationship, symbiotic relationship, etc.).

[0128] Construct an ecological relationship map ERM, where the nodes in the map are species s i and the edges are the relationships r between species ij ∈R species The weight w of the edge ij represents the strength of the relationship (e.g., the degree of dependence of the predator on the prey in a predation relationship).

[0129] The ecological relationship map can be represented by an adjacency matrix A, where A ij is as follows:

[0130]

[0131] Preferably, in a specific application scenario, when establishing a path index, the Dijkstra algorithm is used to establish a path index for the ecological relationship map. Let two nodes s a and s b in the map, the shortest path length d(s a to s b ) is: a ,s b ) is:

[0132]

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

[0134] By calculating the shortest path lengths between all pairs of nodes, an indexed ecological relationship data IERD is established

[0135] Bio_TS_Data: A biodiversity dataset containing information such as species standardization, spatio-temporal annotation, and abundance estimation. In the development of ecological products, it involves the distribution of different species at different times and spaces and their mutual relationships. S: The set of species, which contains various species in the ecosystem. T: The set of time, which records the time points corresponding to the biodiversity data. R: The set of space, which represents the spatial regions corresponding to the biodiversity data. R species: The set of relationships between species, such as predation, symbiosis, etc. These relationships have important impacts on the stability of the ecosystem and the development of ecological products. ERM: The ecological relationship map, which visually shows the relationships between species in a graphical way and helps to understand the structure and function of the ecosystem. A: The adjacency matrix, which is used to mathematically represent the ecological relationship map and is convenient for graph operations and analysis. w_{ij}: The weight of the edge, which reflects the relationship between species s i and s jThe strength of the relationship between them can be used to evaluate the impact degree of species interactions on ecological products in ecological product development. d(s a ,s b ): The shortest path length from species s a to s b . Through the path index, the indirect relationship and influence path between species can be quickly queried. IERD: Indexed Ecological Relationship Data. By establishing a path index, the efficiency of querying and analyzing species relationships in the ecological relationship map is improved, providing a more convenient way to obtain information for the risk assessment of ecological product development.

[0136] Preferably, in a specific application scenario, when performing dimensional modeling and establishing a dimension index for grid economic indicator data, it is assumed that the grid economic indicator data Grid_Eco_Indicators contains multiple economic indicators I = {i1, i2, …, i k}, and the corresponding spatial grid information G = {g1, g2, …, g v}. A star schema dataset SMD is constructed, with the spatial grid as the fact table and the economic indicators as the dimension tables. The fact table F contains the field g (spatial grid identifier) and the measurement values m1, m2, …, m k corresponding to each economic indicator. The dimension table D i (i = 1, …, k) contains the detailed attribute information of the economic indicator i i . For example, a record in the fact table F can be expressed as (g j , m 1j , m 2j , …, m kj ), where m ij is the measurement value of the i-th economic indicator in the j-th spatial grid.

[0137] Preferably, in a specific application scenario, when establishing a dimension index, for each dimension table D i , an index based on the economic indicator values is established. Let the economic indicator values in the dimension table D i be x il (l = 1, …, n i , n i is the number of records in the dimension table D i ). The index structure can adopt an AVL tree (balanced binary search tree). For the query value q, search for the record that satisfies the condition x i = q in the index of the dimension table D il , and the time complexity is O(logn i ). After the dimension index construction, the indexed economic indicator data IEMD is obtained.

[0138] Grid_Eco_Indicators: Socio-economic data after text structuring, spatial mapping, and indicator normalization, which records various economic indicator information in grid units. In the development of ecological products, it is used to analyze the impact of the economic conditions in different regions on product development. I: The set of economic indicators, such as indicators like GDP, per capita income, industrial structure, etc., which reflect different aspects of the socio-economic environment. G: The set of spatial grids, which divides the geographical space into multiple grids for easy association and analysis with economic indicator data. SMD: Star schema dataset, a commonly used data warehouse modeling method, which facilitates data analysis and query through the structure of fact tables and dimension tables. In the risk assessment of ecological product development, it is convenient for multi-dimensional analysis of economic indicators in different spatial regions. F: Fact table, which stores the correspondence between spatial grids and economic indicator measurement values. D i: Dimension table, which is used to store the detailed attribute information of economic indicators for more in-depth analysis and query of economic indicators. m ij: The measurement value of the i-th economic indicator in the j-th spatial grid, which is the key data for analyzing the relationship between the economy and ecological product development. x il : The economic indicator value of the l-th record in dimension table D i . q: Query value, which is used to find records that meet specific economic indicator values in the dimension index. IEMD: Indexed economic indicator data, which improves the efficiency of querying and analyzing economic indicator data by establishing a dimension index, providing faster data support for ecological product development decisions.

[0139] Preferably, in a specific application scenario, during data alignment and transformation, set the spatio-temporal block data TSD, spatially indexed vector data SIVD, indexed ecological relationship data IERD, and indexed economic indicator data IEMD.

[0140] Align the four types of data through spatial and time information. Let the aligned spatio-temporal block data be TSD aligned , the spatially indexed vector data be SIVD aligned , the indexed ecological relationship data be IERD aligned , and the indexed economic indicator data be IEMD aligned .

[0141] Spatially, match the data according to spatial grids or geographical coordinates; temporally, correspond the data according to time points or time ranges.

[0142] Let the transformation functions f env , f spatial , f eco , f econ be used to transform the aligned spatio-temporal 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 group EVG is: EVG = f env (TSD aligned ), the spatial feature vector group SVG is: SVG = f spatial (SIVD aligned ), the ecological feature vector group ECVG is: ECVG = f eco (IERD aligned ), the economic feature vector group EEVG is: EEVG = f econ (IEMD aligned ), TSD aligned 、SIVD aligned 、IERD aligned 、IEMD aligned : The data after alignment processing makes different types of data consistent in space and time, facilitating subsequent joint analysis. f env 、f spatial 、f eco 、f econ : Conversion functions that convert different types of data into feature vector groups according to the characteristics of the data and the analysis requirements. For example, f env may convert environmental features such as temperature and humidity in spatio-temporal block data into a feature vector, and each dimension of the vector represents different environmental features or their combined features. - EVG, SVG, ECVG, EEVG: They are the environmental feature vector group, spatial feature vector group, ecological feature vector group, and economic feature vector group respectively, which are the data forms after conversion, facilitating vector group fusion and subsequent analysis and processing.

[0144] Preferably, in a specific application scenario, when building vector group fusion, let the environmental feature vector group EVG = {e1, e2,..., e p}, the spatial feature vector group SVG = {s1, s2,..., s q}, the ecological feature vector group ECVG = {c1, c2,..., c r}, the economic feature vector group EEVG = {g1, g2,..., g s}. The fusion feature vector FV is obtained by using the weighted fusion method:

[0145] where w ei 、w sj 、w ck 、w gl are weight coefficients, and e i 、s j 、c k 、g l: They are the eigenvectors in the environmental feature vector group, spatial feature vector group, ecological feature vector group, and economic feature vector group respectively. The weight coefficient is used to adjust the contribution degree 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 influence degree of different factors on the risk. For example, if the ecological factor has a greater impact on the risk, the value can be appropriately increased. FV: The fused feature vector synthesizes the feature information in multiple aspects such as environment, space, ecology, and economy, providing a more comprehensive data basis for the generation of subsequent multi-source heterogeneous data streams.

[0146] Preferably, in a specific application scenario, when constructing a multi-source heterogeneous data stream, set the fused feature vector FV, and set the sliding window size as w slide , and the time step as Δt. Starting from t = 1, within each time step Δt, perform a sliding window process on the fused feature vector. Let the fused feature vector within the nth sliding window be FV n , and convert it into 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 the byte stream can adopt the method of binary serialization. Assuming that each element in FV is a floating-point number, convert the floating-point number into a binary representation according to the IEEE754 standard, and then concatenate the binary bits of all elements within the sliding window in sequence to form the byte stream BS n . For example, for a 32-bit floating-point number x, its IEEE754 representation is the sign bit s (1 bit), exponent bit e (8 bits), and 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 way to obtain the byte stream BS n . As the time step progresses, generate BS1, BS2, … in sequence. These byte streams together constitute the multi-source heterogeneous data stream MSDS. w slide : The sliding window size determines the number of fused feature vectors included in each sliding window operation. In the risk prediction scenario of ecological product development, if the comprehensive changes of multiple factors in a short period are concerned, a smaller w can be set slide ; if the trend over a longer time span needs to be analyzed, then increase w slide . Δt: The time step, that is, the time interval of each sliding window operation. A shorter time step can capture more subtle real-time changes and is suitable for the ecological product development scenario that is sensitive to risk changes; a longer time step is more suitable for macro trend analysis. FV n: The subset of the fused feature vectors within the nth sliding window, which contains multi-source feature information within a specific time window. BS n : The byte stream obtained by converting the fused feature vectors within the nth sliding window, which is the basic building block of the multi-source heterogeneous data stream. By converting the fused feature vectors into byte streams, it is convenient for efficient storage, transmission, and subsequent processing in computer systems. MSDS: The multi-source heterogeneous data stream is composed of a series of byte streams BS n that are generated in chronological order, integrating the feature information of multi-source heterogeneous data such as environment, space, ecology, and economy, and providing a unified data input format for the ecological product development risk prediction model.

[0147] In the specific application scenario of ecological product development risk prediction, the technical benefits brought by the above technical solution compared with traditional technologies in data processing, analysis, and utilization are as follows:

[0148] 1. By setting a time window and a spatial region to perform two-dimensional block partitioning on the environmental time-series feature data, this application can discretize continuous spatio-temporal data and observe and analyze the change characteristics of environmental data in different spatio-temporal ranges with a finer granularity. Traditional technologies only perform simple time-series analysis on environmental data or overall statistics of spatial regions, and cannot take into account the changes in both the time and space dimensions simultaneously. For example, when analyzing the temperature change in a certain ecological region, traditional methods may only focus on the change of the overall average temperature of the region over time, while ignoring the local differences at different locations within the region at the same time or at the same location at different times. After the block partitioning in this application, the data scale is reduced, and the processor can process and analyze each small block more efficiently, reducing the consumption of computing resources. Moreover, it can more accurately capture the local characteristics and abnormal changes of environmental data within a specific spatio-temporal range, providing more detailed environmental risk information for ecological product development. For example, in ecological agriculture development, it can timely detect the temperature anomaly of a certain plot within a specific time period and take measures to protect crops in advance.

[0149] 2. This application vectorizes the radiometrically calibrated semantic images and establishes a spatial index, converting the image data into vector features. Through a spatial index structure (such as a quadtree), the vector features are efficiently organized and managed, facilitating fast spatial queries and analysis. Traditional image processing techniques usually analyze images in terms of pixels, resulting in low efficiency for complex feature recognition and spatial queries. For example, when searching for the vegetation distribution within a specific ecological region, traditional methods may need to traverse all the pixels of the entire image, which is time-consuming and error-prone. The establishment of the spatial index in this application enables quick location of relevant vector features during spatial queries (such as range queries, adjacency queries, etc.), greatly improving the efficiency of spatial analysis. Moreover, the vectorization converts the features in the image into clear vector features, facilitating more accurate identification and analysis of different ecological elements, and providing a more reliable basis for the planning and decision-making of ecological product development. For example, in ecological tourism development, the natural landscapes and ecological resources within the scenic area can be accurately determined.

[0150] 3. This application models the relationships of biological spatio-temporal data, constructs an ecological relationship map to represent the mutual relationships between species, and establishes an efficient query mechanism for the map through a path index (such as Dijkstra's algorithm), facilitating the analysis of the indirect relationships and influence paths between species. Traditional biodiversity analysis methods often focus on the individual characteristics and quantity statistics of species, ignoring the complex mutual relationships between species. When analyzing the impact of the ecosystem on ecological product development, it is difficult to comprehensively consider the interactions and chain reactions between species. The ecological relationship map in this application can visually display the relationship network between species, helping developers comprehensively understand the structure and function of the ecosystem, and better evaluate the potential impact of ecological product development on the ecosystem. Moreover, the path index enables more efficient queries of the relationships and influence paths between species, and can promptly detect potential ecological risks. For example, in ecological aquaculture, the impact of changes in a certain species on other related species can be quickly understood, and measures can be taken in advance to maintain the ecological balance.

[0151] 4. This application uses a star schema for dimensional modeling of grid economic indicator data, organizes the data into the structure of fact tables and dimension tables, and improves the query and analysis efficiency of economic indicator data through dimensional indexing (such as AVL trees). Traditional economic data processing methods store and analyze data in a simple tabular form, with limited capabilities for complex multi-dimensional queries and analysis. 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 dimensional modeling of the star schema in this application facilitates multi-dimensional data analysis, enabling in-depth mining of economic data from different perspectives (such as time, space, economic indicator types, etc.), and providing more comprehensive economic information for the decision-making of ecological product development. Moreover, the establishment of dimensional indexing enables quick positioning of relevant data when querying specific economic indicators or conducting range queries, reducing query time and improving decision-making efficiency. For example, when evaluating the economic benefits of an ecological product development project, economic indicator data from different regions and different time periods can be quickly obtained for comparative analysis.

[0152] 5. This application aligns different types of data through spatial and temporal information to ensure the consistency of data in the spatio-temporal dimension, and then converts the aligned data into a set of feature vectors for subsequent fusion and analysis. Traditional technologies often have difficulty solving the spatio-temporal inconsistency problem between data when dealing with multi-source heterogeneous data, resulting in ineffective integration and utilization of data. Data from different sources may vary in time scale, spatial resolution, etc., making it difficult to conduct unified analysis. The data alignment in this application enables different types of data to be integrated within the same spatio-temporal framework, eliminating the spatio-temporal differences between data and providing a basis for subsequent joint analysis. Moreover, converting the data into a set of feature vectors unifies the data representation form, facilitating vector operations and fusion, and improving the generality and flexibility of data processing.

[0153] 6. This application combines different types of feature vectors such as environment, space, ecology, and economy into a fused feature vector using weighted fusion, comprehensively considering the influence of different factors on the risk of ecological product development. Traditional methods only focus on single-type data or simply superimpose different types of data, unable to fully consider the interaction and weight relationship between different factors. When evaluating the risk of ecological product development, the influence of some important factors may be ignored. The fused feature vector in this application can comprehensively reflect information in multiple aspects such as environment, space, ecology, and economy, and more comprehensively evaluate the risk of ecological product development. By adjusting the weight coefficients, the importance of different factors in risk assessment can be flexibly adjusted according to the actual situation. The fused feature vector integrating multi-source data in this application can provide richer information, helping to improve the accuracy and reliability of the risk prediction model and providing a more scientific basis for the decision-making of ecological product development.

[0154] 7. This application performs a sliding window process on the fused feature vector, converts the data within the sliding window into a byte stream, and forms 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, and it is difficult to meet the requirements for real-time data in ecological product development risk prediction. There may also be problems with low efficiency in data storage and transmission. 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 basis for real-time risk prediction. The processor can perform real-time analysis and processing on the data stream, promptly discover potential risks, and take corresponding measures. This application converts the data into byte stream form, reduces the storage space and transmission bandwidth requirements of the data, and improves the efficiency of data storage and transmission. At the same time, the format of the byte stream is convenient for exchange and sharing between different systems, enhancing the generality and scalability of the data.

[0155] Optionally, performing multi-modal feature engineering construction 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 a trend term, and performing Fourier transform on the trend term 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; extracting demographic features, economic development features, and policy and regulation features from the multi-source heterogeneous data stream, and performing spatial feature mining on the demographic features, economic development features, and policy and regulation features to form socio-economic layer feature data.

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

[0157] By the least squares method, it is necessary to minimize the sum of squared errors

[0158] Differentiating Q(β) with respect to β k(k = 0, 1, …, M), take the partial derivative and set it to 0 to obtain the normal equations:

[0159]

[0160] In matrix form, let

[0161] Then the normal equations can be expressed as X T Xβ = X T y, and the solution is Trend term where X: Multi-source heterogeneous data stream. In the ecological product development scenario, it may be comprehensive data of environmental indicators (such as temperature, humidity), ecological indicators (such as changes in the number of species), and economic indicators (such as market demand) over a period of time. N: The number of data points, which reflects the time length of the data or the number of observations. M: The order of the polynomial, which controls the complexity of the regression model and can be adjusted according to the trend of the data. β j : Regression coefficient, β0 is the intercept term, β j (j > 0) represents the coefficients of different order time terms, which reflects the variation law of the data over time. T: Trend term, which describes the long-term variation trend of the multi-source heterogeneous data stream and helps to analyze the overall trend of the data.

[0162] Preferably, in a specific application scenario, when forming the physical layer feature data by Fourier transform, perform the discrete Fourier transform (DFT) on the trend term T. The formula for the discrete Fourier transform is:

[0163] To improve the calculation efficiency, the fast Fourier transform (FFT) algorithm is usually used. Physical layer feature data where F k : The discrete Fourier transform result of the trend term T, which converts the time-domain data to the frequency domain, and k represents the index of the frequency component. P k : Physical layer feature data, which is the modulus of the Fourier transform result and reflects the intensity of different frequency components. It can be used to analyze the periodic changes of the data, such as the seasonal fluctuations of the ecosystem.

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

[0165] Spectral feature S: For multi-spectral images, the spectral feature can be obtained by calculating the statistics of different bands. For example, the mean 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 at (x, y) in the b-th band. Spectral feature vector B is the number of bands. Texture feature T ex : The gray-level co-occurrence matrix (GLCM) is used to extract texture features. Let d be the distance between pixel pairs and θ be the direction of pixel pairs. The gray-level co-occurrence matrix G(i, j; d, θ) represents the number of occurrences of pixel pairs with gray values i and j at a distance d and direction θ. Based on the GLCM, various texture features can be calculated, such as contrast Correlation where L is the number of gray levels, μ i , μ j is the mean of i and j, and σ i , σ j is the standard deviation of i and j. Texture feature vector T ex = {C, R,...}. For the target object in the image, the shape feature can be obtained by calculating its geometric parameters. Let the set of boundary points of the target object be Perimeter The area A can be obtained by integration or pixel counting, and circularity Shape feature vector Sh = {L, A, C r}

[0166] I(x, y): Ecological remote sensing image data, which is the part related to ecological remote sensing in the multi-source heterogeneous data stream and is used to obtain the spectral and texture information of ground objects. B: The number of spectral bands. Different bands correspond to different spectral ranges and can be used to identify different ground object types. d and θ: The distance and direction parameters of the gray-level co-occurrence matrix, which are used to describe the spatial relationship between pixel pairs. L: The number of gray levels, which affects the calculation accuracy of texture features. S: Spectral feature vector, which reflects the reflection characteristics of ground objects in different spectral bands and can be used to distinguish different ecological elements such as vegetation and water bodies. T ex : Texture feature vector, which describes the texture information of the ground object surface and helps to identify different types of ecological landscapes. Sh: Shape feature vector, which 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 the multi-source heterogeneous data stream. Let the species observation data be where s i is the species name, l i is the observation location, and t i is the observation time.

[0168] Species richness characteristic R: In a specific area A and time interval [t start , t end , the species richness R = |{s i | l i ∈ A, t start ≤ t i ≤ t end}|, that is, the number of different species observed in this area and time. Species distribution characteristic D: The spatial distribution of species can be described using kernel density estimation (KDE). Let K(x) be the kernel function (such as Gaussian kernel where x is the spatial vector, d is the spatial dimension, and σ is the bandwidth parameter), and the density estimate of species s at location y is where N s is the number of observations of species s, x i is the observation location of species s, and h is the bandwidth. Species distribution characteristic vector where S is the set of species and Y is the set of locations within the study area. Ecological relationship characteristic E: Construct an ecological relationship graph G = (V, E g ), where V is the set of species nodes and E g is the set of edges, and the weight w ij of the edge represents the relationship strength between species i and j (such as predation relationship, symbiotic relationship, etc.). The characteristics of the graph (such as node degree, clustering coefficient, shortest path length, etc.) can be used to represent the ecological relationship characteristics. For example, the degree of node i Clustering coefficient where E i is the number of edges between the neighbor nodes of node i. Ecological relationship characteristic vector

[0169] O: Species observation data, which records the observation information of different species at different times and locations. A: Study area, which is used to define the spatial range of species richness and distribution. [t start , t end : Time interval, which is used to analyze the changes of species within a specific time period. K(x): Kernel function, which is used for kernel density estimation to describe the spatial distribution of species. σ and h: Bandwidth parameters of the kernel function, which affect the smoothness of density estimation. G: Ecological relationship graph, which intuitively shows the interaction between species. R: Species richness characteristic, which reflects the degree of species diversity in the ecosystem. D: Species distribution characteristic, which shows the spatial distribution of species. E: Ecological relationship characteristic, which reflects the interaction between species and the structure of the ecosystem.

[0170] Preferably, in a specific application scenario, when fusing features 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 Veco-remote = [S; T ex ; Sh], the biodiversity feature vector V bio-diversity = [R; D; E], the fused ecological layer feature data E layer is: E layer = w1V eco-remote + w2V bio-diversity

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

[0172] V eco-remote : The ecological remote sensing feature vector, which synthesizes features such as spectrum, texture, and shape. V bio-diversity : The biodiversity feature vector, which includes features such as species richness, distribution, and ecological relationships. w1 and w2: Weight coefficients, used to adjust the importance of ecological remote sensing features and biodiversity features in the fusion. E layer : The ecological layer feature data, which synthesizes information from both ecological remote sensing and biodiversity, providing a comprehensive basis for evaluating the state of the ecosystem and the ecological impact of ecological product development.

[0173] Preferably, in a specific application scenario, when constructing the social - economic layer feature data, demographic features P demo , economic development features E dev , and policy and regulation features P policy are extracted from multi - source heterogeneous data streams.

[0174] Let the demographic data be where p i is an individual population, a i is the age, g i is the gender, and l i is the place of residence; the economic development data is where r i is the region, t i is the time, and v i is the economic indicator value (such as GDP, industrial output value, etc.); the policy and regulation text data is The demographic feature P demo : The population number N pop = |P data | can be calculated, and the age distribution where n k is the population number within the age interval [a k , a k+1 )), the gender ratio The population density where A region is the area of the research region. The demographic feature vector Pdemo = [N pop ; A dist ; G ratio ; D pop . Economic development feature E dev : For each region r and time t, calculate the mean value of 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 regulation feature P policy : Use natural language processing techniques (such as bag-of-words model, TF-IDF, etc.) to extract features from policy and regulation texts. Let the bag-of-words Policy and regulation text Word frequency vector of where is the frequency of word w k in text . Policy and regulation feature vector where

[0175] P data : Demographic data, recording individual information of the population. E data : Economic development data, containing economic indicator values of different regions and times. T policy : Policy and regulation text data, used to analyze the impact of policies on the development of ecological products. N pop : Population quantity, reflecting the population size of the research area. A dist : Age distribution, helping to understand the age structure of the population. G ratio : Gender ratio, having an impact on the labor market and consumer market. D pop : Population density, reflecting the spatial distribution of the population. μ r,t : Mean value of economic indicators of region r at time t, reflecting the economic development level of the region. g r,t : Economic growth rate of region r at time t, reflecting the development trend of the economy. TF-IDF value of word w k in policy and regulation text , used to measure the importance of the word in the text. P demo : Demographic feature vector, integrating information such as population quantity, age, gender, and distribution. E dev : Economic development feature vector, showing the economic development level and trend of different regions and times. P policy : Policy and regulation feature vector, extracting key information from policy and regulation texts.

[0176] Preferably, in a specific application scenario, when mining spatial features to form socio-economic layer feature data, spatial features of demographic characteristics, economic development characteristics, and policy and regulation characteristics are mined. Let P demo 、E dev 、P policy correspond to spatial distribution data Use spatial autocorrelation analysis (such as Moran's I index) to mine spatial correlation. For the spatial variable 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 is an element of the spatial weight matrix, representing the spatial relationship (such as adjacency relationship) between spatial units i and j, Fill in the missing values of spatial data through spatial interpolation (such as Kriging interpolation). Let the known data points The point to be predicted is x0, and the Kriging interpolation formula is where λ i is the weight coefficient, obtained by solving the semivariogram function and the Kriging equations. The socio-economic layer feature data S layer is a vector that synthesizes spatial features such as spatial correlation and interpolation results.

[0177] are the spatial distribution data of demographic characteristics, economic development characteristics, and policy and regulation characteristics respectively. Z(x): Spatial variable, such as population density, economic indicators, etc. w ij : Element of the spatial weight matrix, describing the adjacency or distance relationship 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 result, used to predict the value of the spatial variable at an unknown location. S layer : Socio-economic layer feature data, synthesizing the spatial information of socio-economic characteristics, which helps to analyze the impact of socio-economic differences in different regions on the development of ecological products.

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

[0179] 1. This application uses multiple linear regression to fit multi-source heterogeneous data streams, determines the regression coefficients by minimizing the sum of squared errors, and thus captures the long-term trends of the data. Subsequently, the discrete Fourier transform is used to transform the trend terms in the time domain to the frequency domain to analyze the periodic changes of the data. Traditional methods mostly use simple linear regression, which can only describe a single trend of the data and cannot cope with complex changes. When analyzing in the frequency domain, there may be a lack of precise transformation means, 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 the complex temperature change trend, and the analysis of periodic changes such as seasons is also relatively rough. The multiple linear regression of this application can adapt to more complex data changes. By adjusting the polynomial order, it can more accurately fit the trends of multi-source heterogeneous data streams, providing a more practical basis for risk prediction. The Fourier transform clearly shows the components of the data at different frequencies, accurately reveals the periodic fluctuations of the ecosystem, such as the laws of ecological products affected by seasons, and helps to plan production and sales in advance.

[0180] 2. The spectral features of this application are obtained by calculating statistics such as the mean and variance of different bands of multi-spectral images, reflecting the response of ground objects to different spectra; the texture features are extracted by means of the gray-level co-occurrence matrix based on the spatial relationship of pixel pairs; the shape features are determined by calculating geometric parameters such as the perimeter, area, and circularity of the target object. Traditional spectral feature extraction only focuses on a few bands and is difficult to comprehensively reflect the characteristics of ground objects. The means of texture feature extraction are limited and cannot fully explore complex texture information. The calculation of shape features may not be accurate enough, and the 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 is likely to lead to misjudgment 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 the development of ecological products. The texture feature extraction based on the gray-level co-occurrence matrix can finely distinguish different ecological landscapes, helping to accurately plan the areas for the development of ecological products. The accurate calculation of shape features helps to identify specific ecological objects and improve the accuracy of ecological environment assessment in the development of ecological products.

[0181] 3. The species richness of this application is determined by counting the number of species within a specific area and time; the species distribution is estimated using kernel density, with the kernel function describing the spatial distribution density of species; the ecological relationships are reflected by constructing an ecological relationship map, using the characteristics of the map to represent the interactions between species. Traditional species richness statistics lack clear spatio-temporal definitions, affecting the accuracy of data. The means of species distribution analysis are single, making it difficult to accurately present complex distribution situations. Ecological relationship research mostly relies on simple qualitative descriptions and lacks quantitative analysis. When evaluating 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. The species richness statistics with clear spatio-temporal definitions in this application provide accurate data for the ecological impact assessment of ecological product development. Kernel density estimation can intuitively display species distribution, providing a scientific basis for the spatial planning of ecological product development. The quantified characteristics of the ecological relationship map 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 uses a weighted fusion method to combine ecological remote sensing features and biodiversity features with different weights to comprehensively reflect various aspects of information in the ecosystem. Traditional fusion methods are mostly simple superpositions, without considering the importance differences of different features, making it difficult to fully utilize the advantages of multi-source data. In the risk prediction of ecological product development, ecological remote sensing and biodiversity information cannot be effectively integrated, resulting in incomplete risk assessment. The weighted fusion in this application can flexibly adjust the feature weights according to the ecological product development scenario, highlighting key information, enabling the ecological layer feature data to more comprehensively and accurately reflect the ecosystem state, and improving the reliability of risk prediction for ecological product development.

[0183] 5. The demographic characteristics of this application are obtained by calculating indicators such as population quantity, age distribution, gender ratio, and population density; the economic development characteristics are reflected by calculating the means, growth rates, etc. of economic indicators; the policy and regulation characteristics are extracted from policy texts using techniques such as the bag-of-words model and TF-IDF in natural language processing to obtain key information. Traditional demographic characteristic analysis lacks in-depth exploration of details such as age distribution. Economic development characteristic analysis mostly focuses on the total amount and pays insufficient attention to dynamic indicators such as growth rates. Policy and regulation feature extraction relies on manual reading, with low efficiency and strong subjectivity. When analyzing the relationship between ecological product development and social economy, traditional methods cannot comprehensively and accurately grasp the influence of social and economic factors. The comprehensive demographic characteristic analysis in this application provides detailed basis for the assessment of market demand and labor resources for ecological product development. Paying attention to the dynamic economic development indicators helps to grasp the market trend and optimize the investment strategy for ecological product development. The natural language processing technology efficiently and objectively extracts key policy and regulation information, providing a policy orientation for ecological product development decision-making.

[0184] 6. This application uses spatial autocorrelation analysis (such as Moran's I index) to measure the correlation of spatial variables, fills in the missing values of spatial data through spatial interpolation (such as Kriging interpolation), and synthesizes these spatial features to form the feature data of the socio-economic layer. Traditional spatial analysis lacks effective means to measure spatial correlation, and the methods for processing missing values in spatial data are simple, resulting in inaccurate and incomplete spatial feature analysis. In the regional planning of ecological product development areas, the spatial differences of socio-economic factors cannot be accurately analyzed. The spatial autocorrelation analysis of this application clearly shows the spatial distribution law of socio-economic factors, providing a reference for the regional layout of ecological product development. Spatial interpolation accurately fills in the missing values, improves the quality of spatial data, enables the feature data of the socio-economic layer to more comprehensively and accurately reflect regional differences, and helps the scientific planning of ecological product development.

[0185] Optionally, the multi-modal feature fusion of the physical layer feature data, ecological layer feature data, and socio-economic layer feature data to generate the comprehensive ERIFM ecological development risk feature data includes: extracting local pattern features in the physical layer feature data based on a 1D convolutional capsule layer to generate physical capsules; extracting spatial hierarchical features in the ecological layer feature data based on a 2D convolutional capsule layer to generate ecological capsules; extracting spatial dependence adjustment in the socio-economic layer feature data based on a graph capsule network to generate economic capsules; calculating the global development risk correlation of the physical capsules, ecological capsules, and economic capsules to generate a fusion weight matrix; and performing multi-modal feature fusion on the physical layer feature data, ecological layer feature data, and socio-economic layer feature data based on the fusion weight matrix to generate the comprehensive ERIFM ecological development risk feature data.

[0186] Preferably, in a specific application scenario, when extracting local pattern features in the physical layer feature data based on a 1D convolutional capsule layer to generate physical capsules, let the physical layer feature data be where N is the number of features. Use a 1D convolutional kernel to perform a convolution operation with a convolution step size of s and a padding of p.

[0187] The convolution output C l is calculated as follows: where l satisfies 1 ≤ l ≤ N - M + 1 + 2p and l increases in steps of s. Preferably, in a specific application scenario, when generating physical capsules using the dynamic routing algorithm, let the convolution output C = {C l}, and input it into the dynamic routing algorithm. Assume there are n p low-level capsules (convolution output units) and m p high-level capsules (physical capsules). The initial routing weight b ij = 0, where i = 1,..., n p , j = 1,..., m pIterate r times and perform the following steps: Calculate the coupling coefficient Calculate the input of the high-level capsule For s j Apply the squeeze function Obtain the physical capsule v j , update the routing weight b ij = b ij + v j ·C i , and finally obtain the set of physical capsules P: Physical layer feature data, which contains the 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 in the ecological product development process. K: 1D convolution kernel, used to extract local patterns in the physical layer feature data. Different convolution kernels can capture different types of local features. M: The length of the convolution kernel, which determines the size of the local range considered by the convolution operation. s: Convolution step size, which controls the movement interval of the convolution operation on the feature data. p: Padding size, used to handle boundary cases to ensure that the convolution operation can cover the entire feature data. n p : The number of low-level capsules, corresponding to the number of convolution output units. m p : The number of high-level capsules, that is, the number of generated physical capsules. b ij : Routing weight, used to measure the connection strength between low-level capsules and high-level capsules. c ij : Coupling coefficient, reflecting the contribution ratio of low-level capsules to high-level capsules. s j : The input of the high-level capsule, which is the weighted sum of the outputs of low-level capsules. v j : Physical capsule, representing the local pattern features in the physical layer feature data.

[0188] Preferably, in a specific application scenario, when extracting the spatial hierarchical features in the ecological layer feature data based on the 2D convolutional capsule layer to generate ecological capsules, let the ecological layer feature data be where H and W are the height and width of the feature data respectively. Use the 2D convolution kernel to perform the convolution operation, and the convolution step size is (s h , s w ), and the padding is (p h , p w ). The convolution output is calculated 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 stepped by s h and s wIncreases. Preferably, in a specific application scenario, when the dynamic routing algorithm generates ecological capsules, similar to generating physical capsules, the 2D convolution output is input into the dynamic routing algorithm. Assume there are n e low-level capsules (2D convolution output units) and m e high-level capsules (ecological capsules).

[0189] After r iterations of the dynamic routing process, an ecological capsule set E: Ecological layer feature data, which combines ecological remote sensing features and biodiversity features and has spatial distribution characteristics. K 2D : 2D convolution kernel, used to extract the spatial hierarchical features in the ecological layer feature data. M and N: The height and width of the 2D convolution kernel, which determine the spatial range considered by the convolution operation. (s h , s w ): Convolution stride, which respectively controls the moving interval of the convolution operation in the height and width directions. (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. n e : Number of low-level capsules, corresponding to the number of 2D convolution output units. m e : Number of high-level capsules, that is, the number of generated ecological capsules. V e : Ecological capsule, representing the spatial hierarchical features in the ecological layer feature data.

[0190] Preferably, in a specific application scenario, when generating economic capsules by adjusting the spatial dependence in the social and economic layer feature data based on the graph capsule network, let the social and economic layer feature data be S, construct a graph G = (V, E), where V is the set of nodes, each node corresponds to a social and economic feature region, and E is the set of edges, and the weight w ij of the edge represents the spatial dependence relationship between nodes i and j (for example, it can be determined by spatial distance, economic connection, etc.). Perform a graph convolution operation on the graph G. Let the feature vector of node i be x i , and the output h i of the graph convolution layer is calculated as follows:

[0191] where N(i) is the set of neighbor nodes of node i, d i 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 high-level capsules (economic capsules).

[0192] S: Socio - economic layer feature data, which contains the spatial distribution information of demographic characteristics, economic development characteristics, and policy and regulation characteristics. G: Graph, used to represent the spatial dependence relationship between socio - economic feature regions. w ij : The weight of the edge, which reflects the spatial dependence intensity between nodes i and j. x i : The eigenvector of node i, representing the socio - economic characteristics of this region. h i : The output of the graph convolutional layer, which fuses the feature information of the node and its neighbor nodes. n s : The number of low - level capsules, corresponding to the number of graph convolutional output units. m s : The number of high - level capsules, that is, the number of generated economic capsules. V s : Economic capsule, representing the spatially - dependent adjustment features in the socio - economic layer feature data.

[0193] Preferably, in a specific application scenario, when calculating the global development risk correlation of physical capsules, ecological capsules, and economic capsules and generating the fusion weight matrix, let the physical capsule ecological capsule economic capsule

[0194] Calculate the correlation between pairwise capsules. For example, the physical capsule and the ecological capsule correlation can be calculated by cosine similarity: Similarly, calculate (the correlation between the physical capsule and the economic capsule) and (the correlation between the ecological capsule and the economic capsule). Combine these correlations into a matrix R, and then obtain the fusion weight matrix W through normalization. For example:

[0195] V p 、V e 、V s : Respectively represent the sets of physical capsules, ecological capsules, and economic capsules. respectively represent the correlations between the physical capsule and the ecological capsule, the physical capsule and the economic capsule, and the ecological capsule and the economic capsule. 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 multi - modal feature fusion.

[0196] Preferably, in a specific application scenario, when performing multi-modal feature fusion on physical layer feature data, ecological layer feature data, and socio-economic layer feature data based on a fusion weight matrix to generate comprehensive ERIFM ecological development risk feature data, assume that the physical layer feature data P, ecological layer feature data E, and socio-economic layer feature data S are respectively converted into vector forms P, E, and S. The comprehensive ERIFM ecological development risk feature data F after fusion is calculated as follows: F = W1P + W2E + W3S, where W1, W2, and W3 are the weight sub-matrices corresponding to the physical layer, ecological layer, and socio-economic layer in the fusion weight matrix W. P, E, S: respectively represent the vector representations of the physical layer, ecological layer, and socio-economic layer feature data. W1, W2, W3: the weight sub-matrices corresponding to the feature data of each layer in the fusion weight matrix W. F: the comprehensive ERIFM ecological development risk feature data, which integrates multi-faceted feature information such as physical, ecological, and socio-economic, and can be used for ecological product development risk prediction.

[0197] In the specific scenario of ecological product development risk prediction, compared with traditional technology processing, the technologies involved above have the following technical advantages:

[0198] 1. The above solution of this application uses 1D convolutional capsule layers, 2D convolutional capsule layers, and graph capsule networks to extract features from physical layer, ecological layer, and socio-economic layer feature data respectively. The capsule network can better capture local patterns, spatial hierarchies, and spatial dependencies in the data through the dynamic routing algorithm. For example, the dynamic routing algorithm adaptively adjusts the routing weights according to the correlation between low-level capsules and high-level capsules, enabling high-level capsules to focus on more meaningful features. When traditional convolutional neural networks (CNNs) process features, they only focus on the presence or absence of features and ignore the direction and spatial relationships between features. For example, when identifying the terrain features of an ecological product development area, traditional CNNs may not be able to accurately distinguish the spatial hierarchical structure of different terrain features. The capsule network of this application can retain the direction and spatial information of features, and the generated physical capsules, ecological capsules, and economic capsules can more accurately represent the features of their respective layers. In ecological layer feature extraction, it can more precisely identify the spatial hierarchical relationships of different elements in the ecosystem, such as the distribution structures of forests, rivers, farmlands, etc. This application has better adaptability to data pose changes, occlusions, etc. In actual ecological product development, there may be situations where some data is occluded or missing, and the capsule network can still effectively extract key features, improving the stability of feature extraction.

[0199] 2. According to the different characteristics of the feature data in the physical layer, ecological layer, and socioeconomic layer, this application adopts different feature extraction methods. For example, a 1D convolutional capsule layer is used for the physical layer, which is suitable for processing one-dimensional time series features; a 2D convolutional capsule layer is used for the ecological layer, which can effectively extract two-dimensional spatial features; a graph capsule network is used for the socioeconomic layer, which can process graph structure data with spatial dependence relationships. Traditional technologies use a unified method to process all types of data and cannot fully explore the inherent characteristics of different data types. For example, using the method for processing images to process socioeconomic data ignores the spatial dependence relationships in the socioeconomic data. The dedicated methods for different data types in this application can give full play to the characteristics of the data and improve the efficiency and quality of feature extraction. When processing the data in the socioeconomic layer, the graph capsule network can better capture the economic connections and policy impacts between different regions and provide more valuable information for risk prediction. This application can extract features from multiple dimensions, covering aspects such as physics, ecology, and socioeconomic, providing a more comprehensive information basis for subsequent risk prediction.

[0200] 3. This application generates a fusion weight matrix by calculating the global development risk correlation between physical capsules, ecological capsules, and economic capsules. This method takes into account the mutual relationships between the features at different levels, making the fusion process more reasonable. For example, by calculating the correlation between different capsules using cosine similarity, the degree of their association in risk prediction can be quantified. Traditional feature fusion methods usually use fixed weights for fusion and do not consider the internal connections between different features. For example, simply adding the features at 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 in the fusion process, the features with a high correlation with risk prediction can obtain more weights, improving the quality of the fused features. In the risk prediction of ecological product development, when the correlation between the features in the ecological layer and the risk is relatively high, the weight of the ecological capsule in the fusion will increase accordingly. This application can organically combine the features at 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. The application performs multi-modal feature fusion on the physical layer, ecological layer, and socio-economic layer feature data based on the fusion weight matrix to generate the comprehensive feature data of ERIFM ecological development risk. This fusion method integrates information from multiple levels and can more comprehensively reflect various risk factors in the ecological product development process. Traditional risk prediction methods only consider single or a few factors at some levels and ignore the interactions between factors at different levels. For example, only considering ecological environment factors while ignoring the impact of socio-economic factors on ecological product development leads to inaccurate risk prediction results. The comprehensive feature data of this application contains information from multiple aspects such as physical, ecological, and socio-economic, and can more comprehensively evaluate the risks that may be faced in the ecological product development process. For example, when considering the market demand for ecological products, combining characteristics such as demographics and economic development at the socio-economic layer and the resource status at the ecological layer can more accurately predict the sales risk of the product. The more accurate risk prediction of this application can provide early warnings for ecological product development, help developers adjust development strategies in a timely manner, and make more informed decisions. For example, when it is predicted that the ecological environment risk in a certain area is relatively high, developers can take measures in advance for ecological protection or adjust the development plan.

[0202] Optionally, the vectorization of the comprehensive feature data of ERIFM ecological development risk is performed to obtain the ecological development risk feature vector, including: grouping the comprehensive feature data of ERIFM ecological development risk into capsule groups according to modalities to obtain several modality capsule groups; performing dynamic pooling on the modality capsule groups to calculate the information entropy between the modality capsule groups; based on the information entropy, performing norm processing on each modality capsule group to obtain the capsule norm vector; and performing semantic enhancement on the capsule norm vector to generate the ecological development risk feature vector.

[0203] Preferably, in a specific application scenario, when grouping the comprehensive feature data of ERIFM ecological development risk into capsule groups according to modalities to obtain several modality capsule groups, let the comprehensive feature data of ERIFM ecological development risk be where N is the total number of features. It is divided into M modality capsule groups according to modalities Here I m is the set of feature indices belonging to the m-th modality, and In the ecological product development risk prediction scenario, usually M = 3, corresponding to the physical layer, ecological layer, and socio-economic layer respectively. F: The comprehensive feature data of ERIFM ecological development risk, which is a complex feature set integrating information from different levels and comprehensively reflects the potential risk factors in the ecological product development process. N: The total number of features in the comprehensive feature data. G m: The m-th modal capsule group contains relevant features under a specific modality. For example, the physical layer modal capsule group G1 contains physical environment features such as temperature and humidity; the ecological layer modal capsule group G2 contains ecological relevant features such as species richness and ecological landscape texture; the socio-economic layer modal capsule group G3 contains socio-economic features such as population density and economic growth rate. I m : The feature index set belonging to the m-th modality, which is used to accurately divide features of different modalities.

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

[0205] Define the attention weight where W m is a learnable weight matrix, b m is a bias vector, and q m is a query vector. The pooling result

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

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

[0208] Information entropy In actual calculation, approximate calculation can be performed by 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, which is used to measure the importance of this capsule in the pooling process. W m , b m , q m : They are respectively the learnable weight matrix, bias vector, and query vector in the attention mechanism, which are used to adaptively allocate attention. h m: The vector of the m-th modal capsule group 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 modality, controlling the smoothness of the kernel function. H: The information entropy, reflecting the uncertainty and information redundancy between different modal capsule groups.

[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 m = H| -m (i.e., the information entropy of this modality under the condition of fixing other modalities).

[0211] Define the weight where β is the adjustment parameter. For each capsule in G m calculate its p-norm where D is the dimension of the capsule. The capsule norm vector

[0212] H m : The conditional information entropy of the m-th modality, reflecting the uncertainty of this modality in the overall information. ω m : The weight of the m-th modality, adjusted according to the conditional information entropy, and β is used to control the adjustment degree of the weight. 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, measuring the feature strength of the capsule. n m : The capsule norm vector of the m-th modality, integrating the norm information of all capsules in this modality and considering the weight of this modality.

[0213] Preferably, in a specific application scenario, when performing semantic enhancement on the capsule norm vector to generate the ecological development risk feature vector, concatenate the M capsule norm vectors n1, n2, …, n M into a vector v = [n1; n2; …; n M . Use a deep residual network (ResNet) for semantic enhancement. Suppose ResNet has L residual blocks, and the input of the l-th residual block is x l , and 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 an activation function (such as ReLU), is the weight matrix, and b is the bias vector. After being processed by L residual blocks, the final output vector r, that is, the ecological development risk feature vector, is obtained. v: The concatenated vector contains capsule norm information of different modalities. L: The number of residual blocks in ResNet, which controls the depth of the network. F(x l ,W l ): The residual function is used to learn the residual information of the input features, helping the network training and avoiding the problem of gradient disappearance. r: The ecological development risk feature vector, after semantic enhancement, can more accurately represent the risk features in the process of ecological product development and can be used in subsequent risk prediction models.

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

[0215] 1. The above solution accurately groups the comprehensive feature data of ERIFM ecological development risks according to modalities, and through the clear index set I m ensures the accurate division of different modality features, and the modality feature sets do not intersect with each other, ensuring the rigor of grouping. Traditional technologies often lack clear modality distinctions when grouping features, and simply divide them according to data sources or general categories, resulting in insufficiently fine grouping, confusion between different modality features, and inability to accurately reflect the risk information at different levels in ecological product development. For example, when distinguishing physical layer and ecological layer features, traditional methods may misclassify some indirect features of the impact of the physical environment on the ecology, affecting subsequent analysis. The precise modality grouping of this application can clearly separate features at different levels such as the physical layer, ecological layer, and socio-economic layer, making the features contained in each modality capsule group highly homogeneous, providing a basis for subsequent specialized processing for different modalities. For example, when analyzing the market risk of ecological product development, relevant features can be directly obtained from the socio-economic layer modality capsule group, avoiding interference from features at different levels. The clear grouping of 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 the physical layer modality capsule group, the potential impact of physical environmental factors such as temperature and humidity on ecological product development can be intuitively understood.

[0216] 2. This application adopts a dynamic pooling method based on the attention mechanism, and adaptively assigns attention weights α through the learnable weight matrix W m , bias vector b m and query vector q m m j ​, highlighting the role of important capsules. When calculating information entropy, the kernel density estimation method is used to estimate the joint probability distribution, which can more accurately reflect the relationship between different modal capsule groups. Traditional pooling methods (such as max pooling and average pooling) usually use fixed rules for pooling, without considering the importance differences between capsules, and may lose some key 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 the attention mechanism in this application can adaptively assign weights according to the importance of capsules, enabling key information to be focused on during the pooling process 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 to more accurately capture ecological risk information. The kernel density estimation method in this application can better fit complex data distributions, thereby more accurately calculating information entropy. Accurate information entropy can help developers understand the degree of association 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 modality based on information entropy m , and controls the adjustment degree of the weight by adjusting the parameter β. The p-norm is calculated for each capsule, considering the measurement of feature intensity by norms of different orders, and the capsule norm vector n is comprehensively obtained m . Traditional norm processing usually uses fixed weights or simple normalization methods, without considering the information entropy differences between different modalities, and cannot be adaptively adjusted according to the uncertainty and importance of different modalities. Moreover, traditional methods may only use a single norm order and cannot comprehensively reflect the feature intensity of capsules. This application dynamically adjusts the weights of modalities according to information entropy, so that in the risk prediction of ecological product development, higher weights are given to modalities with higher uncertainty, and information from different modalities is more reasonably integrated. For example, if the information entropy of the socio-economic layer is high, it indicates that the uncertainty of this modality is large, and higher weights will be given in norm processing to highlight its impact on risk prediction. This application uses the p-norm, and the appropriate order can be selected according to different application scenarios to measure the feature intensity of capsules from multiple dimensions. For example, the p = 1 norm pays more attention to the sparsity of features, and the p = 2 norm pays more attention to the overall intensity of features. By selecting different p values, the feature information of capsules can be more comprehensively reflected.

[0218] 4. This application uses a deep residual network (ResNet) to semantically enhance the concatenated vectors, and learns the residual information of the input features through the residual block F(\mathbf{x}_l,\mathbf{W}_{l}), avoiding the gradient vanishing problem and improving the training effect of the network. Traditional semantic enhancement methods may use a simple multi-layer perceptron (MLP). As the network depth increases, it is prone to gradient vanishing or gradient explosion problems, resulting in network training difficulties and inability to effectively learn complex semantic information. This application's ResNet can learn deeper feature information through multiple residual blocks and explore the potential semantic relationships between different modal features. In the risk prediction of ecological product development, the complex interactions between the physical layer, ecological layer, and socio-economic layer features can be discovered to improve the accuracy of risk prediction. The introduction of the residual block of this application enables the network to update parameters more stably during the training process, avoiding the gradient vanishing problem and improving the training efficiency and generalization ability of the network. It can converge to the optimal solution faster and has better adaptability when facing different data sets and tasks.

[0219] Optionally, the ecological development risk feature vector is input into the development risk quantification model to calculate the ecological development risk score, including: based on the input layer, the ecological development risk feature vector is difference-filled to obtain a continuous standardized feature tensor; based on the dynamic feature enhancement layer, local feature patterns are extracted from the continuous standardized feature tensor to obtain a local feature vector; based on the feature relationship layer, spatial dependency extraction is performed on the local feature vector to obtain a spatially enhanced feature vector; based on the multimodal attention fusion layer, multi-head attention fusion is performed on the spatially enhanced feature vector 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 context features; based on the residual network, residual processing is performed on the context features to obtain abstract development risk features; based on the probability distribution prediction layer, the abstract development risk features are mapped into the development risk level space to output a development risk probability vector; based on the decision output layer, confidence judgment is performed on 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 difference-filled based on the input layer to obtain a continuous standardized feature tensor, the ecological development risk feature vector is set to be v = [v1, v2, ..., v n ], where n is the vector dimension. When there are missing values ​​in the vector, the linear interpolation method is used. Assume that the value at the i-th position is missing, and its preceding and following non-missing values ​​are v i-k and v i+l (k,l≥1), then fill value for: After filling, the feature vector is normalized to obtain the normalized feature vector v std , and the normalization formula is: where is the mean value, is the standard deviation.

[0221] Finally, the normalized feature vector is reshaped into a continuous normalized feature tensor T. Assuming it is reshaped into a tensor with shape (c, h, w), satisfying c×h×w = n. v: The ecological development risk feature vector contains multi-faceted feature information about ecological development risk obtained from the previous steps. n: The dimension of the feature vector, reflecting the number of features. The value obtained after linearly interpolating and filling the missing values ensures the integrity of the data. v std : The normalized feature vector eliminates the influence of the dimension between different features. μ: The mean value of the feature vector, reflecting the average level of the data. σ: The standard deviation of the feature vector, reflecting the degree of dispersion of the data. T: The continuous normalized feature tensor, which is convenient for subsequent layers to process. Different dimensions may represent different feature categories or the spatial structure of features.

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

[0223] The output y(p) of the deformable convolution is: where p is the position on the output feature map, K is the number of convolution kernels, and p k is the offset position of the convolution kernel. The output feature map is 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: The continuous normalized feature tensor, serving as the input of the deformable convolution. K: The deformable convolution kernel, used to extract local feature patterns. Δp: The offset, enabling the convolution kernel to adaptively focus on features at different positions and enhancing the ability to extract local features. y(p): The output of the deformable convolution, reflecting the response of local features. K: The number of convolution kernels, determining the types of local features extracted. p k : The fixed offset position of the convolution kernel. f local : The local feature vector, which compresses the feature map into a one-dimensional vector through global average pooling for subsequent processing.

[0224] Preferably, in a specific application scenario, when extracting the spatial dependence of local feature vectors based on the feature relationship layer to obtain spatially enhanced feature vectors, a graph neural network (GNN) is used to extract the spatial dependence relationship between local feature vectors. Construct a graph G=(V, E), where the nodes V are the elements in the local feature vectors, and the edges E represent the relationships between the nodes. The weight w of the edge ij can be calculated by cosine similarity: The propagation rule of GNN is: where is the feature representation of node i at the l-th layer, N(i) is the set of neighbor nodes of node i, d i is the degree of node i, W (l) is the weight matrix at the l-th layer, b (l) is the bias vector, and σ is the activation function (such as ReLU). After L layers of GNN propagation, the feature representations of all nodes are concatenated to obtain the spatially enhanced feature vector f space . G=(V, E): The constructed graph is used to represent the relationship between local feature vectors. w ij : The weight of the edge, which reflects the similarity between nodes i and j. The feature representation of node i at the l-th layer gradually incorporates the information of neighbor nodes as the number of GNN layers increases. N(i): The set of neighbor nodes of node i, which defines the scope of information propagation. d i : The degree of node i, which is used to normalize the weight of the edge. W (l) : The weight matrix at the l-th layer, which is used to learn the relationship between nodes. b (l) : The bias vector at the l-th layer, which helps to adjust the output result. f space : The spatially enhanced feature vector, which contains the spatial dependence relationship between local feature vectors.

[0225] Preferably, in a specific application scenario, when performing multi-head attention fusion on the spatially enhanced feature vector based on the multi-modal attention fusion layer to obtain the fused feature vector, a multi-head attention mechanism is used to fuse the spatially enhanced feature vector. Let the spatially enhanced feature vector be f space , and project it 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 , where W Q , W K and W V are learnable projection matrices. Split Q, K, and V into h heads, and the attention calculation for each head is: where d kFor the dimension of the key. Concatenate the attention results of h heads, and then obtain the fused feature vector f through a linear transformation fusion : f fusion = Concat(Attention1, Attention2, …, Attention h )W O , where W O is the output projection matrix. f space : Spatial enhanced feature vector, as the input of 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 : Learnable projection matrices, used to adjust the direction and scale of the projection. h: Number of heads, different heads can focus on different feature subspaces. d k : Dimension of the key, used to scale the attention scores to avoid gradient vanishing or explosion. Attention i : Attention result of the i-th head, reflecting the degree of attention to different feature subspaces. W O : Output projection matrix, which linearly combines the multi-head attention results. f fusion : Fused feature vector, integrating information from different feature subspaces.

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

[0227] Use a long short-term memory network (LSTM) to model the sequence. The cell state update formula of the LSTM is:

[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 is the input at the t-th time step, h t-1 is the hidden state at the previous moment, C t-1 is the cell state at the previous moment, i t , f t , o t are the input gate, forget gate and output gate respectively, g t is the candidate cell state, ⊙ is element-wise multiplication, W is the weight matrix, b is the bias vector, σ is the sigmoid function, and tanh is the hyperbolic tangent function. Finally, the hidden state h T at the last time step is used as the context feature f context . f fusion : The fused feature vector, reshaped into a sequence form as the input of the LSTM. T: The sequence length, which determines the number of time steps processed by the LSTM. d: The feature dimension of each time step. x t : The input at the t-th time step, from the sequence representation of the fused feature vector. h t-1 : The hidden state at the previous moment, used to transmit historical information. C t-1 : The cell state at the previous moment, used for long-term memory information. i t , f t , o t : The input gate, forget gate and output gate, controlling the inflow, retention and output of information. g t : The candidate cell state, used to update the cell state. W: The weight matrix, used to learn patterns and relationships in the sequence. b: The bias vector, assisting in adjusting the output result. f context : The context feature, containing the global context information of the fused feature vector.

[0229] Preferably, in a specific application scenario, when performing residual processing on the context feature based on a residual network to obtain the abstract development risk feature, use the residual network (ResNet) to process the context feature f contextProcessing is carried out. 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, \(F(x)=\sigma(W_2\sigma(W_1x + b_1)+b_2)\), \(W_1\) and \(W_2\) are weight matrices, \(b_1\) and \(b_2\) are bias vectors, and \(\sigma\) is an activation function (such as ReLU). After being processed by \(L\) res residual blocks, the abstract development risk feature \(f\) abstract is obtained.

[0230] \(f\) context : Context feature, as the input of the residual network. \(x\): The input of the residual block, initially the context feature. \(y\): The output of the residual block, which retains the input information through the residual connection. \(F(x)\): The residual function, used to learn the residual information of the input features. \(W_1\), \(W_2\): Weight matrices, used to learn the abstract representation of the features. \(b_1\), \(b_2\): Bias vectors, assisting in adjusting the output results. \(L\) res : The number of residual blocks, controlling the depth of the network. \(f\) abstract : Abstract development risk feature, after being processed by the residual network, extracts more advanced abstract features.

[0231] Preferably, in a specific application scenario, in the probability distribution prediction layer, when mapping the abstract development risk feature to the development risk level space to output the development risk probability vector, a fully connected layer is used to map the abstract development risk feature \(f\) abstract to the development risk level space. Let there be \(m\) development risk levels, and the output of the fully connected layer is: \(z = f\) abstract \(W\) out +\(b\) out , where \(W\) out is the output weight matrix and \(b\) out is the output bias vector. The \(z\) is converted into a probability distribution through the softmax function to obtain the development risk probability vector \(p\):

[0232] \(f\) abstract : Abstract development risk feature, as the input of the fully connected layer. \(m\): The number of development risk levels, representing different degrees of risk. \(z\): The output of the fully connected layer, which is the unnormalized score of the development risk level. \(W\) out : Output weight matrix, used to map the abstract feature to the development risk level space. \(b\) out : Output bias vector, assisting in adjusting the output results. \(p\): Development risk probability vector, each element representing the probability of the corresponding risk level.

[0233] Preferably, in a specific application scenario, in the decision output layer, when making a confidence judgment on the development risk probability vector based on the confusion matrix to calculate the ecological development risk score, let the confusion matrix be \(M\), and its element \(M\) ijDenote the number of samples with the true risk level being i and predicted as risk level j.

[0234] First, calculate the confidence conf of each risk level j : Then, calculate the ecological development risk score S based on the development risk probability vector p and the confidence conf: where r j is the weight of the j-th risk level, reflecting the importance of this risk level. M: Confusion matrix, used to evaluate the prediction performance of the model. M ij : Elements of the confusion matrix, recording the prediction situations of different risk levels. conf j : Confidence of the j-th risk level, reflecting the reliability of the prediction of this risk level. p: Development risk probability vector, providing the prediction probability of each risk level. r j : Weight of the j-th risk level, determining the importance of different risk levels according to the actual application scenario. S: Ecological development risk score, comprehensively considering the prediction probability, confidence, and risk level weight.

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

[0236] 1. The scheme of this application adopts a series of innovative feature processing methods, such as deformable convolution, graph neural network, multi-head attention mechanism, etc., to perform multi-level processing and enhancement on the ecological development risk feature vector. Traditional technologies use simple convolution operations, single fully connected layers, etc. in feature processing, and cannot fully explore the complex relationships and spatial dependencies between features. For example, traditional convolution operations can only extract features at fixed positions and scales, and it is difficult to capture subtle changes and potential patterns for the complex and variable risk features in ecological development. The deformable convolution of this application can adaptively adjust the position of the convolution kernel, more flexibly capture local feature patterns, and can extract features more accurately for the irregularly distributed risk factors (such as local ecological damage areas) in the ecological environment. The graph neural network can explore the spatial dependency relationships between features, considering the mutual influence between different elements in the ecosystem, such as the correlation between ecological indicators in different regions, so as to provide more comprehensive risk information. The multi-head attention mechanism of this application can focus on features from multiple perspectives, fuse the information in different feature sub-spaces, and improve the feature expression ability. 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 network (LSTM) to perform global context modeling on the fused feature vectors, taking into account the sequential information and temporal dependencies of the features. Traditional methods ignore the temporal sequence characteristics of the features and treat the data as independent samples for processing. In ecological development, risk factors often have temporal continuity and cumulative effects, and traditional methods cannot effectively capture this information. The LSTM of this application can process long sequence data and effectively retain and transmit historical information through the mechanisms of forget gates, input gates, and output gates. In ecological development risk prediction, the impact of changes in the ecological environment over a past period on the current risk can be considered. For example, the cumulative effect of climate change in the past few years on the stability of the ecosystem can be considered to more accurately predict future risks. This application models using context information, which can reduce the impact caused by short-term fluctuations or noise and improve the stability and reliability of risk prediction. During the ecological product development process, the long-term risk trend can be more accurately evaluated, providing more stable support for decision-making.

[0238] 3. This application judges the confidence of the development risk probability vector based on the confusion matrix and calculates the ecological development risk score by combining the risk level weights, comprehensively considering the accuracy of the prediction and the importance of different risk levels. Traditional methods simply determine the risk level based on the prediction 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 evaluate their actual risks. By introducing confidence and risk level weights, this application can more comprehensively evaluate ecological development risks. For risk levels with high prediction accuracy, higher weights are given, and at the same time, the importance differences between different risk levels are considered, making the risk score more in line with the actual situation. In ecological product development decision-making, high-risk areas and key risk factors can be more accurately identified, and corresponding measures can be taken for prevention and response. Based on the accurate risk score, decision-makers can more scientifically formulate ecological development plans, reasonably allocate resources, and reduce risks during the development process. For example, for projects with a high risk score, monitoring and protection measures can be increased to ensure the sustainable development of the ecological environment.

[0239] The above description is only a preferred embodiment of this application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solution formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, technical solutions formed by mutually replacing the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for predicting the development risks of ecological products, characterized in that, Including: Obtain multi-source heterogeneous data of ecological products, which includes at least one of the following: environmental physical data, ecological remote sensing data, biodiversity data, and socioeconomic data; Perform streaming processing on the multi-source heterogeneous data to form a multi-source heterogeneous data stream; Conduct multi-modal feature engineering construction on the multi-source heterogeneous data stream to form physical layer feature data, ecological layer feature data, and socioeconomic layer feature data; Perform multi-modal feature fusion on the physical layer feature data, ecological layer feature data, and socioeconomic layer feature data to generate comprehensive ERIFM ecological development risk feature data; Vectorize the comprehensive ERIFM ecological development risk feature data to obtain an ecological development risk feature vector; Input the ecological development risk feature vector into a development risk quantification model to calculate the ecological development risk score.

2. The development risk prediction method for an ecological product according to claim 1, characterized in that The method further includes: Perform time window difference, anomaly detection, and feature derivation processing on the environmental physical data to obtain environmental time-series feature data; Perform radiometric correction, spatial registration, and semantic segmentation on the ecological remote sensing data to obtain a radiometrically corrected semantic image; Perform species standardization, spatio-temporal annotation, and abundance estimation on the biodiversity data to obtain bio-spatio-temporal data; Perform text structuring, spatial mapping, and index normalization on the socioeconomic data to obtain grid economic index data.

3. The development risk prediction method of an ecological product according to claim 1, wherein, The performing streaming processing on the multi-source heterogeneous data to form a multi-source heterogeneous data stream includes: Perform two-dimensional partitioning on the environmental time-series feature data according to a set time window and spatial region to obtain spatio-temporal partitioned data; Vectorize the radiometrically corrected semantic image to obtain a vector feature set, and establish a spatial index for the vector feature set to obtain spatially indexed vector data; Perform relationship modeling on the bio-spatio-temporal data to obtain an ecological relationship map, and establish a path index for the ecological relationship map to obtain indexed ecological relationship data; Perform dimensional modeling on the grid economic index data to obtain a star schema data set, and establish a dimensional index for the star schema data set to obtain indexed economic index data; Align the spatio-temporal partitioned data, spatially indexed vector data, indexed ecological relationship data, and indexed economic index data, and perform conversion on the aligned spatio-temporal partitioned data, spatially indexed vector data, indexed ecological relationship data, and indexed economic index data respectively to obtain an environmental feature vector group, a spatial feature vector group, an ecological feature vector group, and an economic feature vector group; Perform vector group fusion on the environmental feature vector group, spatial feature vector group, ecological feature vector group, and economic feature vector group to obtain a fused feature vector; Based on a set sliding window, perform sliding window processing on the fused feature vector according to a set time step to convert the data within the sliding window into a byte stream to form a multi-source heterogeneous data stream.

4. The development risk prediction method of an ecological product according to claim 1, wherein Performing multi-modal feature engineering construction on the multi-source heterogeneous data stream to form physical layer feature data, ecological layer feature data, and socio-economic layer feature data, including: Performing linear regression analysis on the multi-source heterogeneous data stream to obtain a trend term, and performing Fourier transform on the trend term 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; Extracting demographic features, economic development features, and policy and regulation features from the multi-source heterogeneous data stream, and performing spatial feature mining on the demographic features, economic development features, and policy and regulation features to form socio-economic layer feature data.

5. The development risk prediction method of an ecological product according to claim 1, wherein, Performing multi-modal feature fusion on the physical layer feature data, ecological layer feature data, and socio-economic layer feature data to generate comprehensive ERIFM ecological development risk feature data, including: Extracting local pattern features in the physical layer feature data based on a 1D convolutional capsule layer to generate physical capsules; Extracting spatial hierarchical features in the ecological layer feature data based on a 2D convolutional capsule layer to generate ecological capsules; Extracting spatial dependence adjustment in the socio-economic layer feature data based on a graph capsule network to generate economic capsules; Calculating the global development risk correlation of the physical capsules, ecological capsules, and economic capsules to generate a fusion weight matrix; Performing multi-modal feature fusion on the physical layer feature data, ecological layer feature data, and socio-economic layer feature data based on the fusion weight matrix to generate comprehensive ERIFM ecological development risk feature data.

6. The development risk prediction method for an ecological product according to claim 1, wherein Vectorizing the comprehensive ERIFM ecological development risk feature data to obtain an ecological development risk feature vector, including: Grouping the comprehensive ERIFM ecological development risk feature data into capsule groups according to modalities to obtain several modality capsule groups; Performing dynamic pooling on the modality capsule groups to calculate the information entropy between the modality capsule groups; Performing norm processing on each modality capsule group based on the information entropy to obtain a capsule norm vector; Performing semantic enhancement on the capsule norm vector to generate an ecological development risk feature vector.

7. The development risk prediction method of an ecological product according to claim 1, characterized in that, Inputting the ecological development risk feature vector into a development risk quantification model to calculate the ecological development risk score, including: Performing difference filling on the ecological development risk feature vector based on the input layer to obtain a continuous standardized feature tensor; Extracting local feature patterns from the continuous standardized feature tensor based on a dynamic feature enhancement layer to obtain local feature vectors; Performing spatial dependence extraction on the local feature vectors based on a feature relationship layer to obtain spatially enhanced feature vectors; Performing multi-head attention fusion on the spatially enhanced feature vectors based on a multi-modal attention fusion layer to obtain fused feature vectors; Reshaping the fused feature vectors into a sequence form based on a global context modeling layer to obtain context features; Performing residual processing on the context features based on a residual network to obtain abstract development risk features; Based on the probability distribution prediction layer, map the abstract development risk features into the development risk level space to output a development risk probability vector; Based on the decision output layer, perform confidence judgment on the development risk probability vector based on the confusion matrix to calculate the ecological development risk score.

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