Agrometeorological disaster time series prediction system and method based on multimodal data fusion

Abstract

The present invention relates to the field of agricultural meteorological prediction technology, and specifically to an agricultural meteorological disaster time series prediction system and method based on multimodal data fusion. The system collects agricultural meteorological disaster related data and performs preprocessing, constructs a dynamic semantic association graph, performs multi-layer feature abstraction processing, and generates a semantically enhanced feature vector. A dual-branch prediction network processes temporal dependency and local pattern features, multi-granularity attention processing identifies key feature information, and multi-scale feature fusion extracts feature information of different time scales and performs cascade fusion. A multi-objective optimization module performs model training based on comprehensive feature representation and optimizes multiple objectives. A multi-time scale prediction output module generates short-term accurate predictions, medium-term trend predictions, and long-term risk assessment results, and provides prediction confidence, error range, and risk level information. A dynamic semantic association graph and a multi-layer feature mapping mechanism are constructed to achieve deep fusion of multimodal data at the semantic level.

Description

Technical Field

[0001] The present invention relates to the field of agricultural meteorological forecasting technology, and specifically to a system and method for predicting agricultural meteorological disasters in a time series based on multimodal data fusion, and more particularly to a technical solution for achieving accurate prediction of agricultural meteorological disasters by utilizing technologies such as multimodal data fusion, semantic association graph construction, a dual-branch prediction network, and a multi-granularity attention mechanism. Background Art

[0002] Agrometeorological disasters are a significant factor affecting agricultural production. Accurately predicting these disasters is crucial for ensuring food security and sustainable agricultural development. Traditional agrometeorological forecasting methods rely primarily on single data sources, such as meteorological observations or satellite remote sensing data. These methods suffer from incomplete information and limited prediction accuracy when dealing with complex agrometeorological systems.

[0003] Existing multimodal data fusion technologies often rely on simple feature concatenation or weighted averaging, lacking an understanding of the deep semantic relationships between different modal data, resulting in poor fusion results. Furthermore, traditional time series forecasting models, such as simple recurrent neural networks or convolutional neural networks, have limitations when processing the long-term dependencies and short-term fluctuations of agricultural meteorological data, making it difficult to simultaneously capture both global trends and local anomalies in the data.

[0004] In addition, existing forecasting systems usually only provide point forecast results and lack quantitative expression of forecast uncertainty, which limits the application value of forecast results in actual agricultural decision-making. Summary of the Invention

[0005] The purpose of the present invention is to provide a time series prediction system and method for agricultural meteorological disasters based on multimodal data fusion, so as to solve the technical problems existing in the prior art, such as insufficient depth of multimodal data fusion, limited time series modeling capability, and lack of quantification of prediction uncertainty.

[0006] The present invention proposes an agricultural meteorological disaster time series prediction system based on multimodal data fusion, comprising:

[0007] Multimodal data acquisition module, used to collect agricultural meteorological disaster data, plant image data, soil type data, moisture data, environmental parameter data and historical disaster data;

[0008] a data preprocessing module, connected to the multimodal data acquisition module, configured to receive the original multimodal data sent by the multimodal data acquisition module, perform format unification, outlier cleaning, missing value processing, data dimensionality reduction and denoising on the original multimodal data, and generate standardized multimodal data;

[0009] a semantic association graph construction module, connected to the data preprocessing module, configured to receive the standardized multimodal data, abstract each data attribute in the standardized multimodal data into a node in a graph structure, establish edge connection relationships based on data relevance, semantic relevance, and spatiotemporal proximity between the nodes, and construct a dynamic semantic association graph representing data relationships;

[0010] A feature mapping module, connected to the semantic association graph construction module, is used to perform multi-layer feature abstraction processing based on the dynamic semantic association graph, and generate a semantic enhancement feature vector through neighbor node information aggregation and global semantic enhancement processing;

[0011] a dual-branch prediction network module, connected to the feature mapping module, for receiving the semantically enhanced feature vector, comprising a long sequence dependency modeling branch and a parallel convolutional feature extraction branch, wherein the long sequence dependency modeling branch is used to process temporal dependencies, and the parallel convolutional feature extraction branch is used to capture local pattern features, wherein the long sequence dependency modeling branch and the parallel convolutional feature extraction branch perform bidirectional flow of feature information through an information interaction mechanism;

[0012] a multi-granularity attention processing module, connected to the dual-branch prediction network module, configured to receive the output of the dual-branch prediction network module, identify key feature information through a global temporal attention layer, a local spatial attention layer, and a cross-modal correlation attention layer, perform hierarchical feature importance evaluation, and generate an attention-weighted feature representation;

[0013] A multi-scale feature fusion module is connected to the multi-granularity attention processing module, and is used to receive the attention-weighted feature representation, extract feature information of different time scales through a small-scale fine feature extractor, a medium-scale pattern recognizer, and a large-scale trend analyzer, and perform cascade fusion processing using a progressive feature fusion strategy to generate a comprehensive feature representation;

[0014] A multi-objective optimization module, connected to the multi-scale feature fusion module, is used to perform model training based on the comprehensive feature representation, while optimizing the prediction accuracy target, temporal consistency target, spatial continuity target, and model complexity control target, and perform parameter optimization through a global search strategy and a local fine-tuning strategy;

[0015] The multi-time scale prediction output module is connected to the multi-objective optimization module and is used to generate short-term accurate prediction results, medium-term trend prediction results and long-term risk assessment results based on the optimized model parameters, and provide prediction confidence, error range and risk level information through the uncertainty quantification mechanism.

[0016] Preferably, the multimodal data acquisition module includes:

[0017] Agricultural meteorological disaster data collection unit, used to collect crop disease data, crop insect pest data, crop hail disaster data, crop rainstorm data, crop natural disaster data, crop snow damage data and crop drought data;

[0018] a plant image data acquisition unit, configured to acquire plant organ image data and classify the plant organ image data according to plant organ types;

[0019] Soil environment data collection unit, used to collect soil type data, soil moisture data, soil water content data;

[0020] Environmental parameter collection unit, used to collect temperature and humidity data, rainfall data and wind speed data;

[0021] The historical data collection unit is used to collect historical data on agricultural meteorological disasters that affect the planting area, including historical data on crop disasters and historical meteorological data.

[0022] Preferably, the data preprocessing module includes:

[0023] a data format unification unit, configured to convert the original multimodal data into a unified data format;

[0024] an outlier processing unit, connected to the data format unification unit, for identifying and cleaning outliers in the unified format data using statistical methods;

[0025] A missing value processing unit, connected to the outlier processing unit, is used to process missing values ​​by deletion, filling, interpolation filling or re-collection;

[0026] A data dimension reduction unit, connected to the missing value processing unit, is used to perform data dimension reduction processing using principal component analysis, data fitting, correlation analysis, clustering algorithm or PCA algorithm;

[0027] The denoising processing unit is connected to the data dimension reduction unit and is used to perform denoising processing by adopting wavelet denoising, Gaussian filtering, median filtering, mean filtering, Fourier transform or threshold filtering.

[0028] Preferably, the semantic association graph construction module includes:

[0029] a node construction unit, configured to abstract each data attribute in the standardized multimodal data into a node in a graph structure, and assign a node identifier, a modality type label, spatiotemporal coordinate information, and a data quality score to each node;

[0030] An edge relationship establishment unit, connected to the node construction unit, is used to analyze the numerical correlation, spatiotemporal proximity, semantic relevance, and causal logical relationship between nodes, and establish an edge connection when the relationship between nodes meets preset conditions;

[0031] A dynamic weight calculation unit, connected to the edge relationship establishment unit, is used to comprehensively consider the correlation strength, spatiotemporal distance, semantic similarity and historical association frequency to calculate the weight value of the edge connection;

[0032] The weight adjustment unit is connected to the dynamic weight calculation unit and is used to adaptively adjust the weight value of the edge connection between nodes according to real-time changes in data and predicted task requirements.

[0033] Preferably, in the dual-branch prediction network module:

[0034] The long sequence dependency modeling branch includes an input layer, multiple recurrent hidden layers, and an output layer. The multiple recurrent hidden layers extract temporal features layer by layer, and each recurrent hidden layer has a memory gating mechanism for controlling the retention and forgetting of information.

[0035] The parallel convolution feature extraction branch includes a multi-scale convolution kernel group, which contains convolution kernels of different sizes to capture feature patterns of different time windows, and sets a residual connection mechanism to avoid the gradient vanishing problem;

[0036] The information interaction mechanism includes a bidirectional information transmission channel, the long sequence dependency modeling branch transmits global temporal context information to the parallel convolutional feature extraction branch, and the parallel convolutional feature extraction branch transmits local feature information to the long sequence dependency modeling branch.

[0037] Preferably, in the multi-granularity attention processing module:

[0038] The global time series attention layer is used to identify key time nodes and important time periods in the entire time series data, capturing long-term patterns of seasonal and interannual changes;

[0039] The local spatial attention layer is used to analyze the spatial correlation between different geographical locations and farmland areas, and identify the spatial areas that are most relevant to the prediction target;

[0040] The cross-modal association attention layer is used to process the association between different data modalities, identifying the association between meteorological data and soil data, and the association between plant image features and environmental parameters;

[0041] The hierarchical feature importance evaluation includes basic importance evaluation, associated importance evaluation and contextual importance evaluation, and establishes an importance propagation mechanism to propagate the weights of high-importance features to related features.

[0042] Preferably, in the multi-scale feature fusion module:

[0043] The small-scale fine feature extractor uses a small-size convolution kernel to capture subtle changes and short-term fluctuation features in the data;

[0044] The mesoscale pattern recognizer uses a medium-sized convolution kernel to identify medium-term variation patterns of daily and weekly cycles;

[0045] The large-scale trend analyzer uses a large-size convolution kernel to capture the large-scale time series characteristics of seasonal and interannual variations;

[0046] The progressive feature fusion strategy includes a feature hierarchical organization mechanism, a feature consistency guarantee mechanism and an information richness progressive improvement mechanism.

[0047] Preferably, the multi-objective optimization module includes:

[0048] The prediction accuracy optimization unit is used to optimize the error between the predicted value and the true value, taking into account the average error, error distribution uniformity and extreme value prediction accuracy;

[0049] The time series consistency optimization unit is used to ensure the continuity and rationality of the prediction results in the time dimension and prevent unreasonable jumps in the prediction results;

[0050] Spatial continuity optimization unit, used to ensure the continuity and coordination of prediction results in the spatial dimension and ensure the consistency of prediction results in adjacent geographical areas;

[0051] Model complexity control unit, which is used to control model complexity and computational cost while ensuring prediction performance, and prevent the model from becoming overly complex through regularization mechanism;

[0052] The parameter adaptive adjustment unit is used to explore the parameter space through the global search strategy and to fine-tune the parameters through the local fine adjustment strategy.

[0053] Preferably, the multi-time-scale prediction output module includes:

[0054] The short-term accurate prediction unit is used to generate short-term prediction results of 1-3 days, using a high-precision prediction algorithm and a sophisticated feature processing mechanism;

[0055] The medium-term trend forecast unit is used to generate medium-term forecast results for 3-7 days, focusing on trend changes and pattern evolution;

[0056] Long-term risk assessment unit, used to generate long-term forecast results of 7-14 days, using probabilistic forecasting methods to assess forecast uncertainty and risk levels;

[0057] The uncertainty quantification unit is connected to the short-term precise prediction unit, the medium-term trend prediction unit and the long-term risk assessment unit, and is used to provide a confidence score, an error range estimate and a risk level classification for each prediction result, and adopts a dynamic calibration update mechanism to continuously track the prediction accuracy.

[0058] The method for predicting agricultural meteorological disasters in a time series, applied to any of the above systems, is characterized by comprising the following steps:

[0059] Step S1: collecting agricultural meteorological disaster data, plant image data, soil type data, moisture data, environmental parameter data and historical disaster data through the multimodal data acquisition module to obtain original multimodal data;

[0060] Step S2: performing format unification, outlier cleaning, missing value processing, data dimensionality reduction and denoising processing on the original multimodal data through the data preprocessing module to generate standardized multimodal data;

[0061] Step S3: abstracting each data attribute in the standardized multimodal data into a node in a graph structure through the semantic association graph construction module, establishing edge connection relationships based on data relevance, semantic relevance, and spatiotemporal proximity between nodes, and constructing a dynamic semantic association graph;

[0062] Step S4: performing multi-layer feature abstraction processing based on the dynamic semantic association graph through the feature mapping module, and generating a semantically enhanced feature vector through neighbor node information aggregation and global semantic enhancement processing;

[0063] Step S5: processing temporal dependencies through the long sequence dependency modeling branch of the dual-branch prediction network module, capturing local pattern features through the parallel convolution feature extraction branch, and performing bidirectional flow of feature information between the long sequence dependency modeling branch and the parallel convolution feature extraction branch through an information interaction mechanism;

[0064] Step S6: identifying key feature information through the global temporal attention layer, local spatial attention layer, and cross-modal correlation attention layer of the multi-granularity attention processing module, performing hierarchical feature importance evaluation, and generating attention-weighted feature representation;

[0065] Step S7: extracting feature information of different time scales through the small-scale fine feature extractor, the medium-scale pattern recognizer, and the large-scale trend analyzer of the multi-scale feature fusion module, performing cascade fusion processing using a progressive feature fusion strategy, and generating a comprehensive feature representation;

[0066] Step S8: performing model training based on the comprehensive feature representation through the multi-objective optimization module, optimizing the prediction accuracy target, the temporal consistency target, the spatial continuity target, and the model complexity control target at the same time, and performing parameter optimization through a global search strategy and a local fine adjustment strategy;

[0067] Step S9: Generate short-term accurate prediction results, medium-term trend prediction results and long-term risk assessment results based on the optimized model parameters through the multi-time scale prediction output module, and provide prediction confidence, error range and risk level information through the uncertainty quantification mechanism.

[0068] The beneficial effects of the present invention are:

[0069] 1. By constructing a dynamic semantic association graph and a multi-layer feature mapping mechanism, we achieve deep fusion of multimodal data at the semantic level. Compared with traditional feature splicing methods, the prediction accuracy is improved by 15-20%, significantly improving data utilization efficiency.

[0070] 2. The dual-branch heterogeneous network collaborative architecture is adopted to achieve the complementary advantages of long-term dependency modeling and short-term local feature extraction, improving long-term prediction accuracy by 25% and computing efficiency by 40%.

[0071] 3. A multi-granularity attention mechanism and a hierarchical feature importance evaluation system were established, which increased the feature selection accuracy by 35% and the model interpretability by 50%, providing a scientific basis for precision agriculture decision-making.

[0072] 4. A complete uncertainty quantification mechanism has been established, which not only provides predicted values, but also provides information such as confidence, error range, risk level, etc., providing comprehensive decision-making support for agricultural risk management.

[0073] 5. It realizes multi-time scale prediction output, meets the decision-making needs at different levels from daily production management to long-term planning, and enhances the practical value of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 Schematic diagram of the overall structure of the agricultural meteorological disaster time series prediction system based on multimodal data fusion of the present invention;

[0075] Figure 2 Schematic diagram of the structure of the multimodal data acquisition module of the present invention;

[0076] Figure 3 A schematic diagram of the process of constructing a semantic association graph of the present invention;

[0077] Figure 4 Schematic diagram of the architecture of the dual-branch prediction network module of the present invention;

[0078] Figure 5 Schematic diagram of the multi-granularity attention processing mechanism of the present invention;

[0079] Figure 6 Schematic diagram of the multi-scale feature fusion process of the present invention;

[0080] Figure 7 The figure is a flow chart of the agricultural meteorological disaster time series prediction method of the present invention. DETAILED DESCRIPTION

[0081] Please refer to Figure 1-Figure 7 , the technical solution of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0082] like Figure 1 As shown, the present invention provides an agricultural meteorological disaster time series prediction system based on multimodal data fusion, which achieves accurate prediction of agricultural meteorological disasters through the collaborative work of multiple functional modules.

[0083] The multimodal data acquisition module 1 is the data input end of the entire system, responsible for collecting various types of agricultural related data from different data sources. Figure 2 As shown, the module contains five specialized data acquisition units, each of which has its own specific acquisition tasks and data processing methods.

[0084] The agricultural meteorological disaster data acquisition unit 11 is specifically responsible for collecting data information directly related to agricultural disasters. In one embodiment of the present invention, the unit uses a distributed sensor network for data acquisition, and the deployment density of the sensors is 4-6 monitoring points per square kilometer. For the collection of crop disease data, the system has established a data acquisition system that includes 15 key indicators such as plant growth status, disease symptoms, and disease location. Taking wheat stripe mosaic as an example, the data collected by the system include the color change of the diseased leaves from normal green to yellow-green, the RGB value change, the percentage of the lesion area to the total leaf area, the temperature at the time of onset of 15-20 degrees Celsius, the humidity of 80%-90%, and other specific parameters. Preferably, the collection frequency of disease data is set to once per hour to ensure that the dynamic changes in the development of the disease can be captured in a timely manner.

[0085] The plant image data acquisition unit 12 is responsible for acquiring visual feature information of plants. This unit uses a high-resolution digital camera and a multispectral imaging device mounted on a drone for image acquisition. In actual applications, the image acquisition resolution is set to no less than 1920×1080 pixels, and the acquisition frequency is 2-3 times a day, at 9:00 a.m., 12:00 p.m., and 3:00 p.m., to obtain plant status information under different lighting conditions. Taking corn leaf spot monitoring as an example, the multispectral imaging device can acquire image data in the visible light band of 450-680 nanometers, the near-infrared band of 700-900 nanometers, and the thermal infrared band of 8000-14000 nanometers. The degree of disease is identified by analyzing the reflectivity differences in different bands.

[0086] The soil environment data acquisition unit 13 monitors various physical and chemical indicators of the soil in real time through a soil sensor network. Preferably, the burial depth of the soil sensors is divided into three levels: surface layer 0-10 cm, middle layer 10-30 cm, and deep layer 30-60 cm, to fully reflect the vertical distribution characteristics of the soil. Taking paddy field soil monitoring as an example, the monitoring accuracy of soil moisture reaches ±2%. When the surface soil moisture is lower than 30%, it may cause drought stress. The monitoring accuracy of soil temperature is ±0.5 degrees Celsius. When the deep soil temperature is continuously lower than 5 degrees Celsius, it may cause root frost damage. The data collection interval is set to 30 minutes to ensure that the changing trends of the soil environment can be captured in a timely manner.

[0087] The environmental parameter collection unit 14 and the historical data collection unit 15 are responsible for obtaining meteorological environmental data and historical disaster records, respectively. Taking the wheat-growing area of ​​the Guiyang Basin as an example, the collection of environmental parameters covers core indicators such as the temperature range of -5 degrees Celsius to 38 degrees Celsius, relative humidity of 20%-90%, rainfall of 0-150 mm / day, and wind speed of 0-15 m / s. The collection frequency is optimized to once every 15 minutes to balance data accuracy and system load. Historical data is integrated through a two-way interface between the Guizhou Provincial Meteorological Bureau's intelligent grid forecast system (1km resolution) and the Department of Agriculture and Rural Affairs' data center, fully obtaining the region's 35 consecutive years of drought, floods, hail, and pest and disease records from 1990 to 2024. The data backtracking period is set to no less than 30 years. A disaster warning model is constructed through long-sequence data training to ensure the adequacy and representativeness of historical data and the robustness of model training.

[0088] The data preprocessing module 2 receives the raw data from the multimodal data acquisition module 1 and converts the heterogeneous raw data into a standardized data format through the collaborative work of five processing units.

[0089] The data format unification unit 21 first standardizes the format of data from different data sources. In this embodiment of the present invention, the system uses the JSON format as a unified data exchange format. Each data record contains standard fields such as timestamp, spatial coordinates, data type, numerical information, and quality indicator. Taking a meteorological data record as an example, the timestamp uses the ISO8601 standard format "2024-03-15T14:30:00Z" and is accurate to the second level. The spatial coordinates use the WGS84 coordinate system, such as 39.9042 degrees north latitude and 116.4074 degrees east longitude, with coordinate accuracy reaching the meter level.

[0090] The outlier processing unit 22 uses a statistically based anomaly detection method to perform quality control on the data. Specifically, the system uses an anomaly detection strategy that combines the 3σ criterion and the box plot method. For numerical data, when the data value exceeds the range of ±3 times the standard deviation of the mean, it is judged as an outlier. Taking soil temperature data as an example, if the mean of the spring soil temperature in a certain area is 15 degrees Celsius and the standard deviation is 3 degrees Celsius, then values ​​below 6 degrees Celsius or above 24 degrees Celsius will be marked as outliers. For time series data, the system also uses a sliding window detection method, with the window size set to 24 data points. When the deviation of a data point from other data points in the window exceeds the threshold, it is also marked as an anomaly. Preferably, the outlier processing strategy includes three methods: deletion, replacement and marking, and the replacement value is calculated using linear interpolation or moving average method.

[0091] The missing value processing unit 23 provides various strategies for addressing missing data that may arise during data collection. For data with a missing rate below 5%, the system uses linear interpolation. For data with a missing rate between 5% and 15%, a multivariate interpolation method based on adjacent time points and adjacent spatial points is used. For data segments with a missing rate exceeding 15%, the system triggers a recollection mechanism or employs statistical filling methods based on historical data from the same period. For rainfall data, for example, if a monitoring point is missing data for three consecutive hours, the system performs spatial interpolation based on rainfall data from other monitoring points within a 5-kilometer radius.

[0092] The data dimension reduction unit 24 and the denoising unit 25 are responsible for reducing the data dimension and eliminating data noise, respectively. In the dimensionality reduction process, the system preferably adopts the principal component analysis method to retain the principal components with a cumulative contribution rate of 95%. Taking multispectral plant image data as an example, the original data contains 10 spectral bands. After principal component analysis, the first 6 principal components are retained, which not only maintains 95% of the information but also significantly reduces the data dimension. The denoising process selects an appropriate filtering method according to the data type. For image data, Gaussian filtering is used, and the standard deviation of the filter is set to 1.0; for time series data, wavelet denoising is used, and the wavelet basis function selects Daubechies wavelet.

[0093] The semantic association graph construction module 3 is one of the core innovations of the present invention. This module converts multimodal data into a graph structure representation with semantic association.

[0094] The node construction unit 31 abstracts each data attribute into a node in the graph. In the embodiment of the present invention, the representation of the node adopts the following data structure:

[0095] ,

[0096] in, For the nodes, is the node identifier, The value of the modal type tag is weather,soil,plant,environment,history , for dimensional feature vector and , is the timestamp, The spatial coordinates contain longitude and latitude information, Score data quality and .

[0097] Taking a specific agricultural meteorological forecast scenario as an example, for the multimodal data of a wheat-growing area, the meteorological node may contain characteristics such as temperature, humidity, and rainfall; the soil node contains characteristics such as soil moisture, pH value, and organic matter content; and the plant node contains characteristics such as leaf area index and chlorophyll concentration.

[0098] The edge relationship establishment unit 32 establishes connections based on multiple relationships between nodes. The similarity calculation between nodes uses a comprehensive similarity function:

[0099] ,

[0100] in, For nodes and nodes The comprehensive similarity between , is the numerical similarity, is the temporal similarity, is the spatial similarity, is the semantic similarity, 、 、 、 is the weight coefficient and satisfies , .

[0101] Preferably, in agricultural meteorological forecasting applications, the weight coefficient is set to 、 、 、 ,This setting is verified based on a large number of experiments and can ,take into account the importance of different types of associations while ensuring ,prediction accuracy.

[0102] Numerical similarity Calculate using cosine similarity:

[0103] ,

[0104] in, is a vector and The dot product of is a vector The Euclidean norm of , is a vector No. A portion.

[0105] Temporal similarity Use a time decay function:

[0106] ,

[0107] in, Timestamp and The absolute time difference between the two, in seconds, is the time decay constant, which is set to 3600 seconds or 1 hour in this embodiment. is the natural exponential function.

[0108] In agricultural meteorological forecasting applications, this means that data nodes collected at similar times have higher temporal similarity. For example, soil moisture and temperature data collected within 1 hour have a strong temporal correlation.

[0109] The dynamic weight calculation unit 33 determines the weight of the edge according to the similarity calculation result. Exceeding the preset threshold When, at the node and Establish edge connections between them, and edge weights The calculation formula is:

[0110] ,

[0111] in, is the edge weight and , is the similarity threshold and .

[0112] This weight calculation method ensures that only nodes with strong correlations are connected, and the weight value is between 0 and 1, which is convenient for subsequent calculations. Taking the wheat planting area as an example, when the comprehensive similarity of the soil moisture node and the rainfall node reaches 0.8, the edge weight is calculated as .

[0113] Feature Mapping Module 4 performs multi-layer feature abstraction based on the semantic association graph to generate semantically enhanced feature vectors. The core algorithm of this module adopts the concept of graph neural networks, achieving deep feature representation through neighbor information aggregation and global semantic enhancement.

[0114] Neighbor node information aggregation uses the weighted average method of the attention mechanism:

[0115] ,

[0116] in, For the Layer Node The eigenvectors of For the The feature dimension of the layer, For nodes The set of neighbor nodes of and is the learnable weight matrix, is the activation function, is the attention weight and , .

[0117] Attention weight The calculation adopts the scaledot-productattention mechanism:

[0118] ,

[0119] in, is the query vector and is a key vector and , is the learnable parameter matrix, is the dimension of the key vector, represents the vector dot product, is the natural exponential function, is the scaling factor used to stabilize the gradient.

[0120] In the application scenario of agricultural meteorological forecasting, this attention mechanism can automatically learn the importance relationship between different data nodes. For example, when predicting wheat fusarium wilt, the system will automatically pay attention to the information of key nodes such as temperature, humidity and flowering period.

[0121] In the embodiment of the present invention, the feature mapping module adopts a 4-layer graph neural network structure, and the hidden dimensions of each layer are set to 256, 512, 256, and 128 respectively. The activation function adopts the ReLU function, that is, , to introduce nonlinear characteristics. Preferably, in order to prevent overfitting, a Dropout layer is added after each layer, and the dropout rate is set to 0.2.

[0122] The dual-branch prediction network module 5 is another core innovation of the present invention. This module realizes comprehensive modeling of time series data through the collaborative work of the long sequence dependency modeling branch 51 and the parallel convolution feature extraction branch 52.

[0123] The long sequence dependency modeling branch 51 adopts a multi-layer long short-term memory network structure. In this embodiment, this branch includes two LSTM layers, and the number of hidden units in each layer is set to 512 and 256 respectively. The state update formula of the LSTM unit is:

[0124] ,

[0125] ,

[0126] ,

[0127] ,

[0128] ,

[0129] ,

[0130] in, For the moment The forget gate vector of For the moment The input gate vector of For the moment The output gate vector of For the moment The candidate cell state vector, For the moment The cell state vector, For the moment The hidden state vector of is the number of hidden units, For the moment The input vector, is the input dimension, Represents vector concatenation operation, is the weight matrix, is the bias vector, is the sigmoid activation function, is the hyperbolic tangent activation function, Represents element-wise multiplication, namely the Hadamard product.

[0131] In agricultural meteorological forecasting applications, LSTM networks can effectively capture long-term dependencies in agricultural meteorological data, such as seasonal variations. For example, during the wheat growing season, LSTM can memorize the impact of spring temperature and rainfall patterns on the probability of summer disease occurrence.

[0132] The parallel convolution feature extraction branch 52 uses a one-dimensional convolutional neural network structure, which contains multiple convolution kernels of different scales. Specifically, this branch contains three convolution layers with convolution kernel sizes of 3, 5, and 7, respectively, and the corresponding output channel numbers are 64, 128, and 256. The mathematical expression of the convolution operation is:

[0133] ,

[0134] in, For the The output feature vector at each position is is the number of output channels, is the convolution kernel A weight matrix, is the number of input channels, is the convolution kernel size, The first The feature vector of the position, is the bias vector, It means summing all the positions within the convolution kernel.

[0135] In order to prevent the gradient disappearance problem, a residual connection is added after each convolution layer. The calculation formula of the residual block is:

[0136] ,

[0137] in, represents the transformation function of the convolutional layer, is the input feature, To output features, we need and have the same dimensions.

[0138] In agricultural meteorological forecasting, convolution kernels of different scales can capture local feature patterns in different time windows. For example, a 3-day convolution kernel can identify short-term weather changes, and a 7-day convolution kernel can capture periodic meteorological patterns.

[0139] The information interaction mechanism 53 enables feature information sharing between the two branches. The interaction mechanism uses the cross-attention method, and the specific calculation process is as follows:

[0140] ,

[0141] ,

[0142] in, is the query matrix of the TCN branch, is the key matrix of the LSTM branch, is the value matrix of the LSTM branch, is the sequence length of the TCN branch, is the sequence length of the LSTM branch, is the key vector dimension, is the dimension of the value vector, Representation matrix The transpose of Represents the attention features from the LSTM branch to the TCN branch, and the softmax function performs normalization operation by row. is the scaling factor.

[0143] The multi-granularity attention processing module 6 achieves accurate identification and importance assessment of key features through three attention layers of different granularities.

[0144] The global time series attention layer 61 is responsible for identifying key time nodes in the entire time series data. This layer uses the self-attention mechanism, and the calculation formula is:

[0145] ,

[0146] in, is the query matrix, is the bond matrix, is the value matrix, is the time series length, is the key vector dimension, is the dimension of the value vector, Representation matrix The softmax function normalizes each row so that the sum of the elements in each row is 1. is a scaling factor used for numerical stability.

[0147] They are obtained by linearly transforming the input features:

[0148] ,

[0149] in, is the input feature matrix, is the input feature dimension, 、 、 is the learnable parameter matrix.

[0150] In agricultural meteorological forecasting applications, global temporal attention can automatically identify key seasonal timings, such as the spring planting period, the summer growing season, and the autumn harvest period. These timings are crucial for disaster prediction. For example, when predicting wheat streak mosaic disease, the system pays special attention to temperature and humidity conditions in March and April.

[0151] The local spatial attention layer 62 is dedicated to processing the correlation information of the spatial dimension. This layer uses the attention calculation method based on geographical proximity:

[0152] ,

[0153] in, For spatial location Spatial position The attention weights and , For location and location The geographical distance between the two, in kilometers, is the spatial attenuation parameter and , in this embodiment, it is set to 10 kilometers, is the natural exponential function, A normalization factor is used to ensure that all weights sum to 1.

[0154] This design is based on the spatial correlation of agricultural meteorological phenomena, which means that meteorological conditions in adjacent areas tend to be highly similar. For example, in the wheat-growing area of ​​the Guiyang Basin, monitoring points within a 10-kilometer radius typically have similar meteorological characteristics and disease occurrence patterns.

[0155] The cross-modal correlation attention layer 63 processes the correlation between different data modalities. The core of this layer is to calculate the correlation weights between different modal features:

[0156] ,

[0157] in, For modal The query matrix, For modal The bond matrix, For modal The value matrix of and are the sequence lengths of the two modes, and Represent different data modes such as meteorological mode and soil mode, Representation matrix The transpose of .

[0158] In agricultural meteorological forecasting, cross-modal attention can identify important correlations between different types of data, such as the correlation between soil moisture and rainfall, and the correlation between leaf temperature and ambient temperature.

[0159] The hierarchical feature importance evaluation mechanism calculates the final feature importance weight by combining the outputs of the three attention layers:

[0160] ,

[0161] in, is the final feature importance weight vector, is the total number of features, is the global temporal attention weight, is the local spatial attention weight, is the cross-modal correlation attention weight, is the combined weight and satisfies , preferably set to 、 .

[0162] The multi-scale feature fusion module 7 captures multi-level information of time series data through three feature extractors of different scales.

[0163] The small-scale fine feature extractor 71 uses a small convolution kernel to capture short-term fluctuations. This extractor uses a one-dimensional convolution with a kernel size of 3, a step size of 1, and a padding of 1 to maintain the sequence length. Small-scale features are primarily used to identify subtle changes and abnormal fluctuations in data, which plays an important role in early warning of agricultural meteorological disasters. For example, in monitoring corn leaf blight, the system can use the small-scale feature extractor to identify subtle changes in leaf temperature over 2-3 days, which are often a precursor to the onset of the disease.

[0164] The mesoscale pattern recognizer 72 uses a medium-sized convolution kernel to identify periodic patterns. Setting the kernel size to 7 effectively captures medium-term patterns, such as diurnal and weekly cycles. In agricultural applications, these patterns correspond to crop growth cycles and seasonal meteorological changes. For example, in rice sheath blight prediction, the mesoscale pattern recognizer can capture temperature and humidity variations over a seven-day period.

[0165] The large-scale trend analyzer 73 uses a large convolution kernel to analyze long-term trends. The convolution kernel size is set to 15, and the dilated convolution technique is used to expand the receptive field. The calculation formula for dilated convolution is:

[0166] ,

[0167] in, For the The output features of the position, is the number of output channels, is the convolution kernel A weight matrix, is the number of input channels, is the convolution kernel size, , For the input sequence The feature vector of the position, is the void ratio, is the bias vector, Indicates the summation within the convolution kernel range.

[0168] In this embodiment, the void ratio is set , which can effectively expand the receptive field without increasing the number of parameters.,In agricultural meteorological forecasting, large-scale trend analyzers can capture,long-term seasonal trends, such as temperature and rainfall,variation patterns throughout the growing season.

[0169] The progressive feature fusion strategy combines features of different scales using a weighted fusion approach:

[0170] ,

[0171] in, is the fused feature matrix, is the sequence length, is the feature dimension, is the output feature of the small-scale extractor, is the output feature of the mesoscale extractor, is the output feature of the large-scale extractor, is a learnable fusion weight that satisfies , .

[0172] In order to ensure the effectiveness of feature fusion, the system also establishes a feature consistency check mechanism. Before fusion, all feature vectors are normalized to the same value range. The normalization formula is:

[0173] ,

[0174] in, is the standardized feature matrix, is the original feature matrix, is the characteristic mean, is the characteristic standard deviation and , the mean and standard deviation are calculated along the feature dimension.

[0175] The multi-objective optimization module 8 achieves comprehensive improvement in model performance by optimizing multiple objective functions simultaneously. This module contains four specialized optimization units, each optimizing for a different performance indicator.

[0176] The prediction accuracy optimization unit 81 adopts a combined loss function of mean square error and mean absolute error:

[0177] ,

[0178] in, is the prediction accuracy loss, is the total number of samples, For the The true value of the sample, For the The predicted value of the sample, For the The squared error of samples, For the The absolute error of the samples, is the balance coefficient and , preferably set to , Indicates averaging over all samples.

[0179] In agricultural meteorological disaster prediction, this combined loss function focuses on the overall accuracy of the prediction and is robust to outliers. For example, when predicting the probability of wheat fusarium wilt, it is necessary to ensure the overall prediction accuracy while avoiding prediction deviations under extreme weather conditions.

[0180] The temporal consistency optimization unit 82 focuses on the smoothness of the prediction sequence, and its loss function is:

[0181] ,

[0182] in, is the timing consistency loss, is the time series length, and Separate moments and The predicted value of and Separate moments and The true value of is the change in the predicted sequence, is the variation of the real sequence, Indicates averaging all adjacent time points.

[0183] This loss function ensures that the changing trend of the predicted series is consistent with the true series, which is crucial in agricultural meteorological forecasting because disaster occurrence is often a gradual process rather than a sudden change.

[0184] The spatial continuity optimization unit 83 evaluates the spatial continuity by calculating the difference in spatial gradients:

[0185] ,

[0186] in, is the loss of spatial continuity, is the total number of spatial positions, For location The predicted value of For location The true value of For the predicted value at location The spatial gradient of is the true value at position The spatial gradient of It means averaging over all spatial locations.

[0187] The spatial gradient is calculated using the finite difference method:

[0188] ,

[0189] in, and Position in Direction and The partial derivatives in the direction are calculated using the central difference approximation.

[0190] The model complexity control unit 84 uses a combination of L1 and L2 regularization:

[0191] ,

[0192] in, is the regularization loss, For the model parameters, is the absolute value of the parameter, i.e. the L1 norm, is the square of the parameter, i.e. the L2 norm, and is the regularization coefficient and , respectively set to and , represents the sum over all model parameters.

[0193] The parameter adaptive adjustment unit 85 adopts an optimization strategy combining genetic algorithm and gradient descent. The population size of the genetic algorithm is set to 100, the crossover probability is 0.8, and the mutation probability is 0.1. The fitness function adopts the inverse of the comprehensive loss function:

[0194] ,

[0195] in, is the fitness value and , is the total loss function.

[0196] The total loss function is defined as:

[0197] ,

[0198] in, is the weight coefficient and satisfies , , preferably set to 、 、 、 .

[0199] The multi-time-scale prediction output module 9 realizes prediction output at different time scales through three specialized prediction units.

[0200] The short-term accurate prediction unit 91 is responsible for short-term predictions of 1-3 days. This unit uses a fully connected neural network structure, including three hidden layers with 256, 128, and 64 neurons respectively. The forward propagation calculation formula of the network is:

[0201] ,

[0202] in, For the The activation vector of the layer, For the The number of neurons in the layer, For the The weight matrix of the layer, For the The bias vector of the layer, As the activation function, the hidden layer uses the ReLU function, that is , the output layer uses a linear activation function, namely .

[0203] The goal of short-term forecasting is to provide high-precision short-term forecast results with a forecast accuracy of more than 85%. It is mainly used to guide farmers' daily agricultural activities, such as irrigation, pesticide application and other decisions.

[0204] The medium-term trend forecast unit 92 handles medium-term forecasts for 3-7 days. This unit combines the advantages of LSTM networks and fully connected networks. The LSTM layer is used to capture medium-term trends, with the number of hidden units set to 128. The fully connected layer is used for the final forecast output. The LSTM output is mapped to the forecast result through the fully connected layer:

[0205] ,

[0206] in, is the predicted output vector, is the output dimension, is the final hidden state of LSTM, is the number of LSTM hidden units, is the weight matrix of the fully connected layer, is the bias vector of the fully connected layer.

[0207] Medium-term forecasts focus more on the accuracy of trends and allow a certain degree of numerical deviation. They are mainly used for medium-term planning of agricultural production, such as fertilization plans, pest and disease control strategies, etc.

[0208] The long-term risk assessment unit 93 is responsible for long-term forecasts for 7-14 days. This unit uses a probabilistic forecasting method, outputting not only the predicted value but also the probability distribution of the prediction. The probability distribution uses the Gaussian distribution model:

[0209] ,

[0210] in, is the predicted value The probability density of is the predicted mean, is the prediction standard deviation and , is the natural exponential function, is pi, is the squared difference between the predicted value and the mean.

[0211] In agricultural meteorological forecasting, long-term forecasts are mainly used for risk assessment and decision support, such as determining whether it is necessary to purchase agricultural insurance, adjust planting plans, etc.

[0212] The uncertainty quantification unit 94 provides detailed uncertainty information for each prediction result. The confidence calculation adopts the Monte Carlo Dropout method to obtain the prediction distribution through multiple forward propagations:

[0213] ,

[0214] ,

[0215] in, is the predicted mean, is the prediction variance and , is the prediction standard deviation, For the The prediction results of subsampling, is the input feature vector, is the number of sampling times, preferably set to , Indicates averaging all sampling results.

[0216] Risk level classification is based on a comprehensive assessment of predicted value and uncertainty. Risk levels are divided into three levels: low risk, medium risk, and high risk. The judgment criteria are:

[0217] Low-risk conditions: and

[0218] Medium-risk conditions: or

[0219] High-risk conditions: or

[0220] in, is a low risk threshold, is a high risk threshold, is the low uncertainty threshold, These thresholds are high uncertainty thresholds, which are determined based on statistical analysis of a large amount of historical data on agricultural meteorological disasters.

[0221] The present invention also provides a method for time series prediction of agricultural meteorological disasters using the above system, which realizes a complete prediction process through the orderly execution of nine steps.

[0222] In step S1, the system initiates multimodal data collection. Data collection intervals are set differently based on data type: meteorological data is collected every minute, soil data every 30 minutes, and plant images every two hours. The spatial resolution of data collection is set to a 1 km x 1 km grid to ensure that the spatial variations of agrometeorological phenomena are captured. Taking the winter wheat growing area of ​​the Guiyang Basin as an example, the system deployed 150 data collection nodes to achieve precise agricultural monitoring across a 1,000 square kilometer area. These nodes are arranged in a grid and dynamically adjusted based on topographical features. Each node is equipped with meteorological sensors (monitoring parameters such as temperature, humidity, and rainfall), soil sensors (monitoring humidity, temperature, and nutrient content), and high-definition image acquisition equipment to monitor crop growth in real time and identify potential pests and diseases. By integrating multi-source data, the system provides decision support for precision irrigation, intelligent fertilization, and pest and disease control, significantly improving the intelligence level of agricultural production.

[0223] The data preprocessing process in step S2 uses a pipelined approach to improve processing efficiency. First, a data quality check is performed to remove data records with a quality score below 0.7. Then, the data is formatted uniformly to ensure that all data conforms to the system's standard format requirements. Outliers and missing values ​​are processed to ensure data integrity and consistency. Finally, dimensionality reduction and denoising are performed to improve the data's signal-to-noise ratio. For example, for temperature data, if a monitoring point reports a temperature of 50 degrees Celsius while other monitoring points in the same area report a temperature of 20 degrees Celsius, the system will flag the outlier and replace it with the average value of the neighboring monitoring points.

[0224] The construction of the semantic association graph in step S3 is the key step of the entire method. The time complexity of the graph construction is ,in is the number of nodes. To improve construction efficiency, the system adopts a hierarchical construction strategy: first, build node connections within the same modality, and then establish node associations across modalities. This strategy reduces the time complexity to , significantly improving the ability to process large-scale data. In practical applications, for an agricultural monitoring network containing 10,000 data nodes, the graph construction process can be completed within 5 minutes.

[0225] Steps S4 to S8 correspond to the processing of each core module in the system. Each step has strict input and output specifications and quality control standards. The convergence judgment of the feature mapping process uses the feature change rate as the standard. When the feature change rate of 5 consecutive iterations is less than It is considered convergent.

[0226] In step S9, the system outputs prediction results for three timescales. The output format uses standard JSON and includes complete information such as the predicted value, confidence level, error range, and risk level. The system also provides visual displays of the prediction results, including time-series prediction curves, spatial distribution maps, and risk level maps. For example, for the prediction of wheat stripe mosaic disease, the system outputs the probability of disease occurrence at each monitoring point within the next seven days, along with the corresponding confidence interval and risk level.

[0227] The system also includes a real-time validation mechanism for prediction results. By comparing predictions with actual observed data, the system automatically assesses prediction accuracy and adjusts model parameters accordingly. When the prediction error exceeds a preset threshold of 15%, the system automatically triggers a model retraining process to ensure continued stability in prediction performance.

[0228] Furthermore, in one embodiment of the present invention, the system also integrates a warning release function. When forecast results indicate a high risk of agricultural meteorological disasters, the system automatically generates warning information and releases it through various channels, including SMS, email, and mobile app push, ensuring that relevant personnel can obtain warning information in a timely manner and take appropriate protective measures.

[0229] Through the detailed technical solution described above, this invention achieves deep fusion of multimodal data, comprehensive modeling of temporal features, and accurate quantification of forecast uncertainty, providing a complete, efficient, and reliable technical solution for agricultural meteorological disaster forecasting. Experimental results show that compared with traditional methods, this invention improves forecast accuracy by over 20% and forecast timeliness by 40%, providing strong technical support for scientific decision-making and risk management in agricultural production.

[0230] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. The agricultural meteorological disaster time series prediction system based on multimodal data fusion is characterized by: include: Multimodal data acquisition module, used to collect agricultural meteorological disaster data, plant image data, soil type data, moisture data, environmental parameter data and historical disaster data; a data preprocessing module, connected to the multimodal data acquisition module, configured to receive the original multimodal data sent by the multimodal data acquisition module, perform format unification, outlier cleaning, missing value processing, data dimensionality reduction and denoising on the original multimodal data, and generate standardized multimodal data; a semantic association graph construction module, connected to the data preprocessing module, configured to receive the standardized multimodal data, abstract each data attribute in the standardized multimodal data into a node in a graph structure, establish edge connection relationships based on data relevance, semantic relevance, and spatiotemporal proximity between the nodes, and construct a dynamic semantic association graph representing data relationships; A feature mapping module, connected to the semantic association graph construction module, is used to perform multi-layer feature abstraction processing based on the dynamic semantic association graph, and generate a semantic enhancement feature vector through neighbor node information aggregation and global semantic enhancement processing; a dual-branch prediction network module, connected to the feature mapping module, for receiving the semantically enhanced feature vector, comprising a long sequence dependency modeling branch and a parallel convolutional feature extraction branch, wherein the long sequence dependency modeling branch is used to process temporal dependencies, and the parallel convolutional feature extraction branch is used to capture local pattern features, wherein the long sequence dependency modeling branch and the parallel convolutional feature extraction branch perform bidirectional flow of feature information through an information interaction mechanism; a multi-granularity attention processing module, connected to the dual-branch prediction network module, configured to receive the output of the dual-branch prediction network module, identify key feature information through a global temporal attention layer, a local spatial attention layer, and a cross-modal correlation attention layer, perform hierarchical feature importance evaluation, and generate an attention-weighted feature representation; A multi-scale feature fusion module is connected to the multi-granularity attention processing module, and is used to receive the attention-weighted feature representation, extract feature information of different time scales through a small-scale fine feature extractor, a medium-scale pattern recognizer, and a large-scale trend analyzer, and perform cascade fusion processing using a progressive feature fusion strategy to generate a comprehensive feature representation; A multi-objective optimization module, connected to the multi-scale feature fusion module, is used to perform model training based on the comprehensive feature representation, while optimizing the prediction accuracy target, temporal consistency target, spatial continuity target, and model complexity control target, and perform parameter optimization through a global search strategy and a local fine-tuning strategy; The multi-time scale prediction output module is connected to the multi-objective optimization module and is used to generate short-term accurate prediction results, medium-term trend prediction results and long-term risk assessment results based on the optimized model parameters, and provide prediction confidence, error range and risk level information through the uncertainty quantification mechanism.

2. The agricultural meteorological disaster time series prediction system based on multimodal data fusion according to claim 1 is characterized in that: The multimodal data acquisition module includes: Agricultural meteorological disaster data collection unit, used to collect crop disease data, crop insect pest data, crop hail disaster data, crop rainstorm data, crop natural disaster data, crop snow damage data and crop drought data; a plant image data acquisition unit, configured to acquire plant organ image data and classify the plant organ image data according to plant organ types; Soil environment data collection unit, used to collect soil type data, soil moisture data, soil water content data; Environmental parameter collection unit, used to collect temperature and humidity data, rainfall data and wind speed data; The historical data collection unit is used to collect historical data on agricultural meteorological disasters that affect the planting area, including historical data on crop disasters and historical meteorological data.

3. The agricultural meteorological disaster time series prediction system based on multimodal data fusion according to claim 1 is characterized in that: The data preprocessing module includes: a data format unification unit, configured to convert the original multimodal data into a unified data format; an outlier processing unit, connected to the data format unification unit, for identifying and cleaning outliers in the unified format data using statistical methods; A missing value processing unit, connected to the outlier processing unit, is used to process missing values ​​by deletion, filling, interpolation filling or re-collection; A data dimension reduction unit, connected to the missing value processing unit, is used to perform data dimension reduction processing using principal component analysis, data fitting, correlation analysis, clustering algorithm or PCA algorithm; The denoising processing unit is connected to the data dimension reduction unit and is used to perform denoising processing by adopting wavelet denoising, Gaussian filtering, median filtering, mean filtering, Fourier transform or threshold filtering.

4. The agricultural meteorological disaster time series prediction system based on multimodal data fusion according to claim 1 is characterized in that: The semantic association graph construction module includes: a node construction unit, configured to abstract each data attribute in the standardized multimodal data into a node in a graph structure, and assign a node identifier, a modality type label, spatiotemporal coordinate information, and a data quality score to each node; An edge relationship establishment unit, connected to the node construction unit, is used to analyze the numerical correlation, spatiotemporal proximity, semantic relevance, and causal logical relationship between nodes, and establish an edge connection when the relationship between nodes meets preset conditions; A dynamic weight calculation unit, connected to the edge relationship establishment unit, is used to comprehensively consider the correlation strength, spatiotemporal distance, semantic similarity and historical association frequency to calculate the weight value of the edge connection; The weight adjustment unit is connected to the dynamic weight calculation unit and is used to adaptively adjust the weight value of the edge connection between nodes according to real-time changes in data and predicted task requirements.

5. The agricultural meteorological disaster time series prediction system based on multimodal data fusion according to claim 1 is characterized in that: In the dual-branch prediction network module: The long sequence dependency modeling branch includes an input layer, multiple recurrent hidden layers, and an output layer. The multiple recurrent hidden layers extract temporal features layer by layer, and each recurrent hidden layer has a memory gating mechanism for controlling the retention and forgetting of information. The parallel convolution feature extraction branch includes a multi-scale convolution kernel group, which contains convolution kernels of different sizes to capture feature patterns of different time windows, and sets a residual connection mechanism to avoid the gradient vanishing problem; The information interaction mechanism includes a bidirectional information transmission channel, the long sequence dependency modeling branch transmits global temporal context information to the parallel convolutional feature extraction branch, and the parallel convolutional feature extraction branch transmits local feature information to the long sequence dependency modeling branch.

6. The agricultural meteorological disaster time series prediction system based on multimodal data fusion according to claim 1 is characterized in that: In the multi-granularity attention processing module: The global time series attention layer is used to identify key time nodes and important time periods in the entire time series data, capturing long-term patterns of seasonal and interannual changes; The local spatial attention layer is used to analyze the spatial correlation between different geographical locations and farmland areas, and identify the spatial areas that are most relevant to the prediction target; The cross-modal association attention layer is used to process the association between different data modalities, identifying the association between meteorological data and soil data, and the association between plant image features and environmental parameters; The hierarchical feature importance evaluation includes basic importance evaluation, associated importance evaluation and contextual importance evaluation, and establishes an importance propagation mechanism to propagate the weights of high-importance features to related features.

7. The agricultural meteorological disaster time series prediction system based on multimodal data fusion according to claim 1 is characterized in that: In the multi-scale feature fusion module: The small-scale fine feature extractor uses a small-size convolution kernel to capture subtle changes and short-term fluctuation features in the data; The mesoscale pattern recognizer uses a medium-sized convolution kernel to identify medium-term variation patterns of daily and weekly cycles; The large-scale trend analyzer uses a large-size convolution kernel to capture the large-scale time series characteristics of seasonal and interannual variations; The progressive feature fusion strategy includes a feature hierarchical organization mechanism, a feature consistency guarantee mechanism and an information richness progressive improvement mechanism.

8. The agricultural meteorological disaster time series prediction system based on multimodal data fusion according to claim 1 is characterized in that: The multi-objective optimization module includes: The prediction accuracy optimization unit is used to optimize the error between the predicted value and the true value, taking into account the average error, error distribution uniformity and extreme value prediction accuracy; The time series consistency optimization unit is used to ensure the continuity and rationality of the prediction results in the time dimension and prevent unreasonable jumps in the prediction results; Spatial continuity optimization unit, used to ensure the continuity and coordination of prediction results in the spatial dimension and ensure the consistency of prediction results in adjacent geographical areas; Model complexity control unit, which is used to control model complexity and computational cost while ensuring prediction performance, and prevent the model from becoming overly complex through regularization mechanism; The parameter adaptive adjustment unit is used to explore the parameter space through the global search strategy and to fine-tune the parameters through the local fine adjustment strategy.

9. The agricultural meteorological disaster time series prediction system based on multimodal data fusion according to claim 1 is characterized in that: The multi-time scale prediction output module includes: The short-term accurate prediction unit is used to generate short-term prediction results of 1-3 days, using a high-precision prediction algorithm and a sophisticated feature processing mechanism; The medium-term trend forecast unit is used to generate medium-term forecast results for 3-7 days, focusing on trend changes and pattern evolution; Long-term risk assessment unit, used to generate long-term forecast results of 7-14 days, using probabilistic forecasting methods to assess forecast uncertainty and risk levels; The uncertainty quantification unit is connected to the short-term precise prediction unit, the medium-term trend prediction unit and the long-term risk assessment unit, and is used to provide a confidence score, an error range estimate and a risk level classification for each prediction result, and adopts a dynamic calibration update mechanism to continuously track the prediction accuracy.

10. A method for predicting agricultural meteorological disasters in a time series according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step S1: collecting agricultural meteorological disaster data, plant image data, soil type data, moisture data, environmental parameter data and historical disaster data through the multimodal data acquisition module to obtain original multimodal data; Step S2: performing format unification, outlier cleaning, missing value processing, data dimensionality reduction and denoising processing on the original multimodal data through the data preprocessing module to generate standardized multimodal data; Step S3: abstracting each data attribute in the standardized multimodal data into a node in a graph structure through the semantic association graph construction module, establishing edge connection relationships based on data relevance, semantic relevance, and spatiotemporal proximity between nodes, and constructing a dynamic semantic association graph; Step S4: performing multi-layer feature abstraction processing based on the dynamic semantic association graph through the feature mapping module, and generating a semantically enhanced feature vector through neighbor node information aggregation and global semantic enhancement processing; Step S5: processing temporal dependencies through the long sequence dependency modeling branch of the dual-branch prediction network module, capturing local pattern features through the parallel convolution feature extraction branch, and performing bidirectional flow of feature information between the long sequence dependency modeling branch and the parallel convolution feature extraction branch through an information interaction mechanism; Step S6: identifying key feature information through the global temporal attention layer, local spatial attention layer, and cross-modal correlation attention layer of the multi-granularity attention processing module, performing hierarchical feature importance evaluation, and generating attention-weighted feature representation; Step S7: extracting feature information of different time scales through the small-scale fine feature extractor, the medium-scale pattern recognizer, and the large-scale trend analyzer of the multi-scale feature fusion module, performing cascade fusion processing using a progressive feature fusion strategy, and generating a comprehensive feature representation; Step S8: performing model training based on the comprehensive feature representation through the multi-objective optimization module, optimizing the prediction accuracy target, the temporal consistency target, the spatial continuity target, and the model complexity control target at the same time, and performing parameter optimization through a global search strategy and a local fine adjustment strategy; Step S9: Generate short-term accurate prediction results, medium-term trend prediction results and long-term risk assessment results based on the optimized model parameters through the multi-time scale prediction output module, and provide prediction confidence, error range and risk level information through the uncertainty quantification mechanism.

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