Multi-modal neural network fusion system fusing attention mechanism

Abstract

The invention discloses a multi-modal neural network fusion system fusing an attention mechanism, and particularly relates to the technical field of environmental monitoring artificial intelligence. The defects of high meteorological / water quality monitoring false alarm rate and large response delay caused by static weighted fusion, insufficient domain feature extraction and edge deployment bottleneck of an existing multi-modal fusion system are overcome. According to the method, meteorological satellite / water quality sensor data are fused in a cross-modal manner by adopting a dynamic attention mechanism, domain features are extracted in combination with space-time convolution and graph convolution, and the domain features are deployed to edge equipment through hybrid quantization compression. The severe convection weather identification and pollutant traceability precision is improved, and the real-time early warning capability of a field terminal is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology for environmental monitoring, and more specifically, to a multimodal neural network fusion system that incorporates an attention mechanism. Background Technology

[0002] With the increasing demand for refined environmental monitoring, multimodal data fusion technology has become a core tool for meteorological disaster early warning and water pollution analysis. Traditional methods acquire multi-dimensional information on the atmosphere and water bodies from heterogeneous data sources such as satellites, radars, and sensor networks, and utilize neural networks for feature extraction and prediction. However, in dynamic scenarios such as the rapid evolution of severe convective weather and the diffusion and migration of pollutants, existing fusion systems have limitations in feature interaction mechanisms, domain adaptability, and real-time response capabilities, which restrict the accuracy of early warnings and the timeliness of response. Current technological shortcomings: 1. Static fusion mechanisms lack dynamic perception capabilities. Traditional multimodal neural networks often use feature splicing or fixed-weight weighted fusion, which cannot dynamically adjust the modality contribution based on data quality. For example, in meteorological monitoring, when satellite cloud images are obscured by clouds, causing a sharp drop in the signal-to-noise ratio, the system still mechanically fuses low-quality data, leading to misjudgments of storm paths; in water quality monitoring, when chemical sensors are affected by biofilm contamination and produce drift errors, the pollution level assessment results deviate significantly from the true value.

[0003] 2. Disconnect between domain feature extraction and physical laws: Existing general network structures are unable to capture the professional characteristics in environmental monitoring: meteorological forecasting ignores the fluid dynamic continuity constraints of cloud movement and treats satellite images as independent frames, resulting in broken predictions of storm development trajectories; water quality analysis ignores the fluid dynamics model of pollutant diffusion and relies solely on statistical correlation modeling, which cannot accurately trace the location of pollution sources.

[0004] 3. Edge deployment bottlenecks hinder real-time response. Complex neural network models are difficult to adapt to resource-constrained field monitoring terminals. Existing solutions compress the model by reducing input resolution or network layers. However, in critical scenarios such as the early stage of rainstorm formation and the critical point of algal bloom, lightweight models miss high-risk events due to the loss of subtle features (such as cloud top ice crystallization and abrupt changes in algal filament morphology), thus delaying the emergency response window.

[0005] Therefore, a multimodal neural network fusion system incorporating attention mechanisms is proposed to address the above problems. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a multimodal neural network fusion system that incorporates an attention mechanism to address the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a multimodal neural network fusion system incorporating an attention mechanism, comprising: a multimodal input interface receiving satellite remote sensing images, radar-based data, and time-series data from ground meteorological stations for meteorological monitoring scenarios, or chemical sensor network data, plankton microscopic images, and hydrological monitoring time-series data for water quality monitoring scenarios; an intramodal feature extraction module using a three-dimensional convolutional neural network to analyze the spatial-temporal joint features of meteorological data, using a graph convolutional network to model the topological correlation features of water quality monitoring stations, and using gated recurrent units to capture long-short-term dependencies in time-series data; a cross-modal attention fusion module realizing heterogeneous modal feature interaction through a learnable multi-head attention mechanism; and a joint prediction module outputting a meteorological disaster level assessment or a water quality safety risk index.

[0008] Furthermore, the cross-modal attention fusion module includes: a configurable number of attention heads that compute the correlation weight matrix between features of different modalities in parallel, with each attention head focusing on a feature interaction pattern of a specific scale or type; a feature reconstruction unit that generates fused features through a combination of weighted summation and tensor concatenation; and an adaptive gating unit that dynamically generates modulation factors based on the signal-to-noise ratio of real-time input data to suppress the contribution intensity of low-quality modalities within a predetermined threshold range.

[0009] Furthermore, the intramodal feature extraction module includes: a spatiotemporal convolutional network for meteorological satellite images to simultaneously extract cloud morphological features and movement trajectory vectors; a depth-separable convolutional network for water quality microscopic images to extract algal biomorphological texture features; and a bidirectional gated recurrent unit for sensor time-series data combined with a self-attention mechanism to capture periodic patterns and anomalous mutation points in the data stream.

[0010] Furthermore, it also includes an adaptive feature calibration module: a dynamic range normalization layer maps the original features of each modality to a unified numerical distribution range, and its scaling parameters are automatically adjusted according to the statistical characteristics of the input data; a spatiotemporal alignment layer uses an interpolation algorithm to eliminate the differences in sampling frequency and spatial resolution between multimodal data.

[0011] Furthermore, the joint prediction module includes the following in the meteorological scenario: an extreme weather classifier outputs the probability distribution of disaster types such as rainstorms, thunderstorms, and strong winds based on fusion features; a disaster impact regressor generates radius prediction and duration estimation of the affected area; and in the water quality scenario: a pollutant identifier classifies chemical pollutants and biological pollution sources; a risk diffusion model generates a heat map of pollutant migration paths; and a safety assessor comprehensively outputs a water quality health index that conforms to international standards.

[0012] Furthermore, the pollutant identifier adopts a multi-task collaborative architecture: the classification branch identifies pollutant types and toxicity levels through a fully connected network; the regression branch generates a high-resolution concentration spatial distribution map through a transposed convolutional network; the two branches share cross-modal attention fusion features and are jointly optimized using a weighted adaptive loss function.

[0013] Furthermore, it also includes an edge computing optimization unit: compressing neural network parameters to a predetermined bit width range through hybrid precision quantization technology while retaining key feature extraction capabilities; activating or suspending specific data processing channels based on the device's computing resource status through a dynamic modality selector; and reducing memory access latency during inference through layer fusion technology to achieve real-time response of the monitoring terminal in resource-constrained environments.

[0014] An environmental monitoring method includes: in a meteorological early warning process, integrating visible / infrared dual-band satellite imagery, atmospheric pressure gradient sequences, and lightning location data, dynamically calculating the weight coefficients of each data source using a multi-head attention mechanism, and outputting the probability of severe convective weather and the geographical range of disaster impact within a predetermined future time window; in a water quality monitoring process, integrating fluorescence microscopic images, turbidity sensing sequences, and heavy metal ion concentration data, constructing a multi-parameter pollution feature map through a feature reconstruction unit, identifying cyanobacterial bloom events in real time, and generating an early warning report on pollution diffusion trends.

[0015] The technical effects and advantages of this invention are as follows: Compared with existing technologies, this invention introduces a learnable dynamic attention mechanism to establish a cross-modal interaction weight allocation strategy at the feature fusion layer. It automatically adjusts the contribution of each modality based on the quality of real-time input data (such as satellite cloud image signal-to-noise ratio and sensor drift), effectively suppressing interference from low-reliability data. Combined with a domain-customized feature extraction architecture, it employs a spatiotemporal convolutional network to simultaneously capture the spatial correlation and temporal continuity of cloud evolution in meteorological monitoring, and utilizes a graph convolutional network to model the fluid dynamic topological constraints between monitoring stations in water quality analysis, embedding physical laws into the feature learning process. Furthermore, through adaptive edge compression technology, it adopts a hybrid precision quantization and dynamic modality selection mechanism to achieve intelligent allocation of computational load while retaining key feature extraction capabilities. This technical solution improves the accuracy of severe convective weather identification in meteorological disaster early warning scenarios, enhances pollutant source tracing capabilities in water pollution monitoring, and ensures real-time system response at resource-constrained field terminals, forming a closed-loop optimization from data fusion and feature learning to edge deployment. Attached Figure Description

[0016] Figure 1 This is a system framework diagram of the present invention.

[0017] Figure 2 This is a flowchart of the feature fusion process of the present invention.

[0018] Figure 3 This is a dual-scenario workflow diagram of the present invention. Detailed Implementation

[0019] The following will refer to the appendices in the embodiments of the present invention. Figure 1-3 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Severe Convective Weather Warning System Application scenario: Provincial meteorological observatories integrate multi-source data to predict severe convective weather. Step 1: Multimodal data acquisition and preprocessing This system integrates meteorological data from three sources: meteorological satellites receive visible light (VIS) and infrared (IR) band images with a resolution of 1-5km, providing data on cloud top temperature and cloud morphology; a network of ground-based meteorological stations continuously collects temporal data on atmospheric pressure (P), wind speed (WS), and wind direction (WD) at a sampling frequency of 5-60 seconds; and a radar system generates reflectivity factor (Z) profile data within an elevation angle range of 0.5-20 degrees. The data alignment module downsamples the satellite images to the radar grid space using cubic spline interpolation and uses a temporal sliding window (60-180 minutes) to unify the timestamps of the multi-source data, enabling spatiotemporal collaborative analysis.

[0021] At the data processing level, cubic spline interpolation technology adapts high-resolution satellite imagery (original resolution 1-5km) to the radar data grid using piecewise polynomial functions, effectively preserving the gradient characteristics of cloud top temperature and cloud boundary morphology, avoiding the loss of detail caused by conventional interpolation. Temporal alignment employs a dynamic sliding window mechanism, with the window length adaptively adjusted according to the intensity of the meteorological process (e.g., a 60-minute window for strong convection and a 180-minute window for continuous precipitation). Within the window, the time series of each station and radar scan are resampled, aligning all meteorological element timestamps to the most recent radar scan time. This processing allows the macroscopic cloud structure of the satellite to spatially superimpose with the radar's three-dimensional reflectivity field. Simultaneously, high-frequency wind pressure data from ground stations, calibrated along the time axis, can accurately match transient meteorological events (e.g., squall lines), providing a multi-dimensional fusion data foundation for the vertical thermal structure analysis of strong convective clouds and the tracking of storm system evolution, thus improving the spatiotemporal continuity of mesoscale weather process diagnosis.

[0022] Step 2: Intramodal Feature Extraction Satellite imagery is input into a 3D tensor spatiotemporal convolutional network (ST-Conv), where features are extracted using 3×3 spatial convolution kernels and 5-step long-time convolution kernels. After batch normalization (BN) and ReLU activation, a feature map F_sat is output, from which the cloud movement speed (±30 km / h range) and cloud top cooling rate (≥0.5℃ / min) are analyzed. Radar data is segmented into strong echo regions with Z≥40 dBZ using a 3D U-Net structure, and a feature F_radar is generated through convolution-deconvolution-max pooling operations. Based on this, the vertical integral liquid water content (5-50 kg / m³) is calculated. 2 The data includes the storm top height (2-15km); the ground station data is modeled by a two-way GRU network to dynamically capture the pressure change process (ΔP / Δt ≥1hPa / 3min) using an attention mechanism (F_ground = Attention(h_t) * h_t), and finally outputs the wind speed shear (≥15m / s / km) and convective effective potential energy (500-3000 J / kg).

[0023] This framework adapts to the physical characteristics of different modal data through heterogeneous network design: the ST-Conv network uses spatiotemporal separation convolution in a three-dimensional space (height × time × band), and its 3×3 spatial kernel accurately identifies changes in cloud boundary morphology. The 5-step long-time kernel extracts the movement vector from 30 consecutive frames of imagery (covering 150 minutes when sampled at 5-minute intervals). At this time, the velocity field calculation is optimized based on the optical flow loss function of adjacent feature maps. The radar 3D U-Net learns the echo spatial correlation through the encoder downsampling process (8x downsampling captures 10km-level storm structure), and the decoder upsampling fuses shallow high-resolution features to improve the accuracy of hail kernel boundaries. The output F_radar feature is mapped to meteorological indicators through a physical parameter transformation layer. That is, the VIL value is obtained by weighted integral of reflectivity density at different height layers, and the storm top height is taken as the height threshold where the reflectivity vertical profile decays to 20dBZ. The ground-based GRU network uses bidirectional gating units for collaborative forward / reverse time-series modeling. An attention mechanism automatically enhances the weights of corresponding hidden states during periods of steep ΔP / Δt increases. The output wind shear characteristics are verified by calculating wind speed gradients between adjacent stations (requiring data support within a 1km station range). The CAPE index is derived from the network learning the nonlinear relationship between temperature-dew point difference and boundary layer energy transfer, forming a quantitative criterion for multi-scale strong convection triggering conditions. The entire system achieves deep integration of macroscopic cloud system motion, three-dimensional storm structure, and boundary layer energy field, providing an interpretable physical feature space for short-term forecasting.

[0024] Step 3: Cross-modal attention fusion A multi-head attention mechanism is employed to fuse heterogeneous meteorological features: satellite feature F_sat is projected onto query matrix Q via weight matrix W_q; radar feature F_radar and ground feature F_ground are concatenated and mapped to key matrix K and value matrix V by W_k and W_v, respectively. Four to eight attention heads are set up, each calculating Head_i = Softmax((Q·K^T) / √d_k) · V (d_k=64-256). The output heads are concatenated and linearly transformed by W_o to obtain the fused feature F_fused. An adaptive signal-to-noise ratio (SNR) gating is introduced: when SNR < 10, a suppression gate G is generated through σ(W_g·[SNR; F_fused]), and the final output F_final = G ⊙ F_fused completes feature denoising.

[0025] Step 4: Joint Forecasting and Early Warning Based on the fusion feature F_final, a dual-branch output is constructed: the disaster classifier outputs the probability distribution of thunderstorms, hail, and short-duration heavy precipitation through a fully connected layer + Softmax, and sets a red warning to be triggered when P(hail) ≥ 0.7; the impact range regressor uses a transposed convolutional network to decode spatial features and outputs a 100×100 heat map H (pixel resolution 1km). 2 The forecast predicts the storm's path. A final warning example is: "Within the next 2 hours, there is an 82% probability of hail in the XX region (longitude X.XX-XX.XX, latitude Y.YY-YY.YY), with a maximum diameter of 2-5 cm and an impact radius of 15±3 km."

[0026] The classifier's fully connected layer learns cross-modal associations of meteorological disasters: the first hidden layer (128 dimensions) establishes the relationship between hail probability and vertical integral liquid water content (VIL≥45kg / m³). 2 The nonlinear relationship between cloud top cooling rate (≥0.8℃ / min) and boundary layer parameters such as wind speed shear (≥20m / s / km) and CAPE mutation (Δ>1000J / kg / 10min) is investigated in the second hidden layer (64-dimensional). The softmax output layer solves the sample imbalance problem (hail events only account for 5% of the training set) through the focus loss function (γ=2.0). When P(hail)∈[0.7,0.85], an orange warning is triggered, and when it is ≥0.85, it is upgraded to a red warning and the emergency response protocol is activated.

[0027] The impact range regressor employs a cascaded transposed convolutional structure: the first layer, Conv2DTranspose (kernel=7×7, stride=2), upsamples the feature map to 25×25, capturing the initial storm location at a 10km scale; the second layer, Conv2DTranspose (kernel=5×5, stride=2), expands to 50×50, learning storm splitting features; the third layer, bilinear interpolation, refines to 100×100, preserving the strong echo kernel movement trajectory with 1km accuracy. The heatmap H undergoes non-maximum suppression processing, extracting the center points of connected components to generate predicted paths. Geographic mapping errors are corrected using WRF-mode terrain height data (horizontal error <500m). A dynamic extrapolation module is added to the operational system: using the cloud movement vector (dx, dy) extracted by F_sat, an optical flow-constrained particle filter algorithm predicts the impact radius for the next 30-120 minutes, with a confidence interval of ±3km derived from the root mean square error statistics of historical echo tracing, achieving automated output of a three-dimensional early warning product based on "intensity-range-time".

[0028] Example 2: Water Pollution Source Tracing System Application scenario: Identifying algal bloom pollution sources at lake water quality monitoring stations Step 1: Synchronous Acquisition of Multimodal Data The system integrates three sources of water environment data: a microscope camera captures multispectral images of plankton at a resolution of 2048×1536 (wavelengths covering the visible light band of 450-750nm); a sensor array collects pH values ​​(6.0-9.0), dissolved oxygen (2-15mg / L), and chlorophyll a concentration (0-100μg / L) in real time; and a hydrological radar simultaneously acquires flow velocity vector V (0-2m / s) and flow direction θ (0-360°). The plankton images were processed using a multi-band method to analyze their ecological characteristics: the 450nm blue light band enhanced the refractive imaging of diatom shells, the 680nm red light band captured chlorophyll fluorescence features, and the 750nm near-infrared band suppressed water scattering noise. A convolutional neural network was used to classify algae from the microscopic images (achieving 92% accuracy in morphological segmentation of dinoflagellates and diatoms) and quantify the spatial distribution of algal density. Water quality parameters were dynamically correlated using a gradient boosting tree model. A potential algal bloom warning was triggered when pH > 8.5 and chlorophyll a > 50 μg / L, while a sudden change in dissolved oxygen (ΔDO > 3 mg / L / h) was marked as a water stratification or pollution event.

[0029] Hydrological radar data was processed using a eddy covariance algorithm: the standard deviation of vertical velocity profile σ_v > 0.15 m / s characterizes turbulence intensity, and the variance of flow direction angle σ_θ > 25° identifies vortex structure. Coupled Granger causality tests revealed that chlorophyll a concentration lags the velocity field by approximately 10-30 minutes (R0). 2=0.78), confirming the controlling effect of hydrodynamics on algal dispersal. The system ultimately generates a three-dimensional situation map of "flow velocity-water quality-biomass", predicting the migration path of cyanobacterial blooms with a grid accuracy of 1 meter (e.g., when the flow velocity is 1.2 m / s and the southeast wind is blowing, the center of algal density shifts by 432 meters every 6 minutes), providing minute-level decision support for ecological scheduling.

[0030] Step 2: Feature Extraction and Topology Modeling Microscopic image processing: Depthwise separable convolutional networks (8-12 layers) are used to extract algal features: F_img = DW_Conv(ReLU(PW_Conv(Input))); Identification criteria: algal cell density ≥10^6 cells / mL, filamentous algae ≥30%; Chemical parameter processing: Graph Convolutional Network (GCN) modeling of monitoring station topology: adjacency matrix A_ij = 1 / (1 + α·distance_ij^2); node feature update: F_chem^{l+1} = ReLU( D^{-1 / 2} AD^{-1 / 2} F_chem^l W^l ) outputs pollution diffusion direction vector D ∈ R^2; Hydrological Data Processing: Long Short-Term Memory (LSTM) Network Prediction of Velocity Anomalies: f_t = σ(W_f·[h_{t-1}, x_t]) / / Forget gate; i_t = σ(W_i·[h_{t-1}, x_t]) / / Input gate; Ĉ_t = tanh(W_c·[h_{t-1}, x_t]); C_t = f_t ⊙ C_{t-1} + i_t ⊙ Ĉ_t; Output velocity anomalies (|ΔV / Δt|≥0.5m / s) 2 ); Step 3: Attention Fusion and Multi-Task Learning Cross-modal attention weight calculation: Energy = tanh( W_a·[F_img; F_chem] + b_a ); α =exp(Energy) / ∑exp(Energy); F_fused = α ⊙ F_img + (1-α) ⊙ F_chem; Multi-task branching: Main task (pollutant classification): y_type = Softmax(W_cls·F_fused); Output: [blue-green algae, green algae, chemical pollution]; Auxiliary task (diffusion prediction): H_map = Deconv2D(ReLU(ConvT(F_fused))); Outputs a concentration distribution map with a resolution of 0.5m / pixel; Step 4: Pollution Source Tracing and Emergency Response Source tracing algorithm: Combining diffusion map and velocity vector, the coordinates of pollution source are solved in reverse: Source_xy = argmin(∑||H_map(x,y) - f(V,θ,t)||^2).

[0031] Example of an early warning output: "A blue-green algae bloom has been detected at XX.XXXX North latitude (92% confidence level). The pollution source is located at the sewage outlet on the west bank (error radius ≤ 50m). It is expected to spread to the water intake within 12 hours. It is recommended to activate a Level III response." Example 3: Edge Computing Deployment Scheme Application scenario: Real-time water quality monitoring using drones for river patrol Step 1: Dynamic Mode Selection and Quantization Compression The design incorporates a resource sensor to dynamically manage device load: when available memory is ≤50MB, the microscopic image acquisition channel is shut down; when CPU load is ≥80%, the time-series data sampling rate is reduced from 60 seconds to 300 seconds. The model employs 8-bit fixed-point quantization: weight parameters are compressed to the 0-255 range via a linear mapping W_q = round(255×(W-minW) / (maxW-minW)); feature map activation values ​​are quantized using dynamic range quantization F_q = clamp(round(F / Δ), -128, 127), where the scaling factor Δ=max(|F|) / 127.

[0032] The resource sensor employs a hierarchical decision-making mechanism: memory-triggered image channel shutdown takes precedence over radar data (microscopic images occupy 83%±7% of memory), and a 10MB buffer is reserved to store snapshots of sudden water quality events; CPU load reduction is implemented gradually: when the initial load exceeds 75%, median filtering in image preprocessing is disabled (saving 23% computational power), and the sensor sampling rate is only reduced when the load consistently exceeds 80%. The quantization algorithm is optimized for convolutional layers: extreme values ​​are calculated for each channel of weights (minW / maxW are independently calculated for each convolutional kernel) to avoid the influence of outliers on all parameters; activation value quantization introduces EMA to update the Δ value (smoothing coefficient β=0.95), and the scaling factor is dynamically adjusted every 200 inference iterations to track feature distribution drift.

[0033] In actual deployment, the peak memory usage of the quantized model on the ARM Cortex-M7 core decreased to 28.1% of the original 32-bit model (model size compressed from 16.7MB to 4.2MB), and the inference speed increased by 3.8 times. The chlorophyll a concentration compensation model (Chla=0.83×DO) is automatically enabled when the microscopic channel is closed. 2-1.2pH+ε), maintaining continuous algae monitoring. Verified in the Taihu Lake basin, this solution reduced the equipment failure rate by 67% after 72 hours of continuous operation, and ensured complete acquisition of hydrological radar millimeter-wave current velocity data even during algal blooms (when CPU load is normally ≥85%).

[0034] Step 2: Lightweight Feature Extraction Microscopic image processing adopts a bottleneck structure replacement scheme of MobileNetV3: F_out = Conv1x1(ReLU(DW_Conv3x3(Conv1x1(F_in))), which reduces the computational cost to 10-20% of the original model; chemical parameter time-series processing uses a linear structure to replace GRU: F_chem = W_2·ReLU(W_1·[x_t, x_{t-1}, x_{t-2}] + b_1) + b_2, which achieves efficient feature extraction.

[0035] The bottleneck layer of MobileNetV3 decouples spatial and channel dimension calculations through depthwise separable convolutions (DW_Conv3x3). 1×1 convolutions compress the number of channels to one-quarter of the original (e.g., reducing to 64 channels when the input is 256). 3×3 depthwise convolution kernels use ReLU6 activation (upper bound 6.0) to suppress feature outliers. The final 1×1 convolution restores the number of channels by introducing sparse regularization (L1 coefficient 5e-4). Experimental results show that this structure reduces inference time on the RK3399 chip from 187ms to 24ms, reduces memory usage by 89%, while maintaining the accuracy of plankton classification (92.1% on the ResNet34 baseline → 90.3% after lightweighting).

[0036] A simplified chemical parameter model preserves key dynamics through a three-order time-series sliding window: the input vector [x_t, x{t-1}, x{t-2}] contains the combined features of pH, DO, and Chla at the current and two previous time points (3×3=9 dimensions), the hidden layer W_1 (32 dimensions) learns parameter mutation patterns (e.g., a sudden drop in DO > 4 mg / L / 10 min corresponds to an anoxic event), and ReLU activates the negative drift of filtration. Compared to the original bidirectional GRU, this structure reduces the number of parameters from 18.7K to 1.2K, improves inference speed by 17 times on the STM32H7 chip, and increases the chlorophyll a concentration prediction error (RMSE) by only 0.8 μg / L, meeting the minute-level response requirements for water quality mutations. This design overcomes the computing power bottleneck of edge devices, enabling multimodal ecological monitoring to run continuously in environments with ≤100MB of memory.

[0037] Step 3: Attention Fusion Optimization Single-head attention replaces multi-head attention: Attention = Softmax( (Q·K^T) / √d_k ) · V, reducing computational complexity from O(N^2·H) to O(N^2); The gating module is simplified: the confidence estimation is changed to rule judgment: if the sensor data missing rate is > 30% or SNR < 5, then G=0.2, otherwise G=1.0.

[0038] Single-head attention achieves an exponential reduction in computational cost through feature dimension compression (d_k is set to 64). When the input feature sequence length N=100, the theoretical number of floating-point operations is reduced from 512KFLOPs in the original 8-head structure to 64KFLOPs. In actual measurements, the inference latency on the Cortex-M4 chip is reduced by 82%. The Softmax scaling factor √d_k retains the constraint on the feature magnitude (√64=8) to prevent overflow of dot products in high-dimensional space, and avoids redundant calculations through symmetry optimization of pre-computed Q·K^T (utilizing the properties of the Gram matrix). The cost is the sacrifice of multi-feature subspace modeling capability (such as the original 8-head mechanism can simultaneously focus on algal migration direction and water quality mutations). In actual measurements during the Taihu Lake cold wave event, the single-head structure increased the capture delay of the sudden drop in chlorophyll a concentration by 4 minutes.

[0039] The gating rules are designed with hard-decision logic based on the device's operating status: Data missing rate statistics use a sliding window counting (60 sampling points per window). Sensor malfunction is determined when at least two of pH, DO, and Chla are continuously missing (i.e., missing rate > 30%). The SNR < 5 threshold is set for hydrological radar multipath interference scenarios (typical attenuation from reflections from shore-side buildings is 4-6 dB). Compared to native neural network gating, the rule-based system loses adaptive adjustment capability (the original W_g could learn a nonlinear range of -0.5 to 1.2), but eliminates the need for 37.5K parameter storage and remains stable in electromagnetic interference environments—when a sudden pump start-up or shutdown causes the SNR to drop instantaneously to 3, the system immediately reduces the feature confidence to 20%, avoiding abnormal fluctuations in flow velocity prediction (the maximum measured error decreased from 1.2 m / s to 0.3 m / s). This scheme enables edge devices to maintain effective 24 / 7 monitoring of key parameters (chlorophyll a, flow velocity vector) under a 128KB memory constraint. Step 4: Real-time response and linkage control Prediction result trigger action: When heavy metal pollution (Pb>0.01mg / L) is detected: Once the drone hovers at its coordinates (GPS positioning error ≤ 2m), the automatic water sample collection device is activated, and an alarm is sent to the command center via LoRa.

[0040] Performance metrics: Inference latency ≤ 500ms (original system ≥ 3s); Memory usage ≤ 35MB (original model ≥ 200MB).

[0041] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change. Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multimodal neural network fusion system incorporating an attention mechanism, characterized in that... Includes: a multimodal input interface for receiving satellite remote sensing images, radar-based data, and time-series data from ground meteorological stations for meteorological monitoring scenarios, or chemical sensor network data, plankton microscopic images, and hydrological monitoring time-series streams for water quality monitoring scenarios; an intramodal feature extraction module uses a three-dimensional convolutional neural network to analyze the spatial-temporal joint features of meteorological data, uses a graph convolutional network to model the topological association features of water quality monitoring stations, and uses gated recurrent units to capture long-short-term dependencies in time-series data; The cross-modal attention fusion module achieves heterogeneous modal feature interaction through a learnable multi-head attention mechanism; the joint prediction module outputs meteorological disaster level assessment or water quality safety risk index.

2. The multimodal neural network fusion system with an attention mechanism according to claim 1, characterized in that... The cross-modal attention fusion module includes: a configurable number of attention heads that compute the correlation weight matrix between features of different modalities in parallel, with each attention head focusing on a feature interaction pattern of a specific scale or type; and a feature reconstruction unit that generates fused features through a combination of weighted summation and tensor concatenation. The adaptive gating unit dynamically generates modulation factors based on the signal-to-noise ratio of real-time input data, suppressing the contribution intensity of low-quality modes within a predetermined threshold range.

3. The multimodal neural network fusion system with an attention mechanism according to claim 1, characterized in that... The intramodal feature extraction module includes: a spatiotemporal convolutional network for meteorological satellite imagery to simultaneously extract cloud morphological features and movement trajectory vectors; a depth-separable convolutional network for water quality microscopic images to extract algal biomorphological texture features; and a bidirectional gated recurrent unit for sensor time-series data combined with a self-attention mechanism to capture periodic patterns and anomalous mutation points in the data stream.

4. A multimodal neural network fusion system incorporating an attention mechanism according to claim 1, characterized in that... It also includes an adaptive feature calibration module: the original features of each modality are mapped to a uniform numerical distribution range through a dynamic range normalization layer, and its scaling parameters are automatically adjusted according to the statistical characteristics of the input data; the spatiotemporal alignment layer uses an interpolation algorithm to eliminate the differences in sampling frequency and spatial resolution between multimodal data.

5. A multimodal neural network fusion system incorporating an attention mechanism according to claim 1, characterized in that... The joint prediction module includes the following in meteorological scenarios: an extreme weather classifier outputs the probability distribution of disaster types such as rainstorms, thunderstorms, and strong winds based on fusion features; a disaster impact regressor generates radius prediction and duration estimation of affected areas; and in water quality scenarios: a pollutant identifier classifies chemical pollutants and biological pollution sources; a risk diffusion model generates a heat map of pollutant migration paths; and a safety assessor comprehensively outputs a water quality health index that conforms to international standards.

6. A multimodal neural network fusion system with an attention mechanism according to claim 5, characterized in that... The pollutant identifier adopts a multi-task collaborative architecture: the classification branch identifies pollutant types and toxicity levels through a fully connected network; the regression branch generates a high-resolution concentration spatial distribution map through a transposed convolutional network; the two branches share cross-modal attention fusion features and are jointly optimized using a weighted adaptive loss function.

7. A multimodal neural network fusion system incorporating an attention mechanism according to claim 1, characterized in that... It also includes an edge computing optimization unit: compressing neural network parameters to a predetermined bit width range through hybrid precision quantization technology while retaining key feature extraction capabilities; activating or suspending specific data processing channels based on the device's computing resource status through a dynamic modality selector; and reducing memory access latency during inference through layer fusion technology to achieve real-time response of the monitoring terminal in resource-constrained environments.

8. An environmental monitoring method, characterized in that... The system described in any one of claims 1-7 is applied and comprises: in the meteorological early warning process, visible light / infrared dual-band satellite imagery, atmospheric pressure gradient sequence and lightning location data are integrated, and the weight coefficients of each data source are dynamically calculated using a multi-head attention mechanism to output the probability of severe convective weather and the geographical range of disaster impact within a predetermined future time window; in the water quality monitoring process, fluorescence microscopic images, turbidity sensing sequence and heavy metal ion concentration data are integrated, and a multi-parameter pollution feature map is constructed through a feature reconstruction unit to identify cyanobacterial bloom events in real time and generate a pollution diffusion trend early warning report.

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