Animal husbandry early warning system based on multi-modal perception and edge intelligence

Abstract

The invention provides an animal husbandry early warning system based on multi-mode perception and edge intelligence, and belongs to the technical field of animal husbandry intelligent monitoring. The system comprises a multi-mode sensing module, an edge intelligent processing module, an early warning decision and intervention module and a cloud platform management module. The multi-modal sensing module is used for collecting multi-dimensional physiological and behavior data such as acoustics, thermal imaging, odor molecules and micro-vibration of livestock; the edge intelligent processing module adopts a cross-modal space-time fusion algorithm and a lightweight model library to carry out local real-time analysis on the sensing data; the early warning decision module generates a three-level early warning signal based on the abnormal recognition result and is linked with the response device; and the cloud platform module is used for model updating, remote maintenance, data visualization and system management. The method has the advantages of fast early warning response, flexible deployment and adaptive evolution of the algorithm, the intelligence level and the operation and maintenance efficiency of livestock health management are remarkably improved, and the method is suitable for large-scale farms and multi-scene livestock application.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring and early warning technology for animal husbandry, and in particular to an early warning system for animal husbandry based on multimodal perception and edge intelligence. Background Technology

[0002] In modern animal husbandry, real-time monitoring and early warning of livestock health status have become crucial for ensuring both farming efficiency and animal welfare. Existing health monitoring methods primarily rely on manual inspections, single-sensor monitoring (such as thermometers, cameras, or environmental gas sensors), and passive analysis methods based on historical statistical data. While these methods can reflect physiological or environmental changes in livestock to some extent, they have the following significant limitations: On the one hand, the sensing dimensions are limited and the coverage is insufficient. For example, while infrared imaging technology can detect local temperature changes, it is difficult to reflect abnormal behavior or subtle changes in state such as breathing and chewing; and environmental gas sensors can only monitor indicators such as ammonia and hydrogen sulfide in the air, failing to reflect the physiological characteristics of individual livestock. In existing systems, acoustic signals and micro-vibration information are almost not effectively integrated and utilized, resulting in insufficient sensing capabilities.

[0003] On the other hand, data processing relies on centralized computing, resulting in large response delays and a lack of adaptability. Many existing systems require transmitting sensed data to the cloud or servers for centralized analysis, which, limited by network bandwidth and latency, makes them unsuitable for real-time identification and rapid response to health anomalies, stress behaviors, and other situations. Furthermore, existing models generally lack self-learning and local adaptation capabilities, making it difficult to dynamically optimize algorithm performance based on the differences in various farm environments.

[0004] Furthermore, existing early warning logic is mostly based on fixed threshold triggering mechanisms, lacking multimodal collaboration and tiered response strategies. Most systems rely on single-indicator judgments, such as issuing an early warning when body temperature exceeds a set threshold, failing to fully integrate data from multiple sensing dimensions, and failing to effectively distinguish between mild abnormalities, moderate risks, and severe crises, easily leading to false alarms and missed alarms.

[0005] At the hardware level, traditional sensor devices are susceptible to environmental factors such as dust and humidity in aquaculture, resulting in problems such as frequent maintenance, short lifespan, and difficult deployment. Furthermore, some systems still rely on external power supplies and wired networks, lacking the ability to operate independently for extended periods, thus limiting their widespread application in large-scale ranch environments.

[0006] In summary, current livestock health monitoring systems generally face problems such as limited perception dimensions, slow data processing, rigid algorithms, low early warning accuracy, and poor equipment environmental adaptability. There is an urgent need for a new technical solution that can integrate multimodal perception, has edge intelligent processing capabilities, and supports adaptive early warning response, in order to improve breeding efficiency, reduce disease risks, and promote the intelligent upgrading of livestock farming. Summary of the Invention

[0007] Given that existing livestock health monitoring methods suffer from limited sensing dimensions, insufficient real-time performance, low intelligence levels, and inaccurate early warning responses, the purpose of this invention is to provide a livestock early warning system based on multimodal perception and edge intelligence. This system aims to achieve comprehensive perception of livestock physiological and behavioral states, rapid identification of abnormal events, and tiered intervention responses, thereby improving the accuracy and timeliness of disease early warning, reducing labor costs, and enhancing the level of intelligent livestock management.

[0008] To achieve the above objectives, the present invention provides the following technical solution: In one embodiment of the present invention, a livestock early warning system based on multimodal perception and edge intelligence is provided, comprising: a multimodal perception module for real-time acquisition of multi-dimensional data of livestock, including acoustic, thermal imaging, odor molecules, and micro-vibrations; an edge intelligence processing module connected to the multimodal perception module for cross-modal fusion analysis and anomaly detection of the acquired data; an early warning decision module for generating corresponding level early warning signals and triggering intervention mechanisms based on the analysis results of the edge intelligence processing module; and a cloud platform management module for model updates, remote configuration, data storage, and visualization management.

[0009] Furthermore, the multimodal sensing module includes the following sub-modules: The bioacoustic sensing submodule uses a directional microphone array to collect the sounds of livestock, chewing, and breathing, in order to identify estrus, disease, or hunger. The thermal imaging vision submodule monitors the surface temperature distribution of livestock based on millimeter-wave infrared imaging equipment, and is used to detect early signs of inflammation. The odor molecule sensing submodule uses an electronic nose array to detect the concentration of gases such as NH3, H2S, and CH4, as well as volatile organic compounds. The micro-vibration sensing submodule monitors livestock chewing frequency, lying time, and activity rhythm based on piezoelectric thin film sensors.

[0010] In one embodiment of the present invention, the edge intelligence processing module includes: Heterogeneous computing chipsets include GPU chips for visual processing, general-purpose processing chips for multimodal fusion, and neural network acceleration chips; A cross-modal spatiotemporal fusion engine is used for time synchronization, spatial mapping, and feature fusion of multimodal data; Anomaly detection model library, including lightweight models for thermal imaging, acoustic semantics, and behavior recognition; The adaptive learning engine has incremental learning and federated learning capabilities, enabling local model optimization and periodic updates.

[0011] Furthermore, the cross-modal spatiotemporal fusion engine employs a multi-scale pyramid fusion mechanism and combines an attention mechanism to dynamically weight thermal imaging, acoustic, and odor distribution features to improve the confidence and accuracy of anomaly detection.

[0012] Preferably, the adaptive learning engine supports zero-shot anomaly detection and builds a baseline model of normal behavior based on generative adversarial networks, thereby enabling the identification of unlabeled novel diseases or behavioral abnormalities.

[0013] In one embodiment of the present invention, the early warning decision module includes: The three-tiered early warning decision-making mechanism corresponds to three warning levels: yellow, orange, and red. The automatic intervention execution submodule is used to coordinate with isolation doors, drug delivery equipment, environmental controllers, etc. to execute corresponding intervention measures. The knowledge base management unit supports visual configuration and dynamic updates of the rule base; A multi-channel push mechanism is used to notify relevant personnel of early warning information through mobile apps, text messages, voice calls, and other means.

[0014] Furthermore, in the aforementioned three-level early warning mechanism: A yellow alert corresponds to a slight anomaly in a single sensing data item that continues to exceed the threshold. An orange alert corresponds to two or more sensing data points showing moderate anomalies, and these anomalies are consistent after cross-modal verification. A red alert corresponds to the detection results of severe abnormalities in multiple systems or acute disease symptoms, and automatically triggers emergency response procedures.

[0015] Alternatively, the system may further include the following structure: The self-cleaning sensor assembly uses ultrasonic vibration and photocatalytic coating technology to ensure stable operation of the sensor in high dust and high humidity environments; The energy self-sustaining system, including flexible solar cells and piezoelectric energy harvesting modules, enables the system to continuously supply energy in the field environment; The anti-interference communication system combines LoRaWAN and 5G networks to form redundant communication links, supporting mesh structure and self-healing mechanism.

[0016] In one embodiment of the present invention, the cloud platform management module includes: The model version control submodule supports remote deployment and rollback of models. The data visualization platform provides interactive display capabilities for multi-dimensional historical and real-time data; The API provides an open interface, supporting integration and calls between the system and external aquaculture management platforms or analysis systems.

[0017] Furthermore, the system deployment density can be optimized based on the activity behavior and stocking density of different livestock species. Preferred deployment strategies include: One edge node is deployed for every 8-10 cows in the dairy cow area, with a coverage radius of 15-20 meters; In the beef cattle area, one node is deployed for every 6-8 head of cattle, with a coverage radius of 20-25 meters; In the sow area, one node is deployed for every 12-15 sows, with a coverage radius of 8-12 meters; One node is deployed for every 30-40 sheep in the sheep area, with a coverage radius of 30-40 meters.

[0018] Based on the above technical solutions, the livestock early warning system based on multimodal perception and edge intelligence of the present invention combines various biological behavior perception methods (acoustics, thermal imaging, odor and vibration) with an edge intelligence processing platform to achieve multimodal fusion analysis and real-time anomaly identification of livestock health status. Based on a hierarchical early warning mechanism, it automatically links intervention equipment, thereby effectively improving the advance warning and accuracy of disease warning, reducing the cost of manual intervention, and enhancing the level of intelligent and automated management of farms.

[0019] Specifically, this invention comprehensively covers the physiological and behavioral characteristics of livestock at different times and under different behavioral states by deploying sensing terminals such as directional microphones, infrared thermal imagers, electronic noses, and piezoelectric vibration sensors. The system can capture weak abnormal signals such as coughing, abnormal breathing, localized increases in body temperature, reduced feed intake, and changes in gas metabolism. By utilizing a heterogeneous computing platform and lightweight model on edge nodes for local processing, the system significantly reduces its dependence on cloud computing power and enhances response speed and real-time performance. Through a cross-modal attention fusion mechanism, the system can perform time synchronization and spatial alignment of multi-source data and dynamically weight the data based on the importance of features from different modalities, thereby improving the accuracy and robustness of abnormal state identification.

[0020] Furthermore, this invention introduces an adaptive learning engine to implement incremental learning and federated update mechanisms for the model at the edge, enabling it to continuously adapt to new environments and scenarios, significantly improving the system's generalization performance and practicality in different aquaculture scenarios; the zero-shot anomaly detection algorithm used enables the system to identify new diseases and sudden health events in the absence of labeled data, further improving the system's foresight and intelligence level.

[0021] In terms of early warning response, this invention constructs a three-level early warning system, which combines a livestock expert knowledge base and a rule engine to automatically implement intervention measures such as isolation, medication, and environmental adjustment based on the level of abnormality. It also pushes the disposal plan to relevant management personnel through multiple channels, effectively avoiding the risk of major epidemic spread and group outbreaks.

[0022] From an engineering perspective, this invention also addresses issues such as dust, high humidity, and harsh weather in aquaculture environments by designing a sensor structure with self-cleaning capabilities. It employs an energy self-sustaining system composed of flexible solar cells and vibration energy harvesting modules to ensure that the sensing device can operate stably for a long time without external power supply. At the same time, the system supports the construction of a low-latency, high-reliability anti-interference communication network through a hybrid LoRaWAN and 5G network to ensure timely and reliable data transmission and rapid response to control commands.

[0023] In terms of deployment strategy, this invention proposes the principle of dynamically optimizing deployment density according to the characteristics of livestock species, which achieves cost optimization without sacrificing monitoring accuracy, making the system a realistic basis for large-scale promotion and application.

[0024] In summary, this invention not only represents a systematic innovation across multiple dimensions, including multimodal information fusion, edge intelligence algorithms, hardware structure optimization, and automated response mechanisms, but also demonstrates significant advantages in technical performance, deployment efficiency, and commercialization capabilities, providing a comprehensive and sustainable solution for livestock health management, intelligent farming, and risk control. Attached Figure Description

[0025] Figure 1 This is a block diagram of the overall structure of the livestock early warning system based on multimodal perception and edge intelligence as described in an embodiment of the present invention.

[0026] Figure 2 This is a functional structure diagram of the multimodal sensing module according to an embodiment of the present invention, including the configuration relationship of the bioacoustic sensing submodule, the thermal imaging vision submodule, the odor molecule sensing submodule, and the micro-vibration sensing submodule.

[0027] Figure 3 This is a flowchart of the edge intelligence processing module described in an embodiment of the present invention, illustrating the overall processing path of cross-modal spatiotemporal fusion, anomaly detection model execution, and adaptive learning.

[0028] Figure 4 This is a logical flowchart of the three-level early warning decision-making and intervention mechanism described in the embodiments of the present invention, illustrating the system's response actions and linkage devices under yellow, orange and red early warning levels. Detailed Implementation

[0029] To make the objectives, technical solutions, and beneficial effects of this invention clearer and more complete, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the described embodiments are merely illustrative and do not constitute a limitation on the scope of protection of this invention. Equivalent substitutions or technical modifications made by those skilled in the art in specific implementations without departing from the spirit of this invention should all fall within the scope of protection of this invention.

[0030] I. System Overall Structure and Deployment Strategy In one embodiment of the present invention, such as Figure 1 As shown, a livestock early warning system based on multimodal perception and edge intelligence includes the following functional modules: 1. Multimodal sensing module, used to be deployed in livestock and poultry farming environments to collect multi-dimensional biological information and behavioral data of individual livestock and groups, including acoustic characteristics, body temperature distribution, odor molecule concentration and vibrational behavior signals, etc. 2. An edge intelligent processing module, which is communicatively connected to the multimodal perception module and deployed in a local edge node, is used to perform localized, low-latency fusion analysis, anomaly identification, and model updating of the perceived data; 3. Early warning decision and intervention module, used to classify early warning levels based on the analysis results output by the edge intelligent processing module, and automatically control relevant intervention devices to implement response actions; 4. The cloud platform management module is used to realize functions such as remote system management, data storage and visualization, model version control, knowledge base update, system configuration and user permission management.

[0031] The modules mentioned above can establish data connections through wireless communication networks (such as LoRaWAN, Wi-Fi, 5G) or industrial Ethernet to form an intelligent monitoring and response system architecture that is collaborative between the end, edge, and cloud.

[0032] In practical deployment, to adapt to the breeding density and activity characteristics of different livestock species, the system adopts a differentiated deployment strategy, that is, dynamically configuring the density of multimodal sensing nodes and edge processing units according to livestock species. Preferably, the deployment density and coverage are as follows: Dairy farming area: One edge node is configured for every 8 to 10 dairy cows, with a node sensing coverage radius of approximately 15 to 20 meters; Beef cattle breeding area: 1 node is set up for every 6 to 8 beef cattle, with a coverage radius of 20 to 25 meters; Sow breeding area: 1 node is configured for every 12-15 sows, with a coverage radius of 8-12 meters; Sheep breeding area: 1 node is set up for every 30-40 sheep, with a coverage radius of 30-40 meters.

[0033] The above deployment parameters can be flexibly adjusted according to the actual pasture terrain, livestock shed layout, and wireless network propagation performance.

[0034] To improve the long-term stability and field adaptability of the system, preferably, the present invention integrates an energy self-sustaining device and an environmentally adaptable structural design in each edge node, including: Flexible solar thin-film modules with a conversion efficiency of no less than 23% are used for daytime power supply and charging. The vibration energy harvesting module utilizes the mechanical vibrations caused by livestock activities to provide auxiliary power. The self-cleaning housing structure, coated with a photocatalytic anti-fouling coating, combined with ultrasonic dust removal devices, effectively reduces the impact of dust and fecal matter on sensor performance.

[0035] In summary, the system architecture of this invention is highly modular, energy-efficient, environmentally adaptable, and flexible in deployment. It is suitable for various scenarios such as large-scale farms and distributed ranches, and effectively solves the technical problems of existing livestock health monitoring systems, such as difficulty in real-time response, difficulty in achieving full coverage, and high maintenance costs.

[0036] II. Composition and Hardware Structure Innovation of Multimodal Sensing Module In one embodiment of the present invention, such as Figure 2 As shown, the multimodal sensing module is deployed in key behavioral areas within the livestock farm, including feeding troughs, drinking points, lying areas, and passageway intersections, to achieve comprehensive monitoring of individual livestock and groups in multiple dimensions such as sound, temperature, odor, and vibration.

[0037] The multimodal sensing module includes the following four types of sensing units: 1. Bioacoustic sensing unit This unit includes a directional microphone array, a voiceprint recognition module, and an abnormal sound detection module, with the following specific configuration: Directional microphone array: Employs ≥4 channels of high-sensitivity microphones with spatial directional capabilities, installed above feed troughs and watering devices, used to collect livestock vocalizations, chewing sounds, rumination sounds, and breathing sounds; Voiceprint recognition module: Based on the MFCC (Mel frequency cepstral coefficients) + LPC (linear predictive coding) feature extraction method, combined with a lightweight CNN network, voiceprint modeling is performed on individual livestock. Abnormal sound detection module: The TinyBERT acoustic semantic model is used to semantically encode the audio stream, which can identify respiratory abnormal sounds such as coughing, groaning, and wheezing.

[0038] By establishing a livestock voiceprint database and combining it with an acoustic anomaly model, this module can achieve highly sensitive detection of individual animal health status and stress behavior.

[0039] 2. Thermal imaging vision unit This unit consists of an uncooled infrared thermal imager, an image processing chip, and a thermal analysis module, and is installed on the top of the livestock shed or on both sides of the passageway. Specifically, it includes: Infrared thermal imager: operating wavelength 8–14μm, preferred temperature measurement accuracy ±0.2℃, frame rate ≥25fps; Dynamic heat map analysis module: It adopts a semantic segmentation algorithm based on the U-Net architecture to perform partition recognition of the heat distribution map of livestock body surface (such as hoof, shoulder, abdomen) and output temperature change curves; Group thermal density monitoring module: Based on local thermal density clustering analysis, it calculates the distance between animals and the heat stress index, which is suitable for early warning of heat stress or early signs of infectious diseases caused by overcrowding.

[0040] This unit can achieve dynamic body temperature monitoring at the individual and group levels, and identify early symptoms such as hoof inflammation and febrile diseases.

[0041] 3. Odor molecule sensing unit This unit mainly includes a multi-channel electronic nose array, a gas concentration acquisition module, and a VOCs fingerprint recognition module. It is installed in the manure and urine concentration area and the middle section of the exhaust ventilation path, forming a distributed sensing network for changes in livestock metabolic gases.

[0042] Electronic nose array: integrates metal oxide semiconductor (MOS) sensors, electrochemical gas sensors and photoionization detectors (PID), with a total of ≥16 channels; Concentration monitoring module: used to detect real-time changes in the concentration of metabolic-related gases such as ammonia (NH3), hydrogen sulfide (H2S), and methane (CH4); VOCs identification module: Principal component analysis (PCA) + support vector machine (SVM) are used to establish a mapping model between volatile organic compounds and disease states, so as to realize the early correlation between changes in fecal odor and digestive system abnormalities.

[0043] By continuously monitoring changes in environmental gas characteristics, a 24–48 hour early warning window can be provided before disease symptoms manifest.

[0044] 4. Micro-vibration sensing unit This unit is based on a piezoelectric thin film sensor design and is embedded in the livestock lying area, the bottom of the feed trough, and the floor of the passageway to collect behavioral vibration signals of livestock, including chewing, drinking, standing, rumination, and lying down.

[0045] Piezoelectric thin film sensor: preferably made of polyvinylidene fluoride (PVDF), with a response frequency range of 0.1–50Hz, high sensitivity, and resistance to moisture and corrosion; Behavior recognition module: A one-dimensional convolutional neural network (1D-CNN) is used to classify temporal vibration signals, with a preferred recognition accuracy of ≥92%; Activity Analysis Module: Calculates changes in livestock activity levels by integrating daily vibration intensity, used to assess the risk of stress response or metabolic diseases.

[0046] This unit can replace traditional video behavior analysis solutions and has advantages such as strong privacy, anti-occlusion, and low power consumption.

[0047] Hardware structure and environmental adaptability design To ensure the long-term stable operation of the sensing module, this invention has implemented engineering optimization design for the sensor hardware structure, specifically including: 1. Self-cleaning structural design All sensor housings are coated with a nano-photocatalytic coating (such as a TiO2 thin film), which can decompose attached organic contaminants under ultraviolet light irradiation; By combining an ultrasonic oscillation module, deposited dust and fecal matter are periodically removed, improving sensing accuracy and response stability.

[0048] 2. Energy self-sustaining system Each node integrates flexible solar cells (efficiency ≥23%), combined with a lithium battery energy storage system, to achieve independent power supply in outdoor pastures; Piezoelectric energy harvesting devices are installed in areas where livestock frequently move around, and mechanical vibration is used to supplement power supply, thereby improving the endurance of nighttime operation.

[0049] In summary, the multimodal perception module described in this invention achieves innovative breakthroughs in perception dimension, structural layout, hardware selection, and environmental adaptability, providing a high-quality and highly reliable data foundation for subsequent intelligent analysis and early warning judgment.

[0050] III. Structure and Core Algorithm Flow of the Edge Intelligent Processing Module In one embodiment of the present invention, such as Figure 3 As shown, the edge intelligent processing module is deployed in local or zoned control nodes of the livestock shed, communicating with multiple front-end sensing units and possessing local data processing, autonomous reasoning and decision-making, and model update functions. This module mainly includes the following components and processing flow: 3.1 Heterogeneous Computing Platform To achieve parallel processing of multimodal data and efficient model inference, the edge processing module is preferably configured with the following heterogeneous computing units: NVIDIA Jetson Orin chip for processing high-resolution thermal imaging images and semantic segmentation tasks; The Qualcomm QCS6490 chip is used for low-power multimodal data fusion computing and wireless communication scheduling; The Cambricon MLU220 neural network accelerator is used to deploy deep neural network inference models and improve the execution efficiency of edge-side models.

[0051] Through heterogeneous configuration of hardware and software collaboration, the system achieves a dynamic balance between power consumption and performance, ensuring real-time performance and stability for all-weather operation.

[0052] 3.2 Cross-modal spatiotemporal fusion engine This invention proposes a cross-modal spatiotemporal fusion algorithm (CMAF), which is the core mechanism for achieving high-precision anomaly detection. Its structure includes the following steps: (1) Time alignment submodule To address the differences in sampling frequencies among different modalities (such as thermal imaging at 25fps, sound at 44.1kHz, and odor at 1Hz), interpolation and window sliding are used to unify them to a unified time reference, thus forming a synchronized data stream.

[0053] (2) Spatial registration submodule The data from each modality are unified into a spatial representation of an individual or group of livestock through a coordinate mapping model. For example, thermal imaging pixels are spatially mapped and bound to the coordinates of the sound source and the odor distribution area.

[0054] (3) Attention mechanism weighted fusion A multi-head self-attention mechanism is introduced to calculate the relative weights of different modalities within the current time period. The specific calculation formula is as follows: ; Here, Q (query), K (key), and V (value) are the feature codes for each modality, and d_k is the dimension scaling factor. This mechanism can dynamically focus on the most informative modal features.

[0055] (4) Multi-scale pyramid fusion structure The original and multimodal fused feature maps are convolutionally downsampled at scales of 1x, 0.5x, and 0.25x, respectively, and then concatenated and fused to take into account both global trends and local anomalies. This structure improves the model's ability to identify subtle symptoms and anomalous behaviors.

[0056] (5) Output of the anomaly scoring function Finally, an anomaly score (confidence level) is generated through a fully connected layer and a sigmoid activation function, and an adjustable threshold is set to drive the subsequent early warning process.

[0057] This fusion engine can complete fusion and identification locally at the edge node with a latency of less than 200ms, meeting the dual requirements of real-time performance and accuracy in aquaculture sites.

[0058] To further illustrate the specific implementation process of the "cross-modal spatiotemporal fusion engine" in this invention, the following provides simplified pseudocode and a schematic diagram of the core steps, demonstrating the specific path by which the system completes multimodal data fusion and anomaly identification at edge nodes.

[0059] 1. Pseudocode for cross-modal attention fusion process (illustrated) Python #Modal input: Thermal imaging T, Acoustics S, Odor G, Micro-vibration V (different sampling rates) # Output: Anomaly score Score ∈ [0,1] defcross_modal_attention_fusion(T,S,G,V): #Step 1: Time Synchronization (Time Window Alignment) T_aligned = time_align(T) S_aligned = time_align(S) G_aligned = time_align(G) V_aligned = time_align(V) #Step 2: Feature Extraction (using a pre-trained lightweight model) T_feat = CNN_T(T_aligned) S_feat = CNN_S(S_aligned) G_feat=MLP_G(G_aligned) V_feat = CNN_V (V_aligned) #Step 3: Cross-modal attention weighting (with thermal imaging as the primary modality) fusion=multi_head_attention([T_feat,S_feat,G_feat,V_feat]) #Step 4: Multi-scale pyramid fusion fusion_pyramid=pyramid_pooling(fusion) #Step5: Output Anomaly Score score=sigmoid(FC_layer(fusion_pyramid)) returnscore ``` 2. Module Description step Function Description time_align() Use sliding window resampling to unify the modal time reference (e.g., 0.5 seconds). CNN_T etc. Modal features extracted using lightweight models (such as MobileNetV3 and TinyBERT) multi_head_attention() Multi-head self-attention mechanism, calculating inter-modal importance and weighted fusion. pyramid_pooling() Local and global features were extracted using 1×1, 2×2, and 4×4 scale windows. FC_layer+sigmoid() Output the anomaly probability value to drive the early warning trigger logic. 3. Explanation of Algorithm Advantages Strong time alignment robustness: can tolerate differences in sampling frequencies between different modal sensors; Adaptive focusing mechanism: The attention mechanism can dynamically adjust modal weights at different stages of health abnormalities; Multi-scale structure: ensures dual perception of minor local anomalies (such as local high temperature, soft cough) and global trends (such as group stress); Strong edge deployability: When used with a lightweight model library, the average inference latency is less than 200ms, meeting real-time requirements.

[0060] 3.3 Lightweight Model Library and Scheduling Mechanism To adapt to the computing power limitations of edge devices, this invention constructs a lightweight, multi-tasking model library that supports hot switching and scheduling optimization, mainly including: MobileNetV3-α: Used for anomaly detection and segmentation in thermal imaging images, with low computational complexity and excellent accuracy; TinyBERT: Used for semantic recognition and anomalous sound annotation of livestock calls, significantly compressing the size of the original BERT model; Edge-YOLOv7: Used for behavior recognition and posture discrimination, such as extracting key behavioral features like limping, frequent lying down, and interrupted feeding.

[0061] The system dynamically switches model combination strategies based on device idle rate, current task load, and modal data complexity to maximize resource utilization and optimize model inference.

[0062] In summary, the edge intelligent processing module of this invention takes into account both the complexity of multimodal computing and the limitations of edge computing resources in its structural design. It innovatively proposes a cross-modal attention fusion mechanism in its algorithm path and achieves an organic unity of real-time performance and accuracy through a lightweight model library and local scheduling strategy, providing strong technical support for subsequent early warning logic and intervention response.

[0063] IV. Adaptive Learning Engine Mechanism and Privacy Protection Capabilities In one embodiment of the present invention, the adaptive learning engine integrated in the edge intelligent processing module is used to realize continuous optimization of the model on edge nodes and collaborative learning across nodes, thereby improving the system's generalization ability and timely response capability in diverse aquaculture environments.

[0064] This learning engine comprises the following three core sub-modules: 4.1 Incremental Learning Submodule This submodule is primarily designed for local learning tasks at edge nodes and has the following functions: Dynamic sample caching mechanism: A circular cache structure is used to store the latest observed samples and their anomaly feedback labels to realize short-term behavior trajectory modeling; Fine-Tuning strategy: Update the parameters of the deployed lightweight model based on gradient constraints to avoid model overfitting or catastrophic forgetting; Triggered learning mechanism: When the frequency of false alarms / false negatives of a certain type exceeds a threshold within a certain time window, the model correction process is automatically triggered.

[0065] This module allows the system to perform fine-grained local model adaptation based on subtle differences in livestock breeds, behavioral patterns, and environmental conditions in the local environment, avoiding the problem that "one model is difficult to adapt to all farms".

[0066] 4.2 Federated Learning Coordination Module To achieve collaborative evolution among multiple edge nodes while avoiding privacy leaks caused by centralized data transmission, this invention introduces an incremental federated learning framework, which specifically includes the following steps: 1. Local training and parameter extraction: Each edge node periodically trains the model on local data and extracts model parameters (weights, gradients, etc.). 2. Encrypted transmission: Before uploading parameters, homomorphic encryption algorithms (such as Paillier encryption or RSA encryption) are used to encrypt and encapsulate the model parameters; 3. Central coordination and model aggregation: The cloud platform or edge supernodes execute a weighted average aggregation algorithm to generate a new global model; 4. Update Distribution: The new model is sent back to each edge node to achieve unified evolution of the system across multiple scenarios.

[0067] Preferably, the model aggregation cycle is 24 hours, but it can also be adjusted to 6 to 72 hours depending on the scenario, to ensure that the system can respond quickly in the event of a sudden epidemic or a sudden change in behavior patterns.

[0068] 4.3 Zero-sample anomaly detection module To address the issues of frequent emerging diseases or unknown behavioral patterns and a lack of labeled data in livestock farming, this invention designs a zero-shot detection module based on Generative Adversarial Networks (GANs) to establish the distribution boundaries of normal behavior. Normal sample modeling: Use a large amount of historical data without anomalies to train the generator and discriminator to form a standard behavioral feature space; Anomaly boundary inference: If new observation data deviates significantly from the normal behavior space, it is considered a potential anomaly and can be identified without pre-defined labels; Confidence output: By combining cross-modal fusion features, the anomaly scoring function is corrected to reduce false alarms.

[0069] This module effectively compensates for the limitations of traditional deep learning models in identifying small samples or unknown events, and enhances the system's early warning capabilities in changing environments.

[0070] Considering the sensitivity of livestock data and farmers' need for data control, this invention introduces multiple privacy protection mechanisms during the adaptive learning process: Differential privacy mechanism: Add noise perturbation that conforms to the differential privacy mechanism before uploading model parameters. The privacy budget ε can be configured in the range of [0.1, 1.0]. Transmission security protocol: All parameter transmission processes are based on TLS encrypted channels to prevent man-in-the-middle attacks or parameter leakage; Node authentication mechanism: Only authorized nodes can participate in federated model training to prevent malicious model injection.

[0071] Through the above measures, this invention achieves systematic protection of data privacy and node security while ensuring learning efficiency, and is suitable for commercial ranches or insurance cooperation scenarios with high data security requirements.

[0072] In summary, the adaptive learning engine described in this invention possesses the technical characteristics of sustainable evolution, cross-scenario generalization, unsupervised recognition, and secure collaboration. It breaks through the limitations of traditional livestock intelligent systems, such as "rigid models, data silos, and high privacy risks," and provides a solid foundation for the efficient deployment and reliable operation of intelligent early warning systems.

[0073] V. Early Warning Decision-Making Mechanism and Automatic Response Logic In one embodiment of the present invention, such as Figure 4 As shown, the livestock early warning system is based on the anomaly scoring results of the edge intelligent processing module. It uses a hierarchical decision tree mechanism to intelligently judge the severity of events and links multiple response devices to execute personalized intervention measures, so as to achieve early detection, early isolation and early intervention of livestock health risks.

[0074] 5.1 Three-level early warning decision-making mechanism This invention constructs a three-tiered early warning and response system that meets the needs of livestock farms, corresponding to different types and severity of abnormal events, as specifically defined below: Level 1 Warning (Yellow): Triggered when a single sensory indicator (such as body temperature, odor, behavior, etc.) deviates slightly but does not reach a clearly defined pathological threshold. This type of warning indicates that the individual may be in a state of stress, sub-health, or early abnormality. System response: Record the abnormal event and push the information to the zookeeper's APP; Recommended measures: Observe and increase patrols, and do not activate intervention equipment for the time being.

[0075] Level 2 Warning (Orange): Triggered when two or more perception indicators show moderate abnormalities and are verified to be correlated by a cross-modal fusion algorithm. Such abnormalities have a high probability of indicating health problems.

[0076] System response: Automatically initiate intervention procedures, including individual isolation, nutritional regulation, and environmental control; Intervention methods: Control the isolation gate to guide the target individuals into the isolation area, accurately feed them immune-enhancing feed, and adjust the temperature, humidity and light environment inside the shed; Personnel coordination: Notify the veterinary assistant or technician to review the treatment recommendations.

[0077] Level 3 warning (red): Triggered when the system identifies a multimodal major abnormality (such as high fever + dyspnea + abnormal VOC) or meets the characteristics of a major disease template; System response: Emergency activation of advanced response plan; Intervention methods: Deploy drones or automated delivery devices to administer medication, enforce isolation, and generate detailed diagnostic reports; Notification mechanism: Automatically contact licensed veterinarians and activate the emergency plan.

[0078] This early warning mechanism allows users to customize various thresholds and judgment logic to adapt to the needs of different breeds, age groups and farming models.

[0079] 5.2 Intervention Response Device Linkage Mechanism To achieve rapid and automated intervention, this invention integrates multiple types of controllable devices. The system can drive the following devices to respond collaboratively based on the warning level: Isolation system: including automatically controlled isolation doors, partition doors and ground guidance signs, used to flexibly guide and safely isolate suspected abnormal individuals; Precision feeding device: It realizes the quantitative delivery of functional feeds such as vitamins, minerals, and immunomodulators through electronic metering and variety identification; Drug delivery device: integrates atomizing sprayer, smart medicine cabinet or unmanned vehicle / drone delivery system to realize remote or automatic drug administration; Environmental control equipment: controls temperature, humidity, ventilation, and light parameters to reduce stress response and intervene in potential causes of disease; Voice and image prompting devices: prompting personnel to check individual status or assisting in manual judgment.

[0080] All of the above devices are connected to edge nodes or central control servers through a unified protocol, and the system dispatch center issues and responds to instructions in a closed loop according to the early warning logic.

[0081] 5.3 Knowledge Base and Decision Engine Support This invention integrates a scalable livestock health knowledge base and rule engine for constructing intelligent decision-making logic. Its main features include: Visual configuration of the rule base: Keepers or technicians can edit rule triggering conditions, intervention combinations and response sequences through a graphical interface; Expert template embedding: The system has built-in diagnostic rule templates for a variety of common diseases (such as laminitis, respiratory infections, and heat stress), and supports local expert customization and expansion; Historical case reasoning mechanism: The system can retrieve similar historical events, response effects, and handling results to help form optimization suggestions or generate standard handling paths.

[0082] 5.4 Multi-channel information notification and data closed loop This invention provides a multi-channel push strategy to ensure efficient delivery of early warning information: Message push paths include: mobile app push notifications, SMS notifications, voice broadcasts, management platform pop-ups, and third-party interface synchronization; Record archiving: All early warning events, intervention actions, personnel responses, and recovery data are automatically archived, forming a complete closed loop of health records; Tracking and retrospection: Individual livestock can be queried at any time for early warning history, response path and intervention effectiveness, supporting etiology analysis and strategy evaluation.

[0083] In summary, the early warning decision-making and response mechanism constructed by this invention has comprehensive capabilities including hierarchical judgment, automatic intervention, knowledge support, and information closed loop. It breaks through the limitations of traditional systems, such as only being able to indicate anomalies, slow response, and passive intervention, and provides livestock farms with an efficient, intelligent, and autonomous full-cycle health protection system.

[0084] VI. Cloud Platform Management Functions and Remote Maintenance Support In one embodiment of the present invention, a fully functional cloud platform management module is provided to enable remote centralized management, model update and maintenance, and multi-site collaborative control of the system. This module serves as the core backend support platform of the system, interacting with edge nodes and issuing control commands to the terminal user interface, ensuring the stable, secure, and intelligent operation of the system.

[0085] The cloud platform mainly includes the following functional subsystems: 6.1 Model Version Management and Scheduling Mechanism This invention supports centralized management and remote operation and maintenance of models deployed on various edge nodes, and has the following capabilities: Model version control: Each model is configured with a unique version number, supporting the "release-rollback-hot switch" process to ensure the security and controllability of the model upgrade process; Cluster deployment strategy: Customized model combinations can be deployed in different regions, different farms, and different livestock species to improve the generalization performance of the models; Scheduling mechanism: The system can dynamically push the most suitable lightweight model version based on the hardware specifications, load status and network bandwidth of the edge nodes to ensure optimal inference efficiency.

[0086] 6.2 Multidimensional Data Visualization and Analysis To facilitate user understanding and monitoring of aquaculture status, the platform incorporates various types of visualization and analysis tools to achieve data-driven intelligent operation, primarily including: Real-time data dashboard: Displays multimodal sensing data, anomaly warning distribution, equipment operating status, node online status, etc.; Historical data curves: Supports trend playback and comparative analysis of health curves for specified livestock and time periods; Group behavior heatmap: Presents the status of group activities and the frequency distribution of behaviors through heat density maps, behavior tag clouds, and other forms; Abnormal event review: Automatically marks abnormal time points, sensing data snapshots, and response process records for managers to review and analyze.

[0087] 6.3 User Permissions and Multi-Level Role Management This invention supports multi-role, multi-level user access control policies to adapt to the diverse needs of farm organizational structures, including: Hierarchical access control: Access is divided into roles such as system administrator, regional operation and maintenance personnel, breeders, veterinarians, and third-party technical service providers, and different permissions such as data access, model configuration, and response review are assigned to each. Multi-tenant environment support: The platform supports simultaneous deployment of multiple farms / projects, data logic isolation, and unified operation management; Operational auditing and behavior tracking: Every user operation (such as rule modification, model update, alarm handling) is recorded and archived to ensure traceability and security compliance.

[0088] 6.4 Third-party system integration and data interface services To achieve open system integration and business collaboration, this invention provides standardized RESTful API interfaces and data exchange capabilities: Livestock Management System Integration: Achieve data interoperability with existing ERP, breeding management, breeding registration, and slaughter statistics systems; Insurtech platform integration: Connecting the output results of disease risk assessment, mortality prediction and other models to the insurance claims system to support dynamic premiums and automatic claims; Supply chain traceability platform connection: Automatically synchronize health records, disease records and other information to the traceability system to achieve full life cycle health transparency; Multi-terminal interconnection capability: Supports access from multiple types of terminals, including web management terminals, mobile apps, SMS platforms, and IoT protocol (MQTT, CoAP) devices.

[0089] 6.5 Anomaly Handling and Remote Maintenance Mechanism To ensure the long-term stable operation of the system, this invention provides the following remote maintenance and troubleshooting capabilities: Online equipment status monitoring: Periodic inspections of the status of each edge node, sensing module, and power system; Remote log collection and diagnosis: Automatically transmits key logs and identifies hardware failures or communication anomalies through anomaly pattern recognition; OTA firmware update mechanism: Supports remote firmware upgrades for sensing and edge devices, facilitating future feature expansion and vulnerability patching; Automatic fault recovery mechanism: It has mechanisms such as device disconnection and reconnection, self-diagnosis and self-recovery, and primary and backup link switching to ensure high system availability.

[0090] The cloud platform management module described in this invention not only constructs a centralized operation and maintenance hub for the livestock early warning system, but also forms a manageable, scalable, integrable, and evolvable intelligent early warning platform through model scheduling, data visualization, user permission management, and system interface. This significantly improves the system's management efficiency and commercial adaptability, providing strong platform support for large-scale deployment and industrialization.

[0091] Example 1: Application of Early Warning System for Respiratory Diseases in Dairy Cows In a specific application embodiment of the present invention, the livestock early warning system based on multimodal perception and edge intelligence is applied to large-scale dairy farms to monitor and warn of respiratory diseases in dairy cows in the early stage, so as to achieve early detection, rapid treatment and risk control of diseases.

[0092] (I) Application Scenarios and System Deployment In this embodiment, the dairy farm is a centralized, enclosed cattle shed structure, with each shed housing 100 to 300 dairy cows. The shed is equipped with a feeding area, a drinking area, a lying area, and passageways.

[0093] Based on the body size and activity radius characteristics of dairy cows, the system is deployed with one multimodal sensing and edge intelligence integrated node for every 8-10 cows. The nodes are evenly distributed above the feeding troughs, at the edges of the lying areas, and along main passageways. The effective sensing coverage radius of a single node is 15-20 meters. Each node establishes a communication connection with the cloud platform management module via a hybrid LoRaWAN and 5G communication network.

[0094] (II) Multimodal sensing data acquisition process During normal system operation, the multimodal sensing module continuously collects the following data from the target dairy cow: 1. Bioacoustic data The bioacoustic sensing submodule collects sound signals from dairy cows' breathing, coughing, and rumination processes using a directional microphone array, and performs real-time frame-by-frame processing on the audio stream. The system focuses on abnormal coughing frequency, respiratory rhythm changes, and abnormal vocal characteristics per unit time.

[0095] 2. Thermal imaging data The thermal imaging vision submodule continuously monitors the surface temperature of dairy cows, focusing on acquiring the temperature distribution in the nasal cavity, chest cavity, and neck areas, and generating corresponding thermal image sequences for analyzing local or overall abnormal increases in body temperature.

[0096] 3. Odor molecule data The odor molecule perception submodule detects the concentrations of volatile organic compounds (VOCs) and gases such as ammonia and hydrogen sulfide in the cattle shed and the individual activity area, in order to identify changes in odor characteristics caused by respiratory metabolic abnormalities or secondary infections.

[0097] 4. Micro-vibration behavior data The micro-vibration sensing submodule collects vibration signals from dairy cows in the lying and feeding areas to analyze their rumination frequency, changes in standing and lying time, and overall activity levels.

[0098] (III) Edge Intelligence Fusion Analysis and Anomaly Detection In this embodiment, each multimodal sensing data is first preprocessed and feature extracted in the local edge intelligent processing module, and then fused and analyzed through a cross-modal spatiotemporal fusion engine.

[0099] When the system detects one or a combination of the following situations, it will calculate an anomaly score and determine the risk: In bioacoustic data, the frequency of cough sounds per unit time was significantly higher than the historical baseline; In thermal imaging data, the surface temperature of the nasal cavity or chest cavity area is consistently higher than the normal threshold; In the odor molecule data, the distribution pattern of respiratory-related VOCs deviated significantly from that of normal samples; In the microvibration data, the rumination frequency decreased or the lying-down time was abnormally prolonged.

[0100] The system dynamically weights and fuses the aforementioned multimodal features using an attention mechanism, generating corresponding anomaly confidence scores. When an anomaly score exceeds a preset threshold and persists for a certain time window, the system determines that the cow is at risk of respiratory disease.

[0101] (iv) Tiered early warning and automatic intervention response In this application example, the system triggers a tiered early warning mechanism based on the anomaly scoring results: 1. Yellow Alert When only minor anomalies are observed in bioacoustic or thermal imaging data and the duration is short, the system triggers a yellow alert and pushes observation tips to the zookeeper via mobile terminal.

[0102] 2. Orange Alert When bioacoustic anomalies and thermal imaging anomalies occur simultaneously and are consistent after cross-modal verification, the system triggers an orange alert and automatically activates the isolation gate device to guide the target dairy cow into the isolation area. At the same time, it suggests adjusting the feeding formula and notifies the veterinary assistant to conduct on-site verification.

[0103] 3. Red Alert When the system detects a significant increase in cough frequency, abnormally high body surface temperature, and abnormal odor molecule characteristics simultaneously, it determines a high-risk respiratory disease state and triggers a red alert. The system automatically generates a diagnostic auxiliary report and notifies the licensed veterinarian through multiple channels, initiating emergency response procedures.

[0104] (V) Technical Effects and Application Advantages Through the above application embodiments, the livestock early warning system of the present invention can provide early warning of respiratory diseases before dairy cows show obvious clinical symptoms. Compared with traditional manual inspection or single sensor monitoring methods, this embodiment has the following technical advantages: It can identify the risk of respiratory diseases 12 to 48 hours in advance; Significantly reduces false alarm and false negative rates, and improves the accuracy of early warning; Reduce the intensity of manual inspections and improve the management efficiency of farms; It effectively reduces the risk of disease spread and improves the overall safety level of aquaculture.

[0105] In summary, this embodiment fully verifies the feasibility and practical value of the livestock early warning system based on multimodal perception and edge intelligence described in this invention in the early warning scenario of respiratory diseases in dairy cows.

[0106] Example 2: Application Example of Group Heat Stress Early Warning In another specific application embodiment of the present invention, the livestock early warning system based on multimodal perception and edge intelligence is applied to the monitoring and early warning of heat stress in high-density farming environments. It is particularly suitable for dairy or beef cattle farms under high temperature and high humidity conditions in summer, and is used to identify the risk of heat stress in the group in advance and automatically link environmental control devices.

[0107] (I) Application Scenarios and System Deployment Methods In this embodiment, the application scenario is a semi-enclosed or fully enclosed cattle shed structure, with 200 to 500 livestock raised in a single cattle shed. The top of the cattle shed is equipped with a ventilation device, and the sides are equipped with spray cooling and environmental control equipment.

[0108] Based on the characteristics of group behavior and environmental monitoring needs, the system is deployed in the cattle shed in the following manner: Multimodal sensing and edge intelligence nodes are evenly deployed on the top of the cattle shed and in the passageway area; Each node mainly covers areas with dense group activity, including feeding areas, drinking areas, and lying areas; Preferably, one edge node is configured for every 6 to 8 dairy cows, and the sensing coverage radius of a single node is 20 to 25 meters; All nodes aggregate data via the LoRaWAN network and maintain communication with the cloud platform management module via the 5G network.

[0109] This deployment method focuses on group-level status monitoring rather than fine-grained tracking of individual entities.

[0110] (II) Collection of multimodal sensing data of the population In this embodiment, the system focuses on collecting and analyzing multimodal data highly correlated with population heat stress, including: 1. Group thermal imaging data The thermal imaging vision submodule continuously collects thermal images of livestock in the cattle shed. Through overall statistical analysis of the distribution of body surface temperature, it calculates the average body surface temperature of the group, the standard deviation of temperature, and the local high temperature accumulation areas.

[0111] 2. Group behavior and microvibration data The micro-vibration sensing submodule collects ground vibration signals in the lying and passage areas to analyze the proportion of people standing, changes in lying time, and decreases in activity frequency.

[0112] 3. Environmental Odor and Gas Data The odor molecule sensing submodule monitors the concentration changes of ammonia, hydrogen sulfide and volatile organic compounds in the cattle shed, serving as an auxiliary criterion for insufficient ventilation and stress under high temperature conditions.

[0113] 4. Bioacoustic population characteristic data The bioacoustic perception submodule collects data on the overall sound environment of the cattle shed to analyze group behavioral characteristics such as changes in the intensity of breathing sounds and increased agitation.

[0114] (III) Group-level edge intelligence fusion analysis process In this embodiment, the edge intelligence processing module performs fusion analysis on multimodal data based on a group perspective, and its processing flow includes: Population-level feature extraction is performed on thermal imaging data to generate a population thermal density distribution map; Statistical modeling was performed on micro-vibration and acoustic data to calculate group activity index and stress behavior indicators; Trend analysis of odor molecule data can identify abnormal gas accumulation under high-temperature conditions.

[0115] Through a cross-modal spatiotemporal fusion engine, the system jointly models abnormal body surface temperature, decreased activity levels, and abnormal ambient gases in the population, and outputs a population heat stress risk score.

[0116] When the group risk score is consistently higher than the preset threshold and there is temporal and spatial consistency among the multimodal features, the system determines that there is a risk of heat stress in the current cattle shed.

[0117] (iv) Group heat stress classification early warning and environmental intervention mechanism In this application embodiment, the system triggers a graded early warning mechanism based on a population heat stress risk score: 1. Yellow Alert (Mild Heat Stress Risk in the Population) When the average body surface temperature of the group is slightly higher than the historical baseline and the activity level decreases slightly, the system triggers a yellow alert and sends a notification to the management personnel, suggesting that they strengthen environmental monitoring.

[0118] 2. Orange Alert (Moderate Risk of Heat Stress in the Population) When the surface temperature of the cattle herd continues to rise, their activity level decreases significantly, and the gas concentration increases abnormally, the system triggers an orange alert and automatically activates the ventilation system and sprinkler cooling equipment to regulate the temperature and humidity environment inside the cattle shed.

[0119] 3. Red Alert (Risk of Severe Heat Stress in the Population) When the system detects a significant increase in the surface temperature of the herd, an abnormally increased proportion of people lying down for extended periods, and obvious agitation or abnormal respiratory acoustic features, it determines that the herd is in a state of severe heat stress and triggers a red alert. The system automatically implements enhanced cooling strategies and notifies farm managers and technicians through multiple channels to activate emergency response plans.

[0120] (v) Technical effects and application value Through the above-described application examples of group heat stress early warning, the system of the present invention can realize real-time monitoring and early intervention of group health risks under high temperature environments. Specific technical effects include: It can identify the risk of heat stress in groups in advance and avoid large-scale health events; By automating environmental control, the intensity of human intervention is significantly reduced; Improve the precision of cooling measures to avoid excessive energy consumption; To improve the operational safety and stability of high-density farms under extreme climatic conditions.

[0121] In summary, this embodiment fully demonstrates the feasibility and technical advantages of the livestock early warning system described in this invention in scenarios of group-level risk monitoring and intelligent environmental intervention, and further verifies the wide applicability of the system under different breeding models and application conditions.

[0122] In summary, this invention organically integrates multimodal sensing technology, edge intelligent computing, adaptive learning mechanisms, and a hierarchical early warning response system to construct an intelligent early warning system suitable for on-site deployment in animal husbandry. This system possesses real-time performance, high accuracy, and good scalability. It can achieve continuous sensing and intelligent response to livestock health status without relying on centralized computing or manual interpretation, providing solid technical support for modern, digitalized animal husbandry and demonstrating broad application prospects and industrialization value.

[0123] It should be noted that the above embodiments are only used to illustrate the technical principles and application scenarios of the present invention, and do not constitute a limitation on the scope of protection of the present invention. Equivalent substitutions or technical modifications made by those skilled in the art without departing from the core concept of the present invention should be included within the scope of protection of the present invention. To determine the scope of protection of the present invention, the content defined in the claims shall prevail.

Claims

1. A livestock early warning system based on multimodal perception and edge intelligence, characterized in that, include: The multimodal sensing module is used to collect multi-dimensional data of livestock in real time, including acoustics, thermal imaging, odor molecules, and micro-vibrations. An edge intelligence processing module, connected to the multimodal perception module, is used to perform cross-modal fusion analysis and anomaly detection on the collected data; The early warning decision module generates early warning signals of corresponding levels and triggers intervention mechanisms based on the analysis results of the edge intelligent processing module. The cloud platform management module is used for model updates, remote configuration, data storage, and visualization management.

2. The system according to claim 1, characterized in that, The multimodal sensing module includes: The bioacoustic sensing submodule uses a directional microphone array to collect the sounds of livestock, chewing, and breathing, in order to identify estrus, disease, or hunger. The thermal imaging vision submodule monitors the surface temperature distribution of livestock based on millimeter-wave infrared imaging equipment, and is used to detect early signs of inflammation. The odor molecule sensing submodule uses an electronic nose array to detect the concentration of gases such as NH3, H2S, and CH4, as well as volatile organic compounds. The micro-vibration sensing submodule monitors livestock chewing frequency, lying time, and activity rhythm based on piezoelectric thin film sensors.

3. The system according to claim 1, characterized in that, The edge intelligence processing module includes: Heterogeneous computing chipsets include GPU chips for visual processing, general-purpose processing chips for multimodal fusion, and neural network acceleration chips; A cross-modal spatiotemporal fusion engine is used for time synchronization, spatial mapping, and feature fusion of multimodal data; Anomaly detection model library, including lightweight models for thermal imaging, acoustic semantics, and behavior recognition; The adaptive learning engine has incremental learning and federated learning capabilities, enabling local model optimization and periodic updates.

4. The system according to claim 3, characterized in that, The cross-modal spatiotemporal fusion engine employs a multi-scale pyramid fusion mechanism, which dynamically weights thermal imaging, acoustic, and odor distribution features through an attention mechanism to improve the confidence of anomaly detection.

5. The system according to claim 3, characterized in that, The adaptive learning engine supports zero-shot anomaly detection and builds a baseline model of normal behavior based on generative adversarial networks to identify unlabeled novel diseases or behavioral abnormalities.

6. The system according to claim 1, characterized in that, The early warning decision module includes: The three-tiered early warning decision-making mechanism corresponds to yellow, orange, and red warning levels, respectively. The automatic intervention execution submodule coordinates with facilities such as isolation doors, drug delivery equipment, and environmental controllers to implement response measures. The knowledge base management unit supports visual configuration and dynamic updates of the rule base; A multi-channel push mechanism is used to send notifications to keepers, veterinary assistants, and managers.

7. The system according to claim 6, characterized in that: A yellow alert indicates that a single sensing data point is slightly abnormal and continues to exceed the threshold. An orange alert indicates that two or more data points are moderately abnormal and consistent after cross-modal verification. A red alert indicates the occurrence of severe anomalies in multiple systems or the detection of acute symptoms, automatically triggering emergency response procedures.

8. The system according to claim 1, characterized in that, The system further includes: The self-cleaning sensor assembly uses ultrasonic vibration and photocatalytic coating technology to operate stably in dusty and high-humidity environments. The energy self-sustaining system includes flexible solar cells and a piezoelectric energy harvesting module; An anti-interference communication system that combines LoRaWAN with 5G networks and supports a self-healing mesh topology.

9. The system according to claim 1, characterized in that, The cloud platform management module includes: The model version control submodule supports remote deployment and rollback of models; A data visualization platform that provides multi-dimensional, interactive displays of historical and real-time data; The API provides an open interface, supporting integration with third-party platforms or livestock management systems.

10. The system according to claim 1, characterized in that, The deployment density of the system is dynamically adjusted according to different livestock species: One node is deployed for every 8-10 cows in the dairy cow area, covering a radius of 15-20 meters; In the beef cattle area, one node is deployed for every 6-8 head of cattle, covering a radius of 20-25 meters; One node is deployed for every 12-15 sows in the sow area, covering a radius of 8-12 meters; One node is deployed for every 30-40 sheep in the sheep area, covering a radius of 30-40 meters.

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