Livestock health early warning and intelligent driving mechanical auxiliary system based on bioelectricity signal monitoring

Abstract

The invention relates to a livestock health early warning and intelligent driving mechanical auxiliary system based on bio-electricity signal monitoring, and belongs to the technical field of animal husbandry intelligent management. Aiming at the problems of single parameter, strong driving irritability and low efficiency of a traditional monitoring method, the system is used for generating a health score in real time by adopting a CNN-LSTM hybrid model through data fusion of a bio-electricity signal acquisition module (electrocardio, myoelectricity and respiratory frequency) and an environment sensor, and driving an intelligent driving machine in combination with a deep reinforcement learning algorithm, so that the driving effect is improved. The abnormal livestock is guided to be isolated in a non-intrusive mode through ultrasonic directional wave beams and low-intensity laser. The monitoring accuracy is larger than or equal to 95%, the driving efficiency is improved by 60%, all-weather stable operation (availability is larger than or equal to 98%) is achieved, the epidemic disease risk and animal stress are effectively reduced, and an efficient intelligent solution is provided for large-scale breeding.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent management of animal husbandry, and relates to a livestock health warning and intelligent driving mechanical assistance system based on bioelectrical signal monitoring. Background Art

[0002] Traditional livestock health monitoring technologies mostly rely on single parameters such as body temperature and exercise volume, making it difficult to comprehensively evaluate the physiological state and prone to missing early diseases; manual inspection is inefficient and difficult to adapt to large-scale breeding scenarios. Existing driving systems are mostly fixed mechanical or manual operations, with poor flexibility and prone to causing stress reactions in livestock, resulting in a decline in animal welfare. Although the Internet of Things and bio-signal analysis technologies have been gradually applied to animal husbandry, existing solutions still have problems such as insufficient data fusion, low monitoring accuracy, and strong invasiveness of driving means, and cannot achieve closed-loop management of "monitoring - warning - intervention". In addition, complex electromagnetic environments, extreme weather, and group abnormal events further restrict the reliability and practicality of the system. Therefore, there is an urgent need for an intelligent system integrating multi-modal data fusion, non-invasive driving, and robust design to improve the efficiency of livestock management and the level of animal health protection. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a livestock health warning and intelligent driving mechanical assistance system based on bioelectrical signal monitoring. This system takes bioelectrical signals (such as electrocardiogram and electromyogram) as the core monitoring indicators, combines environmental data and machine learning algorithms to achieve dynamic assessment of the health status; at the same time, it integrates intelligent driving machinery, and uses non-invasive technology to guide abnormal livestock to be isolated, forming a closed-loop management of "monitoring - warning - intervention".

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] A livestock health warning and intelligent driving mechanical assistance system based on bioelectrical signal monitoring, comprising:

[0006] A bioelectrical signal acquisition module 1, which is used to collect electrocardiogram, electromyogram and respiratory frequency signals of livestock in real time;

[0007] A data processing center 2, connected to the bioelectrical signal acquisition module 1, including an edge computing chip and a machine learning model, which is used to analyze bioelectrical signals and generate a health score;

[0008] A health warning module 3, connected to the data processing center 2, which is used to trigger local sound and light alarms and remote notifications according to the health score;

[0009] An intelligent driving machine 4, connected to the health warning module 3, including an adaptive mobile chassis, an ultrasonic transmitter and a laser guiding device, which is used to direct abnormal livestock to the isolation area;

[0010] Energy module 5, connected to each module, including a solar cell and lithium battery dual power supply system;

[0011] Environmental sensor 6, used for monitoring temperature, humidity and air quality parameters, and connected to the data processing center (2);

[0012] The system achieves closed-loop management of health monitoring, early warning and expulsion through the fusion analysis of bioelectric signals, environmental data and machine learning models.

[0013] Furthermore, the bioelectric signal acquisition module 1 includes an implantable or wearable sensor, which integrates a flexible silicone shell, an adjustable strap and an RFID / BLE identification code, and is suitable for wearing by livestock of different sizes.

[0014] Furthermore, the machine learning model is a CNN-LSTM hybrid model, including:

[0015] The CNN branch and LSTM branch are set in parallel to extract the local features and time series features of the bioelectric signal respectively;

[0016] A fusion layer is used to concatenate the output features of the CNN branch and the LSTM branch to generate a health score;

[0017] The model was pre-trained using a transfer learning strategy and fine-tuned based on local livestock data, with a test set accuracy of ≥95%.

[0018] Furthermore, the driving control strategy of the intelligent driving machine 4 is based on a deep reinforcement learning framework, including:

[0019] State space, covering livestock position, obstacle distribution and driving machinery power;

[0020] Action space, covering movement direction, ultrasonic emission intensity and laser projection angle;

[0021] Reward function, combining expulsion efficiency and animal stress penalty;

[0022] The strategy is trained by the PPO algorithm, with a response time of <1 second and a path overlap rate of <5%.

[0023] Furthermore, the data processing center 2 includes a redundant sensor network and an adaptive Kalman filter algorithm for counteracting electromagnetic interference, with a signal-to-noise ratio improvement of ≥30dB and an ECG / EMG signal error of <5%.

[0024] Furthermore, the solar cell of the energy module 5 is a 100W monocrystalline silicon solar panel, and the lithium battery is a 48V rechargeable battery that supports 72 hours of continuous operation, and has a built-in heating film to ensure discharge performance in a -30°C environment.

[0025] Furthermore, the intelligent driving machine 4 is equipped with a GPS / UWB / IMU multi-mode positioning system with a positioning accuracy of ±0.3 m, and integrates infrared and ultrasonic obstacle avoidance sensors, with a climbing ability of up to 30°.

[0026] Furthermore, the cloud platform of the health warning module 3 supports epidemic trend prediction and transmits data through a LoRa+4G dual-mode communication module, and can still synchronize alarms when the network is interrupted.

[0027] Furthermore, the system is built-in with a hardware self-check program, supports hot-swap replacement of sensors and battery modules, and the edge computing unit adopts a dual-system image, with a fault repair time < 15 minutes.

[0028] Furthermore, the driving unit of the intelligent driving machine 4 includes a dynamic beamforming ultrasonic array, 20 kHz to 40 kHz; and a 650 nm low-intensity laser with a power < 5 mW and an action range of 1 to 20 m, avoiding interference with other livestock.

[0029] The beneficial effects of the present invention are as follows: Through multi-modal data fusion (bioelectrical signals, environmental parameters) and a CNN-LSTM hybrid model, the present invention significantly improves the comprehensiveness and accuracy of health monitoring (test set accuracy ≥ 95%), and solves the problem of missed diagnosis caused by traditional methods relying on a single parameter; The intelligent driving machine realizes dynamic path planning based on a deep reinforcement learning algorithm (response time < 1 s, path overlap rate < 5%), combines ultrasonic directional beam and low-intensity laser non-invasive driving technology, and improves the driving efficiency by 60%, while reducing the animal stress response; The edge-cloud collaborative architecture and redundant design (signal-to-noise ratio improvement ≥ 30 dB, ECG / EMG error < 5%) ensure the all-weather stable operation of the system in a complex electromagnetic environment and extreme weather (-30°C to 85°C tolerance) (availability ≥ 98%); In addition, the multi-machine collaboration mechanism and priority dynamic allocation strategy optimize the processing efficiency of group abnormal events (isolation time for 50 heads ≤ 10 minutes), significantly reduce the risk of epidemic spread, and have both ecological breeding benefits and large-scale application potential.

[0030] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent description, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0032] Figure 1Structural diagram of the device of the present invention;

[0033] Figure 2 Schematic diagram of the operation of the monitoring system;

[0034] Figure 3 Schematic diagram of the operation of the early warning system;

[0035] Figure 4 Schematic diagram of the operation of the execution system;

[0036] Figure 5 Schematic diagram of the working process of the system of the present invention.

[0037] Reference numerals in the drawings: 1 - Bioelectric signal acquisition module; 2 - Data processing center; 3 - Health early warning module; 4 - Intelligent driving mechanism; 5 - Energy module; 6 - Environment sensor. Detailed implementation manners

[0038] The following uses specific specific examples to illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0039] Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and cannot be understood as a limitation to the present invention; in order to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, which does not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0040] In the drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only for illustrative purposes and cannot be understood as a limitation to the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0041] The intelligent mechanical assistance system designed in this article has the following characteristics:

[0042] (1) Structure: It adopts a modular design, including four subsystems: bioelectric signal collection, data processing, health warning and intelligent driving, which supports flexible deployment and functional expansion; implantable / wearable sensors work together with mobile driving machinery to adapt to the complex terrain of the pasture.

[0043] (2) Principle: Based on multimodal data fusion technology, bioelectric signals (ECG, EMG), respiratory rate and environmental parameters (temperature, humidity, air quality) are integrated to identify abnormal patterns through convolutional neural network (CNN) and long short-term memory network (LSTM) models to improve the accuracy of health assessment; the driving system uses ultrasonic directional beam and low-intensity laser guidance to avoid the harm to livestock caused by traditional electric shock or noise driving.

[0044] (3) Safety: Built-in dual safety mechanism - the bioelectric sensor uses flexible materials and low-power design to avoid physical damage to animals due to long-term wearing; the driving mechanism is equipped with a real-time obstacle avoidance algorithm and emergency shutdown function to prevent safety accidents caused by misoperation.

[0045] (4) Software: Equipped with an edge-cloud collaborative computing architecture, the local end can achieve millisecond-level exception response, and the cloud supports multi-pasture data integration and disease trend prediction; the algorithm model is continuously optimized through incremental learning to adapt to the characteristics of livestock of different breeds and growth stages.

[0046] (5) Durability: The system uses a dual power supply system of solar energy and lithium batteries to ensure continuous operation in the field; the sensor and driving mechanism casing reach IP67 protection level and can withstand harsh environments such as high temperature, high humidity, and dust.

[0047] (6) In the face of special situations: Design redundant sensor networks and self-repair programs to ensure system robustness in response to signal interference, system failures, extreme weather, and other scenarios; multi-machine collaborative algorithms support efficient handling of group abnormal events to avoid the spread of the disease.

[0048] 1. Device technical design

[0049] The device structure is as follows Figure 1 shown.

[0050] Bioelectric signal acquisition module 1: The implantable / wearable sensor integrates a micro RFID chip (compliant with ISO11784 / 85 standards) or a low-power Bluetooth ID (BLE ID) to assign a unique identity code to each livestock. The sensor housing is made of flexible silicone material, equipped with an adjustable strap and anti-drop buckle, suitable for wearing on the neck or ears of livestock of different sizes such as cattle and sheep.

[0051] Data Processing Center 2: Equipped with an edge computing chip (such as NVIDIA Jetson TX2), it runs a machine learning model to analyze signal characteristics and generate a health score.

[0052] Health Warning Module 3: Comprising an audible and visual alarm and a cloud data platform, it synchronously triggers local alarms and remote notifications in case of abnormalities.

[0053] Intelligent Driving Machine 4: Equipped with an adaptive mobile chassis, ultrasonic transmitters, and laser guiding devices, it drives target livestock according to instructions.

[0054] Energy Module 5: A dual-power supply system of solar cells and rechargeable lithium batteries to ensure long-term operation in the wild.

[0055] Environmental Sensor 6: Temperature, humidity, and air quality monitoring units to assist in evaluating the stress state of livestock.

[0056] Technical Preparation: (1) Signal Acquisition and Transmission: The bioelectric sensor uploads data to the data processing center via low-power Bluetooth (BLE), with a sampling frequency ≥ 100Hz to ensure signal integrity. (2) Data Analysis Algorithm: A CNN-LSTM hybrid model using multi-modal data fusion is adopted. Its architecture consists of parallel branches - the CNN branch (3 convolutional + pooling layers) extracts local features of ECG / EMG signals (such as QRS wave morphology, EMG burst frequency), and the LSTM branch (bidirectional layer + attention mechanism) captures temporal dependencies (such as heart rate variability, respiratory rhythm); the features of both are concatenated in the fusion layer (fully connected network) and then a health score (0 - 100 points) is output. The model is trained using a transfer learning strategy, pre-trained based on a publicly available livestock physiological dataset (such as Cattle-ECG), and then fine-tuned with local ranch data (≥ 1000 samples). Finally, the accuracy of the test set is ≥ 95%, and the F1-score is ≥ 0.93. (3) Driving Control Strategy: A deep reinforcement learning (DRL) framework is adopted, with the driving machine as the agent, constructing a state space (livestock position, obstacle distribution, driving machine power), action space (moving direction, ultrasonic emission intensity, laser projection angle), and reward function (driving efficiency + animal stress penalty). The algorithm is trained based on PPO (Proximal Policy Optimization), and the simulator integrates the Unity3D ranch environment and livestock behavior models (evasion, aggregation, etc.). After training, the policy network is deployed to the embedded controller of the driving machine to achieve dynamic path planning (response time < 1 second) and multi-objective coordination (path overlap rate < 5%).

[0057] Device Classification: This system mainly consists of three major parts: a monitoring system, a warning system, and an execution system.

[0058] Such as Figure 2As shown in the figure, the monitoring system consists of a bioelectric sensor, an environmental sensor, and an edge computing unit. The implanted electrocardiogram sensor (ECG, sampling frequency ≥200Hz) and the wearable electromyogram sensor (EMG) are used to collect the electrocardiogram and muscle activity signals of livestock in real time, and the flexible piezoelectric respiration monitoring ring is used to accurately detect the change of respiration frequency. At the same time, the environmental sensor covers the wide-temperature-range temperature and humidity monitoring from -40°C to 85°C and the detection of harmful gases such as NH3 and H2S to comprehensively evaluate the environmental state of livestock. All data is transmitted to the edge computing unit equipped with NVIDIA Jetson TX2 through low-power Bluetooth (BLE), stored in a 256GB encrypted solid-state drive after real-time analysis by a machine learning model, and abnormal data immediately triggers an early warning response. The system dynamically binds each physiological parameter to a specific livestock file by parsing the RFID / BLE identification code built into the sensor, and the cloud platform supports querying the real-time health status by a unique number, realizing the full-link accurate monitoring from data collection, processing to individualized management.

[0059] As Figure 3 shown, the early warning system is based on the local and cloud collaborative mechanism to achieve the immediate warning and remote management of abnormal signals. The local audible and visual alarm is equipped with a 120dB buzzer and an RGB three-color warning light, with a coverage radius ≥50 meters and can be triggered wirelessly. The cloud management server integrates the data of multiple ranches, supports the analysis of disease trends and the docking of API interfaces. The administrator receives real-time push through the mobile or PC terminal, and uses the redundant communication module (LoRa+4G dual mode, transmission distance 10km) to ensure that the alarm can still be synchronized when the network is interrupted. The system determines the health score through thresholds, and automatically links to execute the system to start intervention when it is lower than the critical value.

[0060] As Figure 4 shown, the execution system takes the intelligent driving machine as the core, integrates multi-mode positioning and dynamic driving technologies to achieve non-invasive accurate intervention. The four-wheel all-terrain chassis (speed 1.5m / s, climbing slope 30°) is equipped with a lidar and an IMU inertial navigation module, combines GPS+UWB indoor positioning (accuracy ±0.3 meters) to track the position of the target livestock in real time, and plans the optimal path through a reinforcement learning algorithm. The driving unit uses a dynamic beamforming ultrasonic array (20kHz~40kHz) and a 650nm low-intensity laser (power <5mW) to cooperate and guide, and applies a driving stimulus in a targeted manner according to the target coordinates (action range 1~20 meters), effectively avoiding interference with other individuals. The system is equipped with a 100W monocrystalline silicon solar panel + 48V lithium battery dual power supply system, supporting 72 hours of continuous operation; the obstacle avoidance sensor group (infrared + ultrasonic) and the folding robotic arm (servo torque 20kg·cm) ensure the operation safety under complex terrains. In addition, the edge-cloud collaborative instruction system synchronizes data in real time, drives the "monitoring-warning-action" closed-loop management, and significantly improves the response efficiency and operation reliability.

[0061] II. Device Working Process

[0062] Health Monitoring Mode:

[0063] (1) The bioelectric sensor continuously collects data, which is input into the model for analysis after filtering and noise reduction.

[0064] (2) The data processing center calculates the health score. Under normal conditions, the score ≥ 80 points. If it is lower than the threshold, an alarm will be triggered.

[0065] (3) The health data is synchronized to the cloud for remote viewing by the management staff.

[0066] Abnormal Response Mode:

[0067] (1) When the health score is lower than 60 points, the system determines it as "high risk", activates the sound and light alarm and marks the target livestock.

[0068] (2) The intelligent driving machine automatically navigates to the target location and guides the livestock to the isolation area through the combination of ultrasonic and laser. The system calls its positioning data according to the identification code of the abnormal livestock. The driving machine shares the target coordinates through the Mesh network, plans the optimal path (avoiding obstacles and other livestock), and guides with the combination of directional ultrasonic (20kHz - 40kHz) and low-intensity laser (650nm), and only applies the driving signal to the target animal.

[0069] (3) After the driving is completed, the system sends a task report to the cloud and enters the standby state.

[0070] Figure 5 It is a schematic diagram of the system working process.

[0071] III. Response to Special Situations

[0072] Signal Interference: Regarding the influence of the complex electromagnetic environment on bioelectric signals, this system adopts differential signal processing technology to cancel the common-mode noise through dual-line input, combines the adaptive Kalman filtering algorithm to dynamically eliminate sudden interference, and the signal-to-noise ratio is increased by ≥ 30dB; at the same time, a redundant sensor network is deployed, and the main and backup sensors are switched in real time to ensure that the data loss rate is < 0.1% in case of a single-point failure. This design enables the system to still maintain an ECG / EMG signal error < 5% in scenarios such as high-voltage lines and strong magnetic fields, ensuring the continuity of monitoring.

[0073] System failure: The system has a built-in hardware self-check program, which automatically diagnoses the status of sensor power supply, communication module and mechanical components every hour. The fault code is synchronized with the cloud through a three-color LED light, supporting remote location of problems; key modules (such as sensors and batteries) use standardized interfaces, support hot-swap replacement within 10 seconds, and the edge computing unit dual system mirroring seamlessly switches with an interruption time of less than 1 second. This solution shortens the average fault repair time to less than 15 minutes, and the availability of key components reaches 99.9%, significantly reducing operation and maintenance costs.

[0074] Group abnormality: When multiple livestock are abnormal at the same time, the system dynamically assigns priorities based on health scores (0-100 points), GPS positioning (accuracy ±1 meter) and disease risk levels, giving priority to driving away high-risk targets (such as those with scores less than 40 points and those close to water sources); multiple driving machines share real-time locations through the Mesh network and use auction algorithms to work together - for example, machine A blocks the entrance to the quarantine area, and machine B drives away the peripheral livestock. This mechanism ensures that the collaborative quarantine time of 50 livestock is ≤10 minutes, the path overlap rate is less than 5%, and the efficiency is improved by 60%.

[0075] Extreme weather: The expelling machine is equipped with an IP68 protective shell and a hydrophobic film laser lens. The projection distance in rain and fog is ≥15 meters. The ultrasonic frequency can be adjusted to 40kHz to reduce the scattering of water droplets. The lithium battery pack has a built-in heating film and can discharge normally at -30℃. The communication module uses the Beidou / GPS dual-mode satellite link, and the data transmission delay in heavy rain is less than 2 seconds. This design ensures that the mechanical movement error is less than 0.5 meters in 8-level strong winds or heavy rains, and the system's all-weather availability is ≥98%, adapting to harsh environments such as grasslands and mountains.

[0076] The "livestock health early warning and intelligent driving mechanical auxiliary system based on bioelectric signal monitoring" proposed in the present invention uses the CNN-LSTM fusion model to perform multimodal analysis of livestock electrocardiogram, electromyography and environmental data, and combines RFID / BLE identification codes to achieve individualized health monitoring (accuracy ≥ 95%), and relies on reinforcement learning algorithms to drive multi-mode positioning (GPS / UWB / IMU) and path planning, and uses ultrasonic directional beams and low-intensity laser guidance to achieve non-invasive precision driving (efficiency increased by 60%). The system uses edge-cloud collaborative architecture and redundant design to ensure all-weather stable operation in complex environments (availability ≥ 98%), significantly reducing the risk of disease transmission and labor costs. This system solves the pain points of traditional animal husbandry, such as lagging monitoring and strong stress response of driving, and provides an efficient and scalable solution for the intelligent transformation of large-scale breeding, with both the driving force of ecological breeding and significant social and economic benefits.

[0077] Example 1: Health monitoring and abnormal warning process

[0078] Signal acquisition: The wearable ECG sensor (sampling frequency 200 Hz) and EMG sensor continuously collect the electrocardiogram and electromyogram signals of the cattle herd, and the flexible breathing monitoring ring synchronously detects the breathing frequency (10 - 40 breaths per minute); the environmental sensor continuously obtains the temperature and humidity in the cowshed (15 - 25 °C) and the NH3 concentration (< 20 ppm).

[0079] Data transmission: The sensor encrypts and transmits the data to the edge computing unit (NVIDIA Jetson TX2) via BLE 5.0, and sends a data packet every 5 seconds.

[0080] Model analysis: The CNN branch (with a 3-layer convolution kernel size of 3×3) extracts the morphological features of the QRS wave of the ECG signal, and the LSTM branch (with 128 hidden units in the bidirectional layer) captures the heart rate variability trend; the fusion layer splices the two types of features and inputs them into the fully connected network to output the health score (such as the score suddenly drops from 85 points to 55 points).

[0081] Warning trigger: When the score is continuously < 60 points for 3 times, the local sound and light alarm is activated (120 dB buzzer + red warning light), and the cloud platform pushes the warning of "High risk for cattle No. 3" to the administrator and marks the GPS coordinates (accuracy ±1 meter).

[0082] Example 2: Intelligent driving and mechanical collaborative operation process

[0083] Task reception: The cloud platform sends the RFID codes and coordinates of 5 abnormal cattle (scores are all < 50 points) to 3 driving machines.

[0084] Path planning: Each machine constructs a state space (target cattle position, remaining battery 80%, obstacle distribution) through the DRL algorithm, selects the optimal action (moving direction 30°, ultrasonic intensity 25 kHz, laser projection angle 15°) based on the PPO strategy, and plans the path to avoid overlap (overlap rate < 3%).

[0085] Directional driving: Machine A uses dynamic beamforming ultrasonic waves (30 kHz, action distance 15 meters) to drive cattle No. 2 near the water source, and at the same time emits a 650 nm laser (power 3 mW) to project a guiding light spot on the ground; Machine B blocks the entrance of the isolation area, and the ultrasonic array forms a fan-shaped barrier to prevent other cattle from entering.

[0086] Task feedback: After the driving is completed, the machine uploads the trajectory log (time-consuming 8 minutes and 30 seconds) to the cloud, and the system automatically generates an epidemic risk assessment report.

[0087] Example 3: System redundant operation process in extreme weather

[0088] Environmental perception: Heavy rain causes sudden changes in temperature and humidity (temperature drops to -5°C, humidity 95%). The lithium battery heating film starts to maintain the battery temperature > 0°C, and the hydrophobic film automatically cleans the laser lens to ensure a projection distance ≥ 12 meters.

[0089] Communication switching: After the 4G signal is interrupted, the LoRa module immediately takes over data transmission. The Beidou satellite positioning error is corrected to ±0.5 meters, and cloud instructions are distributed to each machine through the Mesh network.

[0090] Signal anti-interference: The main ECG sensor fails due to lightning strike. The backup sensor takes over the acquisition through differential signal processing (common mode rejection ratio ≥ 100 dB). Kalman filtering eliminates rain droplet noise, and the ECG waveform error is controlled within 4.2%.

[0091] Self-check and repair: The system performs a hardware self-check every 20 minutes. After detecting an abnormality in the obstacle avoidance sensor of the driving machine C, the LED light displays the fault code F03. The maintenance personnel replace the backup module within 5 minutes, and the system resumes operation.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring, characterized in that: Including: A bioelectric signal acquisition module (1) for real-time acquisition of livestock electrocardiogram, electromyogram and respiratory frequency signals; A data processing center (2) connected to the bioelectric signal acquisition module (1), including an edge computing chip and a machine learning model, for analyzing bioelectric signals and generating a health score; A health warning module (3) connected to the data processing center (2), for triggering local audible and visual alarms and remote notifications according to the health score; An intelligent driving machine (4) connected to the health warning module (3), including an adaptive mobile chassis, an ultrasonic transmitter and a laser guiding device, for directionally driving abnormal livestock to an isolation area; An energy module (5) connected to each module, including a dual power supply system of a solar cell and a lithium battery; An environmental sensor (6) for monitoring temperature, humidity and air quality parameters and connected to the data processing center (2); The system realizes closed-loop management of health monitoring, warning and driving through the fusion analysis of bioelectric signals, environmental data and machine learning models.

2. The livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, characterized in that: The bioelectric signal acquisition module (1) includes an implantable or wearable sensor, and the sensor integrates a flexible silica gel shell, an adjustable strap and an RFID / BLE identification code, which is suitable for wearing by livestock of different body types.

3. The livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, characterized in that: The machine learning model is a CNN-LSTM hybrid model, including: Parallel CNN branches and LSTM branches for extracting local features and temporal features of bioelectric signals respectively; A fusion layer for splicing the output features of the CNN branch and the LSTM branch to generate a health score; The model is pre-trained through a transfer learning strategy and fine-tuned based on local livestock data, and the accuracy of the test set is ≥95%.

4. The livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, wherein: The driving control strategy of the intelligent driving machine (4) is based on a deep reinforcement learning framework, including: A state space covering livestock position, obstacle distribution and driving machine power; An action space covering moving direction, ultrasonic emission intensity and laser projection angle; A reward function combining driving efficiency and animal stress penalty; The strategy is trained through the PPO algorithm, the response time <1 second, and the path overlap rate <5%.

5. The livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, characterized in that: The data processing center (2) includes a redundant sensor network and an adaptive Kalman filtering algorithm for canceling electromagnetic interference, the signal-to-noise ratio is increased by ≥30dB, and the ECG / EMG signal error <5%.

6. The livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, characterized in that: The solar cell of the energy module (5) is a 100W monocrystalline silicon solar panel, and the lithium battery is a 48V rechargeable battery, which supports 72 hours of continuous operation and is built-in with a heating film to ensure the discharge performance in an environment of -30°C.

7. The livestock health warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, characterized in that: The intelligent driving machine (4) is equipped with a GPS / UWB / IMU multi-mode positioning system with a positioning accuracy of ±0.3 meters, and integrates infrared and ultrasonic obstacle avoidance sensors, and the climbing ability reaches 30°.

8. The livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, characterized in that: The cloud platform of the health warning module (3) supports epidemic trend prediction and transmits data through a LoRa+4G dual-mode communication module, and can still synchronize alarms when the network is interrupted.

9. The livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, characterized in that: The system is built-in with a hardware self-check program, supports hot-swap replacement of sensors and battery modules, and the edge computing unit adopts a dual-system mirror, and the fault repair time <15 minutes.

10. The livestock health early warning and intelligent driving mechanical assistance system based on bioelectric signal monitoring according to claim 1, wherein: The driving unit of the intelligent driving machine (4) includes a dynamic beamforming ultrasonic array, 20 kHz to 40 kHz; and a 650 nm low-intensity laser with a power < 5 mW and an action range of 1 to 20 meters to avoid interfering with other livestock.

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