Intelligent inspection robot for farm

Abstract

The invention belongs to the technical field of intelligent breeding equipment, and particularly discloses an intelligent inspection robot for a farm. Comprising a track system installed at the top of a farm, a driving mechanism arranged on a robot body and driving a robot to move along a track, and an image acquisition module, a sensor module, a data processing unit and a wireless communication module which are located on the robot body. The image acquisition module comprises a depth camera, a visible light camera and an infrared camera and is used for acquiring a depth image, a visible light image and an infrared image of a pig in a non-contact manner; the sensor module comprises a temperature and humidity sensor, a gas sensor and a sound sensor; the data processing unit processes the collected data in real time, operates an algorithm model and outputs weight, body condition, quantity and body temperature information of the pigs; the wireless communication module is in communication connection with a cloud or a local server and is used for data transmission. According to the invention, non-contact automatic monitoring can be realized, and the problems of low efficiency and poor precision of traditional manual monitoring are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent aquaculture equipment technology, and in particular to an intelligent inspection robot for aquaculture farms. Background Technology

[0002] The core contradiction facing the pig farming industry today lies in the fact that traditional extensive management models cannot meet the demands of modern farming for precise data and intelligent management. In weight monitoring, the manual weighing method has significant drawbacks: firstly, the operation takes an average of 15-20 minutes per pen, resulting in high labor costs; secondly, the pigs' heart rate rises by 30-40 beats per minute during weighing, taking 2-3 hours to recover, severely impacting daily weight gain. Regarding environmental monitoring, existing handheld monitoring devices suffer from data silos; key parameters such as ammonia concentration can only be collected instantaneously at single points, making it impossible to construct gas distribution heat maps.

[0003] The field of sow estrus monitoring has long relied on experience-based judgment, with an accuracy rate of less than 65% in identifying the estrus period. Existing computer vision solutions perform poorly in complex pigsty environments: dust accumulation leads to a false recognition rate of up to 25% for cameras, and infrared thermometry has an error of ±1.2℃ under dynamic lighting conditions. Fixed sensor networks have monitoring blind spots and cannot collect targeted data by following the movement of the pig herd.

[0004] While some companies have attempted to apply automated inspection equipment, its functionality is severely limited: some only have environmental monitoring capabilities and lack biometric analysis modules, while others are equipped with simple cameras but lack edge computing capabilities. The raw data collected needs to be transmitted back to the server for processing, resulting in a decision-making delay of 5-8 minutes. This fragmented technological solution is insufficient to support critical application scenarios such as real-time precision feeding and early disease warning.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent inspection robot for farms, which has the advantages of improving monitoring accuracy, realizing automated management, and reducing labor costs.

[0007] To achieve the above objectives, the present invention provides the following solution: an intelligent inspection robot for farms, comprising a track system installed on the top of the farm to provide a path for the robot's movement; a drive mechanism mounted on the robot body, cooperating with the track system to drive the robot to move along the track; an image acquisition module including a depth camera, a visible light camera, and an infrared camera for non-contact acquisition of depth images, visible light images, and infrared images of pigs; a sensor module including a temperature and humidity sensor, a gas sensor, and a sound sensor; a data processing unit for real-time processing of the acquired data and running an algorithm model to output information on the pigs' weight, body condition, number, and body temperature; and a wireless communication module for communication with a cloud or local server for data transmission.

[0008] Furthermore, the algorithm model of the data processing unit is based on deep learning technology and edge computing technology, which supports the real-time generation of pig growth curves and stocking density analysis reports.

[0009] Furthermore, the intelligent inspection robot for farms in this application also includes a sow estrus monitoring module, which receives image information collected by a visible light camera and identifies whether the sow exhibits estrus posture or behavior.

[0010] Furthermore, the gas sensor also includes a biomimetic sensor of olfactory receptors for monitoring the concentration of estrus odor in sows.

[0011] Furthermore, the gas sensor includes an ammonia sensor and a carbon dioxide sensor, used to monitor the concentration of harmful gases in the pigsty in real time and send early warning signals through a wireless communication module.

[0012] Furthermore, the intelligent inspection robot for farms in this application also includes a lidar located in front of the robot body for obstacle avoidance.

[0013] Furthermore, the drive mechanism includes a drive wheel and an auxiliary wheel that cooperate with the track system, as well as a servo motor and a gear reducer that drive the drive wheel.

[0014] Furthermore, the intelligent inspection robot for farms in this application also includes a power monitoring module and a self-charging system. When the robot's power is below a threshold, it automatically returns to the charging point to recharge. The self-charging system includes a transmitting coil located at the charging point and a receiving coil located on the robot.

[0015] Furthermore, the intelligent inspection robot for farms in this application also includes an ultrasonic insect repellent module, integrated into the robot body, which emits 20-60kHz high-frequency sound waves to repel mosquitoes and flies.

[0016] Furthermore, the robot body has an IP65 protection rating.

[0017] Compared with the prior art, the present invention discloses at least the following beneficial effects:

[0018] This application provides an intelligent inspection robot for farms, which achieves non-contact automated monitoring through the coordinated work of a track system, drive mechanism, image acquisition module, sensor module, data processing unit and wireless communication module. It solves the problems of low efficiency and poor accuracy of traditional manual monitoring, and has the advantages of improving monitoring accuracy, realizing automated management and reducing labor costs. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a front structural diagram of the intelligent inspection robot for farms according to the present invention;

[0021] Figure 2 This is a side view of the intelligent inspection robot for farms according to the present invention.

[0022] In the diagram: 1. Track system; 2. Drive wheel; 3. Auxiliary wheel; 4. Robot body; 5. LiDAR; 6. Temperature and humidity sensor; 7. Depth camera; 8. Visible light camera; 9. Gas sensor; 10. Sound sensor; 11. Infrared camera. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Reference Figure 1 and Figure 2As shown, this embodiment provides an intelligent inspection robot for farms, including a track system 1, a drive mechanism, an image acquisition module, a sensor module, a data processing unit, and a wireless communication module. The track system 1 is installed on the top of the farm to provide a path for the robot's movement. The drive mechanism is mounted on the robot body 4 and works in conjunction with the track system 1 to drive the robot along the track. The image acquisition module includes a depth camera 7, a visible light camera 8, and an infrared camera 11, used for non-contact acquisition of depth, visible light, and infrared images of the pigs. The sensor module includes a temperature and humidity sensor 6, a gas sensor 9, and a sound sensor 10. The data processing unit processes the acquired data in real time and runs an algorithm model to output information on the pigs' weight, body condition, number, and body temperature. The wireless communication module communicates with a cloud or local server for data transmission.

[0026] The track system 1 can use I-beam rails or aluminum alloy rails. I-beam rails have higher load-bearing capacity, while aluminum alloy rails are lightweight and corrosion-resistant. In this embodiment, I-beam rails are used. The drive mechanism can include a drive wheel 2 and an auxiliary wheel 3. The drive wheel 2 is driven by a servo motor, and the auxiliary wheel 3 is used to maintain the robot's balance. In the image acquisition module, the depth camera 7 can use TOF or structured light technology, the visible light camera 8 can be a high-resolution CMOS sensor, and the infrared camera 11 is an uncooled microbolometer. In the sensor module, the temperature and humidity sensor 6 can be a digital sensor, the gas sensor 9 can be an electrochemical or semiconductor type, and the sound sensor 10 can be a MEMS microphone array. The data processing unit is based on an ARM or x86 architecture processor and runs a deep learning algorithm model. The wireless communication module can support 4G, 5G, or Wi-Fi communication protocols.

[0027] This embodiment utilizes a track-mounted mobile platform equipped with multimodal sensors to achieve non-contact data acquisition of pig growth. Specifically, a depth camera 7 acquires three-dimensional contour data of the pigs and calculates their weight using an algorithm model; a visible light camera 8 identifies the number of pigs and their physical characteristics; and an infrared camera 11 measures the surface temperature distribution of the pigs. A temperature and humidity sensor 6 monitors environmental parameters, a gas sensor 9 detects the concentration of harmful gases, and a sound sensor 10 analyzes the pigs' behavioral status. The data processing unit performs real-time calculations at the edge, avoiding the stress caused to pigs by traditional manual measurement methods and solving the problems of high susceptibility to environmental interference and low data accuracy associated with fixed sensors. The processed structured data is uploaded to the management platform via a wireless communication module, providing data support for precise feeding and health management.

[0028] In one specific embodiment, the algorithm model of the data processing unit is based on deep learning technology and edge computing technology, which supports the real-time generation of pig growth curves and stocking density analysis reports.

[0029] Deep learning technology uses convolutional neural networks (CNNs) to extract features and perform pattern recognition on acquired depth, visible light, and infrared images. The CNNs can employ ResNet or YOLO architectures for pig target detection and keypoint localization. Edge computing technology deploys lightweight models locally on the robot, preprocessing and extracting features from raw image data at the acquisition end, uploading only structured data to the cloud. Edge computing nodes can be implemented using Jetson series embedded AI modules. Growth curve generation uses a time-series data analysis module to perform multinomial fitting on continuously acquired weight data, and stocking density analysis uses spatial distribution algorithms to calculate the overlap rate between the number of pigs per unit area and their activity range.

[0030] Specifically, this technical solution achieves real-time calculation of pig vital signs parameters through a deep learning model deployed locally on the robot, avoiding the latency issues associated with data uploads to the cloud in traditional solutions. The edge computing architecture distributes image recognition algorithms on the track-based inspection robot, transmitting only structured data such as weight and length to the server, effectively reducing network bandwidth requirements. The growth curve module establishes a regression model by analyzing historical weight data, dynamically predicting slaughter time; the stocking density analysis module combines pig location information from infrared image recognition to automatically generate regional heat maps for optimizing pen distribution. Compared to existing fixed camera solutions, this mobile acquisition system can cover a larger area of ​​the pigsty and eliminates blind spots through track-based inspection. Through multimodal data fusion, the algorithm model can correct measurement errors caused by pig movement postures, improving weight estimation accuracy to within ±3%.

[0031] In one specific embodiment, a sow estrus monitoring module is added to the intelligent inspection robot used in the farm. This module receives image information collected by the visible light camera 8 and identifies whether the sow exhibits estrus-like postures or behaviors.

[0032] The sow estrus monitoring module analyzes images of sow behavior captured by the visible light camera 8 to identify specific posture features. Specifically, a behavior recognition algorithm based on a convolutional neural network can be used to extract and classify features of typical estrus behaviors such as mounting and standing responses. As a preferred implementation, the algorithm can be combined with temporal analysis techniques to model the motion trajectories in consecutive frames of images to improve recognition accuracy. Furthermore, the module can integrate posture key point detection technology to assist in determining estrus status by tracking spatial position changes of key parts of the sow, such as the ears, back, and tail.

[0033] The aforementioned technical solution replaces traditional manual observation with automated visual recognition, solving the technical problems of low efficiency and high missed detection rate in sow estrus identification. Compared to existing solutions relying on fixed cameras, the mobile nature of the inspection robot enables multi-angle, full-coverage monitoring, avoiding blind spots caused by obstructions in pigpens. Furthermore, compared to static monitoring systems, this solution can more accurately capture the interactive behavioral characteristics of sows, reducing misjudgments caused by environmental interference. The data processing unit performs real-time behavioral analysis through edge computing, ensuring the timeliness of the identification results and providing technical support for accurate judgment of mating timing.

[0034] In one specific embodiment, in the intelligent inspection robot used in the farm, the gas sensor 9 also includes an olfactory receptor biomimetic sensor for monitoring the concentration of estrus odor in sows.

[0035] The olfactory receptor biomimetic sensor, by mimicking the working principle of a biological olfactory system, can specifically identify specific odor molecules released by sows during estrus. Specifically, the sensor can employ polymer-sensitive materials based on molecular imprinting technology or semiconductor sensor arrays based on nanomaterials for gas sensing, enabling trace detection of estrus-related hormones such as estradiol and androstenone. As a preferred embodiment, the sensor can integrate a miniature air pump and a filter to eliminate interference from other odors in the pigsty. The data processing unit, by analyzing odor concentration trends and combining them with posture recognition results from the image acquisition module, can improve the accuracy of estrus detection.

[0036] Based on the above structure, this technical solution solves the problems of low efficiency and high false negative rates associated with traditional manual observation methods. By fusing olfactory and visual information, it enables non-contact, 24 / 7 monitoring of sows' estrus status. Compared to solutions relying solely on image recognition, the addition of odor as a biomarker significantly reduces the false positive rate. Simultaneously, sensor data is uploaded in real-time via a wireless communication module, providing objective evidence for accurate mating.

[0037] In one specific embodiment, the gas sensor 9 in the intelligent inspection robot for farms includes an ammonia sensor and a carbon dioxide sensor, which are used to monitor the concentration of harmful gases in the pigsty in real time and send early warning signals through a wireless communication module.

[0038] Specifically, the ammonia sensor employs electrochemical or semiconductor principles, capable of detecting ammonia in the 0-100 ppm concentration range with an accuracy of ±2 ppm. The carbon dioxide sensor utilizes non-dispersive infrared (NDIR) technology, covering a detection range of 0-5000 ppm with a response time of less than 30 seconds. Both sensors are integrated within the robot body 4, communicating with the external environment through a waterproof and breathable membrane, achieving an IP65 protection rating. The wireless communication module employs LoRa or NB-IoT low-power wide-area network technology, with a transmission interval adjustable from 1 to 30 minutes. A real-time alarm is immediately triggered when the detected value exceeds a preset threshold.

[0039] As a preferred implementation, the ammonia sensor can be a three-electrode electrochemical sensor with an operating temperature range of -20℃ to 50℃ and a service life of over 2 years. The carbon dioxide sensor can be equipped with an automatic calibration function, performing zero-point calibration by periodically introducing standard gas to ensure long-term measurement stability. The sensor module incorporates a temperature compensation algorithm to eliminate the influence of ambient temperature changes on the measurement results. The warning signal includes the specific gas type, concentration value, detection time, and the robot's current position information, encapsulated and transmitted in JSON format.

[0040] This embodiment achieves multi-point dynamic monitoring of the pigsty environment through a mobile detection platform, solving the monitoring blind spot problem of traditional fixed gas detection devices. Compared with manual inspection, it can continuously acquire accurate gas concentration data and promptly detect local gas concentration exceedances caused by poor ventilation or manure accumulation. Remote monitoring is achieved through wireless transmission, avoiding the interference to the breeding environment caused by frequent personnel entering the pigsty. At the same time, the mobile detection method significantly reduces equipment investment costs compared to deploying multiple fixed sensors, and is easier to maintain and calibrate.

[0041] In one specific embodiment, a lidar 5 is installed in front of the robot body 4 for obstacle avoidance. The lidar 5 detects the distance and outline of obstacles in front by emitting laser beams and receiving reflected signals. Specifically, a 16-line or 32-line lidar 5 based on the TOF (Time-of-Flight) principle can be used, with a scanning angle of 270 degrees, a detection distance of 0.1-10 meters, and a scanning frequency of 10Hz. As a preferred implementation, the lidar 5 can be installed at a height of 1.2 meters above the ground in front of the robot, a position that can effectively detect pig activity while avoiding fixed facilities such as feed troughs. Furthermore, the obstacle data collected by the lidar 5 is transmitted to a data processing unit, where a three-dimensional environment map is generated in real time through point cloud processing algorithms. When an obstacle is detected at a distance of less than 1 meter, a deceleration command is triggered; when it is less than 0.5 meters, movement stops and an audible and visual alarm is issued.

[0042] This embodiment utilizes LiDAR 5 to enable the robot to autonomously avoid obstacles in the complex environment of a pigsty. In a pigsty, the ground may contain feed residue, excrement, and other debris, and pig activity is often unpredictable. Traditional infrared or ultrasonic sensors are susceptible to environmental interference, leading to misjudgments. In contrast, LiDAR 5 offers higher measurement accuracy and anti-interference capabilities, accurately identifying both dynamic obstacles (such as moving pigs) and static obstacles (such as equipment and fences), thus solving the collision problem caused by obstacle avoidance failure in narrow passages. Specifically, through 3D environment modeling and real-time path planning, the robot can automatically avoid obstacles without interrupting the inspection task, ensuring equipment safety and inspection continuity.

[0043] In one specific embodiment, the drive mechanism includes a drive wheel 2 and an auxiliary wheel 3 that cooperate with the track system 1, as well as a servo motor and a gear reducer that drive the drive wheel 2.

[0044] Specifically, the drive wheel 2 adopts a rubber-coated metal hub structure, with anti-slip patterns on the wheel surface that mesh with the flanges of the I-shaped track. The diameter of the drive wheel 2 ranges from 80-120mm, preferably 100mm. The auxiliary wheels 3 are made of nylon and are symmetrically distributed on both sides of the drive wheel 2 to maintain the stability of the robot's operation. The diameter of the auxiliary wheels 3 ranges from 50-80mm. The servo motor is a 400W AC servo motor with a rated speed of 3000rpm, directly connected to the gear reducer via a coupling. The gear reducer adopts a two-stage helical gear transmission structure with a reduction ratio of 1:15 and an output torque of up to 50N·m. As a preferred embodiment, the reducer housing is made of aluminum alloy and has built-in grease for lifetime maintenance-free operation.

[0045] The drive mechanism, through high-precision speed control of the servo motor and torque amplification of the gear reducer, enables the inspection robot to start and stop smoothly and operate at a constant speed on the track. The meshing design of the drive wheel 2 with the track effectively prevents slippage, while the auxiliary wheel set 3 significantly reduces lateral sway during operation. Compared with the conventional DC motor drive scheme used in existing technologies, this mechanism has advantages such as high positioning accuracy (error ±2mm), strong load capacity (maximum load 50kg), and low noise (≤60dB), making it particularly suitable for long-term continuous operation in pigsty environments. Precise speed ratio control of the gear reducer ensures that the robot's movement speed remains stably maintained within the set range of 0.3-0.5m / s under different load conditions.

[0046] To further optimize the solution, this embodiment also includes a power monitoring module and a self-charging system. When the robot's power is below a threshold, it automatically returns to the charging point to recharge. The self-charging system includes a transmitting coil located at the charging point and a receiving coil located on the robot.

[0047] The power monitoring module uses a voltage sensor to detect the remaining battery power of the robot in real time and feeds the data back to the data processing unit. When the power level falls below a preset threshold, the data processing unit generates a charging command and sends it to the drive mechanism. The drive mechanism controls the robot to move along track system 1 to the charging point. The transmitting coil at the charging point and the receiving coil on the robot achieve wireless charging through electromagnetic induction. During the charging process, the power monitoring module continuously monitors the battery status until the power level reaches the set upper limit, at which point it automatically disconnects the charging connection.

[0048] In a preferred embodiment, the charging point can be located at the end or middle of the track system 1, and robot positioning is achieved through radio frequency identification (RFID) technology. The data processing unit can store historical charging data and optimize charging threshold settings and path planning strategies.

[0049] Based on the above results, this embodiment solves the power supply problem of the inspection robot during continuous operation. By combining automatic power monitoring and a wireless charging system, the tedious manual charging operation is avoided, ensuring the robot's continuous working capability. Compared with existing technologies, this solution uses a non-contact charging method, eliminating the mechanical wear problem of the charging interface and reducing electrical safety hazards in the pigsty environment. Automated control of the charging process improves system reliability and reduces the risk of data acquisition interruptions due to insufficient power.

[0050] To further optimize the solution, this embodiment also incorporates an ultrasonic insect repellent module, integrated into the robot body 4, which emits 20-60kHz high-frequency sound waves to repel mosquitoes and flies.

[0051] The ultrasonic insect repellent module converts electrical signals into mechanical vibrations using a piezoelectric ceramic transducer, generating sound waves of a specific frequency. Specifically, the module includes a signal generator, a power amplifier, and a transducer array. The signal generator is programmable and adjustable to output a frequency within the range of 20-60kHz to adapt to different environmental requirements. As a preferred implementation, a frequency sweeping mode is used, periodically switching frequencies within the effective frequency band to prevent mosquitoes and flies from developing adaptation. The module's sound pressure level is controlled within the range of 75-85dB, ensuring insect repellency while avoiding acoustic stress on pigs. Furthermore, the transducer is mounted at a 30° tilt angle inside the robot's shell, radiating energy directionally through a waveguide structure to improve energy utilization.

[0052] This embodiment addresses the practical problem of mosquito and fly breeding in pigsty environments by using high-frequency sound waves to interfere with the insects' nervous systems, achieving physical repulsion. Compared to traditional chemical spraying methods, it avoids drug residues and environmental pollution, and does not affect the health of the pigs. Because it is directly integrated into the inspection robot, it can move with the robot to achieve full coverage of the pigsty, solving the problem of blind spots in fixed pest control equipment. Simultaneously, this module works in conjunction with other functions of the robot to complete the pest control operation without adding extra inspection procedures, improving the overall system efficiency.

[0053] In one specific embodiment, the robot body 4 has an IP65 protection rating. It should be understood that the "6" in IP65 represents complete dustproof, meaning dust cannot enter the device; and the "5" represents water jet resistance, meaning low-pressure water jets aimed at the device from any angle will not cause harmful effects. This protection rating can be achieved through the following technical means: the robot shell is made of a one-piece molded aluminum alloy, with silicone sealing rings at all seams; waterproof aviation connectors are used for the camera and sensor interfaces; stainless steel protective covers are added to the motor drive components; and the control panel uses a tempered glass full-lamination process. As another implementation, key electronic components can be additionally treated with a nano-coating process for tri-proofing.

[0054] This embodiment effectively solves practical problems such as equipment failure caused by dust accumulation in the pigsty environment and damage to circuits due to liquid seepage during high-pressure rinsing and disinfection by adopting a standardized protection level design. Compared with the existing technology where ordinary inspection equipment only has an IP54 protection level, this solution significantly improves the reliability of the equipment under harsh conditions such as high temperature and humidity, dusty environments, and regular rinsing while maintaining the integrity of the equipment's functions, enabling the robot to adapt to the special operating environment requirements of pig farms.

[0055] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this invention, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0056] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An intelligent inspection robot for livestock farms, characterized in that, include: A track system (1) is installed on the top of the farm to provide a path for the robot to move; The drive mechanism is located on the robot body (4) and works in conjunction with the track system (1) to drive the robot to move along the track; The image acquisition module includes a depth camera (7), a visible light camera (8) and an infrared camera (11), which are used to acquire depth images, visible light images and infrared images of pigs in a non-contact manner. The sensor module includes a temperature and humidity sensor (6), a gas sensor (9), and a sound sensor (10); The data processing unit processes the collected data in real time and runs the algorithm model to output information on the pigs' weight, body condition, number, and body temperature. The wireless communication module communicates with a cloud or local server for data transmission.

2. The intelligent inspection robot for farms according to claim 1, characterized in that, The algorithm model of the data processing unit is based on deep learning technology and edge computing technology, and supports the real-time generation of pig growth curves and stocking density analysis reports.

3. The intelligent inspection robot for farms according to claim 1, characterized in that, It also includes a sow estrus monitoring module, which receives image information collected by a visible light camera (8) and identifies whether the sow exhibits estrus behavior.

4. The intelligent inspection robot for farms according to claim 1 or 3, characterized in that, The gas sensor (9) also includes a biomimetic sensor of olfactory receptors for monitoring the concentration of estrus odor in sows.

5. The intelligent inspection robot for farms according to claim 1, characterized in that, The gas sensor (9) includes an ammonia sensor and a carbon dioxide sensor, which are used to monitor the concentration of harmful gases in the pigsty in real time and send early warning signals through a wireless communication module.

6. The intelligent inspection robot for farms according to claim 1, characterized in that, It also includes a laser radar (5) located in front of the robot body (4) for obstacle avoidance.

7. The intelligent inspection robot for farms according to claim 1, characterized in that, The drive mechanism includes a drive wheel (2) and an auxiliary wheel (3) that cooperate with the track system (1), as well as a servo motor and a gear reducer that drive the drive wheel (2).

8. The intelligent inspection robot for farms according to claim 1, characterized in that, It also includes a power monitoring module and a self-charging system. When the robot's power is below a threshold, it automatically returns to the charging point to recharge. The self-charging system includes a transmitting coil located at the charging point and a receiving coil located on the robot.

9. The intelligent inspection robot for farms according to claim 1, characterized in that, It also includes an ultrasonic insect repellent module, which is integrated into the robot body (4) and emits 20-60kHz high-frequency sound waves to repel mosquitoes and flies.

10. The intelligent inspection robot for farms according to claim 1, characterized in that, The robot body (4) has an IP65 protection rating.

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