A sheep breath detection device based on data fusion radar

Abstract

The utility model belongs to the field of wisdom livestock technology, and relates to a sheep only breath detection device based on data fusion radar, five big parts including radar module, visual tracking and holder control module, environment perception and edge calculation module, mobile platform and remote control system, mobile power. Its core adopts AM modulation radar constructed by software radio technology, combines visual tracking to let radar beam accurate aim sheep chest and abdomen to obtain respiratory information, reduces sheep only movement interference through data fusion technology. Meanwhile, the environment perception module can detect key environmental parameters such as temperature and humidity in real time. The device fuses non-contact breath detection, target tracking and other leading technologies, provides efficient and accurate intelligent solution for sheep health detection, supports real-time monitoring of breeding environment, can also optimize breeding strategy through data analysis, and helps fine and intelligent breeding management.

Description

Technical Field

[0001] This utility model belongs to the field of smart animal husbandry technology, specifically relating to a sheep breathing detection device based on data fusion radar. Background Technology

[0002] The sheep respiration detection device based on data fusion radar is a multi-functional intelligent monitoring device for sheep farming, applicable to large-scale farms, research institutions, wildlife conservation, and other scenarios. However, existing technologies suffer from the following problems:

[0003] 1. Although the patent CN201620160893.0 "An Infrared Sensing Sheep Collar" has high precision, it is easy to cause stress reactions in animals, and long-term wear may cause skin abrasion, and the maintenance cost is high.

[0004] 2. Patent CN202410808420.6, "A non-contact real-time physiological sign monitoring system based on millimeter-wave radar", monitors human physiological signs by analyzing the micro-movement information of the human chest cavity in the signal through various modules. However, it cannot move the device, which limits its scope of use, makes it susceptible to various factors, and has a single function.

[0005] 3. Patent 201621427433.6, "A Calibration and Testing Instrument for an Abdominal Breathing Sensor", makes it difficult to manually and accurately align and synchronize the time waveform when conducting breathing tests for a long time (e.g., more than 30 minutes). Due to the large amount of data, it is difficult to achieve accurate synchronization of the time waveform manually.

[0006] In view of this, the development of an intelligent monitoring and management device for sheep farming that integrates respiratory detection, target tracking, environmental monitoring, AI diagnosis and remote control has become a key issue that urgently needs to be addressed in the field of smart animal husbandry. Utility Model Content

[0007] Based on the shortcomings of existing technologies, this utility model provides a sheep breathing detection device based on data fusion radar, which aims to solve the problems of stress response caused by contact detection in animals in traditional farms, blind spots in nighttime detection, high cost, insufficient accuracy, and slow execution speed of existing detection solutions.

[0008] The technical solution adopted in this utility model is to provide a sheep respiration detection device based on data fusion radar, comprising five main parts: a radar module, a visual tracking and gimbal control module, an environmental perception and edge computing module, a mobile platform and remote control system, and a mobile power supply. The radar module includes a directional antenna and software-defined radio. The visual tracking and gimbal control module includes a video acquisition device and a two-dimensional gimbal. The environmental perception and edge computing module includes a microcontroller, sensors, a positioning module, an antenna, and edge computing devices. The sensors include a temperature and humidity detection sensor and a CO2 concentration detection sensor. The mobile platform and remote control system includes a stepper motor, a control switch, an expansion board, a voltage regulator module, and a four-wheel drive mobile platform.

[0009] A further improvement of this utility model's technical solution lies in the following: a directional antenna is provided above the video acquisition device; the video acquisition device is supported at the upper end of the two-dimensional gimbal; the CO2 concentration detection sensor is located at the temperature and humidity detection sensor; the positioning module is located at the right end of the sensor; the expansion board is located at the left side of the voltage regulator module; and a stepper motor is provided below the voltage regulator module. The four-wheel drive mobile platform is divided into upper and lower layers. The upper layer includes: a video acquisition device, a directional antenna, a two-dimensional gimbal, a four-wheel drive mobile platform, an edge computing device, and an antenna. The lower layer includes: a stepper motor, an expansion board, a voltage regulator module, a software radio, a sensor, a four-wheel drive mobile platform, a sensor, a control switch, a positioning module, a microcontroller, and a power bank.

[0010] A further improvement of this utility model is that the four-wheel drive mobile platform can be remotely controlled, with remote commands issued by the Alibaba Cloud IoT platform and the breathing waveform transmitted synchronously.

[0011] A further improvement of this utility model is that the radio frequency transceiver stage of the software radio signal adopts the AD9363 radio frequency agile transceiver.

[0012] A further improvement of this utility model's technical solution lies in the fact that the software radio is used in conjunction with the GNU Radio environment and the directional antenna.

[0013] A further improvement of this utility model is that the visual tracking 2D gimbal is used in conjunction with the PyCharm environment, and uses YOLOv5 to record sheep displacement data and transmit sheep displacement information to the 2D gimbal.

[0014] A further improvement of this utility model's technical solution is that the video acquisition device uses a Linux system and deploys a GNU Radio environment and a PyCharm environment to upload data to a cloud platform.

[0015] A further improvement of this utility model is that the edge computing device uses a Linux system and uploads data to the cloud via the MQTT protocol.

[0016] The technological advancements achieved by this invention compared to existing technologies are as follows:

[0017] 1. This device uses AM modulated radar to acquire target information by using reflected radar signals. When radio signals are transmitted to the sheep's body surface, the sheep's breathing causes micro-movements in the chest and abdomen, which modulate the amplitude of the radio signals, thereby encoding the breathing information into the signals. By demodulating these signals, the sheep's breathing can be detected. This solves the problems of existing wearable sensors, which, although highly accurate, are prone to causing stress reactions in animals, may cause skin abrasion with long-term wear, and have high maintenance costs.

[0018] 2. This device is based on an Internet of Things (IoT) architecture to enable remote control of a mobile platform. User commands are sent to the WiFi module via Alibaba Cloud MQTT service, parsed, and then sent as control commands via serial port. The motor controller generates step pulses through timer interrupts, adjusts the pin levels and pulse frequency according to the commands, and drives the motor to achieve motion control, forming a complete IoT control link. This solves the problem that existing devices are limited in scope of use and are easily affected by various factors because they cannot be moved.

[0019] 3. This device calculates the similarity between the radar-measured waveform and the standard waveform using correlation coefficients to calibrate the signal morphology; it performs a large-point fast Fourier transform to convert the time-domain signal into a frequency-domain spectrum, analyzes the peak frequency errors of the top three energy levels in the spectrum, and assesses the accuracy of the respiratory rate. Compared to existing methods, this device reduces the time synchronization requirements, enables accurate monitoring over long periods, and combines the advantages of waveform calibration and frequency error analysis. It solves the problem that existing equipment struggles to manually and accurately align and synchronize the time waveforms during long-term respiratory detection (e.g., over 30 minutes) due to the large amount of data. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the upper structure of this utility model;

[0021] Figure 2 This is a schematic diagram of the lower layer structure of this utility model;

[0022] Figure 3 This is a schematic diagram of the overall structure of this utility model;

[0023] Figure 4 This is a flowchart of the present utility model;

[0024] Figure 5 This is a system block diagram of the present invention;

[0025] In the diagram: 1. Stepper motor; 2. Control switch; 3. Power bank; 4. Expansion board; 5. Microcontroller; 6. Voltage regulator module; 7. Software radio; 8. Video acquisition device; 9. Directional antenna; 10. 2D pan-tilt unit; 11. Sensor; 12. Four-wheel drive mobile platform; 13. Sensor; 14. Positioning module; 15. Antenna; 16. Edge computing device. Detailed Implementation

[0026] Example 1

[0027] Please see Figure 1-5 This utility model provides a technical solution: a sheep respiration detection device based on data fusion radar, comprising five main parts: a radar module, a visual tracking and gimbal control module, an environmental perception and edge computing module, a mobile platform and remote control system, and a mobile power supply. The radar module is a 2.4GHz radar based on Pluto SDR, which acquires respiration data non-contactly by modulating the amplitude of signals caused by micro-movements in the chest and abdomen during respiration, with an error of only 0.34% (compared to the professional HKH respiration detector). The visual tracking and gimbal control module uses a YOLOv5 model to detect sheep in real time, dynamically adjusting the radar beam to align with the chest and abdomen for accurate tracking. The environmental perception and edge computing module integrates sensors for temperature, humidity, carbon dioxide, etc., processes data locally through an edge computing device, and uploads it to Alibaba Cloud. The mobile platform and remote control system are based on a four-wheel drive platform supporting remote control, and achieve cloud command transmission and device linkage through the MQTT protocol.

[0028] Example 2

[0029] like Figure 1-5 As shown, based on Embodiment 1, this utility model provides a technical solution: wherein,

[0030] The sensors include: a temperature and humidity detection sensor and a CO2 concentration detection sensor. Sensor 11 is a DHT11 sensor used for temperature and humidity detection, and sensor 13 is a JW01 sensor used for CO2 concentration detection.

[0031] Positioning module 14 is a Neo-6m positioning module used for GPS positioning, and antenna 15 is an SMA long antenna used to receive GPS signals.

[0032] The four-wheel drive mobile platform 12 is remotely controlled, and remote commands are issued through the Alibaba Cloud IoT platform, with the breathing waveform being transmitted synchronously.

[0033] Software radio 7, Pluto SDR, is used in conjunction with the GNU Radio environment and the directional antenna 9 to define a bio-radar and implement a respiratory detection radar. Its RF transceiver stage employs the AD9363 RF agile transceiver, enabling fast and accurate signal transmission and reception and frequency conversion over a wide frequency range.

[0034] The visual tracking 2D gimbal 10 is used in conjunction with the PyCharm environment and utilizes YOLOv5 to achieve sheep recognition and visual tracking functions, record sheep body displacement data, and transmit sheep body displacement information to the 2D gimbal 10.

[0035] The video acquisition device 8 uses a Linux system and deploys the GNU Radio and PyCharm environments. It is responsible for recording radar data and visual tracking data, performing data fusion, and uploading the data to the cloud platform.

[0036] Edge computing device 16 is an edge computing device equipped with Intel N100, running a Linux system, used for respiratory signal and multi-source data fusion, and uploaded to the cloud via MQTT protocol.

[0037] Example 3

[0038] like Figure 5 As shown, this utility model integrates a respiratory detection radar, a mobile control platform, cloud connectivity, and environmental perception functions into a comprehensive detection device. The device uses a transmitting antenna connected to a power amplifier, transmit (TX), and a digital-to-analog converter (DAC) to a USB port for transmitting. A receiving antenna connects to a receiving port consisting of a low-noise amplifier (LNA), receive (RX), and an analog-to-digital converter (ADC) to collect respiratory signals. The device is controlled by a mobile platform (including a driver chip and stepper motors) based on an ESPB266 and Arduino main control expansion board, powered by a 12V power supply. Simultaneously, the device is equipped with a router to connect to Alibaba Cloud for data decoupling and cloud processing, supporting APP access and external data visualization. It also integrates a GPS positioning module, a CO2 concentration detection environmental perception module, and a visual tracking system composed of a USB camera and two stepper motors, enabling real-time detection of position changes, forming a complete respiratory detection and tracking solution. This utility model utilizes an AI sheep respiratory detection and analysis system. First, the collected respiratory signals are preprocessed and analyzed in both the time and frequency domains. A fast Fourier transform is then applied to generate a respiratory spectrum. The respiratory feature data is input into the AI ​​analysis module to achieve intelligent diagnosis of the sheep's health status.

[0039] The present invention has been described in detail above. However, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, any modifications or improvements that do not depart from the spirit of the present invention are within the protection scope of the present invention.

Claims

1. A sheep respiration detection device based on data fusion radar, characterized in that, It includes five main parts: radar module, visual tracking and gimbal control module, environmental perception and edge computing module, mobile platform and remote control system, and mobile power supply (3); the radar module includes: directional antenna (9) and software radio (7); the visual tracking and gimbal control module includes: video acquisition device (8) and two-dimensional gimbal (10); the environmental perception and edge computing module includes: microcontroller (5), sensor, positioning module (14), antenna (15), and edge computing device (16); the sensor includes temperature and humidity detection sensor (11) and CO2 concentration detection sensor (13); the mobile platform and remote control system includes: stepper motor (1), control switch (2), expansion board (4), voltage regulator module (6), and four-wheel drive mobile platform (12).

2. The sheep respiration detection device based on data fusion radar according to claim 1, characterized in that, A directional antenna (9) is provided above the video acquisition device (8), the video acquisition device (8) is supported on the upper end of the two-dimensional gimbal (10), the CO2 concentration detection sensor (13) is located to the left of the temperature and humidity detection sensor (11), the positioning module (14) is located to the right of the temperature and humidity detection sensor (11), the expansion board (4) is located to the left of the voltage regulator module (6), and a stepper motor (1) is provided on the lower side of the voltage regulator module (6). The four-wheel drive mobile platform (12) is divided into upper and lower layers. The upper layer includes: a video acquisition device (8), a directional antenna (9), a two-dimensional gimbal (10), the four-wheel drive mobile platform (12), an edge computing device (16), and an antenna (15); the lower layer includes: a stepper motor (1), an expansion board (4), a voltage regulator module (6), a software radio (7), a temperature and humidity detection sensor (11), the four-wheel drive mobile platform (12), a CO2 concentration detection sensor (13), a control switch (2), a positioning module (14), a microcontroller (5), and a mobile power supply (3).

3. The sheep respiration detection device based on data fusion radar according to claim 1, characterized in that, The four-wheel drive mobile platform (12) is remotely controlled, and remote commands are issued by the Alibaba Cloud IoT platform, with the breathing waveform being synchronously transmitted back.

4. The sheep respiration detection device based on data fusion radar according to claim 1, characterized in that, The radio frequency transceiver stage of the software radio (7) uses the AD9363 radio frequency agile transceiver.

5. The sheep respiration detection device based on data fusion radar according to claim 1, characterized in that, The software radio (7) is used in conjunction with the GNU Radio environment and a directional antenna (9).

6. The sheep respiration detection device based on data fusion radar according to claim 1, characterized in that, The visual tracking 2D gimbal (10) is used in conjunction with the PyCharm environment.

7. The sheep respiration detection device based on data fusion radar according to claim 1, characterized in that, The video acquisition device (8) uses a Linux system and deploys a GNU Radio environment and a PyCharm environment to upload data to a cloud platform.

8. The sheep respiration detection device based on data fusion radar according to claim 1, characterized in that, The edge computing device (16) uses a Linux system and uploads data to the cloud via the MQTT protocol.

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