Pig herd monitoring management system and method

Abstract

The invention discloses a pig herd monitoring management system and method, and relates to the technical field of pig herd monitoring management, the system comprises an individual identification module, a data acquisition module and a data analysis and storage module, the data acquisition module is respectively connected with the individual identification module and the data analysis and storage module; the individual identification module is used for identifying pig herd numbers and corresponding positions; the data acquisition module is used for acquiring a plurality of monitoring data of pig group behaviors and the environment where the pig group is located; and the data analysis and storage module is used for analyzing, processing and storing the acquired monitoring data. According to the invention, the problem of low monitoring efficiency and accuracy in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of pig herd monitoring and management, and in particular to a pig herd monitoring and management system and method. Background Art

[0002] The pig farming industry occupies a very important position in China's economy. With the development of the pig farming industry, how to achieve intensive, refined and intelligent production management has become an important issue facing the industry. The biggest advantage of structured and factory farming is that it saves land area and reduces transportation costs. Factory farming also has a series of problems. For example, due to the increase in breeding density, it is easier to cause large-scale infection when the disease invades, and manual inspections cannot be carried out 24 hours a day. In traditional farming, the health of pigs depends on manual records. As the number continues to increase, manual records are inefficient and prone to errors. At present, more and more intelligent management and monitoring systems are becoming popular. However, the existing monitoring methods still use a single sensor for monitoring, and the monitoring accuracy is low. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a pig herd monitoring management system and method to solve the problems of low monitoring efficiency and accuracy in the prior art.

[0004] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a pig herd monitoring and management system, including an individual identification module, a data acquisition module and a data analysis and storage module, wherein the data acquisition module is connected to the individual identification module and the data analysis and storage module respectively; The individual identification module is used to identify the pig group number and the corresponding position; The data acquisition module is used to obtain various monitoring data of the pig herd's behavior and the environment in which the pig herd is located; The data analysis and storage module is used to analyze, process and store the acquired monitoring data.

[0005] Furthermore, the data acquisition module includes an RGB camera and an environmental sensor, and the environmental sensor includes a temperature and humidity sensor, a carbon dioxide sensor and an ammonia sensor.

[0006] The present invention also adopts a technical solution: a pig herd monitoring and management method, comprising the following steps: S1: Use the individual identification module to identify the pig group number and the corresponding position, and input the pig group number and the corresponding position into the data collection module; S2: using the data acquisition module to obtain the monitoring data of the pig herd, and inputting the monitoring data into the data analysis and storage module; S3: Analyze, process and store the monitoring data using the data analysis and storage module, and output the pig herd analysis results; S4: Provide targeted decisions and complete pig herd monitoring and management based on the results of pig herd analysis.

[0007] Furthermore, in the individual identification module in S1, each pig is given an ear tag with RFID, and the ear tag is bound to the pig's number.

[0008] Furthermore, S3 includes the following sub-steps: S3-1: Preprocess the monitoring data obtained by the RGB camera and environmental sensor; S3-2: Input the pre-processed monitoring data into the data analysis and storage module to judge the behavior status of the pigs; S3-3: Input the pre-processed monitoring data into the data analysis and storage module to judge the environment in which the pigs are located; S3-4: Obtain the results of the herd analysis based on the pigs’ behavioral status and the environment in which they are located.

[0009] Furthermore, the preprocessing in S3-1 includes performing image enhancement processing on the image acquired by the RGB camera based on bilateral filtering and adaptive histogram equalization method, and screening and cleaning the data acquired by the environmental sensor.

[0010] Furthermore, in S3-2, the behavior status of the pigs is judged, including the pigs' eating, posture and body temperature. The calculation formula for eating is:

[0011] in, The target pigs’ food intake in a specific period of time. is the proportional coefficient corresponding to the feeding time period, is the feeding amount of the trough each time, The remaining amount in the trough each time.

[0012] Further, the posture is judged by a posture detection algorithm, comprising the following steps: A1: Extract the bottom, middle and high-level features of the image through the backbone network of GS-yolov7-pose, and input the bottom, middle and high-level features into the neck network; A2: Multi-scale fusion of bottom-level, middle-level, and high-level features through the neck network; A3: The detection head processes the fused multi-scale features to obtain three anchor boxes of different shapes for the cell; A4: Use anchor boxes for regression, calculate bounding box loss, key point loss, and total loss, and update the network value based on the total loss to obtain updated bounding boxes and key points to achieve posture detection.

[0013] Furthermore, the step of determining the environment of the pigs in S3-3 includes the following steps: B1: Select temperature, humidity, carbon dioxide and ammonia to establish the indicator set; B2: Normalize the indicator set. The formula is:

[0014] in, is the normalized value of the environmental index, is the measured value of the environmental indicator, is the average value of environmental indicators, is the standard deviation of environmental indicators; B3: Constructing environmental scores based on normalized processing results and air quality index for:

[0015] in, Modeled after modeling the air quality index; B4: Judge the environment in which the pigs live based on the environmental score.

[0016] Furthermore, the step B4 of determining the environment in which the pigs are located includes: when , the level of the environment in which the pigs live is poor; when , the level of the pigs’ environment is good; when , the level of the environment in which the pigs live is superior.

[0017] The beneficial effects of the present invention are as follows: the present invention adopts RGB cameras and multiple sensors to obtain physiological monitoring data of pigs, which can improve the monitoring accuracy compared with the traditional single sensor. At the same time, the present invention adopts GS-yolov7-pose and scoring model to judge the behavioral state and environment of pigs, and makes targeted strategy adjustments according to the judgment results, so as to better provide a suitable environment for pigs and monitor the health status of pigs in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a block diagram of a pig herd monitoring and management system.

[0019] Figure 2 The present invention is a flow chart of a pig herd monitoring and management method. DETAILED DESCRIPTION

[0020] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0021] Embodiment 1, as Figure 1 As shown, a pig herd monitoring and management system includes an individual identification module, a data acquisition module and a data analysis and storage module, wherein the data acquisition module is connected to the individual identification module and the data analysis and storage module respectively; The individual identification module is used to identify the pig group number and the corresponding position; The data acquisition module is used to obtain various monitoring data of the pig herd's behavior and the environment in which the pig herd is located; The data analysis and storage module is used to analyze, process and store the acquired monitoring data.

[0022] The data acquisition module includes an RGB camera and an environmental sensor, and the environmental sensor includes a temperature and humidity sensor, a carbon dioxide sensor, and an ammonia sensor. In this embodiment, multiple RGB cameras and environmental sensors are provided, and are installed at multiple locations in the pig house.

[0023] Embodiment 2, as Figure 2 As shown, a method for monitoring and managing a pig herd comprises the following steps: S1: Use the individual identification module to identify the pig group number and the corresponding position, and input the pig group number and the corresponding position into the data collection module; S2: using the data acquisition module to obtain the monitoring data of the pig herd, and inputting the monitoring data into the data analysis and storage module; S3: Analyze, process and store the monitoring data using the data analysis and storage module, and output the pig herd analysis results; S4: Provide targeted decisions and complete pig herd monitoring and management based on the results of pig herd analysis.

[0024] In the individual identification module in S1, each pig is equipped with an ear tag with RFID, and the ear tag is bound to the pig's number.

[0025] By giving each pig an ear tag with RFID and binding each ear tag to the pig's number, when the pig enters the monitoring area, its ear tag number can be automatically identified.

[0026] The S3 includes the following sub-steps: S3-1: Preprocess the monitoring data obtained by the RGB camera and environmental sensor; S3-2: Input the pre-processed monitoring data into the data analysis and storage module to judge the behavior status of the pigs; S3-3: Input the pre-processed monitoring data into the data analysis and storage module to judge the environment in which the pigs are located; S3-4: Obtain the results of the herd analysis based on the pigs’ behavioral status and the environment in which they are located.

[0027] The preprocessing in S3-1 includes performing image enhancement processing on the image acquired by the RGB camera based on bilateral filtering and adaptive histogram equalization method, and screening and cleaning the data acquired by the environmental sensor.

[0028] In S3-2, the behavior status of the pigs is judged, including the pigs' eating, posture and body temperature. The calculation formula for eating is:

[0029] in, The target pigs’ food intake in a specific period of time. is the proportional coefficient corresponding to the feeding time period, is the feeding amount of the trough each time, The remaining amount in the trough each time.

[0030] By analyzing the pigs' eating habits and monitoring their eating behavior within a preset time window, including the amount fed and the remaining amount in the trough each time, it can be used to assess their appetite and digestive function.

[0031] The posture is determined by a posture detection algorithm, comprising the following steps: A1: Extract the bottom, middle and high-level features of the image through the backbone network of GS-yolov7-pose, and input the bottom, middle and high-level features into the neck network; A2: Multi-scale fusion of bottom-level, middle-level, and high-level features through the neck network; A3: The detection head processes the fused multi-scale features to obtain three anchor boxes of different shapes for the cell; A4: Use anchor boxes for regression, calculate bounding box loss, key point loss, and total loss, and update the network value based on the total loss to obtain updated bounding boxes and key points to achieve posture detection.

[0032] In one embodiment of the present invention, GS-yolov7-pose replaces the CBS module in the neck network of the original yolov7 with GSConv, and detects the pig posture through the posture estimation branch GS-yolov7-pose of GS-yolov7.

[0033] The determination of the environment of the pigs in S3-3 includes the following steps: B1: Select temperature, humidity, carbon dioxide and ammonia to establish the indicator set; B2: Normalize the indicator set. The formula is:

[0034] in, is the normalized value of the environmental index, is the measured value of the environmental indicator, is the average value of environmental indicators, is the standard deviation of environmental indicators; B3: Constructing environmental scores based on normalized processing results and air quality index for:

[0035] in, Modeled after modeling the air quality index; B4: Judge the environment in which the pigs live based on the environmental score.

[0036] The judgment of the environment of the pigs in B4 includes: when , the level of the environment in which the pigs live is poor; when , the level of the pigs’ environment is good; when , the level of the environment in which the pigs live is superior.

[0037] In one embodiment of the present invention, a targeted control strategy is formulated based on the judgment of the pig's behavioral state and the environment in which it is located. When the system monitors that the pig's eating and body temperature are outside the healthy threshold, the pig's posture is abnormal, and the pig's environment level is poor, the system will feedback the abnormal data to the management personnel, thereby facilitating the management personnel to carry out targeted management.

[0038] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A pig herd monitoring and management system, characterized in that: It includes an individual identification module, a data collection module and a data analysis and storage module, wherein the data collection module is connected to the individual identification module and the data analysis and storage module respectively; The individual identification module is used to identify the pig group number and the corresponding position; The data acquisition module is used to obtain various monitoring data of the pig herd's behavior and the environment in which the pig herd is located; The data analysis and storage module is used to analyze, process and store the acquired monitoring data.

2. The pig herd monitoring and management system according to claim 1, characterized in that: The data acquisition module includes an RGB camera and an environmental sensor, and the environmental sensor includes a temperature and humidity sensor, a carbon dioxide sensor and an ammonia sensor.

3. A method for monitoring and managing a pig herd, characterized in that: The following steps are involved: S1: Use the individual identification module to identify the pig group number and the corresponding position, and input the pig group number and the corresponding position into the data collection module; S2: using the data acquisition module to obtain the monitoring data of the pig herd, and inputting the monitoring data into the data analysis and storage module; S3: Analyze, process and store the monitoring data using the data analysis and storage module, and output the pig herd analysis results; S4: Provide targeted decisions and complete pig herd monitoring and management based on the results of pig herd analysis.

4. The method for monitoring and managing a pig herd according to claim 3, characterized in that: In the individual identification module in S1, each pig is equipped with an ear tag with RFID, and the ear tag is bound to the pig's number.

5. The method for monitoring and managing pig herds according to claim 3, characterized in that: The S3 includes the following sub-steps: S3-1: Preprocess the monitoring data obtained by the RGB camera and environmental sensor; S3-2: Input the pre-processed monitoring data into the data analysis and storage module to judge the behavior status of the pigs; S3-3: Input the pre-processed monitoring data into the data analysis and storage module to judge the environment in which the pigs are located; S3-4: Obtain the results of the herd analysis based on the pigs’ behavioral status and the environment in which they are located.

6. The method for monitoring and managing a pig herd according to claim 5, characterized in that: The preprocessing in S3-1 includes performing image enhancement processing on the image acquired by the RGB camera based on bilateral filtering and adaptive histogram equalization method, and screening and cleaning the data acquired by the environmental sensor.

7. The method for monitoring and managing a pig herd according to claim 5, characterized in that: In S3-2, the behavior status of the pigs is judged, including the pigs' eating, posture and body temperature. The calculation formula for eating is: in, The target pigs’ food intake in a specific period of time. is the proportional coefficient corresponding to the feeding time period, is the feeding amount of the trough each time, The remaining amount in the trough each time.

8. The method for monitoring and managing a pig herd according to claim 7, characterized in that: The posture is determined by a posture detection algorithm, comprising the following steps: A1: Extract the bottom, middle and high-level features of the image through the backbone network of GS-yolov7-pose, and input the bottom, middle and high-level features into the neck network; A2: Multi-scale fusion of bottom-level, middle-level, and high-level features through the neck network; A3: The detection head processes the fused multi-scale features to obtain three anchor boxes of different shapes for the cell; A4: Use anchor boxes for regression, calculate bounding box loss, key point loss, and total loss, and update the network value based on the total loss to obtain updated bounding boxes and key points to achieve posture detection.

9. The method for monitoring and managing a pig herd according to claim 5, characterized in that: The determination of the environment of the pigs in S3-3 includes the following steps: B1: Select temperature, humidity, carbon dioxide and ammonia to establish the indicator set; B2: Normalize the indicator set. The formula is: in, is the normalized value of the environmental index, is the measured value of the environmental indicator, is the average value of environmental indicators, is the standard deviation of environmental indicators; B3: Constructing environmental scores based on normalized processing results and air quality index for: in, Modeled after modeling the air quality index; B4: Judge the environment in which the pigs live based on the environmental score.

10. The method for monitoring and managing a pig herd according to claim 9, characterized in that: The judgment of the environment of the pigs in B4 includes: when , the level of the environment in which the pigs live is poor; when , the level of the pigs’ environment is good; when , the level of the environment in which the pigs live is superior.

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