An automated measurement system and method for the activity level of meat rabbits

Abstract

The present invention discloses an automated measurement system and method for the activity amount of meat rabbits. The present invention relates to the technical field of meat rabbit breeding, and includes an activity amount measurement platform, which is communicatively connected with a meat rabbit identification module, a data acquisition module, a feature analysis module, an activity type identification module, an activity amount analysis module, and a monitoring and alarm module. This automated measurement system and method for the activity amount of meat rabbits improve the efficiency and accuracy of breeding management by monitoring and analyzing the activity data of meat rabbits, and automatically collect the weight and activity data of meat rabbits through sensors, reducing the workload and potential errors of manual measurement, making the analysis of meat rabbit behavior more accurate, so as to more precisely evaluate the health status and growth of meat rabbits. In addition, it can automatically identify abnormal behaviors, notify the breeding personnel to take measures in a timely manner, reduce the risks of diseases and deaths, and improve the breeding efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of meat rabbit breeding, and specifically to an automated measurement system and method for the activity amount of meat rabbits. Background Technique

[0002] The application of intelligent technologies in the livestock industry is becoming more and more extensive. Especially in livestock and poultry breeding, intelligent technologies have achieved remarkable results. As an important part of the livestock industry, meat rabbit breeding is also at the node of intelligent technology transformation and upgrading. By applying advanced technologies such as sensors, the Internet of Things, big data, and artificial intelligence, it is possible to achieve full-process monitoring and management of the livestock and poultry breeding process, which can not only improve the automation level of breeding, but also provide accurate data support for breeders to assist in making scientific decisions.

[0003] In the prior art, due to the different activity ranges and activity types of meat rabbits, it is difficult to accurately identify various activity types of meat rabbits. Therefore, how to distinguish different activity types of meat rabbits to improve the accuracy of measuring the activity amount of meat rabbits. To solve the above problems, an automated measurement system and method for the activity amount of meat rabbits are urgently needed. Summary of the Invention

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect, an automated measurement system for the activity amount of meat rabbits includes an activity amount measurement platform, which is communicatively connected to a meat rabbit identification module, a data acquisition module, a feature analysis module, an activity type identification module, an activity amount analysis module, and a monitoring and alarm module. Among them, the modules are electrically connected to each other;

[0005] The meat rabbit identification module provides a unique identifier for the activity data of each meat rabbit through RFID identification technology to automatically identify the identity of the meat rabbit;

[0006] The data acquisition module regularly collects the weight data of meat rabbits and real-time collects the activity data of meat rabbits through sensors, including acceleration, displacement, and speed. It preprocesses the collected raw data, including data cleaning (removing noise, outliers, etc.), data formatting, etc., to improve the data quality and provide an accurate data basis for subsequent analysis and identification;

[0007] The feature analysis module extracts features reflecting the activity types of meat rabbits from the preprocessed data and integrates them to obtain an activity feature sequence;

[0008] The activity type identification module constructs an activity type identification model based on historical data and combines the feature data of the activity feature sequence to distinguish different activity types of meat rabbits;

[0009] The activity amount analysis module combines the output results of the activity type recognition model, comprehensively analyzes the activity data of the meat rabbits, and clarifies the degree of association between the change rate of the activity amount of the meat rabbits and their body weights;

[0010] The monitoring and alarm module monitors the activity of the meat rabbits in real time, identifies abnormal behaviors of the meat rabbits, and triggers an alarm mechanism to remind the breeders to pay attention to the health status of the meat rabbits.

[0011] Preferably, in the meat rabbit identification module, the process of automatically identifying the identity of the meat rabbits includes:

[0012] According to the characteristics and needs of the meat rabbits, select RFID tags of ear tag type, wear them on the meat rabbits to ensure a one-to-one correspondence between the tags and the meat rabbits, and write the unique identification information of the meat rabbits, including number, breed, and date of birth, into the RFID tags as the only basis for the identity of the meat rabbits;

[0013] Configure a channel-type RFID reader according to the breeding environment, and configure the working frequency and reading range parameters of the reader to ensure that it can accurately read the information in the RFID tags;

[0014] When the meat rabbit enters the reading range of the reader, the reader communicates wirelessly with the RFID tag through the antenna, and reads the unique identification information in the RFID tag through inductive coupling;

[0015] The RFID reader transmits the read tag information to the activity measurement platform through an electrical signal. The activity measurement platform compares the received tag information with the information in the database, and automatically identifies the identity of the meat rabbit through the comparison result, and provides a unique identification for its activity data.

[0016] Preferably, in the data acquisition module, the process of acquiring the activity data and body weight data of the meat rabbits includes:

[0017] Set up multiple weighing platforms in the breeding environment. Each platform is equipped with a high-precision weighing sensor, and motion sensors such as an accelerometer and a displacement sensor are integrated on the wearable device of the meat rabbit. The body weight data and activity data of the meat rabbit are acquired through the weighing sensor and the motion sensor;

[0018] The meat rabbit regularly passes through the weighing platform equipped with the weighing sensor to automatically obtain and record the body weight data of the meat rabbit, and uses the motion sensor to monitor the activity of the meat rabbit in real time to obtain the acceleration, displacement, and speed parameter data of the meat rabbit's activity;

[0019] Preprocess the weight data and activity data output by the sensor, including the operation steps of data cleaning and data formatting. Among them, noise data generated due to sensor errors, environmental interference, etc. are removed, outliers in the data are checked and processed, such as extreme weight changes, unreasonable motion data, etc., duplicate records in the dataset are identified and deleted, and only unique and accurate data is retained. The raw data collected is converted into a format suitable for subsequent analysis through data formatting, including data type conversion and date-time formatting;

[0020] Comprehensively process the preprocessed weight data and activity data, integrate them into a unified dataset, and transmit them to the subsequent feature analysis module.

[0021] Preferably, in the feature analysis module, the process of obtaining the activity feature sequence includes:

[0022] Receive the preprocessed weight data and activity data, and perform feature analysis on the activity data of the meat rabbit, and extract the acceleration, motion trajectory shape, and speed change features from it;

[0023] For the acceleration feature, extract the mean, variance, maximum, and minimum features of the acceleration. Among them, the mean feature is used to calculate the average acceleration during the activity of the meat rabbit to reflect its overall motion intensity, the variance feature is used to calculate the variance of the acceleration to evaluate the fluctuation of the motion intensity of the meat rabbit, and the maximum and minimum values are used to understand the extreme acceleration situation of the meat rabbit during the motion process;

[0024] For the motion trajectory shape feature, use the sensor data to reconstruct the motion trajectory of the meat rabbit, and extract the shape features of the trajectory, including the length and bending degree of the trajectory, and identify the key feature points of the starting point, ending point, and turning point in the motion trajectory to further analyze the motion behavior of the meat rabbit;

[0025] For the speed change feature, calculate the motion speed of the meat rabbit according to the sensor data, and extract the change feature of the speed, and analyze the change pattern of the speed over time to identify different motion states of the meat rabbit, including stationary, uniform motion, and accelerated motion;

[0026] Integrate the extracted acceleration feature, motion trajectory shape feature, and speed change feature to form a comprehensive feature set, and concatenate the feature values at each time point according to the time series to form an activity feature sequence, reflecting the motion state and behavior pattern of the meat rabbit at different time points.

[0027] Preferably, in the activity type recognition module, the process of distinguishing different activity types of the meat rabbit includes:

[0028] Collect and organize historical data, including the activity feature sequence of the meat rabbit and known activity type labels, including feeding, drinking, running, jumping, and resting;

[0029] Extract the features of the associated activity types from the activity feature sequence, and split the activity type feature data extracted from the historical data into a training set and a test set;

[0030] Use the training set data to train an activity type recognition model in combination with a support vector machine. By continuously adjusting the parameters and structure of the model, improve the recognition accuracy and generalization ability of the model. Use the test data set to evaluate the trained model to test its recognition accuracy and stability. According to the evaluation results, adjust and optimize the model;

[0031] Deploy the trained activity type recognition model to the measurement system of meat rabbits, process the real-time collected activity feature sequence, use the deployed activity type recognition model to identify the activity type, and perform real-time recognition of different activity types of meat rabbits, and output the activity type recognition results.

[0032] Preferably, in the activity amount analysis module, the process of comprehensively analyzing the activity data of meat rabbits includes:

[0033] Based on the recognition results of the feeding, drinking, running, jumping, and resting of the activity types of meat rabbits by the activity type recognition model, record the duration and frequency of each activity type, establish an activity type database, and store the activity type, duration, and frequency information;

[0034] According to the historical data (scientific literature and experimental data), determine the metabolic equivalent of meat rabbits under each activity type. The metabolic equivalent reflects the energy consumption rate of meat rabbits under specific activities, and use the duration and frequency information in the activity type database, as well as the metabolic equivalent value of each activity type, to calculate the total activity amount of meat rabbits;

[0035] Use the trend analysis method to calculate the activity amount trend index in combination with the total activity amount of meat rabbits to analyze the change trend of the activity amount of meat rabbits over time, and calculate the change rate of the activity amount according to the activity amount trend index to clarify the trend of the activity amount of meat rabbits increasing, decreasing, or remaining stable;

[0036] According to the regularly measured body weight data of meat rabbits, conduct a correlation analysis with the activity amount change rate, calculate the correlation coefficient, quantify the association degree between the activity amount change rate and the body weight, and judge the positive or negative correlation relationship between the activity amount change rate and the body weight according to the magnitude and sign of the correlation coefficient;

[0037] Output the analysis results of the activity amount trend index, the activity amount change rate, and the association degree with the body weight in the form of a report. Among them, the report includes the duration and frequency statistics of each activity type, the calculation results of the total activity amount, the analysis results of the activity amount trend index and change rate, the statistical data of the correlation between the activity amount change rate and the body weight, and suggestions for the activity habits and body weight management of meat rabbits.

[0038] Preferably, the calculation formula of the activity trend index is as follows:

[0039]

[0040] where DI is the activity trend index, reflecting the trend intensity of the activity volume change of the meat rabbit, and A i is the total activity volume of the i-th measurement, and A i+1 -A i is the change in the activity volume between two consecutive measurements. n is the number of measurements. The value range of DI is from 0 to 100. The closer the value of DI is to 0, the smaller the change in the activity volume and the more stable the trend.

[0041] The calculation formula of the activity volume change rate is as follows:

[0042]

[0043] where AR is the activity volume change rate, expressed as a percentage, reflecting the change trend of the activity volume of the meat rabbit. DI cur is the activity trend index of the current measurement period, and DI pre is the activity trend index of the previous measurement period.

[0044] Preferably, the calculation formula of the correlation coefficient is as follows:

[0045]

[0046] where r is the correlation coefficient, quantifying the linear correlation degree between two variables. x i is the activity volume change rate of the i-th measurement, and y i is the body weight data of the i-th measurement. is the sample mean of the activity volume change rate. is the sample mean of the body weight data. n is the number of measurements. The value range of r is from -1 to 1. The value of r close to 1 indicates a strong positive correlation, that is, there is a positive linear relationship between the activity volume change rate and the body weight, and the body weight increases when the activity volume increases. The value of r close to -1 indicates a strong negative correlation, that is, there is a negative linear relationship between the activity volume change rate and the body weight, and the body weight decreases when the activity volume increases. The value of r close to 0 indicates no linear correlation.

[0047] Preferably, in the monitoring and alarm module, the identification and alarm process of the abnormal behavior of the meat rabbit includes:

[0048] According to the normal behavior patterns of meat rabbits, standards for abnormal behaviors are set, including excessive movement and prolonged stillness. Among them, excessive movement is divided into continuous rapid movement and continuous rapid jumping, and prolonged stillness is divided into long-term immobility and lack of activity. If a meat rabbit moves continuously and rapidly within 5 minutes, with a speed exceeding the normal activity range and showing no signs of stopping or decelerating, it is considered continuous rapid movement. If a meat rabbit frequently makes jumping movements, and the jumping height or frequency is abnormal, with a duration exceeding the normal activity level, it is considered continuous rapid jumping. If a meat rabbit hardly moves for a long time, staying in the same position or posture, it is considered long-term immobility. If the activity level of a meat rabbit within half a day is significantly lower than the normal activity level, manifested as a lack of exploration, foraging, or social behaviors, it is considered lack of activity;

[0049] Compare the identified activity data of the meat rabbit with the set standards for abnormal behaviors to determine whether the meat rabbit has abnormal behaviors. When it is detected that the meat rabbit has abnormal behaviors, the monitoring and alarm module immediately triggers the alarm mechanism;

[0050] Install an audible and visual alarm device in the rabbit house. When the alarm is triggered, the monitoring and alarm module emits sound and light signals through the audible and visual alarm device to alert the breeding personnel. At the same time, send the alarm information to the mobile phone of the breeding personnel in the form of a text message to remind them to pay attention to the health status of the meat rabbits, and record the time, location, and information on the type of abnormal behavior of each alarm for subsequent analysis and summary.

[0051] In a second aspect, an automated measurement method for the activity level of meat rabbits is provided, which is implemented based on an automated measurement system for the activity level of meat rabbits, and includes the following steps:

[0052] Step 1: Install and configure a weighing sensor and a motion sensor to collect the weight data and activity data of the meat rabbit;

[0053] Step 2: Preprocess the weight data and activity data of the meat rabbit, extract the activity type characteristics of the meat rabbit, and integrate them to obtain an activity characteristic sequence;

[0054] Step 3: Build an activity type recognition model based on historical data to distinguish different activity types of the meat rabbit;

[0055] Step 4: Combine the activity type recognition results, calculate the total activity level of the meat rabbit, determine the metabolic equivalent of each activity type, and calculate the activity level trend index to analyze the change trend of the activity level of the meat rabbit over time;

[0056] Step 5: Set the standards for abnormal behaviors, compare the identified activity data of the meat rabbit with the standards for abnormal behaviors, determine whether there are abnormal behaviors, and trigger the alarm mechanism when abnormal behaviors are detected.

[0057] The present invention provides an automated measurement system and method for the activity level of meat rabbits. It has the following beneficial effects:

[0058] 1. The automated measurement system and method for the activity level of meat rabbits improve the efficiency and accuracy of breeding management by monitoring and analyzing the activity data of meat rabbits. The weight and activity data of meat rabbits are automatically collected through sensors, reducing the workload and potential errors of manual measurement, making the analysis of meat rabbit behavior more accurate, thus enabling a more precise assessment of the health status and growth of meat rabbits. In addition, abnormal behaviors can be automatically identified and the breeding personnel can be notified in a timely manner to take measures, reducing the risks of diseases and deaths and improving the breeding efficiency.

[0059] 2. The automated measurement system and method for the activity level of meat rabbits can identify potential health problems by analyzing the correlation between the activity level change rate of meat rabbits and their weight data. Through a timely alarm mechanism, the breeding personnel can respond to abnormal behaviors in a timely manner and conduct necessary inspections and treatments, thereby reducing the spread of diseases and increasing the cure rate. Utilizing proactive health monitoring and disease prevention measures helps improve the overall health level of meat rabbits and reduce breeding losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a module diagram of an automated measurement system for the activity level of meat rabbits according to the present invention;

[0061] Figure 2 It is a flowchart for comprehensively analyzing the activity data of meat rabbits according to the present invention;

[0062] Figure 3 It is a method flowchart according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments. The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limiting the present invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are selected and described to better illustrate the principles and practical applications of the present invention, and enable those of ordinary skill in the art to understand the present invention and design various embodiments with various modifications suitable for specific purposes.

[0064] The first embodiment, as Figure 1 shown, the present invention provides a technical solution: an automated measurement system for the activity level of meat rabbits, including an activity level measurement platform, which is communicatively connected to a meat rabbit identification module, a data acquisition module, a feature analysis module, an activity type identification module, an activity level analysis module, and a monitoring and alarm module, wherein, the modules are electrically connected to each other;

[0065] The meat rabbit identification module uses RFID identification technology to provide a unique identifier for the activity data of each meat rabbit, automatically identify the identity of the meat rabbit, select an ear-tag type RFID tag according to the characteristics and needs of the meat rabbit, wear it on the meat rabbit to ensure a one-to-one correspondence between the tag and the meat rabbit, and write the unique identifier information of the meat rabbit, including the number, breed, and date of birth, into the RFID tag as the only basis for the identity of the meat rabbit. Configure a channel-type RFID reader according to the breeding environment, and configure the working frequency and reading range parameters of the reader to ensure that it can accurately read the information in the RFID tag. When the meat rabbit enters the reading range of the reader, the reader communicates wirelessly with the RFID tag through the antenna, and reads the unique identifier information in the RFID tag through inductive coupling. The RFID reader transmits the read tag information to the activity measurement platform through an electrical signal. The activity measurement platform compares the received tag information with the information in the database, automatically identifies the identity of the meat rabbit through the comparison result, and provides a unique identifier for its activity data;

[0066] The data acquisition module regularly collects the weight data of the meat rabbit and real-time collects the activity data of the meat rabbit through sensors, including acceleration, displacement, and speed. Preprocess the collected raw data, including data cleaning (removing noise, outliers, etc.), data formatting, etc., to improve the data quality and provide an accurate data basis for subsequent analysis and identification. Set up multiple weighing platforms in the breeding environment, each platform is equipped with a high-precision weighing sensor, and integrate motion sensors such as accelerometers and displacement sensors on the wearable device of the meat rabbit. Collect the weight data and activity data of the meat rabbit through the weighing sensor and motion sensors. The meat rabbit regularly passes through the weighing platform equipped with the weighing sensor to automatically obtain and record the weight data of the meat rabbit. Use the motion sensor to monitor the activity of the meat rabbit in real time and obtain the acceleration, displacement, and speed parameter data of the meat rabbit's activity. Preprocess the weight data and activity data output by the sensors, including the operation steps of data cleaning and data formatting. Among them, identify and remove the noise data generated due to sensor errors, environmental interference, etc., check and process the outliers in the data, such as extreme weight changes, unreasonable motion data, etc., identify and delete the duplicate records in the dataset, and retain the unique and accurate data. Convert the collected raw data into a format suitable for subsequent analysis through data formatting, including data type conversion, date and time formatting. Conduct comprehensive processing on the preprocessed weight data and activity data, integrate them into a unified dataset, and transmit them to the subsequent feature analysis module;

[0067] The feature analysis module extracts features reflecting the activity types of meat rabbits from the preprocessed data, integrates them to obtain an activity feature sequence, receives the preprocessed weight data and activity data, and conducts feature analysis on the activity data of meat rabbits. It extracts acceleration, motion trajectory shape, and speed change features from the activity data. For the acceleration feature, it extracts the mean, variance, maximum, and minimum features of acceleration. Among them, the mean feature is used to calculate the average acceleration during the activity of the meat rabbit to reflect its overall motion intensity. The variance feature is used to calculate the variance of acceleration to evaluate the fluctuation of the motion intensity of the meat rabbit. The maximum and minimum values are used to understand the extreme acceleration situation of the meat rabbit during the motion. For the motion trajectory shape feature, it reconstructs the motion trajectory of the meat rabbit using sensor data and extracts the shape features of the trajectory, including the length and curvature of the trajectory, and identifies the key feature points of the starting point, ending point, and turning point in the motion trajectory to further analyze the motion behavior of the meat rabbit. For the speed change feature, it calculates the motion speed of the meat rabbit based on sensor data, extracts the change features of the speed, and analyzes the change pattern of the speed over time to identify different motion states of the meat rabbit, including stationary, uniform motion, and accelerated motion. It integrates the extracted acceleration features, motion trajectory shape features, and speed change features to form a comprehensive feature set, and concatenates the feature values at each time point according to the time series to form an activity feature sequence, reflecting the motion states and behavior patterns of the meat rabbit at different time points;

[0068] The activity type recognition module constructs an activity type recognition model based on historical data and combines the feature data of the activity feature sequence to distinguish different activity types of meat rabbits. It collects and organizes historical data, including the activity feature sequence of meat rabbits and known activity type labels, including feeding, drinking, running, jumping, and resting. It extracts the features associated with the activity type from the activity feature sequence, and divides the activity type feature data extracted from the historical data into a training set and a test set. It uses the training set data to train the activity type recognition model in combination with the support vector machine. By continuously adjusting the parameters and structure of the model, it improves the recognition accuracy and generalization ability of the model. It uses the test data set to evaluate the trained model to test its recognition accuracy and stability. According to the evaluation results, it adjusts and optimizes the model. It deploys the trained activity type recognition model to the measurement system of the meat rabbit, processes the real-time collected activity feature sequence, and uses the deployed activity type recognition model to identify the activity type, and outputs the activity type recognition result for the real-time recognition of different activity types of the meat rabbit;

[0069] The activity amount analysis module combines the output results of the activity type recognition model and comprehensively analyzes the activity data of the meat rabbit to clarify the degree of association between the change rate of the activity amount of the meat rabbit and its weight;

[0070] The monitoring and alarm module monitors the activities of meat rabbits in real time, identifies abnormal behaviors of meat rabbits, and triggers an alarm mechanism to remind the breeding personnel to pay attention to the health status of meat rabbits.

[0071] For the second embodiment, based on the first embodiment, please refer to Figure 2 As shown, in the activity amount analysis module, the process of comprehensively analyzing the activity data of meat rabbits includes:

[0072] Based on the activity type recognition model, the recognition results of the feeding, drinking, running, jumping, and resting activities of the meat rabbits are obtained, and the duration and frequency of each activity type are recorded. An activity type database is established to store the activity type, duration, and frequency information. According to historical data (scientific literature and experimental data), the metabolic equivalent of the meat rabbits under each activity type is determined. The metabolic equivalent reflects the energy consumption rate of the meat rabbits under specific activities. Using the duration and frequency information in the activity type database and the metabolic equivalent value of each activity type, the total activity amount of the meat rabbits is calculated. Using the trend analysis method, combined with the total activity amount of the meat rabbits, the activity amount trend index is calculated to analyze the change trend of the meat rabbit activity amount over time, and the change rate of the activity amount is calculated according to the activity amount trend index to clarify the trend of the meat rabbit activity amount increasing, decreasing, or remaining stable. According to the regularly measured meat rabbit weight data, a correlation analysis is carried out with the activity amount change rate, and the correlation coefficient is calculated to quantify the degree of association between the activity amount change rate and the weight. According to the magnitude and sign of the correlation coefficient, the positive or negative correlation relationship between the activity amount change rate and the weight is judged. The analysis results of the activity amount trend index, the activity amount change rate, and the association degree with the weight are output in the form of a report. Among them, the report includes the duration and frequency statistics of each activity type, the calculation results of the total activity amount, the analysis results of the activity amount trend index and the change rate, the statistical data of the correlation between the activity amount change rate and the weight, and suggestions for the activity habits and weight management of the meat rabbits;

[0073] Furthermore, the calculation formula of the activity amount trend index is:

[0074]

[0075] where DI is the activity amount trend index, reflecting the trend intensity of the change in the meat rabbit activity amount, A i is the total activity amount measured at the i-th time, A i+1 -A iIt is the change in the amount of activity measured twice consecutively, n is the number of measurements, the value range of DI is from 0 to 100. The closer the value of DI is to 0, the smaller the change in activity and the more stable the trend. The closer the value is to 100, the greater the change in activity and the more obvious the trend. If the DI value continues to rise, it indicates that the activity of the meat rabbit is increasing; if the DI value continues to decline, it indicates that the activity of the meat rabbit is decreasing; if the DI value remains stable, it indicates that the change in the activity of the meat rabbit is small and tends to be stable. By calculating the ratio of the sum of the absolute values of the changes in activity to the total activity for all measurement times, an index reflecting the trend of activity change is obtained to assist in judging the overall trend of the activity of the meat rabbit;

[0076] The calculation formula for the activity change rate is:

[0077]

[0078] where AR is the activity change rate, expressed as a percentage, reflecting the trend of the activity of the meat rabbit, and DI cur is the activity tendency index for the current measurement period, and DI pre is the activity tendency index for the previous measurement period. If AR is positive, it indicates that the activity in the current period has increased compared to the previous period. If AR is negative, it indicates that the activity in the current period has decreased compared to the previous period. If AR is close to 0, it indicates that the change in activity is small and tends to be stable. By calculating the activity change rate, the change in the activity of the meat rabbit can be understood more intuitively, so as to evaluate and manage the activity habits and health status of the meat rabbit. If the AR values for multiple consecutive periods are all positive and the values gradually increase, it indicates that the activity of the meat rabbit is gradually increasing, and it may be necessary to adjust the breeding environment or management measures to meet the activity needs of the meat rabbit. If AR continues to be negative, attention needs to be paid to the health problems of the meat rabbit or the comfort of the breeding environment;

[0079] Furthermore, the calculation formula for the correlation coefficient is:

[0080]

[0081] where r is the correlation coefficient, quantifying the linear correlation degree between two variables, x i is the activity change rate for the i-th measurement, and y i is the body weight data for the i-th measurement, is the sample mean of the activity change rate, $\bar{x}$ is the sample mean of the weight data, $n$ is the number of measurements, and the value range of $r$ is from -1 to 1. When the value of $r$ is close to 1, it indicates a strong positive correlation, that is, there is a positive linear relationship between the activity rate of change and the weight. When the activity increases, the weight also increases. When the value of $r$ is close to -1, it indicates a strong negative correlation, that is, there is a negative linear relationship between the activity rate of change and the weight. When the activity increases, the weight decreases. When the value of $r$ is close to 0, it indicates no linear correlation, that is, there is no obvious linear relationship between the activity rate of change and the weight;

[0082] In the monitoring and alarm module, the identification and alarm process of the abnormal behavior of the meat rabbit includes:

[0083] According to the normal behavior pattern of the meat rabbit, set the standards for abnormal behavior, including excessive movement and long-term stillness. Among them, excessive movement is divided into continuous rapid movement and continuous rapid jumping, and long-term stillness is divided into long-term immobility and lack of activity. If the meat rabbit continuously moves rapidly within 5 minutes, the speed exceeds the normal activity range, and there is no sign of stopping or decelerating, it is continuous rapid movement. If the meat rabbit frequently makes jumping movements, and the jumping height or frequency is abnormal, and the duration exceeds the normal activity level, it is continuous rapid jumping. If the meat rabbit hardly moves for a long time and stays in the same position or posture, it is long-term immobility. If the activity level of the meat rabbit is significantly lower than the normal activity level within half a day, manifested as lack of exploration, foraging or social behavior, it is lack of activity. Compare the identified activity data of the meat rabbit with the set standards for abnormal behavior to determine whether the meat rabbit has abnormal behavior. When it is detected that the meat rabbit has abnormal behavior, the monitoring and alarm module immediately triggers the alarm mechanism, installs a sound and light alarm device in the rabbit house. When the alarm is triggered, the monitoring and alarm module emits sound and light signals through the sound and light alarm device to remind the breeding personnel to pay attention, and simultaneously sends the alarm information to the mobile phone of the breeding personnel in the form of a text message to remind them to pay attention to the health status of the meat rabbit, and record the time, location and type information of abnormal behavior of each alarm for subsequent analysis and summary.

[0084] In the third embodiment, on the basis of the first and second embodiments, please refer to Figure 3 As shown, the present invention also proposes an automatic measurement method for the activity amount of meat rabbits, which is realized based on the automatic measurement system for the activity amount of meat rabbits, and includes the following steps:

[0085] Step 1: Install and configure a weighing sensor and a motion sensor to collect the weight data and activity data of the meat rabbit;

[0086] Step 2: Preprocess the weight data and activity data of the meat rabbit, extract the activity type characteristics of the meat rabbit, and integrate them to obtain an activity characteristic sequence;

[0087] Step 3: Build an activity type recognition model based on historical data to distinguish different activity types of the meat rabbit;

[0088] Step 4: Combine the activity type recognition results, calculate the total activity amount of the meat rabbits, determine the metabolic equivalent of each activity type, calculate the activity amount trend index, and analyze the change trend of the activity amount of the meat rabbits over time;

[0089] Step 5: Set the criteria for abnormal behavior, compare the recognized activity data of the meat rabbits with the criteria for abnormal behavior, determine whether there is abnormal behavior, and trigger the alarm mechanism when abnormal behavior is detected.

[0090] Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art and related fields without creative efforts shall fall within the scope of protection of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention shall be implemented by conventional means in the art without special instructions and limitations.

Claims

1. An automated measurement system for the activity level of meat rabbits, including an activity level measurement platform, characterized in that, The activity measurement platform is communicatively connected to a meat rabbit identification module, a data acquisition module, a feature analysis module, an activity type identification module, an activity amount analysis module, and a monitoring and alarm module. Among them, the modules are electrically connected to each other; The meat rabbit identification module provides a unique identifier for the activity data of each meat rabbit through RFID identification technology, and automatically identifies the identity of the meat rabbit; The data acquisition module regularly collects the weight data of the meat rabbits, and real-time collects the activity data of the meat rabbits through sensors, and preprocesses the collected original data; The feature analysis module extracts the features reflecting the activity types of the meat rabbits from the preprocessed data, and integrates them to obtain an activity feature sequence; The activity type identification module constructs an activity type identification model based on historical data, and combines the feature data of the activity feature sequence to distinguish different activity types of the meat rabbits; The activity amount analysis module combines the output results of the activity type identification model, comprehensively analyzes the activity data of the meat rabbits, and clarifies the degree of association between the activity amount change rate of the meat rabbits and their weights. The process of comprehensively analyzing the activity data of the meat rabbits includes: Based on the identification results of feeding, drinking, running, jumping, and resting of the activity types of the meat rabbits by the activity type identification model, record the duration and frequency of each activity type, establish an activity type database, and store the activity type, duration, and frequency information; Determine the metabolic equivalent of the meat rabbits under each activity type according to historical data, and use the duration and frequency information in the activity type database, as well as the metabolic equivalent values of each activity type, to calculate the total activity amount of the meat rabbits; Use the trend analysis method, combine the total activity amount of the meat rabbits to calculate the activity amount trend index, analyze the change trend of the activity amount of the meat rabbits over time, and calculate the change rate of the activity amount according to the activity amount trend index to clarify the trend of the activity amount of the meat rabbits increasing, decreasing, or remaining stable; According to the regularly measured weight data of the meat rabbits, conduct a correlation analysis with the activity amount change rate, calculate the correlation coefficient, quantify the degree of association between the activity amount change rate and the weight, and judge the positive or negative correlation relationship between the activity amount change rate and the weight according to the magnitude and sign of the correlation coefficient; Output the analysis results of the activity amount trend index, the degree of association between the activity amount change rate and the weight in the form of a report; The calculation formula for the activity amount trend index is: Among them, DI is the activity trend index, and A i is the total activity of the i-th measurement, and A i+1 -A i is the change in activity between two consecutive measurements. n is the number of measurements. The closer the value of DI is to 0, the smaller the change in activity and the more stable the trend; The calculation formula for the activity amount change rate is: Among them, AR is the activity change rate, expressed as a percentage, reflecting the change trend of the activity of meat rabbits, and DI cur is the activity trend index of the current measurement period, and DI pre is the activity trend index of the previous measurement period; The calculation formula for the correlation coefficient is: Among them, r is the correlation coefficient, x i is the activity change rate of the i-th measurement, y i is the body weight data of the i-th measurement, is the sample mean of the activity change rate, is the sample mean of the body weight data, n is the number of measurements, the value range of r is from -1 to 1, a value of r close to 1 indicates a strong positive correlation, a value of r close to -1 indicates a strong negative correlation, and a value of r close to 0 indicates no linear correlation; The monitoring and alarm module real-time monitors the activity of the meat rabbits, identifies abnormal behaviors of the meat rabbits, and triggers an alarm mechanism.

2. The automated measurement system for the activity level of meat rabbits according to claim 1, characterized in that: In the meat rabbit identification module, the process of automatically identifying the identity of the meat rabbits includes: According to the characteristics and needs of the meat rabbits, select the RFID tags of the ear tag type, wear them on the meat rabbits, and write the unique identification information of the meat rabbits, including the number, breed, and date of birth, into the RFID tags; Configure a channel-type RFID reader according to the breeding environment, and configure the working frequency and reading range parameters of the reader; When the meat rabbits enter the reading range of the reader, the reader communicates wirelessly with the RFID tags through the antenna, and reads the unique identification information in the RFID tags through inductive coupling; The RFID reader transmits the read tag information to the activity measurement platform via electrical signals. The activity measurement platform compares the received tag information with the information in the database, and automatically identifies the identity of the meat rabbit based on the comparison result, and provides a unique identifier for its activity data.

3. The automated measurement system for the activity level of meat rabbits according to claim 2, wherein: In the data acquisition module, the acquisition process of the meat rabbit's activity data and weight data includes: Set up multiple weighing platforms in the breeding environment. Each platform is equipped with a high-precision weighing sensor, and motion sensors such as an accelerometer and a displacement sensor are integrated on the wearable device of the meat rabbit. The weight data and activity data of the meat rabbit are collected through the weighing sensor and the motion sensor; The meat rabbit regularly passes through the weighing platform equipped with the weighing sensor to automatically obtain and record the weight data of the meat rabbit, and uses the motion sensor to monitor the activity of the meat rabbit in real time to obtain the acceleration, displacement and speed parameter data of the meat rabbit's activity; Perform preprocessing on the weight data and activity data output by the sensor, including the operation steps of data cleaning and data formatting; Perform comprehensive processing on the preprocessed weight data and activity data, integrate them into a unified data set, and transmit them to the subsequent feature analysis module.

4. The automated measurement system for the activity level of meat rabbits according to claim 3, wherein: In the feature analysis module, the acquisition process of the activity feature sequence includes: Receive the preprocessed weight data and activity data, and perform feature analysis on the activity data of the meat rabbit, and extract the acceleration, motion trajectory shape and speed change features from it; For the acceleration feature, extract the mean, variance, maximum and minimum features of the acceleration; For the motion trajectory shape feature, reconstruct the motion trajectory of the meat rabbit using the sensor data, and extract the shape features of the trajectory, including the length and curvature of the trajectory, and identify the key feature points such as the starting point, ending point and turning point in the motion trajectory to further analyze the motion behavior of the meat rabbit; For the speed change feature, calculate the motion speed of the meat rabbit according to the sensor data, and extract the change feature of the speed, and analyze the change pattern of the speed over time to identify different motion states of the meat rabbit, including stationary, uniform motion, and accelerated motion; Integrate the extracted acceleration feature, motion trajectory shape feature and speed change feature to form a comprehensive feature set, and concatenate the feature values of each time point according to the time series to form an activity feature sequence, which reflects the motion state and behavior pattern of the meat rabbit at different time points.

5. The automated determination system for the activity level of meat rabbits according to claim 4, wherein: In the activity type recognition module, the process of distinguishing different activity types of the meat rabbit includes: Collect and organize historical data, including the activity feature sequence of the meat rabbit and known activity type labels, including feeding, drinking, running, jumping, and resting; Extract the features associated with the activity type from the activity feature sequence, and divide the activity type feature data extracted from the historical data into a training set and a test set; Use the training set data to train the activity type recognition model in combination with the support vector machine, use the test data set to evaluate the trained model, and adjust and optimize the model according to the evaluation result. Deploy the trained activity type recognition model to the measurement system of meat rabbits, process the activity feature sequences collected in real time, use the deployed activity type recognition model to identify the activity types, and perform real-time recognition of different activity types of meat rabbits to output the activity type recognition results.

6. The automated measurement system for the activity level of meat rabbits according to claim 1, wherein: In the monitoring and alarm module, the recognition and alarm process of abnormal behaviors of meat rabbits includes: Set the criteria for abnormal behaviors according to the normal behavior patterns of meat rabbits, including excessive movement and long-term stillness; Compare the recognized activity data of meat rabbits with the set criteria for abnormal behaviors to determine whether there are abnormal behaviors in meat rabbits. When it is detected that there are abnormal behaviors in meat rabbits, the monitoring and alarm module immediately triggers the alarm mechanism; Install acoustic and optical alarm devices in the rabbit house. When the alarm is triggered, the monitoring and alarm module emits sound and light signals through the acoustic and optical alarm devices, synchronously sends the alarm information to the mobile phones of the breeding personnel in the form of text messages, and records the time, location, and information on the type of abnormal behavior of each alarm.

7. An automated method for measuring the activity level of meat rabbits, which is implemented based on the automated measurement system for the activity level of meat rabbits according to any one of the above claims 1-6, and is characterized in that, It includes the following steps: Step 1: Install and configure a weighing sensor and a motion sensor to collect the body weight data and activity data of meat rabbits; Step 2: Preprocess the body weight data and activity data of meat rabbits, extract the activity type features of meat rabbits, and integrate them to obtain the activity feature sequences; Step 3: Build an activity type recognition model based on historical data to distinguish different activity types of meat rabbits; Step 4: Combine the activity type recognition results, calculate the total activity amount of meat rabbits, determine the metabolic equivalent of each activity type, and calculate the activity amount trend index to analyze the change trend of the activity amount of meat rabbits over time; Step 5: Set the criteria for abnormal behaviors, compare the recognized activity data of meat rabbits with the criteria for abnormal behaviors, determine whether there are abnormal behaviors, and trigger the alarm mechanism when abnormal behaviors are detected.

Images