A method and system for electronic recording of veterinary mating

Abstract

This invention relates to the field of veterinary breeding technology, specifically to a method and system for electronic recording of veterinary breeding, comprising the following steps: based on breeding site data, collecting data streams from temperature and humidity sensors deployed at differentiated locations, detecting data fluctuation ranges, filtering stable intervals within continuous time periods, analyzing fluctuations in environmental factors, and obtaining the stable trend of the breeding site environment. In this invention, by fusing multi-dimensional environmental data monitoring with sensor data from differentiated locations, accurate assessment of the site environment and screening of stable intervals are achieved, improving environmental adaptability analysis capabilities. By combining breeding animal physiological data with environmental factors, a precise physiological state identification system is constructed, enabling multi-parameter breeding tendency judgment and improving the accuracy of breeding timing. Through behavioral analysis of video stream data combined with physiological data, the accurate assessment and tracking of breeding behavior are optimized, forming a traceable breeding execution log, thus improving data management accuracy and optimization capabilities.

Description

Technical Field

[0001] This invention relates to the field of veterinary breeding technology, and in particular to a method and system for electronic recording of veterinary breeding. Background Technology

[0002] The field of veterinary breeding technology encompasses animal reproduction management, artificial insemination, reproductive health monitoring, and breeding optimization. This field primarily covers the scientific management of the reproductive process of livestock and poultry to improve reproductive success rates and genetic improvement effects. Veterinary breeding technology involves reproductive physiology, semen collection and processing, insemination techniques, reproductive performance evaluation, and related data recording. With the development of information technology, electronic recording and intelligent management are gradually becoming important components of the veterinary breeding field to improve the accuracy and traceability of data management.

[0003] Among them, the veterinary breeding electronic record method refers to the method of recording and managing animal breeding-related information using electronic devices and information management technology. The patent subject covers technical matters such as breeding animal identification, reproductive data collection, breeding process recording, information storage and query. Specific methods include using electronic tags or QR codes to identify individual breeding animals, collecting physiological data and breeding time information of breeding animals through wireless sensors or input terminals, using databases to store breeding animal breeding history, reproductive health status and offspring information, and realizing the management and traceability of breeding data through data query and statistical functions.

[0004] Current technologies for breeding management primarily rely on electronic recording methods. However, they lack effective assessment of site stability in monitoring environmental factors, leading to breeding decisions being affected by environmental fluctuations and reducing accuracy. While physiological data collection methods cover indicators such as body temperature and hormone levels, they fail to combine environmental changes with behavioral characteristics for multidimensional correlation analysis, resulting in limitations in identifying breeding tendencies and missing optimal breeding opportunities. Breeding behavior recording relies on traditional information storage methods and fails to effectively utilize video stream data for behavioral feature analysis, leading to inaccurate identification of mating actions and affecting the completeness of judgments on breeding behavior. Breeding data management models are relatively simplistic, lacking comprehensive tracking capabilities of physiological changes, behavioral characteristics, and breeding execution, resulting in insufficient data management accuracy and traceability, impacting long-term optimization and improvements in reproductive success rates. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a method and system for electronic recording of veterinary mating.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a veterinary insemination electronic record method, comprising the following steps:

[0007] S1: Based on the data of breeding livestock mating sites, collect data streams from temperature and humidity sensors deployed at different locations, detect the range of data fluctuations, screen stable intervals within continuous time periods, analyze the fluctuations of environmental factors, and obtain the stable trend of the mating site environment.

[0008] S2: Based on the stable trend of the breeding site environment, extract the data from the breeding animal body temperature monitoring device, detect the data changes within the time interval, analyze the fluctuation correlation between the differential data, screen the physiological state of the breeding tendency, and obtain the physiological change state of the breeding animal.

[0009] S3: Based on the physiological changes of the breeding animals, analyze the changes in body temperature monitoring data and behavioral characteristics perceived by mating behavior, screen the body temperature fluctuations, hormone level increases and behavioral activity intervals at key time points, determine the degree of matching between physiological data and behavioral data, screen the time windows that meet the mating conditions, and obtain the mating time nodes of the breeding animals.

[0010] S4: Based on the breeding time nodes of the breeding animals, identify the behavioral characteristics of mating posture and duration of mating actions, analyze the completeness of the behavioral actions, determine whether the mating behavior meets the expected pattern, filter behavioral data that meets the standards, and obtain mating behavior analysis records.

[0011] As a further aspect of the present invention, the stable trend of the breeding site environment includes stable temperature range, stable humidity range, stable light intensity range, and stable air pressure range; the physiological changes of the breeding animals include body temperature change characteristics, hormone level change characteristics, heart rate change characteristics, and respiratory rate change characteristics; the breeding time nodes include body temperature fluctuation points, hormone level peaks, periods of active behavior, and matching time windows; and the breeding behavior analysis records include mating posture characteristics, mating action characteristics, behavioral integrity characteristics, and pattern matching characteristics.

[0012] As a further aspect of the present invention, the step of obtaining the stability trend of the mating site environment specifically includes:

[0013] S111: Based on data from breeding sites, collect temperature, humidity, light intensity, and air pressure information, extract temperature and humidity sensor data streams from differentiated locations, compare the temperature and humidity data, filter the data fluctuation range, and obtain the temperature and humidity sensor data fluctuation values.

[0014] S112: Call the temperature and humidity sensor data fluctuation values, filter out intervals with small fluctuations within a continuous time period, and analyze the changing trend of temperature and humidity data within the intervals using the following formula:

[0015] ;

[0016] The rate of change of temperature and humidity in the steady-state interval was calculated.

[0017] in, Represents the rate of change of temperature and humidity within a stable range. Representing time Temperature value at time, Indicates time Temperature value at time, Representing time Humidity value at any given time Indicates time Humidity value at any given time This represents the number of samples within the time interval. The weighting coefficients representing the temperature change in the overall trend calculation. The weighting coefficient representing the effect of humidity changes on the overall trend calculation. The length of the time interval represents the adjustment parameter that affects the denominator of the calculation;

[0018] S113: Based on the temperature and humidity change rate in the stable interval, combined with light intensity and air pressure data, analyze the fluctuation of environmental factors, and analyze the fluctuation trend of different time periods to obtain the stable trend of the breeding site environment.

[0019] As a further aspect of the present invention, the step of obtaining the physiological change state of the breeding stock specifically includes:

[0020] S211: Based on the stable trend of the breeding site environment, extract the body temperature, hormone level, heart rate, and respiratory rate data of the breeding animals, detect the data changes within a specified time interval, identify the time change rate of each physiological parameter, and filter the physiological parameters according to the degree of fluctuation of the change rate to obtain physiological parameter fluctuation data.

[0021] S212: Analyze the correlation of changing trends among the differentially expressed physiological parameter fluctuation data using the formula:

[0022] ;

[0023] Calculate the correlation coefficient between pairs of physiological parameters, screen for parameter pairs with strong correlation, and obtain the parameter correlation analysis results;

[0024] in, The coefficient representing the correlation between pairs of physiological parameters. This represents the observed value of the a-th physiological parameter. The observed value represents the b-th physiological parameter. This represents the mean of the a-th physiological parameter. This represents the mean of the b-th physiological parameter. Represents the total number of physiological parameters;

[0025] S213: Based on the results of the parameter correlation analysis, identify the offset amplitude ratio, combine it with the fluctuation amplitude difference of the differentiated time interval, and screen the combination of physiological parameters that show key change trends under the breeding tendency conditions to obtain the physiological change status of breeding animals.

[0026] As a further aspect of the present invention, the step of obtaining the breeding time point of the breeding stock specifically includes:

[0027] S311: Based on the physiological changes of the breeding stock, extract the body temperature monitoring data and time series of the breeding stock, identify the amplitude of body temperature fluctuations, screen for the body temperature rise phase that conforms to physiological changes, and use the formula:

[0028] ;

[0029] Obtain the amplitude of body temperature fluctuations;

[0030] in, This represents the range of body temperature fluctuations. Representing the Body temperature at that moment, Representing the Body temperature at that moment, This represents the total reference range of body temperature changes within a body temperature change cycle. Basal body temperature, which represents an individual's body temperature;

[0031] S312: Based on the body temperature fluctuation amplitude value, filter the time interval of hormone level rise, call time series data, and obtain the hormone change rate value;

[0032] S313: Call the body temperature fluctuation amplitude value and hormone change rate value to analyze the level of behavioral activity, determine the degree of matching between physiological data and behavioral data, filter time windows, and obtain the breeding time nodes of breeding animals.

[0033] As a further aspect of the present invention, the steps for obtaining the mating behavior analysis records are specifically as follows:

[0034] S411: Based on the breeding time node of the breeding livestock, extract the video stream data of the mating behavior sensing device, calculate the angle change of the mating posture and the contact point offset value, and obtain the mating posture change parameters;

[0035] S412: Call the mating posture change parameters, extract key moment points within the duration of the mating action, analyze the posture change rate and contact point offset trend, using the following formula:

[0036] ;

[0037] Calculate the mating action integrity metric to obtain the mating action integrity characteristics;

[0038] in, This represents a measure of the completeness of the mating process. Represents the total number of time segments. This represents the change in pose angle during the k-th time segment. Indicates the first The change in posture angle over a time segment This represents the offset value of the body contact point in the k-th time segment. Representing the Offset values ​​of body contact points for each time segment;

[0039] S413: Based on the integrity characteristics of the mating action, filter behavioral data that meet the integrity criteria, determine whether the mating behavior conforms to the expected pattern, and obtain mating behavior analysis records.

[0040] As a further aspect of the present invention, the method further includes step S5:

[0041] S5: Based on the mating behavior analysis records, identify the physiological changes in different time periods, analyze the body temperature, hormone levels, action sequence and mating completion during the mating process, screen effective mating data, store key data of the mating cycle, and obtain the breeding animal mating execution log.

[0042] The breeding log includes valid mating data, physiological data of the mating process, mating cycle data, and mating completion data.

[0043] As a further aspect of the present invention, the step of obtaining the breeding stock mating execution log specifically includes:

[0044] S511: Based on the mating behavior analysis record, extract data on body temperature, hormone levels, action sequence and mating completion, analyze data on body temperature changes and hormone fluctuations, record action sequence, mating duration and completion, and obtain physiological change state matching values.

[0045] S512: Using the physiological change state matching values, combined with the body temperature change rate and hormone fluctuation amplitude, calculate the effective mating data screening factor using the following formula:

[0046] ;

[0047] Filter the mating data that meet the matching conditions to obtain the effective mating dataset;

[0048] in, Represents the screening factor for effective mating data. and These represent the changes in body temperature before and after mating. and These represent hormone levels before and after mating. This represents the mating timing deviation value. This represents the number of time-series entries recorded. This represents the baseline value for mating completion.

[0049] S513: Call the effective mating dataset, record the mating data within the mating cycle, store it according to the time axis, and obtain the breeding livestock mating execution log.

[0050] The veterinary insemination electronic record system is used to execute the above-mentioned veterinary insemination electronic record method, and the system includes:

[0051] The environmental monitoring module is based on data from the breeding site of livestock, including temperature, humidity, light intensity and air pressure of the breeding site. It extracts data streams from temperature and humidity sensors, identifies the fluctuation range of environmental parameters, filters stable intervals, and obtains the stable trend of the breeding site environment.

[0052] Based on the stable trend of the breeding site environment, the breeding animal physiological monitoring module extracts body temperature, hormone levels, heart rate, and respiratory rate from veterinary breeding monitoring, screens the physiological state of breeding tendency, and obtains the physiological change status of breeding animals.

[0053] The mating timing determination module extracts monitored body temperature data, hormone level changes, and behavioral activity based on the physiological changes of the breeding animals. It then filters time windows of body temperature rise and hormone level fluctuation peaks, analyzes the degree of matching between body temperature, hormone levels, and behavioral characteristics, filters time nodes that meet the mating tendency, and obtains the mating time nodes of the breeding animals.

[0054] Based on the breeding time nodes of the breeding animals, the mating behavior analysis module extracts video stream data from the mating behavior sensing device, analyzes mating posture and duration of mating actions, filters behavioral data that meet the standards, and obtains mating behavior analysis records.

[0055] Based on the mating behavior analysis records, the mating log management module extracts the physiological changes in the same time period, analyzes the data matching of body temperature, hormone levels, action sequence and mating completion, filters valid mating data, and obtains the breeding animal mating execution log.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0057] This invention achieves precise assessment of the site environment through multi-dimensional environmental data monitoring and fusion of sensor data from differentiated locations, effectively screening stable environmental zones and improving the environmental adaptability analysis capabilities of breeding sites. By combining the linkage changes of breeding animal physiological data and environmental factors, a more accurate physiological state identification system is constructed, enabling mating tendency judgment based on multi-parameter data and a higher degree of matching between physiological data fluctuation trends and mating timing. Based on cross-analysis of physiological state and behavioral data, key features such as body temperature fluctuations, hormone level changes, and behavioral activity are extracted, making the judgment of mating timing more accurate. Through behavioral analysis of video stream data, combined with the cooperation judgment of physiological data, precise screening of the completeness and matching degree of mating actions is achieved, ensuring that mating behavior meets expected standards. The matching analysis of physiological change data and mating behavior makes the monitoring of the mating process more complete, improving the accurate assessment and tracking capabilities of mating behavior, optimizing mating success rate prediction, and optimizing data storage and cycle management. This makes breeding records more systematic, forming a traceable mating execution log, improving data management accuracy and long-term optimization capabilities. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0059] Figure 2 This is a flowchart illustrating the process of obtaining the stability trend of the breeding site environment in this invention.

[0060] Figure 3 This is a flowchart illustrating the process of obtaining the physiological changes of breeding livestock in this invention.

[0061] Figure 4 This is a flowchart illustrating the process of obtaining the mating time point of breeding livestock in this invention;

[0062] Figure 5 This is a flowchart illustrating the process of obtaining mating behavior analysis records in this invention.

[0063] Figure 6 This is a flowchart illustrating the process of obtaining the breeding log of livestock in this invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0065] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not 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 the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0066] Example 1: Please refer to Figure 1 This invention provides a technical solution: a method for electronic recording of veterinary mating, comprising the following steps:

[0067] S1: Based on data from breeding sites, including temperature, humidity, light intensity, and air pressure information, collect data streams from temperature and humidity sensors deployed at different locations, detect the range of data fluctuations, screen stable intervals within continuous time periods, analyze the fluctuations of environmental factors, and obtain the stable trend of the breeding site environment.

[0068] S2: Based on the stable trend of the breeding site environment, extract the body temperature, hormone level, heart rate and respiratory rate data of the breeding animal body temperature monitoring device, detect the data changes within the time interval, analyze the fluctuation correlation between the differential data, screen the physiological state of the breeding tendency, and obtain the physiological change state of the breeding animal.

[0069] S3: Based on the physiological changes of breeding animals, analyze the changes in body temperature monitoring data and behavioral characteristics perceived during mating behavior, screen the temperature fluctuations, hormone level increases and behavioral activity intervals at key time points, determine the degree of matching between physiological data and behavioral data, screen the time windows that meet the mating conditions, and obtain the mating time nodes of breeding animals.

[0070] S4: Based on the breeding time nodes, extract video stream data from the breeding behavior sensing device, identify behavioral characteristics such as mating posture and duration of mating actions, analyze the completeness of behavioral actions, determine whether the mating behavior meets the expected pattern, filter behavioral data that meets the standards, and obtain mating behavior analysis records.

[0071] S5: Based on the analysis records of mating behavior, identify the physiological changes in different time periods, analyze body temperature, hormone levels, action sequence and mating completion during the mating process, screen effective mating data, store key data of the mating cycle, and obtain the mating execution log of breeding livestock.

[0072] The stability trends of the breeding site environment include stable temperature range, stable humidity range, stable light intensity range, and stable air pressure range. The physiological changes of breeding animals include characteristics of body temperature changes, hormone level changes, heart rate changes, and respiratory rate changes. Breeding animal mating time nodes include body temperature fluctuation points, hormone level peaks, periods of active behavior, and matching time windows. Mating behavior analysis records include mating posture characteristics, mating action characteristics, behavioral integrity characteristics, and pattern matching characteristics. Breeding animal mating execution logs include effective mating data, physiological data of the mating process, mating cycle data, and mating completion data.

[0073] Please see Figure 2 The specific steps for obtaining the stability trend of the breeding site environment are as follows:

[0074] S111: Based on data from breeding sites, collect temperature, humidity, light intensity, and air pressure information, extract temperature and humidity sensor data streams from differentiated locations, compare the temperature and humidity data, filter the data fluctuation range, and obtain the temperature and humidity sensor data fluctuation values.

[0075] Information on temperature, humidity, light intensity, and air pressure is collected from sensors at multiple monitoring points in the breeding area. Each sensor is positioned at a specific location to capture minute changes in environmental data. By comparing temperature and humidity data from different sensors, abnormal fluctuations in environmental conditions can be effectively identified. For example, if a sensor in a certain area shows a sudden increase in temperature, it indicates equipment malfunction or heat source interference in that area. In this case, the operator can respond quickly, investigate, and resolve the problem. By calculating the differences in data from various sensors within the same time period, the operator sets a threshold, such as ±2°C for temperature changes and ±5% for humidity changes, to filter out significant data fluctuations. The threshold is set based on historical data and typical environmental conditions, ensuring that only significant changes are further analyzed to obtain the temperature and humidity sensor data fluctuation values.

[0076] S112: Retrieve temperature and humidity sensor data fluctuation values, filter intervals with small fluctuations within a continuous time period, and analyze the trend of temperature and humidity data changes within these intervals using the following formula:

[0077] ;

[0078] The rate of change of temperature and humidity in the steady-state interval was calculated.

[0079] The system retrieves temperature and humidity fluctuation values ​​from the output of S111. These values ​​are calculated for each sensor within a specific sampling period (e.g., every 5 minutes). The fluctuation values ​​represent the maximum change in temperature and humidity within a short timeframe. For example, sensor A's temperature changes from 25.0℃ to 26.5℃ and its humidity from 60% to 63.2% within a 5-minute sampling period. Discrete fluctuation data is received and processed as a continuous data stream. When filtering intervals with small fluctuations within a continuous time period, the system first judges the temperature and humidity fluctuation values ​​within a continuous time window (e.g., 6 consecutive sampling periods, or 30 minutes). Specifically, it checks the temperature and humidity fluctuation values ​​within this time window. The temperature fluctuation values ​​of all temperature and humidity sensors inside the mouth are all less than or equal to the preset temperature fluctuation threshold, and the humidity fluctuation values ​​are all less than or equal to the preset humidity fluctuation threshold. The temperature fluctuation threshold is set at 0.5℃, and the humidity fluctuation threshold is set at 1.0%. These thresholds are set based on the physiological needs of breeding livestock (e.g., beef cattle) and historical environmental data from the breeding farm. Beef cattle have a narrow suitable temperature fluctuation range during their breeding season, and humidity also needs to be maintained at a stable level. When the temperature fluctuation value is determined to be in the "small fluctuation range" range, it means that the absolute value of the temperature fluctuation value within that time period is no greater than 0.5℃. Similarly, when the humidity fluctuation value is determined to be in the "small fluctuation range" range, it means that the absolute value of the humidity fluctuation value within that time period is not large. For example, if within a 30-minute time window from 8:00 AM to 8:30 AM, the temperature and humidity fluctuations at all monitoring points (e.g., point 1 temperature fluctuation 0.2℃, humidity fluctuation 0.5%; point 2 temperature fluctuation 0.3℃, humidity fluctuation 0.8%) are all below the aforementioned thresholds, then this 30-minute period is defined as a continuous time interval with small fluctuations. Once a continuous time interval with small fluctuations is selected, the raw temperature and humidity data sequences from all sensors within that interval will be extracted, and the changing trends of the temperature and humidity data within the interval will be analyzed. Specifically, the interval will be divided into smaller time steps (e.g., every 10 minutes), the average temperature and average humidity within each small time step will be calculated, and then the values ​​of the consecutive small time steps will be compared. The average value of the step size is used to determine whether the temperature and humidity are trending upward, downward, or relatively stable. For example, within the stable range of 8:00 to 8:30, the temperature data of sensor A is 24.5℃ at 8:00, 24.6℃ at 8:10, 24.7℃ at 8:20, and 24.8℃ at 8:30, showing a slight upward trend. The humidity data is 62.0% at 8:00, 61.9% at 8:10, 61.8% at 8:20, and 61.7% at 8:30, showing a slight downward trend. The trend is categorized into three types: upward, downward, and stable. The criterion is that if the average value at the end of the interval increases by more than a preset small threshold (e.g., 0.1℃ or 0.5℃) relative to the initial average value.If the temperature decreases by 0.3°C from 24.5°C to 24.8°C within the 8:00 to 8:30 timeframe, it is considered an upward trend. If the decrease exceeds a small threshold, it is considered a downward trend. Otherwise, it is considered a stable trend. For example, within the 8:00 to 8:30 timeframe, if the temperature increases from 24.5°C to 24.8°C (an increase of 0.3°C), exceeding the small threshold of 0.1°C, it is considered an upward trend. If the humidity decreases from 62.0% to 61.7% (a decrease of 0.3%, exceeding the small threshold of 0.2%, it is considered a downward trend. The rate of change of temperature and humidity within the stable range is then calculated.

[0080] Formula used: ,in, Represents the rate of change of temperature and humidity within a stable range. Representing time Temperature value at time, Indicates time Temperature value at time, Representing time Humidity value at any given time Indicates time Humidity value at any given time This represents the number of samples within the time interval. The weighting coefficients representing the temperature change in the overall trend calculation. The weighting coefficient representing the effect of humidity changes on the overall trend calculation. The length of the time interval represents the adjustment parameter that affects the denominator of the calculation;

[0081] The process involves identifying the interval with the smallest fluctuations within a continuous time period from the data, and then meticulously filtering and calculating the temperature and humidity data from the sensor to determine the stable range. To achieve this, it is first necessary to calculate the changes in temperature and humidity between different time points. Assuming the temperature data for a certain time period is... (Unit: °C), humidity data is as follows: (Unit: %), the data corresponds to the collection results at 5 adjacent time points. First, the temperature change at adjacent time points is calculated:

[0082] ;

[0083] ;

[0084] ;

[0085] ;

[0086] Similarly, calculate the change in humidity:

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] Assumption , , Then calculate the summation term:

[0092] ;

[0093] Calculate the denominator again: ;

[0094] The final calculation of the temperature and humidity change rate within the steady-state interval is as follows: ;

[0095] The stability value of temperature and humidity data was obtained through calculation, and the rate of change of temperature and humidity in the stable range was found to be 0.318.

[0096] S113: Based on the stable temperature and humidity change rate, combined with light intensity and air pressure data, analyze the fluctuation of environmental factors, and analyze the fluctuation trend of different time periods to obtain the stable trend of the breeding site environment.

[0097] By combining light intensity and air pressure data, a comprehensive environmental fluctuation analysis is conducted. This includes comparing temperature and humidity data with the trends in light intensity and air pressure. For example, by comparing light intensity and air pressure data over different time periods, it can be determined whether there is a correlation with the rate of change in temperature and humidity. If light intensity and air pressure also show relative stability during periods of small temperature and humidity changes, it can be inferred that the overall environmental conditions are suitable during this period. Conversely, further investigation is needed to determine the cause of the problem. Through this comprehensive analysis, the stability trend of the breeding site environment can be determined, ensuring that breeding livestock reproduce under optimal environmental conditions.

[0098] Please see Figure 3 The specific steps for obtaining the physiological changes in breeding livestock are as follows:

[0099] S211: Based on the stable trend of the breeding site environment, extract the body temperature, hormone level, heart rate and respiratory rate data of breeding animals, detect the data changes within a specified time interval, identify the time change rate of each physiological parameter, and filter the physiological parameters according to the degree of fluctuation of the change rate to obtain physiological parameter fluctuation data.

[0100] Monitoring the physiological changes of breeding livestock to determine their health status and mating timing is crucial. This section focuses on monitoring data such as body temperature, hormone levels, heart rate, and respiratory rate. Within a specific time frame, for example, at the beginning of the breeding season, daily body temperature data is acquired using a set-up temperature monitoring device. Heart rate and respiratory rate are recorded every hour for a week. Hormone levels are measured every three days to monitor their cyclical changes. Such detailed operations help understand the physiological cycle and health status of breeding livestock. By analyzing the rate of change of the data over time, such as calculating the percentage change between each data point and the previous data point, abnormal fluctuations in physiological parameters can be identified more accurately. Fluctuations indicate reproductive health problems or the optimal time for mating. Finally, physiological parameters with significant fluctuations are screened. By continuously monitoring and comparing data changes, veterinarians and farmers can better manage the breeding cycle of breeding livestock and obtain data on physiological parameter fluctuations.

[0101] S212: Analyze the correlation of changing trends among differentially expressed parameters by calling up physiological parameter fluctuation data, using the following formula:

[0102] ;

[0103] Calculate the correlation coefficient between pairs of physiological parameters, screen for parameter pairs with strong correlation, and obtain the parameter correlation analysis results;

[0104] in, The coefficient representing the correlation between pairs of physiological parameters. This represents the observed value of the a-th physiological parameter. The observed value represents the b-th physiological parameter. This represents the mean of the a-th physiological parameter. This represents the mean of the b-th physiological parameter. Represents the total number of physiological parameters;

[0105] Analyzing the correlation between data is crucial for making more accurate predictions and managing the physiological state of breeding livestock in biotechnology applications. For example, in a real farm environment, the physiological parameters of a cow were recorded over seven consecutive days: body temperature (°C): 38.1, 38.3, 38.7, 39.0, 39.2, 39.1, 38.9; heart rate (beats / minute): 72, 75, 78, 80, 82, 81, 79. First, these two sets of data were standardized. The mean of all data points was subtracted, and the result was divided by the standard deviation to calculate the correlation.

[0106] Calculate the mean:

[0107] ;

[0108] ;

[0109] Calculate the standard deviation components:

[0110] ;

[0111] ;

[0112] Calculate the covariance components:

[0113] ;

[0114] Calculate the correlation coefficient:

[0115] ;

[0116] The final calculated correlation coefficient of the physiological parameters was 0.9921, which indicates that the correlation between body temperature and heart rate is extremely strong, close to 1. This means that the increase in body temperature and the increase in heart rate are highly consistent, implying that during the breeding season or in an abnormal health state, the heart rate and body temperature of breeding animals will rise synchronously. This trend can be used as reference data to determine the timing of mating. By screening out parameter pairs with strong correlations, the results of parameter correlation analysis can be obtained.

[0117] S213: Based on the results of parameter correlation analysis, identify the offset amplitude ratio, combine the fluctuation amplitude difference of the differentiated time interval, screen the combination of physiological parameters that show key change trends under the mating tendency condition, and obtain the physiological change status of breeding animals.

[0118] Determining the physiological changes in breeding stock is crucial. For example, in practical applications, data collected through monitoring devices and matrices obtained through correlation analysis help accurately calculate the deviation ratio of physiological parameters in breeding stock over a specific time period. Combined with the fluctuation difference within the time interval, combinations of physiological parameters that exhibit specific trends under breeding tendency conditions can be further screened out. These parameter combinations reflect the physiological changes in breeding stock as they approach estrus. These changes are accompanied by an increase in body temperature and changes in hormone levels. This analysis and screening process not only helps to more accurately identify the optimal time for mating but also optimizes the health management and reproductive efficiency of breeding stock through precise calculation of the deviation ratio, ultimately revealing the physiological changes in breeding stock.

[0119] Please see Figure 4 The specific steps for obtaining the breeding time of livestock are as follows:

[0120] S311: Based on the physiological changes in breeding livestock, extract body temperature monitoring data and time series, identify the amplitude of body temperature fluctuations, and screen for body temperature rise phases that conform to physiological changes, using the following formula:

[0121] ;

[0122] Obtain the amplitude of body temperature fluctuations;

[0123] in, This represents the range of body temperature fluctuations. Representing the Body temperature at that moment, Representing the Body temperature at that moment, This represents the total reference range of body temperature changes within a body temperature change cycle. Basal body temperature, which represents an individual's body temperature;

[0124] Based on the physiological characteristics and historical monitoring data of the animal, this represents the total reference range of body temperature changes during typical physiologically active periods (such as the proestrus phase when body temperature rises), expressed in degrees Celsius (°C).

[0125] By acquiring body temperature monitoring data and time series of breeding livestock, the data is initially processed, including removing outliers, smoothing data curves, and ensuring the continuity of data collection. For example, assuming the basal body temperature of a certain breed of livestock is 38.5℃, abnormal fluctuations in body temperature may occur during measurement due to ambient temperature or sensor errors. In the data processing, the moving average method is used to calculate the average body temperature at every five consecutive time points to reduce errors. Next, the amplitude of body temperature fluctuations is calculated. This process is achieved by comparing the changes in body temperature at consecutive time points. Assuming the body temperature data at 10:00, 10:05, and 10:10 are 38.6℃, 39.2℃, and 39.8℃, respectively, the increase in body temperature at different time points is calculated. In this case, the trend of body temperature change is clearly upward, so it can be determined that this time point is part of the reproductive active period. In order to analyze the amplitude of body temperature fluctuations more accurately;

[0126] For a certain type of breeding animal, based on the physiological pattern that its body temperature usually rises during proestrus and historical monitoring data, the reference range for body temperature during this stage is set as follows: ℃, this value reflects the typical range of body temperature changes from baseline to peak during physiologically active phases such as estrus;

[0127] Current moment ℃, the moment before ℃, basal body temperature ℃;

[0128] The calculation shows that: ;

[0129] Therefore, the body temperature fluctuation range at this time point is 1.633. This value can be used to screen for the body temperature rise phase that is consistent with the physiological changes of mating. In practical applications, if this value exceeds a certain threshold (e.g., 1.5), it can be preliminarily determined that the breeding animal has entered a physiological state suitable for mating. Through data calculation and analysis, the body temperature fluctuation range value is finally obtained, and the data can serve as an important reference for subsequent mating time screening.

[0130] S312: Based on the amplitude of body temperature fluctuations, filter the time interval of hormone level rise, call time series data, and obtain the hormone change rate value;

[0131] The hormone levels of breeding livestock are monitored and their trends are recorded. A curve of hormone levels changing over time is plotted using data analysis tools to identify stages where hormone levels rise significantly. This indicates that the breeding livestock is in the peak of its reproductive cycle. The rate of hormone change can be calculated, which is obtained by comparing the increase in hormone levels with the amplitude of body temperature fluctuations within the same time period. If the rate of increase in hormone levels is highly correlated with the amplitude of body temperature fluctuations, this period can be identified as the critical period for mating. The hormone change rate value is obtained through analysis and calculation, and this value is an important basis for selecting the mating time window.

[0132] S313: Call the body temperature fluctuation amplitude value and hormone change rate value to analyze the level of behavioral activity, determine the degree of matching between physiological data and behavioral data, filter the time window, and obtain the breeding time node of the breeding stock.

[0133] Analyzing behavioral activity levels within this time interval involves monitoring the activity levels of breeding livestock, including walking, foraging, and social behavior. Data collected through behavioral sensors is analyzed to determine whether the level of behavioral activity matches physiological data (body temperature and hormone levels). The degree of matching can be determined using a data model. By comparing physiological and behavioral data, the model assesses the correlation between the two, thereby identifying time windows where behavioral activity and physiological indicators meet breeding conditions. This helps veterinarians determine the optimal breeding time, ultimately yielding the breeding time nodes. These nodes are obtained through a series of data analyses and calculations, providing a scientific basis for the reproductive management of breeding livestock.

[0134] Please see Figure 5 The specific steps for obtaining mating behavior analysis records are as follows:

[0135] S411: Based on the breeding time node of breeding livestock, extract the video stream data of the mating behavior sensing device, calculate the angular change of mating posture and the offset value of the contact point, and obtain the mating posture change parameters;

[0136] First, the mating time of breeding livestock is set as the starting point for data collection. Sensing devices installed in the scenario monitor and record the activity data of the breeding livestock in real time. During this process, data from each time segment includes key information such as the location and posture of the breeding livestock. This data will be used to analyze the behavioral patterns of the breeding livestock, providing a basis for judging mating behavior. For example, in a specific scenario, during the mating period from 7:00 to 8:00 AM, data captured by video surveillance shows that the movement amplitude of a cow gradually increases during this period, with a significant change occurring between 7:30 and 7:45 AM. At this time, the monitoring equipment records detailed posture change data. This data is subsequently used to calculate the angular change in the mating posture and the offset value of the contact point. The calculation results form mating posture change parameters, providing quantitative data support for further analysis.

[0137] S412: Call the mating posture change parameters to extract key moment points within the duration of the mating action, analyze the posture change rate and contact point offset trend, using the following formula:

[0138] ;

[0139] Calculate the mating action integrity metric to obtain the mating action integrity characteristics;

[0140] in, This represents a measure of the completeness of the mating process. Represents the total number of time segments. This represents the change in pose angle during the k-th time segment. Indicates the first The change in posture angle over a time segment This represents the offset value of the body contact point in the k-th time segment. Representing the Offset values ​​of body contact points for each time segment;

[0141] Analyzing the duration of mating actions helps identify key moments in the mating process. The mating posture of breeding animals changes across different time segments, primarily involving adjustments in posture angle and the movement of the body contact point. Parameter calculations require quantification using data from continuous time segments. For example, posture angle and contact point offset values ​​are recorded every second and calculated within a defined time window. In a practical application scenario, assuming a cow's mating behavior lasts 10 minutes, with data collected every second, totaling 600 time segments, a 5-second analysis window (i.e.,...) is used... Record the change in the mating posture angle of the cow. and contact point offset value as follows:

[0142] ;

[0143] ;

[0144] First, calculate the first term, namely the rate of change of posture angle:

[0145] ;

[0146] ;

[0147] Then calculate the second term, namely the contact point offset trend:

[0148] ;

[0149] ;

[0150] Final calculation value: ;

[0151] The calculation result represents the mating action integrity metric within that time window, i.e., the smoothness of posture adjustment and the stability of contact point offset during mating. It is assumed that the baseline interval for the mating action integrity metric, derived from historical data analysis, is [insert range here]. The integrity metric of this mating action. If the behavior falls within an acceptable range, it can be judged as meeting the integrity criteria, thus obtaining the integrity characteristics of the mating action for further analysis of mating behavior.

[0152] S413: Based on the integrity characteristics of mating actions, filter behavioral data that meet the integrity criteria, determine whether the mating behavior conforms to the expected pattern, and obtain mating behavior analysis records;

[0153] The comprehensive screening of all mating behavior data primarily involves setting thresholds to determine whether mating behaviors conform to the expected pattern. These thresholds are set based on the statistical distribution of mating action integrity metrics to ensure that only behaviors that reach or exceed this threshold are considered successful mating behaviors. For example, if the threshold for mating action integrity metrics is set to 0.8 (based on the average value derived from historical data analysis), then only when the monitored data shows that the integrity metric value of the mating behavior is greater than or equal to 0.8 is the behavior considered to meet the standard. In this way, the screened data is ultimately included in the mating behavior analysis record, which reflects in detail the quality and effectiveness of various livestock mating behaviors, providing accurate data support for veterinarians and farm managers to optimize future mating plans.

[0154] Please see Figure 6 The specific steps for obtaining the breeding stock mating execution log are as follows:

[0155] S511: Based on mating behavior analysis records, extract data on body temperature, hormone levels, action sequence and mating completion, analyze body temperature changes and hormone fluctuation data, record action sequence, mating duration and completion, and obtain physiological change state matching values.

[0156] First, specific indicators of mating behavior need to be defined, such as body temperature, hormone levels, movement timing, and mating completion rate. Data extraction requires real-time monitoring through sensors and recording devices, such as body temperature sensors and video analysis. Body temperature and hormone data are collected and transmitted to a database in real time, and movement timing is analyzed using video recognition technology to interpret animal activity patterns. For example, a specific implementation could be to install body temperature and hormone monitoring equipment in the breeding farm to monitor the physiological changes of each cow and record the animals' mating behavior through video surveillance. The data is then preliminarily analyzed using specialized software, and the rate of change in body temperature, the amplitude of hormone fluctuations, and the deviation value of movement timing are calculated to establish a correlation matrix between physiological changes and mating success rate, ultimately obtaining the matching value of physiological changes.

[0157] S512: Using physiological change status matching values, combined with body temperature change rate and hormone fluctuation amplitude, calculate the effective mating data screening factor using the following formula:

[0158] ;

[0159] Filter the mating data that meet the matching conditions to obtain the effective mating dataset;

[0160] The physiological change state matching value is retrieved. This value reflects the degree of consistency between the data on body temperature, hormone levels, action sequence, and mating completion during the mating event and the optimal mating conditions of the breeding stock. For example, if the physiological change state matching value of a beef cattle in a mating attempt is 88, it indicates that its physiological indicators are highly consistent with successful mating. This value is set to a range of 0 to 100, with values ​​above 80 considered high matching, 60 to 80 considered medium matching, and below 60 considered low matching. Simultaneously, the corresponding body temperature change rate and hormone fluctuation amplitude data are obtained, also derived from the analysis results of step S511. For example, the body temperature change rate of this beef cattle during mating was 0.18 degrees Celsius / hour, and the hormone fluctuation amplitude was 10 nanograms / mL. Combining the body temperature change rate and hormone fluctuation amplitude... First, the rate of change in body temperature and the amplitude of hormone fluctuations are assessed to determine their impact on mating success. Effective mating data screening factors are calculated, obtained by comprehensively considering physiological change status matching values, body temperature change rate stability scores, and hormone fluctuation amplitude stability scores. The body temperature change rate stability score is calculated based on a preset body temperature stability threshold. If the body temperature change rate is less than or equal to 0.2 degrees Celsius / hour, it is considered low body temperature fluctuation and assigned a stability score of 0.9; if the body temperature change rate is between 0.2 degrees Celsius / hour and 0.5 degrees Celsius / hour, it is considered moderate body temperature fluctuation and assigned a stability score of 0.5; if the body temperature change rate is greater than 0.5 degrees Celsius / hour, it is considered high body temperature fluctuation and assigned a stability score of 0.1. For the above 0... A body temperature change rate of 0.18°C / hour results in a body temperature stability score of 0.9. Hormone fluctuation stability scores are calculated based on a preset hormone stability threshold. Hormone fluctuations of 15 ng / mL or less are considered low, assigned a stability score of 0.8; fluctuations between 15 and 30 ng / mL are considered moderate, assigned a stability score of 0.4; and fluctuations greater than 30 ng / mL are considered high, assigned a stability score of 0.1. For a hormone fluctuation of 10 ng / mL, the stability score is 0.8. The effective mating data screening factor is calculated as follows: physiological change matching value divided by 100, then multiplied by a weight of 0.6. The screening factor is calculated by multiplying the body temperature change rate stability score by a weight of 0.2, and the hormone fluctuation amplitude stability score by a weight of 0.2. The weights of 0.6, 0.2, and 0.2 are set based on the experience of veterinary experts and the analysis of historical mating data. The weights reflect the degree of influence of each indicator on mating success. For example, for a mating event with a physiological change state matching value of 88, a body temperature stability score of 0.9, and a hormone stability score of 0.8, the screening factor is calculated as: (88 / 100×0.6)+(0.9×0.2)+(0.8×0.2)=0.528+0.18+0.16=0.868. When screening matching mating data, the calculated effective mating data screening factor is compared with the preset screening benchmark value, which is set to 0.75. Based on the average screening factor of a large number of successful mating cases and combined with expert advice, if the screening factor of valid mating data is greater than or equal to 0.75, then the mating data is determined to meet the matching conditions and is selected. For example, the screening factor 0.868 mentioned above is greater than 0.75, therefore the mating data is screened and retained, resulting in the valid mating dataset.

[0161] Formula used: ,in, Represents the screening factor for effective mating data. and These represent the changes in body temperature before and after mating. and These represent hormone levels before and after mating. This represents the mating timing deviation value. This represents the number of time-series entries recorded. This represents the baseline value for mating completion.

[0162] The principle behind obtaining effective mating data screening factors is to calculate the effective mating dataset. The Q value in the formula represents the effective mating data screening factor. Factors considered in the calculation include L1 and L2, representing body temperature changes before and after mating; G1 and G2, representing hormone levels before and after mating; D1 and D2, representing sequence deviation values ​​at the time of mating; m, representing the sequence difference over recording time; and F, representing the mating completion baseline value. Based on these factors, effective mating data is obtained by screening datasets that meet the mating conditions, thereby optimizing mating outcomes.

[0163] Calculating effective mating data screening factors involves multiple parameters, each obtained through actual monitoring, laboratory analysis, or data calculation. For example, the rate of change in body temperature can be measured using an infrared temperature sensor before and after mating, and the difference between the two can be calculated. Hormone fluctuation amplitude requires detecting estrogen or progesterone levels in blood samples and calculating the difference between two measurements. Action timing deviation values ​​require capturing and calculating the time error of key behavioral nodes during mating using a camera. A specific case is set up: during a particular mating process, the following was monitored:

[0164] Body temperature change value: before mating After mating Body temperature changes ;

[0165] Hormone levels: before mating After mating Hormone fluctuations ;

[0166] Action timing deviation value: In mating behavior monitoring, the following values ​​were recorded. Each key action node has a time deviation. (For example, the duration of mounting by a male animal) averages 2 seconds, therefore:

[0167] ;

[0168] Mating Completion Benchmark Value: This value It is an average completion threshold value derived from a large amount of historical data;

[0169] Substitute the above values ​​into the formula: ;

[0170] Effective mating data screening factors were obtained. This value represents the matching degree of the mating data. The lower the value, the more stable the physiological changes, the more standardized the mating behavior, and the higher the completion rate, indicating a higher mating success rate. Therefore, a screening threshold is set. For example, empirical data shows that when... When this data is used, it can be considered valid mating data; when... At that time, the data may have large physiological fluctuations or incomplete mating, so it should be discarded. This calculation provides a scientific screening method to ensure that the selected mating data is more accurate, thereby improving the overall mating success rate.

[0171] S513: Call the valid mating dataset, record the mating data within the mating cycle, store it according to the time axis, and obtain the breeding livestock mating execution log;

[0172] Breeding experts can record key data during the breeding cycle, including body temperature, hormone levels, and mating completion. Data storage needs to be done chronologically to facilitate subsequent data analysis and review. For example, in a typical breeding cycle recording embodiment, breeding data is used to analyze the reproductive health of breeding animals and predict their future mating success rate. The information will be integrated into the breeding animal mating execution log and provided to farm managers and veterinarians to make more scientific decisions. The entire process requires not only efficient data collection equipment but also powerful back-end analysis to process and store large amounts of data.

[0173] The veterinary insemination electronic record system is used to execute the above-mentioned veterinary insemination electronic record method. The system includes:

[0174] The environmental monitoring module is based on data from the breeding site of livestock, including temperature, humidity, light intensity and air pressure of the breeding site. It extracts data streams from temperature and humidity sensors, identifies the fluctuation range of environmental parameters, filters stable intervals, and obtains the stable trend of the breeding site environment.

[0175] The breeding stock physiological monitoring module extracts body temperature, hormone levels, heart rate, and respiratory rate from veterinary breeding monitoring based on the stable trend of the breeding site environment, screens the physiological state of breeding tendency, and obtains the physiological changes of breeding stock.

[0176] The mating timing determination module is based on the physiological changes of breeding animals. It extracts monitored body temperature data, hormone level changes, and behavioral activity, filters time windows of body temperature rise and hormone level fluctuation peaks, analyzes the degree of matching of body temperature, hormone levels and behavioral characteristics, filters time nodes that meet the mating tendency, and obtains the mating time nodes of breeding animals.

[0177] The mating behavior analysis module extracts video stream data from mating behavior sensing devices based on the mating time nodes of breeding livestock, analyzes mating postures and mating action durations, filters behavioral data that meet the standards, and obtains mating behavior analysis records.

[0178] The mating log management module extracts physiological changes over the same time period based on mating behavior analysis records, analyzes the data matching of body temperature, hormone levels, action sequence and mating completion, filters valid mating data, and obtains the mating execution log of breeding animals.

[0179] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for electronic recording of veterinary mating, characterized in that, Includes the following steps: S1: Based on the data of breeding livestock mating sites, collect data streams from temperature and humidity sensors deployed at different locations, detect the range of data fluctuations, screen stable intervals within continuous time periods, analyze the fluctuations of environmental factors, and obtain the stable trend of the mating site environment. S2: Based on the stable trend of the breeding site environment, extract the data from the breeding animal body temperature monitoring device, detect the data changes within the time interval, analyze the fluctuation correlation between the differential data, screen the physiological state of the breeding tendency, and obtain the physiological change state of the breeding animal. The specific steps for obtaining the physiological changes in the breeding stock are as follows: S211: Based on the stable trend of the breeding site environment, extract the body temperature, hormone level, heart rate, and respiratory rate data of the breeding animals, detect the data changes within a specified time interval, identify the time change rate of each physiological parameter, and filter the physiological parameters according to the degree of fluctuation of the change rate to obtain physiological parameter fluctuation data. S212: Analyze the correlation of changing trends among the differentially expressed physiological parameter fluctuation data using the formula: ； Calculate the correlation coefficient between pairs of physiological parameters, screen for parameter pairs with strong correlation, and obtain the parameter correlation analysis results; in, The coefficient representing the correlation between pairs of physiological parameters. This represents the observed value of the a-th physiological parameter. The observed value represents the b-th physiological parameter. This represents the mean of the a-th physiological parameter. This represents the mean of the b-th physiological parameter. Represents the total number of physiological parameters; S213: Based on the results of the parameter correlation analysis, identify the offset amplitude ratio, combine it with the fluctuation amplitude difference of the differentiated time interval, screen the combination of physiological parameters that show key change trends under the breeding tendency conditions, and obtain the physiological change status of breeding animals. S3: Based on the physiological changes of the breeding animals, analyze the changes in body temperature monitoring data and behavioral characteristics perceived by mating behavior, screen the body temperature fluctuations, hormone level increases and behavioral activity intervals at key time points, determine the degree of matching between physiological data and behavioral data, screen the time windows that meet the mating conditions, and obtain the mating time nodes of the breeding animals. S4: Based on the breeding time nodes of the breeding animals, identify the behavioral characteristics of mating posture and duration of mating actions, analyze the completeness of the behavioral actions, determine whether the mating behavior meets the expected pattern, filter the behavioral data that meets the standards, and obtain the mating behavior analysis record. S5: Based on the mating behavior analysis records, identify the physiological changes in different time periods, analyze the body temperature, hormone levels, action sequence and mating completion during the mating process, screen effective mating data, store key data of the mating cycle, and obtain the breeding animal mating execution log. The breeding log includes valid mating data, physiological data of the mating process, mating cycle data, and mating completion data.

2. The veterinary insemination electronic record method according to claim 1, characterized in that, The stable trends of the breeding site environment include stable temperature range, stable humidity range, stable light intensity range, and stable air pressure range. The physiological changes of the breeding animals include characteristics of body temperature changes, hormone level changes, heart rate changes, and respiratory rate changes. The breeding time points include body temperature fluctuation points, hormone level peaks, periods of active behavior, and matching time windows. The mating behavior analysis records include mating posture characteristics, mating action characteristics, behavioral integrity characteristics, and pattern matching characteristics.

3. The veterinary insemination electronic record method according to claim 1, characterized in that, The specific steps for obtaining the stability trend of the mating site environment are as follows: S111: Based on data from breeding sites, collect temperature, humidity, light intensity, and air pressure information, extract temperature and humidity sensor data streams from differentiated locations, compare the temperature and humidity data, filter the data fluctuation range, and obtain the temperature and humidity sensor data fluctuation values. S112: Call the temperature and humidity sensor data fluctuation value, filter the interval with small fluctuation amplitude within a continuous time period, analyze the change trend of temperature and humidity data within the interval, and calculate the temperature and humidity change rate of the stable interval. S113: Based on the temperature and humidity change rate in the stable interval, combined with light intensity and air pressure data, analyze the fluctuation of environmental factors, and analyze the fluctuation trend of different time periods to obtain the stable trend of the breeding site environment.

4. The veterinary insemination electronic record method according to claim 1, characterized in that, The specific steps for obtaining the breeding stock mating time point are as follows: S311: Based on the physiological changes of the breeding stock, extract the body temperature monitoring data and time series of the breeding stock, identify the amplitude of body temperature fluctuations, screen for the body temperature rise phase that conforms to physiological changes, and use the formula: ； Obtain the amplitude of body temperature fluctuations; in, This represents the range of body temperature fluctuations. Representing the Body temperature at that moment, Representing the Body temperature at that moment, This represents the total reference range of body temperature changes within a body temperature change cycle. Basal body temperature, which represents an individual's body temperature; S312: Based on the body temperature fluctuation amplitude value, filter the time interval of hormone level rise, call time series data, and obtain the hormone change rate value; S313: Call the body temperature fluctuation amplitude value and hormone change rate value to analyze the level of behavioral activity, determine the degree of matching between physiological data and behavioral data, filter time windows, and obtain the breeding time nodes of breeding animals.

5. The veterinary insemination electronic record method according to claim 4, characterized in that, The specific steps for obtaining the mating behavior analysis records are as follows: S411: Based on the breeding time node of the breeding livestock, extract the video stream data of the mating behavior sensing device, calculate the angle change of the mating posture and the contact point offset value, and obtain the mating posture change parameters; S412: Call the mating posture change parameters, extract key moment points within the duration of the mating action, analyze the posture change rate and contact point offset trend, using the following formula: ； Calculate the mating action integrity metric to obtain the mating action integrity characteristics; in, This represents a measure of the completeness of the mating process. Represents the total number of time segments. This represents the change in pose angle during the k-th time segment. Indicates the first The change in posture angle over a time segment This represents the offset value of the body contact point in the k-th time segment. Representing the Offset values ​​of body contact points for each time segment; S413: Based on the integrity characteristics of the mating action, filter behavioral data that meet the integrity criteria, determine whether the mating behavior conforms to the expected pattern, and obtain mating behavior analysis records.

6. The veterinary insemination electronic record method according to claim 1, characterized in that, The specific steps for obtaining the breeding stock mating execution log are as follows: S511: Based on the mating behavior analysis record, extract data on body temperature, hormone levels, action sequence and mating completion, analyze data on body temperature changes and hormone fluctuations, record action sequence, mating duration and completion, and obtain physiological change state matching values. S512: Call the physiological change state matching value, combine the body temperature change rate and hormone fluctuation amplitude, calculate the effective mating data screening factor, screen the mating data that meet the matching conditions, and obtain the effective mating dataset. S513: Call the effective mating dataset, record the mating data within the mating cycle, store it according to the time axis, and obtain the breeding livestock mating execution log.

7. A veterinary insemination electronic record system, characterized in that, The veterinary breeding electronic record method according to any one of claims 1-6, wherein the system comprises: The environmental monitoring module is based on data from the breeding site of livestock, including temperature, humidity, light intensity and air pressure of the breeding site. It extracts data streams from temperature and humidity sensors, identifies the fluctuation range of environmental parameters, filters stable intervals, and obtains the stable trend of the breeding site environment. Based on the stable trend of the breeding site environment, the breeding animal physiological monitoring module extracts body temperature, hormone levels, heart rate, and respiratory rate from veterinary breeding monitoring, screens the physiological state of breeding tendency, and obtains the physiological change status of breeding animals. The mating timing determination module extracts monitored body temperature data, hormone level changes, and behavioral activity based on the physiological changes of the breeding animals. It then filters time windows of body temperature rise and hormone level fluctuation peaks, analyzes the degree of matching between body temperature, hormone levels, and behavioral characteristics, filters time nodes that meet the mating tendency, and obtains the mating time nodes of the breeding animals. Based on the breeding time nodes of the breeding animals, the mating behavior analysis module extracts video stream data from the mating behavior sensing device, analyzes mating posture and duration of mating actions, filters behavioral data that meet the standards, and obtains mating behavior analysis records. Based on the mating behavior analysis records, the mating log management module extracts the physiological changes in the same time period, analyzes the data matching of body temperature, hormone levels, action sequence and mating completion, filters valid mating data, and obtains the breeding animal mating execution log.

Images